Neuroprotection: Method and Protocols (Methods in Molecular Biology, 2761) 1071636618, 9781071636619

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Methods in Molecular Biology 2761

Swapan K. Ray  Editor

Neuroprotection Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Neuroprotection Method and Protocols

Edited by

Swapan K. Ray Department of Pathology, Microbiology, and Immunology, University of South Carolina School of Medicine, Columbia, SC, USA

Editor Swapan K. Ray Department of Pathology, Microbiology, and Immunology University of South Carolina School of Medicine Columbia, SC, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3661-9 ISBN 978-1-0716-3662-6 (eBook) https://doi.org/10.1007/978-1-0716-3662-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A. Paper in this product is recyclable.

Preface Neuroprotection to the diseases and injuries of the central nervous system (CNS), which includes brain and spinal cord, remains a formidable challenge. All the major CNS diseases and injuries after the onset are ongoing, difficult to control with only symptomatic treatments, regrettably incurable, and thus harbinger of early death. It is important that we know and use the contemporary and appropriate methods for understanding the histological, cellular, and molecular mechanisms of both the neuropathogenesis and neuroprotection in preclinical models of the major CNS diseases and injuries to translate this knowledge to the clinical settings. The good news is that we at the present are gaining better understanding than the past of the complex histological, cellular, and molecular mechanisms of neuropathogenesis as well as of neuroprotection in preclinical models of the most visible CNS diseases and injuries due to the advancements in our ability to design creative molecular biology methodology with the advent of the state-of-the-art technology. This volume of the Methods in Molecular Biology (MMB) on “Neuroprotection” is a collection of the cuttingedge chapters mostly on modern protocols (methods) and a few on comprehensive conceptions (reviews) aimed primarily at graduate students, medical students, postdoctoral fellows, and new principal investigators for providing them with opportunities for exploring neurodegenerative and neuroprotective mechanisms in a large number of preclinical models or some clinical samples of the prominent CNS diseases and injuries for discovering and recommending the best drugs and devices to this point for exploring neuroprotection in humans. This volume of MMB on “Neuroprotection” is broadly organized with its chapters in three categories in this thematic order: chapters that describe methods (Chapters 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12) and some reviews (Chapters 13, 14, and 15) for understanding neuropathology and neuroprotection in the CNS diseases and injuries as a whole; chapters that describe mostly methods and some reviews on neuropathology and neuroprotection in specific CNS diseases in an alphabetic order such as Alzheimer’s disease or AD (Chapters 16, 17, 18, 19, 20, 21, 22, 23, and 24 on methods and Chapters 25 and 26 on reviews), amyotrophic lateral sclerosis (no muscle nourishment in spinal cord areas due to scarring) or ALS (Chapters 27 and 28 on methods), Huntington’s disease or HD (Chapter 29 on method), multiple sclerosis or MS (Chapters 30 and 31 on reviews), and Parkinson’s disease or PD (Chapters 32, 33, 34, and 35 on methods and Chapter 36 on review); and chapters that describe examining and understanding neuropathology and neuroprotection in specific CNS injuries such as spinal cord injury or SCI (Chapter 37 on method), traumatic brain injury or TBI (Chapters 38 and 39 on methods and Chapter 40 on review), and ischemic brain injury or just ischemia (Chapter 41 on method). All chapters are carefully edited and thoroughly revised (in some cases repeatedly) for significant improvements and bringing them to the acceptable final forms. Altogether 41 chapters in this volume will give our readers an ample opportunity to gain expertise in conducting various protocols and expand knowledge on neuropathology and neuroprotection in the major CNS diseases and injuries. Each method chapter in this volume of MMB on “Neuroprotection” contains an Abstract followed by four distinct sections: (1) Introduction, (2) Materials, (3) Methods, and (4) Notes. The “Abstract” is a summary of interesting laboratory technique(s) for revealing mechanisms of neurodegeneration and neuroprotection in a CNS disease or injury.

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(1) “Introduction” describes the background information, principle of the procedure with references to the work of the author(s) and other investigators in the field, outlines the plan for major aspects of the method(s), and briefly states results to show feasibility and validity of the procedures. (2) “Materials” is an essential part of the protocol chapter describing the main components, chemicals, biochemicals, buffers, solutions, supplies, and major equipment needed to conduct the procedures successfully. This section also discreetly declares the specific requirements for storage, stability, purity, temperature and light sensitivity, harmful effects of any reagents and solutions, treatment, protection, and disposal of biological and chemical wastes. (3) “Methods” is the next section that systematically and categorically describes how to execute the individual steps in the protocol and achieve results, points out details of the practical pointers, and clarifies logical aspects of these steps in a proper arrangement. (4) “Notes” can be a significant savior to any novice user of the protocol as this section openly points out the possible sources of the problems and disappointments, finds reasonable solutions to the problems, and clearly provides pointers to detect and defeat the difficulties to attain success with the protocol. As I mentioned above, a few review chapters are also included in this volume of MMB on “Neuroprotection” to provide our readers with current conceptions on the specific topics in some of the major CNS diseases and injuries. Each review chapter in general contains Abstract, Introduction, Subtitles (focused on specific parts of the subject matter), and Conclusions (with future directions). The major CNS diseases and injuries are colloquially called neurological disorders or neurodegenerative disorders, all of which are associated with degeneration of the neural cells (neurons and glia) at varying degrees causing deficiencies in neurological functions and behaviors. To inspire beginners in the neuropathology and neuroprotection field with the simplest pointers, each of the major CNS diseases and injuries (with chapters in this volume) is briefly defined below in just one sentence. AD named after the German neuropathologist Alois Alzheimer is a neurodegenerative brain disorder manifested with formation of abnormal amyloid plaques and neurofibrillary tangles, killing nerve cells first in the hippocampus and later in the cerebral cortex, causing loss of connections between the nerve cells, gradually wiping out memory and thinking skills, and eventually causing dementia or obliterating the cognitive functioning of the AD patients to deter to carry out the easiest tasks. ALS described first by the French neurologist Jean-Martin Charcot is also called Lou Gehring’s disease or motoneuron diseases (MND) as it is a neurodegenerative spinal cord disorder that primarily and increasingly kills motoneurons with generally no known cause (sporadic) or rarely a genetic cause (hereditary) such as a mutation in the gene encoding the antioxidant enzyme superoxide dismutase 1 (SOD1), leading to loss of voluntary (intentional) muscle movement and paralysis. HD named after the American physician George Huntington is an inherited neurodegenerative brain disorder that causes neurons in some parts of the brain to die due to presence of the mutant huntingtin protein with a repeat of many glutamine residues (36 or more) resulting from its defective gene with cytosine, adenine, and guanine (CAG) repeat many more times than normal huntingtin gene (the function of which is essential for development and vesicle trafficking in a cell), impairing voluntary movement of the HD patients manifested with unrestrained dance-like movements (chorea), abnormal body postures, tremors, rigidity, slow or abnormal eye movements, impaired gait and balance, difficulty with speech and swallowing, and problems with emotion, thinking, and personality. MS, which is first welldefined as “scle´rose en plaques” (the French name of MS) on the white matter of brain and spinal cord by again the French neurologist Jean-Martin Charcot, is the most common

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neurological disorder that predominantly affects women, and it is now widely recognized as an autoimmune demyelinating and neurodegenerative disease in which the myelin and myelin-producing oligodendrocytes become the targets of T cell-mediated autoimmune response, resulting in depletion of the white matter, progressive axonal damage, neurodegeneration, and loss of neuronal function. PD, which is first described as a “shaking palsy” by the English physician James Parkinson, is an ongoing neurodegenerative movement disorder of the CNS, especially of the brain with loss of its substantia nigra neurons that produce dopamine (a neurotransmitter required by the dopaminergic neurons for regulating motor function, motivation, and sexual arousal), leading to onset of tremor, muscle rigidity, slowness in movement (bradykinesia), stiffness in the limbs or the trunk of the body, impaired balance, loss of smell, sleep dysfunction, mood disorders, excess salivation, and constipation. SCI is a devastating and progressive neurological disorder that mostly affects young people when they experience a primary injury in any part of the spinal cord due to car accident, fall, sports mishap, or violent event. TBI, which is also an advancing neurological disorder, is defined by neuropathogenesis in the brain and deficiency in the brain functions following an external traumatic impact injury. Ischemic brain injury or ischemia is a neurological disorder that occurs if the blood flow to any part of the brain is suppressed or interrupted by a buildup of plaques (atherosclerosis), preventing brain tissue in that area from receiving oxygen and nutrients, triggering degeneration of the brain tissue, and causing neurological problems and paralysis. None of the major CNS diseases and injuries are yet curable. Therefore, we must continue to use available modern methods and innovative ideas to understand their neuropathology at greater depths to figure out the most promising targets for neuroprotection. All the chapters presented in this volume of the MMB are expected to be highly useful to the beginners as well as to the new and seasoned investigators in the field of CNS diseases and injuries for designing and performing their experiments, gaining novel insights, and making new contributions to the field for the benefits of the future generations. Finally, I would like to thank all the authors from around the globe for contributing their thoughtful method chapters and thought-stimulating review chapters on major CNS diseases and injuries to this volume of MMB on “Neuroprotection” for helping other researchers, especially new investigators, stay interested and involved in this complex and ever-intriguing field and continue to carry out groundbreaking research. I would also like to thank the MMB series Editor, Dr. John M. Walker, who is a Professor Emeritus at University of Hertfordshire in the United Kingdom, for emboldening me for pursuing this lengthy mega project and taking this volume to the finish line for its final production by the Springer Nature. Additionally, my special thanks go to the Associate Editor, Anna Rakovsky, and all other Editorial Staffs at the Springer Nature for their dedication and diligence in publishing this elegant volume that we all are proud of. Columbia, SC, USA

Swapan K. Ray

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 TUNEL-n-DIFL Method for Detection and Estimation of Apoptosis Specifically in Neurons and Glial Cells in Mixed Culture and Animal Models of Central Nervous System Diseases and Injuries . . . . . . . . . . . . . . . . . . . . 1 Swapan K. Ray 2 Isolation of Capillaries from Small Amounts of Mouse Brain Tissue . . . . . . . . . . . 27 Junqiao Mi, Aili Sun, Laura H€ a rtel, Christina Dilling, Patrick Meybohm, and Malgorzata Burek 3 Isolation of Extracellular Vesicles Using Formulas to Adapt Centrifugation to Different Centrifuges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Ramon Handerson Gomes Teles, Daniela Engelmayr, Patrick Meybohm, and Malgorzata Burek 4 High-Resolution Respirometry for Mitochondrial Function in Rodent Brain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Aishika Datta, Deepaneeta Sarmah, Bijoyani Ghosh, Nikita Rana, Anupom Borah, and Pallab Bhattacharya 5 Quantification of Neuronal Dendritic Spine Density and Lengths of Apical and Basal Dendrites in Temporal Lobe Structures Using Golgi-Cox Staining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Vivek Dubey, Aparna Banerjee Dixit, Manjari Tripathi, P. Sarat Chandra, and Jyotirmoy Banerjee 6 Quantification of Neuroinflammatory Markers in Blood, Cerebrospinal Fluid, and Resected Brain Samples Obtained from Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Arpna Srivastava, Aparna Banerjee Dixit, Manjari Tripathi, P. Sarat Chandra, and Jyotirmoy Banerjee 7 Targeted Modification of Epigenetic Marks Using CRISPR/dCas9-SunTag-Based Modular Epigenetic Toolkit . . . . . . . . . . . . . . . . . 81 Min Kyung Song and Yoon-Seong Kim 8 Elevated Plus Maze for Assessment of Anxiety and Memory in Rodents . . . . . . . 93 Ravi Chandra Sekhara Reddy Danduga and Phani Kumar Kola 9 Drosophila melanogaster Neuromuscular Junction as a Model to Study Synaptopathies and Neuronal Autophagy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Anushka Chakravorty, Vasu Sheeba, and Ravi Manjithaya 10 Cell-Based Assay to Detect the Autoantibody Serostatus in Patients with Neuromyelitis Optica Spectrum Disorder (NMOSD) . . . . . . . . . . . . . . . . . . . 121 Pallavi Chatterjee, Suparna Saha, and Debashis Mukhopadhyay

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Using Small Molecules for Targeting Heavy Metals in Neurotoxicity and Neuroinflammation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pronama Biswas and Sunil S. More Chromatographic Separation and Quantitation of Sphingolipids from the Central Nervous System or Any Other Biological Tissue . . . . . . . . . . . . Swapan K. Ray and Somsankar Dasgupta Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saima, S. Latha, Ruchika Sharma, and Anoop Kumar Role of Serotonergic System in Regulating Brain Tumor-Associated Neuroinflammatory Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surojit Karmakar and Girdhari Lal Impacts of Omega-3 Fatty Acids, Natural Elixirs for Neuronal Health, on Brain Development and Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Archana S. Rao, Ajay Nair, K. Nivetha, Bibi Ayesha, Kapadia Hardi, Vora Divya, S. M. Veena, K. S. Anantharaju, and Sunil S. More Microglial Uptake of Extracellular Tau by Actin-Mediated Phagocytosis . . . . . . . Hariharakrishnan Chidambaram, Smita Eknath Desale, Tazeen Qureshi, and Subashchandrabose Chinnathambi Internalization and Endosomal Trafficking of Extracellular Tau in Microglia Improved by α-Linolenic Acid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smita Eknath Desale, Hariharakrishnan Chidambaram, Tazeen Qureshi, and Subashchandrabose Chinnathambi Understanding Actin Remodeling in Neuronal Cells Through Podosomes. . . . . Tazeen Qureshi, Smita Eknath Desale, Hariharakrishnan Chidambaram, and Subashchandrabose Chinnathambi Quantitative Investigation of Neuroprotective Role of ROR1 in a Cell Culture Model of Alzheimer’s Disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaushik Chanda and Debashis Mukhopadhyay microRNA Isolation, Expression Profiling, and Target Identification for Neuroprotection in Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saleem Iqbal and Debnath Pal Quantitative Measurement of Tau Aggregation in Genetically Modified Rats with Neurodegeneration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YouJin Lee and Eric M. Morrow Detection and Characterization of Apoptosis-Related Proteins in Hippocampal Neurodegeneration: From mRNA Expression to Protein Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kajal Rawat, Vipasha Gautam, Arushi Sandhu, and Lekha Saha Isolation and Detection of Pathological Tau Species in a Tauopathy Mouse Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhay Kumar Singh, Karthikeyan Selvarasu, and Siva Sundara Kumar Durairajan

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Enzyme Inhibition Assays for Monoamine Oxidase . . . . . . . . . . . . . . . . . . . . . . . . . Bijo Mathew, Jong Min Oh, Della Grace Thomas Parambi, Sachithra Thazhathuveedu Sudevan, Sunil Kumar, and Hoon Kim Role of Amyloid Beta in Neurodegeneration and Therapeutic Strategies for Neuroprotection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Priyam Ghosh, Kavita Narang, and Parameswar Krishnan Iyer Amyloid Beta–Mediated Neurovascular Toxicity in Alzheimer’s Disease . . . . . . . Sayani Banerjee and Sugato Banerjee Fecal Microbiota Transplantation in Amyotrophic Lateral Sclerosis: Clinical Protocol and Evaluation of Microbiota Immunity Axis . . . . . . . . . . . . . . . Elena Niccolai, Ilaria Martinelli, Gianluca Quaranta, Giulia Nannini, Elisabetta Zucchi, Flavio De Maio, Giulia Gianferrari, Stefano Bibbo`, Giovanni Cammarota, Jessica Mandrioli, Luca Masucci, and Amedeo Amedei Integrated Multi-Omics Analysis and Validation in Yeast Model of Amyotrophic Lateral Sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saiswaroop Rajaratnam, Sai Sanwid Pradhan, Ashwin Ashok Naik, and Venketesh Sivaramakrishnan Imaging and Assay of the Dynamics of Cytotoxic Huntingtin (HTT) Protein Aggregates Regulated by lncRNAs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaushik Chanda and Debashis Mukhopadhyay Astrocyte Activation and Drug Target in Pathophysiology of Multiple Sclerosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preeti Bisht, Charul Rathore, Ankit Rathee, and Atul Kabra Promises of Lipid-Based Nanocarriers for Delivery of Dimethyl Fumarate to Multiple Sclerosis Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sreya Subhash, Nishtha Chaurawal, and Kaisar Raza Chrysin for Neurotrophic and Neurotransmitter Balance in Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alagudurai Krishnamoorthy, Riddhi Upadhyay, and Murugan Sevanan Establishment of a 6-OHDA Induced Unilaterally Lesioned Male Wistar Rat Model of Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Namrata Kumari and Pratibha Mehta Luthra Evaluating Motor Dysfunction and Oxidative Stress Induced by Trichloroethylene in Wistar Rats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajnish Srivastava, Kanupriya Chauhan, and Ramaish Sharma Neurobehavioral Analysis to Assess Olfactory and Motor Dysfunction in Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samir Ranjan Panda, Pallabi Panja, Ujjawal Soni, and V. G. M. Naidu Ion Channels and Metal Ions in Parkinson’s Disease: Historical Perspective to the Current Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bhupesh Vaidya, Dibya S. Padhy, Hem C. Joshi, Shyam S. Sharma, and Jitendra Narain Singh

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Creating a Reproducible Model of Spinal Cord Injury in Rats: A Contusion Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Syed Shadab Raza Weight-Drop Method for Inducing Closed Head Diffuse Traumatic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bhagawati Saxena, Bhavna Bohra, and Krishna A. Lad iDISCO Tissue Clearing Whole-Brain and Light Sheet Microscopy for High-Throughput Imaging in a Mouse Model of Traumatic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hannah Flinn, Leonardo Cruz-Pineda, Laura Montier, Philip J. Horner, and Sonia Villapol Insights from Rodent Models for Improving Bench-to-Bedside Translation in Traumatic Brain Injury . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tulasi Pasam and Manoj P. Dandekar Rat Model of Middle Cerebral Artery Occlusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . Syed Shadab Raza

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors AMEDEO AMEDEI • Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy; Internal Interdisciplinary Medicine Unit, Careggi University Hospital, Florence, Italy K. S. ANANTHARAJU • Department of Chemistry, Dayananda Sagar College of Engineering, Bangalore, India BIBI AYESHA • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, India JYOTIRMOY BANERJEE • Department of Biophysics, AIIMS, New Delhi, India SAYANI BANERJEE • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Kolkata, India SUGATO BANERJEE • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Kolkata, India PALLAB BHATTACHARYA • Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, Gandhinagar, Gujarat, India STEFANO BIBBO` • Digestive Disease Center, A. Gemelli University Hospital IRCCS, Catholic University of Sacred Heart, Rome, Italy PREETI BISHT • University Institute of Pharma Sciences, Chandigarh University, Ajitgarh, Punjab, India PRONAMA BISWAS • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India BHAVNA BOHRA • Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, India ANUPOM BORAH • Cellular and Molecular Neurobiology Laboratory, Department of Life Science and Bioinformatics, Assam University, Silchar, Assam, India MALGORZATA BUREK • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany GIOVANNI CAMMAROTA • Digestive Disease Center, A. Gemelli University Hospital IRCCS, Catholic University of Sacred Heart, Rome, Italy ANUSHKA CHAKRAVORTY • Autophagy Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India KAUSHIK CHANDA • Department of Neuroscience, UF Scripps Biomedical Research, Jupiter, FL, USA P. SARAT CHANDRA • Department of Neurosurgery, AIIMS, New Delhi, India PALLAVI CHATTERJEE • Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, A CI of Homi Bhabha National Institute, Kolkata, India KANUPRIYA CHAUHAN • Moradabad Educational Trust Group of Institutions Faculty of Pharmacy, Moradabad, Uttar Pradesh, India NISHTHA CHAURAWAL • Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, Rajasthan, India HARIHARAKRISHNAN CHIDAMBARAM • Neurobiology Group, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Department of Neurochemistry, National Institute

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Contributors

of Mental Health and Neuro Sciences (NIMHANS), Institute of National Importance, Bangalore, Karnataka, India SUBASHCHANDRABOSE CHINNATHAMBI • Neurobiology Group, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Institute of National Importance, Bangalore, Karnataka, India LEONARDO CRUZ-PINEDA • Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Academic Institute, Houston, TX, USA MANOJ P. DANDEKAR • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India RAVI CHANDRA SEKHARA REDDY DANDUGA • Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’s NMIMS, Mumbai, India SOMSANKAR DASGUPTA • Department of Neuroscience and Regenerative Medicine, Institute of Molecular Medicine and Genetics, Augusta University, Augusta, GA, USA AISHIKA DATTA • Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, Gandhinagar, Gujarat, India FLAVIO DE MAIO • Department of Laboratory and Infectious Sciences, A. Gemelli University Hospital IRCCS, Rome, Italy SMITA EKNATH DESALE • Neurobiology Group, Division of Biochemical Sciences, CSIRNational Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Institute of National Importance, Bangalore, Karnataka, India CHRISTINA DILLING • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany VORA DIVYA • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, India APARNA BANERJEE DIXIT • Dr B R Ambedkar Centre for Biomedical Research, University of Delhi, Delhi, India VIVEK DUBEY • Department of Biophysics, AIIMS, New Delhi, India SIVA SUNDARA KUMAR DURAIRAJAN • Molecular Mycology and Neurodegenerative Disease Research Laboratory, Department of Microbiology, Central University of Tamil Nadu, Thiruvarur, India DANIELA ENGELMAYR • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany; Graduate School of Life Sciences, Julius-Maximilians-University Wu¨rzburg, Wurzburg, Germany HANNAH FLINN • Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Academic Institute, Houston, TX, USA VIPASHA GAUTAM • Department of Pharmacology, Research Block B, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India BIJOYANI GHOSH • Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, Gandhinagar, Gujarat, India PRIYAM GHOSH • Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati, Assam, India GIULIA GIANFERRARI • Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

Contributors

xv

KAPADIA HARDI • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, India LAURA HA€ RTEL • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany PHILIP J. HORNER • Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Academic Institute, Houston, TX, USA SALEEM IQBAL • Axe Molecular Endocrinology and Nephrology, CHUL Research Center and Laval University, Quebec City, QC, Canada PARAMESWAR KRISHNAN IYER • Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati, Assam, India; Center for Nanotechnology, Indian Institute of Technology Guwahati, Guwahati, Assam, India; Jyoti and Bhupat Mehta School of Health Sciences and Technology, Indian Institute of Technology Guwahati, Guwahati, Assam, India HEM C. JOSHI • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India ATUL KABRA • University Institute of Pharma Sciences, Chandigarh University, Ajitgarh, Punjab, India SUROJIT KARMAKAR • National Centre for Cell Science (NCCS), Ganeshkhind, Pune, Maharashtra, India HOON KIM • Department of Pharmacy, and Research Institute of Life Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea YOON-SEONG KIM • RWJMS Institute for Neurological Therapeutics, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA PHANI KUMAR KOLA • Department of Biomedical Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND, USA ALAGUDURAI KRISHNAMOORTHY • International Institute of Biotechnology and Toxicology, Padappai, Chennai, India ANOOP KUMAR • Department of Pharmacology, Delhi Pharmaceutical Science and Research University (DPSRU), New Delhi, India NAMRATA KUMARI • Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi, India SUNIL KUMAR • Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India KRISHNA A. LAD • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Ahmedabad, Gandhinagar, Gujarat, India GIRDHARI LAL • National Centre for Cell Science (NCCS), Ganeshkhind, Pune, Maharashtra, India S. LATHA • Department of Pharmacology, Delhi Pharmaceutical Science and Research University (DPSRU), New Delhi, India YOUJIN LEE • Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA; Center for Translational Neuroscience, Carney Institute for Brain Science, and Brown Institute for Translational Science (BITS), Brown University, Providence, RI, USA PRATIBHA MEHTA LUTHRA • Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi, India JESSICA MANDRIOLI • Neurology Unit, Department of Neuroscience, Azienda Ospedaliero Universitaria di Modena, Modena, Italy; Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Contributors

RAVI MANJITHAYA • Autophagy Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India; Neuroscience Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India; Chronobiology and Behavioural Neurogenetics Laboratory, Neuroscience Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India ILARIA MARTINELLI • Neurology Unit, Department of Neuroscience, Azienda Ospedaliero Universitaria di Modena, Modena, Italy; Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy LUCA MASUCCI • Department of Laboratory and Infectious Sciences, A. Gemelli University Hospital IRCCS, Rome, Italy; Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Catholic University of Sacred Heart, Rome, Italy BIJO MATHEW • Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India PATRICK MEYBOHM • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany JUNQIAO MI • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany; Graduate School of Life Sciences, Julius-Maximilians-University Wu¨rzburg, Wurzburg, Germany LAURA MONTIER • Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Academic Institute, Houston, TX, USA SUNIL S. MORE • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India ERIC M. MORROW • Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA; Center for Translational Neuroscience, Carney Institute for Brain Science, and Brown Institute for Translational Science (BITS), Brown University, Providence, RI, USA DEBASHIS MUKHOPADHYAY • Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, A CI of Homi Bhabha National Institute, Kolkata, India V. G. M. NAIDU • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Guwahati, Assam, India ASHWIN ASHOK NAIK • Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Anantapur, Andhra Pradesh, India AJAY NAIR • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, India GIULIA NANNINI • Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy KAVITA NARANG • Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati, Assam, India ELENA NICCOLAI • Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy K. NIVETHA • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, India JONG MIN OH • Department of Pharmacy, and Research Institute of Life Pharmaceutical Sciences, Sunchon National University, Suncheon, Republic of Korea DIBYA S. PADHY • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India

Contributors

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DEBNATH PAL • Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India SAMIR RANJAN PANDA • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Guwahati, Assam, India PALLABI PANJA • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Guwahati, Assam, India DELLA GRACE THOMAS PARAMBI • College of Pharmacy, Department of Pharmaceutical Chemistry, Jouf University, Sakaka, Al Jowf, Saudi Arabia TULASI PASAM • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India SAI SANWID PRADHAN • Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Anantapur, Andhra Pradesh, India GIANLUCA QUARANTA • Department of Laboratory and Infectious Sciences, A. Gemelli University Hospital IRCCS, Rome, Italy TAZEEN QURESHI • Neurobiology Group, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India; Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Institute of National Importance, Bangalore, Karnataka, India SAISWAROOP RAJARATNAM • Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Anantapur, Andhra Pradesh, India NIKITA RANA • Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, Gandhinagar, Gujarat, India ARCHANA S. RAO • School of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, India ANKIT RATHEE • University Institute of Pharma Sciences, Chandigarh University, Ajitgarh, Punjab, India CHARUL RATHORE • University Institute of Pharma Sciences, Chandigarh University, Ajitgarh, Punjab, India KAJAL RAWAT • Department of Pharmacology, Research Block B, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India SWAPAN K. RAY • Department of Pathology, Microbiology, and Immunology, University of South Carolina School of Medicine, Columbia, SC, USA KAISAR RAZA • Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, Rajasthan, India SYED SHADAB RAZA • Laboratory for Stem Cell & Restorative Neurology, Department of Biotechnology, Era’s Lucknow Medical College and Hospital, Era University, Sarfarazganj, Lucknow, India; Department of Stem Cell Biology and Regenerative Medicine, Era’s Lucknow Medical College Hospital, Era University, Sarfarazganj, Lucknow, India LEKHA SAHA • Department of Pharmacology, Research Block B, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India SUPARNA SAHA • NINDS, NIH, Bethesda, MD, USA SAIMA • Department of Pharmacology, Delhi Pharmaceutical Science and Research University (DPSRU), New Delhi, India ARUSHI SANDHU • Department of Pharmacology, Research Block B, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India DEEPANEETA SARMAH • Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Ahmedabad, Gandhinagar, Gujarat, India

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Contributors

BHAGAWATI SAXENA • Department of Pharmacology, Institute of Pharmacy, Nirma University, Ahmedabad, India KARTHIKEYAN SELVARASU • Molecular Mycology and Neurodegenerative Disease Research Laboratory, Department of Microbiology, Central University of Tamil Nadu, Thiruvarur, India MURUGAN SEVANAN • Department of Biotechnology, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore, India RAMAISH SHARMA • ISF College of Pharmacy, Moga, Punjab, India RUCHIKA SHARMA • Centre for Precision Medicine and Pharmacy, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India SHYAM S. SHARMA • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India VASU SHEEBA • Neuroscience Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India; Chronobiology and Behavioural Neurogenetics Laboratory, Neuroscience Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India ABHAY KUMAR SINGH • Molecular Mycology and Neurodegenerative Disease Research Laboratory, Department of Microbiology, Central University of Tamil Nadu, Thiruvarur, India JITENDRA NARAIN SINGH • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India VENKETESH SIVARAMAKRISHNAN • Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Anantapur, Andhra Pradesh, India MIN KYUNG SONG • RWJMS Institute for Neurological Therapeutics, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA; College of Nursing Science, Kyung Hee University, Seoul, Republic of Korea UJJAWAL SONI • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Guwahati, Assam, India ARPNA SRIVASTAVA • Department of Neurology, AIIMS, New Delhi, India RAJNISH SRIVASTAVA • Moradabad Educational Trust Group of Institutions Faculty of Pharmacy, Moradabad, Uttar Pradesh, India SREYA SUBHASH • Department of Pharmacy, School of Chemical Sciences and Pharmacy, Central University of Rajasthan, Ajmer, Rajasthan, India SACHITHRA THAZHATHUVEEDU SUDEVAN • Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India AILI SUN • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany RAMON HANDERSON GOMES TELES • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wu¨rzburg, Wurzburg, Germany; Laboratory of Tumor Microenvironment, Department of Cell and Developmental Biology, Institute of Biomedical Sciences (ICB), University of Sa˜o Paulo, Sao Paulo, Brazil; Graduate School of Life Sciences, Julius-Maximilians-University Wu¨rzburg, Wurzburg, Germany MANJARI TRIPATHI • Department of Neurology, AIIMS, New Delhi, India RIDDHI UPADHYAY • Department of Biotechnology, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore, India

Contributors

xix

BHUPESH VAIDYA • Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India S. M. VEENA • Department of Biotechnology, Sapthagiri College of Engineering, Bangalore, India SONIA VILLAPOL • Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Academic Institute, Houston, TX, USA ELISABETTA ZUCCHI • Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

Chapter 1 TUNEL-n-DIFL Method for Detection and Estimation of Apoptosis Specifically in Neurons and Glial Cells in Mixed Culture and Animal Models of Central Nervous System Diseases and Injuries Swapan K. Ray Abstract Detection of merely apoptosis does not reveal the type of central nervous system (CNS) cells that are dying in the CNS diseases and injuries. In situ detection and estimation of amount of apoptosis specifically in neurons or glial cells (astrocytes, oligodendrocytes, and microglia) can unveil valuable information for designing therapeutics for protection of the CNS cells and functional recovery. A method was first developed and reported from our laboratory for in situ detection and estimation of amount of apoptosis precisely in neurons and glial cells using in vitro and in vivo models of CNS diseases and injuries. This is a combination of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and double immunofluorescent labeling (DIFL) or simply TUNEL-n-DIFL method for in situ detection and estimation of amount of apoptosis in a specific CNS cell type. An anti-digoxigenin (DIG) IgG antibody conjugated with 7-amino-4-methylcoumarin-3-acetic acid (AMCA) for blue fluorescence, fluorescein isothiocyanate (FITC) for green fluorescence, or Texas Red (TR) for red fluorescence can be used for in situ detection of apoptotic cell DNA, which is earlier labeled with TUNEL using alkali-stable DIG-11dUTP. A primary anti-NeuN (neurons), anti-GFAP (astrocytes), anti-MBP (oligodendrocytes), or antiOX-42 (microglia) IgG antibody and a secondary IgG antibody conjugated with one of the above fluorophores (other than that of ani-DIG antibody) are used for in situ detection of apoptosis in a specific CNS cell type in the mixed culture and animal models of the CNS diseases and injuries. Key words Apoptosis, Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), Double immunofluorescence labeling (DIFL), Central nervous system (CNS) cells, CNS diseases and injuries, Mixed culture, Brain and spinal cord sections, TUNEL-n-DIFL method

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Introduction Diseases and injuries in the central nervous system (CNS) are associated with the programmed cell death or apoptosis in neurons and glial cells [1–3]. Various in vitro and in vivo models are used to mimic CNS diseases and injuries to device the appropriate therapeutic strategies for prevention of apoptosis in the CNS cells.

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Monocultures of neurons or glial cells such as astrocytes, oligodendrocytes, and microglia are straightforward to discover the factor that causes induction of apoptosis in a specific CNS cell type and therapeutic inhibition of its apoptosis [4–6]. However, monoculture model of a CNS disease or injury misses the interactions and communications between neurons and glial cells that actually determine the amount of apoptosis occurring in a specific CNS cell type. Therefore, using co-culture or mixed culture of neurons and glial cells is a better option than monoculture to mimic a CNS disease or injury [7–11]. Yet, the best option for understanding CNS degeneration and exploring therapeutic efficacy is the employment of the in vivo models using rodents that fairly recapitulate many human CNS diseases and injuries [12–14]. The problem is how to simultaneously detect in situ cell death in a specific CNS cell type in the mixed culture as well as in the brain and spinal cord sections from the animal models of the CNS diseases and injuries to explore efficacy of specific therapeutic opportunities to prevent cell death. To address this problem, an innovative and novel method has been developed in our laboratory by combination of terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and double immunofluorescent labeling (DIFL), or TUNEL-n-DIFL, which can successfully be applied (with a change of primary antibody for specific CNS cell type) to the mixed culture and animal models of the CNS diseases and injuries for in situ detection and assessment of amount of apoptosis in a specific CNS cell type before and after therapeutic treatments [15]. While TUNEL reagents remain the same, cell marker antibody needs to be changed to NeuN (neuronal nuclei), GFAP (glial fibrillary acidic protein), MBP (myelin basic protein), or CD11b (cluster of differentiation 11b)/OX-42 for detection and estimation of apoptosis in neurons, astrocytes, oligodendrocytes, or microglia, respectively, in the mixed culture and CNS tissue sections. TUNEL-n-DIFL method provides consistent qualitative and quantitative results to the investigators who can use a neat experimental design, appropriate TUNEL reagents, an anti-digoxigenin (DIG) IgG antibody conjugated with 7-amino-4-methylcoumarin3-acetic acid (AMCA), fluorescein isothiocyanate (FITC), or Texas Red (TR) for detecting DIG incorporated genomic DNA (via a preceding TUNEL); an anti-NeuN, anti-GFAP, anti-MBP, or anti-OX-42 primary IgG antibody for marking neurons, astrocytes, oligodendrocytes, or microglia, respectively; a secondary IgG antibody conjugated with any one of the above fluorophores (other than that of ani-DIG antibody) against the cell marker primary IgG antibody; and evidently some practice. Two different antibodies with two fluorophores of distinct emission maxima (with a difference of at least 40 nm wavelength) can be detected simultaneously, providing discernible colors [16, 17]. Fluorescence colors of AMCA (blue), FITC (green), and TR (red) are easily

Detection of Apoptosis Explicitly in Neurons and Glial Cells

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distinguishable. Estimation of two fluorescence colors (e.g., AMCA and TR, FITC and TR) gives quantitative values for amount of apoptosis in a specific CNS cell type. After its initial development and reporting [15], this method has successfully been used by many investigators in our laboratories over the years [18–25] as well as in other laboratories around the world [26–33] for generating and reporting their results. As the name says, TUNEL can detect genomic DNA nick ends in cells undergoing apoptosis, necrosis, and any other cell death mechanism involving DNA fragmentation. To validate that CNS cells are undergoing undoubtedly apoptosis, investigators are encouraged to use the cell culture or tissue samples to perform additional experiments such as internucleosomal DNA fragmentation assay for observing DNA laddering as the hallmark of apoptosis [34–36] or a microscopic study for identification and confirmation of characteristic apoptotic features such as cell shrinkage, chromatin condensation, and membrane blebbing [37–39]. This TUNEL-n-DIFL method is acquiescent to versatility. In addition to the CNS cells and tissue sections, TUNEL-n-DIFL method can be applied to other cell type in mixed culture and any other organ tissue section with the use of specific antibody for in situ detecting and estimating apoptosis in a specific cell type in the mixed culture and organ tissue section employed in the experiment, as already reported by some creative investigators [29, 31, 32]. Contemporary trends indicate the increasing use of brain organoids [40–45] and spinal cord organoids [46–49] for understanding the mechanisms of pathogenesis in three dimensions (3D) and development of treatments for neuroprotection in CNS disorders. TUNEL-n-DIFL method is thus well positioned for its application to 3D brain organoids as well as to 3D spinal cord organoids for assessment of neuronal and glial cell apoptosis before and after therapeutic treatments. The apoptotic mode of cell death needs to be demonstrated and confirmed by the classic internucleosomal DNA fragmentation (DNA laddering) assay, which has also been developed in our laboratory by isolation and agarose gel electrophoresis of the genomic DNA from the CNS tissue of animals with CNS disorder [15]. Appearance of the internucleosomal DNA fragmentation as a DNA ladder of 180 base pairs (bp) on the agarose gel following electrophoresis is the classic biochemical hallmark of apoptosis, while appearance of the DNA smear of random DNA fragmentation on agarose gel is an indication of cell death by necrosis.

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Materials Follow all safe practices including use of laboratory coats, gloves, and protective eyeglasses for handling and using all materials in the laboratory. Use ultrapure deionized water (with a sensitivity of 18 MΩ-cm at a room temperature of around 25 °C) and

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analytical-grade chemical reagents for preparation of all solutions for the experiments. Carefully manage and dispose of all chemical and biological waste materials following the regulatory guidelines. 2.1 Mixed Culture Model of CNS Disease or Injury

1. Employ neurons and glial cells (astrocytes, oligodendrocytes, or microglia) of animal origin (e.g., mouse, rat) or human origin at proper proportions for studies in mixed cultures or co-cultures in 6-well plates (see Note 1). 2. For growth and experimentation with the mixed culture of CNS cells, use appropriate media and instructions for the CNS cells of mouse [50, 51], rat [52, 53], or human [54–56] origin (see Note 2). 3. Use neurotoxins (see Note 3) such as rotenone and 1-methyl4-phenylpyridinium ion or MPP+ to mimic Parkinson’s disease or PD [21, 55, 57], amyloid beta or Aβ to mimic Alzheimer’s disease or AD [58], mutant form of the superoxide dismutase enzyme 1 or SOD1 to mimic amyotrophic lateral sclerosis or ALS [59], and glutamate to mimic spinal cord injury or SCI [60] in the cell culture models following standard procedures.

2.2 Animal Model of CNS Disease or Injury

1. Use an animal model of PD [12, 21], AD [61, 62], MS [24, 63], SCI [64, 65], or any other CNS disorder in which you are interested. Develop and well establish the animal model of the CNS disease or injury with its standard behavioral studies or clinical scores before attempting any histological and molecular studies with the brain and spinal cord tissues (see Note 4). 2. If desired, perform therapeutic treatments following the experimental design in animal model of the CNS disease or injury. After the treatments and behavioral studies, sacrifice the animals in control and treatment groups under anesthesia (e.g., ketamine at 100 mg/kg and xylazine at 5 mg/kg), and perfuse via the left ventricle with 100 mL of phosphate-buffered saline (PBS) (see Note 5). 3. Use standardized surgical tools and procedures to collect the brain and spinal cord tissues.

2.3 Brain and Spinal Cord Tissue Sectioning

1. Tissue Freezing Medium or TFM (Triangle Biochemical Sciences) (see Note 6). 2. Opimal cutting temperature (OCT) compound cryostat embedding medium (Fisher Scientific) (see Note 7). 3. Cut tissue sections of 5 μm thickness using Reichert-Jung cryostat (Leica) (see Note 8). 4. Superfrost Plus Micro Slides (VWR Scientific Products). 5. 95 % ethanol.

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6. 10× phosphate-buffered saline (PBS): 100 mM Na2HPO4, 18 mM KH2PO4, 1.37 M NaCl, 27 mM KCl, and pH 7.4 (see Note 9). 2.4 Special Equipment

1. A battery-operated homogenizer (Kontes Instruments) (see Note 10). 2. A ultraviolet or UV (303 nm) transilluminator. 3. Polaroid film (positive/negative) Type 665. 4. An autoclave for sterilization of the dissection instruments. 5. Reichert-Jung cryostat (Leica) for brain and spinal cord sectioning. 6. Coplin jar. 7. Plastic Coverslip (Promega). 8. OmniSlide Thermal Cycler (Hybaid) for TUNEL reaction. 9. Fluorescence microscope (Olympus Corporation, Japan) equipped with appropriate filters for the fluorescence colors of two fluorescent antibodies (e.g., AMCA and TR, FITC and TR) used in the experiment. 10. Image-Pro Plus software (Media Cybernetics) and ImageJ software (National Institutes of Health, USA).

2.5 Chemicals and Reagents for TUNEL-nDIFL

1. 95 % ethanol. 2. 4 % methanol-free formaldehyde (freshly prepared in PBS). 3. Equilibration buffer (EB): 200 mM K-cacodylate, 25 mM Tris–HCl, pH 6.6, 0.2 mM dithiothreitol (DTT), 0.25 mg/mL bovine serum albumin (BSA), and 2.5 mM CoCl2 (Promega). 4. TUNEL kit (Promega). 5. 10× PCR DIG Labeling mix (2 mM each of dATP, dCTP, dGTP, 1.9 mM dTTP, and 0.1 mM alkali-stable DIG-11dUTP) (Boehringer Mannheim) (see Notes 11 and 12). 6. 20× saline-sodium citrate (SSC) buffer: 3 M NaCl, 0.3 M sodium citrate, pH 7.0. 7. 2 % serum from sheep (lamb), horse, or goat (Sigma) as blocking reagent (see Note 13). 8. Sheep anti-DIG IgG antibody (Fab fragments) conjugated with AMCA (blue), FITC (green), or TR (red) procured from commercial vendor (Roche Molecular Biochemicals). 9. Primary IgG antibodies against NeuN, GFAP, MBP, or OX-42 (BioSource International).

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10. Secondary IgG antibody conjugated with a fluorophore (other than that of anti-DIG antibody) procured from commercial vendor (Vector Laboratories). 11. VectaShield Mounting Medium (Vector Laboratories). 2.6 Chemicals and Reagents for Isolation and Electrophoresis of Genomic DNA from Mixed Culture or Brain and Spinal Cord Tissues

1. Cells or tissue homogenization buffer: 10 mM Tris–HCl, pH 8.0, 150 mM NaCl, and 50 mM EDTA. 2. Cells or tissue homogenate digestion buffer: 10 mM Tris–HCl, pH 8.0, 50 mM NaCl, 10 mM EDTA, 0.5% sodium dodecyl sulfate (SDS), and 250 ng/mL proteinase K (see Note 14). 3. Phenol (which will be used for DNA extraction from cells or tissue digests) for equilibrating with equal volume of 500 mM Tris–HCl, pH 8.0 to avoid partitioning of DNA into the organic phase [15] (see Note 15). 4. Mixture of phenol (pH 8.0) and chloroform (1:1, v/v) for DNA extraction (see Note 15). 5. 70 % ethanol. 6. TE buffer (10 mM Tris–HCl, pH 8.0, 1 mM EDTA) containing RNase A (50 ng/mL) (see Note 16) for dissolving DNA. 7. 6× DNA loading dye: 0.25 % (w/v) bromophenol blue, 0.25 % xylene cyanol, 30 % glycerol in deionized water. DNA loading dye allows the sample to sink visibly into each gel slot and further helps tracking of travel of the DNA samples during gel electrophoresis. 8. Agarose for preparation of 1.8 % agarose gels (see Note 17). 9. 10× TAE buffer: 400 mM Tris–acetate, 10 mM EDTA, pH 8.3. 10. Ethidium bromide or EtBr (1 μg/mL) (see Note 18).

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Methods Perform all procedures at a laboratory temperature of around 25 °C unless specifically mentioned otherwise.

3.1 Processing of CNS Cells in Mixed Culture

1. In one set, grow the cells, and subsequently treat on sterile glass cover slips inserted within 6-well plates [57]. Process and save cells (from control and all treatment groups) for conducting TUNEL-n-DIFL method [15, 57]. 2. In another set, grow and subsequently treat the cells in the 6-well plates (without sterile glass cover). Process and save cells (from control and all treatment groups) for conducting DNA laddering assay [15].

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3. Use the CNS cells grown and subsequently treated on sterile glass cover slips inserted within 6-well plates. Gently spin (at 150× g for 10 min) the plates to settle down less adherent apoptotic cells on cover slips. Fix the cells with 95 % ethanol for 10 min and wash twice with 1× PBS. 4. Further fix the cells with 4 % methanol-free formaldehyde in PBS for 15 min, and wash twice with 1× PBS. 3.2 Processing of Brain or Spinal Cord Tissue Sections

1. After surgical collection, immediately freeze a same small amount (at least 0.5 g) of the same area of brain or spinal cord tissue from each animal in the TFM (Triangle Biochemical Sciences). 2. Coat the brain or spinal cord tissue with the OCT further to avoid disintegration during sectioning. Snap-freeze in liquid nitrogen for 1 min, and store all frozen tissue samples at -80 °C until ready for sectioning. 3. Also, collect and save a same small amount (at least 1.0 g) of the same area of brain or spinal cord tissue from each animal in the 1.5 mL Eppendorf tube. Store all tissue samples at -80 °C until ready for isolation of genomic DNA. 4. Use the frozen tissue samples to cut 4 μm sections on a cryostat (Cryocut 1800, Reichert-Jung, Leica), and thaw mount onto the Superfrost Plus Micro Slides (VWR). Take two tissue sections on one slide (see Note 19). 5. Place slides on a slightly warm surface for approximately 1 min to dry the sections. 6. Immerse the slide in 95 % ethanol in a Coplin jar for 10 min. 7. Wash the slide twice in PBS in a Coplin jar for 5 min to remove all traces of OCT. 8. Place the slides in 4 % methanol-free formaldehyde (freshly prepared in PBS) in a Coplin jar for 15 min. 9. Rinse twice in PBS for 5 min each.

3.3

TUNEL

1. Cover the cells or tissue sections with 50 μL of EB (Equilibration Buffer, Promega TUNEL kit). Place ½ piece of Plastic Coverslip (Promega TUNEL kit) to ensure even spreading and prevent evaporation of the reagent. Equilibrate for 5–10 min (see Note 20). 2. While the cells or tissue sections are equilibrating, prepare sufficient TdT reaction mix to use 25 μL/slide. Prepare 25 μL of TdT reaction mix by adding 21.5 μL of EB, 2.5 μL of nucleotide mix (PCR DIG Labeling mix, Boehringer Mannheim), and 1 μL of TdT (TUNEL kit, Promega). Keep the TdT reaction mix on ice.

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3. Remove the Plastic Coverslip and excess liquid from the slides. Blot around the equilibrated areas with tissue paper (see Note 21), but do not allow the cells or tissue sections to dry out. 4. Immediately, add 25 μL of TdT reaction mix to the tissue sections on each slide. Cover with ½ piece of Plastic Coverslip to ensure even spreading of the reagent and carefully remove any air bubbles. 5. Incubate the slides at 37 °C for 1 h in the humid OmniSlide Thermal Cycler (Hybaid, UK). 6. Dilute 20× SSC (Promega TUNEL kit) to 40 mL of 2× SSC with deionized water. 7. Remove the Plastic Coverslip and terminate the DIG Labeling reaction by immersing the slides in 2× SSC for 15 min. 8. Wash the slides in PBS for 5 min. Repeat two times for a total of three washes to remove unincorporated alkali-stable DIG-dUTP. 3.4

DIFL

1. To reduce nonspecific background fluorescence (following treatments with antibodies), block the cells or tissue sections on each slide with 200 μL of a mixture of two sera (2 % each of the two sera) in PBS for 30 min. 2. Remove sera by tender tapping of the slide on a paper towel. Add 200 μL of primary IgG antibody targeted to specific CNS cell marker (e.g., NeuN, GFAP, MBP, or OX-42) with a desired dilution (1:100) in blocking solution to the cells or tissue sections and incubate at room temperature for 1 h. 3. Rinse the slide for 5 min in PBS. Repeat. 4. Now is the time to add fluorescent antibodies (see Note 22). Treat the tissue sections with 200 μL of a mixture of sheep antiDIG antibody (Fab fragments) conjugated with AMCA, FITC, or TR (1:50) and a secondary antibody conjugated with a fluorophore (1:100), which is different from that of anti-DIG antibody, in blocking solution for 30 min in the dark. 5. Rinse twice in PBS for 5 min each. 6. Rinse once in distilled water for 3 min. 7. Add just one drop of VectaShield Mounting Medium (Vector Laboratories), and keep the sample covered with a glass coverslip. Transfer slides to a slide box to keep them in the dark before imaging. 8. Immediately examine the slide at 400× under fluorescence microscope equipped with epi-illumination and appropriate optical bandpass filters (see Note 23). Use Image Pro Plus 3.0 software (Media Cybernetics) to capture and save double immunofluorescent images of the cells or tissue sections (see Note 24).

Detection of Apoptosis Explicitly in Neurons and Glial Cells

3.5 Statistical Analysis of TUNEL-nDIFL

9

1. Perform quantitative analysis of mean fluorescence intensity (MFI) of images from the TUNEL-n-DIFL using ImageJ software (National Institutes of Health, USA) following our previous report [21]. 2. Obtain images of fluorescent colors of TUNEL and NeuN for detection and estimation of apoptosis in neurons in a CNS disease; for example, rotenone-induced apoptosis in neurons in rat model of PD (Fig. 1). Similarly, obtain images of fluorescent colors of TUNEL and NeuN, GFAP, or OX-42 for in situ detection and estimation of apoptosis in neurons, astrocytes, and microglia, respectively, in a CNS disease; for example, 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) induced apoptosis in neurons, astrocytes, and microglia in mouse model of PD (Fig. 2). TUNEL-n-DIFL method is equally useful for in situ detection and estimation of apoptosis specifically in the neurons in a CNS injury; for example, apoptosis in neurons in rat model of SCI (Fig. 3). 3. For quantitative results, convert the collaged four images of TUNEL and NeuN (for example) staining into 8-bit format and subtract background. Set an intensity threshold and keep it constant for all images analyzed. 4. Outline NeuN positive cells using an outlining tool in any neuronal staining panel of the collage, and move outline over to respective TUNEL staining panel of collage to delineate neurons. 5. Measure the MFI of TUNEL staining in these outlined areas as fluorescence intensity in arbitrary units. Calculate the MFI per unit area by dividing the MFI units by area of outlined neurons and represent data as arbitrary units ± standard error of the mean (SEM). 6. Express data as mean ± SEM of separate experiments (n ≥ 6). Consider differences significant at p ≤ 0.05.

3.6 Internucleosomal DNA Fragmentation (DNA Laddering) Assay

1. Homogenize cells, brain tissue, or spinal cord tissue in the homogenization buffer (10 mM Tris–HCl, pH 8.0, 150 mM NaCl, 50 mM EDTA). 2. Digest the homogenate in the digestion buffer (10 mM Tris– HCl, pH 8.0, 50 mM NaCl, 10 mM EDTA, 0.5 % SDS, 250 ng/mL proteinase K) at 37 °C for 1 day. 3. Extract twice with a mixture of phenol (pH 8.0) and chloroform (1:1, v/v) and once with chloroform only (see Note 25). 4. Add two volumes of absolute ethanol to the aqueous phase to precipitate DNA (see Note 26), and centrifuge to obtain DNA pellet. Wash DNA pellet twice with 70 % ethanol and air-dry.

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Fig. 1 Use of TUNEL-n-DIFL method for in situ detection and estimation of apoptosis (TUNEL-positive) specifically in neurons (NeuN positive) in a rat model of PD. This study was aimed at finding pathology in spinal cord (SC) in cervical and lumbar SC areas in this animal model of PD. (a) Representative photomicrographs of staining for TUNEL (red) and NeuN (green) in coronal sections (5 μm) of cervical and lumbar SC areas. No TUNEL-positive (apoptotic) neurons were identified in the sections from control animals (see Merge, green); however, apoptosis in many neurons occurred in dorsal horn and ventral horn regions of the cervical and lumbar SC sections from rotenone-induced PD animals (see Merge, yellow resulting from mixing of red and green), clearly indicating that rotenone-induced pathogenesis in the SC of the animals. Images were taken at 200× magnification. (b) Estimation of amount of apoptosis in neurons in the SC by analysis of mean fluorescence intensity (MFI) per unit area of TUNEL-positive neurons. Data showed significant differences in the arbitrary units between samples from control animals and rotenone-induced PD animals (n ≥ 4, *p ≤ 0.05). (Reproduced from ref. 21 with permission from Elsevier)

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Fig. 2 Application TUNEL-n-DIFL method to mouse model of MPTP-induced PD for in situ detection and estimation of apoptosis specifically in neurons and glial cells (astrocytes and microglia). This study was aimed at finding pathology in spinal cord (SC) in another animal model of PD. (a) Using the TUNEL-n-DIFL method, cervical SC sections (5 μm) were stained for microglia (top row: A, B, and C), astrocytes (middle row: D, E, and F), and neurons (bottom row: G, H, and I). The cervical SC samples were collected from the C57BL/6 mice after no injection (control) and injection of MPTP at 24 h (Day 1) and 1 week (Day 7). Microglia, astrocytes, and neurons were marked using mouse monoclonal anti-OX-42, anti-GFAP, and anti-NeuN primary antibody, respectively, and then visualized using an anti-mouse secondary antibody conjugated with Texas Red (red). Apoptosis in the CNS cells (TUNEL-positive) was visualized using a rabbit anti-DIG antibody conjugated with fluorescein isothiocyanate or FITC (green). Cells (red) with intracellular inclusion of green indicated apoptotic cells. The specimens were examined under a non-confocal fluorescence microscope at 400× magnification using a fluorescence microscope (Olympus BH-2). (b) The amounts of apoptosis in the CNS cells were obtained through pixel analysis using the NIH ImageJ software and presented as bar graphs. Pixels for apoptosis were quantified as the total number of pixels above background. Results were expressed as mean ± SEM of separate experiments (n ≥ 3, *p ≤ 0.05) and compared by one-way analysis of variance (ANOVA) followed by Fisher’s post hoc test. The total number of pixels for each representative panel was shown in parentheses above each bar. The SC specimens from the MPTP-induced PD animals showed induction of apoptosis nonsignificantly in microglia and astrocytes but significantly in neurons at Day 7. (Reproduced from ref. 19 with permission from Elsevier)

5. Dissolve DNA in 20 μL of TE buffer (10 mM Tris–HCl, 1 mM EDTA, pH 8.0) containing RNase A (50 ng/mL) in an Eppendorf tube (see Note 27). 6. Incubate all tubes at 37 °C for 1 h. 7. Spin briefly to get down the solution at the bottom of all the tubes.

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Fig. 3 Employment of TUNEL-n-DIFL method for in situ detection and estimation of attenuation of neuronal apoptosis by estrogen in acute SCI in rats. (a) A moderately severe (40 g.cm) injury was induced by dropping a constant weight (5 g) from a height of 8 cm onto an impounder (0.3 cm in diameter) gently placed on the spinal cord. Sham animals received laminectomy only. Coronal sections (5 μm) were cut using caudal penumbra of spinal cord (SC) from the sham and SCI rats treated with vehicle or a low dose estrogen (1, 5, or 10 μg/kg) for 48 h. The SC sections were subjected to the TUNEL-n-DIFL method. Images (captured at 200× magnification using an Olympus BH-2 fluorescence microscope) at the dorsal horn region were presented to show TUNEL staining (red), NeuN staining (green), and co-staining (yellow) in the merged (TUNEL + NeuN) panel, whereas images in ventral horn were presented to show the merged panel only. (n = 3–6). (b) The TUNEL-positive neurons (yellow, merged panel) were estimated in SCI tissue. At p = 0.05, sham + vehicle and sham + estrogen groups were not significantly different, indicating estrogen at all low doses provided neuroprotection in acute SCI in rats. Estrogen treatment showed significant differences at *p < 0.05 when compared to sham and @p & lt; 0.05 when compared with injury + vehicle. (Reproduced from ref. 25 with permission from Blackwell Publishing)

8. Add 4 μL of the 6× DNA loading dye to each tube, mix gently, heat at 60 °C for 3 min, and spin down briefly. 9. Take off the tapes from the gel tray ends, put the gel in electrophoresis rig, soak the gel for 10 min in 1× TAE (40 mM Tris–acetate, 1 mM EDTA, pH 8.3), adjust the buffer volume to a 1.0 cm depth from the surface to the submerged

Detection of Apoptosis Explicitly in Neurons and Glial Cells

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Fig. 4 Internucleosomal DNA fragmentation indicating induction of apoptosis of CNS cells in the spinal cord of rat with the experimental autoimmune encephalomyelitis or EAE (an animal model of human MS). Genomic DNA samples isolated from 1 cm-long sections of lumbar spinal cord were subjected to agarose electrophoresis and EtBr staining. M, marker (1 kb DNA ladder obtained from GIBCO/BRL); lanes 1–4, control rats; lanes 5–8, EAE rats. Internucleosomal DNA fragmentation (DNA laddering) was not detected in spinal cords of any of the control rats, while substantial amount of apoptosis in the form of DNA laddering was found in the spinal cords of all EAE rats. (Reproduced from ref. 15 with permission from Elsevier)

gel, and pre-run gel electrophoresis at 2 V/cm for 1 h before loading marker DNA (see Note 28) and subsequently experimental DNA samples onto the gel slots. 10. Load the experimental DNA samples onto the 1.8 % agarose gel for electrophoresis in 1× TAE (40 mM Tris–acetate, 1 mM EDTA, pH 8.3); buffer at 2 V/cm for 3.5 h in ice-cold condition (see Note 29). 11. Stain the agarose gel with ethidium bromide or EtBr (1 μg/ mL) wrapping the container with aluminum foil to keep the agarose gels in the dark, destain the background of agarose gels in water (see Note 30), and photograph agarose gels on a UV (303 nm) transilluminator using Polaroid film (positive/negative) Type 665 (see Note 31). 12. Save the agarose gel photographs (both positive and negative) for scanning and generating digital agarose gel images. Thus, internucleosomal DNA fragmentation (indicating induction of apoptosis) is detected in a CNS disease, for example, rat model of MS (Fig. 4) and in a CNS injury, for example, rat model of SCI (Fig. 5).

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Fig. 5 Agarose gel electrophoresis of genomic DNA samples isolated from spinal cord segments from separate groups of rats (sham and SCI). Induction of SCI (40 g.cm force) at T12 for 24 h caused varying degrees of internucleosomal DNA fragmentation (apoptosis) and random DNA fragmentation (necrosis). Treatment of SCI rats with the vehicle (1.5 % dimethyl sulfoxide or DMSO in saline) did not prevent genomic DNA fragmentation, but treatment with the calpain inhibitor E-64-d (1 mg/kg in 1.5 % DMSO) for 24 h prevented genomic DNA fragmentation and thereby provided neuroprotection. Each agarose gel is representative of at least three separate experiments with genomic DNA samples isolated from 1 cm-long spinal cord segments: S1, distant rostral; S2, near rostral; S3, injury; S4, near caudal; and S5, distant caudal. M, marker (123 bp DNA ladder) obtained from the supplier (GIBCO/BRL). (Reproduced from ref. 64 with permission from Elsevier)

13. Internucleosomal DNA fragmentation assay gives excellentquality qualitative results for visualization of extent of apoptosis. If desired, also perform alkaline comet assay for comparison as well as for determination of amounts of apoptosis (DNA fragmentation appearing in the comet tail) in the CNS cells (see Note 32).

4

Notes 1. Optimal growth and establishment of the animal and human CNS cells in mixed cultures such as double co-culture and triple co-culture require use of step-by-step procedures for addition of each CNS cell type to the plates and plating at different ratios [58, 66, 67]. It is important that the proportions of neurons and glial cells are appropriate, and cells are healthy prior to experimental treatments. 2. For achieving the aims and objectives from the in vitro investigation and for acceptable interpretation of results, primary CNS cells and cell lines of animal or human origin need to be used in the experiments. If the laboratory has the setup and

Detection of Apoptosis Explicitly in Neurons and Glial Cells

15

expertise in growing and maintaining primary CNS cells [68, 69], many sets of experiments can be designed for generating appealing results and statistical analysis. Primary CNS cells are available from commercial vendors, but these cells are expensive limiting the number of experiments, require buying and using of media and supplements usually from the same commercial vendors, resist expansion and stocking, and perhaps die (especially neurons) even before beginning of the experiments. The CNS cell lines are also used for the in vitro studies of CNS diseases [37, 38, 54, 70] and injuries [4, 71, 72]. These are readily available at reasonable prices from specific vendors, amenable to expansion and stocking, and usually not prone to any kind of programmed cell death during subculturing. However, CNS cell lines may require differentiation and subsequent utilization without delay in the experimental design of the specific CNS disease or injury. 3. Neurotoxins, which are used for modeling CNS diseases [73– 75] are destructive to nerve tissue and exposure to these agents can adversely affect the neurological functions in both developing and mature nervous system in humans. Gloves always need to be worn when handling any neurotoxin. If the processing of a neurotoxin is prone to production of aerosol, all procedures with the neurotoxin need to be performed in a fume hood or biosafety cabinet. 4. Know and observe the standard clinical features that are widely accepted as the representation of a specific CNS disease [23, 24, 76] or injury [64, 77] in the animal models before subjecting the animals to treatments, histological, and molecular studies. 5. Typically, combination of ketamine and xylazine is administered as a single intraperitoneal injection to rodent models for a safe and effective anesthesia, without requiring any specialized equipment, before sacrificing the animals [78– 80]. Currently, ketamine is a Schedule III nonnarcotic substance under the Controlled Substances Act in the United States, and it needs to be used with an appropriate license. Xylazine is currently not a controlled substance. Ketamine at high dose may induce DNA damage in vivo [81], which may be undesirable, and then an alternative procedure needs to be used [82]. Following anesthesia, perfusion (non-survival surgery for rat or mouse) is performed by making midline abdominal incisions in the animal to expose the heart, and then a needle (attached to a gravity perfusion system) is inserted into in the left ventricle of the heart. The right ventricle of the heart is cut to allow blood drainage. The animal is then perfused with PBS for 4–5 min (or until liver is cleared of blood) to flush the blood out of the circulatory system. The liver begins to blanch as blood is replaced with PBS.

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6. TFM is an ultrapure advanced formulation of water-soluble glycols and resins to create a solid bond between the tissue and the object holder. This bonding mechanism prevents tissue disintegration during cryotomy. The formulation of TFM freezes tissue quickly leaving no chance of crystallization even at the most extreme temperatures. 7. OCT compound is a convenient matrix used further to embed the tissue sample prior to sectioning at frozen temperatures of -10 °C and below on a cryostat, leaving no residue on the slides during staining procedures and thus eliminating the possibility of uninvited background staining. Use of OCT compound does not dull microtome knives. Ingeniously, OCT compound is created and calibrated for fluorogenic and chromogenic immunohistochemistry experiments. 8. Creating useful tissue sections as thin as 5 μm using ReichertJung cryostat (Leica) will require a great deal of skill and experience. Optimize knife tilt angle, trim blocks to expose the tissue at proper orientation, and cut sections slowly and uniformly from each block. In general, the last few tissue sections are cut at what will be the final thickness of 5 μm, which usually offers a monolayer of CNS cells for impressive microscopic visualization and quantitation of apoptosis in specific CNS cell type after application of the TUEN-n-DIFL method. Duplicate and more layers of CNS cells on the tissue section create problems for microscopic visualization and accurate quantitation of apoptotic cells, giving baffling results to the same investigator and others. If 3D organoids are used for modeling and treating CNS diseases and injuries, a vibrating tissue slicer or vibratome (VT1000 S Vibratome, Leica) can be used for preparing and mounting of organoid blocks for sectioning following a recently developed protocol [83]. Then, organoid sections can be subjected to the TUEN-n-DIFL method. 9. 10× PBS solution should be sterile and ready to use upon dilution to 1× working solution containing 10 mM Na2HPO4, 1.8 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, and pH 7.4. If it is used from a commercial vendor, it is rigorously evaluated and certified for free from endonuclease, exonuclease, and RNase activity. 10. This little device is very handy for uniform homogenization of all CNS tissue samples in the experiment. It needs to be operated (skillfully to avoid spilling) in a 1.5 mL Eppendorf tube containing the tissue and homogenization buffer on an ice bucket to avoid generation of heat that may damage the tissue components.

Detection of Apoptosis Explicitly in Neurons and Glial Cells

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11. DIG is conveniently used as a ligand, which can be integrated into DNA and RNA for its detection after hybridization with an anti-DIG antibody conjugated with a marker fluorescence color (e.g., AMCA, FITC, or TR) or a reporter enzyme [84, 85]. DIG-labeling provides significant advantages for in situ detection of genomic DNA because the hapten DIG is absent in animal cells, eliminating any undesirable background due to endogenous molecules. 12. For labeling of DNA and RNA, DIG is supplied as DIG-11dUTP and DIG-11-UTP, respectively, by the manufacturer (Boehringer Mannheim). DIG is coupled to dUTP or UTP by an alkali-stable linkage and a spacer arm. Alkali-stable DIG-11-dUTP is a substrate for various DNA polymerases including terminal deoxynucleotidyl transferase (TdT), also known as terminal transferase. The most valuable immunological reagents for detection of DIG-labeled DNA are antigenbinding (Fab) fragments of anti-DIG antibody coupled to a fluorophore (e.g., AMCA, FITC, or TR) or alkaline phosphatase for the use in conjunction with a chromophore (see Note 24). 13. Blocking with sera or proteins is essential to prevent nonspecific binding of antibodies to the cells or tissue sections on the slides. A serum or mixture of sera matching the species of the target (primary or secondary) antibody is used as blocking reagent. Proteins such as bovine serum albumin or casein are also used to block nonspecific antibody binding. 14. The proteinase capable of digesting hair keratin is called proteinase K that has broad substrate specificity and is thus used for digestion of various proteins in the cells and tissues in course of isolation of genomic DNA from them. A of pH 8 is optimum for its activity although it is stable in a wide range of pH (4–12) and temperature (37–60 °C). It is functional at an optimum pH 8, and raising the temperature from 37 to 50 or 60 °C can help increase its activity by several folds. An addition of 0.5 % SDS to the cell or tissue homogenate digestion buffer makes the substrate cleavage sites more accessible to proteinase K, thereby enhancing its activity. 15. Phenol is a toxic organic chemical, highly corrosive, and capable of causing severe burns. It needs to be used with extreme caution. Before use of phenol for DNA extraction from the digested cells or tissue homogenate, phenol needs to be buffersaturated to achieve about 72 % phenol, 28 % water, and a slightly alkaline pH of 8.0. Never use phenol that has turned pink. Phenol comes in a brown bottle. Carefully pour about 5 mL of phenol into a transparent glass tube, and check that it is colorless and useful. Pink or brown phenol indicates that it contains oxidation products, which nick DNA and degrade

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RNA. In DNA extraction using phenol, a volume of phenol is added to the aqueous soup containing the proteins and the DNA to be purified. Phenol (less polar) and water (more polar) are immiscible. Being denser than water, phenol comes down to the bottom. When two layers are forced to mix thoroughly, phenol goes into the water layer where it creates an emulsion of droplets all over. The proteins in the water are denatured and partition into the phenol phase, while the polar molecule DNA (with negative charges on its phosphate backbone) remains in the water phase. As mentioned previously, a mixture of buffer-saturated phenol (pH 8.0) and chloroform (1:1, v/v) is used for DNA extraction. In the mixture, isoamyl alcohol may be included as an anti-foaming agent, but it is an inert organic solvent, and its use is optional. Addition of chloroform, which is significantly denser than water, to the organic phase increases its overall density and prevents phase inversion. 16. Degradation of any RNA with RNase A in DNA solution is helpful for visualization of only DNA by agarose gel electrophoresis and EtBr staining. Otherwise, EtBr also stains RNA creating unwanted background in the agarose gels. 17. Use 1.6–2.0 % agarose gels for resolving DNA fragments by electrophoresis. However, use of 1.8 % agarose gels is found to be optimal for resolving internucleosomal DNA fragments isolated from the apoptotic cells or tissue. Agarose is hygroscopic and keep it in a tightly capped bottle. Dissolve agarose completely by heating in the 1× gel electrophoresis buffer. Partially dissolved agarose produces gels with invisible micro lenses that interfere with DNA resolution during electrophoresis; micro lenses become visible after EtBr staining, and micro lenses create high background fluorescence that is hard to get rid of. In course of preparation of agarose gels, adjust the volume of agarose solution (if necessary) by adding warm 1× gel electrophoresis buffer, pour each gel horizontally on the pre-labeled tray (with taped two ends and a comb near one end of each tray) only when the hotness of flask containing the agarose solution feels tolerable to the hands (pouring too hot agarose solution gives too hard gels). 18. EtBr is still the popular DNA-staining dye due to its property of intercalation by slipping neatly between the base pairs. The intercalation of EtBr in human DNA is undesirable, and thus EtBr is widely believed to be a human mutagen that needs to be cautiously dealt with. It may have potentially carcinogenic or teratogenic effects, although no scientific evidence is yet available showing either of these effects. Handling of EtBr should be performed in a fume hood wearing a lab coat, closed-toe shoes, chemical-resistant gloves made of nitrile rubber, and chemical safety goggles.

Detection of Apoptosis Explicitly in Neurons and Glial Cells

19

19. Using two tissue sections per slide is advisable out of an abundance of caution. It saves the day and the experiment if one of the sections is damaged, disintegrated, or disqualified due to improper labeling. 20. Thorough equilibration with the EB is imperative for penetration of the reagents into the cells in subsequent biochemical reactions. 21. The blotting of excess liquid from the slides makes sure that reagents of the subsequent TdT reaction will not be diluted impairing the outcomes. 22. Preparation and addition of the fluorescent antibodies should be performed in the laboratory room with dimmed lights or no lights on. Success of the DIFL part, more precisely the entire TUNEL-n-DIFL method, ultimately depends on preventing the fluorescent colors from fading on the slides as much as possible before taking digital photographs. 23. Optical bandpass filters transmit (pass) a specific range (band) of frequencies of visible light wavelengths or non-visible wavelengths (at the ultraviolet and infrared ends) of the spectrum. The result is an output containing only the desirable frequencies and wavelengths, while the unwanted frequencies and wavelengths of the spectrum are blocked. For the simultaneous detection of the fluorophores AMCA and TR or FITC and TR in combination, appropriate bandpass filters are required for limiting excitation and emission. Use a 324–388 nm bandpass filter for exciting AMCA and a 410–490 nm bandpass filter to limit its emission to blue; use a 475–490 nm bandpass filter for exciting FITC and a 503–535 nm bandpass filter to its limit emission to green; and use a 560–585 nm bandpass filter for exciting TR and a 600–652 nm bandpass filter to limit its emission to red [15]. 24. Keep enough time to capture, and collect double immunofluorescent images of the cells or tissue sections from all the slides, storing of which without imaging (due to lack of time, energy, or both) even in the cold room just overnight for using them next day does not save the fluorescent colors. Fading of fluorescent colors does not make count all your efforts in the whole experiment. Although properly executed TUNEL-n-DIFL method from slide preparation to capturing fluorescent images provides rewarding results for presentations and publications, the method is quite laborious, and honestly there can be a difficult learning curve for some investigators. High-affinity primary antibody against DIG (raised in sheep immunized with the DIG-coated edestin or bovine serum albumin) coupled to alkaline phosphatase (Boehringer Mannheim) and the primary antibody against a CNS cell marker (NeuN, GFAP,

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MBP, or OX-42) coupled to horseradish peroxidase may be used in the TUNEL-n-double immunochromogenic labeling (DICL) or TUNEL-n-DICL method, which is clearly a modified version of the original TUNEL-n-DIFL method, for chromogenic detection of apoptosis in a specific CNS cell type and for completing the whole method in 2 days instead of 1 day. Endogenous alkaline phosphatase and peroxidase activities, which can produce high background, are usually blocked using levamisole hydrochloride and hydrogen peroxide, respectively, as blocking agents. The best chromogenic substrates for alkaline phosphatase are the binary reagents 5-bromo-4-chloro-3-indolyl phosphate (BCIP) and 4-nitroblue tetrazolium chloride (NBT). Commonly used chromogenic substrates for horseradish peroxidase are 3,3′,5,5′-tetramethylbenzidine (TMB), 2,2′ -azino-di[3-ethylbenzthiazoline-6-sulfonic acid] (ABTS), and 3,3′-diaminobenzidine (DAB). 25. A chloroform extraction right after phenol-chloroform extraction is intended for removing any residual phenol (palpable from its smelling inside the tube) from the aqueous solutions of DNA. Keeping any trace of phenol in the DNA solution will partially or completely kill any enzyme (e.g., RNAse A) that is subsequently used to treat the DNA solution to get rid of RNA. 26. To enhance the precipitation of the internucleosomal DNA fragments (if there is any), add 1.0 M MgCl2 to the aqueous phase to a final concentration of 10 mM [86]. Then, add two volumes of absolute ethanol to precipitate total DNA with several inversions. 27. Getting rid of any RNA contamination in the DNA samples is essential for a clear observation of internucleosomal DNA fragmentation (a DNA ladder with the lowest 180 bp band) on the agarose gel after electrophoresis and EtBr staining. The presence of RNA may mask the resolved 180 bp band and other low molecular size bands of the DNA ladder on the agarose gel. Most of the RNA molecules are small, and they do stain with EtBr albeit weakly and appear at the lower portion of the agarose gel if they are not degraded in the DNA samples by treatment with RNase A prior to loading the samples onto the gel. 28. The most appropriate marker DNA is the 123 bp DNA ladder (1 μg/μL) that we have procured (GIBCO/BRL) and used previously in conjunction with internucleosomal DNA fragmentation assay [64, 86]. The 123 bp DNA ladder consists of a series of DNA fragments ranging from 123 to 4182 bp in length, and it is suitable for determining the size of double-

Detection of Apoptosis Explicitly in Neurons and Glial Cells

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stranded DNA from 123 to 3075 bp. More than 15 resolved bands (appearing as a DNA ladder) on 1.8 % agarose gel can easily be observed after ethidium bromide or EtBr staining. The first 180 bp DNA band of the apoptotic DNA sample (a 180 bp DNA ladder) beautifully appears between the first and second bands (123 and 246 bp) of the marker 123 bp DNA ladder after electrophoresis on the 1.8% agarose gels [64, 86]. 29. The concentration and purity of each of the isolated DNA samples can be determined spectrophotometrically (if desired) before loading onto the gel. Perform gel electrophoresis in 1× TAE buffer, keeping the gel rig in a plastic tray containing ice water to avoid heating of the gel during electrophoresis. The resolved internucleosomal DNA fragments (a 180 bp DNA ladder) do not appear sharp on the overheated gel. If needed, optimize agarose gel electrophoresis strength and duration to obtain the well-resolved internucleosomal DNA fragments from the top to bottom of each lane. 30. Destaining of the agarose gels with water may appear to be an easy task. But it is a tricky task because too much destaining (in a stainless steel tray covered with aluminum foil) will decrease not only the background but also the sharpness of the DNA fragments, especially that of the DNA fragments with low molecular weights. Do monitor destaining of the gel on the UV transilluminator (in the dark room), change water and its volume in the destaining tray, and stop destaining when gel background is clean and DNA bands still look sharp. 31. Polaroid film (positive/negative) Type 665 is a medium-speed, medium-contrast, and fine-grain film, which produces high-quality black and white prints and negatives suitable for enlarging. However, its positive requires print coating, and the negative needs to be washed in an 18 % sodium sulfite solution to minimize swelling in gelatin layer of the negative and then air-dried. Otherwise, swelling of its gelatin layer can cause reticulation (a pattern of interlacing lines resembling a net) that remains after the negative dries. To make the negative scratch-resistant, treat the processed negative (after washing it in sodium sulfite solution and air-drying) in a solution of Kodak Rapid Fix with hardener (Parts A and B) for 2 min and air-dry. With the advent and advancement of digital technology in all domains of photography, the supply of Polaroid film (positive/negative) Type 665 may be limited, uncertain, or discontinued. In that case, agarose gels following electrophoresis and EtBr staining can be photographed digitally using the UVDI Compact Digimage System (Major Science), as we reported recently [87, 88].

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32. Internucleosomal DNA fragmentation assay is applicable to genomic DNA samples isolated from both the cultured cells and tissues, but quantitation of the DNA laddering on the agarose gel images is still challenging. Alternately, alkaline comet assay shows apoptosis in terms of DNA fragmentation appearing in the comet tail [89]. Although quantitation of comet images is doable, results are not yet extremely useful, and the quantitation method is still evolving [90, 91]. Besides, application of alkaline comet assay is limited to cultured cells only. There is another issue, which is worth mentioning here. Apoptosis usually occurs with internucleosomal DNA fragmentation due to activation of caspase-3 followed by caspase3-activated DNase (CAD), which is also known as DNA fragmentation factor 40 (DFF40), in the apoptotic cells [92, 93]. However, occurrence of caspase-3-independent apoptosis has also been reported in many different cell types including the CNS cells [94]. Caspase-3-independent apoptosis with no internucleosomal DNA fragmentation in the cells is known to be associated with high molecular weight (HMW) DNA fragmentation (5 to 250 kb), which after isolation from the cells or tissues can be resolved and detected by the application of field inversion gel electrophoresis or FIGE, as we reported previously [95].

Acknowledgments The work was supported in part by the R01 grants (CA-091460 and NS-057811) from the National Institutes of Health (Bethesda, MD, USA) to S.K.R. References 1. Chakrabarti M, Das A, Samantaray S, Smith JA, Banik NL, Haque A, Ray SK (2016) Molecular mechanisms of estrogen for neuroprotection in spinal cord injury and traumatic brain injury. Rev Neurosci 27:271–281 2. Dailah HG (2022) Potential of therapeutic small molecules in apoptosis regulation in the treatment of neurodegenerative diseases: an updated review. Molecules 27:7207 3. Al-Sammarraie N, Mahmood M, Ray SK (2023) Neuroprotective role of Noggin in spinal cord injury. Neural Regen Res 18:492–496 4. Das A, Sribnick EA, Wingrave JM, Del Re AM, Woodward JJ, Appel SH, Banik NL, Ray SK (2005) Calpain activation in apoptosis of ventral spinal cord 4.1 (VSC4.1) motoneurons exposed to glutamate: calpain inhibition

provides functional neuroprotection. J Neurosci Res 81:551–562 5. Das A, Belagodu A, Reiter RJ, Ray SK, Banik NL (2008) Cytoprotective effects of melatonin on C6 astroglial cells exposed to glutamate excitotoxicity and oxidative stress. J Pineal Res 45:117–124 6. Dasgupta S, Ray SK (2017) Insights into abnormal sphingolipid metabolism in multiple sclerosis: targeting ceramide biosynthesis as a unique therapeutic strategy. Ther Targets Neurol Dis 4:e1598 7. Yang IH, Co CC, Ho CC (2005) Spatially controlled co-culture of neurons and glial cells. J Biomed Mater Res A 75:976–984 8. Pang Y, Simpson K, Miguel-Hidalgo JJ, Savich R (2018) Neuron/oligodendrocyte

Detection of Apoptosis Explicitly in Neurons and Glial Cells myelination coculture. Methods Mol Biol 1791:131–144 9. Goshi N, Morgan RK, Lein PJ, Seker E (2020) A primary neural cell culture model to study neuron, astrocyte, and microglia interactions in neuroinflammation. J Neuroinflammation 17: 155. Erratum in: J Neuroinflammation 2022 Feb 12;19(1):49 10. Ismail FS, Faustmann PM, Ku¨mmel ML, Fo¨rster E, Faustmann TJ, Corvace F (2022) Brivaracetam exhibits mild pro-inflammatory features in an in vitro astrocyte-microglia co-culture model of inflammation. Front Cell Neurosci 16:995861 11. Wang X, Liu X, Chen L, Zhang X (2023) The inflammatory injury in the striatal microgliadopaminergic-neuron crosstalk involved in Tourette syndrome development. Front Immunol 14:1178113 12. Samantaray S, Knaryan VH, Butler JT, Ray SK, Banik NL (2008) Spinal cord degeneration in C57BL/6N mice following induction of experimental parkinsonism with MPTP. J Neurochem 104:1309–1320 13. Chakrabarti M, McDonald AJ, Will Reed J, Moss MA, Das BC, Ray SK (2016) Molecular signaling mechanisms of natural and synthetic retinoids for inhibition of pathogenesis in Alzheimer’s disease. J Alzheimers Dis 50:335–352 14. Raghava N, Das BC, Ray SK (2017) Neuroprotective effects of estrogen in CNS injuries: insights from animal models. Neurosci Neuroecon 6:15–29 15. Ray SK, Schaecher KE, Shields DC, Hogan EL, Banik NL (2000) Combined TUNEL and double immunofluorescent labeling for detection of apoptotic mononuclear phagocytes in autoimmune demyelinating disease. Brain Res Protocol 5:305–311 16. Ferri GL, Gaudio RM, Castello IF, Berger P, Giro G (1997) Quadruple immunofluorescence: a direct visualization method. J Histochem Cytochem 45:155–158 17. Krenacs T, Krenacs L, Raffeld M (2010) Multiple antigen immunostaining procedures. Methods Mol Biol 588:281–300 18. Wingrave JM, Schaecher KE, Sribnick EA, Wilford GG, Ray SK, Hazen-Martin DJ, Hogan EL, Banik NL (2003) Early induction of secondary injury factors causing activation of calpain and mitochondria-mediated neuronal apoptosis following spinal cord injury in rats. J Neurosci Res 73:95–104 19. Chera B, Schaecher KE, Rocchini A, Imam SZ, Sribnick EA, Ray SK, Ali SF, Banik NL (2004) Immunofluorescent labeling of increased calpain expression and neuronal death in the

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spinal cord of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated mice. Brain Res 1006: 150–156 20. Sribnick EA, Matzelle DD, Ray SK, Banik NL (2006) Estrogen treatment of spinal cord injury attenuates calpain activation and apoptosis. J Neurosci Res 84:1064–1075 21. Samantaray S, Knaryan VH, Guyton MK, Matzelle DD, Ray SK, Banik NL (2007) The parkinsonian neurotoxin rotenone activates calpain and caspase-3 leading to motoneuron degeneration in spinal cord of Lewis rats. Neuroscience 146:741–755 22. Samantaray S, Sribnick EA, Das A, Knaryan VH, Matzelle DD, Yallapragada AV, Reiter RJ, Ray SK, Banik NL (2008) Melatonin attenuates calpain upregulation, axonal damage and neuronal death in spinal cord injury in rats. J Pineal Res 44:348–357 23. Guyton MK, Brahmachari S, Das A, Samantaray S, Inoue J, Azuma M, Ray SK, Banik NL (2009) Inhibition of calpain attenuates encephalitogenicity of MBP-specific T cells. J Neurochem 110:1895–1907 24. Guyton MK, Das A, Samantaray S, Wallace GC 4th, Butler JT, Ray SK, Banik NL (2010) Calpeptin attenuated inflammation, cell death, and axonal damage in animal model of multiple sclerosis. J Neurosci Res 88:2398–2408 25. Samantaray S, Das A, Matzelle DC, Yu SP, Wei L, Varma A, Ray SK, Banik NL (2016) Administration of low dose estrogen attenuates gliosis and protects neurons in acute spinal cord injury in rats. J Neurochem 136:1064– 1073 26. Bannerman PG, Hahn A, Ramirez S, Morley M, Bo¨nnemann C, Yu S, Zhang GX, Rostami A, Pleasure D (2005) Motor neuron pathology in experimental autoimmune encephalomyelitis: studies in THY1-YFP transgenic mice. Brain 128:1877–1886 27. Nie BM, Jiang XY, Cai JX, Fu SL, Yang LM, Lin L, Hang Q, Lu PL, Lu Y (2008) Panaxydol and panaxynol protect cultured cortical neurons against Abeta25-35-induced toxicity. Neuropharmacology 54:845–853 28. Burns T, Miers L, Xu J, Man A, Moreno M, Pleasure D, Bannerman P (2014) Neuronopathy in the motor neocortex in a chronic model of multiple sclerosis. J Neuropathol Exp Neurol 73:335–344 29. Li W, Zeng Y, Zhao J, Zhu CJ, Hou WG, Zhang S (2014) Upregulation and nuclear translocation of testicular ghrelin protects differentiating spermatogonia from ionizing radiation injury. Cell Death Dis 5:e1248

24

Swapan K. Ray

30. Shukla AK, Pragya P, Chaouhan HS, Patel DK, Abdin MZ, Kar Chowdhuri D (2014) Mutation in Drosophila methuselah resists paraquat induced Parkinson-like phenotypes. Neurobiol Aging 35:2419.e1–2419.e16 31. Ding J, Wang H, Wu ZB, Zhao J, Zhang S, Li W (2015) Protection of murine spermatogenesis against ionizing radiation-induced testicular injury by a green tea polyphenol. Biol Reprod 92:6 32. Jain M, Zellweger M, Frobert A, Valentin J, van den Bergh H, Wagnie`res G, Cook S, Giraud MN (2016) Intra-arterial drug and light delivery for photodynamic therapy using Visudyne®: implication for atherosclerotic plaque treatment. Front Physiol 7:400 33. Sui L, Du Q, Romer A, Su Q, Chabosseau PL, Xin Y, Kim J, Kleiner S, Rutter GA, Egli D (2023) ZnT8 loss of function mutation increases resistance of human embryonic stem cell-derived beta cells to apoptosis in low zinc condition. Cell 12:903 34. Ray SK, Matzelle DD, Wilford GG, Hogan EL, Banik NL (2001) Inhibition of calpainmediated apoptosis by E-64 d-reduced immediate early gene (IEG) expression and reactive astrogliosis in the lesion and penumbra following spinal cord injury in rats. Brain Res 916: 115–126 35. Ray SK, Patel SJ, Welsh CT, Wilford GG, Hogan EL, Banik NL (2002) Molecular evidence of apoptotic death in malignant brain tumors including glioblastoma multiforme: upregulation of calpain and caspase-3. J Neurosci Res 69:197–206 36. Das A, Guyton MK, Smith A, Wallace G 4th, McDowell ML, Matzelle DD, Ray SK, Banik NL (2013) Calpain inhibitor attenuated optic nerve damage in acute optic neuritis in rats. J Neurochem 124:133–146 37. Das A, Garner DP, Del Re AM, Woodward JJ, Kumar DM, Agarwal N, Banik NL, Ray SK (2006) Calpeptin provides functional neuroprotection to rat retinal ganglion cells following Ca2+ influx. Brain Res 1084:146–157 38. Ray SK, Karmakar S, Nowak MW, Banik NL (2006) Inhibition of calpain and caspase-3 prevented apoptosis and preserved electrophysiological properties of voltage-gated and ligandgated ion channels in rat primary cortical neurons exposed to glutamate. Neuroscience 139: 577–595 39. Das A, Banik NL, Ray SK (2010) Flavonoids activated caspases for apoptosis in human glioblastoma T98G and U87MG cells but not in human normal astrocytes. Cancer 116:164– 176

40. Di Lullo E, Kriegstein AR (2017) The use of brain organoids to investigate neural development and disease. Nat Rev Neurosci 18:573– 584 41. Gonzalez C, Armijo E, Bravo-Alegria J, Becerra-Calixto A, Mays CE, Soto C (2018) Modeling amyloid beta and tau pathology in human cerebral organoids. Mol Psychiatry 23: 2363–2374 42. Qian X, Song H, Ming GL (2019) Brain organoids: advances, applications and challenges. Development 146:dev166074 43. Chiaradia I, Lancaster MA (2020) Brain organoids for the study of human neurobiology at the interface of in vitro and in vivo. Nat Neurosci 23:1496–1508 44. Ramirez S, Mukherjee A, Sepulveda S, BecerraCalixto A, Bravo-Vasquez N, Gherardelli C, Chavez M, Soto C (2021) Modeling traumatic brain injury in human cerebral organoids. Cell 10:2683 45. Bubnys A, Tsai LH (2022) Harnessing cerebral organoids for Alzheimer’s disease research. Curr Opin Neurobiol 72:120–130 46. Lai BQ, Feng B, Che MT, Wang LJ, Cai S, Huang MY, Gu HY, Jiang B, Ling EA, Li M, Zeng X, Zeng YS (2018) A modular assembly of spinal cord-like tissue allows targeted tissue repair in the transected spinal cord. Adv Sci (Weinh) 5:1800261 47. Buchner F, Dokuzluoglu Z, Grass T, Rodriguez-Muela N (2023) Spinal cord organoids to study motor neuron development and disease. Life (Basel) 13:1254 48. Tamaki Y, Ross JP, Alipour P, Castonguay CE´, Li B, Catoire H, Rochefort D, Urushitani M, Takahashi R, Sonnen JA, Stifani S, Dion PA, Rouleau GA (2023) Spinal cord extracts of amyotrophic lateral sclerosis spread TDP-43 pathology in cerebral organoids. PLoS Genet 19:e1010606 49. Xu J, Fang S, Deng S, Li H, Lin X, Huang Y, Chung S, Shu Y, Shao Z (2023) Generation of neural organoids for spinal-cord regeneration via the direct reprogramming of human astrocytes. Nat Biomed Eng 7:253–269 50. Schildge S, Bohrer C, Beck K, Schachtrup C (2013) Isolation and culture of mouse cortical astrocytes. J Vis Exp 71:50079 51. Gamble A, Suessmilch M, Bonestroo A, Merits A, Graham GJ, Cavanagh J, Edgar JM, Pingen M (2023) Establishing mixed neuronal and glial cell cultures from embryonic mouse brains to study infection and innate immunity. J Vis Exp (196). https://doi.org/10.3791/ 65331

Detection of Apoptosis Explicitly in Neurons and Glial Cells 52. Shimizu S, Abt A, Meucci O (2011) Bilaminar co-culture of primary rat cortical neurons and glia. J Vis Exp (57):3257 53. de Vellis J, Cole R (2012) Preparation of mixed glial cultures from postnatal rat brain. Methods Mol Biol 814:49–59 54. Podbielska M, Das A, Smith AW, Chauhan A, Ray SK, Inoue J, Azuma M, Nozaki K, Hogan EL, Banik NL (2016) Neuron-microglia interaction induced bi-directional cytotoxicity associated with calpain activation. J Neurochem 139:440–455. Erratum in: J Neurochem 2018 Jan;144(2):234 55. Pistollato F, Canovas-Jorda D, Zagoura D, Price A (2017) Protocol for the differentiation of human induced pluripotent stem cells into mixed cultures of neurons and glia for neurotoxicity testing. J Vis Exp (124):55702 56. Mangiameli E, Freschi M, Luciani M, Gritti A (2022) Generation of neuronal/glial mixed cultures from human induced pluripotent stem cells (hiPSCs). Methods Cell Biol 171: 229–245 57. Samantaray S, Knaryan VH, Le Gal C, Ray SK, Banik NL (2011) Calpain inhibition protected spinal cord motoneurons against 1-methyl-4phenylpyridinium ion and rotenone. Neuroscience 192:263–274 58. Luchena C, Zuazo-Ibarra J, Valero J, Matute C, Alberdi E, Capetillo-Zarate E (2022) A neuron, microglia, and astrocyte triple co-culture model to study Alzheimer’s disease. Front Aging Neurosci 14:844534 59. Kunze A, Lengacher S, Dirren E, Aebischer P, Magistretti PJ, Renaud P (2013) Astrocyteneuron co-culture on microchips based on the model of SOD mutation to mimic ALS. Integr Biol (Camb) 5:964–975 60. Du Y, Chen CP, Tseng CY, Eisenberg Y, Firestein BL (2007) Astroglia-mediated effects of uric acid to protect spinal cord neurons from glutamate toxicity. Glia 55:463–472 61. Stover KR, Campbell MA, Van Winssen CM, Brown RE (2015) Early detection of cognitive deficits in the 3xTg-AD mouse model of Alzheimer’s disease. Behav Brain Res 289:29–38 62. Alavi MS, Fanoudi S, Hosseini M, Sadeghnia HR (2022) Beneficial effects of levetiracetam in streptozotocin-induced rat model of Alzheimer’s disease. Metab Brain Dis 37:689–700 63. Miller LG Jr, Young JA, Ray SK, Wang G, Purohit S, Banik NL, Dasgupta S (2017) Sphingosine toxicity in EAE and MS: evidence for ceramide generation via serinepalmitoyltransferase activation. Neurochem Res 42:2755–2768

25

64. Ray SK, Matzelle DC, Wilford GG, Hogan EL, Banik NL (2000) E-64-d prevents both calpain upregulation and apoptosis in the lesion and penumbra following spinal cord injury in rats. Brain Res 867:80–89 65. Samantaray S, Das A, Matzelle DC, Yu SP, Wei L, Varma A, Ray SK, Banik NL (2016) Administration of low dose estrogen attenuates persistent inflammation, promotes angiogenesis, and improves locomotor function following chronic spinal cord injury in rats. J Neurochem 137:604–617 66. Hernango´mez M, Mestre L, Correa FG, ˜igo PM, Docagne F, WilLorı´a F, Mecha M, In liams RO, Borrell J, Guaza C (2012) CD200CD200R1 interaction contributes to neuroprotective effects of anandamide on experimentally induced inflammation. Glia 60: 1437–1450 67. Correa FG, Hernango´mez M, Guaza C (2013) Understanding microglia-neuron cross talk: relevance of the microglia-neuron cocultures. Methods Mol Biol 1041:215–229 68. Zelenka L, P€agelow D, Kru¨ger C, Seele J, Ebner F, Rausch S, Rohde M, Lehnardt S, van Vorst K, Fulde M (2022) Novel protocol for the isolation of highly purified neonatal murine microglia and astrocytes. J Neurosci Methods 366:109420 69. English J, McSweeney D, Ribbe F, Howell E, Pak C (2023) Generation and co-culture of cortical glutamatergic and GABAergic-induced neuronal cells. Methods Mol Biol 2683:21–37 70. Slanzi A, Iannoto G, Rossi B, Zenaro E, Constantin G (2020) In vitro models of neurodegenerative diseases. Front Cell Dev Biol 8:328 71. Omelchenko A, Singh NK, Firestein BL (2020) Current advances in in vitro models of central nervous system trauma. Curr Opin Biomed Eng 14:34–41 72. Pilipovic´ K, Harej Hrkac´ A, Kucˇic´ N, Mrsˇic´-Pelcˇic´ J (2022) Modeling central nervous system injury in vitro: current status and promising future strategies. Biomedicine 11:94 73. Tieu K (2011) A guide to neurotoxic animal models of Parkinson’s disease. Cold Spring Harb Perspect Med 1:a009316 74. Lama J, Buhidma Y, Fletcher EJR, Duty S (2021) Animal models of Parkinson’s disease: a guide to selecting the optimal model for your research. Neuronal. Signals 5:NS20210026 75. Upadhayay S, Yedke NG, Rahi V, Singh S, Kumar S, Arora A, Chandolia P, Kaur P, Kumar M, Koshal P, Jamwal S, Kumar P (2023) An overview of the pathophysiological mechanisms of 3-nitropropionic acid (3-NPA) as a neurotoxin in a huntington’s disease model

26

Swapan K. Ray

and its relevance to drug discovery and development. Neurochem Res 48:1631–1647 76. Zhang MJ, Zhong JH, Yi XJ (2023) Effect of curcumin on cognitive behavior and pathological characteristics of the hippocampus in mice with inherent Alzheimer’s disease. Bull Exp Biol Med 175:335–340 77. Haque A, Das A, Samantaray S, Matzelle D, Capone M, Wallace G, Husarik AN, Taheri S, Reiter RJ, Varma A, Ray SK, Banik NL (2022) Premarin reduces neurodegeneration and promotes improvement of function in an animal model of spinal cord injury. Int J Mol Sci 23: 2384 78. Van Pelt LF (1997) Ketamine and xylazine for surgical anesthesia in rats. J Am Vet Med Assoc 171:842–844 79. Stokes EL, Flecknell PA, Richardson CA (2009) Reported analgesic and anaesthetic administration to rodents undergoing experimental surgical procedures. Lab Anim 43:149– 154 80. Irwin MR, Curay CM, Choi S, Kiyatkin EA (2023) Basic physiological effects of ketamine-xylazine mixture as a general anesthetic preparation for rodent surgeries. Brain Res 1804:148251 81. Leffa DD, Bristot BN, Damiani AP, Borges GD, Daumann F, Zambon GM, Fagundes GE, de Andrade VM (2016) Anesthetic ketamine-induced DNA damage in different cell types in vivo. Mol Neurobiol 53:5575– 5581 82. Cicero L, Fazzotta S, Palumbo VD, Cassata G, Lo Monte AI (2018) Anesthesia protocols in laboratory animals used for scientific purposes. Acta Biomed 89:337–342 83. Wiley LA, Beebe DC, Mullins RF, Stone EM, Tucker BA (2016) A method for sectioning and immunohistochemical analysis of stem cell-derived 3-D organoids. Curr Protoc Stem Cell Biol 37:1C.19.1–1C.19.11 84. Kessler C, Ho¨ltke HJ, Seibl R, Burg J, Mu¨hlegger K (1990) Non-radioactive labeling and detection of nucleic acids. I. A novel DNA labeling and detection system based on digoxigenin: anti-digoxigenin ELISA principle (digoxigenin system). Biol Chem Hoppe Seyler 371:917–927 85. Ho¨ltke HJ, Ankenbauer W, Mu¨hlegger K, Rein R, Sagner G, Seibl R, Walter T (1995) The digoxigenin (DIG) system for non-radioactive labelling and detection of

nucleic acids--an overview. Cell Mol Biol (Noisy-le-Grand) 41:883–905 86. Ray S, Ponnathpur V, Huang Y, Tang C, Mahoney ME, Ibrado AM, Bullock G, Bhalla K (1994) 1-β-D-arabinofuranosylcytosine-, mitoxantrone-, and paclitaxel-induced apoptosis in HL-60 cells: improved method for detection of internucleosomal DNA fragmentation. Cancer Chemother Pharmacol 34:365–371 87. Chakrabarti M, Banik NL, Ray SK (2014) miR-7-1 potentiated estrogen receptor agonists for functional neuroprotection in VSC4.1 motoneurons. Neuroscience 256: 322–333 88. Chakrabarti M, Ray SK (2015) Direct transfection of miR-137 mimics is more effective than DNA demethylation of miR-137 promoter to augment anti-tumor mechanisms of delphinidin in human glioblastoma U87MG and LN18 cells. Gene 573:141–152 89. Chakrabarti M, Banik NL, Ray SK (2013) Photofrin based photodynamic therapy and miR-99a transfection inhibited FGFR3 and PI3K/Akt signaling mechanisms to control growth of human glioblastoma in vitro and in vivo. PLoS One 8:e55652 90. Collins AR (2004) The comet assay for DNA damage and repair: principles, applications, and limitations. Mol Biotechnol 26:249–261 91. Azqueta A, Collins AR (2013) The essential comet assay: a comprehensive guide to measuring DNA damage and repair. Arch Toxicol 87: 949–968 92. Liu X, Zou H, Slaughter C, Wang X (1997) DFF, a heterodimeric protein that functions downstream of caspase-3 to trigger DNA fragmentation during apoptosis. Cell 89:175–184 93. Enari M, Sakahira H, Yokoyama H, Okawa K, Iwamatsu A, Nagata S (1998) A caspaseactivated DNase that degrades DNA during apoptosis, and its inhibitor ICAD. Nature 391:43–50 94. Didenko VV, Ngo H, Minchew CL, Boudreaux DJ, Widmayer MA, Baskin DS (2002) Caspase-3-dependent and -independent apoptosis in focal brain ischemia. Mol Med 8:347–352 95. Huang Y, Ray S, Reed JC, Ibrado AM, Tang C, Nawabi A, Bhalla K (1997) Estrogen increases intracellular p26Bcl-2 to p21Bax ratios and inhibits taxol-induced apoptosis of human breast cancer MCF-7 cells. Breast Cancer Res Treat 42:73–81

Chapter 2 Isolation of Capillaries from Small Amounts of Mouse Brain Tissue Junqiao Mi, Aili Sun, Laura H€artel, Christina Dilling, Patrick Meybohm, and Malgorzata Burek Abstract The integrity of the blood-brain barrier (BBB) is essential for the normal functioning of the central nervous system (CNS). Isolated brain capillaries are essential for analyzing changes in protein and gene expression at the BBB under physiological and pathological conditions. The standard methods for isolating brain capillaries require the use of at least one or more mouse brains in order to obtain sufficient quantity and purity of brain capillaries. Here, we describe an optimized protocol for isolating and purifying capillaries from tiny amounts of mouse cerebral cortex using manual homogenization, density gradient centrifugation, and filtration while preserving the structural integrity and functional activity of microvessel fragments. Western blotting showed that proteins expressed at the BBB were enriched in mouse brain capillaries isolated by the optimized method compared to cerebral cortex protein homogenates. This approach can be used for the analysis of a variety of rare mouse genetic models and can also help the investigators to understand regional differences in susceptibility to pathological phenomena such as ischemia and traumatic brain injury. This will allow the investigators to better understand the physiology and pathology of the BBB. Key words Blood-brain barrier, Brain capillaries isolation, Endothelial cells, Central nervous system

1

Introduction The central nervous system (CNS) includes the brain and spinal cord, with the brain being considered the most important part of the entire human body and often referred to as the body’s control center [1]. The cerebral vasculature is a complex system composed of endothelial cells, pericytes, astrocytes, and extracellular matrix components, forming the unique blood-brain barrier (BBB) that lies at the interface between circulating blood and the neural tissue [2, 3]. The BBB represents a defensive network of blood vessels that form a complex, dynamic network that prevents the entry of harmful substances such as neurotoxic debris from blood, cells, or microbial pathogens [4]. Effectively blocking the entry of these

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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substances from the blood into the CNS via this pathway is crucial to prevent the onset of various neurological disorders. Neurotransmitters in this barrier help communicate effectively with other cells of the CNS to regulate key events and maintain homeostasis. For instance, they respond to pathological conditions in the body during the onset and progression of the disease [4, 5]. Previous studies suggest that BBB disruption is associated with several neurological disorders such as Alzheimer’s disease [6], Parkinson’s disease [7], ischemic stroke [8], multiple sclerosis [9], traumatic brain injury [10], and subarachnoid hemorrhage [11]. Various transporter proteins at the BBB can transport the necessary nutrient molecules such as vitamins, minerals, and glucose from the blood to the brain and help in the elimination of toxins/metabolites [1]. Despite the emphasis on the importance of the BBB in many pathophysiological diseases, the molecular and cellular mechanisms leading to BBB dysfunction or disruption in these disease conditions remain poorly unknown. Therefore, it is crucial to develop a method to isolate brain microvasculature with preserved structural integrity to identify variations in the BBB in pathological models. It is now generally accepted that the cerebral microvascular endothelium is an integral part of the BBB. The development of methods to isolate brain capillaries from animal and human brains has provided a valuable resource for studying the transport properties of the BBB [12]. In this regard, isolated brain capillaries represent a unique in vitro BBB model that closely resembles barrier properties in vivo, allowing for the study of barrier function and dysfunction in health and disease. Various methods for isolating and purifying brain capillaries are available [13–16]. In general, cerebral capillary suspensions are prepared from brains by a combination of techniques, including mechanical homogenization, enzyme treatment, density gradient centrifugation, filtration through nylon meshes, and glass bead column filtration [17– 20]. However, current methods for isolating brain capillaries have several limitations, including the application of exogenous enzymes, and incubation at 37 °C, which caused unwanted molecular and metabolic changes, led to cell activation and compromised RNA integrity [21]. The use of glass bead columns increases the technical complexity to the procedure and does not yield pure brain microvessel preparations [22]. Brain capillaries isolated by density centrifugation are easily contaminated by large blood vessels and neuronal cells [23]. Capillary isolation usually requires multiple mouse brains to allow sufficient amount and purity of brain capillary fraction for protein expression analysis [24]. In many cases of genetically modified mice and pathological mouse models, it is quite difficult to obtain the appropriate mouse numbers for brain capillary isolation. In addition, it takes longer to dissect multiple brains, and the use of pooled tissues can significantly reduce data quality while increasing experimental costs, as

Isolation of Capillaries from Small Amounts of Brain Tissue

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multiple animals may be required for a single time point, especially when a specific brain region is of interest in models of ischemia and brain trauma. Therefore, establishing a method for isolating brain capillaries from tiny amounts of brain tissue is important to assess changes in the expression profile of the BBB protein and to contribute to the understanding of the role of the BBB in CNS diseases. To overcome these limitations, we describe an optimized protocol for isolation of brain capillaries that allows us to isolate a workable number of capillaries from 30 to 50 mg brain samples. We optimized various details of the capillary isolation process to obtain intact, viable brain capillaries and successfully determined protein expression and activity. This protocol allows researchers to study changes in BBB function under physiological and pathological conditions, thus helping researchers to establish valid in vitro models of the BBB.

2

Materials Prepare fresh solutions with ultrapure water or DPBS at room temperature. Filter the solutions, and then store them at the appropriate temperature indicated below (see Note 1).

2.1

Animals

2.2

Reagents

Use frozen brains of adult C57BL/6 mice (6 weeks to 3 months old) for capillary isolation. Perform the experiments in accordance with the institutional guidelines and law for animal protection. 1. Nonfat dried milk powder. 2. Dulbecco’s phosphate-buffered saline modified (DPBS). 3. Bovine serum albumin (BSA). 4. Sodium pyruvate solution. 5. Dextran 70. 6. NuPAGE™ 4–12%, Bis-Tris gel. 7. PageRuler™ Plus Prestained Protein Ladder (Thermo Scientific). 8. Tween® 20. 9. NucleoSpin® miRNA (Macherey Nagel). 10. Protein Quantification Assay (Macherey Nagel). 11. P-glycoprotein monoclonal antibody. 12. Claudin 5 monoclonal antibody. 13. NeuN antibody.

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14. Anti-rabbit antibody.

IgG,

horseradish

peroxidase

(HRP)-linked

15. Anti-mouse IgG, HRP-linked antibody. 2.3

Equipment

1. Eppendorf microcentrifuge. 2. High speed centrifuge (Beckman Coulter). 3. Fixed-angle rotor (Beckman Coulter). 4. Conical tubes (15 mL). 5. PluriStrainer® 10 μM (pluriSelect Life Science). 6. Kimwipes. 7. Fluorescence microscope (Keyene). 8. Corning® 50 mL centrifuge tubes. 9. Megafuge ST4 Plus (Thermo Scientific).

2.4 Solutions for Brain Capillaries Isolation

1. 2% (v/v) FCS-DPBS: Add 2 mL of FCS to 100 mL of DPBS in a beaker and mix well with a magnetic stirrer. Filter sterile with a bottle top filter and store at 4 °C to prepare a density gradient medium. 2. Density gradient medium: 18% (w/v) dextran–2% (v/v) FCS-DPBS: Weigh 18 g of dextran into a glass bottle, and add a magnetic stir bar. Add 100 mL of 2% (v/v) FCS-DPBS, and shake gently in a 37 °C water bath for 60 min until all the powder is completely dissolved. This solution can be stored at -20 °C for up to 6 months (see Note 2). 3. Isolation Capillary Buffer A: Weigh 103 mM NaCl (12.040 g), 4.7 mM KCl (0.700 g), 2.5 mM CaCl2 dihydrate (0.734 g), 1.2 mM KH2PO4 (0.326 g), 1.2 mM MgSO4·7H2O (0.592 g), and 15 mM HEPES (7.150 g), and transfer to a 2 L graduate beaker. Add 1800 mL of ultrapure water to the cylinder, mix with a magnetic stir bar, and adjust the pH to 7.4 with a solution of 0.1 M NaOH or 0.3 M NaOH. Fill up to 2 L with ultrapure water and mix. Store the buffer at 4 °C until use. 4. Isolation Capillary Buffer B: Weigh 25 mM NaHCO3 (3.150 g), 10 mM glucose (2.973 g), 1 mM pyruvate (15 mL), and 15 g bovine serum albumin into 1.5 L of Isolation Capillary Buffer A to a final BSA concentration of 1%. Stir slowly to avoid bubbles, mix with a magnetic stir bar, and filter with a sterile syringe filter, pore size 0.2 μM. Store the buffer at 4 °C for up to 3 months (see Note 3).

2.5 Solutions for Western Blotting

1. NuPAGE® LDS Sample Buffer: The NuPAGE® LDS Sample Buffer (4×) is available from Invitrogen or dissolve the following reagents to 8 mL of ultrapure water (106 mM Tris-HCl, 141 mM Tris base, 2% LDS, 10% glycerol, 0.51 mM EDTA,

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0.22 mM SERVA Blue G250, 0.175 mM phenol red). Mix the solution thoroughly and adjust the volume to 10 mL with ultrapure water. The buffer is stable for 6 months when stored at 4 °C. The pH of the 1× solution is 8.5. Do not use acid or base to adjust the pH. 2. NuPAGE® MOPS SDS Running Buffer:The NuPAGE® MOPS SDS Running Buffer (20×) is available from Invitrogen or dissolve the following reagents to 400 mL ultrapure water (50 mM MOPS, 50 mM Tris base, 0.1% SDS, 1 mM EDTA). Mix the solution completely, and adjust the volume to 500 mL with ultrapure water. The buffer is stable for 6 months when stored at 4 °C. The pH of the 1× solution is 7.7. Do not use acid or base to adjust the pH. 3. NuPAGE® Transfer Buffer: The NuPAGE® Transfer Buffer (20×) is available from Invitrogen or dissolve the following reagents in 100 mL ultrapure water (25 mM Bicine, 25 mM Bis-Tris (free base), 1 mM EDTA). Mix the solution well and adjust the volume to 125 mL with ultrapure water. The buffer is stable for 6 months when stored at 4 °C. The pH of the 1× solution is 7.2. Do not use acid or base to adjust the pH. 4. 5% blocking solution: Weigh 5 g nonfat dried milk powder and transfer to a 100 mL measuring beaker. Add 100 mL of DPBS and mix with a magnetic stir bar. Use immediately. 5. Washing solution: Add 1 mL Tween® 20 detergent to 1 L DPBS to a final concentration of 0.1% (v/v) Tween-DPBS, and mix well. Use immediately or store at room temperature (see Note 4). 2.6 Preparation of the Experimental Setup

1. Place all buffers on ice and pre-chill all clean tools. 2. Ultracentrifuge: Pre-chill the ultracentrifuge to 4 °C before use. 3. Filtration: Place connector rings and PluriStrainer on 50 mL tubes.

3

Methods

3.1 Capillary Isolation from Small Amounts of Mouse Brain

Figure 1 shows the workflow chart of the whole isolation of capillaries described below. Mouse brain tissue can be from any part of the cerebral cortex, the weight of the selected mouse brain tissue is about 30–50 mg and can be used fresh or frozen. The entire procedure should always be carried out in a cold room (see Note 5). 1. For the isolation of mouse brains, we have successfully obtained the whole mouse brains. Cut the brain sagittal with a razor blade and remove the cerebellum. Isolate the cortex by

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Fig. 1 Flowchart for capillary isolation. The pictogram illustrates the major steps of the procedure for isolating brain capillaries from tiny amounts of mouse brain tissue

removing all deep brain structures, including the hippocampus, and remaining white matter, being careful not to damage the cortex. The desired portion of the cerebral cortex is sectioned with a sterile razor blade and weighed recording the weight of cortical tissue obtained. Isolated brain tissue can be used directly or stored permanently at -80 °C for later use (see Note 6). 2. Homogenize tiny amounts of brain tissue with 100 μL DPBS / 2% FCS with a plastic blunt-ended rod on ice in a 1.5 mL tube (see Note 7). Gently hand grind the tissue 30 times over a period of 3 min, turning the grinder at a steady speed to homogenize the suspension (see Note 8). 3. Resuspend the mixture in 18% (w/v) dextran—2% (v/v) FCS-DPBS. Transfer the brain homogenate to a Sarstedt tube by rinsing the plastic rod and tube with 8 mL dextran solution, adjust with dextran solution until the tare weight of the samples differs by a maximum of 0.1 g before centrifugation. 4. Mix the homogenate, density gradient medium and buffer gently and evenly with a pipette. Close the centrifuge tubes tightly with caps. Centrifuge at 5.800 g for 12 min at 4 °C, decelerate slow (Beckman Centrifuge) (see Note 9). 5. Filtration: Carefully remove the supernatant without shaking and use kimwipes to remove the brain waste adhering to the wall of the tubes from which the supernatant was removed (see Note 10). Resuspend pellet in 3 mL of Isolation Capillary Buffer B, then transfer this 3 mL of solution on a 10 μM PluriStrainer (place on a 50 mL tube), and use a 20 mL syringe attached to the green connector ring to create negative

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Fig. 2 Representative images of capillaries in the mouse cerebral cortex. (a) 40× magnification, (b) 100× magnification

pressure. Rinse the 10 mL white tube with an additional 3 mL of Isolation Capillary Buffer B and filter as well. Wash the cell strain filter with 40 mL of Isolation Capillary Buffer B (see Note 11). 6. Capillary collection: Invert the filter and place it on a new 50 mL tube (flowthrough can be discarded). Wash the inverted cell strain filter with 40 mL of Isolation Capillary Buffer B by applying gentle pressure with the tip of a 5 mL pipette and moving it over the filter (see Note 12). 7. Wash: After collecting the capillaries, centrifuge all samples at 4.000 g for 10 min at 4 °C, accelerate 9, decelerate 8. Remove the supernatant and resuspend the pellet in approximately 1 mL of Isolation Capillary Buffer A. To ensure purity quickly assess the capillaries by a microscopic technique, remove a drop of the capillaries suspension from the solution, plate onto a microscope slide, examine by staining under an inverted microscope for visual validation. Document capillary purity using a microscope and camera. The procedures yielded intact capillary fragments as examined under a light microscope, although some small fragments of brain parenchymal debris could be seen (Fig. 2). 8. Centrifuge at 5000 g for 10 min at 4 °C. This gives the final capillary pellet. 9. Remove supernatant and add 300 μL ML-Buffer to the tube (NucleoSpin® miRNA, Macherey Nagel). The isolated brain capillaries can be stored at -80 °C for 6–12 months or further used directly for RNA and protein isolation and processing.

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3.2 Western Blotting for Protein Expression in Isolated Capillaries from Small Amounts of Brain

For protein extraction and expression in isolated capillaries, the protocol mentioned below can be followed. Reagents and consumables for protein extraction come from the NucleoSpin® miRNA kit. 1. Remove the “capillary sample” tubes (capillaries containing 300 μL ML buffer = lysis buffer) from the -80 °C freezer and thaw on ice. 2. Freshly dissolve rDNase (stored at 4 °C) or thaw already dissolved rDNase (stored at -20 °C) on ice. 3. Homogenize the thawed “capillary sample” with a 200 μL pipette. 4. After the protein extraction and isolation steps of NucleoSpin® miRNA kit, we can obtain the lysate, which contains total RNA and protein. 5. Add 300 μL Buffer MP (protein precipitation buffer) to the lysate from step 4. Vortex the mixture for 5 s and centrifuge for 3 min at 11,000 g to pellet protein (see Note 13). 6. Add 500 μL of 50% ethanol to the protein pellet (no mixing or incubation is required) and centrifuge for 1 min at 11,000 g. 7. Remove the supernatant completely and allow the protein pellet to dry at room temperature for 10 min. 8. Add a total of 20 μL protein lysis buffer with 5 μL NuPAGE® LDS Sample Buffer (4×), 2 μL NuPAGE® Reducing Agent (10×) and 13 μL RIPA lysis buffer. Vortex the mixture to completely dissolve the protein. Protein can be stored at 20 °C for up to 2 months prior to processing. Measure the protein concentration using the Protein Quantification Assay kit. 9. For the optimal results, heat the sample for denaturing electrophoresis at 70 °C for 10 min. 10. Use 1× NuPAGE® SDS Running Buffer for electrophoresis of denatured samples (see Note 14). 11. After performing electrophoresis, proteins can be transferred to membranes for subsequent analysis. For blotting NuPAGE® gels, use 1× NuPAGE® Transfer Buffer (see Note 14). 12. Briefly, each sample was run on a NuPAGE® Bis-Tris Gel and blotted onto a polyvinylidene difluoride (PVDF) membrane. 13. Get the PVDF membrane from the 4 °C cold room, block nonspecific binding sites by incubating the PVDF membrane with 5% blocking solution, placing on a shaker platform for 60 min. 14. Incubate the PVDF membrane overnight with primary antibodies to detect blood-brain barrier proteins. The following antibodies were used: anti-claudin 5 (1:500), anti-P-gp (1:50) and anti-NeuN (1:1000).

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Fig. 3 Western blot analysis of protein extracts from whole cortex and brain capillary fraction. Protein extracts were prepared from tiny amounts of whole cortex or isolated brain capillaries. Western blot of blood-brain barrier marker (claudin 5, P-glycoprotein, P-gp) and the neuronal marker NeuN were performed using specific antibodies. Representative Western blot images are shown (a). (b–d) Quantification of each band intensity was performed by using Image J software. Protein expression was normalized using β-actin as a loading control. * P < 0.05; **P < 0.01; ****P < 0.0001 (t-test)

15. Collect the primary antibody and store in a -20 °C freezer, rinse the PVDF membrane with washing solution on a shaking platform for 30 min at room temperature. Discard and change the washing solution for 10 min each. 16. Incubate the PVDF membrane with secondary antibodies for 1 h with constant shaking at room temperature. The following secondary antibodies were used: anti-mouse IgG (1:3000) and anti-rabbit IgG (1:3000). 17. Discard the secondary antibodies, and rinse the PVDF membrane three times with washing solution for 10 min each on a shaker. 18. Remove the blot from the wash solution and develop it in enhanced chemiluminescence solution (ECL) for at least 1 min (ECL A: ECL B = 1:1), then read under camera (Fig. 3).

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Notes 1. Staff must always wear gloves. Glassware is autoclaved after each use. Waste is collected in a labeled biohazard waste bag and autoclaved. Sharps are collected in puncture-proof and leak-proof containers marked as biohazard. Loss of capillaries is often the cumulative sum of small losses at each step and is due to small variations during the process. Therefore, pay attention to the modification of each step of isolating capillaries. 2. Density gradient medium is a key component affecting capillary number. An incorrect concentration in buffer will result in an incorrect density and the inability to separate capillaries from cellular debris and thus reduce the volume of the capillary pellet. After taking out the dextran solution from the -20 °C freezer, it should be incubated in the 37 °C water bath for 10 min and shaken completely before use to ensure the uniform concentration of the dextran solution. 3. Gently stir Isolation Capillary Buffer B before use; avoid creating bubbles to avoid albumin denaturation. 4. Tween® 20 detergent is very viscous, reverse pipetting, slow aspiration, and slow dispensing can be used in preparing the wash solution. 5. All steps (from brain dissection to the last step) should be performed in a cold room (4 °C). We found that capillaries obtained at cold temperature retained longer viability and better RNA integrity. 6. For isolating mouse brains, note that we used closed forceps to roll the brains gently and evenly on blotting paper to remove the meninges and meningeal vessels, avoiding interference with large vessels. Due to the different densities of capillaries in various parts of the brain, we carefully removed the deep white matter section, preserved, and sectioned the cerebral cortex and removed the section used for capillary isolation and weighed it. 7. If using frozen samples, thaw samples on ice to obtain high yield of intact capillary fragments. 8. Do not stir in the air to avoid bubbling. Grinding time should be less than 3 min, using gentle force to avoid excessive capillary breakage and maintain capillary structure integrity. 9. Note that the acceleration and deceleration speed of the centrifuge can also affect the formation of the brain capillary pellet.

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10. Before opening the centrifuge lid, remember the outer position of the Sarstedt tube, the outer part is the capillaries, and the top and inner parts are lipids and waste. Carefully remove the Sarstedt tube to avoid shaking and reduce contamination from impurities. 11. It is not recommended to use more than 3 mL of DPBS. You will see some debris on the wall of the tube that can be easily picked up by pipetting. Using a small volume is beneficial to minimize contamination. Gently apply even pressure during the negative pressure process, be careful not to build up excess pressure and avoid air bubbles. 12. Avoid creating bubbles when collecting capillaries, because bubbles can cause loss of capillaries, and pay attention to the edge of the filter to obtain more capillaries. 13. Buffer ML contains enormous amounts of chaotropic salt and β-mercaptoethanol. In addition, ethanol is added prior to protein precipitation, resulting in completely denatured protein. Therefore, it can be difficult to resuspend protein pellets from the whole brain. Take a 10–20 μL aliquot of the lysate before adding Buffer MP, and separately precipitate this smaller portion with an appropriate volume of Buffer MP (6.7–13.3 μL Buffer MP, respectively). 14. Do not forget to use proprietary NuPAGE® antioxidants in the running buffer and transfer buffer.

Acknowledgments We thank Elisabeth Wilken for her excellent technical assistance. References 1. Abbott NJ, Patabendige AAK, Dolman DEM et al (2010) Structure and function of the blood–brain barrier. Neurobiol Dis 37:13–25. https://doi.org/10.1016/j.nbd.2009.07.030 2. Abbott NJ, Friedman A (2012) Overview and introduction: the blood-brain barrier in health and disease. Epilepsia 53:1–6. https://doi. org/10.1111/j.1528-1167.2012.03696.x 3. Obermeier B, Daneman R, Ransohoff RM (2013) Development, maintenance, and disruption of the blood-brain barrier. Nat Med 19:1584–1596. https://doi.org/10.1038/ nm.3407 4. Daneman R, Prat A (2015) The blood–brain barrier. Cold Spring Harb Perspect Biol 7: a 0 2 0 4 1 2 . h t t p s : // d o i . o r g / 1 0 . 1 1 0 1 / cshperspect.a020412

5. Furtado D, Bjo¨rnmalm M, Ayton S et al (2018) Overcoming the blood–brain barrier: the role of nanomaterials in treating neurological diseases. Adv Mater 30:1801362. https:// doi.org/10.1002/adma.201801362 6. Nation DA, Sweeney MD, Montagne A et al (2019) Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med 25:270–276. https://doi.org/ 10.1038/s41591-018-0297-y 7. Lan G, Wang P, Chan RB et al (2022) Astrocytic VEGFA: an essential mediator in blood– brain-barrier disruption in Parkinson’s disease. Glia 70:337–353. https://doi.org/10.1002/ glia.24109 8. Kassner A, Merali Z (2015) Assessment of blood–brain barrier disruption in stroke.

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Stroke 46:3310–3315. https://doi.org/10. 1161/strokeaha.115.008861 9. Ortiz GG, Pacheco-Moise´s FP, Macı´as-Islas ´ et al (2014) Role of the blood–brain barMA rier in multiple sclerosis. Arch Med Res 45: 687–697. https://doi.org/10.1016/j. arcmed.2014.11.013 10. Alluri H, Wiggins-Dohlvik K, Davis ML et al (2015) Blood–brain barrier dysfunction following traumatic brain injury. Metab Brain Dis 30:1093–1104. https://doi.org/10. 1007/s11011-015-9651-7 11. Gendosz de Carrillo D, Student S, Bula D et al (2023) The protective effect of low-dose minocycline on brain microvascular ultrastructure in a rodent model of subarachnoid hemorrhage. Histochem Cell Biol 159:91–114. https://doi.org/10.1007/s00418-02202150-9 12. Helms HC, Abbott NJ, Burek M et al (2016) In vitro models of the blood–brain barrier: an overview of commonly used brain endothelial cell culture models and guidelines for their use. J Cereb Blood Flow Metab 36:862–890. h t t p s : // d o i . o r g / 1 0 . 1 1 7 7 / 0271678x16630991 13. Wu Z, Hofman FM, Zlokovic BV (2003) A simple method for isolation and characterization of mouse brain microvascular endothelial cells. J Neurosci Methods 130:53–63. https:// doi.org/10.1016/s0165-0270(03)00206-1 14. Munikoti VV, Hoang-Minh LB, Ormerod BK (2012) Enzymatic digestion improves the purity of harvested cerebral microvessels. J Neurosci Methods 207:80–85. https://doi. org/10.1016/j.jneumeth.2012.03.011 15. Goldstein GW, Wolinsky JS, Csejtey J, Diamond I (1975) Isolation of metabolically active capillaries from rat brain. J Neurochem 25: 715–717. https://doi.org/10.1111/j. 1471-4159.1975.tb04395.x 16. Hartz AMS, Schulz JA, Sokola BS et al (2018) Isolation of cerebral capillaries from fresh human brain tissue. J Vis Exp. https://doi. org/10.3791/57346 17. Takakura Y, Audus KL, Borchardt RT (1991) Blood—brain barrier: transport studies in

isolated brain capillaries and in cultured brain endothelial cells:137–165. https://doi.org/ 10.1016/s1054-3589(08)60034-4 18. Dilling C, Roewer N, Fo¨rster CY, Burek M (2017) Multiple protocadherins are expressed in brain microvascular endothelial cells and might play a role in tight junction protein regulation. J Cereb Blood Flow Metab 37:3391– 3 4 0 0 . h t t p s : // d o i . o r g / 1 0 . 1 1 7 7 / 0271678x16688706 19. Burek M, Ko¨nig A, Lang M et al (2019) Hypoxia-induced MicroRNA-212/132 alter blood-brain barrier integrity through inhibition of tight junction-associated proteins in human and mouse brain microvascular endothelial cells. Transl Stroke Res 10:672–683. https://doi.org/10.1007/s12975-0180683-2 20. Burek M, Burmester S, Salvador E et al (2020) Kidney ischemia/reperfusion injury induces changes in the drug transporter expression at the blood–brain barrier in vivo and in vitro. Front Physiol 11. https://doi.org/10.3389/ fphys.2020.569881 21. Lee Y-K, Uchida H, Smith H et al (2019) The isolation and molecular characterization of cerebral microvessels. Nat Protoc 14:3059– 3081. https://doi.org/10.1038/s41596019-0212-0 22. Silbergeld DL, Ali-Osman F (1991) Isolation and characterization of microvessels from normal brain and brain tumors. J Neuro-Oncol 11: 4 9 – 5 5 . h t t p s : // d o i . o r g / 1 0 . 1 0 0 7 / bf00166997 23. Ogata S, Ito S, Masuda T, Ohtsuki S (2021) Efficient isolation of brain capillary from a single frozen mouse brain for protein expression analysis. J Cereb Blood Flow Metab 41:1026– 1 0 3 8 . h t t p s : // d o i . o r g / 1 0 . 1 1 7 7 / 0271678x20941449 24. Paraiso HC, Wang X, Kuo P-C et al (2020) Isolation of mouse cerebral microvasculature for molecular and single-cell analysis. Front Cell Neurosci 14. https://doi.org/10.3389/ fncel.2020.00084

Chapter 3 Isolation of Extracellular Vesicles Using Formulas to Adapt Centrifugation to Different Centrifuges Ramon Handerson Gomes Teles, Daniela Engelmayr, Patrick Meybohm, and Malgorzata Burek Abstract Extracellular vesicles (EVs) are small lipid bilayer vesicles released by cells to facilitate cell-to-cell communication. To study their biological roles and functions, they need to be isolated and purified, which can be achieved through a variety of methods. Here, we describe different methods for isolating and purifying EVs, with a focus on calculating the required g-force and centrifugation time with different centrifuges and rotors. We have compiled key formulas and tested predicted parameters for EV acquisitions to provide a comprehensive guide for EV isolation. Key words Extracellular vesicles isolation, Exosomes, Centrifugation, Breast cancer, Biomarker

1

Introduction Extracellular vesicles (EVs) are structures composed of a lipidic bilayer that play a key role in cell-to-cell communication. These vesicles can be subdivided into exosomes (70–160 nM) and microvesicles (100–1000 nM) [1, 2] and are released by all cells. Several methods can be applied to isolate and acquire EVs, including the use of commercial kits [3–5], size exclusion chromatography (SEC) columns [6], or differential centrifugation [7]. The choice of method depends on the intended downstream application and the specific characteristics of the required EVs. Differential centrifugation is one of the most used methods for EV isolation and involves a series of centrifugation steps at different speeds, times, and temperatures [8–10]. The different rounds of centrifugation aim to collect distinct subclasses of EVs, with the first round usually being used to remove dead cells and debris, the second to collect larger microvesicles, and the third to collect

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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exosomes [11]. However, it is important to note that the parameters used during the centrifugation process can significantly affect the quality, concentration, and morphology of the EVs [12]. Therefore, it is crucial to optimize the centrifugation parameters to obtain the desired EV profile. One of the biggest challenges in EV isolation is the variability of the protocols used by different research groups. This variability can arise from using different centrifuges or rotors without appropriate adaptations, leading to suboptimal EV profiles [13–15]. Therefore, it is important to calculate a new set of parameters when using a different centrifuge or rotor for EV isolation to ensure optimal results. In this study, our aim was to demonstrate the process of optimizing centrifugation parameters for EV isolation using a breast cancer cell line. First, we followed a set of established parameters in the literature to obtain exosomes through the differential centrifugation process. We then recalculated these parameters to match the specifications of our centrifuges. Finally, we compared our results with those obtained with a commercial kit. We characterized the EVs by Western blotting and measured their size using nano-tracking analysis (NTA). In summary, the isolation and characterization of EVs is a critical step to understand their biological functions and to develop clinical applications. Choosing the appropriate isolation methods and optimizing the centrifugation parameters are crucial to obtain high-quality EVs with the desired profile. Our study highlights the importance of adjusting the centrifugation parameters for each specific instrument used to obtain optimal EV profiles, thereby contributing to the development of reliable and reproducible EV isolation protocols.

2

Materials

2.1 Centrifuges, Rotors, and Their Specifications

To optimize the isolation of EVs from a cell culture medium of breast cancer cell line, adopt a strategy to use preexisting centrifugation parameters (Set A) from previous studies [9, 16, 17]. Then, fine-tune these parameters to match the equipment available in your laboratory (Set B) as shown in Table 1. In addition, see a comparison between EVs isolated using our optimized parameters versus those isolated using a commercially available kit (ExoEasy, Qiagen).

2.2

Use tubes for different centrifuges and ultracentrifuges (Table 2).

Tubes

Isolation of Extracellular Vesicles

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Table 1 Specifications of centrifuges and rotors Set A

Set B

Centrifuge

5810 R (Eppendorf)

Megafuge ST4R Plus (Thermo Fisher Scientific)

Rotor

F-34-6-38

TX 750

r max (cm)

10.74

16.80

5.56

6.8

15,000/17,400

4700/4816

Ultracentrifuge

Optima XE

Avanti J30I

Rotor

70ti

JA-30.50Ti

r max (cm)

91.1

108

39.5

40

36,781/100,000

29,000/69,702b

r min (cm) ω (RPM/×g)

a

r min (cm) ω (RPM/×g)

a

a

When using 50 mL tubes Calculation using rav = 74 mM

b

Table 2 Description of tubes for different centrifuges and ultracentrifuges Description/brand/supplier

Max. speed

Material

Falcon 50 mL High Clarity PP Centrifuge Tube, conical bottom, sterile; Corning

16,000 × g

Polyethylene

Corning® 50 mL PP Centrifuge Tubes, conical bottom with plug seal cap; Corning

15,500 × g

Polypropylene

26.3 mL, polycarbonate bottle with cap assembly, 25 × 89 mM; Beckman Coulter

Uninformed

Polycarbonate

50 mL, open-top thickwall polycarbonate open-top tube, 29 × 104 mM; Beckman Coulter

29,000 × g

Polycarbonate

®

3 3.1

Methods Cell Culture

Grow the triple-negative breast cancer cell line MDA-MB-231 in 150 mM plates and DMEM-HG supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Maintain the cells in a humidified cell incubator under a 95% CO2 atmosphere.

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3.2 Acquisition of Extracellular Vesicles

Culture cells until they reach approximately 80% confluency. Replace the old medium with fresh medium without FBS, and maintain cells in a starved state for 24 h. After this period, collect and centrifuge the medium (as described below in Methods to Isolate Extracellular Vesicles). Use the resulting pellet containing the purified EVs for downstream analysis.

3.3 Extracellular Vesicles Characterization

Western blotting: Tumor exosomes and microvesicles were lysed in an appropriate volume of RIPA buffer (50 nM Tris-HCl pH 7.4, 750 mM NaCl, 0.5% sodium deoxycholate, 0.5% SDS, 5% Triton, 1 mM sodium orthovanadate, 50 mM NaF, 5 μg/mL pepstatin, 5 μg/mL aprotinin, and 5 μg/mL leupeptin) on ice for 15 min, followed by centrifugation at 10,000 × g for 10 min at 4 °C (Eppendorf, 5418R). Supernatant was collected and protein concentrations determined by BCA assay, according to kit instructions. An equal amount of 5 μg of protein from each lysate was mixed with 4× Laemmli buffer (750 mM Tris-HCl pH 6.8, 5% SDS, 40% glycerol and 80 mM DTT) and heated at 95 °C for 5 min. The protein extracts were loaded onto 10% polyacrylamide resolving gels and run at 70 mA for 120 min. Resolved proteins were transferred to a 0.22 μM PVDF membrane for 16 h at 4 °C. The membranes were blocked for 1 h at RT in 1× TBS-containing 3% bovine serum albumin (BSA). Proteins were detected by incubation with the following primary antibodies (1:1000): CD-9, CD-63, CD-81, and β-actin, diluted in blocking solution (TBST containing 3% BSA) for 16 h at 4 °C. After four washes with TBS with 0.5% Tween-20 (TBST) (10 min per wash), the membranes were incubated at the appropriate dilution (1:1000) with anti-rabbit or antimouse antibody, diluted in blocking solution for 1 h at RT. The membranes were again washed four times by 10 min each, incubated in ECL and the bands visualized on Imager. Nanoparticle tracking analysis (NTA): Particle number and size distribution in medium samples were determined using a ZetaView Quatt. Samples were diluted (1:2000) in bi-distilled water.

3.4

k-factor:

Calculations

k=

ln

r max r min ω2

× 2:533 × 1011

In which K = The k-factor is a parameter that represents the sedimentation rate of particles in a conical tube under the influence of centrifugal force, which is determined by various parameters of the centrifuge. ln = natural logarithm. r max = the maximum centrifugation radius of the rotor.

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r min = the minimum centrifugation radius of the rotor. ω = rotational speed. Equivalent time of centrifugation: Kx k Tx = The adjusted time to be used in a new centrifuge. Tx = T ×

T = The standard time used in a previous centrifugation process to be used as a reference for optimizing new centrifugation parameters. Kx = The k-factor of the new centrifuge. K = The k-factor of the comparative/standard centrifuge. 3.5

ExoEasy

3.6 Flowchart of Steps to Isolate Extracellular Vesicles

Follow the manufacturer’s protocol for using the ExoEasy Kit. Briefly, mix the cell culture supernatant with an equal volume of “buffer XBP,” and then centrifuge at 500 × g for 1 min at room temperature (RT). Wash the filter with “buffer XWP,” and elute the extracellular vesicles in 400 μL of “buffer XE” after a further round of centrifugation at 500 × g for 5 min at RT. Adjust all samples with elution buffer to a final volume of 500 μL, and store at -80 °C until further use. 1. Culture MDA-MB-231 cells in eight 150 mM plates, and grow them until they reach 80% confluency. 2. Maintain the cells on a 24-h starvation period by replacing the medium with a medium without FBS (see Note 1). 3. Collect the conditioned medium in four 50 mL Falcon tubes, and keep them on ice (see Note 2). 4. Use three plates to collect and count the cells. Wash them once with PBS, add trypsin, and neutralize it with FBS-supplemented medium. Transfer the trypsinized cells from each plate to individual tubes, and then sweep the solution up and down to dissociate any clumps. Count the cells and centrifuge them at 200 × g for 5 min at RT. Discard the supernatant and store the cell pellets at -20 ° C (see Note 3). 5. Centrifuge the conditioned medium at 4 °C according to the parameters indicated in the flowchart (Fig. 1). After the first centrifugation step at 2000 × g for 10 min, transfer the supernatant to 4 new 50 mL tubes (Corning), centrifuge at 15,000 × g for 30 min at 4 °C, and transfer the resulting supernatant to ultracentrifuge tubes. The pellet after centrifugation contains microvesicles. Add 400 μL of PBS, transfer to smaller tubes (2 mL), and centrifuge again using the same parameters as before. Discard the supernatant and resuspend a new pellet in 120 μL of PBS and store at -80 °C.

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Fig. 1 Flowchart depicting the steps to isolate extracellular vesicles

Use PBS double-filtered with a 0.2 μM filter to wash the microvesicles and exosomes. After the first centrifugation step at 2000 × g for 10 min, transfer the supernatant to 4 new 50 mL tubes (Corning), centrifuge at 15,000 × g for 30 min at 4 °C, and transfer the resulting supernatant to ultracentrifuge tubes. In the Corning tubes, new pellets will be formed, which contain microvesicles. Add 400 μL of double-filtered PBS, transfer to smaller tubes (2 mL), and centrifuge again as before. Discard the supernatant and resuspend the pellet in 120 μL of PBS and store at -80 °C. 6. The first round of ultracentrifugation is performed to obtain exosomes, while the second round is used to wash and concentrate them (see Note 4). Once the first round is completed, carefully remove the supernatant from all tubes. Next, add 50 μL of PBS per tube, and gently wash the bottom, focusing on the most likely site of pellet formation. Transfer this volume to the next tube to obtain a concentrated sample, and repeat this process for all tubes. Add an additional volume of 50 μL of PBS, and repeat the wash and recovery process to obtain more exosomes. 7. After the washing and concentration steps, the final volume of the exosome sample may be slightly more than 100 μL due to residual liquid at the tube wall. To ensure all exosomes are collected, transfer the entire sample to a small tube (1.5 or 2 mL), and add PBS to a final volume of 1.2 mL. 8. A new ultracentrifugation round is required to remove residual media and obtain a concentrated exosome sample. Prepare two new ultracentrifuge tubes, and add one third of the exosome sample (400 μL) to one tube and the remaining two thirds (800 μL) to the other tube (see Note 5). Adjust the final volume to run properly in each rotor and ultracentrifuge. For example, with the 70ti rotor, the working volume per tube will be 20 mL (one tube with 19.6 mL PBS + 400 μL of exosomes and the other tube with 19.2 mL PBS + 800 μL exosomes).

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9. Use the tube containing one third of the exosomes for protein extraction, while the other tube can be used for exosome characterization and other assays specific to the research group. 10. If it is necessary to use new rotors and centrifuges, it is possible to calculate a new set of parameters equivalent between them. In our scenario, we first obtained EVs with the 5810R and the Optima XE centrifuges and repeated the process with the Megafuge ST4R Plus and the Avanti J30I centrifuges. To ensure that the results from different centrifuges are comparable, we use the k-factor formula to determine the appropriate parameters. We find that the first centrifuge used has a k-factor of 741.2 and the second has a k-factor of 9332. Based on these values, calculate the runtime required. We find that it is necessary to run the second centrifuge for 6 h 17 min. Centrifuge

Rotor

Centrifuge (room A2.-3.691)

Rotor

5810R

F-34-6-38

Megafuge ST (ST4R) Plus

TX-750

k=

ln

, ,

10 74 5 56

15,0002

× 2:533 × 1011

k=

ln

, ,

19 5 8 3

48152

× 2:533 × 1011

k~9332

k~741.2 9332 Tx = 30 × 741:2 Tx = 377.7 min ~6 h 17 min

In the ultracentrifuge, using the same methods as before, determine a k-factor of 156 for the first and 299.15 for the second centrifuge. We find the calculated run time is 4 h 9 min. Centrifuge

Rotor

Centrifuge (main lab)

Rotor

Optima XE

70ti

Avanti J30I

Ja-30.50Ti

k=

ln 92 40

36,7512

× 2:533 × 1011

k~156

k=

ln 108 40

29,0002

× 2:533 × 1011

k~299.15

Tx = 30 × 299:15 156 Tx = 233,3 min ~4 h 9 min

11. After obtaining the exosomes, perform protein quantification (Table 3) and Western blotting (Fig. 2) as published elsewhere [18–22] to assess the presence of some biomarkers specific to exosomes [2]. 12. Use 1 μg of exosomes to perform a nano-tracking analysis to check the exosome size (Fig. 3).

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Table 3 Protein concentration of exosomes obtained by the kit ExoEasy or ultracentrifugation Protein concentration (μg/mL) Kit ExoEasy 543.3 ± 58.0

Ultracentrifugation 84.9 ± 12.5

Fig. 2 Characterization of exosomes by Western blotting. Exosomes were isolated using the ExoEasy Kit to compare total protein yield, biomarker content, and size distribution of exosomes to those obtained by centrifugation method. Our results indicate a clear difference in protein concentration between the two methods, with the kit showing higher protein concentration compared to ultracentrifugation. Western blot analysis revealed that exosomes isolated by ultracentrifugation had more specific protein markers than those obtained with the kit

Fig. 3 Characterization of exosomes by nano-tracking analyses. Nano-tracking analysis showed that the kit yielded smaller exosomes than the ultracentrifugation method, but the difference was not significant

Isolation of Extracellular Vesicles

4

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Notes 1. Ensure that all collected extracellular vesicles are exclusively from the cancer cells without interference from FBS-derived EVs. 2. The final volume should be approximately 160 mL divided equally between four tubes. 3. Storage of the pellets for the Western blot is required. 4. The first and second rounds of centrifugation according to the flowchart should be performed in a benchtop centrifuge to remove dead cells and debris and collect microvesicles if necessary. The third and fourth rounds should use ultracentrifuges to collect and wash exosomes. 5. It is recommended to use Falcon and Corning tubes as specified in this protocol as different speeds compromised tube integrity. In this case, we observed cracks and leaks in Falcon tubes at higher speeds, but not in Corning tubes.

References 1. Teles RHG, Yano RS, Villarinho NJ, Yamagata AS, Jaeger RG, Meybohm P et al (2021) Advances in breast cancer management and extracellular vesicle research, a bibliometric analysis. Curr Oncol 28(6):4504–4520. h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / curroncol28060382 2. Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R et al (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 7(1):1535750. https://doi.org/10.1080/20013078.2018. 1535750 3. Burek M, Konig A, Lang M, Fiedler J, Oerter S, Roewer N et al (2019) Hypoxiainduced MicroRNA-212/132 alter bloodbrain barrier integrity through inhibition of tight junction-associated proteins in human and mouse brain microvascular endothelial cells. Transl Stroke Res 10(6):672–683. https://doi.org/10.1007/s12975-0180683-2 4. Curtaz CJ, Reifschlager L, Strahle L, Feldheim J, Feldheim JJ, Schmitt C et al (2022) Analysis of microRNAs in exosomes of breast cancer patients in search of molecular prognostic factors in brain metastases. Int J Mol Sci 23(7). https://doi.org/10.3390/ ijms23073683

5. Skottvoll FS, Berg HE, Bjørseth K, Lund K, Roos N, Bekhradnia S et al (2018) Comparison of ultracentrifugation and a commercial kit for isolation of exosomes derived from glioblastoma and breast cancer cells. bioRxiv:274910. https://doi.org/10.1101/274910 6. Benedikter BJ, Bouwman FG, Vajen T, Heinzmann ACA, Grauls G, Mariman EC et al (2017) Ultrafiltration combined with size exclusion chromatography efficiently isolates extracellular vesicles from cell culture media for compositional and functional studies. Sci Rep 7(1):15297. https://doi.org/10.1038/ s41598-017-15717-7 7. Livshits MA, Khomyakova E, Evtushenko EG, Lazarev VN, Kulemin NA, Semina SE et al (2015) Isolation of exosomes by differential centrifugation: theoretical analysis of a commonly used protocol. Sci Rep 5:17319. https://doi.org/10.1038/srep17319 8. Ortega FG, Roefs MT, de Miguel Perez D, Kooijmans SA, de Jong OG, Sluijter JP et al (2019) Interfering with endolysosomal trafficking enhances release of bioactive exosomes. Nanomedicine 20:102014. https://doi.org/ 10.1016/j.nano.2019.102014 9. Silva TA, Smuczek B, Valadao IC, Dzik LM, Iglesia RP, Cruz MC et al (2016) AHNAK enables mammary carcinoma cells to produce extracellular vesicles that increase neighboring fibroblast cell motility. Oncotarget 7(31):

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49998–50016. https://doi.org/10.18632/ oncotarget.10307 10. Takahashi A, Okada R, Nagao K, Kawamata Y, Hanyu A, Yoshimoto S et al (2017) Exosomes maintain cellular homeostasis by excreting harmful DNA from cells. Nat Commun 8: 1 5 2 8 7 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / ncomms15287 11. Konoshenko MY, Lekchnov EA, Vlassov AV, Laktionov PP (2018) Isolation of extracellular vesicles: general methodologies and latest trends. Biomed Res Int 2018:8545347. https://doi.org/10.1155/2018/8545347 12. Veerman RE, Teeuwen L, Czarnewski P, Gucluler Akpinar G, Sandberg A, Cao X et al (2021) Molecular evaluation of five different isolation methods for extracellular vesicles reveals different clinical applicability and subcellular origin. J Extracell Vesicles 10(9): e12128. https://doi.org/10.1002/jev2. 12128 13. Alvarez-Erviti L, Seow Y, Yin H, Betts C, Lakhal S, Wood MJ (2011) Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nat Biotechnol 29(4): 341–345. https://doi.org/10.1038/nbt. 1807 14. Thery C, Amigorena S, Raposo G, Clayton A (2006) Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr Protoc Cell Biol Chapter 3:Unit 3 22. https://doi.org/10. 1002/0471143030.cb0322s30 15. Willms E, Johansson HJ, Mager I, Lee Y, Blomberg KE, Sadik M et al (2016) Cells release subpopulations of exosomes with distinct molecular and biological properties. Sci Rep 6:22519. https://doi.org/10.1038/ srep22519 16. Coughlan C, Bruce KD, Burgy O, Boyd TD, Michel CR, Garcia-Perez JE et al (2020) Exosome isolation by ultracentrifugation and

precipitation and techniques for downstream analyses. Curr Protoc Cell Biol 88(1):e110. https://doi.org/10.1002/cpcb.110 17. Torres Crigna A, Fricke F, Nitschke K, Worst T, Erb U, Karremann M et al (2021) Interlaboratory comparison of extracellular vesicle isolation based on ultracentrifugation. Transfus Med Hemother 48(1):48–59. https://doi. org/10.1159/000508712 18. Jeppesen DK, Fenix AM, Franklin JL, Higginbotham JN, Zhang Q, Zimmerman LJ et al (2019) Reassessment of exosome composition. Cell 177(2):428–45 e18. https://doi.org/10. 1016/j.cell.2019.02.029 19. Kalra H, Adda CG, Liem M, Ang CS, Mechler A, Simpson RJ et al (2013) Comparative proteomics evaluation of plasma exosome isolation techniques and assessment of the stability of exosomes in normal human blood plasma. Proteomics 13(22):3354–3364. https://doi.org/10.1002/pmic.201300282 20. Kruger S, Abd Elmageed ZY, Hawke DH, Worner PM, Jansen DA, Abdel-Mageed AB et al (2014) Molecular characterization of exosome-like vesicles from breast cancer cells. BMC Cancer 14:44. https://doi.org/10. 1186/1471-2407-14-44 21. Tang YT, Huang YY, Zheng L, Qin SH, Xu XP, An TX et al (2017) Comparison of isolation methods of exosomes and exosomal RNA from cell culture medium and serum. Int J Mol Med 40(3):834–844. https://doi.org/ 10.3892/ijmm.2017.3080 22. Feldheim J, Wend D, Lauer MJ, Monoranu CM, Glas M, Kleinschnitz C et al (2022) Protocadherin gamma C3 (PCDHGC3) is strongly expressed in glioblastoma and its high expression is associated with longer progression-free survival of patients. Int J Mol Sci 23(15). https://doi.org/10.3390/ ijms23158101

Chapter 4 High-Resolution Respirometry for Mitochondrial Function in Rodent Brain Aishika Datta, Deepaneeta Sarmah, Bijoyani Ghosh, Nikita Rana, Anupom Borah, and Pallab Bhattacharya Abstract High-resolution mitochondrial respirometry is a modern technique that enables to measure mitochondrial respiration in various cell types. It contains chambers with oxygen sensors that measure oxygen concentration via polarography and calculate its consumption. The chamber contains plastic stoppers with injection ports that allow the injection of samples and different substrates, inhibitors, and uncoupler substances to measure mitochondrial respiration with high efficiency. These substances act on the mitochondrial electron transport chain (ETC) and help to assess the mitochondrial ATP production capacity and oxidative phosphorylation. The respirograph obtained with the help of software represents the oxygen consumption in each stage after adding different reagents. Key words Mitochondria, High-resolution respirometry, Respiratory control ratio, OXPHOS, SUIT protocol, ATP

1

Introduction Mitochondria are the fundamental organelle that maintains metabolic homeostasis in almost all multicellular organisms [1]. An increased mitochondrial population is found in organs with high energy dependency, such as the brain [2]. A single neuron is reported to contain hundreds to thousands of mitochondria with several copies of mitochondrial genome consisting about 16.5 kb of circular DNA [3]. In CNS, mitochondria play a pivotal role in maintaining Ca2+ and redox signaling, energy-dependent molecular signaling, synaptic plasticity, and cell survival [4]. Mitochondrial dysfunction can be attributed to many neurodegenerative diseases [4]. In stroke pathology, the internal energy balance gets disrupted due to lack of blood and oxygen supply in the brain [1]. Therefore, assessing mitochondrial respiration capacity is important to evaluate its quality which will help delve deeper into various pathology.

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Mitochondrial oxidative phosphorylation (OXPHOS) allows electron flow along the electron transport chain (ETC) where oxygen is consumed to generate adenosine triphosphate (ATP) [5]. The NADH and FADH2 generated in the tricarboxylic acid cycle (TCA cycle) transport their electrons to different ETC complexes (I to IV) in the inner mitochondrial membrane [5]. This electron flow is coupled with the proton flow from mitochondrial matrix to intermembrane space to establish an electrochemical gradient. Complex IV transfers an electron to oxygen to produce two water molecules, whereas complex V shuttles hydrogen molecules back into the mitochondrial matrix and produces ATP [5, 6]. A state-of-the-art approach for mitochondria and cell research to measure mitochondrial respiration with multi-sensor module is high-resolution respirometry [5]. In a single respirometry assay, the substrate uncoupler inhibitor titration protocol (SUIT protocol) helps to assess various mitochondrial pathways and coupling defects (Fig. 1- SUIT protocol) [7]. High-resolution respirometry follows SUIT protocol in the instrument Oroboros O2k which contains specialized chambers, electrochemical sensors and Peltier temperature control featuring DatLab software [8, 9]. In isolated mitochondria five respiratory states can be measured, namely, (state 1) basal mitochondrial respiration, (state 2) oxygen consumption following ETC substrate addition (coupling control following substrate and ADP addition), (state 3) maximum mitochondrial oxygen consumption following saturation of ADP, (state 4) resting respiration following ADP consumption (respiratory control ratio: state 3/state 4), and (state 5) zero calibration of oxygen [5, 10, 11].

Fig. 1 Substrates and inhibitors of mitochondrial ETC. Malate and pyruvate help to produce NADH that transports via complexes I, III, and IV toward proton pump. Succinate generates FADH2 following reaction with complex II. Rotenone, antimycin A, and FCCP function as inhibitors of complex I, complex III, and electron transfer from complex IV to proton pump, respectively. Oligomycin inhibits the proton pump and functions as the uncoupler that results in total inhibition of the ATP gradient in the inner mitochondrial membrane

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This chapter describes a detailed protocol to measure mitochondrial respiration using mitochondria isolated from rat brain. Specifically, we focus on measuring respiratory control ratio from the oxygen consumption of state 3 and state 4 respiration represented in the oxygraph.

2

Materials

2.1 Animals and Anesthetics

1. Animals: Sprague-Dawley rats weighing 220–250 g. 2. Anesthetic agents: Ketamine (80 mg/kg body weight) and xylazine (10 mg/kg body weight). 3. Surgical tools: Use locally procured scissors and forceps.

2.2 Media for Isolation of Mitochondria from Rat Brain

1. Mitochondrial isolation media: 125 mM sucrose, 250 mM mannitol, 10 mM HEPES, 10 mM EGTA, and 0.01% BSA. Add the protease inhibitor (100 μM phenylmethylsulfonyl fluoride or PMSF) at the time of tissue grinding. 2. Mitochondrial respiration media: 3 mM MgCl2, 60 mM lactobionic acid, 20 mM taurine, 10 mM KH2PO4, 20 mM HEPES, 110 mM sucrose, and 0.5 mM EGTA. Adjust the pH at 7.1 in room temperature using potassium hydroxide (KOH) solution. Add BSA (fatty acid-free) at a concentration of 1 g/L. 3. Prepare the stock solutions of substrates, uncouplers, and inhibitors, and store in -20 °C. Freshly prepare 5 mM pyruvate solution in double distilled water (DDW) before performing the respiration study. 4. Prepare other substrates such as ADP, malate, and glutamate at a concentration of 1 mM, 5 mM, and 410 mM, respectively. 5. To estimate the complex I and complex II activity, prepare 10 mM succinate in DDW, and use as substrate. Adjust the pH to 7 using 10 N KOH (malate and glutamate) and 1 N HCl (succinate). 6. Prepare 5 mM oligomycin as ATP synthase blocker in absolute ethanol. 1 mM FCCP, 0.5 μM rotenone, and 2.5 μM antimycin A prepared in absolute ethanol can be used as inhibitors.

2.3 Brain Harvest and Treatment with Specialized Media

1. Anesthetize rats using ketamine (80 mg/kg body weight) and xylazine (10 mg/kg body weight), and euthanize by cervical dislocation. 2. Transfer the brain in ice, and rinse with extraction buffer sufficient to clean residual blood (see Note 1). 3. Isolate the cortex and mince it thoroughly using sharp scissors (see Note 2).

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4. Transfer the minced tissue to a Dounce homogenizer with 3 mL ice-cold extraction buffer. 5. Grind with A (loose) and then B (tight) with ten strokes each. Avoid bubble formation. 6. Transfer the homogenate into a centrifuge tube containing 30–40 mL of extraction buffer. 7. Centrifuge the suspension for 10 min at 700 g in 4 °C. Collect the supernatant. Repeat the step twice. 8. Centrifuge the supernatant again at 10,000 g for 15 min at 4 ° C. 9. Collect the pellet, and dissolve it in extraction buffer containing 0.02% digitonin. 10. Centrifuge the suspension again at 10,000 g for 15 min at 4 ° C. Collect and resuspend the pellet in 100 μL extraction buffer (see Note 3). 2.4

Instruments

1. Dounce homogenizer. 2. Oroboros Oxygraph-2k System (high-resolution respirometry, Oroboros Instruments, Austria). It is a two-chambered instrument with barometric pressure transducers that can comprehensively measure mitochondrial respiration according to SUIT protocol using DatLab software [8]. 3. Instrument maintenance and cleaning solution: Prepare 70% ethanol in double distilled water (DDW).

3

Methods Mitochondrial respiration is assessed in freshly isolated mitochondria from rat brain. The procedure is optimized in the NIPER-A lab from previously reported protocols with modifications [12]. The main aim of this procedure is to efficiently measure mitochondrial respiratory status in a brief time. The whole protocol is a three-step process of (1) sample and reagent preparation, (2) respirometry, and (3) data analysis. Freshly extracted mitochondria provide maximum precision in the data. The reagents and solutions should be cool while performing the respiration. pH is critically maintained to avoid any false-positive or false-negative results. The procedure is based on substrate-uncoupler-inhibitor titration (SUIT) protocol where different substrates of mitochondrial electron transfer chain (ETC) is added along with subsequent uncouplers and inhibitors at different time points. Freshly prepared pyruvate acts as the starting substrate of sample mitochondrial ETC in the respiration chamber. Malate and pyruvate help to generate NADH in the TCA cycle that travels through complexes I, III, and IV to release electrons for

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Fig. 2 Representative graph of Oroboros Oxygraph-2k

F0F1-ATP synthesis. Complex II oxidizes succinate and generates FADH2. Rotenone acts as complex I inhibitor, antimycin A is complex III inhibitor, and oligomycin is F0F1-ATP synthase inhibitor. FCCP titration is performed to collapse the mitochondrial inner membrane proton gradient (Fig. 1). The oxygen concentrations obtained at stage 3 and stage 4 respiration from representative oxygraph (Fig. 2) are considered to calculate the respiratory control ratio (RCR). 3.1 Assessment of Mitochondrial Integrity

3.2 Mitochondrial Respiration Assessment

Perform Western blotting to evaluate the protein expression of mitochondrial outer membrane protein TOM20 (translocase of outer membrane protein) using 14% polyacrylamide gel. The integrity of isolated mitochondria is necessary to evaluate for better precision of the respiration measurement. TOM20 expression will help to ensure the integrity of isolated mitochondria. The higher the expression of TOM20, the better the mitochondrial integrity. 1. Rinse chamber A and chamber B in Oxygraph-2K with DDW, add 2.5 mL Miro5 buffer, and wait until stable oxygen readout (see Note 4). 2. Add 200 μg of fresh mitochondria into the chamber (see Note 5). 3. Add pyruvate, malate, and glutamate to obtain complex I-linked leak respiration (see Notes 5 and 6). 4. Induce complex I linked OXPHOS by adding ADP.

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5. Observe complex I- and complex II-linked OXPHOS following succinate addition. 6. Inhibit ATP synthase by oligomycin and titrate with FCCP for obtaining maximum noncoupled respiration fueled by complex II. 7. To inhibit complex I, add rotenone, and measure complex II-linked OXPHOS (see Note 5). 8. Add antimycin A to inhibit complex III. 9. Clean the Oxygraph-2k lids and chambers with 70% ethanol followed by DDW. 10. Avoid drying of the transducer membrane, and keep it wet with 70% ethanol (see Note 7). 3.3 Interpretation of Graph

1. Check the graph obtained from the software, and note the oxygen consumption at state 3 and state 4. 2. At high concentration of ADP, add substrate to initiate its consumption, which is referred as state 3 respiration. When all the ADP is exhausted and converted to ATP, the state is referred as state 4. Measure these states directly obtained from the graph. 3. Calculate the respiratory control ratio (RCR) from oxygen concentration at state 3 and state 4 respiration state (see Note 8).

4

Notes 1. Once the brain is harvested, it should be subjected to sufficient extraction buffer to clean the blood as early as possible. 2. The brain must be finely chopped. 3. To avoid mitochondrial impairment, the whole procedure must be performed in ice. The RCR measurement should be performed as early as possible. 4. All the buffers and reagents’ pH are crucial to maintain. NaOH should be avoided to adjust the pH. 5. Different Hamilton syringes should be used to inject different reagents in the respiration chambers. 6. Pyruvate should be prepared freshly. 7. Rotenone and other inhibitors are highly toxic. Therefore, the respiration chambers must be thoroughly cleaned with 70% ethanol to completely remove reagent residue.

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8. RCR is a measurement of OXPHOS coupling efficiency of sample mitochondria. It ranges from 1 to infinity. However, if it ranges between 3 and 10, it shows nonlinear output. The oxygen consumption at state 3 and state 4 can be directly measured from the representative oxygraph. The statistical analysis of RCR ± SD should be linearized by ATP flux [11].

Acknowledgments Authors acknowledge the Department of Pharmaceuticals, Ministry of Chemicals and Fertilizers, Government of India; National Institute of Pharmaceutical Education and Research (NIPER)Ahmedabad; Indian Council of Medical Research (ICMR), New Delhi, for the senior research fellowship grant to Ms. Aishika Datta (45/13/2020-PHA/BMS); as well as the NanoBio project grant to Dr. Pallab Bhattacharya (34/5/2019-TF/Nano/BMS). We also acknowledge Oroboros Instruments (Oxygraph-2k) and the experimental protocols provided by them which have been followed with certain modifications at our lab. References 1. Liu F, Lu J, Manaenko A, Tang J, Hu Q (2018) Mitochondria in ischemic stroke: new insight and implications. Aging Dis 9:924–937 2. Norat P et al (2020) Mitochondrial dysfunction in neurological disorders: exploring mitochondrial transplantation. NPJ Regen Med 5(1):22 3. Rango M, Bresolin N (2018) Brain mitochondria, aging, and Parkinson’s disease. Genes 9(5):250 4. Mattson MP, Gleichmann M, Cheng A (2008) Mitochondria in neuroplasticity and neurological disorders. Neuron 60(5):748–766 5. Djafarzadeh S, Jakob SM (2017) Highresolution respirometry to assess mitochondrial function in permeabilized and intact cells. J Vis Exp 120:e54985 6. Silva AM, Oliveira PJ (2012) Evaluation of respiration with clark type electrode in isolated mitochondria and permeabilized animal cells. In: Mitochondrial bioenergetics: methods and protocols. Humana Press, New York, pp 7–24 7. Pesta D, Gnaiger E (2012) High-resolution respirometry: OXPHOS protocols for human cells and permeabilized fibers from small biopsies of human muscle. In: Mitochondrial

bioenergetics: methods and protocols. Humana Press, New York, pp 25–58 8. Long Q et al (2019) Assessing mitochondrial bioenergetics in isolated mitochondria from mouse heart tissues using oroboros 2k-oxygraph. In: Nuclear receptors: methods and experimental protocols. Humana Press, New York, pp 237–246 9. Silva AM, Oliveira PJ (2018) Evaluation of respiration with clark-type electrode in isolated mitochondria and permeabilized animal cells. In: Mitochondrial bioenergetics: methods and protocols. Humana Press, New York, pp 7–29 10. Chance B, Williams GR (1955) Respiratory enzymes in oxidative phosphorylation: I. Kinetics of oxygen utilization. J Biol Chem 217(1):383–393 11. Gnaiger E (2020) Mitochondrial pathways and respiratory control: an introduction to OXPHOS analysis. Bioenerg Commun 2020: 2–2 12. Sarmah D et al (2023) Cardiolipin-mediated alleviation of mitochondrial dysfunction is a neuroprotective effect of statin in animal model of ischemic stroke. ACS Chem Neurosci 14(4):709–724

Chapter 5 Quantification of Neuronal Dendritic Spine Density and Lengths of Apical and Basal Dendrites in Temporal Lobe Structures Using Golgi-Cox Staining Vivek Dubey, Aparna Banerjee Dixit, Manjari Tripathi, P. Sarat Chandra, and Jyotirmoy Banerjee Abstract The objective of this chapter is to provide an overview of the methods used to investigate the connectivity and structure of the nervous system. These methods allow neuronal cells to be categorized according to their location, shape, and connections to other cells. The Golgi-Cox staining gives a thorough picture of all significant neuronal structures found in the brain that may be distinguished from one another. The most significant characteristic is its three-dimensional integrity since all neuronal structures may be followed continuously from one part to the next. Successions of sections of the brain’s neurons are seen with the Golgi stain. The Golgi method is used to serially segment chosen brain parts, and the resulting neurons are produced from those sections. Key words Dendritogenesis, Hippocampus, Neurite morphometry, Golgi-Cox staining, Anterior temporal lobe, Spines, Apical dendrites, Basal dendrites

1

Introduction Golgi staining, also known as “black reaction,” was initially discovered in 1873 by Camillo Golgi. Neuronal morphology research is more important than ever, as Golgi discovered [1]. The non-invasive Golgi staining method, developed in 1873, is still in practice today and used for the investigation of neuronal morphology with axonal and dendritic arborization and spines by allowing for the imaging of only a small fraction of neurons (1–3%). There are three main types of Golgi staining: rapid, Golgi-Kopsch, and Golgi-Cox [2]. The Golgi-Cox method is thought to be the most dependable in proving dendritic arborization with a minimal background [2, 3]. Numerous changes to this methodology have been made, the majority of which have been made to increase its dependability [4–6], decrease the procedural time [7, 8], and improve the

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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selectivity of neuronal vs. glial staining or vice versa [9, 10]. Dendritic spine visualization dates to Camillo Golgi’s drawings, which were inspired by his observations of fixed brain tissues stained by the so-called black reaction, a staining method he developed in his kitchen. The creator of the neuron hypothesis, Santiago Ramo´n y Cajal, who postulates that the entire brain network is made up of discrete individual cells, was acutely aware of the importance of this technique and made considerable use of it in his research. Cajal established the existence of what he called collateral spines in 1895 and first described dendritic spines in 1888 [11]. In addition, Cajal purposefully noted the many dendritic spine morphologies (sessile, mushroom, and thin), as well as the variations in size between various brain regions and species, distribution features, and even the physiological function of this structure [11]. All these incredible achievements were made possible by the best microscope available at the time, as well as his deft handling of Golgi staining. Santiago Ramo´n y Cajal and Camillo Golgi shared the 1906 Nobel Prize in Physiology or Medicine in “honour of their work on the structure of the neurological system.” Here, we are describing the procedural details of the Golgi-Cox staining. Most neuroscience labs have access to the ingredients used, which are adequate to create the Golgi-Cox staining on any sample. This procedure may lessen issues that are experienced frequently and cut down on the time needed to standardize this procedure. The method described here has been used to investigate dendritic and spine organization as substrates of synaptic circuitries in the anterior temporal lobe (ATL) and the hippocampus of rats.

2 2.1

Materials Animals

2.2 Preparation of Golgi-Cox Solutions for Sample Impregnation

Employ adult male Sprague-Dawley rats (7–8 weeks old, weighing 200–250 g). Maintain animals in groups of two per cage at a room temperature of between 24 ± 2 °C, in a 12 h light/dark cycle, with ad libitum access to water and food [12]. 1. To make the impregnation stock solutions, dissolve 15 g of potassium dichromate (K2Cr2O7) in 300 mL of dd-H2O (5%, w/v), 15 g of potassium chromate (K2CrO4) in 300 mL of dd-H2O (5%, w/v), and 15 g of mercuric chloride (HgCl2) in 300 mL of dd-H2O (5%, w/v). 2. To make Golgi-Cox solution over an extended period, keep all three solutions in dark-colored bottles at room temperature and in the dark [10]. 3. Use the three stock solutions indicated above to create a new bottle of Golgi-Cox solution for each experiment [13].

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4. Prepare the working solution as follows: Solution A: Mix 50 mL of the potassium dichromate solution with 50 mL of the mercuric chloride solution. Solution B: Add 40 mL of the potassium chromate solution. 5. To prepare a working solution, mix solutions A and B. Then, add 100 mL of dd-H2O. 6. Keep the working solution in the dark at least for 48 h, during the time reddish precipitates form. Remove the precipitates with filter paper. 7. Store the rat brains in this solution for up to a month. The volume of the working solution depends on the number of tissues, and the working solution can be used until a week after the preparation (see Notes 1 and 2). 8. Avoid metal instruments during the preparation. Protect tissues treated with Golgi-Cox solution from exposure to light whenever possible (see Note 1). 2.3 Preparation of Gelatin-Coated Slides

1. Keep the frosted micro slides, also known as plain microscopic slides, first in three staining racks, thoroughly washed with dd-H2O, and then allow to dry for 2–3 h in a dust-free environment (hot-air oven) (see Note 3). 2. Weigh 9 g of purified gelatin, and dissolve in 300 mL of dd-H2O while being continuously stirred and heated to 50–55 °C. 3. The subsequent step is to filter the solution through clean Whatman filter paper into a histological staining box (coupling jar). Place the cleaned slides on a rack, and submerge them in the heated gelatin solution for 10 min, after that spread the slides out, and keep them the following day at room temperature in a place free of dust (see Note 3). 4. Before submerging a second rack of slides, the gelatin should be rewarmed to 55 °C (see Note 4). 5. These slides are adequate for collecting brain slices that are 200 μM-thick.

3

Methods

3.1 Tissue Preparation

1. Euthanize rats by asphyxiation in a CO2 chamber. 2. Remove the brains carefully from cranial cavities, and tissues from different regions of the temporal lobe (anterior temporal lobe and the hippocampus) are collected for experiments (see Note 5).

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3.2 Brain Tissue Sample Preparation (Impregnation Step)

1. Take small amber-colored vials wrapped in aluminum foil and a multipurpose container with a cover for washing brain tissue. 2. The aluminum foil is carefully removed from the Golgi-Cox bottle without shaking to avoid the solution from mixing with the brownish precipitates at the bottom of the bottle. 3. Small amber-colored vials are filled with 10 mL of the upper, clear portion of the Golgi-Cox solution for each sample. 4. Following CO2 asphyxiation, the brain is carefully dissected, cleaned with dd-H2O, and split in half to facilitate better impregnation. 5. Then, each portion is put into a separate, tiny container with Golgi-Cox solution and kept in the dark at 37–40 °C temperature. 6. Use plastic forceps to transfer brain tissue to another vial containing Golgi-Cox solution after 24 h for another 24 h. 7. The small vials are maintained in the dark and at 37–40 °C temperature for 48 h (Change every 24 h).

3.3 Vibratome Sectioning

1. As discussed below and illustrated in Fig. 1, vibratome should be used for making thin tissue sections. 2. Before the stained tissue is fixed on the holder, rinse the tissue thrice with 70% ethanol, and fix the tissue on the vibratome holder with the help of glue. 3. After fixing the holder into the vibratome bath platform, set the vibratome frequency, speed, and section thickness (200 μM) as per requirement. 4. In our experience, vibrations at a frequency of 80 Hz and a speed of 15 mM/s produce the best results, though these values can be altered depending on the section quality (see Note 6). 5. Fill the vibratome bath with 70% ethanol, and cut sections of 200 μM thickness (see Note 6). 6. Hold the tissue slice on the slides using a paintbrush. After loading all sections onto the slides, absorbent paper is used to remove any excess bath solution surrounding the sections (Fig. 1b). 7. After that, the sections are blotted by applying absorbent paper to the slides that have been saturated with solution. Directly applying mild downward pressure with the palm is the most efficient technique. 8. If the portions stick to the absorbent paper, the bath solution has probably not been soaked into the paper thoroughly enough.

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Fig. 1 Sectioning of stained brain tissue. The brain sample is kept in Golgi-Cox solution at 37–40 °C temperature in the dark. Stained tissue after removal from Golgi-Cox solutions. The block is fixed to a vibratome tissue holder plate, and the plate is placed into the vibratome chamber, which is subsequently filled with ethanol solution just until the block is well covered. Using a brush, sections are collected from the vibratome chamber and transferred onto gelatin-coated slides. (a) Tissue fixed on vibratome platform for sectioning, 200 μM. (b) Brain tissue coronal sections on gelatin-coated slides

3.4 Mounting of Tissue Sections on Slide

1. Process the sections as follows: Rinse thrice (5 min each) in distilled water to remove traces of impregnating solution. 2. Further dehydrate the section in a series of various ethanol concentrations of 50%, 70%, and 90% and then with absolute alcohol for 10 min each. Further, clear the sections with xylene and mount in dibutylphthalate polystyrene xylene (DPX) on gelatinized slides (see Note 7). 3. Tissue sections are covered with cover glass, and gentle pressure is applied to prevent air bubbles (see Notes 8 and 9). 4. The slides were sealed with clear nail paint (transparent) after all samples have been mounted. 5. The slides are placed horizontally on a slide tray for 48 h while being left in the dark for complete drying of the mounting media before imaging (see Note 10).

3.5

Imaging

Perform microscopic imaging using the following steps: 1. The dried slides are observed under a microscope (Olympus BX51, Japan) at low and high magnifications. Images are captured using a charge-coupled device (CCD) digital camera (JVC, Tokyo, Japan, and MBF CX9000) attached to the microscope.

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Fig. 2 Rat brain Golgi-Cox staining. The Golgi-Cox procedure reported here reliably and uniformly stains neurons in all brain areas: (a) Golgi-Cox-stained pyramidal neuron of the anterior temporal lobe, (b) hippocampus CA1 region pyramidal neurons (20× magnification), and (c) spines of apical dendrites (100× magnification)

2. The following criteria are adopted for the identification of staining for evaluation. 3. Staining is considered to have been initiated if a visible black spot (nucleation) could be distinctly identified inside the neurons without spilling over to the surrounding. (see Notes 11 and 12). 4. Neurons are considered completely stained if the soma, axons, and dendrites could be seen well demarcated by the impregnated black stain without spilling outside the neurons (Fig. 2a– c). 5. Spines are clearly visible at higher magnification (see Note 13). 3.6 Morphological Analysis

1. Carry out morphological analyses using the Neurolucida System Version 11.03, and Neurolucida Explorer Software (MBF Bioscience) [14]. 2. Trace pyramidal neurons and analyze under a 40× objective lens (Fig. 3). 3. Use selection criteria for Neurolucida tracing that includes complete filing of the cell body, no beading or breaks in staining along the dendritic branches, limited dendritic branch crossing from other neurons, a minimum number of dendritic branches originating from the soma, and a minimum number of subsequent branch points [15]. 4. Use Neurolucida software used to trace the neurons that match these criteria. 5. Use tracing information that includes soma size, total dendritic length, total spine count, total dendritic spine density, spine density in relation to distance from soma, and spine density in relation to branch order.

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Fig. 3 Neuronal tracing. Tracing of neurons (Golgi-Cox-stained neurons) by Neurolucida software. (a) Complete trace of pyramidal neuron (20× magnification) showing apical dendrites, basal dendrites, and cell body (soma) and (b) traced apical dendrite spines (100× magnification)

6. Using the Neurolucida Explorer Software, perform Sholl analysis on each neuron [16]. 7. Encircle the cell body with a series of concentric rings, the radius of which rose by 20 μM each time. 8. Within a concentric ring area, measure and plot dendrite length. Report the number of branching points (nodes) per concentric ring area. Use the number of sites where the processes intersected a concentric ring to calculate intersections [16, 17]. 9. Measure dendrite length within a concentric ring area and plot. Report branching points (nodes) as the number per concentric ring area. Determine intersections as the number of points where the processes crossed a concentric ring. 10. A segment from a proximal apical dendrite (30–120 μM from the cell body), select a distant apical dendrite (220–340 μM from the cell body), or a basal dendrite for spine density studies. Using a 100× objective lens, count the number of spines along each 10 m length while submerged in immersion oil [17]. 11. Complexity, as defined in Neurolucida Explorer [15, 16], refers to the normalization and comparison of dendrites among fundamentally different neurons. Complexity = [Sum of the terminal orders + Number of terminals] × [Total dendritic length / Number of primary dendrites]. A “terminal” is defined as a dendritic ending, and “Terminal order” is the number of “sister” branches encountered while proceeding from the terminal to cell body (calculate for each terminal). 12. A branched structured analysis is also performed on each neuron. (For details see the website https://www.mbfbioscience.

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com/help/neurolucida_explorer/Content/Analyze/ Branched_Structure.htm?Highlight = total%20nodes) [16]. 13. The analysis includes a neuron summary that provides an overview of the selected neuron components like dendrites and cell body. 14. The overview also includes dendritic lengths, complexity, and the number of nodes and intersections, revealing systematic variation among the different brain regions [14]. 15. Evaluate length of apical and basal dendrite to examine potential alterations to the neuronal soma, and determine whether the population of pyramidal cell bodies is systematically growing or contracting. Show complete tracing of a neuron with apical and basal dendrites (Fig. 3a). 16. Count the number of basal dendrites per cell body to determine the difference with respect to apical dendrites. 17. Choose a segment from a proximal apical dendrite (cell body) or from a basal dendrite [18]. 18. Count the number of spines along each 10 μM length under oil immersion using 100× objective lens. Carry out classification of spine type by measuring the head width and neck length. 19. Classify spines as per following criteria (Fig. 3b): Spines are determined as “long thin,” if the spine’s head diameter is greater than the maximum diameter of the neck (meaning a well-formed head) and if the neck length is greater than its diameter [19]. 20. Classify spines as “mushroom” if the diameter of the head is greater than the diameter of the neck and neck diameter greater than its length, or if the spine had an enlarged head with a narrow neck [20, 21]. 21. Determine spines as “stubby” if the diameter of the neck is like the total length of the spine (no obvious neck). 22. Further categorize spines based on the following measurements: Mushroom: width > 0.6 μM; long thin, length > 1 μM; thin, length/width ratio > 1 μM; stubby, length/width ratio ≤ 1 μM [22]. 23. Spine density = [Total number of spines/Total dendritic length].

4

Notes 1. Golgi solution should be stored in an amber-colored bottle, as it is light-sensitive. So, whenever the Golgi-Cox staining solution is filtered, or the stain is changed after 24 h, it is always

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dark in color. Golgi solutions are toxic, so when you handle them, protect your eyes, and wear gloves when you use or handle them. 2. If the tissue sections are left in the developing solution (GolgiCox) for a long time, brain tissue will get an excess stain. 3. The slide should be cleaned properly to avoid any dust particles. 4. Gelatin should be mildly warm and filtered to remove any particulate matter. 5. Tissues should be harvested carefully to preserve the anatomical structure of the brain. If brain tissues are not carefully removed from the body, it will lead to loss of neuronal morphology. 6. Fill the vibratome bath with 70% ethanol solution, sections should be cut at a fixed thickness of 200 μM. At the time of sectioning vibratome frequency should be constant. 7. Before placing the tissue in a vibratome bath, rinse the tissue two to three times (2 min) in 70% ethanol to remove the excess stain adhering to the tissue. 8. Tissue sections should be completely dry before applying mounting media DPX, try to avoid excess DPX on the slide. 9. Carefully paste coverslips over slides, avoid bubbling. Positive pressure should be evenly and cautiously applied to remove some bubbles. 10. Imaging should be performed only after the complete drying of the slides. 11. The lens of the microscope should be cleaned properly before imaging. Stained neurons should be visible in black color. 12. When using a microscope to visualize the coronal section of the brain from dorsal to ventral, the slide orientation must be accurate. It aids in identifying the area-wise neuronal morphology. 13. Immersion oil should be used for higher magnification images needed for spine density measurement.

Acknowledgments This work is funded by the Department of Biotechnology, Ministry of Science and Technology, Govt. of India [BT/PR20392/MED/ 122/26/2016], and Indian Council for Medical Research (Grant No. BMI/11(58)/2022).

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References 1. Golgi C (1873) Sulla struttura della sostanza grigia del cervello. Gazz Med Ital Lombardia 6: 244–246 2. Koyama Y (2013) The unending fascination with the Golgi method. OA Anat 1:24 3. Buell SJ (1982) Golgi-Cox and rapid golgi methods as applied to autopsied human brain tissue: widely disparate results. J Neuropathol Exp Neurol 41:500–5007 4. Angulo A, Ferna´ndez E, Mercha´n JA, Molina M (1996) A reliable method for Golgi staining of retina and brain slices. J Neurosci Methods 66:55–59 5. Koyama Y, Tohyama M (2012) A modified and highly sensitive Golgi-Cox method to enable complete and stable impregnation of embryonic neurons. J Neurosci Methods 209:58–61 6. Gibb R, Kolb B (1998) A method for vibratome sectioning of Golgi-Cox-stained whole rat brain. J Neurosci Methods 79:1–4. https://doi.org/10.1016/s0165-0270(97) 00163-5 7. Ranjan A, Mallick BN (2010) A modified method for consistent and reliable Golgi-cox staining in significantly reduced time. Front Neurol 1:157. https://doi.org/10.3389/ fneur.2010.00157 8. Levine ND, Rademacher DJ, Collier TJ, O’Malley JA, Kells AP, San Sebastian W (2013) Advances in thin tissue Golgi-Cox impregnation: fast, reliable methods for multiassay analyses in rodent and non-human primate brain. J Neurosci Methods 213:214–227 9. Gull S, Ingrisch I, Tausch S, Witte OW, Schmidt S (2015) Consistent and reproducible staining of glia by a modified Golgi-Cox method. J Neurosci Methods 256:141–150 10. Zaqout S, Kaindl AM (2016) Golgi-Cox staining step by step. Front Neuroanat 10:38 11. Garcia-Lopez P, Garcia-Marin V, Freire M (2007) The discovery of dendritic spines by Cajal in 1888 and its relevance in present neuroscience. Prog Neurobiol 83:110–130 12. Rutledge LT, Duncan J, Beatty N (1969) A study of pyramidal cell axon collaterals in intact and partially isolated adult cerebral cortex. Brain Res 16:15–22 13. Dubey V, Dey S, Dixit AB, Tripathi M, Chandra PS, Banerjee J (2022) Differential glutamate receptor expression and function in the

hippocampus, anterior temporal lobe, and neocortex in a pilocarpine model of temporal lobe epilepsy. Exp Neurol 347:113916 14. Mehder RH, Bennett BM, Andrew RD (2020) Morphometric analysis of hippocampal and neocortical pyramidal neurons in a mouse model of late-onset Alzheimer’s disease. J Alzheimers Dis 74(4):1069–1083 15. Ledergerber D, Larkum ME (2010) Properties of layer 6 pyramidal neuron apical dendrites. J Neurosci 30(39):13031–13044 16. Milatovic D, Montine TJ, Zaja-Milatovic S, Madison JL, Bowman AB, Aschner M (2010) Morphometric analysis in neurodegenerative disorders. Curr Protoc Toxicol Chapter 12:1– 12.16 17. Sholl DA (1953) Dendritic organization in the neurons of the visual and motor cortices of the cat. J Anat 87:387–406 18. Krstonosˇic´ B, Milosˇevic´ NT, Gudovic´ R (2023) Quantitative analysis of the Golgi impregnated human (neo)striatal neurons: observation of the morphological characteristics followed by an emphasis on the functional diversity of cells. Ann Anat 246:152040 19. Harris KM, Jensen FE, Tsao B (1992) Threedimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and longterm potentiation. J Neurosci Off J Soc Neurosci 12(7):2685–2705 20. Schaefer ML, Perez PJ, Wang M, Gray C, Krall C, Sun X, Hunter E, Skinner J, Johns RA (2020) Neonatal isoflurane anesthesia or disruption of postsynaptic density-95 protein interactions change dendritic spine densities and cognitive function in juvenile mice. Anesthesiology 133(4):812–823 21. Perez-Cruz C, Nolte MW, Van Gaalen MM, Rustay NR, Termont A, Tanghe A, Kirchhoff F, Ebert U (2011) Reduced spine density in specific regions of CA1 pyramidal neurons in two transgenic mouse models of Alzheimer’s disease. J Neurosci 31:3926–3934 22. Risher WC, Ustunkaya T, Alvarado JS, Eroglu C (2014) Rapid Golgi analysis method for efficient and unbiased classification of dendritic spines. PLoS One 9:e107591

Chapter 6 Quantification of Neuroinflammatory Markers in Blood, Cerebrospinal Fluid, and Resected Brain Samples Obtained from Patients Arpna Srivastava, Aparna Banerjee Dixit, Manjari Tripathi, P. Sarat Chandra, and Jyotirmoy Banerjee Abstract Cytokines have the potential to be the ideal biomarkers to track the onset and progression of immunemediated diseases, study the development of novel therapeutic strategies, and they can serve as outcome parameters due to their crucial role in the regulation of immune and inflammatory responses. It is vital to keep track of the entire cytokine spectrum due to the complex interactions, pleiotropic effects, and redundancy in the cytokine network. The multiplex immunoassay (MIA) is, therefore, the best method for achieving that goal. This chapter addresses the key methodological processes of this technique, such as sample preparation, antibody coupling to beads, and assay procedure. Key words Multiplex immunoassay, Biomarkers, ELISA, Neuroinflammation, Cytokines

1

Introduction Neuroinflammation is a pathological feature of many central nervous system (CNS) diseases, including classic neuroinflammatory disorders, such as multiple sclerosis (MS); neurodegenerative diseases, such as Alzheimer’s disease (AD) and Huntington’s disease (HD); disorders induced by brain injury; epilepsy; and neuropsychiatric disorders like depression and schizophrenia. Across the spectrum of neurological disorders, similar cell types and inflammatory mediators are induced, but the outcomes range from toxic processes, like the release of pro-inflammatory cytokines or reactive oxygen species, to reparative processes, like the release of antiinflammatory cytokines or stimulation of neuroprotective and angiogenic factors. These inflammatory mediators and other cellular indicators may all serve as biomarkers of neuroinflammation, and their identification may help to clarify the causes [1, 2].

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Biofluids, particularly blood and cerebrospinal fluid (CSF), can be used to analyze the interplay of several molecular pathways that participate in neuroinflammatory and neurodegenerative diseases. This diagnostic and prognostic method based on biomarkers has significant ramifications for the drug development industry. In fact, fluid biomarkers present a special possibility for enhancing the quality and applicability of data from clinical trials, especially considering the accessibility of disease-modifying medications. Numerous cerebrospinal fluid cytokines have been linked to autoimmune and viral disorders in relation to neuroinflammation. However, research on CSF cytokines in individuals with neurological disorders has been limited to certain conditions [3, 4]. A logical source for neuroinflammation biomarkers is CSF, which has a close association with the central nervous system. In fact, since the CSF serves as a barometer of the CNS’s health, pathological changes to the brain’s chemical composition can affect the CSF’s composition [5, 6]. Measuring specific biomarkers in the CSF has been included in the criteria for the in vivo diagnosis of a particular disease [7], which has aided both clinical and research practices over time. The high invasiveness of CSF collection by lumbar puncture along with the risks and costs associated with the procedure has, however, severely hampered its adoption in regular clinical practice since patients are unwilling to routinely undergo invasive and expensive testing [6, 7]. The limitations of CSF have caused the focus of biomarker research to shift to other readily available biological sources. The current focus is mostly on blood collection, which is a routine process and, when compared to lumbar puncture, is unquestionably less intrusive, less expensive, repeatable over time, and simple to adopt in large populations. As a result, blood-based biomarkers are simple to adopt in clinical practice to track the development of the disease or the effectiveness of treatment over time [8]. The evidence that patients with neurological disorders have functionally impaired blood-brain barriers (BBBs), which causes increased molecule leakage from the CSF, which in turn causes those molecules to reflect the pathological changes taking place in the diseased brain and provides the justification for blood being a suitable source of biomarkers for neuroinflammatory and neurodegenerative diseases [6–10]. Although the availability of site-specific tissues for studying neuroinflammation biomarkers is rare, cytokine profiling has been done in specific diseases to gain a better understanding of the neuroinflammation process when patients undergo surgical treatment [11–13]. Herein, we will discuss the method used to measure many biomarkers simultaneously for patient stratification, target engagement, and outcome assessment in tissues, blood, and CSF of neuroinflammatory and neurodegenerative diseases.

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For better diagnosis, prognosis, and therapy, there is an increasing demand for technology that can extract a significant quantity of bioinformation from a small sample volume. Because of this, approaches for the simultaneous detection of several proteins have been developed that are low-cost, flexible, and high-throughput. With the invention of multiplex immunoassays (MIAs), many analytes can be measured simultaneously in a single biological sample with the least amount of assay time, money, and sample [14]. The Multiplex platform-based technologies have been used in clinical diagnostics and biomedical research after overcoming technological obstacles [15, 16]. The precise affinity between the antibody and the antigen is used to advantage in immunoassays. While other immunoassays employ antibody probes to identify antigen targets, certain immunoassays use antigens as the probe to detect the binding of antibody targets. Traditional ELISAs are single-plex devices that need numerous binding and washing steps, an enzymatic system that generates a colorimetric or chemiluminescent label as a quantitative readout of target concentration in a sample, and repeated binding and washing steps. Based on Luminex technology, the goldstandard multiplex immunoassay is conducted. This flow-based method, which employs fluorescent microspheres and detecting antibodies for quantitative readout, operates on the same principles as the ELISA [14, 15, 17]. Which targets are present in the sample can be determined by combining the label wavelength with the bead fluorescence wavelength. Each capturing antibody has a microsphere attached to it with a distinct emission wavelength. If the antigen is present, it will attach to the capture antibody and form a sandwich with the tagged detection antibody. The detecting antibody has a fluorescent tag attached to it. The Luminex system keeps track of each bead’s fluorescence as it moves through the system and gives a quantitative reading of how many beads are linked to the target. Data from numerous studies demonstrate that multiplexed technologies are suitable for screening for trends in cytokine profiles and other secreted proteins to support preclinical and clinical studies [14–18]. Here, we will discuss the method employed to measure neuroinflammatory mediators in serum or CSF and resected tissue samples of neurological disorders using Bio-Plex multiplex systems (BioRad). Bio-Plex Assays are sandwich immunoassays formatted on magnetic beads. The test operates similarly to a sandwich ELISA. Covalent bonds are formed between the desired biomarker-specific capture antibodies and the beads. Coupled beads react with the sample containing the target biomarker. After a series of washing processes to remove unattached protein, a biotinylated detection antibody is added to create a sandwich complex. The best detection complex is produced when streptavidin-phycoerythrin (SA-PE) conjugate is added. Phycoerythrin is a fluorescent indicator or reporter [18].

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Materials Assay Reagents

1. Cytokine/chemokine standard. 2. Cytokine quality controls 1 and 2. 3. Set of 96-well plates with sealers. 4. Assay buffer: Phosphate-buffered saline or PBS (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, and 1.8 mM KH2PO4 with 1% BSA). 5. Wash buffer: PBS with 0.05% Tween. 6. Lysis solution: 10 mM Tris, pH 7.4, 100 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1 mM NaF, 20 mM Na4P2O7, 2 mM Na3VO4, 1% Triton X-100, 10% glycerol, 0.1% SDS, and 0.5% deoxycholate. 7. Protease inhibitor cocktail: 10 μM leupeptin, 0.1 μM aprotinin, 1.0 μM pepstatin, 668 μM AEBSF, 0.3 μM Aprotinin, 3 μM Bestatin, and 14 μM E-64. 8. 1 mM phenylmethylsulfonyl fluoride (PMSF). 9. Bicinchoninic acid (BCA) kit: Reagent A contains 1% w/v BCA-Na2, 2% w/v Na2CO3·H2O, 0.16% w/v Na2 tartrate, 0.4% w/v NaOH, 0.95% w/v NaHCO3, add 50% NaOH or solid NaHCO3 to adjust the pH to 11.25. Reagent B contains 4% w/v CuSO4·5H2O. 10. Cytokine detection multiplex antibodies: Assay-dependent. 11. Detection antibodies diluent: 0.1% BSA/PBS, 0.005% Tween 20. 12. Standard diluent: PBS with 1% BSA. 13. Sample diluent: PBS with 1% BSA. 14. Streptavidin-phycoerythrin: Streptavidin conjugated to R-phycoerythrin in 150 mM NaCl, 50 mM sodium phosphate, 1% BSA, 0.1% glycerol, pH 7.6. 15. Coupled magnetic beads: Immobilize the antibody on the bead surface (direct approach) to set up a magnetic beadbased assay. The desired biomarker-specific capture antibodies and the beads create covalent interactions. Coupled beads react with the target biomarker-containing sample. 16. Validation kit: Bead sets for validation procedures (optics, reporter, classification, and fluidics validation) using the Bio-Plex Manager software and the Bio-Plex MCV plate IV are included in the Bio-Plex validation kit 4.0 (see Notes 1 and 2). 17. Calibration kit: A set of gold-standard calibration beads with consistent fluorescence signals is included in the Bio-Plex calibration kit. Signal output is standardized via calibration across days and instruments. The Bio-Plex system supports two alternative calibration settings. The calibration for broad-range

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standards offers the greatest dynamic range, while the calibration for low-range standards using StatLIA curve fitting in Bio-Plex Manager software offers the highest sensitivity (see Notes 1 and 2). 2.2 Assay Tubes and Pipettes

1. 15 mL propylene tubes. 2. Adjustable pipettes with tips capable of delivering 2–1000 μL (see Note 3). 3. Multichannel pipettes capable of delivering 2–200 μL (see Notes 3 and 4).

2.3 Preparation of Reagents and Samples for Immunoassay

Preparation of samples: 1. Keep all the samples on ice until ready for use. The user should optimize the appropriate sample dilution factor. 2. If required, dilute the samples in a sample diluent. Centrifuge the samples (tissue lysates, serum, CSF, and plasma) at 1000 g for 10 min at 4 °C to remove any particles [11, 19, 20]. Preparation of quality controls: 1. Before use, reconstitute quality control with 250 μL of standard diluent. Invert the vial several times to mix and vortex. 2. Allow the vial to sit for 5–10 min, and then transfer the controls to appropriately labeled polypropylene microfuge tubes. Unused portions may be stored at ≤ -20 °C for up to 1 month [11]. Preparation of cytokine standard: 1. Prior to use, reconstitute the cytokine standard with 250 μL of standard diluent to give a known concentration of standard for all analytes [11]. 2. Invert the vial several times to mix. Vortex the vial for 10 s. Allow the vial to sit for 5–10 min on ice, and then transfer the standard to an appropriately labeled polypropylene microfuge tube. 3. Make a fourfold standard dilution series and blank. Prepare 8 tubes and label them S1 to S8. Add 150 μL of standard diluent to S2 to S8 tubes. 4. Transfer 250 μL of the reconstituted standard to the S1 tube. 5. Prepare serial dilutions by adding 50 μL of the S1 to S2, mix well, and transfer 50 μL of the S2 standard to the S3 tube, mix well and transfer 50 μL of the S3 standard to the S4 tube, and so on.

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6. For samples with extremely low endogenous analytes, adding a second standard point to extend the standard curve’s bottom end may improve sample detectability (see Note 5). 7. The blank tube only contains a standard diluent of 200 μL [11]. Preparation of coupled beads: 1. Add the required volume of assay buffer to a 15 mL polypropylene tube to prepare 1× stock. Vortex the 10× or 20× stockcoupled beads at medium speed for 30 s. 2. Carefully open the cap and pipet any liquid trapped in the cap back into the tube. This is important to ensure maximum bead recovery (see Note 6). 3. Do not centrifuge the vial; doing so it will cause the beads to pellet. Dilute coupled beads to 1× by pipetting the required volume into the 15 mL conical centrifuge tube. Vortex the tube. Protect the beads from light with aluminum foil [11]. Preparation of wash buffer: 1. Bring the 10× Wash Buffer to room temperature, and mix to bring all salts into the solution. 2. Even after the buffer has been warmed to room temperature, crystals may still be present due to the high concentration. To assist dissolve the remaining crystals, softly swirl the container while holding it in one hand. 3. Dilute 1 part of the 10× stock solution with nine parts of deionized water to create the 1× wash buffer [11]. Preparation of detection antibodies: 1. Calculate the volume of detection antibodies and detection antibody diluent required to make a 1× stock while the samples are incubating. 2. Antibodies for detection should be prepared 10 min before use. The required quantity of detection antibody diluent should be added to a 5 mL polypropylene tube. 3. Vortex the 10× or 20× stock detection antibody for 5 s at medium speed to collect the complete volume at the bottom of the tube. 4. To achieve a 1× dilution, pipette the required amount of detection antibody into the 5 mL tube [11]. Preparation of streptavidin-phycoerythrin (SA-PE): 1. Determine the volume of SA-PE and assay buffer required to prepare a 1× stock while the detection antibodies are in incubation.

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2. 10 min prior to use, SA-PE should be prepared. Fill a 15 mL polypropylene tube with the required amount of assay buffer. 3. Vortex the 100× stock SA-PE at a medium speed for 5 s. Spin the tube for 30 s to collect all the volumes at the bottom. 4. Pipette the necessary volume of SA-PE into the 15 mL tube to dilute it to 1×. Until ready to utilize, vortex and shield from light [11]. 2.4 Analysis Software

3

Read and analyze assays with either Bio-Plex Manager™ Software or Luminex xPONENT Software, Luminex® 200™, HTS, FLEXMAP 3D®, or MAGPIX® with xPONENT® software.

Methods

3.1 CSF Collection and Sample Preparation

1. Perform lumber puncture in the left lateral decubitus or sitting position. 2. Let each participant receive local anesthesia by lidocaine hydrochloride injection before puncture. 3. Withdraw CSF from the L3–L4 or L4–L5 interspace using an atraumatic pencil-point needle, collect it in a low-protein absorption tube, and immediately transfer to ice [21]. 4. Centrifuge the CSF (4000 g for 10 min) at 4 °C. Divide the supernatant into 0.5 mL aliquots and store at -80 °C. 5. Perform MIAs after a single melting and refreezing of the sample for the preparation of 96-well plates [19].

3.2 Serum or Plasma Collection and Sample Preparation

1. Collect up to 5 mL whole blood from each participant, before centrifuging for 10 min at 1000 g at 4 °C, allow the blood at least 30 min to clot. 2. Remove serum and perform the test right away, or aliquot samples and keep them at -80 °C. Avoid multiple freeze/ thaw cycles. 3. Thoroughly thaw frozen materials, mix them by using a vortex, and centrifuge them before using them in the assay to get rid of any particles. Use sample diluent when more dilution is necessary [20]. 4. Collect plasma using EDTA as an anticoagulant. After drawing blood, centrifuge it for 10 min at 1000 × g at 4 °C. 5. Remove the plasma, perform the test right away, or aliquot the sample and store it at -80 °C. Steer clear of more than two freeze/thaw cycles.

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6. Thoroughly thaw frozen materials, mix them properly using a vortex, and centrifuge them before using them in the assay to solubilize any undissolved particles. 7. Use sample diluent as the diluent whenever more dilution is necessary [20]. 3.3 Tissue Collection and Preparation of Tissue Lysate

1. Collect patient tissue samples that have undergone surgical resection, immediately frozen, and keep them at -80 °C until needed. 2. Transfer about 25–35 mg of the tissue sample using a sterile disposable scalpel into a sterile 1.5 mL microcentrifuge tube. 3. Once the tissue has been ground 10–15 times, remove any remaining large pieces by adding 100 μL of tissue lysis buffer containing 1 mM PMSF and a protease inhibitor cocktail to the tube. 4. Gently stir and add another 650 μL of lysis buffer before adding it to the tube. 5. Place the tube on ice for 10 min with the cap closed. The tube should be centrifuged at 10,000 g for 10 min at 4 °C. 6. Carefully transfer the clear supernatant to a pre-chilled 1.5 mL microcentrifuge tube. Label the supernatant as total lysate. 7. Determine the protein concentration. Keep lysate concentration at 0.5–1 mg/mL. Prepare aliquots and store them at 80 °C for single use [11–13].

3.4 Instrument Preparation

1. Before configuring the assay, start the Bio-Plex System, and calibrate it using the appropriate Software (see Note 1). 2. To standardize the fluorescence signal, the calibration kit should be run every day or before each usage of the instrument (see Note 1). 3. To ensure the best possible performance of fluidics and optical systems, the validation kit should be run every month. Bring the wash buffer, assay buffer, and diluents to room temperature while the instrument warms up (see Note 2). 4. Keep additional materials chilled until required. If the highthroughput fluidics (HTF) unit is not used, fill the sheath fluid bottle up, and empty the waste bottle before beginning. By doing this, data loss and fluidic system backup will be avoided. 5. The protocol must be written beforehand to read the plate as soon as the experiment is complete. 6. The protocol file contains details about the assay’s analytes, the plate wells that need to be read, the sample information, the standard and control values, and the instrument parameters.

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7. Most Bio-Plex Assay procedures are included in the incorporated software. Choose a protocol from the list or produce your own. To begin a new protocol, select File, then New from the main menu [11]. 3.5

Assay Procedure

1. Plan the plate layout. 2. Prior to use, bring all test components to room temperature (RT). 3. Pipette carefully with calibrated pipets, avoiding bubbles. 4. Follow the conditions mentioned for vortex, shaking, and incubation directions exactly; failing to do so could lead to test variability (see Note 7). 5. Vortex the diluted (1×) beads. Add 50 μL to each well of the assay plate. 6. Wash the plate two times with 100 μL of wash buffer (see Notes 8 and 9). 7. Vortex samples, standards, blanks, and control. Add 50 μL to each well. 8. Cover the plate with sealing tape. Incubate on a shaker at 850 ± 50 rpm at RT for 30 min. 9. Vortex detection antibodies for 5 s, and quick spin to collect liquid with 10 min remaining in the incubation. 10. Wash the plate three times with 100 μL of wash buffer (see Notes 8 and 9). 11. For 5 s, vortex the diluted (1×) detection antibodies at a moderate pace. Add 25 μL to each well. Use a multichannel pipet to transfer it from a reagent reservoir. 12. Apply a fresh sheet of sealing tape over the plate. 30 minutes of 850 ± 50 rpm shaking at room temperature. 13. Meanwhile, prepare Bio-Plex Manager Software protocol; enter standard S1 values and units provided in the assay kit. 14. With 10 min left in the incubation, vortex 100× streptavidinPE (SA-PE) for 5 s and quick spin to collect liquid. Dilute to make it 1×. 15. Wash the plate three times with 100 μL wash buffer (see Notes 8 and 9). 16. Vortex the diluted (1×) SA-PE. Pour into a reagent reservoir, and use a multichannel pipet to transfer 50 μL to each well. 17. Cover the plate with sealing tape, and incubate at 850 ± 50 rpm for 10 min at room temperature. 18. Wash the plate three times with 100 μL of wash buffer (see Notes 8 and 9).

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19. Resuspend beads in 125 μL assay buffer. Cover and shake at 850 ± 50 rpm for 30 s. 20. Remove the sealing tape, and read the plate using the protocol set in the Bioplex system using the appropriate software [11]. 21. After each plate run, choose to wash between plates to lessen the chance of instrument clogging. 3.6

Data Analysis

1. Determine analyte concentrations by comparing sample readings to standard curves generated using a logistical curve fit algorithm. Calibration curves of the 6-plex immunoassay are shown in Fig. 1. Calibration curves of IL-1β, IL6, IL10, MIP1α, MIP1β, and TNFα in the 6-plex immunoassay generated using the five-parameter logistic (5PL) regression model. 2. Present analyte concentrations as pg/mL ± SD, range, or number (% of samples), as appropriate, as shown in Fig. 2. Analysis of cytokines in surgically resected tissues from mesial temporal lobe epilepsy (MTLE) patients and autopsy controls are shown in Fig. 2. 3. Automatically eliminate outliers by software by choosing to optimize in the standard curve panel. Outliers can also be manually chosen. Apply appropriate statistical methods to analyze the data [11].

Fig. 1 Standard curve of the 6-plex immunoassay (IL-1β, IL-10, MIP1A (CCL3), MIP1B (CCL4), IL-6, and TNFα) generated using the five-parameter logistic (5PL) regression model. Fluorescence intensity was measured in arbitrary units and analyte concentration was measured in pg/mL

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Fig. 2 Representative scatter diagram of cytokines and chemokine levels in patients and control using multiplex immunoassay. Protein expression of the inflammatory mediators in surgically resected tissue specimens of patients and controls. Error bar is mean ± SD based on 15 patients and 12 control samples, and each sample is analyzed in triplicates. Analyte concentrations are determined using standard curve. Mean increase in protein levels is statistically significant (unpaired t-test and Mann-Whitney U test, whichever is applicable; *p < 0.05; **p < 0.01; ***p < 0.001; **** p < 0.0001)

4

Notes 1. Before configuring the assay, start the Bio-Plex System, and calibrate it using the appropriate Software. To standardize the fluorescence signal, the calibration kit should be run every day or before each usage of the instrument. 2. To ensure the best possible performance of fluidics and optical systems, the validation kit should be run every month. The software documentation or the online Help can be used to find instructions on how to perform validation. 3. Use sealer correctly, and pipette using multichannel pipettes without touching the reagent in the plate to prevent cross-well contamination. 4. For convenience and effectiveness, a multichannel pipette is strongly advised. 5. Creating an additional standard point to prolong the standard curve’s bottom end may help increase sample detectability for samples with extremely low endogenous analytes.

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6. When transferring beads from the 20× stock tube, use a 200–300 μL capacity pipet to reduce volume loss. If necessary, execute the volume transfer in two steps. Avoid employing a 1000 μL capacity pipet and/or wide bore pipet tip. 7. Be consistent with this incubation time and shaker setting for optimal assay performance and reproducibility. 8. Be careful not to splash the wash buffer from one well to another while doing the washing. After each wash cycle, be sure to keep an eye on the leftover volume. To avoid splashing, make sure the microplate shaker setting is not too high, and lower the shaker speed. 9. Make sure that every washing step fully removes all chemicals from the well.

Acknowledgments This work is funded by the MEG resource facility, funded by the Department of Biotechnology, Ministry of Science and Technology, Govt. of India [BT/MED/122/SP24580/2018]. References 1. Stephenson J, Nutma E, van der Valk P, Amor S (2018) Inflammation in CNS neurodegenerative diseases. Immunology 154:204–219 2. Amor S, Puentes F, Baker D, van der Valk P (2010) Inflammation in neurodegenerative diseases. Immunology 129:154–169 3. Gaetani L, Paolini Paoletti F, Bellomo G, Mancini A, Simoni S, Di Filippo M, Parnetti L (2020) CSF and blood biomarkers in neuroinflammatory and neurodegenerative diseases: implications for treatment. Trends Pharmacol Sci 41:1023–1037 4. Huang S, Wang YJ, Guo J (2022) Biofluid biomarkers of Alzheimer’s disease: progress, problems, and perspectives. Neurosci Bull 38: 677–691 5. Ferreira D, Perestelo-Pe´rez L, Westman E, Wahlund LO, Sarrı´a A, Serrano-Aguilar P (2014) Meta-review of CSF core biomarkers in Alzheimer’s disease: the state-of-the-art after the new revised diagnostic criteria. Front Aging Neurosci 6:47 6. Angiulli F, Conti E, Zoia CP (2021) Bloodbased biomarkers of neuroinflammation in Alzheimer’s disease: a central role for periphery? Diagnostics (Basel) 11:1525 7. Clark C, Richiardi J, Mare´chal B (2022) Systemic and central nervous system

neuroinflammatory signatures of neuropsychiatric symptoms and related cognitive decline in older people. J Neuroinflammation 19:127 8. Hampel H, O’Bryant SE, Molinuevo JL, Zetterberg H, Masters CL, Lista S, Kiddle SJ, Batrla R, Blennow K (2018) Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol 14:639–652 9. Thambisetty M, Lovestone S (2010) Bloodbased biomarkers of Alzheimer’s disease: challenging but feasible. Biomark Med 4:65–79 10. Galasko D, Golde TE (2013) Biomarkers for Alzheimer’s disease in plasma, serum and blood-conceptual and practical problems. Alzheimers Res Ther 5:10 11. Srivastava A, Dixit AB, Paul D, Tripathi M, Sarkar C, Chandra PS, Banerjee J (2017) Comparative analysis of cytokine/chemokine regulatory networks in patients with hippocampal sclerosis (HS) and focal cortical dysplasia (FCD). Sci Rep 7:15904 12. Kan AA, de Jager W, de Wit M et al (2012) Protein expression profiling of inflammatory mediators in human temporal lobe epilepsy reveals co-activation of multiple chemokines and cytokines. J Neuroinflammation 9:207 13. Barroeta-Espar I, Weinstock LD, Perez-Nievas BG, Meltzer AC, Siao Tick Chong M, Amaral

Brain Cytokine Assay by Multiplex ELISA AC, Murray ME, Moulder KL, Morris JC, Cairns NJ, Parisi JE, Lowe VJ, Petersen RC, Kofler J, Ikonomovic MD, Lo´pez O, Klunk WE, Mayeux RP, Frosch MP, Wood LB, Gomez-Isla T (2019) Distinct cytokine profiles in human brains resilient to Alzheimer’s pathology. Neurobiol Dis 121:327–337 14. Ahsan H (2022) Monoplex and multiplex immunoassays: approval, advancements, and alternatives. Comp Clin Pathol 31:333–345 15. Zheng W, He L (2012) Multiplexed immunoassays. Advances in immunoassay technology. Edited by Norman H. L. Chiu and Theodore K. Christopoulos Ahsan H (2019) The biomolecules of beauty: biochemical pharmacology and immunotoxicology of cosmeceuticals. J Immunoassay Immunochem 40: 91–108 16. Ahsan H (2019) The biomolecules of beauty: biochemical pharmacology and immunotoxicology of cosmeceuticals. J Immunoassay Immunochem 40:91–108

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17. Fu Q, Zhu J, Van Eyk JE (2010) Comparison of multiplex immunoassay platforms. Clin Chem 56:314–318 18. Houser B (2012) Bio-Rad’s Bio-Plex(R) suspension array system, xMAP technology overview. Arch Physiol Biochem 118:192–196 19. Hidese S, Hattori K, Sasayama D (2021) Cerebrospinal fluid inflammatory cytokine levels in patients with major psychiatric disorders: a multiplex immunoassay study. Front Pharmacol 11:594394 20. Belzeaux R, Lefebvre MN, Lazzari A, Le Carpentier T, Consoloni JL, Zendjidjian X (2017) How to: measuring blood cytokines in biological psychiatry using commercially available multiplex immunoassays. Psychoneuroendocrinology 75:72–82 21. Jane LA, Wray AA (2022) Lumbar puncture. [Updated 2022 July 25]. In: StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL); 2023 Jan. Available from: https://www. ncbi.nlm.nih.gov/books/NBK557553/

Chapter 7 Targeted Modification of Epigenetic Marks Using CRISPR/dCas9-SunTag-Based Modular Epigenetic Toolkit Min Kyung Song and Yoon-Seong Kim Abstract The epigenome, consisting of chemical modifications to DNA and histone proteins, can alter gene expression. Clustered regularly interspaced short palindromic repeats/dead CRISPR-associated protein 9 (CRISPR/dCas9) systems enable precise target gene-specific gene modulation by attaching different “effector” domains to the dCas9 protein to activate or repress specific genes. CRISPR/dCas9-SunTag is an improved system version, allowing more efficient and precise gene activation or repression by recruiting multiple copies of the protein of interest. A CRISPR/dCas9-SunTag-based modular epigenetic toolkit was developed, enabling gene-specific epigenetic architecture modulation. This protocol generated a stable SH-SY5Y cell line expressing the CRISPR/dCas9-SunTag-JARID1A system to study H3K4Me3-mediated promoter regulation at a 200–400 bp of fine resolution. The procedure involved designing sgRNAs, subcloning dCas9-5XGCN4 into pLvx-DsRed, validating epigenetic mark changes with ChIP, and validating gene expression changes with RT-qPCR. This epigenetic toolkit is valuable for researchers to understand the relationship between gene-specific epigenetic modifications and gene expression. Key words Epigenetic architecture, CRISPR/dCas9-SunTag, α-Synuclein, Histone modifications

1

Introduction The epigenome consists of a record of chemical modifications to DNA and histone proteins, such as DNA methylation and histone modifications. Epigenetic changes are crucial in modifying gene expression [1–6]. Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) systems have been used widely for precise genome editing due to their high target specificity, efficiency, simplicity, and versatility [7]. dCas9, Cas9 endonuclease dead, is a mutated form of Cas9 whose endonuclease activity is removed, making it versatile for numerous genome-targeting applications. By attaching different “effector” domains to the dCas9 protein, researchers can use it to either activate or repress specific genes. This process is called CRISPR activation or CRISPR interference, respectively.

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_7, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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CRISPR/dCas9 epigenetic editing is a technique that uses the CRISPR/dCas9 system to modify epigenetic marks on DNA without altering the underlying DNA sequence itself. Epigenetic marks are chemical modifications of DNA or histone proteins that DNA wraps around and can alter gene expression [8–10]. CRISPR/dCas9-SunTag is an improved version of the CRISPR/dCas9 system, enabling more efficient and precise gene activation or repression. The main advantage of the CRISPR/ dCas9-SunTag is the ability to recruit multiple copies of the effector protein of interest fused to a single-chain variable fragment (scFv) that binds to a tandem repeat of five GCN4-peptide, allowing for more robust effects on the target gene [11, 12]. We developed a CRISPR/dCas9-SunTag-based modular epigenetic toolkit that enables gene-specific modulation of epigenetic architecture [13]. The entire system consists of the CRISPR/dCas9-SunTag with five GCN4-peptide, each epigenetic effector domain (e.g., JARID1A, JMJD3, EZH2, PRDM9, and p300, etc.) tagged with an scFv and target-specific sgRNA, allowing for modifying various epigenetic marks in a target-specific manner [14]. This epigenetic toolkit can be easily altered with various epigenetic effectors and is a valuable tool for researchers to understand the relationship between gene-specific epigenetic changes and gene expression. In this section, we describe a protocol for demonstrating the CRISPR/dCas9-SunTag-effector system to a target gene using the modified CRISPR/dCas9-SunTag platform, resulting in the activation or repression of gene expression [13, 14]. For example, we provide the process of generating a stable SH-SY5Y cell line expressing the CRISPR/dCas9-SunTag-JARID1A (histone demethylase) system for a fine targeting the promoter of SNCA gene, demonstrating the successful removal of a methyl group from H3K4me3 in the SNCA promoter region and reducing α-synuclein protein expression. The procedure involves designing sgRNAs and cloning sgRNA oligo into LentiGuide-Puro for the target gene, subcloning dCas9-5XGCN4 into pLvx-DsRed, generating stable SH-SY5Y cell lines expressing the CRISPR/dCas9-SunTag-JARID1A system, validating epigenetic mark changes with chromatin immunoprecipitation (ChIP), and validating gene changes in expression with RT-qPCR.

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Materials

2.1 Available Plasmids from Addgene or Clontech

1. pHRdSV40-scFv-GCN4-sfGFP-VP64-GB1-NLS (Addgene). 2. pCAG-dCas9-5xPlat2AflD plasmid (Addgene). 3. LentiGuide-Puro (Addgene). 4. pLVX-dsRed-Monomer-N1 (Clontech).

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5. pMD2.G (Addgene). 6. psPAX2 (Addgene). 2.2 Available Plasmids from Yoon’s Lab

1. pHRdSV40-scFv-GCN4-sfGFP-JARID1A-GB1-NLS. 2. pHRdSV40-scFv-GCN4-sfGFP-EZH2-GB1-NLS. 3. pHRdSV40-scFv-GCN4-sfGFP-JMJD3-GB1-NLS. 4. pHRdSV40-scFv-GCN4-sfGFP-PRDM9-GB1-NLS. 5. pHRdSV40-scFv-GCN4-sfGFP-p300-GB1-NLS.

2.3

Cell Culture

1. HEK293T cells. 2. SH-SY5Y cells. 3. DMEM/high glucose medium. 4. DMEM/F12 medium. 5. Fetal bovine serum (FBS).

2.4 Reagents and Biochemicals

1. Zymoclean™ Gel DNA recovery kit (Zymo Research). 2. EZ ChIP™ Chromatin immunoprecipitation kit (Millipore). 3. amfiRivert cDNA Synthesis platinum master mix (GenDepot). 4. PowerUP SYBR Green Master Mix (Applied Biosystems). 5. FastDigest BsmBI (Fermentas). 6. FastAP (Fermentas). 7. T4 polynucleotide kinase (T4 PNK) (NEB). 8. Quick Ligase (NEB). 9. 0.45 um Polyethersulfone (PES) membrane filter. 10. XmalI (NEB). 11. NotI (NEB). 12. Calf intestinal alkaline phosphatase (CIP). 13. T4 DNA ligase. 14. Competent cell. 15. Polybrene. 16. Blasticidin (Acros Organics). 17. Puromycin dihydrochloride (Acros Organics). 18. 40% formaldehyde. 19. Glycine. 20. Protease Inhibitor Cocktail (PI) (Thermosphere). 21. Protein A agarose/salmon sperm DNA (Millipore). 22. Anti-H3K4me3 antibody (Abcam). 23. Normal mouse IgG (EMD Millipore). 24. RNase A.

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25. Proteinase K. 26. Agarose. 27. Ethidium bromide. 2.5

Buffers

1. 100 mM DTT. 2. 0.3 M CaCl2. 3. 2X HEPES-buffered saline (2x HBS). 4. Phosphate buffered saline (PBS) (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4). 5. ChIP lysis buffer (1% SDS, 10 mM EDTA pH 8.0, 50 mM Tris-HCl, pH 8.0). 6. ChIP dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA pH 8.0, 16.7 mM Tris-HCl pH 8.1, 167 mM NaCl). 7. Low-salt wash buffer (0.1% SDS, 1.0% Triton X-100, 2 mM EDTA pH 8.0, 20 mM Tris-HCl, pH 8.1, 150 mM NaCl). 8. High-salt wash buffer (0.1% SDS, 1.0% Triton X-100, 2 mM EDTA pH 8.0, 20 mM Tris-HCl pH 8.0, 500 mM NaCl). 9. Lithium chloride (LiCl) wash buffer (250 mM LiCl, 1.0% IGEPAL, 1 mM EDTA pH 8.0, 10 mM Tris-HCl pH 8.1, 1.0% deoxycholic acid). 10. Tris-EDTA buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). 11. 200 mM NaCl.

2.6

Equipment

1. Thermocycler (Bio-Rad). 2. Agarose gel electrophoresis apparatus. 3. Cell Sorter (Bio-Rad). 4. Sonicator equipped with a probe (Fisher Scientific). 5. Real-Time PCR System (Applied Biosystems™).

3

Methods

3.1 Design of sgRNA and Subcloning sgRNA Oligo into LentiGuidePuro (Addgene)

1. Enter the sequence encompassing the promoter region of each target gene into Broad Institute genetic perturbation platform (https://portals.broadinstitute.org/gpp/public/analysistools/sgrnadesign) to select highly specific target sequences (20-mer). 2. The promoter region of SNCA: over 3200 base pairs. 3. Select 10 sgRNA targets spaced every 200–400 base pairs to thoroughly investigate the promoter regions of SNCA. sgRNAs: sgRNA sequence 5′-3′ SNCA (1): AAAGCAGACATTTTTAGCTC

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SNCA (2): AACAGCAGGCCCAAGTGTGA SNCA (3): GCTTTTCCCCGGGAAACGCG SNCA (4): ATTCCCAAATAATATTTAAT SNCA (5): CACTTCCGCGTCGCGGCGCT SNCA (6): GCGACTCTGACGAGGGGTAG SNCA (7): TGGGAAAATCAGCGTCTGGC SNCA (8): AAGCAAAGGCTTTCTGCTAG SNCA (9): ACTTTAAAACCACAAGGAAC SNCA (10): CAAGTCCAACCTTCTTGCTC 4. Use following primers to clone selected sgRNAs oligo into LentiGuide-Puro (see Note 1): Forward oligo: 5′ – CACCGNNNNNNNNNNNNNNNNNNNN – 3′ Reverse oligo: 3′ – CNNNNNNNNNNNNNNNNNNNNCAAA – 5′ 5. Digest and dephosphorylate LentiGuide-Puro plasmid with BsmBI for 30 min at 37 °C. 5.0 μg: LentiGuide-Puro 3.0 μL: FastDigest BsmBI 3.0 μL: FastAP 6.0 μL: 10x FastDigest Buffer 0.6 μL: 100 mM DTT 42.4 μL: ddH2O 60.0 μL: Total 6. Perform gel purification of digested plasmid using Zymoclean Gel DNA recovery kit and elute in elution buffer (EB) (see Note 2). 7. Phosphorylate and anneal each pair of oligonucleotides for the sgRNA using T4 polynucleotide kinase (T4 PNK). 1.0 μL: Forward oligo (100 μM) 1.0 μL: Reverse oligo (100 μM) 1.0 μL: 10× T4 ligation buffer 0.5 μL: T4 PNK 6.5 μL: ddH2O 10.0 μL: Total 8. Put the phosphorylation/annealing reaction in a thermocycler using the following program. 37 °C: 30 min 95 °C: 30 min and cooling to 25 °C at 1.5 °C/min

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9. Dilute annealed oligos from Step 8 at a 1:200 dilution into EB. 10. Prepare ligation reaction mixture as indicated below and incubate at room temperature (RT) for 10 min (see Note 3). X μL: BsmBI digested plasmid (50 ng) 1.0 μL: Diluted oligo duplex from Step 9 5.0 μL: 2x quick ligase buffer 1.0 μL: Quick ligase X μL: ddH2O 11.0 μL: Total 3.2 Lentiviral Packaging

1. Prepare 10 cm dish of 70–80% confluent HEK293T cells. 2. Co-transfect with packaging plasmids pMD2.G and psPAX2 using calcium phosphate. Prepare the co-transfection mixture as indicated below. 15.0 μg: Transfer plasmid; subclone sgRNA oligo into LentiGuide-Puro 6.0 μg: pMD2 15.0 ug: psPAX2 750.0 μL: 0.3 M CaCl2 750.0 μL: 2x HEPES-buffered saline (2x HBS) 3. After 16 h post-transfection, change the medium with general HEK293T cells medium (see Note 4). 4. After 8 h, change the medium with lentiviral production medium (see Note 5). 5. After 24 h, collect the medium containing lentiviral particles. 6. Centrifuge at 1000 g at RT for 5 min. 7. Transfer supernatant to new 15 mL tube and discard pellet cell debris. 8. Filter through 0.45 μm polyethersulfone (PES) membrane and use immediately.

3.3 Subcloning dCas9-5XGCN4 into pLvx-DsRedMonomer-N1 to Make pLVX-dCas9-5XGCN4

1. Digest 2–5 μg pLVX-dsRed-Monomer-N1 using restriction enzymes (XmalI/NotI) to obtain vector DNA. 2. Incubate a 1:20 mix of calf intestinal alkaline phosphatase (CIP) enzyme and digested insert DNA at 37 °C for 1 h to reduce the chance of self-ligation. 3. Incubate at 65 °C for 30 min to inactivate alkaline phosphatase. 4. Perform gel purification of digested vector DNA.

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5. Digest 2–5 μg pCAG-dCas9-5xPlat2AflD using restriction enzymes (XmalI/NotI) to obtain insert DNA (NLS-dCas95XGCN4). 6. Perform gel purification of digested insert DNA. 7. Prepare ligation reaction as indicated below and incubate at 4 °C for 10 min (see Note 6). X μL: Digested vector DNA (20 ng) X μL: Digested insert DNA (100 ng) 1.0 μg: TF ligase buffer 1.0 μL: T4 DNA ligase X μL: ddH2O 20.0 μL: Total 8. Transform ligation reaction to competent cell using heat shock. 9. Check the result by digestion and sequencing of the plasmid to screen for plasmids carrying the correct inserts. 3.4 Generation of Stable SH-SY5Y Cell Line Expressing the SunTag System

1. Prepare 24-well plate of 70–80% confluent HEK293T cells. 2. Transduce 10 μL pLVX-dCas9-5XGCN4 containing lentiviral particles with 4 μg/mL polybrene into SH-SY5Y cells to generate SH-SY5Y cells stably overexpressing dCas9-5XGCN4. 3. After 48 h of treatment with lentiviral particles mixture, select positive cells under antibiotic blasticidin (5 μg/mL). 4. Expand the blasticidin-resistant cells for 4 days. 5. Transduce 10 μL pHRdSV40-scFv-GCN4-sfGFP-JARID1AGB1-NLS containing lentiviral particles with 4 ug/mL polybrene into SH-SY5Y cells expressing both dCas9-5XGCN4 to generate the CRISPR/dCas9-SunTag-JARID1A system. 6. After 72 h, fluorescence-activated single cell sorting (FACS) for GFP (conferred by pHRdSV40-scFv-GCN4-sfGFP-JARID1A-GB1-NLS) to obtain a pure population of cells containing two transgene expressions. 7. After sorting, expand the sorted cells until sufficient for biochemical analysis. 8. Transduce 10 μL lentiviral particles containing each sgRNA with 4 μg/mL polybrene into SH-SY5Y cells expressing the SunTag-JARID1A system. 9. After 48 h of treatment with lentiviral particles, conduct the following experiments to confirm histone modification and changes in gene expression.

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3.5 Validation of Epigenetic Mark Changes with Chromatin Immunoprecipitation (ChIP) in SH-SY5Y Cells Expressing the CRISPR/dCas9SunTag-JARID1A System

This section provides chromatin immunoprecipitation (ChIP) protocol in stable SH-SY5Y cells expressing the CRISPR/dCas9-SunTag-JARID1A system by modifying the manual of EZ ChIP™ chromatin immunoprecipitation kit. 1. Cross-link proteins to DNA by adding formaldehyde to the media to a final concentration of 1% for 10 min at RT at 4 °C. 2. Add glycine to a final concentration of 125 mM to the media and incubate at RT for 10 min with rotation. 3. Wash cells twice with cold PBS supplemented with Protease Inhibitor Cocktail (PI). 4. Add cold PBS and scrape dishes with a cell scraper and transfer into 15 mL tube. 5. Centrifuge at 1000 g at 4 °C for 4 min. 6. Carefully aspirate supernatant. 7. Resuspend the pellet in ChIP lysis buffer (750 μL per 1 × 107 cells) containing PI and incubate for 10 min on ice. 8. Sonicate lysate to shear DNA to an average fragment size of 200–1000 bp with sonicator equipped with a probe for microcentrifuge tubes (see Note 7). 9. Centrifuge at 8000 g at 4 °C for 10 min. 10. Transfer supernatant to a new EP tube (see Note 8). 11. Dilute 10× in ChIP dilution buffer containing PI. 12. Add protein A agarose/salmon sperm DNA (40 μL/mL) into diluted chromatin. 13. Incubate overnight at 4 °C with rotation. 14. Brief centrifuge 5000 g for 1 min. 15. Remove 10 μL (1%) of the supernatant as input and save at 4 °C. 16. Collect the remaining supernatant and discard agarose pellet. 17. Add each target antibody (appropriate amount of antibody, 1–10 μg each tube) to the supernatant fraction (see Note 9). 18. Incubate at 4 °C for 8 h with rotation. 19. Add 20 μL protein A agarose/salmon sperm DNA to each tube with the target antibody and leave for a combined incubation at 4 °C overnight. 20. Brief centrifuge 5000 g for 1 min. 21. Remove the supernatant fraction. 22. Wash the immune complex with low-salt wash buffer (see Note 10). 23. Wash the immune complex with high-salt wash buffer (see Note 10).

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24. Wash the immune complex with LiCl wash buffer (see Note 10). 25. Wash twice immune complex with Tris-EDTA buffer. 26. Add EB to each tube containing the antibody/agarose complex. 27. Incubate at RT for 15 min. 28. Brief centrifuge 5000 g for 1 min and collect supernatant into new EP tubes. 29. Repeat Steps 26–28. 30. Add 200 mM NaCl to all samples and incubate at 65 °C overnight to reverse the DNA-protein cross links. 31. Add 10 μg RNase A to each tube and incubate at 37 °C for 30 min to remove RNA contamination. 32. Add 10 mM EDTA, pH 8.0; 40 mM Tris-Cl, pH 8.0; 50 μg proteinase K to each tube and incubate at 45 °C for 2 h. 33. Extract of fragmented DNA by phenol/chloroform/isoamyl alcohol (25:24:1), and finally dissolve in DNase/RNase free water. 34. Place the PCR reaction tubes in a thermal cycler and visualize using a 1.5% agarose gel with ethidium bromide. 3.6 Validation of Gene Expression Changes with RT-qPCR

This section presents reverse transcription-quantitative polymerase chain reaction (RT-qPCR) as a representative example for verifying gene expression levels. In addition to this method, western blot or immunostaining can use to confirm the gene expression level. 1. Prepare the RT-PCR mixture with RNA of stable SH-SY5Y cell lines expressing the SunTag system using amfiRivert cDNA Synthesis Platinum Master Mix. X μL: RNA (1 μg) 1.0 μL: RT enzyme 10.0 μL: 2× buffer X μL: DEPC H2O 20.0 μL: Total 2. Use following primers for qPCR: SNCA F′ primer: CCAGAAGACAGTGGAGGGAGCAGG 138 bp SNCA R′ primer: GCCTCATTGTCAGGATCCACAGGC β-actin F′ primer: GGAGTCCTGTGGCATCCACG 322 bp β-actin R′ primer: CTAGAAGCATTTGCGGTGGA

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3. Prepare qPCR reaction with PowerUP SYBR Green Master Mix as indicated below: 3.0 μL: cDNA 1.0 μL: F′ primer 1.0 μL: R′ primer 10.0 μL: Power SYBR® Green PCR Master Mix (2X) 5.0 μL: Water 20.0 μL: Total 4. Perform RT-qPCR in a real-time PCR system using the following program: UDG activation: 50 °C for 2 min Polymerase activation: 95 °C for 2 min Denaturation: 95 °C for 3 s Annealing/extension: 95 °C for 30 s (Denaturation and Annealing/extension step repeat 40 cycles). 5. Calculate fold change (2^(-ΔΔCt)).

4

Notes 1. Example oligo design: The NGG PAM is not included in the designed oligos. 2. If BsmBI is digested, a ~2 kb filler piece should be on the gel. Only gel purifies the larger band. Leave the 2 kb band. 3. For negative control ligation: Vector-only with water. 4. General HEK293T cells medium: DMEM/high glucose medium +10% FBS + 1% penicillin/streptomycin. 5. Lentiviral production medium: OPTI-MEM + 0.5% FBS + 1% penicillin/streptomycin. 6. For negative control to confirm background clones arising from self-ligation of inefficiently phosphatased vector, set a parallel ligation in the absence of insert DNA. 7. Sonication condition: Five pulses at 20 Hz for 20 s with 30 s intervals in between each pulse. This will need optimizing as different cell lines require different sonication times. 8. Each 100 μL aliquot contains 1 × 106 cell equivalents of lysate for immunoprecipitation once heard cross-linked chromatin can be frozen in liquid nitrogen and stored at -80 °C for up to 2 months.

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9. The following primary antibodies were used: H3K4me3 and normal mouse IgG. 10. Incubate with wash buffer for 5 min and brief centrifuge 5000 g for 1 min. Remove the supernatant fraction. References 1. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N et al (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339:819–823 2. Chavez A, Scheiman J, Vora S, Pruitt BW, Tuttle M, Iyer EPR et al (2015) Highly efficient Cas9-mediated transcriptional programming. Nat Methods 12:326–328 3. Hilton IB, D’ippolito AM, Vockley CM, Thakore PI, Crawford GE, Reddy TE et al (2015) Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat Biotechnol 33(5): 510–517 4. Holtzman L, Gersbach CA (2018) Editing the epigenome: reshaping the genomic landscape. Annu Rev Genomics Hum Genet 19:43–71 5. Klann TS, Black JB, Chellappan M, Safi A, Song L, Hiton IB et al (2017) CRISPR-Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome. Nat Biotechnol 35(6): 561–568 6. Thakore PI, Black JB, Hilton IB, Gersbach CA (2016) Editing the epigenome: technologies for programmable transcription and epigenetic modulation. Nat Methods 13(2):127–137 7. Adli M (2018) The CRISPR tool kit for genome editing and beyond. Nat Commun 9(1):1911 8. Xu X, Qi LS (2019) A CRISPR-dCas toolbox for genetic engineering and synthetic biology. J Mol Biol 431(1):34–47

9. Pickar-Oliver A, Gersbach CA (2019) The next generation of CRISPR-Cas technologies and applications. Nat Rev Mol Cell Biol 20(8): 490–507 10. Qi LS et al (2013) Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152(5): 1173–1183 11. Pflueger C, Tan D, Swain T, Nguyen T, Pflueger J, Nefzger C et al (2018) A modular dCas9-SunTag DNMT3A epigenome editing system overcomes pervasive off-target activity of direct fusion dCas9-DNMT3A constructs. Genome Res 28(8):1193–1206 12. Artyuhov AS, Dorovskiy DA, Sorokina AV, Shakirova KM, Momotyuk ED, Dashinimaev EB (2022) The efficiency of gene activation using CRISPR/dCas9-based transactivation systems depends on the system run time. Mol Biol (Mosk) 56(6):1014–1022 13. Guhathakurta S, Kim J, Adams L, Basu S, Song MK, Adler E et al (2021) Targeted attenuation of elevated histone marks at SNCA alleviates alpha-synuclein in Parkinson’s disease. EMBO Mol Med 13(2):e12188 14. Guhathakurta S, Adams L, Jeong I, Sivakumar A, Cha M, Bernardo FM et al (2022) Precise epigenomic editing with a SunTag-based modular epigenetic toolkit. Epigenetics 17(13):2075–2081

Chapter 8 Elevated Plus Maze for Assessment of Anxiety and Memory in Rodents Ravi Chandra Sekhara Reddy Danduga and Phani Kumar Kola Abstract The elevated plus maze is the most widely used paradigm to evaluate anxiety-associated behavioral alterations in rodent models of central nervous system (CNS) disorders. Unconditioned aversive behavior for open and elevated areas is a measure of anxiety and can be assessed by the plus maze. Plus maze consists of perpendicularly arranged open arms and closed arms crossed in the middle with a central platform. Rodents are allowed to explore the maze between the open and closed arms. The number of entries and time spent in the open arms and the closed arms are used as indicators for the anxiety nature of the animals. Transfer latency is a memory indicator that measures the amount of time it takes to move an animal from an open arm to a closed arm. This chapter describes the pretest conditions, materials required, and protocol for the conductance and evaluating the results for the anxiety and cognition-related behavior in rodents. Key words Rodents, Anxiety, Cognition, Plus maze, Behavioral test, Unconditioned aversive behavior

1 Introduction Anxiety disorders are the most prevalent psychiatric disorders with a prevalence of 7.3% globally [1]. Anxiety disorder refers to hyperarousal, excessive worry, and counterproductive fear and is more associated with muscle tension and avoidance behavior [2]. All mammalian species exhibits avoidance toward the potentially dangerous situations at the cost of opportunities, which is a hallmark of anxious behavior. The maladaptive and excessive avoidance leads to the development and maintenance of anxiety disorder and prevents and eliminates the fearful response to the situations in humans and rodents [3]. Preclinical models are extremely useful testing and understanding the pathophysiology of anxiety disorders, as well as screening and drug development for the effective treatment of anxiety disorders. This chapter describes the behavioral test anxiety and cognition that can be carried out by using plus maze.

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Plus maze was initially invented as Y maze by Montgomery and then modified as elevated plus maze by Pillow et al. [4]. The natural exploratory behavior of rats is suppressed under certain conditions like elevated positions and open space. The elevated plus maze test is based on the unconditioned avoidance toward the aversive environments over the natural exploratory behavior in rodents. Even though plus maze is simple in its structure, it can be effectively used for the estimation of anxiety in rodents. Testing is carried out using a typical plus maze comprised of two open arms with the dimensions 25 × 5 × 0.5 cm and (50 × 10 × 0.5 cm); the two closed arms, oppositely arranged with the dimensions 25 × 5 × 16 cm and 50 × 10 × 40 cm; and a central platform with the dimensions 5 × 5 × 0.5 cm and 10 × 10 × 0.5 cm for mice, rats, respectively [5]. The entire plus maze is elevated to the height of 50 cm. The mouse/rat is placed in the center area and is released toward a closed arm, i.e., head is directed toward a closed arm.

2

Materials 1. Control and test animals (rats or mice) (see Note 1). 2. Weighing machine. 3. Separate rooms for testing of the animals and for the investigator to minimize the errors (see Note 2). 4. The plus maze consists of two closed arms (with side walls) and two open arms (without side walls) extended from a central platform. The maze needs to be elevated to a height of 50 cm from the ground level. 5. Camera and retort stand for elevated plus maze (see Note 3). 6. 70% ethanol.

3 3.1

Methods Acclimatization

1. Acclimatize rats or mice for at least 7 days before the experiment so that the animals can become accustomed to the workplace and the investigator/experimenter. 2. Measure the animal (rat or mouse) weight and log the metadata (include the type of study, study title, and strain of animal). 3. To reduce bias, the plus maze should be cleaned with 70% ethanol before each test (without clues). 4. Animals should be placed in the experimentation room, 1 h before initiating the study so they can get used to the surroundings.

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3.2 Assessment of Anxiety

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Perform the following steps [6]: 1. Label the pretesting animals that were chosen in accordance with the therapy. 2. To reduce anxiety, place the testing animal softly in the middle of the plus maze. 3. Allow each animal to explore the plus maze for 5 min freely. 4. Remove the animal from the maze and back into the cage. 5. To reduce the cue-based bias in the behavior, each arm should be cleansed with 70% ethanol prior to each trial (see Note 4). 6. The time spent in open arms and the number of entries in each arm were recorded by video recording. 7. The anxiety index was calculated using the given formula, and the anxiety index ranged from 0 to 1. The increased values indicate the increased anxiety of the animals.

Anxiety index = 1 - f½ðtime spent in open arm=total timeÞ þ ðopen arm entries=total number of entriesÞ=2g:

3.3 Assessment of Memory [9]

Perform the following steps [7–9]: 1. Label the pretesting animals that are chosen in accordance with the therapy. 2. Allow each animal to explore the maze for 10 s. 3. Position the animal at the open arm’s far end, facing away from the platform in the middle. 4. Remove the rat from the maze and back into the cage. 5. Consider the time taken by the animal to transfer from open arms to closed arms as initial transfer latency (ITL) (see Note 5). 6. After 24 h, repeat the same procedure to measure the retention memory as retention transfer latency (RTL).

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Notes 1. All the experimental procedures should be approved by the appropriate institutional animal ethical committee [7]. 2. During the test and prior to the test period ambient should be as quiet as possible to minimize the undue influence of noises, smell, or movements on anxiety and cognition [8]. 3. Use of camera can minimize the undue influence of direct observer on the test results.

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4. Residual odor and matters could influence our test results. So, the cleaning before and after each test procedure with 70% ethanol simply minimizes the stress and anxiety in the animals. 5. If the animal was reluctant to move into the closed arms, within 90 s, the animal was pushed to move into the closed arms and record 90 s as the ITL or RTL. References 1. Thibaut F (2017) Anxiety disorders: a review of current literature. Dialogues Clin Neurosci 19: 87–88 2. Remes O, Brayne C, van der Linde R, Lafortune L (2016) A systematic review of reviews on the prevalence of anxiety disorders in adult populations. Brain Behav 6:e00497 3. Biedermann SV, Biedermann DG, Wenzlaff F, Kurjak T, Nouri S, Auer MK, Wiedemann K, Briken P, Haaker J, Lonsdorf TB, Fuss J (2017) An elevated plus-maze in mixed reality for studying human anxiety-related behavior. BMC Biol 15:1–3 4. Pellow S, Chopin P, File SE, Briley M (1985) Validation of open: closed arm entries in an elevated plus-maze as a measure of anxiety in the rat. J Neurosci Methods 14:149–167 5. Pawlak CR, Karrenbauer BD, Schneider P, Ho YJ (2012) The elevated plus-maze test:

differential psychopharmacology of anxietyrelated behavior. Emot Rev 4:98–115 6. Lee B, Sur B, Yeom M, Shim I, Lee H, Hahm DH (2014) L-tetrahydropalmatine ameliorates development of anxiety and depression-related symptoms induced by single prolonged stress in rats. Biomol Ther (Seoul) 22:213–122 7. Ellenbroek B, Youn J (2016) Rodent models in neuroscience research: is it a rat race? Dis Model Mech 9(10):1079–1087 8. Leo LM, Pamplona FA (2014) Elevated plus maze test to assess anxiety-like behavior in the mouse. Bio-protocol 4(16):e1211 9. Danduga RCSR, Dondapati SR, Kola PK, Grace L, Tadigiri RVB, Kanakaraju VK (2018) Neuroprotective activity of tetramethylpyrazine against 3-nitropropionic acid induced Huntington’s disease-like symptoms in rats. Biomed Pharmacother 105:1254–1268

Chapter 9 Drosophila melanogaster Neuromuscular Junction as a Model to Study Synaptopathies and Neuronal Autophagy Anushka Chakravorty, Vasu Sheeba, and Ravi Manjithaya Abstract Neuronal synapse dysfunction is a key characteristic of several neurodegenerative disorders, such as Alzheimer’s disease, spinocerebellar ataxias, and Huntington’s disease. Modeling these disorders to study synaptic dysfunction requires a robust and reproducible method for assaying the subtle changes associated with synaptopathies in terms of structure and function of the synapses. Drosophila melanogaster neuromuscular junctions (NMJs) serve as good models to study such alterations. Further, modifications in the microenvironment of synapses can sometimes reflect in the behavior of the animal, which can also be assayed in a high-throughput manner. The methods outlined in this chapter highlight assays to study the behavioral changes associated with synaptic dysfunction in a spinocerebellar ataxia type 3 (SCA3) model. Further, molecular assessment of alterations in NMJ structure and function is also summarized, followed by effects of autophagy pathway upregulation in providing neuroprotection. These methods can be further extended and modified to study the therapeutic effects of drugs or small molecules in providing neuroprotection for any synaptopathy models. Key words Autophagy, Neurodegeneration, Neuromuscular junction, Drosophila, Synaptopathy, Locomotion

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Introduction Synaptopathies are a class of neurodegenerative disorders that are characterized by deleterious changes to the presynaptic or postsynaptic contacts between neurons [1–3]. In many neurodegenerative diseases, which includes Alzheimer’s, Huntington’s, and prion diseases, the cellular manifestation of disease pathology is seen earliest at the presynapse [4–6]. Autophagy is one of the proteostatic machineries wherein a part of the cytoplasm is sequestered in double-membraned vesicles called autophagosomes that eventually fuse with lysosomes resulting in degradation of its contents ranging from cytoplasmic long-lived proteins, aggregated proteins,

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damaged or superfluous organelles, or intracellular pathogen [7, 8]. Emerging studies suggest that proteostatic machineries, especially synaptic autophagy can confer neuroprotection by not only clearing toxic protein aggregates at the presynapse but also via synapse-specific roles of increasing bouton numbers or controlling the degradation of key presynaptic proteins involved in vesicle recycling and/or neurotransmission [9–11]. The Drosophila melanogaster neuromuscular junction (NMJ) serves as a good model system for studying synapse pathogenesis for several reasons. Drosophila are easy to rear and maintain and have rapid generation time, and with the large repertoire of genetic toolkits available, tissue-specific manipulation can be achieved [12, 13]. Moreover, around 60% of human disease genes are well conserved in flies [14, 15]. Drosophila NMJs are big synapses, which can be targeted for genetic manipulation as well as for electrophysiology, ultrastructural, biochemical, and imaging studies. They resemble the vertebrate central nervous system glutamatergic synapses and are accessible through both invasive and noninvasive techniques, through the cuticle of the translucent larva [13, 16]. The current chapter outlines the key methods for utilizing a neurodegenerative model to study associated synaptopathies, using Drosophila NMJs. One of the readouts for dysfunctional motor neuron presynapses is locomotion deficit [17, 18]. Drosophila larvae exhibit several natural crawling behaviors such as turns, bends, rolls, pauses, forward and backward locomotion, and hunches [19]. All these patterns of locomotion are controlled by the central pattern generators (CPGs), which are a group of excitatory and inhibitory neurons in the larval ventral nerve cord, along with motor and sensory neurons [19, 20]. Any dysfunction in these circuits leads to locomotory defects, which can be measured with the aid of a simple yet robust assay for larval locomotion [21]. The GAL4-UAS system is a bipartite transcription activation system derived from yeast [22, 23]. The GAL4 lines (called the driver lines) harbor a construct that carries core sequences from a promoter that bears tissue-specific expression. This core promoter sequence is placed upstream of the coding sequence of GAL4. The UAS lines (called the responder lines), similarly, harbor a construct carrying the enhancer sequences called upstream activation sequence or UAS, to which the GAL4 binds. The gene of interest is placed downstream of the UAS sequences, and, thus, its expression is controlled via the activation and binding of GAL4. Using this method, tissue-specific expression of the proteins of interest can be achieved in vivo by following mating schemes between the driver and responder lines. Moreover, studies involving loss or gain of function of genes of interest can also be achieved in a tissuespecific manner using the GAL4-UAS system, through RNAi and CRISPR toolkits [24, 25].

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The method described here employs the GAL4-UAS system for expressing 78 polyQ repeats of ataxin-3 protein, which is responsible for spinocerebellar ataxia type 3 (SCA3), a progressive movement disorder that worsens with age [26]. The nonpathogenic control contains 27 polyQ repeats. The expression is driven in motor neurons using the driver, D42-GAL4. Since the presynaptic autophagy pathway can confer neuroprotection via either clearance of aggregates or through mechanisms unique to synapses such as degradation of active zones and synaptic vesicles, the methods described herein are simple yet robust to assess neuroprotective effects of autophagy pathway in a SCA3 neurodegenerative model. We describe here the method for assaying the locomotion of Drosophila larvae followed by studying synaptic dysfunction using immunohistochemistry. Considerable focus has been given to analysis and quantification of the acquired data. Although this chapter makes use of a Drosophila model of spinocerebellar ataxia type 3 to study synaptic dysfunction and neuroprotection conferred upon autophagy pathway upregulation, the methods outlined here can be extended to any other neurodegeneration-associated synaptopathy model as well.

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Materials

2.1 Larval Locomotion Assay Tools

1. Paint brush. 2. Conical flask. 3. Microwave oven. 4. Black color adhesive tape. 5. 90 mm petri dish. 6. 60 mm petri dish. 7. Tissue paper soaked in 1× phosphate-buffered saline (PBS). 8. Small paper cut-out with 1 cm marking. 9. Video camera. 10. Makeshift setup for carrying out locomotion assay. 11. LED light strip. 12. Black chart paper. 13. White A4 blank sheets.

2.2 Immunohistochemistry Tools

1. 50 mL conical centrifuge tubes. 2. Tube stand. 3. Vortex. 4. 1.5 mL tubes. 5. Rotospin.

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6. Sylgard plates. 7. Stainless steel minutien pins: 0.1 mm diameter. 8. Micro dissecting spring scissors: Straight/3 mm cutting edge. 9. Long and short forceps. 10. Coverslips. 11. Glass slides. 12. Pipette. 13. Pipette tips. 14. Stereo microscope. 2.3 Larval Locomotion Assay Reagents

1. Agar agar powder. 2. Fine charcoal powder. 3. Distilled water. 4. 1× PBS.

2.4 Immunohistochemistry Reagents

1. Hemolymph-like saline, HL3: 70 mM NaCl, 5 mM KCl, 20 mM MgCl2, 5 mM trehalose, 115 mM sucrose, 5 mM HEPES, 10 mM NaHCO3, pH 7.2. 2. Fixative: 4% paraformaldehyde (PFA). 3. Permeabilizing solution: Prepare 0.1% PBT using 0.1% Triton X-100 dissolved in 1× phosphate buffer saline (PBS). 4. Blocking solution: Prepare 0.2% PBTB using 0.2% bovine serum albumin dissolved in 0.1% PBT. 5. Mounting media such as Vectashield antifade mounting media. 6. Primary antibodies: Mouse anti-Bruchpilot diluted at 1:200 (Developmental Studies Hybridoma Bank), DyLight™ 405 AffiniPure Goat Anti-Horseradish Peroxidase diluted to 1:200 (Jackson ImmunoResearch), Fluorescein (FITC) AffiniPure Goat Anti-Horseradish Peroxidase diluted to 1:200 (Jackson ImmunoResearch). 7. Secondary antibodies: Goat Anti-Mouse Atto 633 diluted to 1: 1000 (Sigma-Aldrich). 8. Stains: Phalloidin-Atto 550 diluted to 1:1000 (Sigma-Aldrich).

2.5

Fly Husbandry

1. Drosophila melanogaster stock information:

Fly lines

Genotype

Bloomington stock number

UAS-Q78

w[*]; P{w[+mC]=UAS-hATXN3.tr-Q78}c211.2

8150

UAS-Q27

w[*]; P{w[+mC]=UAS-hATXN3.tr-Q27}N18.3d

8149 (continued)

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Fly lines

Genotype

Bloomington stock number

D42-GAL4

w[*]; P{w[+mW.hs]=GawB}D42

8816

UAS-mCherryAtg8a

y[1] w[1118]; P{w[+mC]=UASp-mCherry-Atg8a}2; Dr [1]/TM3, Ser[1]

37750

UAS-GFP-mCherry- y[1] w[1118]; P{w[+mC]=UASp-GFP-mCherry-Atg8a}2 37749 Atg8a

2. Cornmeal agar food: Agar agar 12 gm, cornmeal powder 100 gm, sugar 40 gm, yeast 40 gm, benzoic acid 1 gm, ethanol 10 mL, and propionic acid 10 mL. 3. FlyPad and CO2 needle. 4. Cut glass bottle. 5. Charcoal powder. 6. Egg collection plate: Cornmeal agar food mixed with charcoal powder and poured onto a 60 mm petri dish. 2.6

Softwares

1. Fiji ImageJ (https://imagej.net/software/fiji/). 2. VirtualDub (https://virtualdub.sourceforge.net/). 3. GraphPad Prism (https://www.GraphPad.com/).

3

Methods

3.1 Fly Husbandry and Cross Setup

1. Collect female virgin flies of D42-GAL4 and D42-GAL4 > UAS-mCherry-Atg8a flies. When flies are kept at ambient temperatures of 25 °C and at 12 h:12 h light/dark cycle, virgin females can be collected first thing in the morning, after lights are on. Virgin flies can be collected again after 3–4 h gap. 2. Once there are enough (approximately ten females per vial; two vials per cross) virgin females, set up the following crosses:

Cross 1: Progeny from this cross will be the driver controls, as there will be no expression of any transgene because of lack of upstream activation sequence in them.

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Cross 2: Progeny from this cross will serve as control, as there will be expression of nonpathogenic 27 polyQ repeats of ataxin-3 in motor neurons. Cross 3: Progeny from this cross will express pathogenic 78 polyQ repeats of ataxin-3 protein in motor neurons. Cross 4: Progeny from this cross will express Atg8a over and above the endogenous levels. Overexpression of Atg8a can induce autophagy. This will be the control for assessing the effect of Atg8a overexpression in behavior and NMJ morphometry in the larvae. Cross 5: Progeny from this cross will express nonpathogenic 27 polyQ repeats of ataxin-3 in motor neurons, in the background of overexpressed Atg8a. This will be the control for assessing the neuroprotective effect of Atg8a overexpression in motor neurons. Cross 6: Progeny from this cross will express pathogenic 78 polyQ repeats of ataxin-3 in motor neurons, in the background of overexpressed Atg8a. This will be the experimental cross to assess for the neuroprotective effect of Atg8a overexpression in motor neurons, in an ataxic model of Drosophila. 3. Maintain these crosses in 12 h:12 h light/dark cycle, at 25 °C. After 72–96 h, early third instar larvae will emerge (see Note 3). 4. Flip the adults to new vials. Using a fine brush, collect the larvae for further experiments. 3.2 Larval Locomotion Assay

Setup to capture videos of larval locomotion: 1. A makeshift cardboard box can be used as a setup (Fig. 1a). Line the insides of the box with black chart paper. The height of the box can be adjusted such that the camera fixed on the top of the box is able to capture good-resolution videos. Place a paper with a 1 cm mark near the plate, which will serve as the reference scale while analyzing videos. 2. Take the lid of a 90 mm petri dish and cover the sides of the plate with black tape. This is done to avoid reflection of light while capturing videos, which makes it difficult for determining the threshold during analysis. 3. Make 50 mL of 2% agar with 0.5 gm charcoal powder added to it. 4. Pour the agar onto the lid to evenly spread it till the top of the lid. Wait for 20–30 min for the charcoal agar mix to cool down (Fig. 1b). 5. Place the agar plate at the center of the arena and align the camera for video recording.

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Fig. 1 Larval locomotion assay. (a) Setup for larval locomotion assay. (b) Charcoal agar plate for larval locomotion assay, with four third instar larvae.

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Preparing age-matched larvae for carrying out the assay (this should be carried out around 5-6 days prior to the day of larval locomotion assay): 1. Collect approximately 60–70 virgin female flies and 40 males of desired genotypes. 2. Transfer them onto cut glass bottles with yeast-enriched cornmeal agar food plate taped onto the cut end of the bottle. Allow flies to feed overnight on the yeast-enriched food in 12 h:12 h light/dark cycle. 3. After 12 h, transfer the flies onto another bottle with cornmeal agar food plate, and house them in ambient conditions of temperature and humidity for 1 h. 4. Repeat this process, and let the flies lay eggs for 1 h. After 1 h, transfer the flies onto bottles with egg collection plates. House the flies in these bottles for 2 h. 5. At the end of 2 h, collect the eggs from the plates, and transfer approximately 200–300 eggs in fresh vials containing cornmeal agar food. 6. After 72–96 h, at ambient conditions of 25 °C and 12 h:12 h light/dark cycle, the larvae will reach third instar stage and will be ready for carrying out the assay (see Note 1). Videography of larval locomotion: 1. Collect early third instar larvae of the desired genotypes. 2. Wash the larvae in 1× PBS to remove food particles, and place them in tissue papers soaked in 1× PBS. 3. Using a paintbrush, gently place 4 larvae at the center of a 2% charcoal agar plate (Fig. 1b). 4. Let the animals acclimatize for 30 s, following which, place larvae back at the center (see Note 2). 5. Using a good-quality video camera, capture movies of the animals for 1 min and 30 s with a frame rate of 30 frames per

ä

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Fig. 1 (continued) (c) Background subtraction and thresholding after postprocessing to identify the larvae for analysis using wrMTrck plugin. (d) Analysis with wrMTrck gives information of path length, distance, average speed, bends, and body bends per second (BBPS) of all the four animals. Each animal is assigned an identity (numbered from 1 to 4), and individual information for all parameters are generated. (e and f) Representative tracks of larvae generated after analysis through wrMTrcK. (g and h) Representative graphs of total distance traversed and average speed of the animals

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sec. Make around ten videos per genotype. Ensure that the contrast is good while capturing the videos. 6. Transfer the videos immediately and rename them with the genotype names. Post-acquisition video processing: 1. The videos captured should be uncompressed for quantification in ImageJ (an open-source Java-based image-processing program developed at the National Institutes of Health). To uncompress the videos, open them in any third-party software such as VirtualDub (https://virtualdub.sourceforge.net/). If the video format is not supported and cannot be opened in VirtualDub, use FFMpeg plugin (https://codecpack.co/ download/FFInputDriver.html) for opening the files. To reduce the size of the video file, the audio can be turned off (Audio > No audio). 2. Change the color depth of the video file to luminance only (Video > Colour depth > luminance only). 3. Select the frame number corresponding to 10th second as the starting frame (Edit > Set selection start). 4. Select the frame number corresponding to 70th second as the end frame (Edit > Set selection end). 5. Save the movie as an AVI file (File > Save as AVI). Video analysis: 1. Open the saved AVI videos in ImageJ. Draw a line over the 1 cm mark using the line tool. Change the scale from pixels to cm. (Analyze > Set scale, known distance = 1; unit of length = cm). 2. Select the region of interest (ROI) around the plate with the rectangle selection tool, and crop. 3. Go to Edit > invert. 4. Subtract the background from the movie (tick light background; adjust rolling ball radius to obtain sharp contrast). Maintain similar rolling ball radius for all videos. 5. Threshold the videos (Image > Adjust > Threshold). An 8-bit window with the animals thresholded will appear (Fig. 1c, d). 6. Run the wrMTrck plugin [27] using the parameters that have been standardized for third instar larvae. 7. The following parameters will be generated in a separate window: Distance (the actual displacement of the larvae), Length (the distance that the animal travels), Average speed, Bends (number of times the animals make turns and bends in the

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arena), BBPS (body bends per second). The path tracks will also be generated (Fig. 1e, f). Quantification and statistics: 1. Collate all the results for all the animals and save in an excel file. 2. Transfer the results to any plotting software (e.g., GraphPad Prism). 3. Open GraphPad Prism software and select column data. Test for normality distribution of the data. 4. Carry out analysis using one-way ANOVA for more than two genotypes if the data points are normally distributed. If only two genotypes are being compared, use two-tailed Student’s unpaired t-test or Mann-Whitney test for pairwise comparison of non-normally distributed data. 5. Use appropriate post hoc analysis for ANOVA, to find out which groups are significantly different. Note the p-values and degrees of freedom. 6. Plot the graphs. A good way to plot is to show all the individual data points (Fig. 1g, h). 3.3 Immunohistochemistry

Preparing age-matched larvae for carrying out the assay: Refer to Subheading 3.2 for protocol. Dissection of larvae to obtain neuromuscular system: 1. 72–96 h after setting up the cross, collect early third instar larvae. 2. Wash them in 1× PBS to remove excess food particles stuck to the body. Collect around six to eight animals per genotype. 3. Keep the vials containing the animals on ice. 4. Put a small drop of ice-cold HL3 buffer onto a Sylgard plate. Using a paintbrush, transfer an animal onto the buffer. Using forceps and a minutein pin, grab the posterior end, and insert the pin between the posterior spiracles. 5. Using the forceps, grab between the mouth hooks and pull the larva. Fix the other minutein pin just below the mouth hook (Fig. 2a). 6. Using insect scissors, make cuts on the posterior end near the pin. Make the cut vertically along the body wall, between the tracheal tubes, without damaging the brain (see Note 3). 7. Flush the animal with HL3 so that all the organs start floating up. Using forceps, pull out the organs (gut, lymph glands, salivary glands) keeping the brain, long axons, and musculature intact). Dissect minimum three animals per genotype. 8. Pin the muscle prep on all the four sides (Fig. 2c).

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Fig. 2 Drosophila larval neuromuscular preparation. (a) Drosophila third instar larva placed dorsal side up and pinned in the anterior and posterior ends. The tracheal tubes, mouth hook, and posterior spiracles are visible. The cut is made across the midline of the dorsal axis followed by small incisions near the anterior and posterior pins. (b) Cartoon of a larval neuromuscular preparation. Note the stereotypical distribution of muscle fibers in each hemisegment. The abdominal segments have been labelled from A1 to A7. Red box indicates muscle 4 of left and right hemisegment A4. (c) A dissected third instar larva. Note the muscle fibers visible in each hemisegment. The preparation appears yellow because of Bouin’s fixative, which contains picric acid that stains muscles yellow

Preparation of larval fillet samples for immunohistochemistry: 1. Remove the HL3 buffer. Add a small drop (200 μL) of 4% PFA and incubate samples for 30 min. 2. Remove PFA and wash thrice in 1× PBS for 5 min each. 3. Remove the pins from the animals, and collect all the animals of each genotype in separate 1.5 mL tubes containing 500 μL permeabilizing solution. 4. Put the tubes in a tube rotator for 15 min. 5. Remove the solution and add 500 μL permeabilizing solution again, and incubate for 15 min. 6. Remove the solution and add 500 μL of blocking solution. Incubate for 1 h. 7. Prepare primary antibody mix in blocking solution in recommended dilutions. For visualizing overall morphology of NMJs, use Fluorescein (FITC) AffiniPure Goat AntiHorseradish Peroxidase diluted to 1:200 (Jackson ImmunoResearch) combined with a muscle marker such as PhalloidinAtto 550 diluted to 1:1000 (Sigma-Aldrich). Additionally, an active zone marker, such as anti-Bruchpilot (anti-BRP) diluted to 5 μg/mL (Developmental Studies Hybridoma Bank) can be used. Remove the blocking solution and add 500 μL of the antibody mix.

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8. Incubate samples overnight in 4 °C, in the tube rotator. 9. Next day, remove the primary antibody solution and rinse twice in permeabilizing solution. Add 500 μL permeabilizing solution and incubate for 15 min. 10. Remove permeabilizing solution, add fresh solution again, and incubate for another 15 min. 11. Remove the above solution and add 500 μL of blocking solution. Incubate for 1 h. 12. Prepare secondary antibody mix: Goat anti-mouse IgG conjugated to Atto 633 (1:1000 dilution). 13. Incubate in secondary antibody for 2 h at room temperature. 14. Remove secondary antibody mix and wash twice with blocking solution for 15 min each. 15. Put around 10–20 μL of mounting medium on the glass slide, and mount the samples with cuticle side facing the slide and musculature facing the coverslip. 16. Proceed for imaging. Imaging: 1. Adjust the gain and laser power in confocal microscope. Observe the phalloidin-stained muscles in 20×, and capture the entire field of muscles (Fig. 3a). 2. Switch to 60× objective and identify muscle 4 at hemisegment A2. Then, identify the neuromuscular junctions at muscle 4 (Fig. 2b) or at muscle 6/7 (Fig. 3b, c). Both muscle NMJs are routinely used for bouton analysis. 3. Adjust the zoom factor to capture the entire field of the NMJ. While adjusting laser intensities, use lookup tables with range indicators and check for over/undersaturation. Avoid oversaturating the signal. 4. Maintain the same zoom factor while capturing the images of all genotypes. Obtain the images, maintaining optimal Z-stack step size, in accordance with Nyquist equation. 5. Use the same imaging parameters (gain, laser power, step size) while capturing images from different genotypes. 6. Capture muscle 4 and muscle 6/7 NMJs for hemisegments A2–A4 for at least three animals per genotype. While saving the images, name the files appropriately, with the channel and antibody/stain names, hemisegment name, and muscle name in subfolders of each animal. The main folder should contain the genotype name.

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Fig. 3 Representative images of Drosophila third instar larval neuromuscular preparation. (a) Dissected third instar larval body wall with ventral nerve cord and long motor axons visible in green (immunostained with FITC conjugated anti-HRP) and muscles visible in red (stained with Phalloidin-Atto 550). (This figure has been reproduced from Chakravorty et al. [26] under Creative Commons Attribution License (CC BY)). Scale bar 400 μm. (b) Zoom of muscle 6/7 of hemisegment A4. Note the axon branching out to innervate different muscle fibers, terminating into NMJs. Scale bar 100 μm. (c) Zoom of NMJ of muscle 6/7 of hemisegment A4. The boutons can be visualized as the bead-like swollen structures. The active zones in the boutons, visible here in blue, have been immunostained with anti-Bruchpilot. Scale bar 20 μm

Analysis of structural defects in NMJ and quantifying area and perimeter of NMJs: 1. Open the acquired images in ImageJ. Split the channels (Image > Colour > Split channels). 2. In the FITC-HRP channel (Fig. 4a), mark the outline of the NMJ using polygon or freehand selection tool. Press “t” to add the ROI to the ROI manager. 3. Repeat the above steps for all NMJs acquired for a particular genotype. 4. Select all the ROIs from the manager. 5. Go to Analyze > Set measurements. Checkmark the boxes for Area, Perimeter, and Mean Gray Value. 6. Go to Analyze > Measure. Save the results. 7. Repeat the steps for all genotypes and save the results. 8. Open the channel containing muscle images. 9. Using the polygon selection tool, draw an outline around muscle 4 and muscle 6/7. Press “m,” and note the area of the muscle. Save the result. Do this for all animals of all genotypes, and save the results in the respective folders/subfolders.

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Fig. 4 Representative images of NMJ of muscle 4. (a) FITC conjugated anti-HRP immunostained NMJ. The swollen bead-like structures are called boutons. (b) Active zones in NMJ boutons. Each punctum is an active

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Quantifying area and perimeter of boutons of NMJs: 1. Follow the above steps 1–7. This time while drawing the ROIs, select the individual boutons (swollen parts of the NMJ, where the neurons contact muscles). Analysis of functional defects in NMJ: Quantifying active zones in FITC-HRP marked neurons using manual method: 1. Open the acquired images in ImageJ. 2. Go to Image > Stacks > Z-project. 3. Split the channels. Image > Colour > Split channels. Convert the images to 8-bit (Image > Type > 8-bit). 4. In the channel for FITC-HRP-labelled neurons, mark the ROI around the entire NMJ, and press “t” to add the ROI to the ROI manager. 5. Switch to the BRP channel (Fig. 4b). Select the ROI from the ROI manager, and go to Image > Adjust > Threshold. Threshold using default method. Make sure that all the BRP particles are selected. A more restrictive thresholding method (such as Renyi entropy or Yen) can be used if the signal-to-noise ratio is low. Do not press apply. While determining the best thresholding methods, use auto threshold. (Image > Adjust > Auto Threshold (Try All)). A window will open, displaying thresholded result via all the 16 methods (Fig. 4c). Upon zooming in, the name of the method can be found below each thresholded image. 6. Go to Analyze > Analyze Particles. The size can be left as between 0-infinity. Circularity can also be left as 0.00–1.00. Show > Outlines. Checkmark boxes against Display results and summarize. Press OK. 7. A window with the parameters for number of particles, area of particles, mean gray value, integrated density (total intensity), and perimeter will appear. A separate window summarizing the results will also open along with outlines of all the particles (Fig. 4d). Save both the windows. 8. Repeat for all images for each genotype.

ä Fig. 4 (continued) zone (site of active neurotransmitter release) marked by enrichment of Bruchpilot (BRP), a large scaffolding protein that tethers synaptic vesicles close to the plasma membrane. (c) Auto thresholding using 16 methods yields different results for the BRP channel. Out of all methods, IsoData (marked in red) works best for the representative image shown here. (d) Particle analysis of BRP puncta. Upon thresholding the BRP channel using IsoData method, particle analysis yields the count, mean intensity, area, and perimeter of all the BRP puncta in the NMJ

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9. The manual method described above can be time-consuming especially if the number of genotypes involved is huge. For batch-processing many files and multi-parametric quantification, a plugin was developed by Annette Schenck’s group for high-throughput analysis of Drosophila NMJs [28, 29]. Below is a summary of the same. Quantifying active zones in FITC-HRP marked neurons using macro for batch-processing for NMJ morphometry: 1. Open the three-channel image acquired above using Fiji version 20141125. The macro does not work with older versions of Fiji. The “Drosophila NMJ Morphometrics” and “Drosophila NMJ Bouton Morphometrics” can be downloaded from the following website: (https://doi.org/10.6084/m9.figshare. 2077399.v121). Install the two macros: Drosophila_NMJ_Morphometrics.ijm and Drosophila_NMJ_Bouton Morphometrics.ijm (Plugins > Install > Select the .ijm files). Restart Fiji and the macros will appear at the bottom of the dropdown menu. 2. Open the acquired images. Only two channel images (containing HRP and BRP channels are used as input for running the macro). The order of the images needs to be such that channel 1 corresponds to HRP channel and channel 2 corresponds to BRP channel. If they are not in such a manner, the channel order can be reversed. Go to Image > Colour > Arrange channels > Type “2 1” in the box and press OK. This automatically also converts the three-channel image to a two-channel one. 3. Create a directory wherein all the images will be stored. Create subfolders for each genotype. 4. Save the image generated in step 2 in Tiff format. Go to File > Save As > Tiff. Save it in the directory corresponding to its genotype. While saving the file, the filename should start with “stack.” This is the identifier for the macro to work. For example, the filename can be saved as “stack_synapse01.tif.” 5. Open the saved image. Do a Z-projection Image > Stacks > Z-project (Max intensity).

on

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6. Save this file in the same directory where the previous image was saved. While saving, the filename should start with “flatstack.” For example, the file can be saved as “flatstack_synapse01.tif.” 7. Close the images and likewise create further images for all animals. 8. Go to Plugins > Drosophila NMJ morphometrics 20161129. 9. In the window that appears, enter the unique file string with the name of the first image (synapse01.tif).

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10. Deselect “Convert to stack” and “Analyze” (at the bottom of the macro) and press enter. You will be redirected to choose the directory in which you saved your images in the previous steps. Once you choose the directory, the images will open one by one. Draw the ROI (using freehand selection tool) outlining the NMJs for all the images, and keep pressing OK till all ROIs are defined. 11. Once all ROIs are defined and command is finished, open the plugin again (Plugins > Drosophila NMJ morphometrics 20161129). 12. In the window that appears, enter the unique file string with the name of the first image (synapse01.tif). 13. Adjust the parameters for the following: find maxima noise tolerance, small particle size, min bouton size, rolling ball radius, BRP lower threshold, BRP upper threshold, NMJ outline threshold, skeleton threshold, and active zone threshold. 14. Deselect “Convert to stack” and “Define ROI” and press enter. Select the directory where all the images are stored. 15. The macro will start running and generate the following parameters: Area of NMJ, Perimeter of NMJ, Number of boutons, Length of the NMJ, Length of the longest branch, Number of branches, Number of branching points, Number of islands of NMJ, and Number of active zones. 16. Save the result window that appears along with the Tiff images of NMJ outline and active zones. Quantification and statistics: 1. Collate all the results for all the animals and save in an excel file. 2. Transfer the results to any plotting software (for example, GraphPad Prism). 3. Open GraphPad Prism software and select column data. Test for normality distribution of the data. 4. Carry out analysis using one-way ANOVA for more than two genotypes if the data points are normally distributed. If only two genotypes are being compared, use two-tailed Student’s unpaired t-test or Mann-Whitney test for pairwise comparison of non-normally distributed data. 5. Use appropriate post hoc analysis for ANOVA, to find out which groups are significantly different. Note the p-values and degrees of freedom. 6. Plot the graphs for area of NMJs, area of boutons, active zone numbers normalized to area of NMJ (analyzed by manual method) and perimeter, longest branch length, and number of islands, additionally (analyzed by batch-processing using macro).

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Fig. 5 Detection of autophagic flux in Drosophila NMJs can be done using the tandem-fluorescent GFP-mCherry-Atg8a reporter. (a) UAS lines containing the GFP-mCherry-Atg8a transgene are crossed with motor neuron expressing driver (such as D42-GAL4 or OK371-GAL4). As a result, autophagosomes will appear yellow (due to tandem fluorescent GFP and mCherry tagged to Atg8a); however, the green fluorescence will be quenched upon fusion with lysosomes. Thus, autolysosomes will appear red. (b) Zoomed representative image of a single bouton (immunostained with Dylight-HRP), showing mCherry signal (immunostained with anti-RFP antibody), and GFP signal (immunostained with anti-GFP antibody). The total red signal indicates all the autophagic vesicles (autophagosomes + autolysosomes). The merge channel shows the colocalized points in white. These colocalized points are the autophagosomes. Scale bar 5 μm 3.4 Assessing Autophagy Flux in Fly NMJs

Atg8a (homologous to mammalian LC3) will be conjugated to the membrane of growing autophagosomes and will appear as yellow punctate structures in fluorescence assays (because of the presence of both GFP and mCherry reporter tagged to Atg8a). However, the GFP fluorescence is quenched when autophagosomes fuse with lysosomes, because of the low pH of lysosomal compartments (where GFP is less stable compared to RFP or its variant, mCherry) [30]. Thus, autolysosomes will appear as red punctate signal in fluorescence assays (Fig. 5a, b). A colocalization analysis can thus detect the number and size of autophagosomes and number of autolysosomes in NMJs. The following method describes the analysis steps:

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Fly husbandry and cross setup: 1. Collect female virgin flies of D42-GAL4 > UAS-GFP-mCherryAtg8a flies. Here, the progeny will express tandem fluorescent Atg8a (tandemly tagged with GFP and mCherry). 2. Once there are enough (approximately ten females per vial; two vials per cross) virgin females, set up the following crosses: Cross 1: Progeny from this cross will express tandemly tagged GFP-mCherry-Atg8a over and above the endogenous levels. This will be the control for studying the flux of the pathway in absence of ataxin-3.

Cross 2: Progeny from this cross will express tandemly tagged GFP-mCherry-Atg8a in the background of nonpathogenic 27 polyQ repeats derived from ataxin-3. This will be the control for studying the flux of the pathway in the presence of non-aggregating polyQ27. Cross 3: Progeny from this cross will express tandemly tagged GFP-mCherry-Atg8a in the background of pathogenic 78 polyQ repeats derived from ataxin-3. This will be the experimental larvae for studying the flux of the pathway in the presence of aggregating polyQ78. Immunohistochemistry:

Refer to Subheading 3.3 for immunohistochemistry protocol. Use the following antibodies for detecting autophagosomes and autolysosomes in the NMJs: Primary antibodies: Rabbit anti-dsRed (Clontech, 1:200), mouse anti-GFP (Roche, 1:50), Dylight AffiniPure Goat Anti-Horseradish Peroxidase diluted to 1:200 (Jackson ImmunoResearch Laboratories, Inc.).

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Secondary Antibodies: Goat Anti-Mouse Atto 488 diluted to 1:1000 (Sigma-Aldrich), Goat Anti-Rabbit Atto 550 diluted to 1:1000 (Sigma-Aldrich). An additional marker for lysosomes can also be used (see Note 4). Imaging: Refer to Subheading 3.3. Acquire a three-channel image of the NMJ (Dylight-HRP channel) along with anti-GFP and antiRFP channels (Fig. 6a, a′, a″). Colocalization analysis to detect autophagosomes and autolysosomes in NMJs: 1. Open the image. 2. Go to Image > Color > Split Channels. Convert both channels to 8-bit image. Image > Type > 8-bit. 3. In the Dylight-HRP channel, draw outline around NMJ using freehand selection tool and press “t” to add the selection to the ROI manager. Then press “m.” Make sure that the boxes for Area, Mean Gray Value, and Integrated Density are checkmarked in Analyze > Set Measurements. Save the result window. 4. In the channel corresponding to RFP (Fig. 6b), draw some region outside the ROI for measuring the background intensity. Press “t” and add this region to the ROI manager. 5. Now, go to Process > Math > Subtract. Subtract the above mean intensity (m) value from 255. 6. Go to Process > Math > Multiply. Multiply by the factor (255/255-m). 7. Now using lookup table (LUT), pseudocolor the image to Fire. Go to Image > Lookup Tables > Fire (Fig. 6b′). 8. Threshold the image. Go to Image > Adjust > Threshold > Renyi entropy (or any other threshold method that suits the image). Zoom and make sure the yellow and intense points are selected during thresholding (Fig. 6b″). Also select stack histogram, as the thresholding is being done for Z-stacks. 9. Follow the above steps 4–8 for the GFP channel (Fig. 6c, c′, c″). 10. While thresholding, note upper threshold values for both channels. 11. Go to Plugins > Colocalization. Enter the Image names for RFP and GFP channels. Let the ratio be 50%. Enter the threshold values determined in step 10 for each channel. Select Colocalized points 8-bit.

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Fig. 6 Colocalization analysis for detecting autophagy flux in NMJs. Expression of GFP-mCherry-Atg8a driven under D42-GAL4 motor neuronal driver allows for detection of autophagosomes and autolysosomes in NMJs. (a) Muscle 4 NMJ immunostained with Dylight conjugated anti-HRP. (a′) Autophagic vesicles

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12. Two windows will open. The 8-bit result window will display the colocalized points only (Fig. 6d). The RGB window will show the RFP particles in red and GFP particles in green and the colocalized particles as white (Fig. 6d′, d″). Do a Z-projection on the 8-bit result window (Image > Stacks > Z Project > Max Intensity). 13. Go to Image > Adjust > Threshold. The upper slider can be kept at 1. 14. Go to Analyze > Analyze particles. Save the result window and the summary window. Note number of colocalized points and size. These points are the autophagosomes. 15. Now go to the RFP channel obtained in step 7. Follow the steps (like step 12) for doing Z-projection using Maximum Intensity. Go to Image > Adjust > Threshold. Zoom and make sure all RFP puncta are selected. 16. Go to Analyze > Analyze particles (refer to Subheading 3.3). 17. Save the result window containing the number and area of RFP puncta. 18. Now subtract number of colocalized puncta (obtained in step 14) from total red puncta (obtained in step 16) for every image. This population of the puncta will be the autolysosomes. Quantification and statistics: Refer to Subheading 3.3.

4

Notes 1. Take only early third instar larvae (between 72 and 96 h old, grown at 25 °C) for all experiments, as beyond this time, developmental autophagy sets in, and it would be difficult to dissect out the two pathways and assess for basal neuronal autophagy.

ä Fig. 6 (continued) (autophagosomes and autolysosomes) immunostained with anti-RFP antibody visible in the NMJ. These autophagic vesicles appear as punctate structures owing to conjugation of Atg8a onto the membrane of growing autophagosomes. (a″) Autophagosomes immunostained with anti-GFP antibody. (b) Arrowheads indicate autophagic vesicles (autophagosomes and autolysosomes) in the NMJs. (b′) Pseudocolour LUT of the RFP channel shows the bright intensity of the punctate signal which are thresholded in b″. (c) Arrowheads indicate autophagosomes in the NMJs. (c′) Pseudocolour LUT of the GFP channel shows the bright intensity of the punctate signal which are thresholded in c″. (d) Colocalized points displayed as 8-bit window after running the Colocalization plugin. (d′) Colocalized points displayed as white signal in an RGB window after running the Colocalization plugin. (d″) A single bouton zoomed-in to show the colocalized points in white

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2. While making videos of the larvae for locomotion analysis, re-center the animals, and make fresh videos if two or more animals collide during any point in the video. This is because during collision, the macro cannot be used to distinguish between the animals and their paths and identities would be reassigned. 3. While making vertical cuts along the dorsal midline axis of the third instar larva, make sure that the scissor does not damage the brain or poke the muscles underneath. To avoid damage to the brain, flush out the organs with HL3 buffer by pipetting, and take them out once they float up. 4. For assessing autophagy flux in NMJs, along with GFP and RFP antibodies, LysoTracker dye can be used to mark the acidic lysosomal compartments. This will give a better readout of the flux, since sometimes even after fusion, GFP signal is not quenched. Alternatively, UAS-Lamp1 can also be used to mark the lysosomes in these flies.

Acknowledgments This work was supported by Intramural funds from JNCASR to RM and VS and JNCASR doctoral fellowships to AC. References 1. Lepeta K, Lourenco MV, Schweitzer BC et al (2016) Synaptopathies: synaptic dysfunction in neurological disorders – a review from students to students. J Neurochem 138:785–805 2. Brose N, O’Connor V, Skehel P (2010) Synaptopathy: dysfunction of synaptic function? Biochem Soc Trans 38:443–444 3. Jackson J, Jambrina E, Li J et al (2019) Targeting the synapse in Alzheimer’s disease. Front Neurosci 13:735 4. Kashyap G, Bapat D, Das D et al (2019) Synapse loss and progress of Alzheimer’s disease – a network model. Sci Rep 9:6555 5. Martı´nez-Serra R, Alonso-Nanclares L, Cho K, Giese KP (2022) Emerging insights into synapse dysregulation in Alzheimer’s disease. Brain Commun 4:fcac083 6. Hong S, Beja-Glasser VF, Nfonoyim BM et al (2016) Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352:712–716 7. Chakravorty A, Jetto CT, Manjithaya R (2019) Dysfunctional mitochondria and mitophagy as drivers of Alzheimer’s disease pathogenesis. Front Aging Neurosci 11:311

8. Chinchwadkar S, Padmanabhan S, Mishra P et al (2017) Multifaceted housekeeping functions of autophagy. J Indian Inst Sci 97:79–94 9. Decet M, Verstreken P (2021) Presynaptic autophagy and the connection with neurotransmission. Front Cell Dev Biol 9:790721 10. Vijayan V, Verstreken P (2017) Autophagy in the presynaptic compartment in health and disease. J Cell Biol 216:1895–1906 11. Kuijpers M, Kochlamazashvili G, Stumpf A et al (2021) Neuronal autophagy regulates presynaptic neurotransmission by controlling the axonal endoplasmic reticulum. Neuron 109: 299–313.e9 12. Verheyen EM (2022) The power of Drosophila in modeling human disease mechanisms. Dis Model Mech 15:dmm049549 13. Chou VT, Johnson SA, Van Vactor D (2020) Synapse development and maturation at the Drosophila neuromuscular junction. Neural Dev 15:11 14. Mirzoyan Z, Sollazzo M, Allocca M et al (2019) Drosophila melanogaster: a model organism to study cancer. Front Genet 10:51

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15. Banerjee S, Benji S, Liberow S, Steinhauer J (2020) Using Drosophila melanogaster to discover human disease genes: an educational primer for use with “amyotrophic lateral sclerosis modifiers in Drosophila reveal the phospholipase D pathway as a potential therapeutic target”. Genetics 216:633–641 16. Collins CA, DiAntonio A (2007) Synaptic development: insights from Drosophila. Curr Opin Neurobiol 17:35–42 17. Grice SJ, Liu J-L (2022) Motor defects in a Drosophila model for spinal muscular atrophy result from SMN depletion during early neurogenesis. PLoS Genet 18:e1010325 18. Walters R, Manion J, Neely GG (2019) Dissecting motor neuron disease with Drosophila melanogaster. Front Neurosci 13:e1010325 19. Clark MQ, Zarin AA, Carreira-Rosario A, Doe CQ (2018) Neural circuits driving larval locomotion in Drosophila. Neural Dev 13:6 20. Kohsaka H, Takasu E, Morimoto T, Nose A (2014) A group of segmental premotor interneurons regulates the speed of axial locomotion in Drosophila larvae. Curr Biol 24:2632– 2642 21. Post Y, Paululat A (2018) Muscle function assessment using a Drosophila larvae crawling assay. Bio Protoc 8:e2933 22. Caygill EE, Brand AH (2016) The GAL4 system: a versatile system for the manipulation and analysis of gene expression. Methods Mol Biol 1478:33–52

23. Brand AH, Perrimon N (1993) Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development 118:401–415 24. Xu J, Ren X, Sun J et al (2015) A toolkit of CRISPR-based genome editing systems in Drosophila. J Genet Genomics 42:141–149 25. Heigwer F, Port F, Boutros M (2018) RNA interference (RNAi) screening in Drosophila. Genetics 208:853–874 26. Chakravorty A, Sharma A, Sheeba V, Manjithaya R (2022) Glutamatergic synapse dysfunction in Drosophila neuromuscular junctions can be rescued by proteostasis modulation. Front Mol Neurosci 15:842772 27. Brooks DS, Vishal K, Kawakami J et al (2016) Optimization of wrMTrck to monitor Drosophila larval locomotor activity. J Insect Physiol 93–94:11–17 28. Castells-Nobau A, Nijhof B, Eidhof I et al (2017) Two algorithms for high-throughput and multi-parametric quantification of neuromuscular junction morphology. JoVE 123: 55395 29. Nijhof B, Castells-Nobau A, Wolf L et al (2016) A new Fiji-based algorithm that systematically quantifies nine synaptic parameters provides insights into Drosophila NMJ morphometry. PLoS Comput Biol 12: e1004823 30. Yoshii SR, Mizushima N (2017) Monitoring and measuring autophagy. IJMS 18:1865

Chapter 10 Cell-Based Assay to Detect the Autoantibody Serostatus in Patients with Neuromyelitis Optica Spectrum Disorder (NMOSD) Pallavi Chatterjee, Suparna Saha, and Debashis Mukhopadhyay Abstract Cell-based assay (CBA) is an immunofluorescence assay that is extensively used for the confirmatory diagnosis of inflammatory demyelinating diseases of the central nervous system, like neuromyelitis optica spectrum disorder (NMOSD). Detecting the type of autoantibody present in the sera of the patients is the primary goal. CBA is the most sensitive and recommended detection method among all similar tools. Briefly, serum autoantibody is screened by transfecting specific cells seeded on cover glasses with full-length specific antigen fused with green fluorescent protein (GFP), followed by treating them with the patient serum used here as the source of primary antibody. The autoantibody-treated cells are further labeled with a rhodamine-conjugated secondary antibody. The co-localization of GFP and rhodamine is visualized by confocal microscopy, and the intensity of fluorescence is evaluated to determine the presence of autoantibody. A detailed protocol to screen antibodies against AQP4 and MOG in human sera using this method is described. Key words Cell-based assay (CBA), Confocal microscopy, Immunofluorescence, Neuromyelitis optica spectrum disorders (NMOSD), Human sera, Autoantibodies

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Introduction Neuromyelitis optica spectrum disorder (NMOSD) is a rare inflammatory demyelinating autoimmune disorder of the central nervous system where patients could be either seropositive or seronegative for aquaporin4 (AQP4)-IgG [1] or myelin oligodendrocyte glycoprotein (MOG)-IgG [2, 3] and in very rare cases seropositive for both [4]. It is therefore crucial to establish a reliable protocol to detect the serostatus of the patients for accurate diagnosis. In 2004, the presence of anti-AQP4 IgG was first detected in patients with NMO with the help of an immunofluorescence assay which used brain tissue derived from mice [5]. In 2010, a complete cell-based assay was developed to detect anti-AQP4-IgG. It was shown that

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_10, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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living cells transfected with AQP4 M-23-isoform are highly sensitive for detecting anti-AQP4-IgG Abs in definite (97%) and highrisk (65%) NMO patients [6]. Though a scale to evaluate the autoantibody titer present in the sera by this method is still lacking, a meta-analysis revealed that cell-based assay’s (CBA) sensitivity and specificity were 76% and 99%, respectively, and greater than those of tissue-based and enzyme-linked immunosorbent assays [7, 9] like radioimmunoprecipitation, fluorescence-based immunoprecipitation assay (FIPA), Western blotting, enzyme-linked immunosorbent assay (ELISA), and cell-based indirect immunofluorescence assay (IIFA). We have standardized CBA to screen the patient sera against both the autoantibodies by transfecting HEK 293 cells with M23 isoform of AQP4 (based on the theory that the M23 isoform can form OAPs) [6, 8] and full-length alpha1 isoform of MOG, followed by treatment with patient serum containing the autoantibody. The autoantibody was then further labeled by rhodamine-conjugated secondary antibody and was investigated under confocal microscope. In this chapter, the standardized protocol of CBA is described.

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Materials

2.1 Patient Enrollment

2.2 Collection of Blood Samples and Isolation of Serum

Patients satisfying the International Diagnostic Criteria of NMOSD [1], should be chosen with proper consent from patients’ family. The basic characteristics of each patient, e.g. age, gender, past medical history and treatment status should be noted (also see Note 1). Healthy individuals should be chosen to serve as control. 1. Ice bucket and ice packs. 2. Complete protease inhibitor cocktail. 3. Cold centrifuge. 4. Cryogenic freezer (-80 °C).

2.3 Cell Lines and Cell Culture Reagents

1. HEK293 cells are cultured and incubated in an incubator, maintaining a constant temperature of 37 °C ± 0.5 °C, 5% carbon dioxide, and 95% humidity (see Note 2). 2. Dulbecco’s modified Eagle’s medium or DMEM [high glucose (4 gm/L)] (supplemented with 10% fetal bovine serum (FBS) and antibiotics penicillin/streptomycin (1%, v/v). 3. Basic cell culture equipment includes tissue culture dishes (60 mm and 35 mm), cover glass (22 mm), sterile needle and forceps, absorbent cotton, and 70% ethyl alcohol solution (v/v) (see Note 3). 4. 0.25% trypsin/EDTA. 5. Phosphate-buffered saline (PBS) (1×).

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1. pcDNA6.2/C-EmGFP-Topo-hAQP4(M23) that expresses M23 isoform of AQP4 fused C-terminally to an emerald green fluorescence protein (EmGFP) in mammalian cells [6]. 2. pEGFP-N1-hMOG (alpha1) that expresses a human MOG (isoform alpha1) fused C-terminally to enhanced green fluorescence protein in mammalian cells [6].

2.5 Transient Transfection Assays

1. Transfection reagent: Lipofectamine 2000. 2. Plasmid constructs (as described in Subheading 2.2). 3. 1.5 mL Eppendorf tubes. 4. DMEM supplemented with 10% FBS.

2.6 Immunofluorescence

1. 1× PBS. 2. Aliquot of both healthy and NMOSD patient serum according to the dilution. 3. Fixation solution: 4% paraformaldehyde (PFA) is prepared in 1× PBS from a 40% PFA stock as per the requirement (1 mL/ 35 mm dish). To prepare 5 mL of 4% PFA, add 0.5 mL of PFA from the stock and 4.5 mL of 1× PBS (see Notes 4–6). 4. Blocking buffer: PBS containing 10% FBS (1 mL/35 mm dish). To prepare 5 mL of the solution, add 4.5 mL of 1× PBS and 500 μL of FBS (see Notes 6 and 7). 5. Secondary antibody: rhodamine-conjugated anti-human antibody (1:1000) prepared in blocking buffer (1 mL/35 mm dish). To prepare 5 mL of solution, add 5 μL of antibody to 5 mL of blocking buffer (see Notes 6–8). 6. Glass slides (76 mm × 26 mm × 0.95 mm). 7. Needle and forceps. 8. Nail polish as a sealant. 9. Aluminum foil to wrap the slides. 10. Confocal microscope. 11. Lasers used: 488 nm for green fluorescent protein (GFP) and 561 nm for rhodamine.

3

Methods

3.1 Experimental Principle and Design

1. To know whether a patient serum contains autoantibody against AQP4 or MOG, two 35 mm dishes with cells seeded onto cover glasses are assigned for each sample. They are transfected with M23-EmGFP and MOG-EGFP to express M23 isoform of AQP4 (green) and alpha1 isoform of MOG (green), respectively, at the cell membranes. Then both the transfected dishes are treated with the same serum so that

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whichever autoantibody is present in it can bind to its respective antigen being expressed on the cell membrane. The autoantibody present in the serum is then further detected by labeling it with a secondary antibody-rhodamine-conjugated anti-human antibody (red fluorescence). The presence of green fluorescence on cell membranes in the absence of red fluorescence, or the presence of red fluorescence that does not co-localize with green fluorescence under a confocal microscope, is scored as negative for AQP4 or MOG autoantibodies. The presence of red fluorescence on cell membranes that co-localizes with green (giving yellow fluorescence) is scored as positive for either AQP4 or MOG autoantibody. So, according to this principle, a sample can be AQP4+ve, MOG + ve, both AQP4 and MOG double-positive or double-negative [10, 11]. 2. Two other 35 mm dishes with cells seeded onto cover glasses are assigned for each serum sample derived from healthy individuals which served as negative control for the assay. Here, both the dishes are treated with serum of the same healthy individual instead of serum of NMOSD patients post-transfection with M23-EmGFP and MOG-EGFP, respectively. All the other steps are similar as described in Point 3.1. 3. A non-serum control was also included in the assay where two 35 mm dishes with cells seeded onto cover glasses were transfected with M23-EmGFP and MOG-EGFP, respectively, without treating them with any serum sample. The principle of CBA is described in Fig. 1. 3.2 Seeding of Cells in Cover Glasses

Perform this assay in HEK 293 cells (see Note 2). Culture cells on either 60 or 100 mm tissue culture dishes in DMEM high glucose media, and grow to confluency. When the confluency reaches 70–80%, seed the cells onto cover glass following the procedure described below in detail. 1. Before starting the experiment, take 35 mm cell culture dishes according to the need, and place cover glasses in the dishes. Add 250 μL of DMEM (without FBS) on the cover glasses of each dish, and keep within the hood (see Note 3). 2. Take the confluent dish of HEK cells out of the incubator and discard the culture media. 3. Wash the dish with 1× PBS two times. 4. Add 500 μL of 0.25% trypsin-EDTA solution to the side wall of the flask, swirl the content gently to cover the cell layer, and incubate for 5 min at 37 °C in the incubator. 5. Observe the cells under the microscope. The detached cells appear rounded and refractile. Continue the incubation for 2 more minutes if fewer than 90% of the cells are detached.

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Fig. 1 Basic principle of the cell-based assay (CBA)

6. Once cells appear detached, add 2 mL of pre-warmed complete growth medium into the dish to inactivate trypsin. Disperse the medium gently (vigorous pipetting should be avoided as it causes cell damage) by pipetting over the cell layer surface several times to ensure recovery of 95% of cells. 7. Then, transfer the cell suspension to a 15 mL falcon tube, and centrifuge at 2000 rpm for 5 min. Remove the supernatant and resuspend cell pellet in the culture medium. Determine the cell density of viable cells using hemocytometer and trypan blue. Then, seed the cells (10,000 cells/35 mm dish) onto the cover glasses, and thoroughly mix with the medium already placed on the cover glasses before trypsinizing the cells. 8. Keep the dishes for 5–10 min in the hood to attach the cells to the cover glasses. 9. Add dropwise 1 mL of DMEM onto the portions of the 35 mm dishes that are beside the cover glasses to cover each side of the cover glass. 10. Then, calculate the total volume of media added on each dish (250 uL of DMEM added previously on the cover glass + volume of media added on the cover glass containing desired number of cells +1 mL of media added into the dish).

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Table 1 Preparation of Mix A and Mix B and their respective components MIX A

MIX B

Diameter of dish

Concentration of DNA (μg)

Volume of media (μL)

Lipofectamine (μL)

Volume of media (μL)

35 mm

2ug for M23-AQP4

125

4

125

35 mm

1.5ug for MOG-EGFP

125

3

125

11. Add dropwise, 10% FBS to the total volume of medium on the dishes beside the cover glass. 12. Finally, keep the dishes in the incubator for the next 24 h. 3.3 Transfection of Cells

1. Prepare two different solutions for transfection. One solution (Mix A) will contain the plasmid DNAs (for AQP4 and MOG, respectively), diluted in DMEM. The other solution (Mix B) will contain Lipofectamine 2000 diluted in DMEM. The preparation of the solutions is mentioned in Table 1. 2. Incubate Mix A and Mix B individually for 5 min in the incubator. 3. Mix the two solutions for individual plasmids together and incubate for 20 min. 4. Within this period of incubation, take out the 35 mm dishes from the incubator and remove the media. 5. Wash the dishes with PBS (two times) very gently so that cells do not get detached from the cover glasses. 6. Add DMEM and FBS (volume is mentioned below) on the dishes dropwise before adding the transfection mixture. Label the dishes by mentioning the type as “AQP4 transfected” or “MOG transfected.” 7. Take out the transfection mixtures from the incubator after 20 min, add very slowly on the dishes according to the labels, and swirl gently. 8. Keep the dishes back in the incubator for the next 24 h. Note: Total volume of transfection medium for each 35 mm dish = 1 mL Total volume of transfection medium = volume of transfection mixture þ 10%FBS þ rest of the media: 1 mL = 250 μL þ 100 μL þ 650 μL

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3.4 Treating Cells with Serum Derived from NMO Patients and Healthy Individuals

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1. After 24 h of transfection, take out the dishes from the incubator, and observe the cells under the microscope. In the case of HEK293 cells, generally very few cell death is observed posttransfection. Remove the transfection media from each dish. 2. Gently wash with PBS two times. 3. Add 600 μL of DMEM in each dish. No FBS is added during treatment with serum. 4. As mentioned above (Point 3.1), assign two dishes transfected with MOG-EGFP and M23-EGFP, respectively, for each patient serum to be screened for autoantibody. Apply the same criterion for each negative control (treatment with serum derived from each healthy individual) and no-serum control (where the cells are only transfected with the abovementioned plasmid, respectively, without treating them with serum). Assign the names of the dishes as [(P1 to Pn) (first two letters of the patients’ name)], negative control [(H1 to Hn) (first two letters of the healthy individual’s name)], and no-serum control. 5. Tilt the dishes, and apply the serum in 1:20 dilution (30 uL) in the tilted medium according to the assigned dishes and swirl the medium very gently to cover the cell layer (see Note 9). 6. Incubate the dishes for the next 1.5 h in the incubator.

3.5 Immunofluorescence Staining and Confocal Microscopy

1. After 1.5 h of incubation, take out the dishes from the incubator and discard the medium. Gently wash with PBS two times. All the next steps are done outside the hood. 2. Put off the lights. Add 1 mL of 4% PFA solution on the wall of each of the dishes very gently for fixation, and keep in dark for exactly 10 min. 3. Remove the PFA and wash with PBS three times. 4. Put on the lights. Add 1 mL of blocking buffer on the wall of each dish, and keep them for 2 h at room temperature. 5. Remove the blocking buffer and wash the dishes with PBS three times. 6. Turn off the lights again, and add 1 mL of secondary antibody (rhodamine-conjugated anti-human antibody light-sensitive) in 1:1000 dilution in each dish. Keep them for 1.5 h at room temperature (see Note 10). 7. After 1.5 h remove the antibody and wash the dishes with PBS three times. Add 1 mL of fresh PBS to each dish after the third washing step. 8. Place 12 uL of PBS on the labeled slides as mounting media for the cover glasses. Lift the appropriate cover glass up (according to the label on the slide) from the respective dish using one needle.

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9. Remove the excess PBS from the cover glasses before mounting by gently tilting them on tissue paper with the help of forceps. Hold the cover glasses in such a way that the surface containing the cells faces the slides. 10. Mount the cover glasses on the respective slides keeping in mind that no bubble gets trapped under them. Place small pieces of filter paper (Whatman-1) around the edge of the cover glasses to remove the excess of mounting medium. Keep the slides for 5 min in dark before sealing (see Note 11). 11. After 5 min seal the edge of the cover glasses with clear nail polish, and keep them for drying in room temperature for 10–15 min. 12. Cover the slides with aluminum foil and label appropriately. Keep them in a box with a slightly wet tissue, at 4 ° C temperature before imaging. 13. Image the slides using a confocal microscope (lasers needed: 488 nm for GFP and 561 nm for rhodamine). All the images should be taken in similar conditions. Take at least images of 15 different fields from each slide under 60× magnification (Figs. 2 and 3). 14. Two investigators, who are blinded to the laboratory and clinical information of the patients being studied, should independently examine and confirm (as well as the researcher) for green and red fluorescence on the cell membranes, respectively, followed by their co-localization on the membrane showing yellow fluorescence. After proper investigation finally the serostatus of the patients should be confirmed [11]. 15. Assay each serum two times, and both the investigators and the researcher should score the slides as per the following criteria: the presence of red fluorescence on cell membranes that co-localize with green fluorescence (giving yellow fluorescence) is scored as positive for AQP4 or MOG autoantibodies and graded on a 3-point scale: weakly positive (+), positive (+ +), and strongly positive (+++), depending on the fluorescence intensity. Then the median of the four scores given individually by the investigators and the researcher is considered as the final score [11] (see Note 12).

4

Notes 1. It is preferred to select patients with minimum age difference for greater precision of downstream studies. But as this disease is rare, matching this preference was not possible in the case of MOG + ve patients but luckily was satisfied in the case of AQP4 +ve patients.

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Fig. 2 Cell-based assay for the no-serum control and negative control. (a–f) denotes M23-EmGFP (green) and MOG-EGFP (green) transfected cells not treated with any serum. Here, no co-localization is seen in the merged images (c, f). (g–l) denotes transfected cells M23-EmGFP (green) and MOG-EGFP (green) incubated with serum derived from healthy individual 1. Very less and nonspecific binding of rhodamine-conjugated antihuman antibody (red h, k) is seen thereby showing no yellow fluorescence (co-localization i, l) in the cell membrane. This indicates the absence of AQP4- or MOG-specific autoantibody in the healthy individual. (Scale bars corresponding to 10 μm pertain to all the panels of equal magnification)

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Fig. 3 Cell-based assay for the selection of AQP4-IgG- and MOG-IgG-positive patients (here example of one patient from both the categories is shown: cells transfected with M23-EmGFP (green) (a) are treated with a NMOSD patient serum, and a strong fluorescence of the secondary antibody (red) is seen in the cell membrane (b) giving a precise co-localization (yellow fluorescence) (c). This indicates the presence of AQP4 autoantibody in the serum of the patient. No such co-localization is seen (e, f) when cells expressing MOG-EGFP (d) are treated with the same serum. Treatment of MOG-EGFP expressing cells (g) with another patient serum show very strong red fluorescence (h) in the cell membrane thus giving perfect co-localization (yellow fluorescence) (i). This signifies the presence of MOG autoantibody in his serum. No such co-localization is seen (k, l) when cells expressing M23-EmGFP (j) are treated with the same serum. (Scale bars corresponding to 10 μm pertain to all the panels of equal magnification)

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2. This assay can be done in HeLa cells also. HEK 293 cells express a dystroglycan complex which allows stable insertion of AQP4 into the plasma membrane [11]. If these two are not available, then cell lines like CHO or V79 can also be used. The confluency of the cells on the cover glasses before transfection should reach 70–80%. 3. Place a sheet of tissue paper, 60 mm dishes, a box of cover glasses, and 35 mm dishes in the hood, and expose them to UV for 15 min before seeding the cells. Then, take one or two 60 mm dishes. Place cover glasses as per the requirement in the dishes, and add 70% alcohol in the dishes so that the cover glasses get immersed. Keep it for 5 min in the hood. Equipment like cover glass, needle, forceps, and cotton must be kept solely for the use of cell culture and must always be kept within the culture hood. Pull the cover glasses up from the surface of the 60 mm dish with the help of a needle, wipe the alcohol completely with tissue paper, and place them one by one in the 35 mm dishes. 4. The 4% PFA solution can be prepared in a 15 mL amber falcon a day before starting the experiment and can be stored at room temperature. 5. After fixing with 4% PFA, the cells could be kept in 1 mL fresh PBS and be stored in 4 °C temperature overnight in a small box containing a slightly wet tissue paper (This is done only if one will not be able to complete the entire experiment in a day due to shortage of time or any other inconvenience). The next day PBS could be discarded, and blocking can be done. 6. All the solutions like fixative agent, blocking buffer, and secondary antibody dilution should be made more than the needed volume to avoid pipetting error. 7. It is better to prepare blocking buffer and secondary antibody dilution in the tissue culture hood before the respective steps. 8. The secondary antibody solution should be made in 15 mL amber falcon as the antibody is light-sensitive. As it is made in blocking buffer it is convenient to make the buffer at once according to the required volume. 9. Before treating the cells with sera, aliquot required volume of serum individually with properly labeled 1.5 mL Eppendorf tubes. Repeated freeze-thawing of sera is not recommended. The dilution of sera used here is 1:20. The assay could be performed with 1:10 dilution of sera also. The volume of medium per dish should be chosen in such a way that it covers the surface of the 35 mm dishes and the amount of required serum be as little as possible (as a good amount of serum will be needed for the downstream studies.

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10. It is better to mount one cover glass on one slide. During the incubation period with secondary antibody, slides should be taken out as per the need and should be cleaned and labeled appropriately. 11. By removing excess mounting medium after placing the cover glass onto the slide, the chances of the cover glasses floating around on the slides will be less likely, thus decreasing the possibility of specimen damage. This will increase the surface tension between the cover glasses and the slides, and one will also be able to seal the cover glasses in a better way. 12. To score the slides, it would be better to assay each serum three times. But, here in our case, we could not do it because of shortage of serum. 13. The titer of the autoantibody present in each serum derived from NMO patients can also be measured from this assay provided a sufficient volume of sera is available for the downstream studies. Titers can be expressed as the maximum dilution in which the samples are showing positive staining, and thus, the sera are titrated in serial twofold dilutions from 4× to 16,384×, and the assay is performed. Serum containing high titer of autoantibody will show positive staining (yellow fluorescence of co-localization) in a much higher dilution. We could not measure the titer in our serum sample due to shortage of serum [12]. References 1. Wingerchuk DM, Banwell B, Bennett JL, Cabre P, Carroll W, Chitnis T, de Seze J, Fujihara K, Greenberg B, Jacob A, Jarius S, Lana-Peixoto M, Levy M, Simon JH, Tenembaum S, Traboulsee AL, Waters P, Wellik KE, Weinshenker BG (2015) International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 85:177–189 2. Kitley J, Woodhall M, Waters P, Leite MI, Devenney E, Craig J, Palace J, Vincent A (2012) Myelin-oligodendrocyte glycoprotein antibodies in adults with a neuromyelitis optica phenotype. Neurology 79:1273–1277 3. Bernard-Valnet R, Liblau RS, Vukusic S, Marignier R (2015) Neuromyelitis optica: a positive appraisal of seronegative cases. Eur J Neurol 22:1511–1e83 4. Hamid SHM, Whittam D, Mutch K, Linaker S, Solomon T, Das K, Bhojak M, Jacob A (2017) What proportion of AQP4-IgG-negative NMO spectrum disorder patients are MOG-IgG positive? A cross sectional study of 132 patients. J Neurol 264:2088–2094

5. Lennon VA, Wingerchuk DM, Kryzer TJ, Pittock SJ, Lucchinetti CF, Fujihara K, Nakashima I, Weinshenker BG (2004) A serum autoantibody marker of neuromyelitis optica: distinction from multiple sclerosis. Lancet 364:2106–2112 6. Mader S, Lutterotti A, Di Pauli F, Kuenz B, Schanda K, Aboul-Enein F, Khalil M, Storch MK, Jarius S, Kristoferitsch W, Berger T, Reindl M (2010) Patterns of antibody binding to aquaporin-4 isoforms in neuromyelitis optica. PLoS One 5:e10455 ˜ eda C, 7. Ruiz-Gaviria R, Baracaldo I, Castan ˜ o A, Acosta-Hernandez A, Rosselli Ruiz-Patin D (2015) Specificity and sensitivity of aquaporin 4 antibody detection tests in patients with neuromyelitis optica: a meta-analysis. Mult Scler Relat Disord 4:345–349 8. Pisani F, Sparaneo A, Tortorella C, Ruggieri M, Trojano M, Mola MG, Nicchia GP, Frigeri A, Svelto M (2013) Aquaporin-4 autoantibodies in neuromyelitis optica: aqp4 isoformdependent sensitivity and specifcity. PLoS One 8:e79185

Cell Based Assay to Detect Auto-antibody Serostatus 9. Waters P, Reindl M, Saiz A, Schanda K, Tuller F, Kral V, Nytrova P, Sobek O, Nielsen HH, Barington T, Lillevang ST, Illes Z, Rentzsch K, Berthele A, Berki T, Granieri L, Bertolotto A, Giometto B, Zuliani L, Hamann D, van Pelt ED, Hintzen R, Ho¨ftberger R, Costa C, Comabella M, Montalban X, Tintore´ M, Siva A, Altintas A, Deniz G, Woodhall M, Palace J, Paul F, Hartung HP, Aktas O, Jarius S, Wildemann B, Vedeler C, Ruiz A, Leite MI, Trillenberg P, Probst M, Saschenbrecker S, Vincent A, Marignier R (2016) Multicentre comparison of a diagnostic assay: aquaporin-4 antibodies in neuromyelitis optica. J Neurol Neurosurg Psychiatry 87:1005–1015 10. Mader S, Gredler V, Schanda K, Rostasy K, Dujmovic I, Pfaller K, Lutterotti A, Jarius S, Di Pauli F, Kuenz B, Ehling R, Hegen H,

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Deisenhammer F, Aboul-Enein F, Storch MK, Koson P, Drulovic J, Kristoferitsch W, Berger T, Reindl M (2011) Complement activating antibodies to myelin oligodendrocyte glycoprotein in neuromyelitis optica and related disorders. J Neuroinflammation 8:184 11. Chan KH, Kwan JS, Ho PW, Ho JW, Chu AC, Ramsden DB (2010) Aquaporin-4 autoantibodies in neuromyelitis optica spectrum disorders: comparison between tissue-based and cell-based indirect immunofluorescence assays. J Neuroinflamm 7:50 12. Takahashi T, Fujihara K, Nakashima I, Misu T, Miyazawa I, Nakamura M, Watanabe S, Ishii N, Itoyama Y (2006) Establishment of a new sensitive assay for anti-human aquaporin-4 antibody in neuromyelitis optica. Tohoku J Exp Med 210:307–313

Chapter 11 Using Small Molecules for Targeting Heavy Metals in Neurotoxicity and Neuroinflammation Pronama Biswas and Sunil S. More Abstract Pharmaceutical drugs, natural toxins, industrial chemicals, and various environmental toxins negatively impact the nervous system. A significant cause of many neurodegenerative diseases is neurotoxicity. Although trace amounts of heavy metals are required for the proper functioning of several metabolic pathways, their dysregulation can cause many cellular and molecular alterations, which can enhance the risks associated with several neurodegenerative diseases. For example, high levels of heavy metals like manganese (Mn) affect the central nervous system with implications in both higher-order cognitive and motor functions. In addition, the buildup of amyloid aggregates and metal ions in the brain of patients with Alzheimer’s disease is associated with disease pathogenesis. Small molecules capable of targeting neuroinflammation and neuroprotection pathways would be valuable to elucidate the pathological pathways associated with metal toxicity in neurogenerative disease. This chapter will summarize the necessary steps involved in (1) culturing of cell lines and maintenance of animal models, (2) design and preparation of samples of small molecules and treatment methodologies, (3) RNA and protein isolation and preparation of tissue and cell culture samples for quantitative studies, and (4) quantitative estimation of cellular products. Key words Heavy metals, Neurotoxicity, Neuroinflammation, Neurodegenerative diseases, Small molecule, Neuroprotection pathways

1

Introduction Naturally occurring elements containing metal and with relatively higher density (>5 g/cm3) than that of water are defined as heavy metals [1]. Heavy metals are a significant public health concern due to their widespread use in industrial, agricultural, and technological applications, resulting in environmental pollution [2]. They are composed of both biologically essential and nonessential metals, with some, such as iron, manganese, and zinc, being necessary for physiological functions. Other metals, including arsenic, cadmium, lead, and mercury, are nonessential and highly toxic, even at low exposure levels [3]. Heavy metal exposure can cause damage to various organs or systems, with neurotoxicity being a prominent

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_11, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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effect. The mechanisms underlying heavy metal neurotoxicity are not yet fully understood. However, recent research on adult neurogenesis has shed light on this topic, providing new insights into the mechanisms of heavy metal neurotoxicity. Excessive levels of metals in the body can lead to a range of harmful effects, including neurotoxicity, which is a common health problem associated with metal exposure. Toxic heavy metals are known to induce neurotoxicity, and even essential transition metals like Fe, Cu, Zn, or Mn can have harmful effects on the brain [4]. When these metals are not properly distributed, they can result in an excess or deficiency that can cause oxidative stress and molecular damage, which impairs brain cellular function [5]. A wellestablished association exists between increased environmental or occupational exposure to toxic heavy metals or alterations in essential transition metals (ETM) homeostasis, and several neurological disorders, including neurodegenerative diseases like Alzheimer’s disease and Parkinson’s disease [6]. In biological systems, ETM has a critical role in catalyzing significant redox reactions when present in appropriate levels. However, the presence of ETM is a delicate balance, as an excess of ETM can be toxic to cellular processes. The biological effects of metals are closely tied to their chemical properties. Toxic heavy metals can lead to oxidative toxicity and alter protein function by forming a complex with functional side-chain groups or by displacing ETM in metalloproteins. Additionally, toxic heavy metals can interfere with the transport of other metals, including ETM [7, 8]. As a result, exposure to toxic heavy metals can impact ETM homeostasis in the body, potentially leading to the accumulation of ETM and adverse health consequences. Therefore, environmental exposures to ETM must be carefully monitored to prevent such detrimental effects. Small molecules have garnered considerable attention as potential therapeutic agents for combatting heavy metal-induced neurotoxicity and neuroinflammation (Table 1). Small molecules are organic compounds with low molecular weight, and they possess the unique ability to interact with specific molecular targets, including heavy metal ions. Small molecules play a vital role in numerous chemical reactions by detecting the existence of a vast array of cellular metabolites [9]. These compounds offer several advantages in terms of drug development, including their accessibility, modifiability, and diverse chemical structures, enabling the discovery of novel compounds with enhanced efficacy and reduced side effects [10–26]. Trientine, a copper-chelating agent, has been utilized in the treatment of Wilson’s disease, a genetic disorder characterized by copper accumulation [17, 18]. Epigallocatechin gallate (EGCC), on the other hand, has demonstrated potent antiinflammatory and neuroprotective properties against heavy metalinduced neurotoxicity [25]. Quercetin, a flavonoid widely

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Table 1 List of small-molecule inhibitors for targeting heavy metals Small-molecule inhibitor

Targeted heavy metals

MCC950

Mechanism of action

References

Manganese

Increases expression of pro-inflammatory markers and apoptosis

[10]

B355252

Cobalt

Alters mitochondrial dynamics and inhibits autophagy

[11]

DMSA

Lead, mercury

Chelation and enhanced excretion

[12, 13]

DMPS

Mercury, arsenic

Chelation and enhanced excretion

[13, 14]

D-penicillamine

Copper, other metals

Chelation and enhanced excretion

[15]

Deferoxamine

Iron

Chelation and iron complex formation

[16]

Trientine

Copper

Chelation and enhanced excretion

[17, 18]

Succimer

Lead, other metals

Chelation and enhanced excretion

[19]

Alpha-lipoic acid

Lead, zinc, and other metals

Antioxidant and metal chelation properties

[20]

Quercetin

Copper, zinc, and other metals

Anti-inflammatory, antioxidant, and metal chelation

[21, 22]

Melatonin

Lead, other metals

Antioxidant, anti-inflammatory, induces production of acetylcholine

[23]

Resveratrol

Various metals

Antioxidant, anti-inflammatory, and metal chelation

[24]

EGCG (epigallocatechin gallate)

Various metals

Antioxidant, anti-inflammatory, and metal chelation

[25]

Sulforaphane

Various metals

Induction of phase II detoxification enzymes [26]

distributed in fruits and vegetables, has exhibited metal chelation capabilities and antioxidant effects, making it a promising candidate for mitigating heavy metal-mediated neuroinflammation [21, 22]. Small-molecule inhibitors such as MCC950, a selective inhibitor of the NLRP3 inflammasome, can mitigate Mn-induced neurotoxicity [10]. This chapter aims to provide a comprehensive guide to the essential methodologies involved in utilizing small molecules to target heavy metal toxicity in the central nervous system (CNS). It will present a step-by-step approach, detailing the materials and methods required to carry out experiments related to smallmolecule interventions (Fig. 1). This chapter will summarize the necessary steps involved in (1) culturing of cell lines and maintenance of animal models, (2) design and preparation of samples of

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Fig. 1 Schematic overview of the steps involved in the experimental setup for studying the effect of small molecules on heavy metal toxicity. Group 1, control group; Group 2, heavy metal toxicity group; Group 3, heavy metal toxicity + small-molecule group

small molecules and treatment methodologies, (3) RNA and protein isolation and preparation of tissue and cell culture samples for quantitative studies, and (4) quantitative estimation of cellular products.

2

Materials Prepare all reagents and solutions in autoclaved ultrapure water (sensitivity 18 MΩ-cm at 25 °C) using analytical grade reagents. Filter-sterilize all media and reagents before using. Store the stock solutions at the appropriate temperatures and storage conditions. Follow all safety and waste disposal guidelines of the laboratory. Obtain the necessary ethical clearance for handling animal and human tissues. All animal experiments should be done following the guidelines of the appropriate ethics board.

2.1 Culture and Maintenance of Cells

1. Cell culture media: Use the suitable media according to the cell type (DMEM, RPMI, alpha-MEM). Mix the solid media contents in 1 L water. Add the required amount of sodium bicarbonate. Add 10–15% fetal bovine serum (FBS), based on the requirement. Supplement the media with 1% streptomycin and 1% penicillin. Add any other media components such as

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glutamine or sodium pyruvate based on the requirement of the cell type used. Sterilize the complete media using a 0.25 μm membrane filter. Store at 4 °C. 2. Freezing solution: Add 10% DMSO in FBS to make the freezing solution. Store at 4 °C in the dark. 3. Trypsinization: 0.25% trypsin-EDTA. Store at 4 °C. 4. Cell culture ware: T25 and T75 tissue culture flasks for culturing and subculturing cells, 6- and 12-well plates, 96-well plates, steripipettes, and sterile filter units. 5. Biosafety cabinet. 2.2 Treatment of Cells

Cell lysis solution: Tris-HCl (50 mM), EDTA (0.1 mM), EGTA (0.1 mM), Triton-X (1%), and SDS (1%).

2.3 Cell Viability Assay

1. Prepare 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide (MTT) solution by dissolving 5 mg MTT in 1 mL of sterile phosphate-buffered saline (PBS). Store at 4 °C (see Note 1). 2. Prepare solubilizing solution (stop solution) by adding 10% SDS in 0.01 M HCl (see Note 2).

2.4 Nitric Oxide Measurement

1. Lysis buffer: Add 2.5 mL of 50 mM Tris-HCl, 20 μL of 0.1 mM EDTA, 20 μL of 0.1 mM EGTA, 1 mL of 1% triton X (v/v), and 1% SDS (w/v). Make the volume up to 100 mL with distilled water. 2. Griess reagent. 3. Nitrite standards: prepare 100 μM nitrite solution for the nitrite standard reference curve.

2.5 Measurement of Intracellular Reactive Oxygen Species

1. Add 4.85 mg of 2′,7′-dichlorofluorescein diacetate in 1 mL of dimethyl sulfoxide (DMSO) to make 10 mM stock solution. Store at -20 °C. 2. Positive control: Hydrogen peroxide. 3. Negative control: Ascorbic acid.

2.6 RNA and Protein Preparation

1. Tris-acetate EDTA (TAE) buffer: Add 40 mM Tris-acetate and 1 mM EDTA in water. Store at room temperature (RT). 2. RNA isolation: Trizol reagent, chloroform (100 μL/mL of homogenate), 75% ethanol, isopropanol, and diethyl pyrocarbonate (DEPC). 3. Modified radioimmunoprecipitation assay (RIPA) buffer: Add 50 mM Tris-HCl, 150 mM NaCl, 1.0% (v/v) NP-40, 0.5% (w/v) sodium deoxycholate, 1.0 mM EDTA, 0.1% (w/v) SDS, and 0.01% (w/v) sodium azide in water. Adjust the pH to 7.4. Store at 4 °C. Add protease inhibitory cocktail before using.

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Western Blotting

1. Stacking gel (5%): Add the following reagents in the order mentioned; 3.4 mL water, 830 μL of 30% acrylamide (830 μL), 630 μL of 1 M Tric-Cl (pH-6.8), 50 μL of 10% SDS, 50 μL of 10% APS, and 5 μL TEMED (see Note 3). 2. Resolving gel: Make the percentage of the resolving gel according to the molecular weight of the target protein. Water (3 mL), 30% acrylamide (4 μL), 1.5 M Tric-Cl (2.5 μL, pH-8.8), 10% SDS (100 μL), 10% APS (100 μL), and TEMED (5 μL). 3. SDS loading dye (5%): Tris-Cl (250 mM, pH-6.8), SDS (10%, w/v), bromophenol blue (0.5%, w/v), glycerol (50%), and β-mercaptoethanol (200 mM). 4. SDS PAGE running buffer: Tris base (3.03 g), glycine (14.4 g), SDS (1 g). Make up the final volume to 1000 mL. 5. Transfer buffer: Tris base (3.03 g), glycine (14.4 g), methanol (200 mL). Make up the final volume to 1000 mL.

3

Methods

3.1 Cell Culture and Maintenance

1. Seed the cells at a density of 0.7 × 106 for T25 flask and 2.1 × 106 for T75 flask. Maintain the cells at 37 °C and 5% CO2-humified incubator. 2. Maintain the cells in log phase with regular subculturing of cell monolayers when they reach 70–80% confluence. 3. For subculturing, remove the old media. Wash the cells with PBS, and trypsinize by incubating with 0.25% trypsin-EDTA for 2–5 min (see Note 4). 4. Normalize the trypsin using an equal volume of fresh medium and centrifuge the cell suspension at 80 × g for 5 min. 5. Discard the supernatant and resuspend the cell pellet in fresh medium. Reseed 1/5 of the resuspended cell suspension into fresh culture flask. 6. Cell line preservation: Spin down the cells as described in step 4. Resuspend the cell pellet in fresh media. Add equal volume of the freezing mixture and transfer it to cryovials. Place the vials in a cryo-cooler containing isopropanol in the outer compartment which, when placed at lower temperatures, brings about a gradual cooling of the vials stored in the inner compartment. Store the cryo-cooler at -20 °C for 24 h. Transfer the vials to -80 °C for 24 h and finally to liquid nitrogen for long-time storage (see Note 5). 7. Cell line renewal: Place the vials in a water bath at 37 °C for 2 min to thaw the cells quickly. Add the cells to 2 mL of media

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and incubate for 5 min to reacclitimize the cells. Spin down the cells at 80 × g for 3 min, and resuspend the cell pellet in regular culture medium and seed into the culture flask (see Note 6). 3.2 Cellular Experiments

1. Seed the cells at a density of 5 × 105 cells/well in a 6-well-plate, and incubate overnight at 37 °C at 5% CO2 in a humidified incubator. 2. Replace the media on the following day before initiating the experiments. 3. Treat one group of cells with the heavy metal (by dissolving the compound in sterile PBS) at varying concentrations. 4. Preincubate the other group of cells with the small molecule (prepared in the appropriate solvent), e.g., MCC950 at varying concentrations. Choose the concentration of the small molecule based on review of literature. Post-incubation, treat the cells with the heavy metal and incubate for 24 h at 37 °C at 5% CO2 in a humidified incubator. 5. Post-incubation, harvest the cells for further experiments.

3.3 Animals and Treatment

1. Keep all animals under controlled temperature (23 ± 1 °C) and humidity (50 ± 5%) with free access to water and food, on a 12 h dark/light cycle. 2. Allow the animals to acclimatize to the environment for 1 week before performing any experiments.

3.4 Animal Study Design and Treatment

1. Divide the animals randomly into the following groups: normal control, disease control, and treatment group 1, treated with x concentration of small molecule/kg of body weight, and treatment group 2, treated with y concentration of small molecule/kg of body weight. (Increase the treatment groups based on the concentration of the compound used.) 2. Sacrifice the animals after the treatment procedure is over and collect the brain for further analysis.

3.5 Preparation of Brain Samples

1. Collect animal brain and snap-freeze in liquid nitrogen. 2. Wash the brain with sterile PBS and homogenize the tissue using bead-beating for 5 min. 3. Make aliquots of the samples and store at -80 °C to avoid freeze-thaw cycle (Fig. 2).

3.6 Cell Proliferation Assay

1. Seed 5 × 103 cells in each well of a 96-well plate. 2. Treat the cells with the drug or molecule of choice, and incubate for 24 h at 37 °C at 5% CO2 in a humidified incubator. 3. Discard the media and add 10 μL of MTT to each well and incubate for 2–3 h in the dark.

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Fig. 2 Schematic representation of the process of isolation of cells from brain tissue

4. Add 10 μL solubilizing solution or DMSO and incubate for 30 min in constant shaking. 5. Measure the absorbance at 590 nm with a reference filter at 620 nm. 3.7 Measurement of Intracellular Nitric Oxide

1. Add 150 μL of lysis buffer to cells. 2. Incubate at room temperature for 15 min in the dark. 3. Scrape the cells with the help of a cell scraper and centrifuge at 12,000 rpm for 5 min. 4. Collect 100 μL of the supernatant and add 100 μL Griess reagent. 5. Incubate at room temperature for 10 min and measure absorbance at 540 nm.

3.8 Measurement of Intracellular Reactive Oxygen Species

1. Dilute the stock solution of DCHF-DA with pre-warmed culture medium into 10 μM working solution (see Note 7). 2. For imaging: Add 500 μL of working solution to each well of a 24-well plate. Incubate at 37 °C for 30 min in the dark. Remove the DCHF-DA solution and wash with PBS. Add 500 μL PBS to each well and representative fluorescent images for each well using the green fluorescent protein (GFP) channel on a fluorescence microscope (see Note 8). 3. For fluorescence intensity measurement: Harvest cells posttreatment. Wash twice with PBS. Add working solution and incubate at 37 °C for 30 min in the dark. Analyze the cells immediately using flow cytometry to measure the fluorescence intensity.

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RNA Preparation

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1. Centrifuge the cell suspension at 80 × g for 5 min to pellet down the cells. 2. Resuspend the cells in Trizol reagent, mix vigorously, and incubate at room temperature for 5 min to lyse the cells. Add 1 mL Trizol reagent to 1 × 106 cells for total RNA extraction. 3. Transfer the lysates to a 1.5 mL microCT, add 100 μL chloroform/mL of homogenate, and mix well to precipitate the proteins. Incubate the cells at 4 °C for 10 min. 4. Centrifuge at 4 °C and 10,000 × g for 10 min for phase separation. 5. Shift the aqueous phase containing RNA to a fresh CT, and add equal volume of ice-cold isopropanol and incubate overnight at -20 °C for precipitation of RNA (see Note 9). 6. Collect the RNA as a pellet by centrifuging at 12,000 × g for 20 min at 4 °C. 7. Wash the pellet two times by resuspending it in 70% ethanol followed by centrifugation at 7500 × g for 10 min at 4 °C. 8. Partially air-dry the pellet, and dissolve in 40 μL of diethyl pyrocarbonate (DEPC)-treated water by placing the tubes in 62 °C water bath for 10 min. 9. Evaluate the RNA quality by electrophoresis on a 0.8% agarose gel. 10. Measure the absorbance of diluted samples at 260 and 280 nM, and calculate RNA concentration using the following formula: Concentration of RNA ðin μgÞ = A260 reading × 40 × dilution factor

3.10 Quantitative Real-Time PCR

1. Convert 1.5 μg of total RNA to cDNA in a 20 μL reaction volume using PrimeScript RT-PCR kit using oligo-dT primers according to the reaction setup given below: 2. Perform qRT-PCR to analyze the expression profile of the target genes using gene-specific primers. A list of molecular targets for studying neurotoxicity and neuroinflammation is provided in Table 2. Amplify cDNA using two cycles: the first one consisting of a single cycle at 95 °C for 7 min followed by a total of 40 cycles consisting of 95 °C for 10 s and 60 °C for 30 s. 3. After the completion of amplification cycle, perform melting curve analysis to check for primer dimers and false amplification. 4. Calculate the relative expression of mRNA by normalizing against expression of housekeeping gene using Pfaffl equation.

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Table 2 List of protein markers for assessing neurotoxicity and neuroinflammation Protein marker

Function/role

Pro-inflammatory cytokines

Indicators of inflammation and immune response

TNF-α

Promotes inflammation and cell death

IL-1β

Mediates inflammation and immune response

IL-6

Regulates immune response and inflammation

IL-8

Chemotactic factor for immune cells and promotes inflammation

IL-18

Enhances inflammation and immune responses

Anti-inflammatory cytokines Downregulate inflammatory responses and promote tissue repair IL-10

Inhibits pro-inflammatory cytokines and modulates immune response

TGF-β

Regulates immune cell function and tissue repair

Chemokines

Attract immune cells to the site of inflammation

MCP-1

Recruits monocytes and macrophages

IP-10

Attracts activated T cells and monocytes

RANTES

Regulates migration and activation of leukocytes

MIP-1α

Promotes inflammation and recruits immune cells

Glial activation markers

Indicators of activation and response in glial cells

IBA1

Marker for microglial activation

CD11b

Expressed on microglia and other myeloid cells

GFAP

Marker for reactive astrocytes

S100B

Released by astrocytes in response to injury or inflammation

ALDOC

Astrocyte-specific marker

Oxidative stress markers

Indicators of oxidative stress and damage

SOD

Antioxidant enzyme that protects against superoxide radicals

CAT

Breaks down hydrogen peroxide and protects against oxidative stress

GPx

Detoxifies hydrogen peroxide and lipid hydroperoxides

NOX

Enzyme involved in the generation of reactive oxygen species

MDA

Biomarker of lipid peroxidation and oxidative stress

Synaptic markers

Indicators of synaptic integrity and function

Synaptophysin

Protein found in presynaptic vesicles

PSD-95

Postsynaptic protein involved in synaptic organization and signaling

Synapsin I

Regulates neurotransmitter release and synaptic plasticity

Neurotrophic factors

Promote survival, growth, and differentiation of neurons (continued)

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Table 2 (continued) Protein marker

Function/role

BDNF

Enhances neuronal survival, growth, and synaptic plasticity

GDNF

Supports survival and maintenance of dopaminergic neurons

NGF

Promotes the growth and survival of sympathetic and sensory neurons

Microglial activation markers

Indicators of microglial activation and response

TREM2

Key regulator of microglial activation and phagocytosis

MHC-II

Major histocompatibility complex class II molecule expressed on microglia

Astrocytic markers

Indicators of astrocyte activation and function

GS

Key enzyme in the astrocytic glutamate-glutamine cycle

AQP4

Astrocyte-specific water channel protein

Component

Concentration

Volume

Total RNA

1.5 μg

X μL

Oligo dT primer

50 pM

1 μL

dNTPs

10 mM

1 μL

RNase-free water

–

QS

Incubate mixture at 65 °C for 5 min and quick chill on ice To the above mixture, add the following PrimeScript Buffer

5×

4 μL

PrimeScript RT enzyme

100 units

1 μL

RNase-free water

–

QS

Set up a two-step cycling

3.11 Protein Isolation and Estimation

1. Wash the cells with PBS, lyse in ice-cold RIPA lysis buffer containing protease and phosphatase inhibitors, and incubate in rotospin for 1 h. 2. Centrifuge the cells at 10,000 × g for 15 min at 4 °C to remove debris. 3. Estimate the concentration of protein in the cell lysates using the Pierce BCA Protein Assay Kit method. 4. Dilute the samples ten times with PBS. Add 10 μL of each sample to a well of 96-well plate.

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5. Add 200 μL of BCA reagents in the ratio 49:1 (A:B) to 10 μL of sample. Leave the plates undisturbed at 37 °C for 30 min in dark. 6. Measure the absorbance at 560 nM. 7. Estimate the amount of protein in the diluted samples by comparing their absorbance against absorbance values of different concentration of BSA standards. 3.12 Western Blotting

1. Prepare the samples for electrophoresis by mixing the cell lysates with 1/5th the volume of 5× SDS-PAGE loading dye (with reducing agent), and incubate in a boiling water bath for 5 min. 2. Subject the samples containing 30–50 μg of protein to SDSPAGE separation using a discontinuous buffer system (see Note 10). 3. After resolution on gel, transfer the proteins onto a 0.22 μm nitro cellulose membrane in Towbin’s transfer buffer using a wet western transfer apparatus at 100 V/300 mA at 4 °C for 120 min. 4. Incubate the membrane for 16–18 h at 4 °C with primary antibodies at 1:5000 dilution. 5. Remove the primary antibodies and incubate the membrane with appropriate secondary antibodies at 1:5000 dilution for 1 h at RT. 6. Wash the membrane with TBST three times for 10 min at RT with constant shaking.

4

Notes 1. Prepare MTT solution freshly before using. Do not use MTT solution older than 1 week for experiments. 2. DMSO can also be used to solubilize the formazan crystals. 3. Please be careful while handling acrylamide and TEMED. These chemicals should be handled using gloves. Please wear a mask while preparing acrylamide solution. 4. The incubation time for trypsinization should be optimized according to the cell type. Observe the cells under microscope to visualize the cells. Do not exceed 5 min. If cells have not lifted off after 5 min, gently tap the sides of the culture flask. 5. Make sure that the storage temperature of the vials is gradually lowered if a cryo-cooler is not used. Do not snap-freeze the vials.

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6. If this revival protocol leads to stress in the cells, the centrifugation step can be avoided, and the cells can be directly seeded into the culture medium after thawing. In that case, the media should be changed within 12 h to remove DMSO. 7. The stock solution should be diluted with pre-warmed culture media immediately before adding it to the wells. 8. The volume of the working solution should be adjusted depending upon the volume of the culture plate used. 9. The incubation time can be reduced based on requirement. Incubating for a longer time in isopropanol increases nucleic acid precipitation. 10. The concentration of the protein should be optimized based on the molecular weight of the protein. References 1. Al Osman M, Yang F, Massey IY (2019) Exposure routes and health effects of heavy metals on children. Biometals 32:563–573 2. Karri V, Schuhmacher M, Kumar V (2016) Heavy metals (Pb, Cd, As and MeHg) as risk factors for cognitive dysfunction: a general review of metal mixture mechanism in brain. Environ Toxicol Pharmacol 48:203–213 3. Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ (2012) Heavy metal toxicity and the environment. Exp Suppl 101:133–164 4. Wang H, Matsushita MT (2010) Heavy metals and adult neurogenesis. Curr Opin Toxicol 26: 14–21 5. Bonda DJ, Lee HG, Blair JA, Zhu X, Perry G, Smith MA (2011) Role of metal dyshomeostasis in Alzheimer’s disease. Metallomics 3:267– 270 6. Andrade VM, Aschner M, Marreilha Dos Santos AP (2017) Neurotoxicity of metal mixtures. Adv Neurobiol 18:227–265 7. Garza-Lombo´ C, Posadas Y, Quintanar L, Gonsebatt ME, Franco R (2018) Neurotoxicity linked to dysfunctional metal ion homeostasis and xenobiotic metal exposure: redox signaling and oxidative stress. Antioxid Redox Signal 28:1669–1703 8. Mitra J, Vasquez V, Hegde PM, Boldogh I, Mitra S, Kent TA, Rao KS, Hegde ML (2014) Revisiting metal toxicity in neurodegenerative diseases and stroke: therapeutic potential. Neurol Res Ther 1:107 9. Schiavone S, Trabace L (2018) Small molecules: therapeutic application in neuropsychiatric disorders. and neurodegenerative Molecules 23:411

10. Singh S, Shaikh IA, More SS, Mahnashi MH, Almohaimeed HM, El-Sherbiny M, Ghoneim MM, Umar A, Soni HK, Agrawal H, Mannasaheb BA, Khan AA, Muddapur UM, Iqubal SMS (2022) Blockage of KHSRP-NLRP3 by MCC950 can reverse the effect of manganeseinduced neuroinflammation in N2a cells and rat brain. Int J Mol Sci 23:13224 11. Chimeh U, Zimmerman MA, Gilyazova N, Li PA (2018) B355252, a novel small molecule, confers neuroprotection against cobalt chloride toxicity in mouse hippocampal cells through altering mitochondrial dynamics and limiting autophagy induction. Int J Med Sci 15:1384–1396 12. Shaban NZ, Abd El-Kader SE, Mogahed FAK, El-Kersh MAL, Habashy NH (2021) Synergistic protective effect of beta vulgaris with meso2,3-dimercaptosuccinic acid against leadinduced neurotoxicity in male rats. Sci Rep 11:252 13. Bridges CC, Joshee L, Zalups RK (2008) MRP2 and the DMPS- and DMSA-mediated elimination of mercury in TR(-) and control rats exposed to thiol S-conjugates of inorganic mercury. Toxicol Sci 105:211–220 14. Moore DF, O’Callaghan CA, Berlyne G, Ogg CS, Davies HA, House IM, Henry JA (1994) Acute arsenic poisoning: absence of polyneuropathy after treatment with 2,3-dimercaptopropanesulphonate (DMPS). J Neurol Neurosurg Psychiatry 57:1133–1135 15. Matsubara T, Saura R, Hirohata K, Ziff M (1989) Inhibition of human endothelial cell proliferation in vitro and neovascularization in vivo by D-penicillamine. J Clin Invest 83: 158–167

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Pronama Biswas and Sunil S. More

16. Valde´s PA, Samkoe K, O’Hara JA, Roberts DW, Paulsen KD, Pogue BW (2010) Deferoxamine iron chelation increases deltaaminolevulinic acid induced protoporphyrin IX in xenograft glioma model. Photochem Photobiol 86:471–475 17. Wang CY, Xie JW, Xu Y, Wang T, Cai JH, Wang X, Zhao BL, An L, Wang ZY (2013) Trientine reduces BACE1 activity and mitigates amyloidosis via the AGE/RAGE/NF-κ B pathway in a transgenic mouse model of Alzheimer’s disease. Antioxid Redox Signal 19:2024–2039 18. Ala A, Aliu E, Schilsky ML (2015) Prospective pilot study of a single daily dosage of trientine for the treatment of Wilson disease. Dig Dis Sci 60:1433–1439 19. Stangle DE, Smith DR, Beaudin SA, Strawderman MS, Levitsky DA, Strupp BJ (2007) Succimer chelation improves learning, attention, and arousal regulation in lead-exposed rats but produces lasting cognitive impairment in the absence of lead exposure. Environ Health Perspect 115:201–209 20. Deore MS, Keerthana S, Naqvi S, Kumar A, Flora SJS (2021) Alpha-lipoic acid protects co-exposure to lead and zinc oxide nanoparticles induced neuro, immuno and male reproductive toxicity in rats. Front Pharmacol 12: 626238 21. Zubcˇic´ K, Radovanovic´ V, Vlainic´ J, Hof PR, Orsˇolic´ N, Sˇimic´ G, Jazvinsˇc´ak Jembrek M

(2020) PI3K/Akt and ERK1/2 signalling are involved in quercetin-mediated neuroprotection against copper-induced injury. Oxidative Med Cell Longev 2020:9834742 22. Lin MC, Liu CC, Liao CS, Ro JH (2021) Neuroprotective effect of quercetin during cerebral ischemic injury involves regulation of essential elements, transition metals, Cu/Zn ratio, and antioxidant activity. Molecules 26: 6128 23. Omeiza NA, Abdulrahim HA, Alagbonsi AI, Ezurike PU, Soluoku TK, Isiabor H, AlliOluwafuyi AA (2021) Melatonin salvages lead-induced neuro-cognitive shutdown, anxiety, and depressive-like symptoms via oxidoinflammatory and cholinergic mechanisms. Brain Behav 11:e2227 ˜iz AR, 24. Nicola´s-Me´ndez T, Kacew S, Ortiz-Mun ˜ ez Mendoza-Nu´n VM, Garcı´a-Rodrı´guez MDC (2022) Protective effect of resveratrol against hexavalent chromium-induced genotoxic damage in Hsd:ICR male mice. Molecules 27:4028 25. Zwolak I (2021) Epigallocatechin gallate for management of heavy metal-induced oxidative stress: mechanisms of action, efficacy, and concerns. Int J Mol Sci 22:4027 26. Davuljigari CB, Ekuban FA, Zong C, Fergany AAM, Morikawa K, Ichihara G (2021) Nrf2 activation attenuates acrylamide-induced neuropathy in mice. Int J Mol Sci 22:5995

Chapter 12 Chromatographic Separation and Quantitation of Sphingolipids from the Central Nervous System or Any Other Biological Tissue Swapan K. Ray and Somsankar Dasgupta Abstract Chromatographic separation and purification of an individual lipid to homogeneity have long been introduced. Using this concept, a more precise method has been developed to identify and characterize the sphingolipid composition(s) using a small amount (30 mg) of biological sample. Sphingolipids (lipids containing sphingosine or dihydrosphingosine) are well-known regulators of the central nervous system development and play a critical role in neurodegenerative diseases. Introducing a silicic acid column chromatography, sphingolipid components have been separated to individual fractions such as ceramide, glucosyl/galactosylceramide, other neutral and acidic glycosphingolipids, including (dihydro)sphingosine and psychosine; as well as phospholipids from which individual components are quantified employing a single or combination of other advanced chromatography procedures such as thin-layer chromatography, gas chromatography-mass spectrometry, and high-performance liquid chromatography-mass spectrometry. Key words Column chromatography, Gas chromatography, Glycosphingolipids, High-performance liquid chromatography, Mass spectrometry, Sphingolipids

1

Introduction Sphingolipids include ceramide (Cer)/dihydroceramide (dhCer), sphingosine (Sph)/dihydrosphingosine (dhSph), and glucosyl/ galactosylceramide (Glc/GalCer). Gangliosides are enriched in the central nervous system (CNS) and display variegated biological functions such as stimulation of cell growth, CNS development, induction of cell apoptosis leading to nerve degeneration. For example, both Sph and Cer are critical molecules in CNS development, but Sph is an inhibitor of protein kinase C (PKC) [1], and Sph toxicity in degeneration of oligodendrocytes and neurons in multiple sclerosis (MS) leading to demyelination has been reported [2]. To develop a drug, it is critical to determine the precise concentration of sphingolipids using a small amount of

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_12, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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biological samples such as brain and other tissues and to delineate their mechanism of accumulation examining the biosynthetic pathway and metabolism. Here, we describe a chromatography technique to separate individual lipid components employing a silicic acid column chromatography [3] for isolating some purified components and fractions for quantitation using also other advanced chromatography techniques such as thin-layer chromatography (TLC), gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography-mass spectrometry (HPLC-MS). This methodology supersedes the modern lipidomic analysis for its greater accuracy in terms of tissue-specific quantitation due to the removal of other contaminating lipid molecules that may interfere with the quantitative analysis and inspire the discovery of a novel lipid. Moreover, this chromatography separation offers precise characterization of lipid molecule that has not been previously identified in the CNS [4] or in any other biological tissue.

2

Materials 1. Use Dowex resins (Pharmacia LKB Technology) for separation of sphingosine (Sph), dihydrosphingosine (dhSph), and psychosine from the lipid mixture. 2. Use high-performance thin-layer plates (HPTLC) (E. Merck) for analysis of glucosyl (Glc)/galactosyl (Gal) Cer and other neutral and acidic glycosphingolipids (Gsl). 3. Use silicic acid (100–200 mesh, Sigma) for separation and purification of Cer, GlcCer/GalCer, and other higher Gsls including gangliosides and phospholipid fractions. Compare to a standard sphingolipid (Sigma-Aldrich), and further purify in your laboratory. Employ Dowex resin for sphingosine isolation. 4. List all solvents and chemicals such as chloroform, acetone, methanol, tetrahydrofuran (THF), and dichloroethane (DCE) (Fisher Scientific), which are used at different compositions for purification/separation of lipid fractions. 5. Instrument/tools required: Polytron homogenizer, glass columns, high-performance TLC plates (E. Merck) and TLC sprayer, gas chromatography-mass spectrometry (Hewlett Packard), HPLC (Waters), and high-speed centrifuge as necessary tools. A magnetic stirrer is required to extract lipid content and a flash evaporator, which is necessary for rapid evaporation of extracting solvents.

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151

Methods Lipid Extraction

1. Extract approximately 30 mg of the CNS or any biological tissue for lipid extraction (Fig. 1). 2. Extract total lipids using chloroform/methanol/water (2:4:1, v/v), three times using a Polytron homogenizer and stirring on magnetic stirrer for approximately 1 h. 3. Pool three extracts and collect lipids in a flask after drying in a flash evaporator.

3.2 Separation of Lipid Components Using Silicic Acid Column Chromatography

Perform column chromatography (see Note 1) steps as follows: 1. Suspend the pooled dried extract in a minimum volume (approx. 2–3 mL) of chloroform. Purify sphingolipids, e.g., Cer (Fig. 2), monoglycosylceramides (MGCs) (Fig. 3), and GSLs as individual fractions as described below. 2. Apply the lipid extract on a silicic acid column (0.2 × 12 cm) in chloroform/acetone (49:1, v/v), and elute lipid fractions using following solvent systems: elute neutral lipids washing with 15 column volumes of chloroform/acetone (49:1, v/v). 3. Elute the column successively with: (a) Chloroform/acetone (23:2, v/v; 15 column volumes) for Cer,

Fig. 1 Schematic presentation of chromatographic separation of lipids from the CNS tissue. This scheme is also useful for chromatographic separation of lipids from any other tissue. TMS, trimethylsilyl. (Reproduced and modified from ref. 3 with permission from its publisher)

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Fig. 2 Changes in ceramide synthesis in developing rat brain. Column chromatography was used for purification of ceramide fractions, and HPTLC was applied for quantification. The public domain ImageJ program of the National Institutes of Health (NIH, Bethesda, MD, USA) was used for quantification of the resolved individual bands. The structure of ceramide was confirmed by the use of GC-MS and followed by HPLC. Then, the ceramide/ dihydroceramide ratio was determined to be around 4.5:1. The results were obtained with the use of two sets of rats. E, embryonic rat brain; P, postnatal rat brain

Fig. 3 Developing rat brain showing monoglycosylceramide content. Column chromatography was used for purifying monoglycosylceramide fraction and HPTLC was used for resolving individual bands. ImageJ program was used for quantifying each band, and GC-MS was used to determine the structure. GlcCer, glucosylceramide; GalCer, galactosylceramide; E, embryonic rat brain; P, postnatal rat brain

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(b) Chloroform/methanol (22:3, v/v; 10 column volumes) for MGC, i.e., Glc/Gal Cer, (c) Tetrahydrofuran (THF)/water (7:1, v/v; 3.5 column volumes) for long-chain GSL, sphingoids, and some phospholipids [fraction 1, e.g., phosphatidyl ethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS)], (d) Methanol [five column volumes, phospholipid fraction 2, for phosphatidylcholine (PC) and sphingomyelin (SM)]. 4. Dry and store each fraction at 4 °C until use. Dissolve the THF-water elution in chloroform/methanol/water (12:7:1, v/v/v; 15 mL/g tissue). Preserve 50 μL for examining phospholipids (see Note 2). 3.3 Separation of GSLs and Sphingoids (Sph, dhSPH, and Psychosine)

1. Dowex chromatography: Separate Sph/dhSph from the GSLs using a Dowex 50X8-200 (Na+ form) column by resuspending in methanol [column (1 × 2 cm; Na+ form)] previously equilibrated with methanol [5]. 2. Collect NGSLs and gangliosides washing with 15 column volumes of (a) methanol, (b) chloroform/methanol (2:1, v/v), and (c) 10 column volumes of methanol (fractions collected and dried for GSLs). 3. Elute sphingoids with 10 column volumes of methanol: 0.4 M CaCl2 (3:1, v/v). 4. Dry and suspend the eluent in water, and remove the salt with a Sep-Pak C18 cartridge as described [3, 5, 6].

3.4 Detection and Quantitation of Sph/ dhSph

1. Assay the purified sphingoids (Fig. 4) after fluorescent tagging, using HPLC [7–10].

3.5 Separation of Gangliosides and Neutral GSL by Anion Exchange Chromatography

1. Pool and dry the three washings of the Dowex column overnight in a vacuum desiccator before acetylation as described [3, 12].

2. Assay Sph/dhSph bases by chromatography-mass spectrometry (GC-MS) as a trimethylsilyl derivative [5, 11]. Examine the quantitative recovery of standard sphingoids, usually between 60% and 80%.

2. Purify the acetylated GSL through a Florisil column (0.5 × 3 cm) and wash with dichloroethane/acetone (19:1, v/v; discarded) followed by DCE:acetone (1:1, v/v; GSL) [12, 13]. 3. Deacetylate the later fraction [13] and fractionate the deacetylated mixture to NGSL and gangliosides by a DEAE-Sephadex A50 (acetate) column (0.5 × 3 cm).

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Fig. 4 Developing rat brain showing sphingosine and dihydrosphingosine contents. Column chromatography was used for purifying sphingosine fractions, and then HPLC and GC-MS were used for quantification. The sphingosine/dihydrosphingosine ratio was determined to be around 2.5:1. The results were obtained with the use of two sets of rats. E, embryonic rat brain; P, postnatal rat brain 3.6 Separation of Gangliosides and NGSLs

1. Prepare a DEAE-Sephadex A50 (acetate) column (0.5 × 3 cm), and apply the sample using chloroform/methanol/water (10: 15:1, v/v/v). 2. Wash the column with five to ten volumes of the same solvent to collect NGSLs. 3. Elute gangliosides using chloroform/methanol/0.06 M sodium acetate (10:15:1, v/v/v). 4. Dry the elution and remove salt from gangliosides with a Sep-Pak C18 cartridge [3, 6]. 5. Dry and store each fraction at 4 °C until further use. 6. Elute other polar GSL containing sulfoglucuronic acid from the Sephadex column with 0.2 M sodium acetate. 7. At this stage, do not try to identify them [they are minor components of the CNS [14], and their concentration may be beyond the level of detection of this TLC]. 8. Estimate protein content with the bicinchoninic acid reagent [15].

3.7 Thin-Layer Chromatography (TLC) Resolution of Ceramide, MGCs, NGSLs, and Gangliosides

1. High-performance thin-layer chromatography (HPTLC): Purity and quantitate Cer HPTLC. 2. Apply a defined amount of chloroform/acetone (23:2) fraction (15 μL; 1 mL/g tissue) on a HPTLC along with different concentrations of Cer standards. 3. Develop the plate using chloroform/methanol/acetic acid (95: 4.5:0.5, v/v/v) [16] and visualized by benzidine spray [17]. 4. Check the purity of each lane by iodine exposure showing a single band. Scan band from each lane and quantitate using the reference standard.

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5. Resolution of monoglycosylceramide (MGC): Since MGCs is the major component of CNS and its presence interferes with the other NGSLs, elute the silicic acid column with chloroform/methanol (22:3, v/v) to remove the major portion (>90%) of the MGC, which facilitates the resolution of NGSLs by TLC. 6. Dissolve MGCs fraction in chloroform/methanol (2:1, v/v; 2–4 mL/g), and apply quantitatively (5 μL) to an HPTLC plate. 7. Resolve the bands with chloroform/methanol/water (85: 15:0.5, v/v/v) and visualized by diphenylamine-aniline spray [3]. 8. Scan bands from each lane as described above and estimate the quantity with galactosylceramide (GalCer) as the reference standard. 9. Thin-layer chromatography resolution of NGSLs and gangliosides: Examine the compositions of NGSLs and ganglioside (including sulfatide) fractions by HPTLC as described below. 10. Examine NGSL purified from the DEAE-Sephadex A50 column by TLC using chloroform/methanol/water (60:40:9, v/v/v). 11. Stain NGSL bands by digoxigenin immunostaining (DIG-IS) [3, 18]. 12. Spot ganglioside fractions on a duplicate plate for quantitation of sulfatide. 13. Use chloroform/methanol: 0.25% CaCl2 (55:45:10, v/v/v) as solvents for gangliosides. 14. Visualize ganglioside bands with resorcinol spray. 15. Scan both plates and each identified band (compared with a standard). 16. Calculate the individual band as a percentage of total NGSL or ganglioside concentration. 17. Analyze the sulfatide band with a standard and after spraying the duplicate ganglioside-TLC plate with diphenylamineaniline [3]. 3.8 Quantification of Sphingosine Base by High-Performance Liquid Chromatography (HPLC)

1. Assay the purified sphingoids (Sph, dhSph, and psychosine) (see Note 3) after fluorescent tagging, using HPLC (Waters) [5, 8, 9] and/or by GC-MS as a trimethylsilyl derivative [3]. 2. Notice that the quantitative recovery of standard sphingoids examined is between 60% and 80% (see Notes 4 and 5).

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3.9 Characterization of Ceramide and Sphingosine Base with Application of Gas ChromatographyMass Spectrometry (GC-MS)

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1. Hydrolyze Ceramide using methanolic-HCl at 80 °C for 16–18 h. 2. Remove methylated fatty acids by hexane, and convert bases to trimethylsilyl derivatives [3, 5, 11]. 3. Analyze both fatty acids and base derivatives by GC-MS in a Hewlett-Packard GC-MS (GC 5980, MS 5972).

Notes 1. These column chromatography techniques have been designed to separate lipid components from an extract of a small amount of biological samples (30 mg). 2. Separated fractions are further resolved into purified fraction of individual components, such as Cer, gangliosides, NGSLs, Sph/dhSph, and psychosine. 3. Each component is further examined employing TLC and HPLC, and their structures have been confirmed by GC-MS and HPLC-MS. 4. Purification helps quantify the lipid components more accurately compared to other methodology such as lipidomics [2]. 5. It also enables the researcher to characterize the novel compound such as phytoceramide that has not been previously identified in vertebrate brain [4].

Acknowledgments The work was supported in part by the R01 grants (CA-091460 and NS-057811) from the NIH (Bethesda, MD, USA) to S.K.R. References 1. Hannun YA, Bell RM (1987) Lysosphingolipids inhibit protein kinase C: implication for sphingolipidosis. Science 235:670–674 2. Miller LG Jr, Young JA, Ray SK, Wang G, Purohit S, Banik NL, Dasgupta S (2017) Sphingosine toxicity in EAE and MS: evidence for ceramide generation via serinepalmitoyltransferase activation. Neurochem Res 42:2755–2768 3. Dasgupta S, Hogan EL (2001) Chromatographic resolution and quantitative assay of CNS tissue sphingoids and sphingolipids. J Lipid Res 42:301–308

4. Dasgupta S, Kong J, Bieberich E (2013) Phytoceramide in vertebrate tissues: one step chromatography separation for molecular characterization of ceramide species. PLoS One 8:e80841 5. Igishu H, Suzuki K (1984) Analysis of galactosphingosine (psychosine) in brain. J Lipid Res 25:1000–1006 6. Williams MA, McCluer RH (1980) The use of Sep-Pak C18 cartridges during the isolation of gangliosides. J Neurochem 35:266–269 7. Naoi M, Lee YC, Roseman S (1974) Rapid and sensitive determination of sphingosine bases

Chromatographic Separation and Quantitation of Sphingolipids and sphingolipids with fluorescamine. Anal Biochem 58:571–577 8. Kisic A, Rapport MM (1974) Determination of long-chain base in glycosphingolipids with fluorescamine. J Lipid Res 15:179–180 9. Higgins TJ (1984) Simplified fluorometric assay for sphingosine bases. J Lipid Res 25: 1007–1009 10. Shinoda H, Kobayashi T, Katayama M, Goto I, Nagara H (1987) Accumulation of galactosylsphingosine (psychosine) in the twitcher mouse: determination by HPLC. J Neurochem 49:92–99 11. Motta S, Monti M, Sesana S, Caputo R, Carelli S, Ghidoni R (1993) Ceramide composition of the psoriatic scale. Biochim Biophys Acta 1182:147–151 12. Saito T, Hakomori SI (1971) Quantitative isolation of total glycosphingolipids from animal cells. J Lipid Res 12:257–259 13. Dasgupta S, Hogan EL, Spicer SS (1996) Stage-specific expression of fuco-neolacto(Lewis X) and ganglio-series neutral glycosphingolipids during brain development: characterization of Lewis X and related

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glycosphingolipids in bovine, human and rat brain. Glycoconj J 13:367–375 14. Chou DK, Jungalwala FB (1993) N-acetylglucosaminyltransferase regulates the expression of neolactoglycolipids including sulfoglucuronylglycolipids in the developing nervous system. J Biol Chem 268:21727–21733 15. Shibuya T, Watanabe Y, Nalley KA, Fusco A, Salafsky B (1989) The BCA protein determination system an analysis of several buffers incubation temperature and protein standards. J Tokyo Med Coll 47:677–682 16. Bischel MD, Austin JH (1963) A modified benzidine method for the chromatographic detection of sphingolipids and acid polysaccharides. Biochim Biophys Acta 70:598–600 17. Dasgupta S, Everhart MB, Bhat NR, Hogan EL (1997) Neutral monoglycosylceramides in rat brain: occurrence, molecular expression and developmental variation. Dev Neurosci 19: 152–161 18. Kniep B, Mu¨hlradt PF (1990) Immunochemical detection of glycosphingolipids on thinlayer chromatograms. Anal Biochem 188:5–8

Chapter 13 Role of Network Pharmacology in Prediction of Mechanism of Neuroprotective Compounds Saima, S. Latha, Ruchika Sharma, and Anoop Kumar Abstract Network pharmacology is an emerging pioneering approach in the drug discovery process, which is used to predict the therapeutic mechanism of compounds using various bioinformatic tools and databases. Emerging studies have indicated the use of network pharmacological approaches in various research fields, particularly in the identification of possible mechanisms of herbal compounds/ayurvedic formulations in the management of various diseases. These techniques could also play an important role in the prediction of the possible mechanisms of neuroprotective compounds. The first part of the chapter includes an introduction on neuroprotective compounds based on literature. Further, network pharmacological approaches are briefly discussed. The use of network pharmacology in the prediction of the neuroprotective mechanism of compounds is discussed in detail with suitable examples. Finally, the chapter concludes with the current challenges and future prospectives. Key words Neuroprotective, Neurodegenerative disease, Network pharmacology, Molecular docking study, Molecular dynamics studies

1

Introduction Network pharmacology (NP) is a new emerging and ubiquitous approach applied by many researchers in the drug discovery process. It is a combination of genomic technology and computational and system biology which describes the complex relationships between biological systems, drugs, and diseases [1]. It assimilates the principles of polypharmacology [2, 3], in silico pharmacology, and system pharmacology [4, 5]. In network pharmacology, we study and analyze the network which is a net-like structure composed of nodes and edges, where nodes represent targets or proteins while the edges represent the association or interrelationship between them [6]. Figure 1 illustrates the simple network. The principle of network pharmacology is based on targeting multiple nodes in interconnected complex molecular systems which are interconnected to each other and act synergistically and could

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_13, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Fig. 1 Simple network structure where nodes represent targets while the edges represent target-target associations

result in greater efficacy and fewer adverse effects [7]. The conventional drug discovery approach is based on one-drug-one-targetone gene-one disease concept. However, multiple targets, genes are involved in the pathogenesis of any disease. Therefore, network pharmacological approaches could play a significant role in the understanding of multiple targets, genes, or pathways in the pathogenesis of particular disease that will help to design effective and safe molecules. Emerging studies have indicated the potential of network pharmacological approaches in the prediction of possible mechanism of herbal compounds [7–10]. The findings of natural compounds could guide the in vitro and in vivo studies in drug development process [11, 12]. Neurodegenerative diseases, cancers, and diabetes are caused by malfunctions in a whole regulatory network and pathway; treating them with drugs based on singletarget interventions is ineffective and inadequate. Most of the diseases involved multiple pathways in their pathogenesis; therefore, single-target interventions might be not that much effective. Network pharmacological approaches could play an important role to understand pathogenesis as well as in the identification of promising drugs as it considers multiple targets and network with multiple disease signalling molecules [13, 14]. The identification of inevitable disease-related pathways and signalling is the first and foremost step in network pharmacology. Following that, the network is analyzed and visualized using various topological parameters such as nodes, edges, degree of the nodes, shortest path, modules, etc. Based on all these parameters, hub nodes with a greater involvement in disease-related pathways are identified. The traditional herbal formulations contain multiple components and phytoconstituents, each with multiple therapeutic targets; network pharmacology methodology is useful for conforming and decoding its therapeutic mechanism against a wide range of complex diseases [15, 16]. This chapter provides detailed information regarding network pharmacological approaches and their use particularly in the identification of promising neuroprotective compounds.

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Neuroprotective Compounds Neuroprotective compounds are those that can protect the structure and functions of neurons from injury, neuronal loss, or cell death. It postpones and prevents disease progression and neurodegeneration. Neurodegenerative diseases are characterized by a persistent loss of neural structure and function, which eventually results in neuronal cell death [17]. The neurodegeneration of neurons involved in normal functioning of the central nervous system causes the slippage in certain brain functions like memory, muscular coordination, movements, learning, and cognition, and they are rapidly increasing causes of disability and even death, with far-reaching social and economic consequences [18]. Various mechanisms are involved in the development of neurogenerative diseases. The major mechanisms involved in neurodegenerative diseases are oxidative stress, neuroinflammation, excitotoxicity, mitochondrial dysfunction, aberrant protein misfolding and aggregation, apoptosis, synaptic dysfunction, etc. [19–26]. The involvement of various mechanisms in neurodegenerative diseases is presented in Fig. 2. To understand the mechanism and prospective treatment targets for neurodegenerative disorders, a significant amount of research has been done till date. There are presently no effective medications for treating this ailment, even though neurodegenerative disease is progressing and death and morbidity rates are rising. The currently accessible medication doesn’t address the root problem; it just treats symptoms. It is challenging to create a single medication to treat this complicated condition since several genes and intricate pathways are involved in its genesis and progression. The search for new compounds capable of halting or at least slowing the progression of brain degeneration has been sparked by the growing understanding of the molecular and cellular mechanisms underpinning the degenerative process. Hence, by intervening with the pathophysiological change process, neuroprotection techniques and associated processes are most successful in avoiding or deferring the process of neurodegeneration [17]. Natural resources are abundant, a gift from nature to humans. Natural remedies are essential for both human and animal health improvement and illness prevention [27]. Research studies have also shown the neuroprotective effects of selected natural compounds [28]. Flavonoids, alkaloids, and polyphenols have shown neuroprotective effects against various neurodegenerative diseases such as Parkinson, Alzheimer, etc. by improving the memory and cognitive capabilities [16]. Natural compounds have also shown promising neuroprotective effects apart from neurodegenerative diseases [29]. The neuroprotective mechanism of natural compounds was found to be multimodal, i.e., shows its action through

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Fig. 2 Mechanism associated with the neurodegeneration

multiple targets. Animal experimental studies have also shown neuroprotective mechanism of natural compounds in different animal models [16, 30, 31]. Many studies are available in the literature which shows neuroprotective activities of natural compounds. Natural neuroprotective plants and their phytoconstituents are compiled in Table 1 [32–104]. Studies have also been done on a variety of other natural products, such as Cocculus laurifolius leaves [105], Coeloglossum viride [106], Cinnamomum camphora leaves [107], Phragmanthera austroarabica [108], parawixin [108–110], white rose petal extract [111], rosemary extract [112], walnut extract [113], etc., to explore their neuroprotective potential against neurodegenerative diseases. The precise mode of action of these compounds against neurodegenerative disease is still vague. Therefore, the Network Pharmacological Approach, which implements informatics, data analysis, mathematical modelling, and other computational approaches to solve the various pharmacological problems [10, 11], might be effective in the prediction of the mechanisms underlying drug actions.

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Table 1 Natural neuroprotective plants and their phytoconstituents Serial Natural products no.

Common name

Phytoconstituent

References

Bacosides, bacopasides

[32]

Cognitive functions 1

Bacopa monnieri

Brahmi

2

Withania somnifera

Indian ginseng Withanone

[33]

3

Vaccinium angustifolium

Lowbush blueberry

Anthocyanins

[34, 35]

4

Tinospora cordifolia

Gulbel

Whole plant/ethanolic extract

[36, 37]

Alzheimer’s disease 5

Ananas comosus

Pineapple

Bromelain

[38]

6

Magnolia officinalis

Houpo

Magnolol, honokiol, obovatol and 4-Omethylhonokiol

[39]

7

Vaccinium angustifolium

Lowbush blueberry

Anthocyanins

[40]

8

Gynostemma pentaphyllum

Jiaogulan or southern ginseng

GypenosideXXXVII, XXV, XVII

[41, 42]

9

Uncaria rhynchophylla

Cat’s claw

Rhynchophylline and isorhynchophylline

[43–47]

10

Smallanthus sonchifolius

Ground apple

Yacon (Smallanthus sonchifolius (poepp. and [48] endl.) H. Robinson) leaf extract

11

Carthamus tinctorius L.

Safflower

Safflower yellow (natural safflower aqueous extract)

[49]

12

Nicotiana tabacum

Tobacco

Osmotin, a plant protein extracted from Nicotiana tabacum

[50]

13

Lactuca capensis

Lettuce

Methanolic extract of Lactuca capensis thunb. leaves

[51]

14

Huperzia serrata

Water tasselfern

Huperzine A

[52, 53]

Parkinson’s disease 15

Crocus sativus L

Saffron

Crocin, crocetin, picrocrocin, safranal

[54–56]

16

Curcuma longa

Turmeric

Curcumin

[57–61]

17

Ginkgo biloba

Maiden hair tree

18

Camellia sinensis

Green/black tea

[62–69] Polyphenols, Kaempferol, quercetin, myricetin, epigallocatechin-3

[70, 71] (continued)

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Table 1 (continued) Serial no. Natural products

Common name

Phytoconstituent

References

19

Vicia faba

Broadbean

Artemorine

[72]

20

Cannabis sativa

Marijuana

Cannabinoids

[73, 74]

21

Fructus arctii

Great power seed

Arctigenin

[75]

22

Apium graveolens L.

celery

A. graveolens crude extract

[76]

23

Ampelopsis grossedentata

Vine tea

Dihydromyricetin (DHM) (a natural flavonoid extracted from Ampelopsis grossedentata)

[77]

24

Boswellia carteri

Frankincense

Boswellic acids

[78]

25

Phytocannabinoid –

β-Caryophyllene

[79]

26

Oxalis corniculata Creeping wood sorrel

Oxalis corniculata extract

[80]

27

Olea europaea L

Common olive Olea europaea L leaf extract

[81]

28

Perilla frutescens

Beefsteak plant Rosmarinic acid

[82]

29

Sophora tomentosa

Necklace pod

Sophora tomentosa extract

[83]

30

Tinospora cordifolia

Guduchi

Tinospora cordifolia ethanol extract

[84]

31

Tribulus terrestris

Gokharu or puncture vine

Tribulus terrestris extract

[85]

32

Urtica dioica Linn.

Stinging nettle Ethyl acetate fraction of Urtica dioica Linn. [86]

33

Zingiber zerumbet

Wild ginger

Zingiber zerumbet (L.) Smith ethyl acetate extract

[87]

Amyotrophic lateral sclerosis and multiple sclerosis 36

Fragaria ananassa Strawberry

Anthocyanin

[88]

37

Alpinia oxyphylla

Sharp-leaf galangal

Alpinia oxyphylla fruit extract

[89]

38

Garcinia mangostana L. mangosteen

Mangosteen

Isogarcinol extracted from Garcinia mangostana L. mangosteen

[90]

Brahmi

Leaf powder

[91, 92]

Sesamol

[93]

Huntington’s disease 39

Bacopa monnieri

40

Sesamum indicum Sesame (oil)

(continued)

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Table 1 (continued) Serial no. Natural products

Common name

Phytoconstituent

References

41

Tomatoes

–

Lycopene

[94]

42

Curcuma longa

Turmeric

Curcumin

[95, 96]

Ischaemic stroke 43

Acanthopanax senticosus

Devil’s bush

Acanthopanax polysaccharide

[93, 97]

44

Cinnamomum philippinense

Philippine cinnamon

Cinnamophilin

[98]

45

Curcuma longa

Turmeric

Extracted oil from rhizomes

[99]

46

Ginkgo biloba

Maiden hair tree

EGb761 [Ginkgo biloba extract]

[100, 101]

47

Panax ginseng

Radix ginseng

20(S)-ginsenoside Rg/ginsenosides

[102, 103]

48

Magnolia officinalis

Houpou

Honokiol

[104]

3

Network Pharmacological Approaches Network pharmacology typically entails three steps, i.e., target recognition and prognostication, network building (during which the network is examined and confirmed), and pathway enrichment analysis followed by corroboration of the technique’s findings. Building a sophisticated physiological network based on a sizable existing database is a straightforward place to start. Finally, using hierarchical clustering and analytics method, identify the major nodes that make up the network and anticipate the essential biological processes. Lastly, extra network evaluation is necessary to confirm the legitimacy of the results that were projected [114]. Network pharmacology research’s major goal is to find genes that are connected to and common to both drugs and disease-related targets, establish a relationship between them, and then analyze and visualize their networks [114, 115]. The target selection and compound prediction processes employ a variety of databases, including the Swiss database. Forming a protein and gene interaction network using databases like STRING, STITCH, etc. allows us to identify the important nodes implicated in the illness, from which we may then predict the major biological pathways using Kyoto encyclopedia of genes and genomes (KEGG) pathway network analysis [111, 116]. To properly confirm the association between highly active ingredients and their potential targets, further network validation is undertaken [7, 117]. Figure 3

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Fig. 3 Steps involved in network pharmacology

represents the steps involved in network pharmacology approaches followed by experimental validation. The details about each step are described below. 3.1 Target Identification

The first step in network pharmacology is to identify the possible targets of desired compounds as well as the targets which are involved in the pathogenesis of disease of interest. Several databases are available to find possible targets of compounds and diseases. The well-known databases to predict to predict the target of compounds are Swiss Target Prediction Database (http://www. swisstargetprediction.ch/) and ChemBL. However, Swiss target predictor require smiley notation of compounds in a search option. Usually, researchers could get smiley notation of compounds in various databases like PubChem (https://pubchem.ncbi.nlm.nih. gov/), ChEMBL (https://www.ebi.ac.uk/chembl/), etc. The next step is to select the name of species depending upon the requirement of the researcher [118]. The targets which are involved disease also need to be identified. The most used databases for the prediction of targets involved

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in particular disease are Human Gene Database (https://www. genecards.org/) and the DisGeNET Database (https://www. disgenet.org/) [119]. 3.2 Protein-Protein Interaction

As we know that targets are not isolated in a human body, they are interacting among each other. Therefore, it is also important to check the interactions among targets. The most popular and commonly used database to check the protein-protein interactions is STRING database which is freely available at https://string-db. org/. Further, STRING database also provide the option to pick species of interest. The network of protein-protein interactions is analyzed using a variety of topographical variables, comprising node, edge, degree, etc. Nodes may express genes, proteins, chemicals, disease manifestations, and other elements. A bridge node is a node that connects two modules, while an edge is a connection between two nodes that can reflect a relationship between two proteins, a chemical and a target, or the pathways behind a disease and the targets [114].

3.3 Analysis and Visualization of the Network

The most used software for analysis and visualization of network is Cytoscape. Various versions of Cytoscape software are available; the recent version is Cytoscape software, version 3.9.1. [120]. The targets interactions identified by STRING database is usually visualized and analyzed in Cytoscape. The activities of the network’s nodes were analyzed and visualized using network analysis tools and software [121, 122].

3.4 Functional Enrichment Analysis

Pathway analysis is carried out to obtain a better knowledge of the chemicals, proteins, or enzymes and to examine associated biological processes and how they interact with the numerous signalling pathways in our bodies. To determine the biological, molecular, and cellular processes and the linked pathways involved in the gene of our interest, the KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) pathway analysis is the best tool [123]. Furthermore, it aids in the elucidation of the mechanism behind the therapeutic effects of the compounds on the ailment of concern. The GO enrichment was largely utilized to comprehend the target’s primary mechanism, whereas the KEGG pathway evaluation was carried out to investigate the distribution of the targets in the network [124].

3.5 Validation and Confirmation of the Results

Validating the results achieved through the previous procedures is vital. The effectiveness of anticipated molecular targets may be verified using a variety of validation techniques. Although in vitro and in vivo techniques are typically thought to be the most effective, however, these techniques are time-consuming and expensive [8, 125]. Therefore, many computational approaches have been created, which may be utilized for the confirmation and reassurance

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of the results, because of the expanding use of technology in the field of research and development. One of such technique is molecular docking. Molecular docking helps to predict the energetically favored binding conformation of ligand in the active site of target [126]. Usually, researchers are identifying the possible mechanism of selected compounds against disease through identification of possible targets of compounds. However, network pharmacological approaches will not tell us about the interactions of selected compounds against selected targets. To check the interactions of compounds with target, molecular docking is best approach. Various commercial and freely software is available to perform the molecular docking [127, 128]. The well-known commercial software widely used is Maestro tool of Schrodinger, whereas the wellknown freely available is AutoDock Vina. Researchers have employed gene expression microarray data in several network pharmacological techniques as one means for confirming their findings. The activity of hundreds of genes is simultaneously measured by gene expression microarray analysis to give a complete picture of cell activities [129]. These characteristics can be used to identify cells that are actively dividing or to show how cells react to a particular medication. One of the most effective and adaptable techniques for analyzing the patterns of gene expression in several tissues or within a single tissue under various experimental settings is high-density microarray analysis. Real-time polymerase chain reaction (RT-PCR) can be used to validate the differential expression target genes discovered from the analysis of gene expression once this procedure has been successfully completed [130, 131]. The other trustworthy method for validating the results is western blotting. Western blotting is commonly used by researchers to confirm the findings of target-pathway interaction networks. The dependability and precision of Western blot analysis results provide researchers more confidence, which fosters novel medication discovery and development [132, 133].

4 Role of Network Pharmacology in the Prediction of Possible Mechanism of Neuroprotective Compounds Various studies have been conducted so far to find out the possible mechanism of compounds against neurological problems using network pharmacological approaches. Dong et al. [132] have used network pharmacological approaches to predict the possible mechanism of luteolin against stroke. The results of study have indicated the protective mechanism of luteolin in stroke through modulation of TNF signalling pathway, MAPK, NF-kappa B signalling pathway, apoptosis, and P13K-Akt signalling pathways

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Fig. 4 Possible mechanism of luteolin against ischemic stroke identified using network pharmacological approaches

[134]. The possible neuroprotective mechanism of luteolin against ischemic stroke is presented in Fig. 4. Researchers have also used network pharmacological approaches in the identification of possible mechanism of ayurvedic formulation against neurological diseases. Xu et al. (2022) have identified the possible neuroprotective mechanism of Qin-ZhiZhu-Dan (QZZD) formulation in the treatment of Alzheimer’s disease using network pharmacological approaches. They have found the regulation of TNFR1-ERK1/2-NF-Bp65 inflammatory pathways by compounds of Qin-Zhi-Zhu-Dan (QZZD) formulation. Further, they have also conducted animal experimental studies to confirm the results of network pharmacology and found neuroprotective effects through modulation of targets identified by network pharmacological approaches [135]. Red ginseng and radix ophiopogonis make up the majority of the shenmai injection (SMI), a traditional Chinese herbal medicine. According to latest study, SMI possesses antioxidant activity and can enhance myocardial microcirculation by scavenging oxygen free radicals. By reducing ROS (reactive oxygen species) generation, controlling intracellular calcium, and preventing cell apoptosis, it

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Fig. 5 Possible neuroprotective mechanism of shenmai injection identified using network pharmacological approaches

also offers neuroprotective action and safeguards against cardiac dysfunction, ischemic stroke/reperfusion injury, and cellular damage. Jing Wu et al. (2022) have used network pharmacology approach integrated with in vitro studies to predict the possible neuroprotective effects of shenmai injection through modulation of APJ/AMPK/GSK-3beta pathways [136]. The possible neuroprotective mechanism of shenmai injection is presented in Fig. 5. The neuroprotective mechanism of Gynostemma pentaphyllum was also identified against AD using network pharmacological approaches by Wang et al. in 2022. The study results have identified the neuroprotective mechanism of Gynostemma pentaphyllum through modulation of IL-1, IL-6, NOS3, PON1, or EGFR [137]. Ma et al. (2022) implemented a network pharmaceutical approach to gain insight into Coreopsis tinctoria’s neuroprotective impact and its underlying mechanism. Analysis methods such as KEGG and Gene Ontology (GO) were used to investigate possible molecular processes and pathways. Further, the results of network pharmacology are also validated using in vitro studies. The results of the study identified TNF, PTGS2, VEGFA, BCL2, HIF1A, MMP9, PIK3CG, ALDH2, AKT1, and EGFR as possible targets of Coreopsis tinctoria’s [138]. Zhang et al. (2022) have identified possible mechanism of kaempferol against ischemic stroke through modulation of JAK1/STAT3 and BDNFTrkB-PI3K/AKT signalling [139]. Hangbei Xu et al. (2022) have identified the neuroprotective mechanism of epimedium preparations against ischemic stroke through modulation of MAPK/ERK and NF-B signalling pathways [140]. Shankhpushpi (Convolvulus pluricaulis) is widely used in the management of neurological disorders. However, the exact

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mechanism is unclear so far. Researchers have also used network pharmacological approaches in the identification of possible neuroprotective mechanism of Convolvulus pluricaulis and found Convolvulus pluricaulis metabolites, i.e., scopoletin, 4-hydroxycinnamic acid, kaempferol, quercetin, and ayapanin as major neuroprotective molecules through modulation of PI3K/ Akt, the neurotrophin, and the insulin signalling pathways [141]. Network pharmacological approaches are also used to predict neuroprotective mechanism of compounds against Parkinson’s disease. Li et al. (2020) have identified the possible neuroprotective mechanism of Shaoyao Gancao decoction in the management of Parkinson’s disease. Around 48 bioactive constituents modulating 38 targets have been identified [142]. The possible neuroprotective mechanism of compounds identified using network pharmacological approaches is compiled in Table 2.

5

Current Prospectives Currently, various researchers are focusing on the identification of molecules with multimodal mechanism. The network pharmacological approaches help these researchers in a greater way. The pharmacology, computational methods, and data science knowledge are required to perform network pharmacological studies in a better way [1, 5, 148]. Therefore, these studies are usually done in teams of different expertise. The researchers are not using these techniques in the identification of possible mechanism of drugs but also to understand complex pathophysiology of diseases also. The brain is the most complex structure of the body which involves various signalling. Therefore, many therapies have been failed which were tried to develop against neurological disorders [149]. The multidisciplinary approach could help us to understand complex signalling of the brain [150]. Currently, various computational methods are being used in the field of neurology [151, 152]. Network pharmacology is also one of the computational methods. Various studies are being published using network pharmacological approaches [153].

6

Challenges Network pharmacology plays a significant role in the drug discovery and development process. However, there are certain challenges that need to be rectified in the future. First, the biggest challenge is in the selection of suitable database. Various databases are available nowadays. The selection of all available databases could be time-

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Table 2 Possible neuroprotective mechanism of compounds identified using network pharmacological approaches Serial Natural no. compounds

Disease

Mechanism/pathway associated

References

1

Luteolin

Ischemic stroke

TNF; cytokine-cytokine receptor signalling pathway

[134]

2

Qin-Zhi-Dan

Alzheimer’s disease

TNF; Aβ-associated signalling pathway; NF-κB, PI3K-Akt

[135]

3

Shenmai injection

Ischemic/ reperfusion injury

APJ, AMPK; GSK-3β

[136]

4

Gynostemma pentaphyllum

Alzheimer’s disease

HIF-1; amoebiasis; cytokine-cytokine receptor interaction

[137]

5

Coreopsis tinctoria

Neuroprotective BCL2; AKT signalling pathway action

[138]

6

Kaempferol

Ischemic stroke

BDNF-TrkB-PI3K/AKT; JAK1/STAT3 signalling pathway

[139]

7

Epimedium extract

Ischemic stroke

MAPK/ERK and NF-κB signalling pathway

[140]

8

Convolvulus pluricaulis

Dementia

PI3K-AKT; neurotrophin signalling pathway; Insulin signalling pathway

[141]

9

Paeonia Alzheimer’s lactiflora Pall disease

P13K-AKT; MAPK; neurotrophin signalling pathway

[143]

10

Curcumin

Alzheimer’s disease

RAGE mediated signalling pathway; TNF; [144] JNK; MAPK; PI3K/AKT signalling pathway

11

Cordycepin

Alzheimer’s disease

MAPK; NF-Kb signalling pathway

[145]

12

Parkinson’s Paeonia disease lactiflora Pall

Neuroactive ligand-receptor interaction, calcium signalling pathway, PI3-Akt signalling pathway, TNF signalling pathway, and apoptosis signalling pathway

[146]

13

Ginkgo biloba folium

The Akt/GSK3β pathway

[147]

Parkinson’s disease

consuming and possibly can get a large number of duplicates. Second, the used databases should be updated. Various models are also available to perform network pharmacology. The different algorithms generate different results. Further, experimental studies are also required to validate the results of network pharmacology.

Network Pharmacology for Mechanism of Neuroprotective Agents

7

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Conclusions Neurological disorders involve multiple complex signalling; therefore, network pharmacological approaches could play a significant role in the identification of possible mechanism of neuroprotective compounds. However, these techniques should be used carefully to get better results.

Acknowledgments The authors are thankful to the Vice-Chancellor, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi 110017, India, for his continuous support and motivation. Authors also acknowledge the BioRender.com for providing trial version for the creation of figures. References 1. Hopkins AL (2007) Network pharmacology. Nat Biotechnol 25:1110–1111 2. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20 3. Zhao S, Iyengar R (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol 52:505–521 4. Arrell DK, Terzic A (2010) Network systems biology for drug discovery. Clin Pharmacol Ther 88(1):120–125 5. Gowers T, Nielsen M (2009) Massively collaborative mathematics. Nature 461(7266): 879–881 6. Li S, Zhang B (2013) Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med 11(2):110–120 7. Noor F et al (2022) Network pharmacology approach for medicinal plants: review and assessment. Pharmaceuticals (Basel) 15(5): 572 8. Schmidt HHHW et al (2019) From single drug targets to synergistic network pharmacology in ischemic stroke. Proc Natl Acad Sci U S A 116(14):7129–7136 9. Zhang R et al (2019) Network pharmacology databases for traditional Chinese medicine: review and assessment. Front Pharmacol 10: 123

10. Zhang GB et al (2013) Network pharmacology: a new approach for chinese herbal medicine research. Evid Based Complement Alternat Med 2013:621423 11. Zheng S et al (2022) Application of network pharmacology in the study of the mechanism of action of traditional chinese medicine in the treatment of COVID-19. Front Pharmacol 13:926901 12. Jellinger KA (2010) Basic mechanisms of neurodegeneration: a critical update. J Cell Mol Med 14(3):457–487 13. Poornima P et al (2016) Network pharmacology of cancer: from understanding of complex interactomes to the design of multi-target specific therapeutics from nature. Pharmacol Res 111:290–302 14. Zhou Z et al (2020) Application of network pharmacology in traditional Chinese medicine research. Evid Based Complement Alternat Med 2020:1646905 15. Lee WY et al (2019) The methodological trends of traditional herbal medicine employing network pharmacology. Biomol Ther 9(8):362 16. Bagli E et al (2016) Natural compounds and neuroprotection: mechanisms of action and novel delivery systems. In Vivo 30(5): 535–547 17. Berman T, Bayati A (2018) What are neurodegenerative diseases and how do they affect the brain? Front Young Minds 6:70

174

Saima et al.

18. Tao W et al (2013) Network pharmacologybased prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol 145(1):1–10 19. Przedborski S, Vila M, Jackson-Lewis V (2003) Neurodegeneration: what is it and where are we? J Clin Invest 111(1):3–10 20. Salin´ska E, Danysz W, Łazarewicz JW (2005) The role of excitotoxicity in neurodegeneration. Folia Neuropathol 43(4):322–339 21. Gorman AM (2008) Neuronal cell death in neurodegenerative diseases: recurring themes around protein handling. J Cell Mol Med 12(6A):2263–2280 22. Singh A et al (2019) Oxidative stress: a key modulator in neurodegenerative diseases. Molecules 24(8):1583 23. Cenini G, Lloret A, Cascella R (2019) Oxidative stress in neurodegenerative diseases: from a mitochondrial point of view. Oxidative Med Cell Longev 2019:2105607 24. Liu Z et al (2017) Oxidative stress in neurodegenerative diseases: from molecular mechanisms to clinical applications. Oxidative Med Cell Longev 2017:2525967 25. Chitnis T, Weiner HL (2017) CNS inflammation and neurodegeneration. J Clin Invest 127(10):3577–3587 26. Chen WW, Zhang X, Huang WJ (2016) Role of neuroinflammation in neurodegenerative diseases (Review). Mol Med Rep 13(4): 3391–3396 27. Glass CK et al (2010) Mechanisms underlying inflammation in neurodegeneration. Cell 140(6):918–934 28. Kimura I (2006) Medical benefits of using natural compounds and their derivatives having multiple pharmacological actions. Yakugaku Zasshi 126(3):133–143 29. Mohd Sairazi NS, Sirajudeen KNS (2020) Natural products and their bioactive compounds: neuroprotective potentials against neurodegenerative diseases. Evid Based Complement Alternat Med 2020:6565396 30. Faoro F et al (2010) Neuroprotective herbs and foods from different traditional medicines and diets. Molecules 15(5):3517–3555 31. Singh H, Dhawan B (1997) Neuropsychopharmacological effects of the Ayurvedic nootropic Bacopa monniera Linn. (Brahmi). Indian J Pharmacol 29(5):359 32. Pandey A et al (2018) Multifunctional neuroprotective effect of Withanone, a compound from Withania somnifera roots in alleviating cognitive dysfunction. Cytokine 102:211– 221

33. Joseph JA et al (1999) Reversals of age-related declines in neuronal signal transduction, cognitive, and motor behavioral deficits with blueberry, spinach, or strawberry dietary supplementation. J Neurosci 19(18):8114–8121 34. Youdim KA et al (2000) Short-term dietary supplementation of blueberry polyphenolics: beneficial effects on aging brain performance and peripheral tissue function. Nutr Neurosci 3(6):383397 35. Mishra R et al (2016) Tinospora cordifolia ameliorates anxiety-like behavior and improves cognitive functions in acute sleep deprived rats. Sci Rep 6:25564 36. Gupta A et al (2013) Comparative evaluation of ethanolic extracts of Bacopa monnieri, Evolvulus alsinoides, Tinospora cordifolia and their combinations on cognitive functions in rats. Curr Aging Sci 6(3):239–243 37. Sancesario GM et al (2018) Bromelain degrades Aβ1-42 monomers and soluble aggregates: an in vitro study in cerebrospinal fluid of Alzheimer’s disease patients. Curr Alzheimer Res 15(7):628–636 38. Lee YK et al (2009) Protective effect of the ethanol extract of Magnolia officinalis and 4-O-methylhonokiol on scopolamineinduced memory impairment and the inhibition of acetylcholinesterase activity. J Nat Med 63(3):274–282 39. Krikorian R et al (2010) Blueberry supplementation improves memory in older adults. J Agric Food Chem 58(7):3996–4000 40. Cai H, Liang Q, Ge G (2016) Gypenoside attenuates β amyloid-induced inflammation in N9 microglial cells via SOCS1 signaling. Neural Plast 2016:6362707 41. Dong X et al (2014) Attenuation of Aβ25-35induced parallel autophagic and apoptotic cell death by gypenoside XVII through the estrogen receptor-dependent activation of Nrf2/ ARE pathways. Toxicol Appl Pharmacol 279(1):63–75 42. Kang TH et al (2002) Rhynchophylline and isorhynchophylline inhibit NMDA receptors expressed in Xenopus oocytes. Eur J Pharmacol 455(1):27–34 43. Fujiwara H et al (2006) Uncaria rhynchophylla, a Chinese medicinal herb, has potent antiaggregation effects on Alzheimer’s betaamyloid proteins. J Neurosci Res 84(2): 427–433 44. Shim JS et al (2009) Effects of the hook of Uncaria rhynchophylla on neurotoxicity in the 6-hydroxydopamine model of Parkinson’s disease. J Ethnopharmacol 126(2):361–365

Network Pharmacology for Mechanism of Neuroprotective Agents 45. Zheng M et al (2021) Alkaloids extracted from Uncaria rhynchophylla demonstrate neuroprotective effects in MPTP-induced experimental Parkinsonism by regulating the PI3K/Akt/mTOR signaling pathway. J Ethnopharmacol 266:113451 46. Lan YL et al (2018) Uncaria rhynchophylla ameliorates Parkinson’s disease by inhibiting HSP90 expression: insights from quantitative proteomics. Cell Physiol Biochem 47(4): 1453–1464 47. Baek S et al (2018) Smallanthus sonchifolius leaf attenuates neuroinflammation. J Exerc Nutrition Biochem 22(2):31–35 48. Zhang L et al (2019) Safflower yellow attenuates learning and memory deficits in amyloid β-induced Alzheimer’s disease rats by inhibiting neuroglia cell activation and inflammatory signaling pathways. Metab Brain Dis 34(3): 927–939 49. Ali T et al (2015) Osmotin attenuates amyloid beta-induced memory impairment, tau phosphorylation and neurodegeneration in the mouse hippocampus. Sci Rep 5:11708 50. Postu PA et al (2018) Lactuca capensis reverses memory deficits in Aβ1-42-induced an animal model of Alzheimer’s disease. J Cell Mol Med 22(1):111–122 51. Wang R, Tang XC (2005) Neuroprotective effects of huperzine A. A natural cholinesterase inhibitor for the treatment of Alzheimer’s disease. Neurosignals 14(1–2):71–82 52. Xu SS et al (1995) Efficacy of tablet huperzine-A on memory, cognition, and behavior in Alzheimer’s disease. Zhongguo Yao Li Xue Bao 16(5):391–395 53. Zhang C et al (2015) Neuroprotective effects of safranal in a rat model of traumatic injury to the spinal cord by anti-apoptotic, anti-inflammatory and edema-attenuating. Tissue Cell 47(3):291–300 54. Wang K et al (2015) Neuroprotective effects of crocin against traumatic brain injury in mice: involvement of notch signaling pathway. Neurosci Lett 591:53–58 55. Hatziagapiou K et al (2019) Antioxidant properties of Crocus sativus L. and its constituents and relevance to neurodegenerative diseases; Focus on Alzheimer’s and Parkinson’s disease. Curr Neuropharmacol 17(4): 377–402 56. Mythri RB, Bharath MM (2012) Curcumin: a potential neuroprotective agent in Parkinson’s disease. Curr Pharm Des 18(1):91–99 57. Lee WH et al (2013) Curcumin and its derivatives: their application in

175

neuropharmacology and neuroscience in the 21st century. Curr Neuropharmacol 11(4): 338–378 58. Wang J et al (2009) Curcumin attenuates 6-hydroxydopamine-induced cytotoxicity by anti-oxidation and nuclear factor-kappa B modulation in MES23.5 cells. Biochem Pharmacol 78(2):178–183 59. Rajeswari A, Sabesan M (2008) Inhibition of monoamine oxidase-B by the polyphenolic compound, curcumin and its metabolite tetrahydrocurcumin, in a model of Parkinson’s disease induced by MPTP neurodegeneration in mice. Inflammopharmacology 16(2): 96–99 60. Xie CJ et al (2018) Curcumin protects neural cells against ischemic injury in N2a cells and mouse brain with ischemic stroke. Brain Behav 8(2):e00921 61. Nash KM, Shah ZA (2015) Current perspectives on the beneficial role of Ginkgo biloba in neurological and cerebrovascular disorders. Integr Med Insights 10:1–9 62. Yoshikawa T, Naito Y, Kondo M (1999) Ginkgo biloba leaf extract: review of biological actions and clinical applications. Antioxid Redox Signal 1(4):469–480 63. Yin B et al (2014) Ginkgo biloba on focal cerebral ischemia: a systematic review and meta-analysis. Am J Chin Med 42(4): 769–783 64. Riederer P, Jellinger K (1983) Neurochemical insights into monoamine oxidase inhibitors, with special reference to deprenyl (selegiline). Acta Neurol Scand Suppl 95:43–55 65. Myllyl€a VV et al (1992) Selegiline as initial treatment in de novo parkinsonian patients. Neurology 42(2):339–343 66. Baez S et al (1997) Glutathione transferases catalyse the detoxication of oxidized metabolites (o-quinones) of catecholamines and may serve as an antioxidant system preventing degenerative cellular processes. Biochem J 324(Pt 1):25–28 67. Wu WR, Zhu XZ (1999) Involvement of monoamine oxidase inhibition in neuroprotective and neurorestorative effects of Ginkgo biloba extract against MPTP-induced nigrostriatal dopaminergic toxicity in C57 mice. Life Sci 65(2):157–164 68. Ahmad M et al (2005) Ginkgo biloba affords dose-dependent protection against 6-hydroxydopamine-induced Parkinsonism in rats: neurobehavioural, neurochemical and immunohistochemical evidence. J Neurochem 93(1):94–104

176

Saima et al.

69. Choi JY et al (2002) Prevention of nitric oxide-mediated 1-methyl-4-phenyl-1,2,3,6tetrahydropyridine-induced Parkinson’s disease in mice by tea phenolic epigallocatechin 3-gallate. Neurotoxicology 23(3):367–374 70. Singh NA, Mandal AK, Khan ZA (2016) Potential neuroprotective properties of epigallocatechin-3-gallate (EGCG). Nutr J 15(1):60 71. Orlando G et al (2019) Inhibitory effects induced by Vicia faba, Uncaria rhyncophylla, and Glycyrrhiza glabra water extracts on oxidative stress biomarkers and dopamine turnover in HypoE22 cells and isolated rat striatum challenged with 6-hydroxydopamine. Antioxidants (Basel) 8(12):602 72. More SV, Choi DK (2015) Promising cannabinoid-based therapies for Parkinson’s disease: motor symptoms to neuroprotection. Mol Neurodegener 10:17 73. Garcı´a-Arencibia M et al (2007) Evaluation of the neuroprotective effect of cannabinoids in a rat model of Parkinson’s disease: importance of antioxidant and cannabinoid receptorindependent properties. Brain Res 1134(1): 162–170 74. Zhang N et al (2018) Neuroprotective effect of arctigenin against neuroinflammation and oxidative stress induced by rotenone. RSC Adv 8(5):2280–2292 75. Chonpathompikunlert P et al (2018) The antioxidant and neurochemical activity of Apium graveolens L. and its ameliorative effect on MPTP-induced Parkinson-like symptoms in mice. BMC Complement Altern Med 18(1):103 76. Ren ZX et al (2016) Dihydromyricetin protects neurons in an MPTP-induced model of Parkinson’s disease by suppressing glycogen synthase kinase-3 beta activity. Acta Pharmacol Sin 37(10):1315–1324 77. Ojha S et al (2016) β-Caryophyllene, a phytocannabinoid attenuates oxidative stress, neuroinflammation, glial activation, and salvages dopaminergic neurons in a rat model of Parkinson disease. Mol Cell Biochem 418(1–2):59–70 78. Aruna K, Rajeswari PDR, Raja Sankar S (2017) The effect of Oxalis corniculata extract against the behavioral changes induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) in mice. J Appl Pharm Sci 7: 148–153 79. Sarbishegi M et al (2018) The neuroprotective effects of hydro-alcoholic extract of olive (Olea europaea L.) leaf on rotenone-induced

Parkinson’s disease in rat. Metab Brain Dis 33(1):79–88 80. Wang J et al (2012) Neurorescue effect of rosmarinic acid on 6-hydroxydopaminelesioned nigral dopamine neurons in rat model of Parkinson’s disease. J Mol Neurosci 47(1):113–119 81. Chang HC et al (2019) Sophora tomentosa extract prevents MPTP-induced Parkinsonism in C57BL/6 mice via the inhibition of GSK-3β phosphorylation and oxidative stress. Nutrients 11(2):252 82. Kosaraju J et al (2014) Neuroprotective effect of Tinospora cordifolia ethanol extract on 6-hydroxy dopamine induced Parkinsonism. Indian J Pharmacol 46(2):176–180 83. Alzahrani S et al (2018) Standardized Tribulus terrestris extract protects against rotenone-induced oxidative damage and nigral dopamine neuronal loss in mice. J Physiol Pharmacol 69(6):979–994 84. Bisht R et al (2017) Antioxidant-rich fraction of Urtica dioica mediated rescue of striatal mito-oxidative damage in MPTP-induced behavioral, cellular, and neurochemical alterations in rats. Mol Neurobiol 54(7): 5632–5645 85. Ibrahim F et al (2019) Neuroprotective effect of Zingiber zerumbet ethyl acetate extract in paraquat-induced Parkinsonism rats via its anti-inflammatory activity. Front Pharmacol. https://doi.org/10.3389/conf.fphar.2018. 63.00059 86. Huang KK et al (2019) Alpinia oxyphylla fruit extract ameliorates experimental autoimmune encephalomyelitis through the regulation of Th1/Th17 cells. Evid Based Complement Alternat Med 2019:6797030 87. Wang M et al (2016) Amelioration of experimental autoimmune encephalomyelitis by isogarcinol extracted from Garcinia mangostana L. mangosteen. J Agric Food Chem 64(47): 9012–9021 88. Dey A, De JN (2015) Neuroprotective therapeutics from botanicals and phytochemicals against Huntington’s disease and related neurodegenerative disorders. J Herb Med 5:1– 19. https://doi.org/10.1016/j.hermed. 2015.01.002 89. Shinomol GK (2011) Muralidhara, Bacopa monnieri modulates endogenous cytoplasmic and mitochondrial oxidative markers in prepubertal mice brain. Phytomedicine 18(4): 317–326 90. Hsieh PF et al (2011) Sesamin ameliorates oxidative stress and mortality in kainic acidinduced status epilepticus by inhibition of

Network Pharmacology for Mechanism of Neuroprotective Agents MAPK and COX-2 activation. J Neuroinflammation 8(1):57 91. Hsiao G et al (2004) A potent antioxidant, lycopene, affords neuroprotection against microglia activation and focal cerebral ischemia in rats. In Vivo 18(3):351–356 92. Choudhary S, Kumar P, Malik J (2013) Plants and phytochemicals for Huntington’s disease. Pharmacogn Rev 7(14):81–91 93. Rathore P et al (2008) Curcuma oil: reduces early accumulation of oxidative product and is anti-apoptogenic in transient focal ischemia in rat brain. Neurochem Res 33(9):1672–1682 94. Xie Y, Zhang B, Zhang Y (2014) Protective effects of Acanthopanax polysaccharides on cerebral ischemia-reperfusion injury and its mechanisms. Int J Biol Macromol 72:946– 950 95. Wang Y et al (2020) Study on the therapeutic material basis and effect of Acanthopanax senticosus (Rupr. et Maxim.) Harms leaves in the treatment of ischemic stroke by PK-PD analysis based on online microdialysis-LC-MS/MS method. Food Funct 11(3):2005–2016 96. Lee E-J et al (2005) Cinnamophilin reduces oxidative damage and protects against transient focal cerebral ischemia in mice. Free Radic Biol Med 39(4):495–510 97. Zhang Z et al (2012) Experimental evidence of Ginkgo biloba extract EGB as a neuroprotective agent in ischemia stroke rats. Brain Res Bull 87(2–3):193–198 98. Lee EJ et al (2002) Acute administration of Ginkgo biloba extract (EGb 761) affords neuroprotection against permanent and transient focal cerebral ischemia in Sprague-Dawley rats. J Neurosci Res 68(5):636–645 99. Zheng GQ et al (2011) Ginseng total saponins enhance neurogenesis after focal cerebral ischemia. J Ethnopharmacol 133(2):724–728 100. Tian J et al (2005) Neuroprotective effect of 20(S)-ginsenoside Rg3 on cerebral ischemia in rats. Neurosci Lett 374(2):92–97 101. Chen CM et al (2007) Honokiol, a neuroprotectant against mouse cerebral ischaemia, mediated by preserving Na+, K+-ATPase activity and mitochondrial functions. Basic Clin Pharmacol Toxicol 101(2):108–116 102. Maqbool S et al (2019) Neuropharmacological evaluation of anticonvulsant and neuroprotective activity of Cocculus laurifolius leaves in wistar rats. Metab Brain Dis 34(4):991–999 103. Lang XY et al (2022) Coeloglossum viride Var. Bracteatum extract attenuates MPTPinduced neurotoxicity in vivo by restoring BDNF-TrkB and FGF2-Akt signaling axis

177

and inhibiting RIP1-driven inflammation. Front Pharmacol 13:903235 104. Jawaid T et al (2018) Anticonvulsant and neuroprotective effects of methanolic extract of Cinnamomum camphora leaves in rat brain. Orient Pharm Exp Med 18:237–246 105. Aldawsari HM et al (2017) Anticonvulsant and neuroprotective activities of Phragmanthera austroarabica extract in pentylenetetrazole-kindled mice. Evid Based Complement Alternat Med 2017:5148219 106. Fachim HA et al (2015) Neuroprotective activity of parawixin 10, a compound isolated from Parawixia bistriata spider venom (Araneidae: Araneae) in rats undergoing intrahippocampal NMDA microinjection. Pharmacogn Mag 11(43):579–585 107. Fachim HA et al (2011) Neurobiological activity of parawixin 10, a novel anticonvulsant compound isolated from Parawixia bistriata spider venom (Araneidae: Araneae). Epilepsy Behav 22(2):158–164 108. Yon JM, Kim YB, Park D (2018) The ethanol fraction of white rose petal extract abrogates excitotoxicity-induced neuronal damage in vivo and in vitro through inhibition of oxidative stress and proinflammation. Nutrients 10(10):1375 109. Naderali E et al (2018) The role of rosemary extract in degeneration of hippocampal neurons induced by kainic acid in the rat: a behavioral and histochemical approach. J Integr Neurosci 17(1):19–25 110. Thangthaeng N et al (2018) Walnut extract modulates activation of microglia through alteration in intracellular calcium concentration. Nutr Res 49:88–95 111. Luo TT et al (2020) Network pharmacology in research of Chinese medicine formula: methodology, application and prospective. Chin J Integr Med 26(1):72–80 112. Niu W-H et al (2021) Network pharmacology for the identification of phytochemicals in traditional Chinese medicine for COVID-19 that may regulate interleukin-6. Biosci Rep 41(1):BSR20202583. https://doi.org/10. 1042/BSR20202583 113. Gao X et al (2021) A network pharmacology approach to estimate potential targets of the active ingredients of epimedium for alleviating mild cognitive impairment and treating Alzheimer’s disease. Evid Based Complement Alternat Med 2021:2302680 114. Zhang Y et al (2021) Network pharmacology analysis of the mechanisms of compound herba Sarcandrae (Fufang Zhongjiefeng)

178

Saima et al.

aerosol in chronic pharyngitis treatment. Drug Des Devel Ther 15:2783–2803 115. Liang X, Li H, Li S (2014) A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol BioSyst 10(5): 1014–1022 116. Daina A, Michielin O, Zoete V (2019) SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 47 (W1):W357–W364 117. Dai Y et al (2022) Network pharmacologybased identification of miRNA expression of Astragalus membranaceus in the treatment of diabetic nephropathy. Medicine (Baltimore) 101(5):e28747 118. von Mering C et al (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31(1): 258–261 119. Li X et al (2022) INPUT: an intelligent network pharmacology platform unique for traditional Chinese medicine. Comput Struct Biotechnol J 20:1345–1351 120. Shannon P et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504 121. Xie R et al (2022) Identification of core genes and pathways in melanoma metastasis via bioinformatics analysis. Int J Mol Sci 23(2):794 122. Zhang L et al (2021) Exploring the mechanisms underlying the therapeutic effect of Salvia miltiorrhiza in diabetic nephropathy using network pharmacology and molecular docking. Biosci Rep 41(6):BSR20203520 123. Aslam S et al (2021) Designing a multiepitope vaccine against Chlamydia trachomatis by employing integrated core proteomics, immuno-informatics and in silico approaches. Biology (Basel) 10(10):997 124. Tao Q et al (2020) Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19. Drug Dev Ind Pharm 46(8):1345–1353 125. Hsin KY, Ghosh S, Kitano H (2013) Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One 8(12):e83922 126. Gupta M, Sharma R, Kumar A (2020) Docking techniques in toxicology: an overview. Curr Bioinforma 15(6):600–610 127. Navyashree V, Kant K, Kumar A (2021) Natural chemical entities from Arisaema genus

might be a promising break-through against Japanese encephalitis virus infection: a molecular docking and dynamics approach. J Biomol Struct Dyn 39(4):1404–1416 128. Kant K, Lal UR, Kumar A, Ghosh M (2019) A merged molecular docking, ADME-T and dynamics approaches towards the genus of Arisaema as herpes simplex virus type 1 and type 2 inhibitors. Comput Biol Chem 78: 217–226 129. Song S et al (2022) Network pharmacology and experimental verification based research into the effect and mechanism of Aucklandiae Radix-Amomi Fructus against gastric cancer. Sci Rep 12(1):9401 130. Li Z et al (2022) Network pharmacology, molecular docking, and experimental validation to unveil the molecular targets and mechanisms of compound fuling granule to treat ovarian cancer. Oxid Med Cell Longev 2022:2896049 131. Guo Q et al (2017) Wu-Tou decoction in rheumatoid arthritis: integrating network pharmacology and in vivo pharmacological evaluation. Front Pharmacol 8:230 132. Dong R et al (2021) Exploration of the mechanism of luteolin against ischemic stroke based on network pharmacology, molecular docking and experimental verification. Bioengineered 12(2):12274–12293 133. Xu W et al (2022) Network pharmacology analysis reveals neuroprotective effects of the Qin-Zhi-Zhu-Dan Formula in Alzheimer’s disease. Front Neurosci 16:943400 134. Wu J et al (2022) The neuroprotective effect of Shenmai injection on oxidative stress injury in PC12 cells based on network pharmacology. Evid Based Complement Alternat Med 2022:6969740 135. Wang J et al (2022) Network pharmacology analysis reveals neuroprotection of Gynostemma pentaphyllum (Thunb.) Makino in Alzheimer’s disease. BMC Complement Med Ther 22(1):57 136. Ma P et al (2022) The neuroprotective effects of Coreopsis tinctoria and its mechanism: interpretation of network pharmacological and experimental data. Front Pharmacol 12: 791288 137. Zhang SS et al (2022) Network pharmacology analysis and experimental validation of kaempferol in the treatment of ischemic stroke by inhibiting apoptosis and regulating neuroinflammation involving neutrophils. Int J Mol Sci 23(20):12694 138. Xu H et al (2022) Molecular mechanism of Epimedium extract against ischemic stroke

Network Pharmacology for Mechanism of Neuroprotective Agents based on network pharmacology and experimental validation. Oxidative Med Cell Longev 2022:3858314 139. Hannan MA et al (2022) Protective mechanisms of nootropic herb Shankhpushpi (Convolvulus pluricaulis) against dementia: network pharmacology and computational approach. Evid Based Complement Alternat Med 2022:1015310 140. Li L et al (2020) A network pharmacologybased study of the molecular mechanisms of Shaoyao-Gancao decoction in treating Parkinson’s disease. Interdiscip Sci 12(2): 131–144 141. Zeng Q et al (2019) A network pharmacology approach to reveal the underlying mechanisms of Paeonia lactiflora Pall. On the treatment of Alzheimer’s disease. Evid Based Complement Alternat Med 2019:8706589 142. Wu X et al (2022) A network pharmacology approach to identify the mechanisms and molecular targets of curcumin against Alzheimer disease. Medicine (Baltimore) 101(34): e30194 143. Ma X et al (2022) Integration of network pharmacology and molecular docking to explore the molecular mechanism of Cordycepin in the treatment of Alzheimer’s disease. Front Aging Neurosci 14:1058780 144. Du W et al (2020) The underlying mechanism of Paeonia lactiflora Pall. in Parkinson’s disease based on a network pharmacology approach. Front Pharmacol 11:581984 145. Yan YC et al (2023) Uncovering the pharmacology of Ginkgo biloba folium in the cell-

179

type-specific targets of Parkinson’s disease. Front Pharmacol 13:1007556 146. Rojas P et al (2012) Ginkgo biloba extract (EGb 761) modulates the expression of dopamine-related genes in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced Parkinsonism in mice. Neuroscience 223:246– 257 147. Castillo X et al (2019) Re-thinking the etiological framework of neurodegeneration. Front Neurosci 13:728 148. Gitler AD, Dhillon P, Shorter J (2017) Neurodegenerative disease: models, mechanisms, and a new hope. Dis Model Mech 10(5): 499–502 149. Graw S et al (2021) Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics 17(2):170–185 150. Fisch KM et al (2015) Omics Pipe: a community-based framework for reproducible multi-omics data analysis. Bioinformatics 31(11):1724–1728 151. Biswas N, Chakrabarti S (2020) Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. Front Oncol 10:588221 152. Sun YV, Hu YJ (2016) Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv Genet 93:147–190 153. Zhu J et al (2023) Research trends and hotspots of neurodegenerative diseases employing network pharmacology: a bibliometric analysis. Front Pharmacol 13:1109400

Chapter 14 Role of Serotonergic System in Regulating Brain Tumor-Associated Neuroinflammatory Responses Surojit Karmakar and Girdhari Lal Abstract Serotonin signaling regulates wide arrays of both neural and extra-neural functions. Serotonin is also found to affect cancer progression directly as well as indirectly by modulating the immune cells. In the brain, serotonin plays a key role in regulating various functions; disturbance of the normal activities of serotonin leads to various mental illnesses, including the neuroinflammatory response in the central nervous system (CNS). The neuroinflammatory response can be initiated in various psychological illnesses and brain cancer. Serotonergic signaling can impact the functions of both glial as well as the immune cells. It can also affect the tumor immune microenvironment and the inflammatory response associated with brain cancers. Apart from this, many drugs used for treatment of psychological illness are known to modulate serotonergic system and can cross the blood-brain barrier. Understanding the role of serotonergic pathways in regulating neuroinflammatory response and brain cancer will provide a new paradigm in modulating the serotonergic components in treating brain cancer and associated inflammation-induced brain damages. Key words Serotonin, Cancer, Immunity, Glioma, Glioblastoma, Neuroinflammation, Cytokines

Abbreviations 5-HIAA 5-HT BBB cAMP CNS CTLs DAG DC GBM HTR IL IP3 MAO MDSCs

5-HydroxyIndole acetic acid 5-Hydroxytryptamine Blood-brain barrier cyclic Adenosine mono phosphate Central nervous system Cytotoxic T lymphocytes Diacylglycerol Dendritic cells Glioblastoma Serotonin receptors Interleukins Inositol-3 phosphate Monoamine oxidase Monocyte-derived suppressor cells

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_14, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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MMPs NF-κβ NK cell PI3K PKA rDCs SERT SSRI TAM TFIID TGM2 TPH Tregs

1

Matrix metalloproteinases Nuclear factor-κβ Natural killer cells Phosphoionositide-3 kinase Protein kinase A Regulatory DCs Serotonin transporter Serotonin reuptake inhibitors Tumor-associated macrophages Transcription factor IID Transglutaminase 2 Tryptophan hydroxylase Regulatory T cells

Introduction The human brain is a complex organ that regulates many physiological processes, including cognition, emotion, behavior, and homeostasis [1]. To accomplish these tasks, the brain relies on the vast network of neurons and neurotransmitters that communicate among themselves and distal organs to control various functions in the body [2]. One such neurotransmitter is serotonin, which regulates numerous physiological processes, including mood, appetite, sleep, sexual behaviors, and cognitive processes [3]. The enzyme tryptophan hydroxylase synthesizes serotonin from the amino acid tryptophan [4]. Almost 90% of total serotonin within the body is produced in the gut, which controls various functions ranging from controlling gut motility to regulating the metabolic processes within the body [5, 6]. The tryptophan hydroxylase II enzyme produces about 5% of the body’s total serotonin in the brain. It is stored in the vesicles within the serotonergic neurons located in the raphe nuclei of the brainstem [7]. From there, serotonergic neurons project to various brain and periphery regions, where they release serotonin and modulate the activity of neurons or other cells [7, 8]. Serotonergic pathways play a crucial role in the central nervous system (CNS) and have been implicated in the pathophysiology of various neurological disorders, including neuroinflammation and brain cancer [9–11]. Neuroinflammation is the process that occurs in the CNS in response to injury or infections that damage the blood-brain barrier (BBB), and it involves the activation of glial cells, including microglia, astrocytes, which release pro-inflammatory cytokines, chemokines, and other inflammatory mediators [12]. Neuroinflammation is a common feature of various neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and traumatic brain injury [13–

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15]. Neuroinflammation is also associated with glioma, the major form of brain cancer [16, 17]. In recent years, studies have shown that serotonin can modulate the activity of the glial cells and regulate the neuroinflammation [18, 19]. Serotonin also performs wide arrays of function that directly or indirectly modulates different components of the immune system. The immunomodulatory role of serotonin is extended in both the innate and adaptive immune systems. Its diverse role in both the immune system and cancer makes it an interesting molecule to study from the perspective of the immune regulation in cancer. Many therapeutic interventions regulate the serotonin levels within the body, such as serotonin reuptake inhibitors (SRRI) [20, 21] and serotonin receptor modulators such as cariprazine, which are used in treating psychological illnesses such as depression [22], schizophrenia, and bipolar syndrome of multiple sclerosis [23–25]. These drugs directly modulate the serotonergic pathways and can easily pass through the BBB [26]. These drugs also have the potential to affect the neuroinflammatory responses and brain-associated cancer [27]. In the following sections, we have discussed how serotonin signaling modulates immune responses and how it can directly or indirectly impact brain cancer progression and outcome.

2

Serotonergic System Serotonin synthesized in the CNS performs numerous neuropsychotic functions, including regulating appetite, mood, pain, and sexual behavior [3]. However, the extra-neural synthesized serotonin from the gut and the different cells of the immune system, including mast cells, T cells, and dendritic cells (DCs), performs more widespread actions in the body such as regulating angiogenesis and vasoconstriction [5], controlling bone density [6], maintaining various metabolic processes to control blood glucose levels and obesity [28], modulating gastrointestinal motility and gut microbiome balance, etc. [29]. Apart from this, serotonin was also found to modulate the immune response to different pathogens and diseases such as cancer [30]. Serotonin signals through 15 different serotonin receptors (HTRs) which are widely distributed in the different organs and tissues in the body.

2.1 Serotonin Synthesis and Degrading Machineries

Serotonin is synthesized from the essential amino acid L-tryptophan by the action of the enzyme tryptophan hydroxylase (TPH) (Fig. 1). TPH has two isoforms TPH1 and TPH2. The TPH2 is only confined within the CNS, whereas TPH1 is distributed widely in the periphery [4]. Almost 95% of total serotonin in the body is produced from the enterochromaffin cells of the gut, and around 5% of total serotonin is synthesized within the

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Fig. 1 Serotonergic system and its functions in the body. Serotonin is synthesized within the serotonergic neurons in the brainstem by TPH2 enzyme and in the enterochromaffin cells in the gut epithelia by TPH1 enzyme. The brain’s serotonergic system regulates appetite, mood, pain, sexual behavior, neurons, glial cell maintenance, and neuroinflammation. In contrast, the peripheral serotonin synthesized in the gut regulates gut motility, bone density, vasoconstriction, wound healing, metabolic regulation, and controlling immune functions and inflammation. Both the serotonergic system of the brain and gut interact with each other to control these wide arrays of functions. (The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license)

serotonergic neurons originating at the raphe nuclei of the brainstem. Serotonin is taken in by the resident cells such as platelets, mast cells, or DCs using serotonin transporter (SERT) on their surface. These cells release serotonin elsewhere in the body during platelet rupture due to injuries, inflammation, tumor, or during the IgE crosslinking on the mast cells [31–33]. The existence of the serotonergic system and its functions in both the CNS and periphery are summarized in Fig. 1. The concentration of the stored serotonin in these cells is about 65 nmol/mL, and the level of free serotonin in the blood ranges from 0.7 to 2.5 μM which can increase up to the millimolar range during discharge of the serotonergic neurons or during in various pathological conditions including cancer [34, 35]. After its function, serotonin is either taken up by the presynaptic neurons or different blood cell types in the vicinity through SERT or degraded by the enzyme monoamine oxidase-A (MAO-A). After the degradation of the serotonin by MAO-A, 5-hydroxyindoleacetic acid (5-HIAA) and melatonin are produced [34]. The serotonergic pathway is functional within many cell types in the brain, gut, and immune system.

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2.2 Serotonin Receptors and Signaling Mechanisms

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Serotonin responds through a wide repertoire of receptors and serotonin transporters (SERT). To date, scientists have identified 15 different subtypes of serotonin receptors. They are classified into seven major types ranging from HTR1 to HTR7. Most of these receptors, except HTR3, belong to the G protein-coupled receptor family (GPCRs). HTR1 is again divided into six subtypes, such as HTR1A–HTR1F. These receptors coupled to intracellular G protein Gαi/o, and activating these receptors inhibits adenylyl cyclase activity leading to downregulation of intracellular protein kinase A (PKA)/cyclic adenosine monophosphate (cAMP) pathway [36]. HTR1 signaling also induces NF-κβ signaling through ERK phosphorylation in a PI3K/Akt-dependent manner [37]. The HTR2 is subdivided into three subtypes, namely, HTR2A–HTR2C. These subtypes are coupled with the intracellular stimulatory G protein Gq/11. The HTR2 signaling mostly stimulates intracellular calcium release by IP3 via activating phospholipase C [38]. Serotonin is known to exert its mitogenic potential through HTR2 subtypes via the mitogen-activated protein kinase (MAPK) pathway [38]. HTR2 activation also stimulates the protein kinase C pathway via diacylglycerol (DAG) through the phospholipase C pathway. This signaling also activates the PKA/cAMP pathway and performs many functions within the cell via a phosphorylating variety of target proteins [39]. HTR3 is a ligand-gated nonspecific cation channel, and it is subdivided into HTR3A and HTR3B. When it binds to its ligand serotonin, it opens and allows the passage of cations like Na+, K+, Ca2+, and Mg2+ within the cells. This causes the cell to depolarize. In addition to its direct effects on ion channels [36], HTR3 receptor activates several intracellular signaling pathways [40]. For example, it stimulates the cAMP formation and activates protein kinase A. It also stimulates intracellular calcium signaling pathways via the activation of protein kinase C. The signaling through the HTR3 subtype is much more rapid than other subtypes. HTR4/6/7 is also coupled with intracellular stimulatory G protein Gs. Upon activation, it also triggers signaling pathways such as intracellular Ca2+ signaling via phospholipase C, PKA/cAMP molecules through adenylyl cyclase, and protein kinase C pathway [39]. Like HTR1, HTR5 receptor subtypes such as HTR5A and HTR5B are also coupled with inhibitory G protein Gαi/o [36]. The detailed signaling mechanisms of HTR5 are less understood. Serotonin transporters (SERT) are responsible for the reuptake of serotonin from the synaptic cleft back into the cells, and this is essential for serotonin storage and transport. Serotonin that enters through the SERT within the cells can also participate in direct signaling, where it gets incorporated into the glutamine residue of the histone H3 via transglutaminase 2 (TGM2) and helps in the recruitment of the transcription factor II D (TFIID) and initiates the transcription of many essential genes [41]. This

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widespread activation of the signaling pathways generates a pleiotropic effect within the body and controls various functions both in CNS and the periphery.

3

Role of Serotonergic System in the CNS The serotonergic system in the CNS is a complex network of neurons that use serotonin to transmit the signal [8]. Several brain regions are particularly rich in serotonergic neurons, including raphe nuclei in the brainstem, which are the primary source of serotonin in the brain. These neurons project into different brain regions, including the cortex, limbic system, and basal ganglia. The serotonergic system in the brain is involved in many crucial functions, such as regulation of mood, sleep, appetite, and weight, modulation of pain perception, cognition, and learning. It also maintains various autonomic functions in the body through different receptors [7]. Apart from this, peripheral serotonin carried within mast cells is also known to modulate hippocampaldependent behavior and neurogenesis [42]. The absence of mast cells in the brain is also associated with anxiety-like behavior [43]. Taken together, serotonin is an important neurotransmitter that controls various functions with the CNS, signaling through its different receptors. The expressions of the serotonin receptors are widespread, and they control various functions within the brain, which also overlap with each other sometimes. In the following sections, we have discussed the distribution and the functions of the serotonin receptors in the CNS. HTR1A is widely distributed in the brain and regulates anxiety, depression, stress, and aggression. As this signaling leads to a decrease in intracellular cAMP levels, it reduces neuronal activity and decreases the release of neurotransmitters like dopamine and norepinephrine [44–46]. HTR1B is also primarily found in the brain and regulates feeding behavior, appetite, and drug addiction. It can have excitatory and inhibitory effects on neurotransmitter release depending on the localization in the brain [47–49]. HTR1D is primarily found in the brainstem and regulates pain perception, nausea, and cardiovascular functions. HTR1D agonists have been studied as potential therapeutic targets for migraine headaches and hypertension [50–53]. HTR1E is widely present in the brain and regulates sleep and wakefulness [54]. The HTR1F is primarily localized in the brain, but its specific role is not well defined [55]. The presence of HTR2 receptors is more widespread in the CNS and periphery. Studies have shown that HTR2A subtype plays a role in regulating mood, emotion, and cognitive functions such as attention, learning and memory, and sleep-wakefulness [56–58]. Activation of these receptors has improved cognitive performance in the animal model [59]. Scientists have also shown that activating these

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receptors can promote wakefulness, whereas blocking them can induce sleep [60, 61]. This receptor subtype is also associated with depression, anxiety, and schizophrenia. HTR2C receptors have been shown to regulate sensory perception, and activating these receptors can alter visual, auditory, and pain perception [62–64]. The HTR3 are directly gate ion channels in response to the ligand binding, leading to changes in membrane potential and neuronal excitability [65]. Upon serotonin binding, it allows passage of Na+ and Ca2+ into the cell and depolarizes membrane potential, releasing neurotransmitters and propagating signals through the nervous system [66, 67]. The HTR4 receptor subtype is expressed in the brain and gastrointestinal tract. It regulates mood, cognition, and gastrointestinal functions [68, 69]. It has been shown that HTR5 receptor regulates anxiety and stress responses and modulates synaptic transmission and plasticity in the brain [70]. HTR5 receptor activation can increase neural excitability and potentiate NMDA receptor activity in some brain regions [71]. SERTs are important for maintaining the homeostasis of serotonin within the body [72]. SERTs play an essential role in regulating the concentration of free serotonin in the synaptic cleft as the unbound serotonin from the synapse is taken up into the presynaptic serotonergic neurons through SERT [73]. SERTs are also the primary target of antidepressant drugs like selective serotonin reuptake inhibitors (SSRI) such as sertraline and fluoxetine [74]. In the CNS, many brain-related illnesses, such as depression, anxiety, neurodegenerative disorders, autoimmune conditions, and cancer, are associated with the acute or chronic inflammation of the brain. Serotonin signaling is known to regulate the outcome of many of these illnesses directly or indirectly by modulating immune response and inflammation. In the next sections, we discussed how serotonin affects the immune response to inflammation and how it impacts the response to brain-related cancer. 3.1 Role of Serotonin in Immune Response and Inflammation

Emerging evidence suggests the serotonergic system’s involvement in immunity, interorgan communication, and inflammation [75– 77]. There are pieces of evidence suggesting that various immune system components affect the serotonergic system and indirectly impact many physiological processes. For example, it has been shown that the alarmin cytokine IL-33 is sensed by the enterochromaffin cells of the gut (the main serotonin-producing cells of the body) and induces serotonin secretion [78]. The increased serotonin levels can initiate neuro-immune interaction in the gut. In this section, we have discussed how serotonergic signaling impacts the immune system and vice versa and how this interaction impacts neuro-inflammation and associated processes. The expression of the serotonergic system in the various innate and adaptive immune cells and their respective functions are summarized in Table 1.

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Table 1 The expression and functions of the serotonergic system in the immune cells Immune cells

MAO- SE RT Functions TPH1 A

Serotonin receptors

References

Innate immune cells -

+

+

1. Promotes M2 macrophage polarization and inhibits M1 macrophage polarization 2. Reduced nitric oxide synthase (NOS) production. 3. Reduce expression of the type I interferon genes

DCs

Immature: + HTR1A, HTR1E, HTR2A, HTR7 Mature: HTR2A, HTR4, HTR7

+

+

[82–85] 1. Promotes maturation of CD1a+ monocyte to DCs 2. Reduces production and secretion of pro-inflammatory cytokines 3. Suppress DC-mediated T-cell activation 4. Reduces antigen presentation capacity.

Monocytes

HTR1, HTR2, HTR7

-

-

-

1. Reduces cytokine secretion [82, 86, following lipopolysaccharide (LPS) 87] stimulation 2. Promote differentiation of the monocytes into regulatory DCs

Eosinophils

HTR1A, HTR1B, HTR1E, HTR2A

-

-

Affects recruitment, airway hyper responsiveness, and inflammation in allergic asthma reactions

[88–90]

Mast cells

HTR2A

+

-

+

Enhances adhesion and migration

[91, 92]

NK cells

HTR2B, HTR2C

-

-

-

Promotes NK cell cytotoxicity in the [93–97] presence of autologous monocytes

Macrophages HTR2B, HTR7

[35, 79– 81]

Adaptive immune cells T cells

HTR2, HTR3, HTR7

+

+

+

1. Provides accessory signals to T-cell [98, 99] activations 2. Suppress T cell’s ability to elicit the delayed hypersensitivity reaction

B cells

HTR3A

-

-

+

Affects B cell development.

[100, 101]

Effect on the innate immune system: Most of the serotonin is synthesized in the gut, and various immune cells such as platelets, DCs, macrophages, monocytes, and mast cells take up the serotonin and circulate it throughout the body [4]. However, there is a disparity in the serotonin storage capacity of various immune cells between humans and rodents. Rodent’s mast cells have a higher capacity to store serotonin. In contrast, human mast cells have a

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higher capacity to synthesize serotonin owing to higher expression of TPH1, but they sequester less intracellular serotonin [91, 102, 103]. This stored serotonin within these cells is released at various parts of the body during injuries, inflammation, or in various pathological conditions. This distal release of serotonin by these cells affects the functions of various innate and adaptive immune cells in the periphery [31]. Neutrophils are the major type of innate immune cells that reach the site of inflammation early and initiate the inflammatory response. In mice, serotonin abrogates the migration of neutrophils to the site of the acute inflammation, and its absence helps to improve survival after the LPS-induced endotoxic shock in mice [104]. Serotonin has no effect on the cell adhesion and extravasation of the neutrophils at the inflammatory sites [105]. Eosinophils also express several types of serotonin receptors [106], and HTR2A signaling affects its recruitment, airway hyperresponsiveness, and inflammation in allergic asthma reactions [88, 89]. In basophils or mast cells, the effects of serotonin vary among mice and humans. In rodents, mast cells are the major producer of serotonin, whereas, in humans, they are present in low concentrations. But the release of serotonin is enhanced in human mast cells during carcinoid tumors or inflammation [91, 102, 103]. But mast cells from both human and mice can synthesize serotonin [90, 107]. Serotonin is also known to enhance the adhesion and migration of mast cells in rodents and humans via HTR2A signaling [92]. Serotonin signaling through the 5HTR2B receptor promotes in vitro polarization of M2 macrophages [108]. HTR2B signaling stimulates transcription of M2-specific genes such as SERINB2, COL23AI, THBS1, and STAB1 and reduces the expression of M1-specific genes such as ALDH1, CD1B, and matrix metalloproteinase 12 (MMP12) via phosphorylation of ERK1/2. Serotonin through HTR7 receptor promotes the secretion of cytokines such as TNF-α and IL-2 from monocyte-derived M2 macrophages following lipopolysaccharide (LPS) stimulation via NF-κβ signaling [79, 108]. Serotonin supports the expression of TGF-β and abrogates expression of type I IFN-dependent genes (IDO-I, RSAP2, IL-27, CXCL10, CXCL11, and IFIT2) in macrophages via PKA signaling. Serotonin and its metabolites, such as melatonin and N-acetyl serotonin, hinder nitric oxide production in macrophages through their ROS scavenging activities [79]. HTR7 signaling also gives macrophages a pro-fibrogenic anti-inflammatory phenotype and affects macrophage infiltration in skin fibrotic lesions in mice [35]. Concurrently, a recent finding states that serotonindegrading enzyme MAO-A promotes tumor-associated macrophage (TAM) infiltration in the tumor, and inhibition of MAO-A in TAMs reprograms it and promotes anti-tumor response [109].

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Serotonin released from the platelets during inflammation and allergic response enhances the costimulatory molecule expression and cytokine secretion from monocyte following LPS treatment. It also suppresses apoptosis in those cells via HTR1/HTR7 signaling [86]. Serotonin is known to suppress the TNF-α secretion from activated human monocytes via HTR1/2 signaling [87] and promotes the differentiation of monocytes into DCs that express regulatory cytokines like IL-10. These DCs show an impair capacity to stimulate allogeneic T cells [82]. Serotonin signaling has a widespread effect on the dendritic cells (DCs) [110, 111]. DCs express many serotonin receptor subtypes, and they also can synthesize, transport, and degrade serotonin. DCs can take up serotonin through SERT and release it at the immunological synapses with T cells and activates T cells via HTR1 signaling. Serotonin signaling affects maturation of DCs, cytokine production, and T-cell activation potential of DCs [83, 84]. HTR2B signaling promotes the maturation of immature CD1a+ human monocyte upon TLR3 activation [83] by increasing the expression of activation markers (CD80, CD83, and CD86) [83]. Serotonin signaling is known to affect the maturation and migratory properties of the bone marrow-derived DCs (BMDC) via HTR7-mediated activation of a small GTPase Cdc45 [85]. Serotonin could affect the migration and cytokine secretion of the DCs through PKA/cAMP signaling [84]. Serotonin signaling reduces the expression of IL-12 and TNF-α secretion from mature DCs and reduces the DC-mediated IFN-γ+Th1 and IL-17+Th17 polarization and promotes the IL-4+ Th2 polarization [84]. Serotonin signaling through HTR1 and HTR7 is known to reduce the antigen presentation capacity of the monocyte-derived DCs [82]. Overall, serotonin signaling promotes the development of regulatory DC subtypes that negatively impact T-cell activation and their response to diseases. NK cells are one of the most potent arms of the innate immune system that plays a key role against viral infections and tumors [112]. The expression of HTR2C has been observed in the NK cells in Alzheimer’s patient [113]. The SSRI treatment in major depressive disorder patients also affects the number and the cytotoxicity of NK cells in blood [114, 115]. In the 1990s, some work suggested that serotonin affects cytotoxicity and cytokine secretion of NK cells in the presence of autologous monocytes via HTR2 and HTR1 signaling [93, 94]. But there is no direct evidence that indicate the effect of this receptor signaling on NK cells. Serotonin is also known to protect NK cells from oxidative stress-induced damage and apoptosis induced by monocytes [116]. Based on the evidence, it can be assumed that serotonin signaling can directly or indirectly affect the functions of the NK cells.

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Effect on the adaptive immune system: The adaptive arm of the immune system eliminates pathogens and remembers the pathogen for future challenges. When they encounter the pathogens again, it can be reactivated to mount more efficient immune response. T and B cells are the two main pillars of the adaptive immune system. T cells are responsible for mounting the cellmediated immunity where they are activated by the antigenpresenting cells (APCs) of the innate arms, such as DCs and macrophages, or can be activated by B in a cell-dependent manner [117]. At the same time, B cells respond through a humoral response by secreting antigen-specific antibodies that either neutralize the antigen or facilitate their phagocytosis or cell-mediated killing [118–121]. T cells are known to express many serotonin receptors and serotogenic pathway molecules such as TPH1 and MAO-A [122, 123], and their expression is higher in CD8 CTLs than in the CD4 T-helper cells [123, 124]. These T cells can also respond to serotonin in an autocrine manner [123]. Serotonin signaling through HTR3 activates T cells and promotes their proliferation via increasing intracellular Na+ concentration [125]. Serotonin signaling through HTR7 provides accessory signals to the TCRs to activate T cells via the NF-κβ signaling pathway [98]. Through HTR2 signaling, serotonin also suppresses the T cell’s ability to elicit a delayed hypersensitivity reaction [99]. A recent report suggests inhibitors of MAO-A can enhance CD8 T-cell response against tumor and can enhance the efficacy of anti-PD1 immune checkpoint blockers [126]. Though there is truly little evidence regarding the role of serotonin on the B-cell response, it is observed that the B-cell number is increased within major depressive disorder patients following SSRI treatment [127]. Serotonin may also affect B-cell development via HTR3A signaling, but the detailed mechanisms are unclear [100, 102]. Together, it suggests that serotonin can generate an antiinflammatory microenvironment in the tissue which may be essential for controlling inflammation and promoting cancer. In the following sections, we discussed how this immune modulation affects inflammatory responses in the CNS and brain-associated cancers. 3.2 Role of Serotonin in the CNS Inflammatory Response

In the 1960s, the first connection between the inflammatory process and platelet-derived serotonin was observed [128]. Platelets act as a carrier of serotonin in the body and ensure the directed release of serotonin in the periphery in different conditions, such as thrombi associated with vessel injuries and inflammatory processes or tumors. In the tissue microenvironment, several factors such as platelet-activating factors, complement anaphylatoxin C5a and IgE-containing immune complexes, and other factors such as bacteria, parasites, and platelet–endothelial interactions activate platelets and release intracellular vesicular stored contents, including

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serotonin [129–131]. Serotonin is known to activate vascular smooth muscle cells to release pro-inflammatory cytokines such as IL-6 [132]. In major depressive disorders, patients have higher levels of the pro-inflammatory cytokines (IL-2, IL-12, and TNF-α) and monocyte chemoattractant protein-1 (MCP-1) and lower levels of anti-inflammatory cytokines such as IL-4 and TGF-β [27]. This condition can be reversed following antidepressant SSRI treatment [27]. Neuroinflammation refers to the inflammation of the nervous system, caused by numerous factors including injury, infection, or autoimmune disorders. This inflammation can damage the nervous system and contribute to the development of a range of neurological and psychiatric illnesses, including multiple sclerosis, Alzheimer’s disease, and depression. Serotonin levels in the brain are reduced in neuroinflammation after traumatic brain injuries [133]. Platelet-derived serotonin inhibits hemorrhage and increases neuronal survival and plasticity after traumatic brain injury [133]. In anxiety, the catabolites of the kynurenine pathways of tryptophan metabolism are imbalanced by stress or inflammation and reduce serotonin and melatonin levels in the brain leading to aggravation of anxiety reactions [134]. It has been observed that pro-inflammatory cytokines in the brain induce indoamine2,3deoxygenase (IDO) and subsequently reduce serotonin synthesis [135, 136]. Serotonin signaling through HTR2B in microglia prevents its over activity and limits neuroinflammation [137]. Signaling through HTR1 and HTR2 subtypes also attenuates inducible nitric oxide synthase (iNOS) and pro-inflammatory cytokines such as TNF-α and IL-1β from microglia and astrocyte via NF-κβ and p38 MAPK pathways [138]. Moreover, different evidence suggests reduced serotonin levels have been associated with exaggerated responses in disease-induced neuroinflammatory reactions. 3.3 Role of Serotonin in Brain Cancer

The serotoninergic system is expressed on various cancer cell lines and tumors [30]. The serotonin receptors are overexpressed in many forms of cancer tissues compared to their normal counterparts. Many cancer cell lines can also synthesize serotonin due to the expression of the TPH1. Expression of the TPH1 in cancer cell lines is associated with the growth and proliferation in hepatocellular carcinoma [139, 140], breast cancer [141–143], and prostate cancer [144]. Serotonin signaling through different receptor subtypes enhanced the proliferation and reduced apoptosis of hepatocellular [38, 145], prostate [37, 144, 146], colorectal cancer, and cholangiocarcinoma. In the case of lung cancer, serotonin signaling promotes tumor growth by modulating CD8/CD4 T-cell balance within the tumor [147]. Though the role of serotonin in different cancer is established, the effect of serotonin in glioma and brain-

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associated cancer is more relevant. Serotonin is known to perform a mitogenic activity and promote the growth and maintains the functions and viability of glial cells. But how serotonin signaling affects the glial cells during tumorigenesis and cancer progression remains an active area of research. Glioblastoma patients suffer the highest incidences of clinical depression of all cancer patients. Different glioblastoma cell lines such as U-373 MG, U-138MG, U-87 MG, DBTRG-05MG, T98G, H4, CCF-STTG1, and Hs 683 express serotonin receptor HTR7 [148]. Glioma tissues express HTR5A, but its expression is lower in low-grade gliomas compared to high-grade ones [149]. A retrospective study showed that the treatment of psychological illness in glioblastoma patients with SSRI does not affect the overall survival of patients [150]. A recent study demonstrates that SSRI can increase the intracellular Ca2+ that causes mitochondrial damage in astrocytes and cancer cell lines and promotes cell death [151]. The role of brain penetrant antidepressant SSRI, fluoxetine, has been studied in the GBM progression. It was reported that fluoxetine can interact with GBM via AMPA receptor and triggers cell death in mice [152]. A similar study implies that another SSRI, escitalopram, inhibits the proliferation of the xenografted GBM in mice model [153]. Fluoxetine alone or combined with temozolomide, a standard of care in GBM, significantly enhanced the killing of GBM cells in mice [154]. Fluoxetine blocks serotonin reuptake in the nerve endings, enhancing free serotonin levels in the microenvironment. On the contrary, it has been seen that excess TPH1 expression is associated with glioma progression and reduced survival in patients. Elevated serotonin levels with enhanced TPH1 in the glioma promote cell proliferation, invasive migration, and drug resistance of the glioma cells through the NF-κβ signaling pathway and regulate the expression of the L1-cell adhesion molecule (L1-CAM) [155]. Serotonin receptor HTR7 is overexpressed in glioblastoma, and it increases proliferation and reduces apoptosis via increased ERK1/2 activation, enhanced IL-6 synthesis, and increased STAT3 signaling pathway [156]. HTR7 receptor also promotes IL-6 secretion by the U-373MG astrocytoma through p38 MAPK and protein kinase C pathways. This may lead to the development of a chronic pro-inflammatory tumor microenvironment which may promote the progression and metastasis of this tumor [157]. Concurrently, stimulation of HTR5A by valerianic acid inhibits growth, EMT, and metastasis of glioblastoma cells both in vitro and in vivo via increased intracellular ROS and AMPK signaling pathways [149]. However, the role of serotonin signaling in the immune regulation of the glioma microenvironment and associated neuroinflammation is not noticeably clear.

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3.4 Role of Serotonin Signaling in GliomaAssociated Neuroinflammation

The brain is compartmentalized and guarded from the peripheral circulations through the BBB [158, 159]. BBB protects the brain from harmful toxic materials, critically regulates the transport the essential nutrients, and maintains homeostasis in the brain [159, 160]. Through this, BBB can be compromised in a variety of conditions which allow the entry of various immune cells within the brain and cause inflammation. In glioma, the BBB also become compromised, resulting in infiltration of both pro- and antiinflammatory immune cells and resulting in neuroinflammation [161].

3.5 Neuroinflammation Associated with the Glioma Microenvironment

While BBB supports effective immune surveillance of the brain, disruptive changes in the BBB cause excessive immune infiltration resulting in exaggerated inflammatory responses in the brain [162]. In tumor-associated inflamed conditions, cytokines such as TNF-α and IL-1β produced by tumor-associated macrophages affect the functions of other immune and stromal cells in the tumor microenvironment [163–165]. The release of regulatory cytokines such as TGF-β and molecules like matrix metalloproteinases can control the inflammatory states within the brain and modifies the extracellular matrix (ECM) and the basal lamina of the BBB, thereby promoting the migration and metastasis of the cancer cells [162, 164, 166–168]. The microenvironment of the glioma is quite heterogeneous and controls the extent of the neuroinflammation in the brain. Like many types of cancer, the glioma microenvironment comprises many immunosuppressive molecules expressed and secreted by the regulatory immune cells like TAMs, Tregs, monocyte-derived suppressor cells (MDSCs), etc. [169–172]. This immunosuppressive milieu not only affects the anti-tumor activities of the effector and helper T cells but also promotes the growth and aggressiveness of the tumor [173]. The myeloid cells in the glioma are comprised of either macrophages, MDSCs, or microglia. Unlike macrophages and MDSCs, microglia are brain-resident glial cells that do not get recruited following the disruption of the BBB. The macrophages and the MDSCs get recruited into the tumor microenvironment in the brain by chemotactic molecules such as CCL2, CX3CL1, colony-stimulating factor (CSF-1), stromal cell-derived factor-1 (SDF-1), granulocyte/macrophage-colony-stimulating factor (GM-CSF), and lysine oxidase (LOX) following disruption of the BBB [174–177]. Pro-tumorigenic TAMs release many immuneregulatory molecules in the microenvironment, such as TGF-β, and MMPs [178]. Cytokines such as IL-6, IL-10, and IL-1β subsequently suppress anti-tumor T-cell response and promote the progression of the tumor [176, 178]. MDSCs are a heterogeneous group of suppressive immune cell populations derived from either monocyte (M-MDSCs) or granulocytes (PMN-MDSCs) [179]. They are also recruited into the tumor by the tumor-derived

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chemokines (CCL2, CXCL8, SDF-1, and CXCL2) and expanded in the microenvironment by various cytokines such as IL-6, PGE2, IL-10, VEGF, and GM-CSF [180, 181]. T cells are the major tumor-regulating cellular populations categorized into cytotoxic CD8+ and helped CD4+ T cells. Cytotoxic CD8 T lymphocytes (CTLs) perform direct killing of the cancer cells via the release of perforin and granzyme B or by promoting apoptosis of the cancer cells via Fas-FasL and TRAILTRAIL-ligand interactions, and it is associated with better survival in glioma patients [182, 183]. However, in glioma, most CTLs become exhausted due to chronic antigenic exposure or interaction of many immune checkpoints, such as programmed cell death protein-1 (PD-1), T-cell immunoglobulin and mucin domaincontaining protein 3 (TIM3), and lymphocyte activation gene-3 (LAG-3) on the T cells with their ligands [184, 185]. These exhausted and hypo-responsive CTLs are less efficient in cancerkilling and release little pro-inflammatory cytokines such as IFN-γ and TNF-α in the microenvironment [184]. On the other hand, CD4+ T cells are the helper T-cell populations that regulate the functions of the CTLs by releasing cytokines such as IFN-γ, TNF-α, IL-10, and TGF-β [186]. In the glioma microenvironment, the regulatory subsets of the CD4 T cells are greatly enhanced that they suppress the cytotoxic functions of the CTLs [187, 188] either by interaction with immune checkpoint molecules such as PD-L1and CTLA4 or by releasing cytokines such as IL-10 and TGF-β [179, 189]. Apart from immune cells, brain-resident cells such as astrocytes and microglia play a substantial role in regulating the inflammatory status of the GBM microenvironment. Tumor-associated astrocytes (TAAs) are associated with tumor progression and resistance to therapy in GBM [190–192]. Astrocytes in the brain are transformed into the TAAs by NF-κβ signaling and contribute to GBM evasion [193]. Distinct astrocyte populations within the GBM can release different cytokines such as IL-10, TGF-β, and GM-CSF and promote an anti-inflammatory microenvironment within the tumor [191]. In response to the injury, astrocytes can secrete IL-6, TGF-β, TNF-α, and insulin growth factor-1 and contribute to tumor progression [194, 195]. Overall, astrocytes promote the development of a tumor-promoting microenvironment and the progression of the GBM. Different immune cell infiltrations and the presence of cancer and glial cells in the tumor microenvironment are associated with releasing various cytokines in the brain. This cytokine balance is associated with both maintenance of the inflammatory milieu in the brain and the maintenance of the immune response to the tumor. Glioma cells secrete many immune suppressive cytokines such as TGF-β, IL-6, IL-10, IL-4, IL-13, and IL-33 in the microenvironment [196–199]. These cytokines hamper immune response to tumors and promote GBM

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progression. For example, IL-33 released within the tumor is known to promote tumor progression and reduce the overall survival of patients [200]. Apart from that release of pro-inflammatory cytokines such as IL-6 and colony-stimulating factor (CSF-1) also promotes tumor progression by affecting the T-cell functions [201].

4

Potential Role of Serotonin Signaling the Immune Response to Gliomas Studies have indicated serotonin signaling is associated with enhanced anti-inflammatory immune response. Serotonin secretion is enhanced in many forms of inflammation associated with pathogenic encounters, endotoxic shock, traumatic injuries, and conditions such as cancer [31]. This enhanced serotonin level also impacts the recruitment of many early responders of the inflammation, such as neutrophils [105], and eosinophils, in case of an allergic response and helps in the quick resolution of the inflammation [88–90, 92, 107]. Serotonin also affects the activation of the adaptive immune system by affecting the cells that bridge the innate and adaptive immune response. It suppresses the pro-inflammatory polarization of the monocyte toward DCs by downregulating the expression of costimulatory molecules [86]. Apart from that, this signaling also promotes the development of regulatory DCs that suppress the activation and differentiation of T cells toward pro-inflammatory Th1 and Th17 subtypes [82, 84]. Serotonin is also responsible for the polarization of the M2 macrophages [79– 81, 108]. As serotonin suppresses the over-activation of the T cells [122, 123], it controls the exaggerated immune activation during inflammation and resolves inflammatory responses. Though this anti-inflammatory response is beneficial in preventing many forms of inflammation, this kind of response can be detrimental in the case of cancer. Most therapies, including immunotherapies in GBM, are ineffective because the enriched immunosuppressive microenvironment in the GBM comprises more M2 macrophages, MDSCs, and Tregs. The anti-inflammatory attribute exerted by serotonin can also enhance the anti-inflammatory intra-tumoral milieu in gliomas [173–177]. This enhanced anti-inflammatory milieu exerted by the serotonin can affect the recruitment and functions of many antitumor immune cells, such as cytotoxic T-cell lymphocytes and NK cells within the tumor. The roles of serotonin within the tumor microenvironment in the GBM are summarized in Fig. 2. Therefore, the enhanced serotonin levels can promote the progression of the gliomas by reducing the effector immune responses to the tumor and improving the resistance to many therapies that enhance tumor-specific immune responses in the gliomas.

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Fig. 2 Regulation of brain cancer by serotonin. Serotonin regulates brain cancer progression by affecting cancer cells and the immune microenvironment generated from brain cancer-associated neuroinflammation. Serotonin promotes the growth of the glioblastoma cell lines and promotes cancer progression. Serotonin can also affect the immune microenvironment of the GBM, suppressing T-cell activation by promoting regulatory DCs development and recruitment. Serotonin can also promote the polarization of the TAMs toward the pro-tumorigenic M2 subtype and suppress the polarization toward the anti-tumorigenic M1 subtype, thereby can promote cancer progression. (The figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license)

5

Conclusions Serotonergic pathways are one of the major pathways that govern many functions within the body. It not only takes part in the neural regulations, but it also performs wide arrays of extra-neural functions. Its vast roles in regulating immune functions make it an important molecule to study in the case of homeostasis and different diseases and pathologies. The immune-modulatory effects are quite important in inflammation and cancer progression. The serotonergic pathway regulates the neuro-inflammatory response in the brain and the periphery. This neuro-inflammatory response in the brain plays a significant role in regulating the progression of the tumor. Understanding the mechanisms of the serotonin signaling pathways in brain cancer progression not only will help in

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developing better drugs to target the immunological manifestation of brain cancer but also enhances the chance of repurposing the existing pharmacological targets of the serotonergic pathways to target brain cancer and neuro-inflammation associated brain damages. Declaration of Interests The authors declare no competing interests. Funding Supports GL received the Swarna Jayanti Fellowship (DST/SJF/LSA-01/2017–18), from the Department of Science and Technology and a research grant from the Science and Engineering Research Board (CRG/2022/007108), Ministry of Science and Technology, Government of India. SK received a Senior Research Fellowship from the Council of Scientific and Industrial Research (CSIR), Government of India.

References 1. Willis WD (2001) Role of neurotransmitters in sensitization of pain responses. Ann N Y Acad Sci 933:142–156 2. Akyuz E, Polat AK, Eroglu E, Kullu I, Angelopoulou E, Paudel YN (2021) Revisiting the role of neurotransmitters in epilepsy: an updated review. Life Sci 265:118826 3. Veenstra-VanderWeele J, Anderson GM, Cook EH (2000) Pharmacogenetics and the serotonin system: initial studies and future directions. Eur J Pharmacol 410:165–181 4. Walther DJ, Peter JU, Bashammakh S, Ho¨rtnagl H, Voits M, Fink H, Bader M (2003) Synthesis of serotonin by a second tryptophan hydroxylase isoform. Science 299:1078197 5. Rapport MM, Green AA, Page IH (1948) Serum vasoconstrictor, serotonin; isolation and characterization. J Biol Chem 176: 1243–1251 6. Yadav VK, Balaji S, Suresh PS, Liu XS, Lu X, Li Z, Guo XE, Mann JJ, Balapure AK, Gershon MD, Medhamurthy R, Vidal M, Karsenty G, Ducy P (2010) Pharmacological inhibition of gut-derived serotonin synthesis is a potential bone anabolic treatment for osteoporosis. Nat Med 16:308–312 7. Hornung JP (2003) The human raphe nuclei and the serotonergic system. J Chem Neuroanat 26:331–343 8. Charnay Y, Le´ger L (2010) Brain serotonergic circuitries. Dialogues Clin Neurosci 12: 471–487

9. Mann JJ (1999) Role of the serotonergic system in the pathogenesis of major depression and suicidal behavior. Neuropsychopharmacology 21:99–105 10. Lanctoˆt KL, Herrmann N, Mazzotta P (2001) Role of serotonin in the behavioral and psychological symptoms of dementia. J Neuropsychiatry Clin Neurosci 13:5–21 11. Pritchard AL, Pritchard CW, Bentham P, Lendon CL (2007) Role of serotonin transporter polymorphisms in the behavioural and psychological symptoms in probable Alzheimer disease patients. Dement Geriatr Cogn Disord 24:201–206 12. DiSabato DJ, Quan N, Godbout JP (2016) Neuroinflammation: the devil is in the details. J Neurochem 2:136–153 13. Guzman-Martinez L, Maccioni RB, Andrade V, Navarrete LP, Pastor MG, Ramos-Escobar N (2019) Neuroinflammation as a common feature of neurodegenerative disorders. Front Pharmacol 10:1008 14. Kwon HS, Koh S-H (2020) Neuroinflammation in neurodegenerative disorders: the roles of microglia and astrocytes. Transl Neurodegener 9:42 15. Leng F, Edison P (2021) Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol 17:157–172 16. Afridi R, Tsuda M, Ryu H, Suk K (2022) The function of glial cells in the

Serotonin Signaling in Neuroinflammation neuroinflammatory and neuroimmunological responses. Cell 11(4):659 17. Jha MK, Jeon S, Suk K (2012) Glia as a link between neuroinflammation and neuropathic pain. Immune Netw 12:41–47 18. Bhattarai P, Cosacak MI, Mashkaryan V, Demir S, Popova SD, Govindarajan N, Brandt K, Zhang Y, Chang W, Ampatzis K, Kizil C (2020) Neuron-glia interaction through Serotonin-BDNF-NGFR axis enables regenerative neurogenesis in Alzheimer’s model of adult zebrafish brain. PLoS Biol 18:e3000585 19. De-Miguel FF, Leon-Pinzon C, Noguez P, Mendez B (2015) Serotonin release from the neuronal cell body and its long-lasting effects on the nervous system. Philos Trans R Soc Lond B Biol Sci 370:20140196 20. Mead GE, Hsieh CF, Lee R, Kutlubaev MA, Claxton A, Hankey GJ, Hackett ML (2012) Selective serotonin reuptake inhibitors (SSRIs) for stroke recovery. Cochrane Database Syst Rev 11:CD009286 21. Price A, Rayner L, Okon-Rocha E, Evans A, Valsraj K, Higginson IJ, Hotopf M (2011) Antidepressants for the treatment of depression in neurological disorders: a systematic review and meta-analysis of randomised controlled trials. J Neurol Neurosurg Psychiatry 82:914–923 22. Li N, Ghia J-E, Wang H, McClemens J, Cote F, Suehiro Y, Mallet J, Khan WI (2011) Serotonin activates dendritic cell function in the context of gut inflammation. Am J Pathol 178:662–671 ´ , Ne´meth G (2021) 23. Laszlovszky I, Baraba´ssy A Cariprazine, a broad-spectrum antipsychotic for the treatment of schizophrenia: pharmacology, efficacy, and safety. Adv Ther 38: 3652–3673 24. Campbell RH, Diduch M, Gardner KN, Thomas C (2018) Review of cariprazine in management of psychiatric illness. Ment Health Clin 7:221–229 25. Molnar MJ, Molnar V, Fedor M, Csehi R, Acsai K, Borsos B, Grosz Z (2022) Improving mood and cognitive symptoms in Huntington’s disease with cariprazine treatment. Front Psych 12:825532 26. Edinoff AN, Akuly HA, Hanna TA, Ochoa CO, Patti SJ, Ghaffar YA, Kaye AD, Viswanath O, Urits I, Boyer AG, Cornett EM, Kaye AM (2021) Selective serotonin reuptake inhibitors and adverse effects: a narrative review. Neurol Int 13:387–401 27. Sutcigil L, Oktenli C, Musabak U, Bozkurt A, Cansever A, Uzun O, Sanisoglu SY,

199

Yesilova Z, Ozmenler N, Ozsahin A, Sengul A (2007) Pro- and anti-inflammatory cytokine balance in major depression: effect of sertraline therapy. Clin Dev Immunol 2007: 76396 28. Martin AM, Young RL, Leong L, Rogers GB, Spencer NJ, Jessup CF, Keating DJ (2017) The diverse metabolic roles of peripheral serotonin. Endocrinology 158:1049–1063 29. Berger M, Gray JA, Roth BL (2009) The expanded biology of serotonin. Annu Rev Med 60:355–366 30. Karmakar S, Lal G (2021) Role of serotonin receptor signaling in cancer cells and antitumor immunity. Theranostics 11:5296– 5312 31. Arreola R, Becerril-Villanueva E, CruzFuentes C, Velasco-Vela´zquez MA, Garce´sAlvarez ME, Hurtado-Alvarado G, Quintero-Fabian S, Pavo´n L (2015) Immunomodulatory effects mediated by serotonin. J Immunol Res 2015:354957 32. Mondanelli G, Volpi C (2021) The double life of serotonin metabolites: in the mood for joining neuronal and immune systems. Curr Opin Immunol 70:1–6 33. Fung TC, Olson CA, Hsiao EY (2017) Interactions between the microbiota, immune and nervous systems in health and disease. Nat Neurosci 20:145–155 34. Herr N, Bode C, Duerschmied D (2017) The effects of serotonin in immune cells. Front Cardiovasc Med 4:48 ´ , Usategui A, Casas-Engel 35. Domı´nguez-Soto A ML, Simo´n-Fuentes M, Nieto C, Cuevas VD, ´ L (2017) Vega MA, Luis Pablos J, Corbı´ A Serotonin drives the acquisition of a profibrotic and anti-inflammatory gene profile through the 5-HT7R-PKA signaling axis. Sci Rep 7:14761 36. Masson J, Emerit MB, Hamon M, Darmon M (2012) Serotonergic signaling: multiple effectors and pleiotropic effects. Wiley Interdiscip Rev Membr Transp Signal 1:685–713 37. Dizeyi N, Hedlund P, Bjartell A, Tinzl M, Austild-Taske´n K, Abrahamsson P-A (2011) Serotonin activates MAP kinase and PI3K/ Akt signaling pathways in prostate cancer cell lines. Urol Oncol 29:436–445 38. Soll C, Jang JH, Riener MO, Moritz W, Wild PJ, Graf R, Clavien PA (2010) Serotonin promotes tumor growth in human hepatocellular cancer. Hepatology 51:1244–1254 39. Quintero-Villegas A, Valde´s-Ferrer SI (2019) Role of 5-HT(7) receptors in the immune system in health and disease. Mol Med 26:2

200

Surojit Karmakar and Girdhari Lal

40. Irving H, Turek I, Kettle C, Yaakob N (2021) Tapping into 5-HT3 receptors to modify metabolic and immune responses. Int J Mol Sci 22:11910 41. Farrelly LA, Thompson RE, Zhao S, Lepack AE, Lyu Y, Bhanu NV, Zhang B, Loh YE, Ramakrishnan A, Vadodaria KC, Heard KJ, Erikson G, Nakadai T, Bastle RM, Lukasak BJ, Zebroski H 3rd, Alenina N, Bader M, Berton O, Roeder RG, Molina H, Gage FH, Shen L, Garcia BA, Li H, Muir TW, Maze I (2019) Histone serotonylation is a permissive modification that enhances TFIID binding to H3K4me3. Nature 567:535–539 42. Nautiyal KM, Dailey CA, Jahn JL, Rodriquez E, Son NH, Sweedler JV, Silver R (2012) Serotonin of mast cell origin contributes to hippocampal function. Eur J Neurosci 36:2347–2359 43. Nautiyal KM, Ribeiro AC, Pfaff DW, Silver R (2008) Brain mast cells link the immune system to anxiety-like behavior. Proc Natl Acad Sci U S A 105:18053–18057 44. Akimova E, Lanzenberger R, Kasper S (2009) The serotonin-1A receptor in anxiety disorders. Biol Psychiatry 66:627–635 45. Borg J (2008) Molecular imaging of the 5-HT(1A) receptor in relation to human cognition. Behav Brain Res 195:103–111 46. King MV, Marsden CA, Fone KC (2008) A role for the 5-HT(1A), 5-HT4 and 5-HT6 receptors in learning and memory. Trends Pharmacol Sci 29:482–492 47. Mostany R, Pazos A, Castro ME (2005) Autoradiographic characterisation of [35S] GTPgammaS binding stimulation mediated by 5-HT1B receptor in postmortem human brain. Neuropharmacology 48:25–33 48. O’Hearn E, Battaglia G, De Souza EB, Kuhar MJ, Molliver ME (1988) Methylenedioxyamphetamine (MDA) and methylenedioxymethamphetamine (MDMA) cause selective ablation of serotonergic axon terminals in forebrain: immunocytochemical evidence for neurotoxicity. J Neurosci 8:2788–2803 49. Gallezot JD, Nabulsi N, Neumeister A, Planeta-Wilson B, Williams WA, Singhal T, Kim S, Maguire RP, McCarthy T, Frost JJ, Huang Y, Ding YS, Carson RE (2010) Kinetic modeling of the serotonin 5-HT(1B) receptor radioligand [(11)C]P943 in humans. J Cereb Blood Flow Metab 30:196–210 50. MacLean MR (2007) Pulmonary hypertension and the serotonin hypothesis: where are we now? Int J Clin Pract Suppl 156:27–31 51. Thompson MD, Noble-Topham S, Percy ME, Andrade DM, Ebers GC (2012)

Chromosome 1p36 in migraine with aura: association study of the 5HT(1D) locus. Neuroreport 23:45–48 52. Varn€as K, Hall H, Bonaventure P, Sedvall G (2001) Autoradiographic mapping of 5-HT (1B) and 5-HT(1D) receptors in the post mortem human brain using [(3)H]GR 125743. Brain Res 915:47–57 53. Li J, Wang Y, Zhou RL, Yang L, Zhang HB, Wang B (2007) Association between serotonin 1D gene polymorphisms and attention deficit hyperactivity disorder comorbid or not comorbid learning disorder. Beijing Da Xue Xue Bao 39:535–538 54. Bai F, Yin T, Johnstone EM, Su C, Varga G, Little SP, Nelson DL (2004) Molecular cloning and pharmacological characterization of the guinea pig 5-HT1E receptor. Eur J Pharmacol 484:127–139 55. Lucaites VL, Krushinski JH, Schaus JM, Audia JE, Nelson DL (2005) [3H] LY334370, a novel radioligand for the 5-HT1F receptor. II. Autoradiographic localization in rat, guinea pig, monkey and human brain. Naunyn Schmiedeberg’s Arch Pharmacol 371:178–184 56. Zhang G, Stackman RW Jr (2015) The role of serotonin 5-HT2A receptors in memory and cognition. Front Pharmacol 6:225 57. Sˇvob Sˇtrac D, Pivac N, Mu¨ck-Sˇeler D (2016) The serotonergic system and cognitive function. Transl Neurosci 7:35–49 58. Wood WE, Lovell PV, Mello CV, Perkel DJ (2011) Serotonin, via HTR2 receptors, excites neurons in a cortical-like premotor nucleus necessary for song learning and production. J Neurosci 31:13808–13815 59. Landolt HP, Wehrle R (2009) Antagonism of serotonergic 5-HT2A/2C receptors: mutual improvement of sleep, cognition and mood? Eur J Neurosci 29:1795–1809 60. Monti JM (2011) Serotonin control of sleepwake behavior. Sleep Med Rev 15:269–281 61. Portas CM, Bjorvatn B, Ursin R (2000) Serotonin and the sleep/wake cycle: special emphasis on microdialysis studies. Prog Neurobiol 60:13–35 62. Drago A, Serretti A (2009) Focus on HTR2C: a possible suggestion for genetic studies of complex disorders. Am J Med Genet B Neuropsychiatr Genet 150B:601–637 63. Bonhaus DW, Bach C, DeSouza A, Salazar FH, Matsuoka BD, Zuppan P, Chan HW, Eglen RM (1995) The pharmacology and distribution of human 5-hydroxytryptamine2B (5-HT2B) receptor gene products:

Serotonin Signaling in Neuroinflammation comparison with 5-HT2A and 5-HT2C receptors. Br J Pharmacol 115:622–628 64. Giorgetti M, Tecott LH (2004) Contributions of 5-HT(2C) receptors to multiple actions of central serotonin systems. Eur J Pharmacol 488:1–9 65. Wang Q, Zhang H, Xu H, Guo D, Shi H, Li Y, Zhang W, Gu Y (2016) 5-HTR3 and 5-HTR4 located on the mitochondrial membrane and functionally regulated mitochondrial functions. Sci Rep 6:37336 66. Barnes NM, Hales TG, Lummis SC, Peters JA (2009) The 5-HT3 receptor--the relationship between structure and function. Neuropharmacology 56:273–284 67. Holbrook JD, Gill CH, Zebda N, Spencer JP, Leyland R, Rance KH, Trinh H, Balmer G, Kelly FM, Yusaf SP, Courtenay N, Luck J, Rhodes A, Modha S, Moore SE, Sanger GJ, Gunthorpe MJ (2009) Characterisation of 5-HT3C, 5-HT3D and 5-HT3E receptor subunits: evolution, distribution and function. J Neurochem 108:384–396 68. Marner L, Gillings N, Madsen K, Erritzoe D, Baare´ WF, Svarer C, Hasselbalch SG, Knudsen GM (2010) Brain imaging of serotonin 4 receptors in humans with [11C] SB207145-PET. NeuroImage 50:855–861 69. Lucas G, Rymar VV, Du J, Mnie-Filali O, Bisgaard C, Manta S, Lambas-Senas L, ˜ eyro G, Sadikot Wiborg O, Haddjeri N, Pin AF, Debonnel G (2007) Serotonin(4) (5-HT (4)) receptor agonists are putative antidepressants with a rapid onset of action. Neuron 55: 712–725 70. Thomas DR (2006) 5-HT5A receptors as a therapeutic target. Pharmacol Ther 111:707– 714 71. de Kock CP, Cornelisse LN, Burnashev N, Lodder JC, Timmerman AJ, Couey JJ, Mansvelder HD, Brussaard AB (2006) NMDA receptors trigger neurosecretion of 5-HT within dorsal raphe nucleus of the rat in the absence of action potential firing. J Physiol 577:891–905 72. De Felice LJ (2016) A current view of serotonin transporters. F1000Res 28:1884 73. Yang D, Gouaux E (2021) Illumination of serotonin transporter mechanism and role of the allosteric site. Sci Adv 7:1 74. Willeit M, Sitte HH, Thierry N, Michalek K, Praschak-Rieder N, Zill P, Winkler D, Brannath W, Fischer MB, Bondy B, Kasper S, Singer EA (2008) Enhanced serotonin transporter function during depression in seasonal affective disorder. Neuropsychopharmacology 33:1503–1513

201

75. Huh JR, Veiga-Fernandes H (2020) Neuroimmune circuits in inter-organ communication. Nat Rev Immunol 20:217–228 76. Wan M, Ding L, Wang D, Han J, Gao P (2020) Serotonin: a potent immune cell modulator in autoimmune diseases. Front Immunol 11:186 77. Wu H, Denna TH, Storkersen JN, Gerriets VA (2019) Beyond a neurotransmitter: the role of serotonin in inflammation and immunity. Pharmacol Res 140:100–114 78. Chen Z, Luo J, Li J, Kim G, Stewart A, Urban JF Jr, Huang Y, Chen S, Wu LG, Chesler A, Trinchieri G, Li W, Wu C (2021) Interleukin33 promotes serotonin release from enterochromaffin cells for intestinal homeostasis. Immunity 54:151–163 ˇ ´ızˇ M (2020) Serotonin 79. Vasˇ´ıcˇek O, Lojek A, C and its metabolites reduce oxidative stress in murine RAW264.7 macrophages and prevent inflammation. J Physiol Biochem 76:49–60 80. de Las Casas-Engel M, Corbı´ AL (2014) Serotonin modulation of macrophage polarization: inflammation and beyond. Adv Exp Med Biol 824:89–115 81. de Las Casas-Engel M, Domı´nguez-Soto A, Sierra-Filardi E, Bragado R, Nieto C, PuigKroger A, Samaniego R, Loza M, Corcuera MT, Go´mez-Aguado F, Bustos M, Sa´nchezMateos P, Corbı´ AL (2013) Serotonin skews human macrophage polarization through HTR2B and HTR7. J Immunol 190:2301– 2310 82. Katoh N, Soga F, Nara T, TamagawaMineoka R, Nin M, Kotani H, Masuda K, Kishimoto S (2006) Effect of serotonin on the differentiation of human monocytes into dendritic cells. Clin Exp Immunol 146:354– 361 83. Szabo A, Gogolak P, Koncz G, Foldvari Z, Pazmandi K, Miltner N, Poliska S, Bacsi A, Djurovic S, Rajnavolgyi E (2018) Immunomodulatory capacity of the serotonin receptor 5-HT2B in a subset of human dendritic cells. Sci Rep 8:1765 84. Idzko M, Panther E, Stratz C, Mu¨ller T, Bayer H, Zissel G, Du¨rk T, Sorichter S, Di Virgilio F, Geissler M, Fiebich B, Herouy Y, Elsner P, Norgauer J, Ferrari D (2004) The serotoninergic receptors of human dendritic cells: identification and coupling to cytokine release. J Immunol 172:6011–6019 85. Holst K, Guseva D, Schindler S, Sixt M, Braun A, Chopra H, Pabst O, Ponimaskin E (2015) The serotonin receptor 5-HT7R regulates the morphology and migratory

202

Surojit Karmakar and Girdhari Lal

properties of dendritic cells. J Cell Sci 128: 2866–2880 86. Soga F, Katoh N, Inoue T, Kishimoto S (2007) Serotonin activates human monocytes and prevents apoptosis. J Invest Dermatol 127:1947–1955 87. Arzt E, Costas M, Finkielman S, Nahmod VE (1991) Serotonin inhibition of tumor necrosis factor-α synthesis by human monocytes. Life Sci 48:2557–2562 88. Boehme SA, Lio FM, Sikora L, Pandit TS, Lavrador K, Rao SP, Sriramarao P (2004) Cutting edge: serotonin is a chemotactic factor for eosinophils and functions additively with eotaxin. J Immunol 173:3599–3603 89. Lima C, Souza VM, Soares AL, Macedo MS, Tavares-de-Lima W, Vargaftig BB (2007) Interference of methysergide, a specific 5-hydroxytryptamine receptor antagonist, with airway chronic allergic inflammation and remodelling in a murine model of asthma. Clin Exp Allergy 37:723–734 90. Kubes P, Gaboury JP (1996) Rapid mast cell activation causes leukocyte-dependent and -independent permeability alterations. Am J Phys 271:H2438–H2446 91. Kushnir-Sukhov NM, Brown JM, Wu Y, Kirshenbaum A, Metcalfe DD (2007) Human mast cells are capable of serotonin synthesis and release. J Allergy Clin Immunol 119:498–499 92. Kushnir-Sukhov NM, Gilfillan AM, Coleman JW, Brown JM, Bruening S, Toth M, Metcalfe DD (2006) 5-Hydroxytryptamine induces mast cell adhesion and migration. J Immunol 177:6422–6432 93. Hellstrand K, Hermodsson S (1993) Serotonergic 5-HT1A receptors regulate a cell contact-mediated interaction between natural killer cells and monocytes. Scand J Immunol 37:7–18 94. Hellstrand K, Czerkinsky C, Ricksten A, Jansson B, Asea A, Kylefjord H, Hermodsson S (1993) Role of serotonin in the regulation of interferon-gamma production by human natural killer cells. J Interf Res 13:33–38 95. Hellstrand K, Hermodsson S (1987) Role of serotonin in the regulation of human natural killer cell cytotoxicity. J Immunol 139:869– 875 96. Hellstrand K, Hermodsson S (1990) Enhancement of human natural killer cell cytotoxicity by serotonin: role of non-T/ CD16+ NK cells, accessory monocytes, and 5-HT1A receptors. Cell Immunol 127:199– 214

97. Hellstrand K, Asea A, Dahlgren C, Hermodsson S (1994) Histaminergic regulation of NK cells. Role of monocyte-derived reactive oxygen metabolites. J Immunol 153:4940–4947 98. Leo´n-Ponte M, Ahern GP, O’Connell PJ (2007) Serotonin provides an accessory signal to enhance T-cell activation by signaling through the 5-HT7 receptor. Blood 109: 3139–3146 99. Ameisen JC, Meade R, Askenase PW (1989) A new interpretation of the involvement of serotonin in delayed-type hypersensitivity. Serotonin-2 receptor antagonists inhibit contact sensitivity by an effect on T cells. J Immunol 142:3171–3179 100. Rinaldi A, Chiaravalli AM, Mian M, Zucca E, Tibiletti MG, Capella C, Bertoni F (2010) Serotonin receptor 3A expression in normal and neoplastic B cells. Pathobiology 77:129– 135 101. Meredith EJ, Holder MJ, Chamba A, Challa A, Drake-Lee A, Bunce CM, Drayson MT, Pilkington G, Blakely RD, Dyer MJ, Barnes NM, Gordon J (2005) The serotonin transporter (SLC6A4) is present in B-cell clones of diverse malignant origin: probing a potential anti-tumor target for psychotropics. FASEB J 19:1187–1189 102. Meredith EJ, Chamba A, Holder MJ, Barnes NM, Gordon J (2005) Close encounters of the monoamine kind: immune cells betray their nervous disposition. Immunology 115: 289–295 103. Morishima T (1970) 5-Hydroxytryptamine (serotonin) and 5-hydroxytryptophan in mast cells of human mastocytosis. Tohoku J Exp Med 102:121–126 104. Duerschmied D, Suidan GL, Demers M, Herr N, Carbo C, Brill A, Cifuni SM, Mauler M, Cicko S, Bader M, Idzko M, Bode C, Wagner DD (2013) Platelet serotonin promotes the recruitment of neutrophils to sites of acute inflammation in mice. Blood 121:1008–1015 105. Mauler M, Schanze N, Krauel K, Schoenichen C, Glatzki F, Poeschl S, Stallmann D, Mezger J, Gauchel N, Sharipova D, Rieder M, Hilgendorf I, Witsch T, Bode C, Duerschmied D (2022) Peripheral serotonin lacks effects on endothelial adhesion molecule expression in acute inflammation. J Thromb Haemost 20:222– 229 106. Kang BN, Ha SG, Bahaie NS, Hosseinkhani MR, Ge XN, Blumenthal MN, Rao SP, Sriramarao P (2013) Regulation of serotonininduced trafficking and migration of eosinophils. PLoS One 8:23

Serotonin Signaling in Neuroinflammation 107. Inoue H, Nagata N, Koshihara Y (1995) Participation of serotonin in capsaicin-induced mouse ear edema. Jpn J Pharmacol 69:61–68 108. Nieto C, Rayo I, de Las Casas-Engel M, Izquierdo E, Alonso B, Be´chade C, ´ L (2020) Maroteaux L, Vega MA, Corbı´ A Serotonin (5-HT) shapes the macrophage gene profile through the 5-HT(2B)-dependent activation of the aryl hydrocarbon receptor. J Immunol 204:2808–2817 109. Wang Y-C, Wang X, Yu J, Ma F, Li Z, Zhou Y, Zeng S, Ma X, Li Y-R, Neal A, Huang J, To A, Clarke N, Memarzadeh S, Pellegrini M, Yang L (2021) Targeting monoamine oxidase A-regulated tumor-associated macrophage polarization for cancer immunotherapy. Nat Commun 12:3530 110. Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D (2020) Dendritic cells in cancer immunology and immunotherapy. Nat Rev Immunol 20:7–24 111. Wylie B, Macri C, Mintern JD, Waithman J (2019) Dendritic cells and cancer: from biology to therapeutic intervention. Cancers 11: 521 112. Karmakar S, Pal P, Lal G (2021) Key activating and inhibitory ligands involved in the mobilization of natural killer cells for cancer immunotherapies. Immunotargets Ther 10: 387–407 113. Martins LC, Rocha NP, Torres KC, Dos Santos RR, Franc¸a GS, de Moraes EN, Mukhamedyarov MA, Zefirov AL, Rizvanov AA, Kiyasov AP, Vieira LB, Guimara˜es MM, Yalvac¸ ME, Teixeira AL, Bicalho MA, Janka Z, Romano-Silva MA, Palota´s A, Reis HJ (2012) Disease-specific expression of the serotoninreceptor 5-HT(2C) in natural killer cells in Alzheimer’s dementia. J Neuroimmunol 251:73–79 114. Frank MG, Hendricks SE, Johnson DR, Wieseler JL, Burke WJ (1999) Antidepressants augment natural killer cell activity: in vivo and in vitro. Neuropsychobiology 39:18–24 115. Park EJ, Lee JH, Jeong DC, Han SI, Jeon YW (2015) Natural killer cell activity in patients with major depressive disorder treated with escitalopram. Int Immunopharmacol 28: 409–413 116. Betten A, Dahlgren C, Hermodsson S, Hellstrand K (2001) Serotonin protects NK cells against oxidatively induced functional inhibition and apoptosis. J Leukoc Biol 70:65–72 117. Waldman AD, Fritz JM, Lenardo MJ (2020) A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat Rev Immunol 20:651–668

203

118. Rubtsov AV, Rubtsova K, Kappler JW, Jacobelli J, Friedman RS, Marrack P (2015) CD11c-expressing B cells are located at the T cell/B cell border in spleen and are potent APCs. J Immunol 195:71–79 119. Iglesia MD, Vincent BG, Parker JS, Hoadley KA, Carey LA, Perou CM, Serody JS (2014) Prognostic B-cell signatures using mRNA-seq in patients with subtype-specific breast and ovarian cancer. Clin Cancer Res 20:3818– 3829 120. Nielsen JS, Sahota RA, Milne K, Kost SE, Nesslinger NJ, Watson PH, Nelson BH (2012) CD20+ tumor-infiltrating lymphocytes have an atypical CD27- memory phenotype and together with CD8+ T cells promote favorable prognosis in ovarian cancer. Clin Cancer Res 18:3281–3292 121. Gilbert AE, Karagiannis P, Dodev T, Koers A, Lacy K, Josephs DH, Takhar P, Geh JL, Healy C, Harries M, Acland KM, Rudman SM, Beavil RL, Blower PJ, Beavil AJ, Gould HJ, Spicer J, Nestle FO, Karagiannis SN (2011) Monitoring the systemic human memory B cell compartment of melanoma patients for anti-tumor IgG antibodies. PLoS One 6:e19330 122. O’Connell PJ, Wang X, Leon-Ponte M, Griffiths C, Pingle SC, Ahern GP (2006) A novel form of immune signaling revealed by transmission of the inflammatory mediator serotonin between dendritic cells and T cells. Blood 107:1010–1017 123. Chen Y, Leon-Ponte M, Pingle SC, O’Connell PJ, Ahern GP (2015) T lymphocytes possess the machinery for 5-HT synthesis, storage, degradation and release. Acta Physiol (Oxf) 213:860–867 124. Wu JI, Wang LH (2019) Emerging roles of gap junction proteins connexins in cancer metastasis, chemoresistance and clinical application. J Biomed Sci 26:8 125. Khan NA, Poisson JP (1999) 5-HT3 receptor-channels coupled with Na+ influx in human T cells: role in T cell activation. J Neuroimmunol 99:53–60 126. Wang X, Li B, Kim YJ, Wang YC, Li Z, Yu J, Zeng S, Ma X, Choi IY, Di Biase S, Smith DJ, Zhou Y, Li YR, Ma F, Huang J, Clarke N, To A, Gong L, Pham AT, Moon H, Pellegrini M, Yang L (2021) Targeting monoamine oxidase A for T cell-based cancer immunotherapy. Sci Immunol 6:eabh2383 127. Hernandez ME, Martinez-Fong D, PerezTapia M, Estrada-Garcia I, Estrada-Parra S, Pavo´n L (2010) Evaluation of the effect of selective serotonin-reuptake inhibitors on lymphocyte subsets in patients with a major

204

Surojit Karmakar and Girdhari Lal

depressive disorder. Eur Neuropsychopharmacol 20:88–95 128. Davis RB, Meeker WR, McQuarrie DG (1960) Immediate effects of intravenous endotoxin on serotonin concentrations and blood platelets. Circ Res 8:234–239 129. Mo¨ssner R, Lesch KP (1998) Role of serotonin in the immune system and in neuroimmune interactions. Brain Behav Immun 12: 249–271 130. Wagner DD, Frenette PS (2008) The vessel wall and its interactions. Blood 111:5271– 5281 131. Endo Y, Shibazaki M, Nakamura M, Takada H (1997) Contrasting effects of lipopolysaccharides (endotoxins) from oral blackpigmented bacteria and Enterobacteriaceae on platelets, a major source of serotonin, and on histamine-forming enzyme in mice. J Infect Dis 175:1404–1412 132. Ito T, Ikeda U, Shimpo M, Yamamoto K, Shimada K (2000) Serotonin increases interleukin-6 synthesis in human vascular smooth muscle cells. Circulation 102:2522– 2527 133. Dukhinova M, Kuznetsova I, Kopeikina E, Veniaminova E, Yung AWY, Veremeyko T, Levchuk K, Barteneva NS, Wing-Ho KK, Yung W-H, Liu JYH, Rudd J, Yau SSY, Anthony DC, Strekalova T, Ponomarev ED (2018) Platelets mediate protective neuroinflammation and promote neuronal plasticity at the site of neuronal injury. Brain Behav Immun 74:7–27 134. Kim Y-K, Jeon WS (2018) Neuroinflammation and the immune-kynurenine pathway in anxiety disorders. Curr Neuropharmacol 16: 574–582 135. Correia AS, Vale N (2022) Tryptophan metabolism in depression: a narrative review with a focus on serotonin and kynurenine pathways. Int J Mol Sci 23:8493 136. Miura H, Ozaki N, Sawada M, Isobe K, Ohta T, Nagatsu T (2008) A link between stress and depression: shifts in the balance between the kynurenine and serotonin pathways of tryptophan metabolism and the etiology and pathophysiology of depression. Stress 11:198–209 137. Be´chade C, D’Andrea I, Etienne F, Verdonk F, Moutkine I, Banas SM, Kolodziejczak M, Diaz SL, Parkhurst CN, Gan WB, Maroteaux L, Roumier A (2021) The serotonin 2B receptor is required in neonatal microglia to limit neuroinflammation and sickness behavior in adulthood. Glia 69: 638–654

138. Hwang J, Zheng LT, Ock J, Lee MG, Suk K (2008) Anti-inflammatory effects of m-chlorophenylpiperazine in brain glia cells. Int Immunopharmacol 8:1686–1694 139. Abdel-Hamid NM, Shehata DE, AbdelGhany AA, Ragaa A, Wahid A (2016) Serum serotonin as unexpected potential marker for staging of experimental hepatocellular carcinoma. Biomed Pharmacother 83:407–411 140. Shu B, Zhai M, Miao X, He C, Deng C, Fang Y, Luo M, Liu L, Liu S (2018) Serotonin and YAP/VGLL4 balance correlated with progression and poor prognosis of hepatocellular carcinoma. Sci Rep 8:9739 141. Pai VP, Marshall AM, Hernandez LL, Buckley AR, Horseman ND (2009) Altered serotonin physiology in human breast cancers favors paradoxical growth and cell survival. Breast Cancer Res 11:R81 142. Hejazi SH, Ahangari G, Deezagi A (2015) Alternative viewpoint against breast cancer based on selective serotonin receptors 5HTR3A and 5HTR2A antagonists that can mediate apoptosis in MCF-7 cell line. Curr Drug Discov Technol 12:240–249 143. Sonier B, Arseneault M, Lavigne C, Ouellette RJ, Vaillancourt C (2006) The 5-HT2A serotoninergic receptor is expressed in the MCF-7 human breast cancer cell line and reveals a mitogenic effect of serotonin. Biochem Biophys Res Commun 343:1053–1059 144. Dizeyi N, Bjartell A, Nilsson E, Hansson J, Gadaleanu V, Cross N, Abrahamsson PA (2004) Expression of serotonin receptors and role of serotonin in human prostate cancer tissue and cell lines. Prostate 59:328–336 145. Soll C, Riener MO, Oberkofler CE, Hellerbrand C, Wild PJ, DeOliveira ML, Clavien PA (2012) Expression of serotonin receptors in human hepatocellular cancer. Clin Cancer Res 18:5902–5910 146. Dizeyi N, Bjartell A, Hedlund P, Taske´n KA, Gadaleanu V, Abrahamsson PA (2005) Expression of serotonin receptors 2B and 4 in human prostate cancer tissue and effects of their antagonists on prostate cancer cell lines. Eur Urol 47:895–900 147. Liu Y, Zhang H, Wang Z, Wu P, Gong W (2019) 5-Hydroxytryptamine1a receptors on tumour cells induce immune evasion in lung adenocarcinoma patients with depression via autophagy/pSTAT3. Eur J Cancer 114:8–24 148. Mahe C, Bernhard M, Bobirnac I, Keser C, Loetscher E, Feuerbach D, Dev KK, Schoeffter P (2004) Functional expression of the serotonin 5-HT7 receptor in human glioblastoma cell lines. Br J Pharmacol 143:404–410

Serotonin Signaling in Neuroinflammation 149. Lu Q, Ding Y, Li Y, Lu Q (2020) 5-HT receptor agonist Valerenic Acid enhances the innate immunity signal and suppresses glioblastoma cell growth and invasion. Int J Biol Sci 16:2104–2115 150. Otto-Meyer S, DeFaccio R, Dussold C, Ladomersky E, Zhai L, Lauing KL, Bollu LR, Amidei C, Lukas RV, Scholtens DM, Wainwright DA (2020) A retrospective survival analysis of Glioblastoma patients treated with selective serotonin reuptake inhibitors. Brain Behav Immun Health 2:16 151. Then CK, Liu KH, Liao MH, Chung KH, Wang JY, Shen SC (2017) Antidepressants, sertraline and paroxetine, increase calcium influx and induce mitochondrial damagemediated apoptosis of astrocytes. Oncotarget 8:115490–115502 152. Liu KH, Yang ST, Lin YK, Lin JW, Lee YH, Wang JY, Hu CJ, Lin EY, Chen SM, Then CK, Shen SC (2015) Fluoxetine, an antidepressant, suppresses glioblastoma by evoking AMPAR-mediated calcium-dependent apoptosis. Oncotarget 6:5088–5101 153. Chen VC, Hsieh YH, Chen LJ, Hsu TC, Tzang BS (2018) Escitalopram oxalate induces apoptosis in U-87MG cells and autophagy in GBM8401 cells. J Cell Mol Med 22:1167–1178 154. Bi J, Khan A, Tang J, Armando AM, Wu S, Zhang W, Gimple RC, Reed A, Jing H, Koga T, Wong IT-L, Gu Y, Miki S, Yang H, Prager B, Curtis EJ, Wainwright DA, Furnari FB, Rich JN, Cloughesy TF, Kornblum HI, Quehenberger O, Rzhetsky A, Cravatt BF, Mischel PS (2021) Targeting glioblastoma signaling and metabolism with a re-purposed brain-penetrant drug. Cell Rep 37:109957 155. Zhang J, Guo Z, Xie Q, Zhong C, Gao X, Yang Q (2022) Tryptophan hydroxylase 1 drives glioma progression by modulating the serotonin/L1CAM/NF-κB signaling pathway. BMC Cancer 22:457 156. Kast RE (2010) Glioblastoma chemotherapy adjunct via potent serotonin receptor-7 inhibition using currently marketed high-affinity antipsychotic medicines. Br J Pharmacol 161: 481–487 157. Lieb K, Biersack L, Waschbisch A, Orlikowski S, Akundi RS, Candelario-Jalil E, Hull M, Fiebich BL (2005) Serotonin via 5-HT7 receptors activates p38 mitogenactivated protein kinase and protein kinase C epsilon resulting in interleukin-6 synthesis in human U373 MG astrocytoma cells. J Neurochem 93:549–559

205

158. Varatharaj A, Galea I (2017) The blood-brain barrier in systemic inflammation. Brain Behav Immun 60:1–12 159. Da Ros M, De Gregorio V, Iorio AL, Giunti L, Guidi M, de Martino M, Genitori L, Sardi I (2018) Glioblastoma chemoresistance: the double play by microenvironment and blood-brain barrier. Int J Mol Sci 19:2879 160. Bergers G, Benjamin LE (2003) Tumorigenesis and the angiogenic switch. Nat Rev Cancer 3:401–410 161. Erickson MA, Dohi K, Banks WA (2012) Neuroinflammation: a common pathway in CNS diseases as mediated at the blood-brain barrier. Neuroimmunomodulation 19:121– 130 162. Sonar SA, Lal G (2018) Blood-brain barrier and its function during inflammation and autoimmunity. J Leukoc Biol 103:839–853 163. Allavena P, Sica A, Solinas G, Porta C, Mantovani A (2008) The inflammatory microenvironment in tumor progression: the role of tumor-associated macrophages. Crit Rev Oncol Hematol 66:1–9 164. Sethi G, Sung B, Aggarwal BB (2008) TNF: a master switch for inflammation to cancer. Front Biosci 13:5094–5107 165. Kore RA, Abraham EC (2014) Inflammatory cytokines, interleukin-1 beta and tumor necrosis factor-alpha, upregulated in glioblastoma multiforme, raise the levels of CRYAB in exosomes secreted by U373 glioma cells. Biochem Biophys Res Commun 453:326–331 166. Ko¨nnecke H, Bechmann I (2013) The role of microglia and matrix metalloproteinases involvement in neuroinflammation and gliomas. Clin Dev Immunol 914104:14 167. Joseph JV, Balasubramaniyan V, Walenkamp A, Kruyt FA (2013) TGF-β as a therapeutic target in high grade gliomas— promises and challenges. Biochem Pharmacol 85:478–485 168. Wolburg H, Noell S, Fallier-Becker P, Mack AF, Wolburg-Buchholz K (2012) The disturbed blood-brain barrier in human glioblastoma. Mol Asp Med 33:579–589 169. Huettner C, Czub S, Kerkau S, Roggendorf W, Tonn JC (1997) Interleukin 10 is expressed in human gliomas in vivo and increases glioma cell proliferation and motility in vitro. Anticancer Res 17:3217–3224 170. Van Meir EG (1995) Cytokines and tumors of the central nervous system. Glia 15:264–288 171. Crane CA, Ahn BJ, Han SJ, Parsa AT (2012) Soluble factors secreted by glioblastoma cell lines facilitate recruitment, survival, and

206

Surojit Karmakar and Girdhari Lal

expansion of regulatory T cells: implications for immunotherapy. Neuro-Oncology 14: 584–595 172. Perng P, Lim M (2015) Immunosuppressive mechanisms of malignant gliomas: parallels at non-CNS sites. Front Oncol 6:153 173. Moore KW, de Waal Malefyt R, Coffman RL, O’Garra A (2001) Interleukin-10 and the interleukin-10 receptor. Annu Rev Immunol 19:683–765 174. Zhang J, Sarkar S, Cua R, Zhou Y, Hader W, Yong VW (2012) A dialog between glioma and microglia that promotes tumor invasiveness through the CCL2/CCR2/interleukin6 axis. Carcinogenesis 33:312–319 175. Roesch S, Rapp C, Dettling S, Herold-Mende C (2018) When immune cells turn badtumor-associated microglia/macrophages in glioma. Int J Mol Sci 19:436 176. Gutmann DH, Kettenmann H (2019) Microglia/brain macrophages as central drivers of brain tumor pathobiology. Neuron 104:442– 449 177. Chen R, Keoni C, Waker CA, Lober RM, Chen YH, Gutmann DH (2019) KIAA1549-BRAF expression establishes a permissive tumor microenvironment through NFκB-mediated CCL2 production. Neoplasia 21:52–60 178. Hambardzumyan D, Gutmann DH, Kettenmann H (2016) The role of microglia and macrophages in glioma maintenance and progression. Nat Neurosci 19:20–27 179. Grabowski MM, Sankey EW, Ryan KJ, Chongsathidkiet P, Lorrey SJ, Wilkinson DS, Fecci PE (2021) Immune suppression in gliomas. J Neuro-Oncol 151:3–12 180. Mi Y, Guo N, Luan J, Cheng J, Hu Z, Jiang P, Jin W, Gao X (2020) The emerging role of myeloid-derived suppressor cells in the glioma immune suppressive microenvironment. Front Immunol 11:737 181. Miyazaki T, Ishikawa E, Sugii N, Matsuda M (2020) Therapeutic strategies for overcoming immunotherapy resistance mediated by immunosuppressive factors of the glioblastoma microenvironment. Cancers 12:1960 182. Yang I, Tihan T, Han SJ, Wrensch MR, Wiencke J, Sughrue ME, Parsa AT (2010) CD8+ T-cell infiltrate in newly diagnosed glioblastoma is associated with long-term survival. J Clin Neurosci 17:1381–1385 183. Mohme M, Neidert MC (2020) Tumorspecific T cell activation in malignant brain tumors. Front Immunol 11:205 184. Woroniecka K, Chongsathidkiet P, Rhodin K, Kemeny H, Dechant C, Farber SH,

Elsamadicy AA, Cui X, Koyama S, Jackson C, Hansen LJ, Johanns TM, Sanchez-Perez L, Chandramohan V, Yu YA, Bigner DD, Giles A, Healy P, Dranoff G, Weinhold KJ, Dunn GP, Fecci PE (2018) T-cell exhaustion signatures vary with tumor type and are severe in glioblastoma. Clin Cancer Res 24:4175–4186 185. Koyama S, Akbay EA, Li YY, Herter-Sprie GS, Buczkowski KA, Richards WG, Gandhi L, Redig AJ, Rodig SJ, Asahina H, Jones RE, Kulkarni MM, Kuraguchi M, Palakurthi S, Fecci PE, Johnson BE, Janne PA, Engelman JA, Gangadharan SP, Costa DB, Freeman GJ, Bueno R, Hodi FS, Dranoff G, Wong KK, Hammerman PS (2016) Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints. Nat Commun 7:10501 186. Tay RE, Richardson EK, Toh HC (2021) Revisiting the role of CD4+ T cells in cancer immunotherapy-new insights into old paradigms. Cancer Gene Ther 28:5–17 187. El Andaloussi A, Lesniak MS (2006) An increase in CD4+CD25+FOXP3+ regulatory T cells in tumor-infiltrating lymphocytes of human glioblastoma multiforme. NeuroOncology 8:234–243 188. Mu L, Yang C, Gao Q, Long Y, Ge H, DeLeon G, Jin L, Chang YE, Sayour EJ, Ji J, Jiang J, Kubilis PS, Qi J, Gu Y, Wang J, Song Y, Mitchell DA, Lin Z, Huang J (2017) CD4+ and perivascular Foxp3+ T cells in glioma correlate with angiogenesis and tumor progression. Front Immunol 8: 1415 189. Alghamri MS, McClellan BL, Hartlage CS, Haase S, Faisal SM, Thalla R, Dabaja A, Banerjee K, Carney SV, Mujeeb AA, Olin MR, Moon JJ, Schwendeman A, Lowenstein PR, Castro MG (2021) Targeting neuroinflammation in brain cancer: uncovering mechanisms, pharmacological targets, and neuropharmaceutical developments. Front Pharmacol 12:680021 190. Guan X, Hasan MN, Maniar S, Jia W, Sun D (2018) Reactive astrocytes in glioblastoma multiforme. Mol Neurobiol 55:6927–6938 191. Henrik Heiland D, Ravi VM, Behringer SP, Frenking JH, Wurm J, Joseph K, Garrelfs NWC, Str€ahle J, Heynckes S, Grauvogel J, Franco P, Mader I, Schneider M, Potthoff AL, Delev D, Hofmann UG, Fung C, Beck J, Sankowski R, Prinz M, Schnell O (2019) Tumor-associated reactive astrocytes aid the evolution of immunosuppressive environment in glioblastoma. Nat Commun 10: 2541

Serotonin Signaling in Neuroinflammation 192. Yang N, Yan T, Zhu H, Liang X, Leiss L, Sakariassen P, Skaftnesmo KO, Huang B, Costea DE, Enger P, Li X, Wang J (2014) A co-culture model with brain tumor-specific bioluminescence demonstrates astrocyteinduced drug resistance in glioblastoma. J Transl Med 12:278 193. Kim JK, Jin X, Sohn YW, Jin X, Jeon HY, Kim EJ, Ham SW, Jeon HM, Chang SY, Oh SY, Yin J, Kim SH, Park JB, Nakano I, Kim H (2014) Tumoral RANKL activates astrocytes that promote glioma cell invasion through cytokine signaling. Cancer Lett 353:194–200 194. Seike T, Fujita K, Yamakawa Y, Kido MA, Takiguchi S, Teramoto N, Iguchi H, Noda M (2011) Interaction between lung cancer cells and astrocytes via specific inflammatory cytokines in the microenvironment of brain metastasis. Clin Exp Metastasis 28:13–25 ˜ ones-Hinojosa A, Searson 195. Placone AL, Quin PC (2016) The role of astrocytes in the progression of brain cancer: complicating the picture of the tumor microenvironment. Tumour Biol 37:61–69 196. Debinski W, Gibo DM, Slagle B, Powers SK, Gillespie GY (1999) Receptor for interleukin 13 is abundantly and specifically overexpressed in patients with glioblastoma multiforme. Int J Oncol 15:481–486 197. Gomez GG, Kruse CA (2006) Mechanisms of malignant glioma immune resistance and sources of immunosuppression. Gene Ther Mol Biol 10:133–146

207

198. Brown CE, Alizadeh D, Starr R, Weng L, Wagner JR, Naranjo A, Ostberg JR, Blanchard MS, Kilpatrick J, Simpson J, Kurien A, Priceman SJ, Wang X, Harshbarger TL, D’Apuzzo M, Ressler JA, Jensen MC, Barish ME, Chen M, Portnow J, Forman SJ, Badie B (2016) Regression of glioblastoma after chimeric antigen receptor T-cell therapy. N Engl J Med 375:2561–2569 199. Harshyne LA, Nasca BJ, Kenyon LC, Andrews DW, Hooper DC (2016) Serum exosomes and cytokines promote a T-helper cell type 2 environment in the peripheral blood of glioblastoma patients. NeuroOncology 18:206–215 200. De Boeck A, Ahn BY, D’Mello C, Lun X, Menon SV, Alshehri MM, Szulzewsky F, Shen Y, Khan L, Dang NH, Reichardt E, Goring KA, King J, Grisdale CJ, Grinshtein N, Hambardzumyan D, Reilly KM, Blough MD, Cairncross JG, Yong VW, Marra MA, Jones SJM, Kaplan DR, McCoy KD, Holland EC, Bose P, Chan JA, Robbins SM, Senger DL (2020) Glioma-derived IL-33 orchestrates an inflammatory brain tumor microenvironment that accelerates glioma progression. Nat Commun 11:4997 201. Hao C, Parney IF, Roa WH, Turner J, Petruk KC, Ramsay DA (2002) Cytokine and cytokine receptor mRNA expression in human glioblastomas: evidence of Th1, Th2 and Th3 cytokine dysregulation. Acta Neuropathol 103:171–178

Chapter 15 Impacts of Omega-3 Fatty Acids, Natural Elixirs for Neuronal Health, on Brain Development and Functions Archana S. Rao, Ajay Nair, K. Nivetha, Bibi Ayesha, Kapadia Hardi, Vora Divya, S. M. Veena, K. S. Anantharaju, and Sunil S. More Abstract Omega-3 fatty acids play a seminal role in maintaining the structural and functional integrity of the nervous system. These specialized molecules function as precursors for many lipid-based biological messengers. Also, studies suggest the role of these fatty acids in regulating healthy sleep cycles, cognitive ability, brain development, etc. Dietary intake of essential poly unsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are foundational to the optimal working of the nervous system. Besides regulating health, these biomolecules have great therapeutic value in treating several diseases, particularly nervous system diseases and disorders. Many recent studies conclusively demonstrated the beneficial effects of Omega-3 fatty acids in treating depression, neuropsychiatric disorders, neurodegenerative disorders, neurochemical disorders, and many other illnesses associated with the nervous system. This chapter summates the multifaceted role of poly unsaturated fatty acids, especially Omega-3 fatty acids (EPA and DHA), in the neuronal health and functioning. The importance of dietary intake of these essential fatty acids, their recommended dosages, bioavailability, the mechanism of their action, and therapeutic values are extensively discussed. Key words EPA, DHA, ALA, Neurodegenerative disorders, Neurochemical disorders, Health, Physiology, Diseases

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Introduction Since its discovery in the early twentieth century as an essential dietary nutrient, an array of well-documented work has been done on the biological role of fatty acids. Studies on essential fatty acid deficiency in infants have a long-standing history. Long-chain fatty acids, viz., omega-6 and omega-3 polyunsaturated fatty acids (PUFA), are particularly abundant in the central nervous system. These fatty acids are structural elements of neuronal membranes that have an impact on cellular activity both directly through changes to membrane properties and indirectly by serving as a source of messengers formed from lipids. For proper cerebral

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_15, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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growth and visual function, omega-3 PUFA consumption is crucial. Additionally, there is mounting evidence that consuming more long-chain omega-3 PUFAs like eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) may have positive effects on a number of neurological and psychiatric problems, particularly neurodegenerative diseases. Extensive work in the recent past has definitively shown that in addition to multifaceted positive effects, omega-3 fatty acids possess a seminal neuroprotective potential in several neurological injuries.

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Dietary Intake of EPA/DHA for Optimal Working of the Nervous System Brain weight starting from birth till age two is regarded as the human brain’s major development phase. Yet, certain parts of the brain are still developing by the time a person is 2 years old, and growth and development continue throughout infancy and adolescence [1]. Docosahexaenoic acid (DHA, 22:6n-3), an n-3 LC-PUFA with a high tissue content, is crucial for this development. Planning, problem-solving, and focused attention are examples of executive and higher-order cognitive functions that are attributed to frontal lobes rich in DHA [2]. Researchers claim that these prefrontal brain structures are linked to the limbic system, where a child’s social, emotional, and behavioral development coheres with the development of high-level cognitive function [3–5]. The biophysical characteristics of brain membranes are regulated by docosahexaenoic acid (DHA; 22:6n-3) [6]. Many areas of the brain, including the cerebral cortex, synapses, and retinal rod photoreceptors, exhibit unusually high concentrations of DHA, according to research in animals [7–9]. Since it accumulates in fetal tissue at a particularly high rate throughout the third trimester, adequate DHA consumption is particularly crucial during pregnancy [10]. Farquharson et al. in an autopsy study found that newborns who had been fed breast milk, which is known to contain DHA, had cerebral cortex DHA concentrations that were greater than those of infants who had been fed formula without DHA [11]. Findings of neurophysiologic tests that directly measured brain activity show that DHA supplementation causes alterations in the brains of healthy youngsters. Standardized cognitive testing does not reveal consistent changes, nevertheless. The increase in reading and spelling abilities shown after DHA intake may be the result of little adjustments made across numerous domains that are difficult to identify on other types of testing. These alterations might be especially modest in young, healthy youngsters. The examined pediatric studies support animal research showing that DHA is a crucial brain component influencing learning and behavior [5].

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According to research, ingesting EPA, or fish oil with at least 60% EPA, will lessen depressive symptoms. It could function best when taken in conjunction with antidepressants. EPA supplementation appears to aid in the prevention of depression in patients receiving the medication interferon-alpha. Taking EPA has proven to be positive in lowering the aggression and decreasing the depression in women. Recent research suggests that omega-3 polyunsaturated fatty acids (also known as n-3 PUFAs or n-3 fatty acids) offer a promising path for resolving the mysteries of depression, illness behavior, and the relationship between the mind and body. The two main bioactive components of n-3 PUFAs, eicosapentaenoic acid and docosahexaenoic acid, cannot be effectively produced by humans and must therefore be acquired from food, particularly fish. Docosahexaenoic acid shortage is linked to problems with serotonin, norepinephrine, and dopamine transmission and neuronal membrane integrity, which may be related to the cause of depression‘s cognitive and mood problems. Eicosapentaenoic acid, via lowering membrane arachidonic acid (an n-6 PUFA) and prostaglandin E2 production, may be connected to the somatic symptoms and physical comorbidity in depression. It is also crucial for maintaining immunological function and physical health. The potential theory of psychoneuroimmunology of depression is supported by the role of n-3 PUFAs in immunity and mood function, which also serves as a superb interface between the “mind” and the “body” [12]. According to increasing evidence, DHA may have an impact on the developing brain. DHA helps to lessen psychological and cognitive issues. Both DHA and EPA elevate depression and neuroinflammatory condition in the brain [13]. Maternal DHA, which is reliant on the mother’s dietary intake, becomes a crucial component for the fetal brain development because the fetus and placenta capacity to biosynthesize DHA from its precursors is insufficient to meet the demand of the rapidly developing neural tissues [14]. DHA for infants has been linked to a number of advantages, including increased hand-eye coordination [15], improved problem-solving abilities [10, 16], sustained attention [17, 18], and improved cognitive function [19, 20]. 2.1

Bioavailability

DHA is a necessary nutrient since it is only minimally produced by the human body and is mostly received from food. The main dietary sources of DHA are cold-water marine fish. The main sources of DHA are marine microalgae, and as you move up the food chain, the content of DHA keeps becoming higher [21]. The greatest concentration of DHA was reported in Tenualosa ilisha, followed by Trichiurus lepturus. Similarly Sardinella longiceps and Crassostrea madrasensis had the greatest EPA content. Whereas Schizothorax richardsonii and Neolissochilus hexagonolepis

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and Oncorhynchus mykiss were rich in EPA, N. hexagonolepis and O. mykiss were rich in DHA. Gudusia chapra, a SIF (Small fresh water Fish), was discovered to have elevated levels of DHA, and P. sophore, elevated levels of EPA [21, 22]. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), two marine omega-3 polyunsaturated fatty acids, are found in krill and are mostly bonded in phospholipids. Krill meal and krill oil are typical krill products. Fish oils mostly comprise EPA and DHA bound in triglycerides [23]. The best and most natural source of DHA for term newborns is human breastmilk. Worldwide, the typical DHA content of human breastfeeding is 0.32–0.22% of all fatty acids by weight [24]. Cold-water fish, especially fatty species, such as salmon, mackerel, herring, and trout, are known to be important dietary sources of DHA. In 100 g of flesh from this fish, 0.68–1.43 g of DHA may be found. Moreover, it has been demonstrated that DHA is present in the meat, milk, and eggs of animals fed marine byproducts or PUFA-producing microalgae [25]. For vegetarians, people who do not consume fish, and women who are pregnant, sources of n-3 other than fish are crucial. Rich in n-3 fatty acids, flaxseed oil has been shown in several studies to have high absorption whether used in bulk or as an emulsion. The levels of n-3 and LC3PUFAs in research participants were also enhanced by echium seed oil and walnut consumption [26]. 2.2 Recommended Dosage

The recommended daily intake of DHA and other n-3 PUFAs has been established by experts. To avoid CVD, it is advised to eat eight ounces of a variety of seafood each week, which is equal to 250 mg of DHA and EPA per day [27]. The recommended daily intake of DHA alone for pregnant and nursing women is typically 200 mg, which is greater than the recommended daily intake of DHA plus EPA for healthy individuals, which is 250 mg [28]. In a similar vein, there are specific recommended daily intakes (RDI) of n-3 PUFA, particularly DHA, for certain age groups; however, they vary from nation to nation due to a variety of reasons, including such as hormones, dietary habits, and environment [26]. Twenty patients with a current diagnosis of major depressive illness participated in a double-blind, 4-week, parallel-group trial. The addition of 2 g of EPA/day to antidepressant medication reduced sleeplessness, a gloomy mood, and feelings of guilt and worthlessness. One gram of EPA per day decreased aggressiveness and the intensity of depressive symptoms in people with borderline personality disorder, according to a double-blind placebo-controlled trial [29]. Many expert authorities in various countries have recommended dietary intakes of DHA or n-3 PUFA based on the expanding body of research proving the health advantages of DHA supplementation for different life stages. The RDI for DHA is

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between 70 and 250 mg per day or between 40 and 2300 mg per day when combined with other n-3 PUFA. The National Health and Medical Research Council of Australia and the Ministry of Health, Labour, and Welfare of Japan (2006) proposed that the age groups and genders should be considered when recommending n-3 PUFA consumption. For instance, whereas 0.9 g of total n-3 PUFA per day is advised for newborns 0–5 months old, this advice rises with age to 2.0 g for males and 2.4 g for women aged 50–69, respectively. Except for newborns between the ages of 0 and 2 years, where there is no difference in the RDI for either gender, the RDI of total n-3 PUFA for males in Japan is typically greater than that for females. The RDI for DHA for pregnant and breastfeeding women is typically approximately 200 mg, while Japan has a higher RDI (1800 mg n-3 PUFA). The RDI for DHA and EPA can be raised for persons at risk to 1000 mg to prevent CHD or CVD [25]. It was discovered that erythrocyte ALA and EPA levels were increased by flaxseed dosages of 2.4 and 3.6 g/d by 0.23 and 0.42 (mol %) [26].

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Importance of Omega-3 Fatty Acids in Brain and Vision Development Essential fatty acids (EFAs), especially omega-3 fatty acids (n-3 FAs), are crucial for optimum health maintenance and must be obtained via dietary intake as they cannot be synthesized endogenously by the body. Sixty percent of human brain comprises fat, of which n-3 FAs represent around 35% holding approximately 40% of DHA in the neuronal tissue, especially the gray matter [34]. This determines the brain integrity and thereby supports its performance and brain development [30, 34]. Omega-3 fatty acids play a significant role in the development of synaptic processing of neural cell interaction and the expression of genes regulating cell differentiation, aiding the overall development of neuronal health. Dietary docosahexaenoic acid (DHA) aids in the optimum functional maturation of the retina and visual cortex. DHA acts as the precursor of oxygenation products/docosanoids, which mediate in counteracting oxidative stress-triggered apoptotic DNA damage in retinal pigment epithelial cells. Decrease in DHA intake may lead to cognitive impairment and problems with neurogenesis leading in altered learning and vision that may in turn affect the overall brain development [30, 35]. Deficiency of n3 FAs reduces dopamine vesicle density in the cortex and causes malfunction of the dopaminergic mesocorticolimbic pathway [1]. Thus n-3 FAs are very essential for brain and vision development [35].

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Role of EFAs (n-3 FAs) in Brain Structure and Functions The brain comprises fat and hence it is essential to understand its crucial dependence on n-3 FAs for maintaining its structural integrity. Arachidonic acid (AA) and DHA are critical for fetal brain development. They are typically obtained by the developing fetus from the mother [30, 34]. It is present in the breast milk, which when obtained for about 1 year contributes enough AA levels that aids in the formation of inflammatory substances, such as leukotrienes, prostaglandin E2, and thromboxane A2 [30]. Elevated levels of n-3 FAs are found majorly in the neuronal membranes and myelin sheath. Studies suggest that unavailability or metabolic blocking of n-3 FAs may lead to amyelination, dysmyelination, or demyelination. This could be accompanied by impaired learning, vision, and auditory abnormalities. Neuronal membranes are composed of phospholipid pools, which function as reservoirs to produce specific lipid messengers involved in essential brain functions and neuronal stimulations. n-3 FAs operate as key messengers in the synthesis of brain neurotransmitters that take part in signaling cascades, involving in promoting neuronal injury or neuroprotection. n-3 FAs are crucial for the synthesis of an array of prostaglandins such as PGE1 (affects release of antiinflammatory compounds from nerve cells that transmit nerve impulses), PGE2 (highly inflammatory substance, responsible for swelling increased pain sensitivity and blood viscosity), and PGE3 (mildly anti-inflammatory and immune enhancing, which counters the powerful inflammatory effects of PGE2), which are involved in multiple physiological and neurological processes [30].

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Omega-3 Fatty Acids in the Regulation of Cognition and Brain Development Omega-3 fatty acid alters cognitive processes and improve the overall mental performance. n-3 FA supplementation has shown increased hemoglobin oxygen saturation and total hemoglobin concentrations. This in turn improves the blood circulation in the brain and inhibits neuronal cell death. Studies suggest DHA and eicosapentaenoic acid (EPA) intake has proven to improve early memory and learning deficiencies in relation to cognitive aging and highlights higher cognitive performances, respectively [34]. The intake of n-3 FAs plays a vital role in the immune/inflammatory and neurological pathways, which develop the overall circadian clock regulation, thereby influencing the biological clock. Evidence has shown that dietary n-3 FA intake and plasma concentration of EPA and DHA have shown to regulate the molecular mechanisms involved in maintaining the circadian rhythm in healthy adults. Studies suggest that n-3 FA-enriched diet during

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pregnancy was found to elevate the expression of brain-derived neurotrophic factor (BDNF), cAMP response-element binding protein (CREB), and tropomyosin receptor kinase B (TRkB), all which aid in developing the behavior and memory of the offspring [31]. Omega-3 fatty acid-enriched diet leads to the decrease in the production of proinflammatory cytokines in peripheral blood mononuclear cells such as interleukins IL-1, IL-6. n-3 FAs represents around 35% of total lipids in the brain, which actively take part in modulating neuronal membrane fluidity through their incorporation into the phospholipid tails [31, 32, 34]. They are also involved in maintaining dendrite elongation, neurotransmission, and synaptic plasticity [33]. Neurotransmission is influenced by both neuronal membrane fluidity and neurotransmitter release. These mechanisms participate in diminishing brain apoptosis that may be affected by the presence of n-3 FAs. Brain apoptosis could be alleviated by decreasing reactive oxygen species (ROS) responses that imposes an antiapoptotic action, or by upregulating antiapoptotic proteins and downregulating apoptotic proteins, respectively. Reduced DHA intake has shown to decrease the onset of dementia and mental deterioration [34]. Patients reported with pshycopathologies such as autism spectrum disorder (ASD), attentiondeficit/hyperactivity disorder (ADHD), Alzheimer’s disease, and major depression are found to possess decreased serum EPA and DHA levels [31, 33]. Thus, n-3 FA intake contributes majorly toward vision development, learning improvement, early memory, blood flow in the brain, and the overall cognitive well-being [34].

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Influence of n-3 FAs in Maintaining Healthy Sleep Cycle and Sleep Wellness Healthy sleep cycle is crucial for regulating daily physiological functions and for maintain overall good health. Poor sleep cycle creates higher risk of cardiovascular diseases, disrupted glucose metabolism, obesity in children, decreased attentiveness, and alertness, thus imposing negative effects on cognition and brain development. Several sleep outcomes are used to detect an individual’s sleep health, including sleep efficiency (SEff), sleep latency (SL), sleep quality (SQ), and total sleep duration (TSD) [32]. Concentration of n-3 FAs in the adipose tissues of obese patients suffering from sleep apnea were found to be in positive association with SEff and sleep wellness, as well as the minutes spent in slow wave sleep, and rapid eye movement (REM) sleep [33, 35]. Beneficial effects of consuming n-3 FA-rich diet include increased SEff and reduced SL observed in studies using animal models [33]. Recently, studies have shown that dietary/energy intake and nutrient implications based on the distribution of micro- and

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macronutrients and the overall dietary pattern play a key role in regulating healthy sleep cycle of humans [32, 33]. Among several nutrients, long-chain polyunsaturated fatty acid (LC-PUFA), n-3 FA, is a promising nutrient supplement that contributes majorly to sleep health. n-3 FAs are found to regulate the composition of melatonin and neuronal membrane structure, which is very essential to maintain healthy sleep in children and adults [32]. Reports suggest that higher blood DHA levels in children and adolescents are associated with early sleep timing, increase in sleep duration, shorter and fewer night-wakings, and longer weekend sleep, thereby significantly improving sleep wellness [35]. Studies have suggested that n-3 FA-deficient diet causes disturbed nocturnal sleep by affecting the melatonin rhythm and circadian clock functions [35]. Thus, n-3 FA-rich diet aids in improving sleep disturbances and the overall sleep quality in children and adults [32].

7 Therapeutics Value of Omega-3 Fatty Acids in Treating Nervous System Diseases and Disorders Improvement in life expectancy has brought in a fair share rising incidences of age-related non-communicable diseases (NCDs) such as Type 2 diabetes, cancer, obesity, cardio-vascular disorders, respiratory, endocrine diseases, chronic hepatic and renal disorders, musculoskeletal disorders (periodontal and spinal cord), rheumatoid arthritis, and of most concerned brain disorders, including neuropsychiatric, neurodegenerative, neurochemical, and depression, to name few [36, 37] An overview of all stipulated benefits of omega-3 fatty acids pertaining to various biological and molecular pathways involved in huge array of disorders is highlighted in figure below (Fig. 1). Among the roles lipids play in body, omega-3 fatty acids remarkably have been observed to help in not only physical but also mental health [38]. Inflammation and oxidative stress play a chief role in a majority of age- and lifestyle-related disorders. The primary function of long-chain omega-3 polyunsaturated fatty acids (LCPUFAs), DHA and EPA, is to limit inflammation and potential anti-oxidative properties, reducing their amount in the body and promoting overall human health [39–42]. Statistics show the number of people with dementia in their old ages are nearly getting doubled every 20 years, and one of the key reasons is the decreased levels of brain DHA as age progresses [43]. DHA and EPA account for their crucial role in neurotransmission and neuronal cell function along with immune reactions involved in nervous system disorders [44]. Higher DHA levels in plasma phosphatidylcholine are linked with 47% reduction of risk in all-cause dementia

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Fig. 1 Comprehensive illustration of the roles O3FAs play in metabolic pathways in human body

(RR = 0.53, 95% CI 0.29–0.97; p = 0.04) and a 39% reduction of risk in Alzheimer’s disease (RR = 0.61, 95% CI 0.31–1.18; p = 0.14) as described by a cohort from the Framingham Heart Study [45]. With increasing concerns and prevalence of such disorders come into light the effects of nutrition and supplementations of omega-3 fatty acids (O3FAs) for prevention and showing therapeutic value [46]. O3FAs’ therapeutic values are attributed to their integration into neuronal membranes owing to activation of recently found intracellular and cell surface receptors like FreeFatty Acid Receptors (FFAR). To investigate the role of O3FAs as antagonists of FFAR molecular docking, studies are being conducted, leading to advancement of receptor-specific targeted agonists as drugs [47]. Reduced levels of O3FAs in brain bring changes in the brain dopamine system in a way that enhances the risk of developing neurological disorders such as Parkinson and Huntington along with neuropsychiatric disorders such as depression, substance dependence, and schizophrenia particularly when combined with genetic factors and environmental risks [48] in cases of environmental abuses that can disrupt brain dopamine systems. DHA treatments may reverse the adverse effects by acting upon nuclear receptors as O3FAs are their ligands [49]. Substantia nigra and Ventral segmented areas are the regions of brain involved in dopamine synthesis where the RXR heterodimerizes in conjunction with nuclear receptor-related-1 protein (Nurr1) and nerve growth factor 1B (Nur77), implying the potential roles of O3FAs in dopaminergic neuron development and survival, in addition to

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neuropsychiatric and neurological disorders [50]. Besides being building blocks in membranes, O3FAs are bio-transformed into endocannabinoids and eicosanoids (leukotrienes, thromboxane, prostaglandins) as well as other lipid-based signaling molecules being constitutive of many immune and inflammatory responses, for example, prostacyclin I3 and thromboxane A3 reflect antithrombotic, vasodilatory, and antineoplastic activities in brain tumors and neurodegenerative disorders [51]. Global authorities and scientific nutrition societies prescribe specific amounts of daily intake of O3FAs, but does not provide reference ranges for their blood levels [52]. As an alternative, however, free serum O3FAs, erythrocyte membrane PUFAs, and whole blood phospholipid O3FAs are being measured by various laboratories and exercised as biomarkers and for risk estimation alongside monitoring efficacy when supplemented by commercial preparation [53]. The O3FA concentration in the plasma acts as a marker of the fatty acid supplied through diet; on the other hand, O3FA concentration in blood cells guides long-term bioavailability [54]. Properties such as high lipophilicity, long carbon chain, and ability to cross blood-brain barrier make O3FAs suitable candidates for central nervous system (CNS) drug delivery [55]. In addition, the use of specific carriers such as 1-lyso, 2-docosahexaenoyl-glycerophosphocholine (LysoPC), and Ace-DoPC (1- acetyl,2-docosahexaenoyl-glycerophosphocholine), in combination with O3FA, further improves their delivery in brain by increased intracerebral DHA transport which is almost ten-fold, ensuring DHA transport specifically to the brain [56]. Subsequently after recurrent ingestion of EPA and DHA, their half-life is stated to be 37 h and 48 h, respectively [57]. For the development of pharmacotherapeutics out of O3FAs, we need to consider several factors that can alter bioavailability especially under low-fat environment for better absorption of O3FA aggregation within nanoparticles and nanocapsules should be studied [58]. Moreover, O3FAs are susceptible to oxidation and hence microencapsulation helps in stabilizing and protecting from oxidation [59]. Thus, novel formulation strategies in hands with solid bioavailability studies can promote therapeutic development of O3FA.

8 Beneficial Effects of Omega-3 Fatty Acids in Treating Depression, Neuropsychiatric, Neurodegenerative, and Neurochemical Diseases Homo sapiens have evolved in an environment that was plush in dietary omega-3 PUFAs. Paleontological evidence has conclusively shown the link between food accessibility and brain size and function [60]. Relation of dietary intake of omega-3 PUFAs to brain evolution is evidently documented [61], and when we study this

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co-relation with a perspective of varied range of neural disorders, we can imbibe into the benefits of the O3FAs and stand to comment on the potency of them to be used as druggable compounds. 8.1 O3FA for Treating Depression

Mental disorders in the recent times have presented itself as one of the most prevalent public health issues. They are among the foremost causes of disability globally and account for nearly one-fifth of years living along with disabilities in adults [62]. Depressed mood, reduced interest, and inability to get pleasure from activities are the characteristic features of depressive disorder, limiting psychosocial functioning and severely diminishing quality of life [63]. By 2030, depression will become a major cause of burdening diseases globally and hence O3FAs are emerging as an approach to its management. Preclinical findings indicate the influence of O3FAs on diverse neurobiological mediators that are most likely to be implicated in the pathophysiology of depression. For instance, membrane lipids such as O3FAs act as a barrier and medium for typical transmitter signaling; thus, any changes in membrane lipids lead to pathophysiology of depression and underscores their potential as targets for lipid-based therapeutic interventions [64]. O3FAs execute their antidepression activity via several mechanisms. Most of them involved modulation of G-protein signaling via G-protein coupled receptors, which has its effects on lipid raft formation [65]. Other mechanisms include variation of pro-inflammatory mediators, and deviations in telomerase levels also have significance [66].

8.2 O3FA for Treating Neuropsychiatric Disorders

Neuropsychiatric disorder is a broad medical term that covers a vast range of medical conditions that involve neurology and psychiatry among which most common ones are seizures, attention deficit disorders, schizophrenia, bipolar disorders, and cognitive deficit disorders. Even though in infancy stages there are studies to rule out potential benefits of O3FAs in treating these disorders. Schizophrenia (SZ) Schizophrenia is a complicated cognitive and behavioral syndrome with symptoms such as dysfunction in cognition abilities and positive (e.g., hallucinations, delusions) and negative symptoms (e.g., social withdrawal, impaired motivation) [67]. Currently major therapy is antipsychotic medications, for which long-term outcomes are modest [46]. Documented evidence from clinical studies has driven to ‘membrane hypothesis’ which states nutritional changes PUFA in turn affects the membrane function which serves as key pathological mechanisms in schizophrenia [68, 69]. Disturbances in cortical membrane FA homeostasis have been recognized as a pathological attribute of schizophrenia, which is a probable explanation for the efficacy of O3FAs [68, 69]. Within membrane-related impacts on neuronal cell signaling pathways,

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O3FAs act by altering dopaminergic pathways in the mesolimbic system [70]. Moreover, clinical studies in schizophrenic patients stated that n-3 PUFA changeover leads to hype in telomerase levels besides reduction in oxidative stress [71]. Bipolar Disorder (BD) Bipolar disorder is a persistent condition characterized by swapping states of depression and elevated mood (hypomania) and alternating phases of euthymia [72]. The causes and mechanism of bipolar disorder is not clearly understood, but studies indicate low levels of O3FAs, DHA, in erythrocyte membranes in subjects having this disorder [45]. Additives of O3FAs in standard psychotic drugs have demonstrated improved scores in depression and mania [73]. Also, there are reported studies where O3FAs fail to have impacts on bipolar patients. Reasons may include small sample sizes, trial durations that are too short to show subsequent alterations in brain fatty acid composition, and disparateness of doses and ratio of admixes [74]. 8.3 O3FA for Treating Neurodegenerative Disorders

Neurodegenerative disorders occur when nerve cells in the brain or peripheral nervous system lose function over time and die, resulting in shrinkage in the brain size. These disorders get worse with time and have no absolute cure. Dysregulation or absolute loss of O3FA in brain has been linked to neurodegenerative disorders as these molecules account for resolving major neuroinflammatory and oxidative stress [75]. Alzheimer’s Disease (AD) In Alzheimer’s the main etiology includes formation of beta amyloid plaques and neurofibrillary tangles, but few less studied causes include increased lipid peroxidation and declined levels of O3FA [76]. In the brain tissues of AD patients, increased lipid peroxidation resulted in formation of acrolein which is a strong electrophile reacting with DNA bases [77]. Novel studies have shown relation between decreased incidence of neurodegenerative disorders like AD and intake of dietary O3FAs [78]. Ongoing studies imply that DHA has protective effects against pathophysiological traits of AD such as amyloid-beta plaque accumulation, cognitive dysfunction, and tau protein hyperphosphorylation in transgenic mouse models [79]. Furthermore, O3FAs have reported to improve amyloid-β phagocytosis by macrophages in patients suffering from mild cognitive impairment [80]. The therapeutic role of supplementation of O3FA for cognitive function and AD is majorly decided by the extent of cognitive dysfunction and the stage and severity of AD. Parkinson’s Disease (PD) PD is described as progressive loss of dopaminergic neurons, in the substantia nigra, leading to substantial dopamine depletion, accounting for the motor symptoms of PD [81]. In animal models

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of PD induced by neurotoxin-6-hydroxydopamine (6-OHDA), the ingestion of O3FAs prevented neurochemical and behavioral disturbances, suggesting their potential as a neuroprotective agent [82]. EPA and DHA generate compounds such as resolvins and protectins via pathways including cyclooxygenase and lipoxygenase enzymes, which are connected in the anti-inflammatory effects of O3FA supplementation [83]. Another clinical study found that supplementation with O3FAs decreased C-reactive protein and increased glutathione concentrations along with total antioxidant capacity, which was followed by experimental data indicating decrease in amounts of inducible nitric oxide synthase activity in the CNS after supplementation with O3FAs [84–86]. In PD, other than neuroprotective effects, O3FAs may improve dopaminergic signal transduction varied mechanisms [87]. 8.4 O3FA for Treating Neurochemical Disorders

Multiple Sclerosis Multiple sclerosis (MS), defined as a chronic autoimmune demyelinating disease of the central nervous system (CNS), has an unclear and multifactorial etiology having varied clinical symptoms with varying severity. Major pathophysiology sums up to systemic inflammation, demyelination of the white matter and activation of autoimmune processes in body [88]. O3FAs are associated with decline in inflammation by conversion into anti-inflammatory prostaglandins E1 and E2, which can potentially affect leukocyte migration, cytokine production, and other immune responses [89]. Global studies demonstrated proven anti-inflammatory effect of O3FAs by varied mechanisms such as, arachidonic acid (AA) is substituted in leukocyte membranes and checked production of pro-inflammatory eicosanoids, along with triggering biomarkers with less inflammatory action and metabolism of intermediators participating in immune processes (protectins and resolvins) which in turn aids in controlling inflammatory reactions [90, 91]. The other explanatory mode of activity of O3FAs is advancement and assistance of remyelination [91]. Autism Early appearing deficits in communication, mental skills, cognition, and repetitive sensory motor behavior are features of Autism. People having autism spectrum disorders have abnormal blood levels of O3FAs, which is understood to be a result of low dietary intake of O3FAs or alterations in fatty acid metabolism along with the assimilation of PUFAs into cellular membranes [92, 93]. Very few studies demonstrate alterations in O3FAs and O6FAs, and their ratios have a role in autism [93] but others have failed to find any such changes [94, 95]. In a meta-analysis by Mazahery et al. in 2017 including 15 case-control subjects with autism and showing atypical neurodevelopmental disorders, revealed that DHA, EPA, and AA levels were lower and total O6FAs to O3FAs ratio was high in

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them [96]. Considering such available studies, the evidence fell short to recommend O3FAs supplementation to manage autism [97]. According to diverse interventional studies on the efficacy of n-3 PUFA supplementation in various neuropsychiatric disorders found positive outcomes, and a huge body of experimental data presents biological and pharmacological foundation for these discoveries. Furthermore, prescribing O3FAs for neurodegenerative disorders is put under validation by various clinical trials that will assist in expanding the rigor of the findings and reduce the use of non-validated and non-standardized dietary supplements [75]. Here, we have accumulated the escalating clinical trials for O3FAs’ composite drugs in recent times which have been completed or ongoing but have conclusive outcomes. Among several existing clinical trials related to depression, the described trial encompasses 40 studies, with 31 being the placebocontrolled randomized trials. These trials investigated the efficacy of O3FAs as additional antidepressants in the handling of MDD (major depression disorder) wherein reduction in the depressive symptoms were observed when compared to the placebos [98]. Several studies showed no significant effects on symptoms in older adults aged 60 or above, but dosage of 1.5 g/d omega-3 showed significant reduction in symptoms of depression. The data from the metanalysis and one of the reviews suggested that intake of omega3 PUFA has no effect on the depressed mood of adults having good mental health; however, a considerable impact was seen among adults having depression [99], although these profits were only seen among those having mild-to-moderate depression [67]. The existing literature review recommends the intake of EPA-rich oil, which helps reduce the symptoms of depression during pregnancy and post-partum. Long-term consumption has helped in the decrease of postpartum depression risk in healthy women [66]. To conclude, supplementation of dietary PUFA has therapeutic and preventive effects, whereas low intake of O3FA might predispose certain people to depression. The pragmatic base on which the “membrane phospholipid hypothesis” of schizophrenia initiates implies that O3FA deficiencies function as hallmarks of psychosis in adolescents with elevated risk [100–103]. Rebuilding of O3FA levels is recommended for the reduction of psychotic symptoms [70]. Another study for progression rate of first episode of schizophrenia continued 81 individuals observed for 12 weeks showed 5% and 27.5% development of psychotic disorder [104]. At present schizophrenia therapy does not suggest the intake of omega-3 PUFAs since one of the studies with ethyl-EPA (2 g/d) administration into 71 subjects initially showed improvement, but the outcomes were restricted to only 6 weeks [105]. On the other hands trial studies with individuals suffering from bipolar disorder

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have shown decreased levels of O3FAs, specifically DHA within erythrocyte membrane [71, 106]. Upon the findings that individual consuming less PUFAs and more fats are at risk of developing bipolar, [106] several therapeutic trials showed variable results in the clinical manifestation of bipolar disorder, suggesting that omega-3 supplementation helps in pharmacotherapeutic improvement [107–110]. Another trial considered the efficacy of IcosaPent-ethyl therapy on predisposed Alzheimer’s include 150 cognitive healthy veterans (50–75 years) and are observed for 18 months, which will end in 2023 further evaluation of biomarkers will be achieved by MRI. Meanwhile phase 3 clinical trial data (NCT00440050) suggested chronic administration of DHA was effective in slowing AD progression from mild to moderate [111]. Another study on Omega AD for 12 months showed positive effects of omega-3, plasma transthyretin on cognition [112]. Supporting the benefits in Parkinson’s disorder following controlled, randomized, double-blind clinical trial was carried out to know the role of co-administration of vitamin E and O3FAs on the expression of genes linked to lipids and insulin in individual with (PD) involving 40 individuals and doses of 1000 mg/day of O3FA and 400 IU/day of vitamin E were administered for 12 weeks, results of which concluded down regulation of TNF-α (tumor necrosis factor-alpha) and upregulation of peroxisome proliferator-activated receptor gamma and LDLR downregulation [113]. Further improved health was observed in MS patient in the study of 80 subjects from varied groups for 30 months administered with DHA & EPA at 3:1 wt./wt. aiming to study the reduction of relapsing rates and pro-inflammatory eicosanoid formation and reduction of the free radical. Forty-one subjects completed the trail with no adverse effect [114]. The existing evidence of a Cochrane review pertaining to autism including meta-analyses and two randomized controlled trials conclude inadequate results to recommend O3FA administration. Four randomized trials with 107 subjects showed improvement in social interaction and autism spectrum with O3FA administration. Another meta-analysis of five randomized trails having 183 subjects showed no evidence of performance-improving effects of O3FA in 121 subjects with autism disorder, reasons being small size of the sample and less duration. In the nutshell it would be early to mention the benefits of O3FAs in the management of autism disorder, but several findings suggest it may help improve certain fundamental symptoms. Future research that can address the above-discussed issues raised by the ambiguity of clinical trials hopes to promote a more inclusive approach regarding omega-3 supplementation in human health.

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Recommendations for Future Research Most clinical trials that assessed the effect of dietary supplementation with O3FAs in neurological conditions yielded positive results. However, there were also neutral or even negative results. The conflicting findings may be due to several reasons [36]. Most commercial preparations from fish oil are oxidized (rancid) to varying degrees [37]. Variations in the source, origin, amount, ratio, and chemical composition of omega-3 preparations may be the reason for different results [38]. The impacts of O3FA substitution on the synthesis of lipid-based signaling molecules and inflammatory cytokines are often seen after a few days (due to the less biological halflife); their inclusion into cell membranes and onset of associated biological effects takes longer duration, depending on the turnover rate of cells. Thus, the period of clinical observation is critical, and the gold standard 8 weeks may not be enough. For the same reason, O3FAs appear less suitable as an acute treatment of neuropsychiatric disorders, but more useful for long-term prevention.

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Conclusions Omega-3 fatty acid consumption has shown significant improvement in learning, memory function, cognitive health, and cerebral blood flow. Long-chain PUFA has been found to significantly impact various aspects of neurological health. The use of omega-3 supplements is advantageous, bioavailable, and very less risky. O3Fa has shown its unparalleled potential in the prevention of complex diseases such as cardiovascular, psychiatric, neurological, etc. Therefore, given the mounting evidence supporting the ability of O3Fa to curb neurodegenerative disorders as well as maintain neurological wellbeing, research has been focused on their pharmacological roles and the subsequent translation to clinical trials.

References 1. Brown TT, Jernigan TL (2012) Brain development during the preschool years. Neuropsychol Rev 22:313–233 2. Anderson V, Fenwick T, Manly T, Robertson I (1998) Attentional skills following traumatic brain injury in childhood: a componential analysis. Brain Inj 12:937–949 3. Barkley RA (2001) The executive functions and self-regulation: an evolutionary neuropsychological perspective. Neuropsychol Rev 11:1–29 4. Kuratko CN, Barrett EC, Nelson EB, Norman S Jr (2013) The relationship of

docosahexaenoic acid (DHA) with learning and behavior in healthy children: a review. Nutrients 5(7):2777–2810 5. Mitchell DC, Gawrisch K, Litman BJ, Salem N (1998) Why is docosahexaenoic acid essential for nervous system function? Biochem Soc Trans 26(3):365–370. https://doi.org/10. 1042/bst0260365 6. Bowen RA, Clandinin MT (2002) Dietary low linolenic acid compared with docosahexaenoic acid alter synaptic plasma membrane phospholipid fatty acid composition and

Omega-3 Fatty Acids in Brain Health and Diseases sodium–potassium ATPase kinetics in developing rats. J Neurochem 83(4):764–774 7. Bazan NG, Scott BL (1990) Dietary omega-3 fatty acids and accumulation of docosahexaenoic acid in rod photoreceptor cells of the retina and at synapses. Ups J Med Sci 48:97– 107 8. Sarkadi-Nagy E, Wijendram V, Diau GY, Chao AC, Hsieh AT, Turpeinen A, Brenna JT (2003) The influence of prematurity and long chain polyunsaturated supplementation in 4-week adjusted age baboon neonate brain and related tissues. Pediatr Res 54(2): 244–252 9. Van Aerde JE, Wilke MS, Feldman M, Clandinin MT (2011) Accretion of lipid in the fetus and newborn. Accretion of lipid in the fetus and newborn. In: Fetal and neonatal physiology. WB Saunders, pp 454–470 10. Judge MP, Harel O, Lammi-Keefe CJ (2007) Maternal consumption of a docosahexaenoic acid–containing functional food during pregnancy: benefit for infant performance on problem-solving but not on recognition memory tasks at age 9 mo. Am J Clin Nutr 85(6):1572–1577 11. Su KP (2009) Biological mechanism of antidepressant effect of omega–3 fatty acids: how does fish oil act as a ‘mind-body interface’? Neurosignals 17(2):144–152 12. Khalid W, Gill P, Arshad MS, Ali A, Ranjha MMAN, Mukhtar S, Maqbool Z (2022) Functional behavior of DHA and EPA in the formation of babies brain at different stages of age, and protect from different brain-related diseases. Int J Food Prop 25(1):1021–1044 13. Uauy R, Mena P, Rojas C (2000) Essential fatty acids in early life: structural and functional role. Proc Nutr Soc 59(1):3–15 14. Dunstan J, Simmer K, Dixon G, Prescott SL (2008) Cognitive assessment of children at age 2½ years after maternal fish oil supplementation in pregnancy: a randomised controlled trial. Arch Dis Child Fetal Neonatal Ed 93(1):F45–F50 15. Drover J, Hoffman DR, Castaneda YS, Morale SE, Birch EE (2009) Three randomized controlled trials of early long-chain polyunsaturated fatty acid supplementation on means-end problem solving in 9-montholds. Child Dev 80(5):1376–1384 16. Colombo J, Gustafson KM, Gajewski BJ, Shaddy BJ, Kerling EH, Thodosoff JM, Carlson SE (2016) Prenatal DHA supplementation and infant attention. Pediatr Res 80(5): 656–662 17. Jensen CL, Voigt RG, Llorente AM, Peters SU, Prager TC, Zou YL, Heird WC (2010)

225

Effects of early maternal docosahexaenoic acid intake on neuropsychological status and visual acuity at five years of age of breast-fed term infants. J Pediatr 157(6):900–905 18. Birch EE, Garfield S, Hoffman DR, Uauy R, Birch DG (2000) A randomized controlled trial of early dietary supply of long-chain polyunsaturated fatty acids and mental development in term infants. Dev Med Child Neurol 42(3):174–181 19. Drover JR, Hoffman DR, Castaneda YS, Morale SE, Garfield S, Wheaton DH, Birch EE (2011) Cognitive function in 18-monthold term infants of the DIAMOND study: a randomized, controlled clinical trial with multiple dietary levels of docosahexaenoic acid. Early Hum Dev 87(3):223–230 20. Mohanty BP, Ganguly S, Mahanty A, Sankar TV, Anandan R, Chakraborty K, Sridhar N (2016) DHA and EPA content and fatty acid profile of 39 food fishes from India. Biomed Res Int 2016:4027437 21. Hernandez-Ledesma B, Hsieh CC, MartinezVillaluenga C (2017) Food bioactive compounds against diseases of the 21st century. Biomed Res Int 75(1):3–9 22. Kohler A, Sarkkinen E, Tapola N, Niskanen T, Bruheim I (2015) Bioavailability of fatty acids from krill oil, krill meal and fish oil in healthy subjects – a randomized, single-dose, crossover trial. Lipids Health Dis 14:1–10 23. Brenna JT, Varamini B, Jensen RG, DiersenSchade DA, Boettcher JA, Arteburn LM (2007) Docosahexaenoic and arachidonic acid concentrations in human breast milk worldwide. Am J Clin Nutr 85(6):1457–1464 24. Li J, Pora BL, Dong K, Hasjim J (2021) Health benefits of docosahexaenoic acid and its bioavailability: a review. Food Sci Nutr 9(9):5229–5243 25. Lane K, Derbyshire E, Li W, Brennan C (2014) Bioavailability and potential uses of vegetarian sources of omega-3 fatty acids: a review of the literature. Crit Rev Food Sci Nutr 54(5):572–579 26. Marshall TA (2011) Dietary guidelines for Americans, 2010: an update. J Am Dental Assoc 142(6):654–656 27. Sposito AC, Caramelli B, Fonseca FA, Bertolami MC, Neto AA, Souza AD, Sociedade Brasileira de Cardiologia (2007) IV Brazilian guideline for dyslipidemia and atherosclerosis prevention: Department of Atherosclerosis of Brazilian Society of Cardiology. Arq Bras Cardiol 88:2–19 28. Zanarini MC, Frankenburg FR (2003) Omega-3 fatty acid treatment of women with borderline personality disorder: a

226

Archana S. Rao et al.

double-blind, placebo-controlled pilot study. Am J Psychiatry 160(1):167–169 29. Chang CY, Ke DS, Chen JY (2009) Essential fatty acids and human brain. Acta Neurol Taiwanica 18(4):231–241 30. Checa-Ros A, D’Marco L (2022) Role of omega-3 fatty acids as non-photic zeitgebers and circadian clock synchronizers. Int J Mol Sci 23(20):12162 31. Dai Y, Liu J (2021) Omega-3 long-chain polyunsaturated fatty acid and sleep: a systematic review and meta-analysis of randomized controlled trials and longitudinal studies. Nutr Rev 79(8):847–868 32. Patan MJ, Kennedy DO, Husberg C, Hustvedt SO, Calder PC, Middleton B, Jackson PA (2021) Differential effects of DHA-and EPA-rich oils on sleep in healthy young adults: a randomized controlled trial. Nutrients 13(1):248 33. Dighiri IM, Alsubaie AM, Hakami FM, Hamithi DM, Alshekh MM, Khobrani FA, Tawhari M (2022) Effects of omega-3 polyunsaturated fatty acids on brain functions: a systematic review. Cureus 14(10):e30091 34. Dyall SC (2015) Long-chain omega-3 fatty acids and the brain: a review of the independent and shared effects of EPA, DPA and DHA. Front Aging Neurosci 7:52 35. GBD 2016 Causes of Death Collaborators (2017) Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England) 390(10100): 1151–1210 36. GBD 2016 dalys and HALE Collaborators (2017) Global, regional, and national disability-adjusted life-years (dalys) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England) 390(10100):1260–1344 37. Lange KW (2020) Omega-3 fatty acids and mental health. J Glob Health 4(1):18–30 38. Calder PC (2018) Very long-chain n-3 fatty acids and human health: fact, fiction and the future. Proc Nutr Soc 77(1):52–72 39. Calder PC (2015) Marine omega-3 fatty acids and inflammatory processes: effects, mechanisms and clinical relevance. Biochim Biophys Acta Gen Subj 1851(4):469–484 40. Chiang N, Serhan CN (2020) Specialized pro-resolving mediator network: an update on production and actions. Essays Biochem 64(3):443–462

41. Guisto NM, Salvador GA, Castagnet PI, Pasquare SJ, Ilincheta de Boschero MG (2002) Age-associated changes in central nervous system glycerolipid composition and metabolism. Neurochem Res 27(11):1513–1223 42. Reimers A, Ljung H (2019) The emerging role of omega-3 fatty acids as a therapeutic option in neuropsychiatric disorders. Ther Adv Psychopharmacol 9:204512319858901 43. Korotkova M, Jakobson PJ (2014) Persisting eicosanoid pathways in rheumatic diseases. Nat Rev Rheumatol 10(4):229–241 44. Sarris J, Logan AC, Akbaraly TN, Amminger GP, Balanza-Martinez V, Freeman MP, Hibbeln J, Matsuoka Y, Mischoulon D, Mizoue T, Nanri A, Nishi D, Ramsey D, Rucklidge JJ, Sanchez-Villegas A, Scholey A, Su KP, Jacka FN, International Society for Nutritional Psychiatry Research (2015) Nutritional medicine as mainstream in psychiatry. Lancet Psychiatry 2(3):271–274 45. Chitre NM, Moniri NH, Murnane KS (2019) Omega-3 fatty acids as druggable therapeutics for neurodegenerative disorders. CNS Neurol Disord Drug Targets 18(10):735–749 46. Healy-Stoffel M, Levant B (2018) N-3 (omega-3) fatty acids: effects on brain dopamine systems and potential role in the etiology and treatment of neuropsychiatric disorders. CNS Neurol Disord Drug Targets 17(3):216–232 47. Lengqvist J, Mata De Urquiza A, Bergman AC, Willson TM, Sjovall J, Perlmann T, Griffiths WJ (2004) Polyunsaturated fatty acids including docosahexaenoic and arachidonic acid bind to the retinoid X receptor alpha ligand-binding domain. Mol Cell Proteomics 3(7):692–703 48. Simpoulos AP (2008) The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases. Exp Biol Med (Maywood) 233(6):674–688 49. Biesalki HK, Erdman JW Jr, Hathcock J, Ellwood K, Beatty S, Johnson E, Marchioli R, Lauritzen L, Rice HB, Shao A, Griffiths JC (2013) Nutrient reference values for bioactives: new approaches needed? A conference report. Eur J Nutr 52(Suppl 1):1–9 50. Harris WS (2018) The Omega-6:omega-3 ratio: a critical appraisal and possible successor. Prostaglandins Leukot Essent Fatty Acids Prostag Leukotr Ess 132:34–40 51. Miura K, Hughes MCB, Ungerer JPJ, Smith DD, Green AC (2018) Absolute versus relative measures of plasma fatty acids and health outcomes: example of phospholipid omega-3 and omega-6 fatty acids and all-cause mortality in women. Eur J Nutr 57(2):713–722

Omega-3 Fatty Acids in Brain Health and Diseases 52. Bazinet RP, Laye S (2014) Polyunsaturated fatty acids and their metabolites in brain function and disease. Nat Rev Neurosci 15(12): 771–785 53. Hachem M, Geloen A, Van AL, Foumaux B, Fenart L, Gosselet F, Da Silva P, Breton G, Lagarde M, Picq M, Bernoud-Hubac N (2016) Efficient docosahexaenoic acid uptake by the brain from a structured phospholipid. Mol Neurobiol 53(5):3205–3215 54. Reimers A, Ljung H (2019) The emerging role of omega-3 fatty acids as a therapeutic option in neuropsychiatric disorders. Ther Adv Psychopharmacol 9: 2045125319858901 55. Lopez-Toledano MA, Thorsteinsson T, Daak A, Maki KC, Johns C, Rabinowicz AL, Sancilio FD (2017) A novel ω-3 acid ethyl Ester formulation incorporating advanced lipid technologies™ (ALT®) improves docosahexaenoic acid and eicosapentaenoic acid bioavailability compared with Lovaza®. Clin Ther 39(3):581–591 56. Hinriksdottir HH, Jonsdottir VL, Sveinsdottir K, Martinsdottir E, Ramel A (2015) Bioavailability of long-chain n-3 fatty acids from enriched meals and from microencapsulated powder. Eur J Clin Nutr 69(3): 344–348 57. Crawford MA, Bloom M, Cunnane S, Holsmen H, Ghebremeskel K, Parkington J, Broadhurst CL (2001) Docosahexaenoic acid and cerebral evolution. World Rev Nutr Diet 88:6–17 58. Gibbons A (2007) Paleoanthropology. Food for thought. N Y Sci J 316(5831):1558–1560 59. Whiteford HA, Ferrari AJ, Degenhardt L, Feigin V, Vos T (2015) The global burden of mental, neurological and substance use disorders: an analysis from the Global Burden of Disease Study 2010. PLoS One 10(2): e0116820 60. Malhi GS, Mann JJ (2018) Depression. Lancet (London-England) 392(10161): 2299–2312 61. Muller CP, Reichel M, Muhle C, Rhein C, Gulbins E, Kornhuber J (2015) Brain membrane lipids in major depression and anxiety disorders. Biochim Biophys Acta Gen Subj 185(8):1052–1065 62. Czysz AH, Rasenick MM (2013) G-protein signaling, lipid rafts and the possible sites of action for the antidepressant effects of n-3 polyunsaturated fatty acids. CNS Neurol Disord Drug Targets 12(4):466–473 63. Su KP, Huang SY, Peng CY, Lai HC, Huang CL, Chen YC, Aitchison KJ, Pariante CM (2010) Phospholipase A2 and cyclooxygenase

227

2 genes influence the risk of interferon-alphainduced depression by regulating polyunsaturated fatty acids levels. Biol Psychiatry 67(6): 550–557 64. Owen MJ, Sawa A, Mortensen PB (2016) Schizophrenia. Lancet (London, England) 388(10039):86–97 65. Alvarez-Jimenez M, Parker AG, Hetrick SE, McGorry PD, Gleeson JF (2011) Preventing the second episode: a systematic review and meta-analysis of psychosocial and pharmacological trials in first-episode psychosis. Schizophr Bull 37(3):619–630 66. Horrobin DF (1998) The membrane phospholipid hypothesis as a biochemical basis for the neurodevelopmental concept of schizophrenia. Schizophr Res 30(3):193–208 67. Horrobin DF, Glen AI, Vaddadi K (1994) The membrane hypothesis of schizophrenia. Schizophr Res 13(3):195–207 68. Pawelczyk T, Grancow-Grabka M, Trafalska E, Szemraj J, Pawelczyk A (2017) Oxidative stress reduction related to the efficacy of n-3 polyunsaturated fatty acids in first episode schizophrenia: secondary outcome analysis of the OFFER randomized trial. Prostaglandins Leukot 121:7–13 69. Phillips ML, Kupfer DJ (2013) Bipolar disorder diagnosis: challenges and future directions. Lancet (London, England) 381(9878):1663–1671 70. McNamara RK, Welge JA (2016) Metaanalysis of erythrocyte polyunsaturated fatty acid biostatus in bipolar disorder. Bipolar Disord 18(3):300–306 71. Clayton EH, Hanstock TL, Hirneth SJ, Kable CJ, Garg ML, Hazell PL (2009) Reduced mania and depression in juvenile bipolar disorder associated with long-chain ω-3 polyunsaturated fatty acid supplementation. Eur J Clin Nutr 63(8):1037–1040 72. Montgomery P, Richardson AJ (2008) Omega-3 fatty acids for bipolar disorder. Cochrane Database Syst Rev 2 73. Pamplona R, Dalfo E, Ayala V, Bellmunt MJ, Prat J, Ferrer I, Portero-Otin M (2005) Proteins in human brain cortex are modified by oxidation, glycoxidation, and lipoxidation: effects of Alzheimer disease and identification of lipoxidation targets. J Biol Chem 280(22): 21522–21530 74. Whitfield JF (2006) Can statins put the brakes on Alzheimer’s disease? Expert Opin Investig Drugs 15(12):1479–1485 75. Wang L, Fan H, He J, Wang L, Tian Z, Wang C (2018) Protective effects of omega-3 fatty acids against Alzheimer’s disease in rat brain endothelial cells. Brain Behav 8(11):e01037

228

Archana S. Rao et al.

76. Calon F, Cole G (2007) Neuroprotective action of omega-3 polyunsaturated fatty acids against neurodege. Prostaglandins Leukot 77(5–6):287–293 77. Olivera-Perez HM, Lam L, Dang J, Jiang W, Rodriguez F, Rigali E, Fiala M (2017) Omega-3 fatty acids increase the unfolded protein response and improve amyloid-β phagocytosis by macrophages of patients with mild cognitive impairment. FASEB J 31(10):4359 78. Tarasova TV, Lytkina OA, Roman AY, Bachurin SO, Ustyugov AA (2016) The role of alpha-synuclein in the development of the dopaminergic neurons in the substantia nigra and ventral tegmental area. Dokl Biol Sci 466: 5–7 79. Barros AS, Crispim RYG, Cavalcanti JU, Souza RB, Lemos JC, Filho C, Aguiar LMV (2017) Impact of the chronic omega-3 fatty acids supplementation in Hemiparkinsonism model induced by 6-hydroxydopamine in rats. Basic Clin Pharmacol Toxicol 120(6): 523–531 80. Serhan CN, Chiang N, Van Dyke TE (2008) Resolving inflammation: dual antiinflammatory and pro-resolution lipid mediators. Nat Rev Immunol 8(5):349–361 81. Nakamoto K (2017) A new pain regulatory system via the brain long chain fatty acid receptor GPR40/FFA1 signal. Yakugaku Zasshi 137(2):199–204 82. Ichimura A, Hirasawa A, Hara T, Tsujimoto G (2009) Free fatty acid receptors act as nutrient sensors to regulate energy homeostasis. Prostaglandins Other Lipid Mediat 89(3–4): 82–88 83. Cheshmehkani A, Senatorov IS, Kandi P, Singh M, Britt A, Hayslett R, Moniri NH (2015) Fish oil and flax seed oil supplemented diets increase FFAR4 expression in the rat colon. J Inflamm Res 64(10):809–815 84. Grosso G, Pajak A, Marventano S, Castellano S, Galvano F, Bucolo C, Drago F, Caraci F (2014) Role of omega-3 fatty acids in the treatment of depressive disorders: a comprehensive meta-analysis of randomized clinical trials. PLoS One 9(5):e96905 85. Ovcharova EM, Danovska MP, Marinova DL, Pendicheva DI, Tonchev PT, Shepherd NM (2022) Role of diet and supplementation with omega-3 polyunsaturated fatty acids for managing chronic fatigue in patients with relapsing-remitting multiple sclerosis. Biomed Res J 15(2):99–104 86. AlAmmar WA, Albeesh FH, Ibrahim LM, Algindan YY, Yamani LZ, Khattab RY (2021) Effect of omega-3 fatty acids and fish oil supplementation on multiple sclerosis: a

systematic review. Nutr Neurosci 24(7): 569–579 87. Langer-Gould A, Black LJ, Waubant E, Smith JB, Wu J, Gonzales EG, Shao X, Koebnick C, Lucas RM, Xiang A, Barcellos LF (2020) Seafood, fatty acid biosynthesis genes, and multiple sclerosis susceptibility. Mult Scler 26(12): 1476–1485 88. Li X, Bi X, Wang S, Zhang Z, Li F, Zhao AZ (2019) Therapeutic potential of ω-3 polyunsaturated fatty acids in human autoimmune diseases. Front Immunol 10:2241 89. Mostafa GA, Al-Ayadhi LY (2015) Reduced levels of plasma polyunsaturated fatty acids and serum carnitine in autistic children: relation to gastrointestinal manifestations. Behav Brain Funct 11:4 90. Bell JG, Miller D, MacDonald DJ, MacKinlay EE, Dick JR, Cheseldine S, O’Hare AE (2010) The fatty acid compositions of erythrocyte and plasma polar lipids in children with autism, developmental delay or typically developing controls and the effect of fish oil intake. Br J Nutr 103(8):1160–1167 91. Bu B, Ashwood P, Harvey D, King IB, Water JV, Jin LW (2006) Fatty acid compositions of red blood cell phospholipids in children with autism. Prostaglandins Other Lipid Mediat 74(4):215–221 92. Mazahery H, Stonehouse W, Delshad M, Kruger MC, Conlon CA, Beck KL, Von Hurst PR (2017) Relationship between long chain n-3 polyunsaturated fatty acids and autism spectrum disorder: systematic review and meta-analysis of case-control and randomised controlled trials. Nutrients 9(2):155 93. Akbar U, Yang M, Kurian D, Mohan C (2017) Omega-3 fatty acids in rheumatic diseases: a critical review. J Clin Rheumatol 23(6):330–339 94. Sarris J, Murphy J, Mischoulon D, Papakostas GI, Fava M, Berk M, Ng CH (2018) Adjunctive nutraceuticals for depression: a systematic review and meta-analyses. Focus (Am Psychiatr Publ) 16(3):328–340 95. Bai ZG, Bo A, Wu SJ, Gai QY, Chi I (2018) Omega-3 polyunsaturated fatty acids and reduction of depressive symptoms in older adults: a systematic review and meta-analysis. J Affect Disord 241:241–248 96. Bae JH, Kim G (2018) Systematic review and meta-analysis of omega-3-fatty acids in elderly patients with depression. Nutr Res 50:1–9 97. Hsu MC, Tung CY, Chen HE (2018) Omega-3 polyunsaturated fatty acid supplementation in prevention and treatment of maternal depression: putative mechanism

Omega-3 Fatty Acids in Brain Health and Diseases and recommendation. J Affect Disord 238: 47–61 98. Glen AI, Glen EM, Horrobin DF, Vaddadi KS, Spellman M, Morse-Fisher N, Ellis K, Skinner FS (1994) A red cell membrane abnormality in a subgroup of schizophrenic patients: evidence for two diseases. Schizophr Res 12(1):53–61 99. Amminger GP, Schafer MR, Klier CM, Slavik JM, Holzer I, Holub M, Goldstone S, Whitford TJ, McGorry PD, Berk M (2012) Decreased nervonic acid levels in erythrocyte membranes predict psychosis in help-seeking ultra-high-risk individuals. Mol Psychiatry 17(12):1150–1152 100. Schlo¨gelhofer M, Amminger GP, Schaefer MR, Fusar-Poli P, Smesny S, McGorry P, Berger G, Mossaheb N (2014) Polyunsaturated fatty acids in emerging psychosis: a safer alternative? Early Interv Psychiatry 8(3):199–208 101. Amminger GP, Schafer MR, Papageorgiou K, Klier CM, Cotton SM, Harrigan SM, Mackinnon A, McGorry PD, Berger GE (2010) Long-chain omega-3 fatty acids for indicated prevention of psychotic disorders: a randomized, placebo-controlled trial. Arch Gen Psychiatry 67(2):146–154 102. Emsley R, Niehaus DJ, Koen L, Oosthuizen PP, Turner HJ, Carey P, van Rensburg SJ, Maritz JS, Murck H (2006) The effects of eicosapentaenoic acid in tardive dyskinesia: a randomized, placebo-controlled trial. Schizophr Res 84(1):112–120 103. Chiu CC, Huang SY, Su KP, Lu ML, Huang MC, Chen CC, Shen WW (2003) Polyunsaturated fatty acid deficit in patients with bipolar mania. Eur Neuropsychopharmacol 13(2): 99–103 104. Evans SJ, Ringrose RN, Harrington GJ, Mancuso P, Burant CF, McInnis MG (2014) Dietary intake and plasma metabolomic analysis of polyunsaturated fatty acids in bipolar subjects reveal dysregulation of linoleic acid metabolism. J Psychiatr Res 57:58–64 105. Fristad MA, Young AS, Vesco AT, Nader ES, Healy KZ, Gardner W, Wolfson HL, Arnold LE (2015) A randomized controlled trial of individual family psychoeducational psychotherapy and omega-3 fatty acids in youth with subsyndromal bipolar disorder. J Child Adolesc Psychopharmacol 25(10):764–774 106. Sarris J, Mischoulon D, Schweitzer I (2012) Omega-3 for bipolar disorder: meta-analyses

229

of use in mania and bipolar depression. J Clin Psychiatry 73(1):81–86 107. Olloquequi J, Ettcheto M, Cano A, SanchezLo´pez E, Carrasco M, Espinosa T, BeasZarate C, Gudino-Cabrera G, UrenaGuerrero ME, Verdaguer E, Folch J, Auladell C, Camins A (2022) Impact of new drugs for therapeutic intervention in Alzheimer’s disease. Front Biosci 27(5):146 108. Faxen-Irving G, Freund-Levi Y, EriksdotterJonhagen M, Basun H, Hjorth E, Palmblad J, Vedin I, Cederholm T, Wahlund LO (2013) Effects on transthyretin in plasma and cerebrospinal fluid by DHA-rich n-3 fatty acid supplementation in patients with Alzheimer’s disease: the OmegAD study. J Alzheimers Dis 36(1):1–6 109. Tamtaji OR, Taghizadeh M, Aghadavod E, Mafi A, Dadgostar E, Daneshvar Kakhak R, Abolhassani J, Asemi Z (2019) The effects of omega-3 fatty acids and vitamin E co-supplementation on gene expression related to inflammation, insulin and lipid in patients with Parkinson’s disease: a randomized, double-blind, placebo-controlled trial. Clin Neurol Neurosurg 176:116–121 110. Pantzaris MC, Loukaides GN, Ntzani EE, Patrikios IS (2013) A novel oral nutraceutical formula of omega-3 and omega-6 fatty acids with vitamins (PLP10) in relapsing remitting multiple sclerosis: a randomised, doubleblind, placebo-controlled proof-of-concept clinical trial. BMJ Open 3(4):e002170 111. Bent S, Bertoglio K, Hendren RL (2009) Omega-3 fatty acids for autistic spectrum disorder: a systematic review. Rev J Autism Dev Disord 39(8):1145–1154 112. James S, Montgomery P, Williams K (2011) Omega-3 fatty acids supplementation for autism spectrum disorders (ASD). Cochrane Database Syst Rev 11:CD007992 113. Mazahery H, Stonehouse W, Delshad M, Kruger MC, Conlon CA, Beck KL, Von Hurst PR (2017) Relationship between long chain n-3 polyunsaturated fatty acids and autism spectrum disorder: systematic review and meta-analysis of case-control and randomised controlled trials. Nutr J 9(2):155 114. Noroozieseyedhosseini H, Imani R, Sumra B, Tariq M (2023) The effects of omega-3 and omega-6 fatty acids on the mental and physical health of children, adolescents, and adults

Chapter 16 Microglial Uptake of Extracellular Tau by Actin-Mediated Phagocytosis Hariharakrishnan Chidambaram, Smita Eknath Desale, Tazeen Qureshi, and Subashchandrabose Chinnathambi Abstract Microglia are scavengers of the brain environment that clear dead cells, debris, and microbes. In Alzheimer’s disease, microglia get activated to phagocytose damaged neurons, extracellular Amyoid-β, and Tau deposits. Several Tau internalization mechanisms of microglia have been studied which include phagocytosis, pinocytosis, and receptor-mediated endocytosis. In this chapter, we have visualized microglial phagocytic structures that are actin-rich cup-like extensions, which surrounds extracellular Tau species by widefield fluorescence and confocal microscopy. We have shown the association of filamentous actin in Tau phagocytosis along the assembly of LC-3 molecules to phagosomes. The 3-dimensional, orthogonal and gallery wise representation of these phagocytic structures provides an overview of the phagocytic mechanism of extracellular Tau by microglia. Key words Tau, Microglia, Phagocytosis, Actin cytoskeleton

1

Introduction Microglia are the brain scavenging cells that provide first-line defense by its constant surveillance of the micro-environment by undergoing morphological changes through its extensive actin dynamics [1, 2]. They are the resident macrophage cells of the central nervous system that sense the extracellular environment by its actin cytoskeleton coupled to a range of surface receptors [2]. During brain development, microglia are involved in the shaping of synapses by synaptic pruning and promotes maturation of neuronal networks [3]. Tau, a microtubule-associated protein, is involved in microtubule stabilization and cargo trafficking [4, 5]. Human Tau is a 441 amino acid protein expressed in neuronal cells with a microtubule-binding domain and two flanking ends [5]. In Alzheimer’s disease (AD), Tau protein misfolds due to several

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post-translational modifications such as phosphorylation, acetylation, and glycation [6–9]. Misfolded Tau self-aggregates to form oligomers and aggregates in the intra- and extracellular sites of neuronal cells that act as seeds and infect healthy neurons by prion-like propagation [10]. Several small molecules have been characterized for Tau aggregation inhibition and disaggregation of pre-formed filaments formed under AD conditions [11– 18]. Apart from this, microglia are activated by these extracellular Tau deposits and are involved in its clearance by active internalization [19–22]. Microglia internalize Tau via heparan sulfate proteoglycan (HSPGs)-mediated mechanism which either accumulates in the cytosol as seeds or degraded by lysosomes [23, 24]. Monomeric Tau is internalized by micropinocytosis and aggregated Tau by dynamin-dependent pathways that are enveloped in endosomes for Tau degradation [25]. Tau is also reported to be internalized by receptor-mediated endocytosis by interacting through microglial membrane receptors, such as P2Y12 and CX3CR1 [19, 21, 26, 27]. Microglia, as migratory cells, undergo actin cytoskeleton remodeling for the phagocytosis of extracellular particles such as Tau [20]. The ratio of filamentous actin (F-actin) to globular actin (G-actin) plays a significant role in microglial migration and phagocytosis. Actin-binding proteins such as actin-related proteins 2/3, Rho family of GTPases, Cofilin, and profiling maintain the physiological levels of G- and F-actin ratio in microglial cells [28]. Actin polymerization plays a role in several physiological functions of microglia such as membrane ruffling, phagosome formation, endocytosis, and migratory structures such as uropod, filopodia formation for the migration [29]. Specific cell surface receptors such as toll-like receptors (TLRs), triggering receptor expressed on myeloid cells 2 (TREM-2), Fc receptors, and G-protein-coupled receptors participate in sensing and clearance of the extracellular toxic materials by active phagocytosis. Apoptotic debris such as amyloid proteins induces phagocytosis through ERK/PKC signaling pathways by interacting with TREM-2 receptor [30, 31]. Extracellular nucleotides induce phagocytosis through purinergic receptors such as P2Y6 that mediates PLC/InsP3 pathway [32]. Whereas other components such as lipopolysaccharides, viral nucleotides, and amyloid-β provoke phagocytosis either by activation of scavenger receptors pathway or Cdc42/Rac signaling pathway [33–35]. In postnatal developing cerebellum of Npc1 nmf164 mice, the phagocytic cups diameter ranged from 6 to 7 μm [36]. Similarly, in the developing amygdala of juvenile rats, microglial phagocytic cups ranged up to 8–9 μm depending on the size of target [37]. In this chapter, we discuss the visualization actin phagocytic structures of N9 microglial cells by fluorescence microscopy. The z-stack analysis showed the association of LC-3 molecules in phagosomes containing extracellular Tau. The confocal microscopy provided a better resolved structures of microglial phagocytosis.

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Materials

2.1 Reagents and Antibodies

1. Tau species preparation: Soluble monomeric Tau, dithiothreitol (DTT), heparin (17,500 Da), sodium azide, protease inhibitor cocktail (PIC), N, N-bis(2-hydroxyethyl)2-aminoethanesulfonic acid, Glutaraldehyde, 0.45 μm membrane filter, and PBS buffer pH 7.4. 2. 100 mM DTT preparation (50 mL): Add 694 mg in 45 mL of sodium acetate buffer pH: 5.4. Made up volume to 50 mL. Filter using 0.45 μM membrane syringe filter. 3. 1 mM Heparin (17,500 Da): Dissolve 17.5 mg heparin in 1000 μL water and filter using 0.45 μM membrane syringe filter. 4. Alexa-fluor 647 labelling of Tau: Alexa fluor 647 labelled C2 Maleimide, tris (2-carboxyethyl) phosphine (TCEP), and 5 kDa molecular weight cut-off centricons. 5. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE): Acrylamide, bis-acrylamide, tris, sodium dodecyl sulphate (SDS), ammonium persulfate (APS), N,N,N’,N’tetramethylethylenediamine (TEMED), 6x protein loading dye, bio-rad dual color protein marker. 6. Thioflavin-S assay: Thioflavin-S, methanol, ammonium acetate, 384 black well micro plates. 7. Negative staining: Uranyl acetate, glutaraldehyde, 400 mesh carbon-coated copper grid. 8. Cell culture: N9 microglial cell stocks, RPMI 1640 GlutaMax media, fetal bovine serum (FBS), dimethyl sulfoxide (DMSO), trypsin-EDTA, penicillin-streptomycin, 0.22 μm syringe filters, syringes, centrifuge tubes, sterile petridishes, multi well plates, and sterile coverslips. 9. Freezing media (5%): Add 5% sterile DMSO to fetal bovine serum and filter sterilize with 0.22 μm syringe filter. 10. Four percent paraformaldehyde solution: Add 4 g of paraformaldehyde in 100 mL PBS buffer (pH 7.4) and heat it to 60 ° C. Add NaOH drop wise, until paraformaldehyde is completely dissolved. Adjust the pH back to 7.4 and filter sterilize with 0.22 μm syringe filter. 11. Immunofluorescence assay: Paraformaldehyde, Triton-X 100, horse serum, parafilm sheets, DAPI, Prolong Diamond Antifade mounting media, immersion oil, tweezers, glass slides. 12. Antibodies: Alexa-fluor 488 tagged Phalloidin, primary antibody: LC-3 rabbit antibody, secondary antibody: Goat antirabbit antibody conjugated to Alexa-fluor 555.

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Instruments

1. AKTA pure chromatography system. 2. Tecan plate reader Infinite 200 Pro. 3. SDS-PAGE unit (Bio-Rad). 4. Tecnai T20 transmission electron microscope 200 KeV (TEM). 5. FEI high-resolution 300 KeV (HR-TEM).

transmission

electron

microscope

6. Laminar flow chambers with HEPA filters, cell culture incubators with CO2 supply. 7. Zeiss Axio observer 7.0 with Apotome 2.0 microscope with Plan Apochromat 63×/1.40 Oil DIC M27 objective and Axiocam 503 camera. 8. Leica Stellaris 5 confocal microscope with Plan Apochromat 100×/1040 objective and HyD S silicon detectors. 9. -80 °C deep freezers, Incubators, centrifuges, shakers, etc., 2.3

Software

1. Zen 2.3 software. 2. Leica Application Suite X 4.5 software. 3. GraphPad Prism 8.0.1.

3

Methods

3.1 Preparation of Tau (Monomer and Aggregate)

1. Follow the protocol for recombinant Tau protein expression and purification by Chidambaram et al. 2020 and 2022 [19, 38]. Higher order Tau aggregates are prepared in vitro with the polyanionic co-factor, heparin. 2. Incubate 100 μM monomeric Tau with heparin in the ratio of 4:1 in 20 mM BES buffer containing 25 mM NaCl and 1 mM DTT. 3. Incubate the mixture for 72 h at 37 °C and confirm by SDSPAGE (see Note 1).

3.2 Labeling and Characterization of Tau Species

1. Label Tau species at cysteine residues with Alexa-fluor 647 conjugated C2 maleimide. 2. Mix Tau and C2 maleimide at 1:2 molar ratio (see Note 2) and incubate with shaking conditions at 4 °C for 12 h. 3. Wash off excess dye with PBS buffer using 5 kDa centrifugal filters. 4. Estimate the final concentration of the protein by BCA assay with 1 mg/mL BSA solution as a standard protein. 5. Labelled Tau monomer and aggregates can be characterized by various biochemical and biophysical such as SDS-gel

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electrophoresis, ThS fluorescence assay, transmission electron microscopy, and high-resolution transmission electron microscopy. 3.3 Culturing of N9 Microglial Cells

1. Revive N9 microglial cells (mouse) stored in freezing media with excess RPMI media containing 10% FBS and centrifuge at 800 rpm for 5 min. 2. Resuspend the cells in RMPI media and incubate at culture plates at 37 °C with 5% CO2. 3. Passage the cells and allow it to attain at least 80% confluency and split into different culture dishes to acquire the desired quantity of cells. 4. Excess cells can be resuspended in freezing media and stored at -80 °C.

3.4 ImmunofluorescenceImmunofluorescence Assay

1. Extracellular exposure of Tau to N9 microglial cells: Place clean and sterile coverslips (12 mm) in multi-well plates (24 well) and seed N9 microglial cells with a count of 15,000 cells per well (see Note 3). Incubate the cells in RPMI media containing 10% FBS for 24 h at 37 °C with 5% CO2 supply. Expose microglial cells to 1 μm monomeric and aggregated Tau labelled with Alexa-fluor 647 individually in reduced serum media (0.5% FBS) for different time intervals such as 3, 6, 12, and 24 h. 2. Fixation and permeabilization: Fix the Tau-exposed microglial cells at different time points to determine the role of microglia in Tau phagocytosis in a time-dependent manner. Add ice-cold paraformaldehyde solution (4%) and incubate for 20 min at room temperature (see Note 4). The cells can be stored at 4 °C in PBS buffer followed by fixation. Permeabilize cells with PBS buffer containing 0.2% Triton-X (PBS-t) for 20 min at shaking conditions (see Note 5). 3. Antibody staining: Before staining the cells with desired antibodies, block the cells with PBS-t buffer containing 5% horse serum for 1 h at shaking conditions. This prevents non-specific antibody binding to the cells. Incubate coverslips with primary antibodies at desired dilutions (with blocking buffer) (see Note 6) and in humid condition/chamber for 12–16 h at 4 °C. LC3B polyclonal antibody is used at a dilution of 1:400 and Alexa-fluor 488 tagged phalloidin at 1:40 dilution that stains the F-actin cytoskeleton of microglial cells. After primary incubation, wash coverslips with PBS-t buffer thrice for 10 min each. Add Alexa-four 555 tagged anti-rabbit antibody at a dilution of 1:500 with blocking buffer and incubate coverslips for 1 h at room temperature followed by PBS-t wash thrice. At last, incubate coverslips with 300 nM DAPI in PBS buffer for 10 min at room temperature for nuclear staining. Mount

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coverslips on glass glides with Prolong Diamond Antifade mounting media and cure for 24 h at room temperature (see Note 7). 3.5 Imaging of N9 Microglial Cells for Tau Phagocytosis by Fluorescence Microscopy

1. Observe control groups and Tau-treated coverslips under Zeiss Axio Observer 7.0 with Apotome 2.0 wide-field fluorescence microscope. 2. Identify microglial cells with actin-rich migratory structures such as lamellipodia and uropod by phalloidin staining that binds to F-actin. 3. Identify phagocytic structures and finger-like projections along actin-rich filaments that surround extracellular materials for active internalization at all-time intervals of monomer and aggregate-treated groups. 4. Quantify the internalized Tau intensity at different time intervals by using the contour tool of Zen 2.3 software. Quantify and plot Tau monomer and aggregate internalization using GraphPad Prism 8.0.1.

3.6 Analysis of Microglial Phagocytic Structures

1. Use Zen 2.3 software to acquire images from fluorescence microscopy. Once the image is captured, adjust the black and white balance of the image to get the representative view of phagocytic-cup structures at different time points (Fig. 1a) (see Note 8). 2. Focus on fields having microglial cells with cup-like actin structures to show the phagocytosis of extracellular Tau. Further, analyze the same field by acquiring Z-stack images at an interval of 0.24 μm (20–25 slices) that covers a thickness of about 5–6 μm along Z-axis (Fig. 1b). 3. Z-stack images provide the 3-dimensional information of the field, which could be used to visualize the LC-3-associated phagocytosis by microglia. The channel-wise gallery of z-stack provides the localization of different targets such as internalized Tau, LC-3, and F-actin at different planes along Z-axis (Fig. 1c) (see Note 9). 4. Select the field of microglial cells with phagocytic cups (field of interest) and visualize in 3D representation showing clear colocalization of F-actin with Tau and LC-3 (Fig. 1d) (see Note 10).

3.7 Imaging of Phagocytic Microglia by Leica Stellaris 5 Confocal Microscopy

1. Next, observe microglial cells under Leica Stellaris 5 confocal microscope to visualize the phagocytic structures with better resolution using 100× objective and Hydra detectors. 2. Acquire the images by confocal scanning of the desired field. From the LAS-X window, the required lasers must be turned

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Fig. 1 Analysis of microglial phagocytic structures by fluorescence microscopy. (a) N9 microglial cells treated with Alexa-fluor 647-labelled Tau for 24 h are observed for phagocytic structures and compared with cell control by fluorescence microscopy. The phagosomes are visualized by phalloidin and LC-3 that stains filamentous actin and LC-3 molecules. Scale bar represents 10 μm. (b) Orthogonal representation of actinrich phagocytic structures showing three-point colocalization between LC-3, F-actin, and labeled Tau. The left and bottom axis shows colocalization between LC3, F-actin, and Tau whereas right and top axis represents colocalization between LC3 and F-actin for better visualization. (c). Z-stack gallery showing individual channels on Y-axis and z-stack images of every channel on X-axis. (d) Three-dimensional representation of phagocytic cups enriched with filamentous actin and LC-3 at the site of phagocytosis

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on from the software before adding the excitation, emission range, and detectors (see Note 11). 3. Once the fluorophore has been added, the image scanning sequence must be selected with “line sequential” or “frame sequential” or “stack sequential” as per the requirement (see Note 12). 4. The default gain value of any laser in set to 2.5. This value could be set to a minimum of 100 without losing the image quality. Set the gain values accordingly and the laser intensities must be optimized and minimized that has been set to 2% as default. 5. Laser optimization: Select the desired laser window and start the “fast live” option to optimize for a minimum laser emission and the images are captured at least laser intensities (see Notes 13 and 14). 6. All the images could be captured at 2048 × 2048 format with a speed of 100× for a better image resolution. N9 microglial cells treated with Alexa-fluor 647-labelled Tau for different time intervals up to 24 h are observed for phagocytic structures and compared with cell control (Fig. 2a) (see Note 15). 7. 3D image analysis: Z-stack window provides 3D view of samples upon setting the begin and end Z-positions of your sample. Either of the option could be selected “number of steps” or “step size” for 3D imaging and the same can be visualized in 3D viewer option. A step size of 0.24 μm has been used to generate z-stack files. The orthogonal visualization of the phagocytic actin structures has been performed from z-stack images (Fig. 2b). 8. Open the 3D tab of display window for visualization of captured z-stack images. The required parameters such as intensity adjustment can be performed and generate the high-quality images as .tiff files (Fig. 2c) (see Note 16).

4

Notes 1. Tau aggregates are SDS stable filaments which could be visualized in SDS-gel electrophoresis as a smear of higher order proteins with longer filaments deposited at the wells. 2. C2 maleimide binds to -SH group of cysteine residues. Human full-length Tau consists of two cysteine residues, and hence, double molar ratio of dye should be added to Tau. 3. Wash the coverslips thoroughly in 70% ethanol solution, dry at room temperature, and sterilize to prevent any contamination from coverslips.

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Fig. 2 Microglial phagocytosis visualized by Leica Stellaris 5 confocal microscope. (a) Confocal microscope images of N9 microglia treated with Alexa-fluor 647-labelled monomeric and aggregated Tau for 24 h are stained with LC3 and phalloidin (F-actin) observed for phagocytosis. The 2-dimensional representation of microglial cells phagocyting extracellular Tau colocalizes with LC3 and F-actin (white arrows indicate phagocytic structures in Tau-treated groups). The scale bar represents 5 μm. (b) Orthogonal representation of Tau phagocytosis by microglial cells. The left and bottom axis shows colocalization between LC3 and F-actin, whereas right and top axis shows colocalization between Tau, LC3, and F-actin. (c) Orthogonal and 3-dimensional view of phagocytic cup formation (region of interest from the Fig. 4b), showing a 3-point colocalization between Tau, LC3, and F-actin

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4. Ice cold 4% paraformaldehyde should be used to prevent the cellular stress that could lead to change in morphology, rounding up of cells and loss of actin-rich migratory structures such as filopodia and uropod. 5. The permeabilization step removes few lipid molecules from the cell membrane that create pores for the antibody molecules to reach the intracellular target proteins. 6. Optimize the primary antibody dilution with different dilutions before using for the experiment. To minimize the use of antibody, coverslips can be placed on petridishes containing parafilm sheets with cells on the upper side. Add 50–100 μL of diluted antibody per coverslip and maintain humid condition to prevent drying of coverslips. 7. For long-term storage of the coverslips, seal the edges with sealant (e.g., Nail paint), aluminum wrap to prevent photo bleaching, and store at 4 °C. 8. The black and white intensity must maintain uniform between different treatment groups and time intervals for comparison. 9. Process z-stack images by adjusting the black and white balance across the z-axis. Open the gallery tab with different channels along Y-axis and z-stack images along X-axis. The phagocytic cup can be selected as a region of interest and focused for the z-stack gallery visualization. 10. The same region of interest (actin-rich phagocytic cups) can be visualized in 3D along X-, Y-, and Z-axes from the 3D tab of Zen 2.3 software. Rotating the structure along different axis shows the association of actin structures along extracellular Tau. 11. The necessary lasers should be turned on before scanning from the “LASER” tab of LAS X window, and the required dye could be added from the selection tab and dragged to the laser parameter window. 12. Dye assistant could be used to select the best configuration for a maximum yield and minimum cross talk. 13. The intensity saturation should be checked from the “Over/ under exposure” option provided on the right top of display window. This option comes with three displays—saturation, gray scale, and RGB images. The blue represents saturation, and the laser intensities should be optimized to avoid saturation points in the image; red—signal and green—no signal. 14. You can also save your laser parameters and load for later use provided as “Save "” and “load#” option provided in the LAS X window.

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15. Select your required format from a wide range of configurations and the imaging speed from xyz window. The format decides your pixel size that corresponds to the image resolution. The speed corresponds to noise reduction; i.e., reduced speed improves the image quality by reducing the background noise. This may also lead to increased laser exposure. 16. The orthogonal and 3D view of the desired region of interest can be better visualized and captured from the 3D tab of display window.

Acknowledgments The author is grateful to Chinnathambi’s lab members for their scientific suggestions on the manuscript. CHK, SD, and TQ acknowledges the Department of Science and Technology, Department of Biotechnology, and Government of India for the fellowship. The authors acknowledge the Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences (NIMHANS), and the Institute of National Importance, Bengaluru, for their internal support. References 1. Cowan M, Petri WA Jr (2018) Microglia: immune regulators of neurodevelopment. Front Immunol 9:2576 2. Franco-Bocanegra DK, McAuley C, Nicoll JA, Boche D (2019) Molecular mechanisms of microglial motility: changes in ageing and Alzheimer’s disease. Cell 8(6):639 3. Bilbo SD, Schwarz JM (2012) The immune system and developmental programming of brain and behavior. Front Neuroendocrinol 33(3):267–286 4. Binder LI, Frankfurter A, Rebhun LI (1985) The distribution of tau in the mammalian central nervous system. J Cell Biol 101(4): 1371–1378 5. Conde C, Ca´ceres A (2009) Microtubule assembly, organization and dynamics in axons and dendrites. Nat Rev Neurosci 10(5): 319–332 6. Gong C-X, Liu F, Grundke-Iqbal I, Iqbal K (2005) Post-translational modifications of tau protein in Alzheimer’s disease. J Neural Transm 112(6):813–838 7. Martin L, Latypova X, Terro F (2011) Posttranslational modifications of tau protein: implications for Alzheimer’s disease. Neurochem Int 58(4):458–471

8. Sonawane SK, Chinnathambi S (2018) Prionlike propagation of post-translationally modified tau in Alzheimer’s disease: a hypothesis. J Mol Neurosci 65(4):480–490 9. Sonawane SK, Dubey T, Balmik AA, Das R, Chinnathambi S (2021) Alzheimer’s disease pathology: a tau perspective. In: Govindaraju T (ed) Alzheimer’s disease: recent findings in pathophysiology, diagnostic and therapeutic modalities. The Royal Society of Chemistry 10. Das R, Chinnathambi S (2019) Microglial priming of antigen presentation and adaptive stimulation in Alzheimer’s disease. Cell Mol Life Sci 76(19):3681–3694 11. Balmik AA, Das R, Dangi A, Gorantla NV, Marelli UK, Chinnathambi S (2020) Melatonin interacts with repeat domain of tau to mediate disaggregation of paired helical filaments. Biochim Biophys Acta Gen Subj 1864(3):129467 12. Das R, Balmik AA, Chinnathambi S (2020) Effect of melatonin on tau aggregation and tau-mediated cell surface morphology. Int J Biol Macromol 152:30–39 13. Gorantla NV, Das R, Chidambaram H, Dubey T, Mulani FA, Thulasiram HV, Chinnathambi S (2020) Basic limonoid modulates

242

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chaperone-mediated proteostasis and dissolve tau fibrils. Sci Rep 10(1):4023 14. Gorantla NV, Landge VG, Nagaraju PG, Priyadarshini CGP, Balaraman E, Chinnathambi S (2019) Molecular cobalt (II) complexes for tau polymerization in Alzheimer’s disease. ACS Omega 4(16):16702–16714 15. Sonawane SK, Ahmad A, Chinnathambi S (2019) Protein-capped metal nanoparticles inhibit tau aggregation in Alzheimer’s disease. ACS Omega 4(7):12833–12840 16. Sonawane SK, Balmik AA, Boral D, Ramasamy S, Chinnathambi S (2019) Baicalein suppresses repeat tau fibrillization by sequestering oligomers. Arch Biochem Biophys 675: 108119 17. Sonawane SK, Chidambaram H, Boral D, Gorantla NV, Balmik AA, Dangi A, Ramasamy S, Marelli UK, Chinnathambi S (2020) EGCG impedes human tau aggregation and interacts with tau. Sci Rep 10(1):12579 18. Sonawane SK, Chinnathambi S (2021) Epigallocatechin-3-gallate modulates tau posttranslational modifications and cytoskeletal network. Oncotarget 12(11):1083 19. Chidambaram H, Das R, Chinnathambi S (2022) G-protein coupled purinergic P2Y12 receptor interacts and internalizes TauRDmediated by membrane-associated actin cytoskeleton remodelling in microglia. Eur J Cell Biol:151201 20. Das R, Balmik AA, Chinnathambi S (2020) Phagocytosis of full-length tau oligomers by actin-remodeling of activated microglia. J Neuroinflammation 17(1):1–15 21. Das R, Chinnathambi S (2021) Microglial remodeling of actin network by tau oligomers, via G protein-coupled purinergic receptor, P2Y12R-driven chemotaxis. Traffic 22(5): 153–170 22. Desale SE, Chinnathambi S (2021) α–Linolenic acid modulates phagocytosis and endosomal pathways of extracellular tau in microglia. Cell Adhes Migr 15(1):84–100 23. Kolay S, Vega AR, Dodd DA, Perez VA, Kashmer OM, White CL, Diamond MI (2022) The dual fates of exogenous tau seeds: lysosomal clearance versus cytoplasmic amplification. J Biol Chem 298(6) ´ vila J, 24. Perea JR, Lo´pez E, Dı´ez-Ballesteros JC, A Herna´ndez F, Bolo´s M (2019) Extracellular monomeric tau is internalized by astrocytes. Front Neurosci 13:442

25. Evans LD, Wassmer T, Fraser G, Smith J, Perkinton M, Billinton A, Livesey FJ (2018) Extracellular monomeric and aggregated tau efficiently enter human neurons through overlapping but distinct pathways. Cell Rep 22(13): 3612–3624 26. Bolo´s M, Llorens-Martı´n M, Perea JR, JuradoArjona J, Ra´bano A, Herna´ndez F, Avila J (2017) Absence of CX3CR1 impairs the internalization of tau by microglia. Mol Neurodegener 12(1):59 27. Chidambaram H, Das R, Chinnathambi S (2020) Interaction of tau with the chemokine receptor, CX3CR1 and its effect on microglial activation, migration and proliferation. Cell Biosci 10(1):1–9 28. Pelucchi S, Stringhi R, Marcello E (2020) Dendritic spines in Alzheimer’s disease: how the actin cytoskeleton contributes to synaptic failure. Int J Mol Sci 21(3):908 29. Bosch M, Castro J, Saneyoshi T, Matsuno H, Sur M, Hayashi Y (2014) Structural and molecular remodeling of dendritic spine substructures during long-term potentiation. Neuron 82(2):444–459 30. Jones B (2013) TREM2 linked to late-onset AD. Nat Rev Neurol 9(1):5–5 31. Zhao Y, Wu X, Li X, Jiang L-L, Gui X, Liu Y, ˜ a-Crespo JC, Zhang M Sun Y, Zhu B, Pin (2018) TREM2 is a receptor for β-amyloid that mediates microglial function. Neuron 97(5):1023–1031.e1027 32. Koizumi S, Shigemoto-Mogami Y, NasuTada K, Shinozaki Y, Ohsawa K, Tsuda M, Joshi BV, Jacobson KA, Kohsaka S, Inoue K (2007) UDP acting at P2Y6 receptors is a mediator of microglial phagocytosis. Nature 446(7139):1091–1095 33. Hanke ML, Kielian T (2011) Toll-like receptors in health and disease in the brain: mechanisms and therapeutic potential. Clin Sci 121(9): 367–387 34. Ribes S, Ebert S, Regen T, Czesnik D, Scheffel J, Zeug A, Bunkowski S, Eiffert H, Hanisch UK, Hammerschmidt S (2010) Fibronectin stimulates Escherichia coli phagocytosis by microglial cells. Glia 58(3):367–376 35. Song M, Jin J, Lim J, Kou J, Pattanayak A, Rehman J, Kim H, Tahara K, Lalonde R, Fukuchi K (2011) TLR4 mutation reduces microglial activation, increases Abeta deposits and exacerbates cognitive deficits in a mouse model of Alzheimer’s disease. J Neuroinflammation 8:92

Uptake of Extracellular Tau by Actin-mediated Phagocytosis 36. Rava A, La Rosa P, Palladino G, Dragotto J, Totaro A, Tiberi J, Canterini S, Oddi S, Fiorenza MT (2022) The appearance of phagocytic microglia in the postnatal brain of Niemann Pick type C mice is developmentally regulated and underscores shortfalls in fine odor discrimination. J Cell Physiol 237(12):4563–4579

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37. VanRyzin JW, Marquardt AE, Argue KJ, Vecchiarelli HA, Ashton SE, Arambula SE, Hill MN, McCarthy MM (2019) Microglial phagocytosis of newborn cells is induced by endocannabinoids and sculpts sex differences in juvenile rat social play. Neuron 102(2):435–449.e436 38. Chidambaram H, Chinnathambi S (2020) Role of cysteines in accelerating tau filament formation. J Biomol Struct Dyn:1–10

Chapter 17 Internalization and Endosomal Trafficking of Extracellular Tau in Microglia Improved by α-Linolenic Acid Smita Eknath Desale, Hariharakrishnan Chidambaram, Tazeen Qureshi, and Subashchandrabose Chinnathambi Abstract Alzheimer’s disease (AD) is distinguished by extracellular accumulation of amyloid-beta plaques and intracellular neurofibrillary tangles of Tau. Pathogenic Tau species are also known to display “prion-like propagation,” which explains their presence in extracellular spaces as well. Glial population, especially microglia, tend to proclaim neuroinflammatory condition, disrupted signaling mechanisms, and cytoskeleton deregulation in AD. Omega-3 fatty acids play a neuroprotective role in the brain, which can trigger the anti-inflammatory pathways as well as actin dynamics in the cells. Improvement of cytoskeletal assembly mechanism by omega-3 fatty acids would regulate the other signaling cascades in the cells, leading to refining clearance of extracellular protein burden in AD. In this study, we focused on analyzing the ability of α-linolenic acid (ALA) as a regulator of actin dynamics to balance the signaling pathways in microglia, including endocytosis of extracellular Tau burden in AD. Key words α-Linolenic acid (ALA), Tau seed, Endosomal trafficking, Actin dynamics, Rab7

1

Introduction Endocytosis is a process of antigen engulfment, where the membrane-associated invagination is formed for intracellular vesicle formation. Endocytosis-associated events such as membrane curvature formation, invagination, vesicle detachment from the membrane, and intracellular trafficking of the vesicle during endosomal maturation are actin cytoskeleton dependent processes [1]. Constant assembly of F-actin via actin nucleator proteins such as Arp2/3 and WASP drives the force to create the membrane invagination and vesicle trafficking in the cell [2]. Here, along with actin, microtubule tracks are also responsible for the endosomal maturation before meeting lysosomal-mediated degradation of an antigen [3]. The cytosolic pool of F- and G- actin is a limiting step for vesicle trafficking and signaling events in the cell. Continuous

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_17, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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flow of F-actin synthesis decides the fate of a signaling event, including phagocytosis, vesicle trafficking, and migration [4– 6]. Hence inhibition of F-actin assembly in the cell by various inhibitors such as latrunculin and cytochalasin would hamper the phagocytosis and endocytosis events, which denotes the dependency of these processes on actin remodeling [6, 7]. In neurodegenerative diseases like Alzheimer’s disease (AD), the progressive accumulation of aggregated proteins such as Tau fibrils and amyloid-beta plaques in intra and extracellular environment leads to neuroinflammation and cell death [8, 9]. In this condition, malfunctioning of immune cells of the brain such as microglia would propel the severity of disease. Due to disrupted signaling and cytoskeletal control, these cells tend to show abrupt activation with the loss of surveillant state, leading to neuroinflammation [10]. This also resists the propensity of microglia to clear the extracellular aggregated amyloid-beta and Tau seeds. Omega-3 fatty acids are known as potent neuroprotective agents, which effect neuroplasticity, membrane composition, receptor expression, and signaling events [11, 12]. The insertion of long-chain fatty acids in cell membranes by omega-3 fatty acids increases their flexibility and promotes an anti-inflammatory response in microglia. This is because the excess of omega-3 fatty acids can modify the composition of the cell membrane, making it more flexible and less prone to inflammation [13, 14]. Here, we hypothesize to induce actin dynamics with extracellular ALA exposure in microglia to increase clearance of extracellular aggregated protein, especially Tau seed in AD.

2 2.1

Materials Cell Culture

1. N9 microglia cells. 2. RPMI 1640 media. 3. 1× pen-strep solution. 4. 1× anti-anti solution. 5. 10% fetal bovine serum (FBS). 6. 1× phosphate-buffered saline (PBS). 7. 1× trypsin-EDTA solution. 8. Latrunculin-A (100 nM). 9. ALA. 10. Sterile centrifuge tubes, sterile multiwell dishes, and sterile coverslips (12 mm and 18 mm).

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2.2 Tau Protein Purification

hTau40 WT protein expressed in BL21* cells, LB media, ampicillin solution (100 μg/mL), IPTG solution (0.5 mM), dithiothreitol (DTT) (2 mM), NaCl (1 M), cation exchange sepharose fast flow column, Superdex 75 Hi load 16/600 column for size exclusion chromatography, 1× PBS, bicinchoninic acid, copper sulfate, BSA solution.

2.3 Tau Aggregation Assay

Tau monomer (mg/ml), NaCl (25 mM), 1x protease inhibitor cocktail (PIC), sodium azide (0.01%), DTT (1 mM), heparin (17,500 Da), BES pH 7.4, ThS fluorescent dye, ammonium acetate, 384 black well micro plates 2% uranyl acetate, 400 mesh carbon-coated copper grid. Acrylamide, bis-acrylamide, sodium dodecyl sulphate (SDS), 10% ammonium persulfate (APS), tetramethylethylenediamine (TEMED), tris, glycine, 6x protein loading dye, bio-rad dual color protein marker.

2.4

Tau monomer, Tau aggregates, Alexa flour 647 labelled C2 maleimide, tris (2-carboxyethyl) phosphine (TCEP), 1x PBS, 3 kDa molecular weight cut-off centricons.

Tau Labeling

2.5 ImmunofluorescenceImmunofluorescence Assay

Four percent of Paraformaldehyde solution in PBS, 1× PBS, horse serum, Triton X-100, moist tray, parafilm sheet, DAPI (300 nM), Prolong Antifade mounting media, and glass slide.

2.6

Antibodies

Primary antibody—Rab7 (Rabbit), Phalloidin Alexa fluor 488 tagged, and secondary antibody—Alexa fluor anti-rabbit 555 tagged antibody.

2.7

Instruments

1. Laminar air flow with HEPA filter, CO2 incubator at 5% CO2 level. 2. Centrifuge and refrigerator. 3. AKTA pure chromatography system. 4. Optima MAX-XP ultracentrifuge (Beckman Coulter). 5. Avanti high-speed centrifuge. 6. Tecan plate reader Infinite 200 Pro. 7. SDS-gel electrophoresis unit (Bio-Rad). 8. Tecnai T20 transmission electron microscope 200 KeV (TEM). 9. FEI high-resolution 300 KeV (HR-TEM).

transmission

electron

microscope

10. Zeiss Axio observer 7.0 with Apotome 2.0 microscope. 2.8

Software

1. ZEN 2.3 software. 2. ImageJ software. 3. SigmaPlot 10. 4. GraphPad Prism 8.0.1.

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Methods

3.1 Culturing Microglia for Internalization of Tau

Evaluate the dynamic effect of ALA on microglial actin remodeling and its consequence on endosomal trafficking of internalized Tau by internalization assay. Assess the internalization of Tau and its endosomal sorting by fluorescence microscopy (Fig. 1a). 1. Culture N9 microglia cells in RPMI media supplemented with 10% FBS, 1% pen-strep solution and 1% anti-anti solution, until the confluency reaches 90%. Then, passage cells in the presence of trypsin-EDTA solution and seed on coverslips at a density of 20,000 cells/well for immunofluorescence assay. 2. Before starting immunofluorescence assay, label Tau species with C2 maleimide conjugated Alexa fluor 674 tag. Pretreat Tau species (100 μM) with TCEP for 10 min and then incubate with Alexa fluor 647 label (2 M excess) for overnight at 4 °C in shaking condition (see Note 1). 3. Followed by the incubation, remove excess of untagged Alexa fluor by buffer-exchange with 1× PBS using 3 kDa cutoff centrifugal filters. After buffer exchange, determine the concentration of labelled Tau species by BCA assay. To study the morphology of higher-order aggregates of Tau proteins using TEM, it is guided to stain the grids with a 2% Uranyl acetate solution after protein incubation to generate contrast (see Note 2) (Fig. 1b). 4. Similarly, prepare ALA in 100% ethanol and solubilization the solution at 50 °C for homogenous mixture of ALA. Analyze vesicle-like structures of ALA by TEM by 2% Uranyl acetate staining (see Note 3) (Fig. 1c) [15]. 5. For immunofluorescence assay, seed N9 microglia cells on 18 mm coverslip (20,000 cells) in complete RPMI media; later treat the N9 microglia cells with Tau monomer, Tau aggregates (1 μM) along with ALA (40 μM) in a 0.5% FBS serum-deprived media for 1, 3, 6, 12, and 24 h time points depending upon the experiment. 6. Once the incubation time of treatment is completed, discard the treatment media and wash cells with 1× PBS wash and fix with 4% paraformaldehyde in PBS for 15 min. Give PBS wash to cells after fixation and supplement with permeabilization media, 0.2% Triton X-100 in PBS for 15 min. After permeabilization block the cells with 5% horse serum in PBS for 1 h [16, 17]. 7. Remove the blocking media and further treat the coverslips with primary antibodies; Rab7 (1:200) and phalloidin (1:40) for overnight at 4 °C in a moist chamber (see Note 4).

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Fig. 1 Characterization of Tau for endosomal pathway analysis. (a) Extracellular Tau behaves as a foreign entity, which is cleared by microglia through endocytosis pathway. Latrunculin-A is a potent inhibitor of endocytosis, whereas ALA increases internalization of Tau. In this study, we focused on understanding the ability ALA to increase the endocytosis in presence of latrunculin-A. (b) Higher-order aggregated Alexa Fluor 647 labeled Tau visualized by Transmission electron microscopy (TEM). (c) Vesicle-like morphology of ALA characterized by TEM

8. After primary antibody treatment wash the coverslips with 1× PBST (1× PBS + 0.2% Triton X 100) 3 times for 10 min each on a parafilm sheet by inverting coverslips with the help of fine tweezers. 9. Later, treat the coverslips with secondary antibody treatment, Alexa fluor anti-rabbit 555 (1:500) antibody for 1 h at RT in moist chamber. 10. Post secondary antibody treatment, again follow washing step with 1x PBST similarly on parafilm sheet, and lastly treat coverslips with DAPI (300 nM) for 10 min before mounting on a glass slide with Prolong Antifade mounting media. Dry the coverslips for at RT overnight before scanning in the microscope. 3.2 Analysis of Internalized Tau and Associated Endosomal Trafficking

1. Study the internalization pattern of Tau by Z-stack imaging to evaluate section-wise internalization of Tau from cortical plane (peripheral) to internal planes (perinuclear) of the cell (Fig. 2a, b, f). 2. For Z-stack image acquisition, select Z plane 3D distance of the cell from as start and end of an experiment and customize the number of Z slices according to the desired distance between each plane (see Note 5).

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Fig. 2 Endosomal translocation of Tau via Rab7 in microglia. (a) Internalization and cytosolic localization of Tau was studied by Rab7-mediated endosomal sorting. 3D Z stack imaging was conducted to check subcellular localization of Tau. (b) Z-stack image analysis of Tau in association with Rab7 for a distance of 1.44 μm. (c) Colocalization analysis of Rab7 and internalized Tau by orthogonal section view, to denote the colocalization on X and Y axis projection. (d) Distribution of internalized Tau in cell body (perinuclear) versus extensions (peripheral), studied through intensity mapping. (e) Increase in internalization pattern of Tau studied by fold change analysis with respect to control and test groups treated with ALA. (f) Representation of cellular distribution of Tau and its association with Rab7 in the presence of ALA

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3. After the end of an experiment, collect gallery of images for given distance and step size consisting of images for each fluorophore. 4. To evaluate the pattern of internalization of Tau, analyze sections from bottom (peripheral planes) to top planes (perinuclear planes). 5. The intensity of Tau from bottom to top planes denotes its peripheral versus perinuclear localization (see Note 6). 6. Maximum percentage of internalized Tau in the bottom planes indicates the membrane-associated and cytosolic localization of Tau, whereas its percentage in the top planes points out internal trafficking of Tau toward perinuclear region. 7. For representation purposes select six planes of 1.44 μm distance from every treatment condition to map the localization of Tau in microglia after internalization (Fig. 2b). 8. Calculate the percentage of Tau in cell body versus extensions from these Z-stack planes via its intensity values (Fig. 2d). 9. Select Z-stack slices by considering the lower panel of Tau instead of Rab7; separate the background plane versus internalized plane using software ZEN 2.3. Delete the desired planes by using ZEN software for 3D construction of the image. 10. Calculate the fold change of Tau internalization with respect to control and test conditions (+ALA) by mean fluorescence intensity of Tau of every group. Then, plot the Graph as a fold change variable on Y axis and conditions on X axis, where the comparison is drawn within control and test groups (see Note 7) (Fig. 2e). 11. Similarly, evaluate the contact of internalized Tau with Rab7 (late endosomal marker) during trafficking of Tau in cell body via colocalization coefficient analysis. Then, estimate the association of internalized Tau with Rab7 by ImageJ plugin Coloc2, which denotes the extent of colocalization between Rab7 and Tau (see Note 8) (Fig. 2c). 12. Estimate the colocalization coefficient study by ROI analysis in ImageJ software; also designate the trafficking of internalized Tau in cell body versus extension. Present colocalization between Tau and Rab7 using orthogonal projection view from the Z-stack experiment to indicate the substantial colocalization inside the cell. Generate, ROI of single cells from the 3D field view and present colocalization with the help of X and Y projection on axis.

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3.3 Actin-Mediated Internalization of Tau and Associated Endosomal Trafficking

1. Know that the process of internalization is highly dependent on membrane-associated actin remodeling, where continuous F-actin generation drives the force for membrane modulation to internalize the vesicle and further trafficking. 2. Evaluate the involvement of actin in the internalization and Rab7-mediated endosomal degradation by coherent localization of Tau, Rab7, and actin by fluorescence microscopy. 3. Observe the internalized Tau to be positive for Rab7 and actin by intensity profile graph and colocalization coefficient analysis. 4. Test the colocalization of internalized Tau in perinuclear region with Rab7 and Tau by introducing pseudo colors during 2D and orthogonal view projection. Then, generate ROI to indicate the coherent localization of Tau, Rab7, and actin. 5. Further, generate intensity profile, a 4 μm distance to confirm the colocalization using ZEN software for control and test groups. 6. Intensity profile gives point-wise intensity values of desired 4 μm distance for different fluorophore present in the marked area (see Note 9). 7. Coinciding peaks of the intensity values represent their respective co-occurrence at a given point; hence, it can be used to denote the colocalization between more than two proteins at a time. 8. Also, the co-occurrence of Tau, Rab7, and actin at perinuclear region or a cell center signifies the translocation of internalized Tau from the peripheral region of the cell. 9. Coinciding peaks of actin with a Rab7 positive Tau mark the assistance of actin cytoskeleton in an endosomal trafficking mechanism. 10. Additionally, evaluate the colocalization coefficient for Tau, Rab7, and F-actin; orthogonal projection view represents the three-protein coherent localization on X and Y axis.

3.4 Actin-Dependent Endocytic Internalization and Trafficking of Tau

1. Test dependency of internalization on actin remodeling with the help of latrunculin-A, a potent inhibitor of F-actin assembly by sequestering G-actin subunits from the cytoplasm. Inhibition of F-actin assembly generates morphological destability, polarization of the cell, and signaling obstruction. 2. Seed N9 microglia cells on 18 mm coverslips (20,000/well) and pre-treat with latrunculin-A at 100 nM for 1 h in 0.5% serum-deprived media at 37 °C; 5% CO2 level, to inhibit the F-actin assembly. 3. Later, change the media and supplement the cells with Tau species and ALA for 1, 3, 6, 12 h time points to observe the recovery phase of the cells after latrunculin-A exposure (see Note 10).

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4. Fix the respective treatment groups after desired time points and immunostained with Rab7 (1:200), phalloidin (1:40), and DAPI for nuclear stain. Then, observe the time-dependent change in morphology of microglia, internalization of Tau, and its association with Rab7 by fluorescence microscopy. 5. Analyze 2D images for the internalization of Tau in microglia and its morphological changes in recovery phase. 6. Measure intracellular intensity of Tau through the contour tool from ZEN software to estimate time-dependent internalization Tau after latrunculin-A treatment to microglia. 7. Plot the time-dependent intracellular intensity of Tau to estimate the logarithmic phase, which indicates recovery phase of the cell. 8. The Chase experiment reveals that ALA induces the ability of microglia to normalize the endocytosis and endosomal trafficking of Tau after latrunculin-A exposure. 9. Similarly, evaluate Rab7-mediated trafficking of internalized Tau by colocalization coefficient analysis by ImageJ software. Plot the intensity values by Sigma Plot 10.0 and GraphPad Prism 8.0.1.

4

Notes 1. Considering only 2 cysteine residues in full-length Tau protein, the C2 maleimide Alexa fluor 647 is taken as 2 M excess of Tau protein. For other proteins it is recommended as 10 M excess of the protein concentration. 2. While concentrating the labelled Tau to remove excess of unbound tag, temperature is maintained at 4 °C and fresh filtered 1× PBS is used. 3. ALA sample was freshly prepared before the experiment to avoid oxidation. During solubilization at 50 °C the microcentrifuge tube is covered with parafilm to avoid evaporation of ethanol. 4. Antibody staining should be kept in a moist tray to avoid evaporation and treatment time of 12–16 h at 4 °C is maintained during staining. 5. Avoid lower-most plane while designating the 3D distance to avoid protein background, as Tau protein tends to adhere to coverslip during treatment. 6. Select sections indicating internalization of Tau and the number of sections should be same for all the treatment conditions.

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7. Fold change within the group is compared between control groups treated with Tau species and test group treated with Tau species and ALA. 8. To calculate Pearson’s colocalization coefficient, ROI should be selected of same area from the Tiff images. 9. To generate an intensity profile graph, the same distance should be selected for every treatment group. 10. Recovery time point should be selected according to the morphology change of the cells upon latrunculin-A treatment, which may differ for different cell lines or primary culture.

Acknowledgments The author is grateful to Chinnathambi’s lab members for their scientific suggestions on the manuscript. SD, HKC and TQ acknowledges the Department of Science and Technology, Department of Biotechnology, Government of India for the fellowship. Authors acknowledge the Department of Neurochemistry, National Institute of Mental Health and Neuro Sciences (NIMHANS), Institute of National Importance, Bengaluru for their internal support. References 1. Galletta BJ, Cooper JA (2009) Actin and endocytosis: mechanisms and phylogeny. Curr Opin Cell Biol 21:20–27 2. Derivery E, Sousa C, Gautier JJ, Lombard B, Loew D, Gautreau A (2009) The Arp2/3 activator WASH controls the fission of endosomes through a large multiprotein complex. Dev Cell 17:712–723 3. Day CA, Baetz NW, Copeland CA, Kraft LJ, Han B, Tiwari A, Drake KR, De Luca H, Chinnapen DJF, Davidson MW (2015) Microtubule motors power plasma membrane Tubulation in Clathrin-independent endocytosis. Traffic 16:572–590 4. Bershadsky AD, Gluck U, Denisenko ON, Sklyarova TV, Spector I, Ben-Ze’ev A (1995) The state of actin assembly regulates actin and vinculin expression by a feedback loop. J Cell Sci 108:1183–1193 5. Capitani N, Baldari CT (2021) F-actin dynamics in the regulation of endosomal recycling and immune synapse assembly. Front Cell Dev Biol 9:670882 6. Oliveira CA, Kashman Y, Mantovani B (1996) Effects of latrunculin a on immunological phagocytosis and macrophage spreading-

associated changes in the F-actin/G-actin content of the cells. Chem Biol interact 100:141– 153 7. de Oliveira CA, Mantovani B (1988) Latrunculin a is a potent inhibitor of phagocytosis by macrophages. Life Sci 43:1825–1830 8. Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K (2006) Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol 112:389– 404 9. Sonawane SK, Chinnathambi S (2018) Prionlike propagation of post-translationally modified tau in Alzheimer’s disease: a hypothesis. J Mol Neurosci 65:480–490 10. Leng F, Edison P (2021) Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol 17: 157–172 11. Desale SE, Chinnathambi S (2021) α-Linolenic acid induces clearance of tau seeds via actin-remodeling in microglia. Mol Biol 2: 1–14 12. Desale SE, Chinnathambi S (2021) α–Linolenic acid modulates phagocytosis and

ALA in Regulation of Endocytosis of Extracellular Tau endosomal pathways of extracellular tau in microglia. Cell Adh Migr 15:84–100 13. Desale SE, Chinnathambi S (2020) Role of dietary fatty acids in microglial polarization in Alzheimer’s disease. J Neuroinflamm 17:1–14 14. English JA, Harauma A, Fo¨cking M, Wynne K, Scaife C, Cagney G, Moriguchi T, Cotter DR (2013) Omega-3 fatty acid deficiency disrupts endocytosis, neuritogenesis, and mitochondrial protein pathways in the mouse hippocampus. Front Genet 4:208 15. Desale SE, Dubey T, Chinnathambi S (2021) α-Linolenic acid inhibits tau aggregation and

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modulates tau conformation. Int J Biol Macromol 166:687–693 16. Das R, Balmik AA, Chinnathambi S (2020) Phagocytosis of full-length tau oligomers by actin-remodeling of activated microglia. J Neuroinflamm 17:1–15 17. Das R, Chinnathambi S (2021) Microglial remodeling of actin network by tau oligomers, via G protein-coupled purinergic receptor, P2Y12R-driven chemotaxis, vol 22. Traffic, p 153

Chapter 18 Understanding Actin Remodeling in Neuronal Cells Through Podosomes Tazeen Qureshi, Smita Eknath Desale, Hariharakrishnan Chidambaram, and Subashchandrabose Chinnathambi Abstract Cytoskeletal dysregulation forms an important aspect of many neurodegenerative diseases such as Alzheimer’s disease. Cytoskeletal functions require the dynamic activity of the cytoskeletal proteins—actin, tubulin, and the associated proteins. One of such important phenomena is that of actin remodeling, which helps the cell to migrate, navigate, and interact with extracellular materials. Podosomes are complex actin-rich cytoskeletal structures, abundant in proteins that interact and degrade the extracellular matrix, enabling cells to displace and migrate. The formation of podosomes requires extensive actin networks and remodeling. Here we present a novel immunofluorescence-based approach to study actin remodeling in neurons through the medium of podosomes. Key words Podosomes, Actin remodeling, Cytoskeleton, Neurons, Alzheimer’s disease

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Introduction Neurodegenerative diseases like Alzheimer’s disease (AD) are known for symptoms such as memory loss, caused due to degeneration of neurons and the connections between them. At the molecular level, AD is characterized by deposits of amyloid beta plaques outside and neurofibrillary tangles inside the cells [1, 2]. The link between molecular pathology and the visible symptoms has not entirely been realized. The loss of synaptic connections is both caused by and affects cytoskeletal dysregulation [3–5]. Moreover, the trafficking and transport of proteins and other cargo are greatly affected in the AD brain. For example, the immune cells of the brain or the microglia are responsible for clearing the extracellular space of any pathogenic Tau deposits that may have been released by diseased neurons [6, 7]. But in AD, this mechanism is weakened. This endocytosis function is orchestrated by many cytoskeletal proteins in coordination. This example demonstrates the integral

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_18, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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contribution of the cytoskeleton to the cell and its various functions. Thus, attention must be given to cytoskeletal dysregulation as an important link between the pathology and symptoms [8–10]. The cytoskeleton is dominated by two main proteins—actin and tubulin. The crosstalk between these two facilitates the various functions of the cytoskeleton in the cell. Actin remodeling is the most important cytoskeletal machinery involved in functions such as migration, cell shape, and endocytosis. It is the process of restructuring actin monomers to polymers to filaments and the consequent formation of extensive actin networks. These networks make up complex cytoskeletal structures such as lamellipodia, filopodia, podosomes, and phagocytic cups. in the cell [11]. The major events of the actin remodeling pathway include a stepwise activation of tyrosine kinases Pyk2 [12] and Fyn [13], which form a Pyk2-Fyn complex, followed by Cdc42 and Rac activation [14, 15]. Activated Rac mediates the activation of the protein complexes of WASP (Wiskott-Aldrich Syndrome protein) [16] and WAVE (WASP-Associated Verprolin Homology) [14, 17]. These complexes are described as nucleators of actin and are responsible for the activation of the most important protein complex in the pathway—the Arp2/3 complex [18, 19]. Arp2/3 physically sits at the nodes of actin filaments to facilitate actin branching and ultimately network formation [20, 21]. These networks lead to the formation of structures like podosomes, along with other important proteins required for the respective functions [22]. Podosomes are actin-rich structures, found majorly in migratory cells such as transformed cells, fibroblasts, and microglia [23– 25]. They are abundant in matrix metalloproteinases that degrade the extracellular matrix (ECM) and help the cell to displace and navigate. In addition, TKS5 is found to be localized in the podosome structures [26–28], along with other important cytoskeletal proteins—vinculin and talin—that help in adhering to and interacting with the ECM [29]. Here, we present podosomes as a cytoskeletal model to study the actin remodeling phenomenon and to further understand its response to any therapeutic strategy for AD.

2

Materials

2.1 ImmunofluorescenceImmunofluorescence

1. Neuro2a cells. 2. Advanced DMEM F12 medium. 3. Fetal bovine serum. 4. Phosphate buffered saline (1×, sterile). 5. Trypsin-EDTA (0.05% in 1× phosphate-buffered saline (PBS) and filtered through 0.22 μm membrane). 6. Culture plates (12-well), dishes (60 mm), coverslips (18 mm).

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7. Paraformaldehyde (4% in 1× PBS): Add 2 g Paraformaldehyde in 50 mL 1× PBS, in a water bath set at 60 °C. Stir/shake constantly and add concentrated NaOH dropwise. Keep adding NaOH under constant agitation, until PFA dissolves completely. Cool down the solution and adjust pH to 7.4. Filter through 0.22 μm membrane. 8. Blocking buffer: 5% horse serum and 0.2% Triton X-100 in 1× PBS. 9. PBST: 0.2% Triton X-100 in 1× PBS. 10. Primary antibodies: TKS5 (rabbit), Arp2 (rabbit), and WASP (rabbit). 11. Secondary antibodies: Anti-rabbit Alexa Fluor 555. 12. Phalloidin (with Alexa Fluor 488 tag for F-actin staining). 13. DAPI (300 nM in 1× PBS; for nucleus staining). 14. Mounting medium (Prolong Gold Antifade Mountant)/ glycerol). 15. Immersion oil. 2.2

Instrumentation

1. Laminar flow chambers with HEPA filters, cell culture incubators with 5% CO2 supply. 2. Zeiss Axio Observer 7.0 with Apotome 2.0 microscope. 3. -80 °C and -20 °C freezers, centrifuges, vortex machine.

2.3 Analysis Software

1. Zen 3.4 for Immunofluorescence image processing and analysis. 2. FIJI (Fiji Is Just ImageJ) for colocalization analysis and podosome counting. 3. GraphPad Prism 8 for plotting and representation of data and statistical significance.

3

Methods

3.1 Confirmation of Podosomes—TKS5 Immunomapping

Podosome structures can be confirmed in the cells though immunomapping of the protein TKS5 in the podosomes. Since podosomes are actin-rich structures, use F-actin (filamentous actin) to map the structures with the help of fluorescently tagged (Alexa Fluor 488) phalloidin. The colocalization (or the point at which the localization of the proteins coincides, implying their interaction or functional correlation) between the proteins suggests the abundance of TKS5 in these structures, thereby attributing them as podosomes. 1. Seed cells on coverslips in complete media (0.25 × 105 Neuro2a cells; 18 mm diameter coverslips washed and autoclaved; Media—Advanced DMEM F12 supplemented with 10% FBS;

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Plate—12-well cell culture plate) and keep for incubation at 37 °C with 5% CO2 in humid conditions. See step 1 in Fig. 1a. 2. After the cells have settled on the coverslips and attained the normal morphology (usually after 18–24 h), replace the complete media with the serum-starved media (Advanced DMEM F12 supplemented with 0.5% FBS) for treatment (see Note 1). 3. Add the protein or chemical to be studied for its effects on actin remodeling and podosomes to each cell culture plate well as treatment group, keeping one of the wells as control (with no treatment/with only vehicle control, used for preparing the treatment chemical) (see Note 2), 4. Incubate the cells with the treatment for the desired time point at 37 °C, 5% CO2 in humid conditions. 5. After the incubation time, replace the media with 4% PFA (paraformaldehyde) for fixation of the cells on the coverslips, at room temperature for 20 min (Fig. 1a, step 2). 6. Remove PFA and give a single wash with sterile 1X PBS (phosphate buffered saline) to remove any remaining PFA. 7. Allow the coverslips to undergo blocking with 5% horse serum and 0.2% Triton X-100 (prepared in sterile 1× PBS) for 1 h at room temperature. 8. Coat petridishes with parafilm at their base (to avoid spillage) and place the coverslips (with the cell side facing up) in these dishes for antibody incubation (see Note 3). 9. Prepare the primary antibody TKS5 against the protein (Host – rabbit; dilution 1:100) in the blocking buffer along with phalloidin (Alexa Fluor 488 tagged; dilution 1:40) (Fig. 1a, step 4). 10. For the 18 mm coverslip, add 100 μL of the antibody mixture gently on each coverslip. 11. Place the dishes in a moist chamber (to avoid evaporation of the antibody) overnight (12–14 h) at 4 °C or 3 h at RT (see Note 4). 12. Aspirate the antibody and perform rigorous washing of the coverslips with 1× PBST thrice for 10 min to remove excess unbound primary antibodies (see Note 5) (Fig. 1a, step 5). 13. Place the washed coverslips in parafilm base dishes and add 100 μL of the respective secondary antibody (Anti Rabbit Alexa Fluor 555 tagged; dilution 1:500; prepared in blocking buffer) to each coverslip. Incubate for 1 h at room temperature. 14. Wash the coverslips similarly to “Point 12” to remove unbound secondary antibodies. 15. After three washes, incubate the cells with 50 μL DAPI for 5 min at RT, to counterstain the nuclei. Give a single PBST wash to remove excess DAPI (Fig. 1a, step 6).

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Fig. 1 Identification of podosomes. (a) The schematic showing the protocol for sample preparation immunofluorescence microscopy. (b) The gallery view of images with all individual channels and the merge. A cluster of podosome has been marked in white. This region is zoomed in, focusing on the point in both the channels. The yellow spots indicate colocalization of the proteins TKS5 and F-actin, thus confirming podosome structures

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16. Rinse the glass slides with 70% ethanol, to remove lint and air dry. 17. For the 18 mm coverslip, add 12 μL Mounting medium (Prolong Gold Antifade Mountant) as a drop on the glass slide and invert the coverslip (cells facing down) on the drop (see Note 6) (Fig. 1a, step 7). 18. Allow the mounted coverslips to dry for 24 h at RT, before imaging. 19. Observe the coverslips in an epifluorescence or confocal microscope at 63× or 100× magnification respectively (oil immersion) (Fig. 1a, step 8). 20. For imaging, adjust the intensities and the laser exposure times for each channel (rhodamine for red, FITC for green, DAPI for blue and DIC for phase contrast) such that the final merged image is optimal. Since podosomes are actin-based structures, set F-actin as the focus for imaging. 21. For image-acquisition, try to accommodate around 4–5 cells in each field and capture about 10–12 fields for covering all regions of the coverslip. 22. Process the images to get rid of the noise and background fluorescence, followed by analysis and quantification (see Note 7). 23. Analyze the imaged cells for yellow spots in the actin-rich punctae, implying co-incidence of TKS5 (red) and F-actin (green), which confirms their attribution as podosomes (Fig. 1). 3.2 Differential Localization Analysis—Actin Remodeling Proteins

Follow the above immunofluorescence protocol for mapping actin remodeling proteins Arp2 and WASP with F-actin, respectively. 1. Analyze the processed images in FIJI software for colocalization between Arp2-Factin, WASP-Factin, and TKS5-Factin, respectively. 2. In the FIJI software, open the processed image with the merge of all channels and split the channels using the “Image – Colors – Split Channels” option. Except the two channels whose colocalization is to be studied, close the others. 3. In the channel that covers most of the cell area (here, F-actin), using “freehand selection” tool, select the region of interest (ROI). 4. Perform the colocalization analysis for the selected ROI using “Analyze – Colocalization – Coloc 2.” 5. The colocalization parameters for the ROI will be displayed in a separate dialogue box. The “Pearson’s R value (no threshold)” gives the Pearson’s value for average colocalization coefficient for all the points in the region selected.

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6. For differential localization analysis, two different kinds of ROIs must be analyzed for each cell: a. Overall colocalization, implying selection of the entire cell and b. podosome colocalization, where only single podosome punctae or a podosomerich region would be selected as ROI (Fig. 2). 7. Note the values for different ROIs and save in a separate sheet for the analysis of significance between different groups and the trends followed. 8. The difference in the colocalization of the proteins between the two ROIs in the control and treated groups implies the effect of the treatment on the translocation of the protein from or to the podosome regions from other parts of the cell. 9. For example, if the treated group shows higher colocalization between Arp2 and F-actin in the podosomes as compared to the whole cell (overall), while the control group shows relatively lower colocalization in the podosome region, this suggests that the treatment is inducing the translocation or the increased localization of Arp2 towards/within podosomes. 10. Determine the statistical significance of the effect of treatment using unpaired t-test at 99% confidence interval, where p > 0.1 = non-significant (n.s.), p < 0.1 = *, p < 0.01 = **, p < 0.001 = ***.

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Notes 1. Care needs to be taken during the replacement of the media so that the cells do not dry up in the interval between the two media. The cells could start floating or lose morphology if left dried for longer. 2. The treatment media can also be prepared separately for each group with the serum-starved media. 3. The coverslips must be carefully removed from the culture plates and placed on the parafilm base dish with the cells facing up and the antibody is directly introduced on the coverslip. 4. The moist chamber is created by wetting a piece of filter paper or tissue paper and placing it in the tray with the coverslip dishes. The tray is then covered with an aluminum foil. 5. The washing is carried out as follows: A parafilm is stuck to the workspace desk and a 100 μL drop of PBST added onto the parafilm for each coverslip. Individual coverslips are lifted and inverted onto this drop and kept for 10 min. This step is repeated for two more washes with two new drops of PBST. The coverslip is lifted and dabbed on tissue paper in between two washes.

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Fig. 2 Differential localization analysis. Flowchart for the colocalization analysis in FIJI software. The images are immunofluorescent microscopy images. For the demonstration of ROI selection, a single cell has been zoomed in from the merge image. On the left, the region selected for overall colocalization has been shown, and on the right, region for the podosome colocalization. The colocalization coefficients for each group are analyzed and plotted as shown

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6. Mounting can also be done using 50% glycerol. Coverslips are inverted on a drop of glycerol. Excess glycerol is removed by placing tissue paper on the coverslip and pressing gently. The coverslip is then sealed in place with a transparent nail paint along the circumference of the coverslip. 7. Image processing is done in the imaging software (Zen), where the black component of each channel represents the background, while the white component represents the brightness of the color intensity. The black and white components are adjusted to improve the signal to noise ratio for each channel. The processed images are saved in compatible formats like TIFF or JPEG.

Acknowledgments This project is supported by the in-house CSIR-National Chemical Laboratory grant MLP101726. The author is grateful to Chinnathambi’s lab members for their scientific suggestions on the manuscript. TQ acknowledges the Department of Science and Technology – Innovation in Science Pursuit for Inspired Research (DST-INSPIRE), the Government of India for her fellowship. The authors greatly acknowledge the Department of Neurochemistry, the National Institute of Mental Health and Neuro Sciences (NIMHANS), and the Institute of National Importance, Bangalore, for their internal support. References 1. Breijyeh Z, Karaman R (2020) Comprehensive review on Alzheimer’s disease: causes and treatment. Molecules 25:5789. https://doi.org/ 10.3390/molecules25245789 2. Brion JP, Couck AM, Passareiro E, FlamentDurand J (1985) Neurofibrillary tangles of Alzheimer’s disease: an immunohistochemical study. J Submicrosc Cytol 17:89–96 3. Chen Y, Fu AKY, Ip NY (2019) Synaptic dysfunction in Alzheimer’s disease: mechanisms and therapeutic strategies. Pharmacol Ther 195:186–198. https://doi.org/10.1016/j. pharmthera.2018.11.006 4. Penzes P, VanLeeuwen J-E (2011) Impaired regulation of synaptic actin cytoskeleton in Alzheimer’s disease. Brain Res Rev 67:184–192. https://doi.org/10.1016/j.brainresrev.2011. 01.003 5. Tracy TE, Gan L (2018) Tau-mediated synaptic and neuronal dysfunction in neurodegenerative disease. Curr Opin Neurobiol 51:134–

138. https://doi.org/10.1016/j.conb.2018. 04.027 6. Das R, Chinnathambi S (2020) Actinmediated microglial chemotaxis via G-protein coupled purinergic receptor in Alzheimer’s disease. Neuroscience 448:325–336. https://doi. org/10.1016/j.neuroscience.2020.09.024 7. Das R, Chinnathambi S (2021) Microglial remodeling of actin network by tau oligomers, via G protein-coupled purinergic receptor, P2Y12R-driven chemotaxis. Traffic 22:153– 170. https://doi.org/10.1111/tra.12784 8. Bamburg JR, Bloom GS (2009) Cytoskeletal pathologies of Alzheimer disease. Cell Motil Cytoskeleton 66:635–649. https://doi.org/ 10.1002/cm.20388 ˜ oz-Lasso DC, Roma´-Mateo C, Pallardo´ 9. Mun FV, Gonzalez-Cabo P (2020) Much more than a scaffold: cytoskeletal proteins in neurological disorders. Cell 9:358. https://doi.org/ 10.3390/cells9020358

266

Tazeen Qureshi et al.

10. Vickers JC, Kirkcaldie MT, Phipps A, King AE (2016) Alterations in neurofilaments and the transformation of the cytoskeleton in axons may provide insight into the aberrant neuronal changes of Alzheimer’s disease. Brain Res Bull 126:324–333. https://doi.org/10.1016/j. brainresbull.2016.07.012 11. Khan AN (2012) Involvement of actin pathology in Alzheimer’s disease. Cell Dev Biol 2012: 02. https://doi.org/10.4172/2168-9296. 1000e121 12. Gil-Henn H, Destaing O, Sims NA et al (2007) Defective microtubule-dependent podosome organization in osteoclasts leads to increased bone density in Pyk2-/- mice. J Cell Biol 178:1053–1064. https://doi.org/10.1083/ jcb.200701148 13. Liu Z, Hao K-M, Wang H-Y, Qi W-X (2020) Histone deacetylase-6 modulates amyloid betainduced cognitive dysfunction rats by regulating PTK2B. Neuroreport 31:754–761. h t t p s : // d o i . o r g / 1 0 . 1 0 9 7 / W N R . 0000000000001481 14. Palazzo AF, Joseph HL, Chen Y-J et al (2001) Cdc42, dynein, and dynactin regulate MTOC reorientation independent of rho-regulated microtubule stabilization. Curr Biol 11:1536– 1541. https://doi.org/10.1016/S0960-9822 (01)00475-4 15. Gomes ER, Jani S, Gundersen GG (2005) Nuclear movement regulated by Cdc42, MRCK, myosin, and actin flow establishes MTOC polarization in migrating cells. Cell 121:451–463. https://doi.org/10.1016/j. cell.2005.02.022 16. Weaver AM, Heuser JE, Karginov AV et al (2002) Interaction of Cortactin and N-WASp with Arp2/3 complex. Curr Biol 12:1270– 1278. https://doi.org/10.1016/S0960-9822 (02)01035-7 17. Nakanishi O, Suetsugu S, Yamazaki D, Takenawa T (2007) Effect of WAVE2 phosphorylation on activation of the Arp2/3 complex. J Biochem 141:319–325. https://doi.org/10. 1093/jb/mvm034 18. Korobova F, Svitkina T (2008) Arp2/3 complex is important for filopodia formation, growth cone motility, and neuritogenesis in neuronal cells. MBoC 19:1561–1574. https://doi.org/10.1091/mbc.e07-09-0964 19. San Miguel-Ruiz JE, Letourneau PC (2014) The role of Arp2/3 in growth cone actin dynamics and guidance is substrate dependent. J Neurosci 34:5895–5908. https://doi.org/ 10.1523/JNEUROSCI.0672-14.2014

20. Qureshi T, Chinnathambi S (1869) Histone deacetylase-6 modulates tau function in Alzheimer’s disease. Biochimica et Biophysica Acta (BBA) Mol Cell Res 2022:119275. https://doi.org/10.1016/j.bbamcr.2022. 119275 21. Hurst IR, Zuo J, Jiang J, Holliday LS (2004) Actin-related protein 2/3 complex is required for actin ring formation. J Bone Miner Res 19: 499–506. https://doi.org/10.1359/JBMR. 0301238 22. Albiges-Rizo C, Destaing O, Fourcade B et al (2009) Actin machinery and mechanosensitivity in invadopodia, podosomes and focal adhesions. J Cell Sci 122:3037–3049. https://doi. org/10.1242/jcs.052704 23. Murphy DA, Courtneidge SA (2011) The “ins” and “outs” of podosomes and invadopodia: characteristics, formation and function. Nat Rev Mol Cell Biol 12:413–426. https:// doi.org/10.1038/nrm3141 24. Linder S, Aepfelbacher M (2003) Podosomes: adhesion hot-spots of invasive cells. Trends Cell Biol 13:376–385. https://doi.org/10. 1016/S0962-8924(03)00128-4 25. Vincent C, Siddiqui TA, Schlichter LC (2012) Podosomes in migrating microglia: components and matrix degradation. J Neuroinflammation 9:190. https://doi.org/10.1186/ 1742-2094-9-190 26. Courtneidge SA, Azucena EF, Pass I et al (2005) The SRC substrate Tks5, podosomes (invadopodia), and cancer cell invasion. Cold Spring Harb Symp Quant Biol 70:167–171. https://doi.org/10.1101/sqb.2005.70.014 27. Iizuka S, Abdullah C, Buschman MD et al (2016) The role of Tks adaptor proteins in invadopodia formation, growth and metastasis of melanoma. Oncotarget 7:78473–78486. https://doi.org/10.18632/oncotarget.12954 28. Pe˛zin´ski M, Maliszewska-Olejniczak K, Daszczuk P et al (2021) Tks5 regulates synaptic podosome formation and stabilization of the postsynaptic machinery at the neuromuscular junction. Int J Mol Sci 22:12051. https://doi. org/10.3390/ijms222112051 29. Zambonin-Zallone A, Teti A, Grano M et al (1989) Immunocytochemical distribution of extracellular matrix receptors in human osteoclasts: a β3 integrin is colocalized with vinculin and talin in the podosomes of osteoclastoma giant cells. Exp Cell Res 182:645–652. https://doi.org/10.1016/0014-4827(89) 90266-8

Chapter 19 Quantitative Investigation of Neuroprotective Role of ROR1 in a Cell Culture Model of Alzheimer’s Disease Kaushik Chanda

and Debashis Mukhopadhyay

Abstract Cytoskeletal and microtubule atrophy are major hallmarks of Alzheimer’s disease (AD). A method to investigate endogenous proteins that can interact/stabilize the cytoskeleton (under pathological cues) is rare. Here, we describe how receptor tyrosine kinase-like orphan receptor 1 (ROR1), a receptor tyrosine kinase (RTK), can act as a neuroprotective molecule by promoting neurite outgrowth, stabilizing cytoskeletal components, and altering the dynamics of actin assembly in a cell culture model of AD. Key words Alzheimer’s disease, RTK, ROR1, Neuroprotection, Confocal microscopy

1

Introduction The cellular abnormalities evident in Alzheimer’s disease (AD) pathology comprise, among other things, a gross dysregulation of the cytoskeleton network [1]. While both β-amyloid (Aβ) plaques [2, 3] and neurofibrillary tangles (NFTs) cause the same, the result is the loss of the neuronal cytoskeleton integrity, both spatially and temporally. ROR1, a member of the receptor tyrosine kinase-like orphan receptor (ROR) family, is implicated in the process of neurite extension and neurogenesis—two events critical to establish neuronal network [4–6]. Here we describe our method of establishing an Aβ1–42-treated cell culture model of AD, with both biochemical and fluorescence microscopy-based readouts of cytoskeletal degradation. Additionally, we also provide methods to study how ROR1, which is an RTK, can counter such abnormality by positively regulating neurite growth and altering the dynamics of actin assembly. In this study, we focus on ROR1 with the motivation that cytoskeleton disruption in AD due to Aβ1–42 is a wellrecognized hallmark [7]. Further microtubule-associated ROR1 has been implicated in the reinforcement of neuronal networks [4, 8], which we find to be true on ROR1 overexpression and

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subsequent neurogenesis with the caveat that AD involves significant disruption of the same even with transient overexpression of ROR1 in the presence of Aβ1–42.

2

Materials Use sterile glassware for the preparation of all solutions unless otherwise mentioned. While some reagents are stored in -80 °C, -20 °C, or 4 °C, all solutions must be brought to room temperature before being used.

2.1 Cell Culture and Transfection

1. Human neuroblastoma cell lines SHSY-5Y from American Type Cell Culture Collection, Virginia, United States (see Note 1). 2. DMEM-F12 (Gibco, USA). 3. Heat-inactivated fetal bovine serum (FBS) (Gibco, USA). 4. Antibiotics 1% penicillin/streptomycin (PS), (5000 U/mL) (Gibco, USA). 5. Geneticin selective antibiotic (G418 Sulfate) (50 mg/mL) (Gibco, USA) at 400 μg/mL. 6. Lipofectamine 2000 (Invitrogen, USA) (see Note 2). 7. DPBS (Gibco, USA). 8. pCMV3-C-GFP Spark Control Vector (CV026) (SinoBiological Inc., China). 9. Human ROR1 ORF mammalian expression plasmid, C-GFP Spark tag (Sino-Biological Inc., China).

2.2

Reagents

1. Cytochalasin D. 2. Jasplakinolide. 3. DRAQ5™. 4. Aβ1–42 protein fragment. 5. Phalloidin-iFluor 594 reagent. 6. Lysis buffer (1 M Tris-HCl, pH 7.5, 1 N NaCl, 0.5 M EDTA, 1 M NaF, 1 M Na3VO4, 10% SDS, 20 mM PMSF, 10% Triton X-100, 50% glycerol). 7. TBST (50 mM Tris-HCl, 150 mM NaCl, pH 7.5 containing 0.05% Tween 20). 8. Antibodies—Mouse monoclonal anti-ROR1—1:500; Rabbit polyclonal anti-α-tubulin—1:3000; mouse monoclonal antiSMA—1:2000; rabbit monoclonal anti-vimentin—1:2000; mouse monoclonal anti-MAP2—1:1000; rabbit monoclonal anti-vinculin—1:2000; rabbit monoclonal anti-GAPDH—1:

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3000; mouse monoclonal anti-actin (pan) antibody [C4]—1: 2000.

3

Methods

3.1 Cytoskeletal Degradation in Aβ1–42Treated Cell Models of AD

1. Weigh and reconstitute lyophilized Aβ1–42 protein fragment in DMSO to make a stock of 2 μM. 2. From this stock, add the requisite amount of Aβ1–42 to the petri-dishes (0.5 μL for 35 mm, 1 μL for 60 mm, and 2 μL for 90 mm plates) to make the working concentration of 1 μM. 3. Treat cells with this Aβ1–42 for 24 h (see Note 3). 4. Use only dimethyl sulfoxide (DMSO) in the same concentration and amount as a control. 5. After 24 h, lyze treated cells and process the lysate for western blotting. 6. Lyse phosphate-buffered saline (PBS)-washed pellet from cells on ice in lysis buffer (for 30 min) in the presence of complete protease inhibitor (Roche Diagnostics) and centrifuge at 13,000 ×g for 15 min. 7. Quantify protein amounts using Bradford spectrophotometric assay (see Note 4). 8. Separate the cell lysate (total protein, 20–30 μg) on SDS gel according to molecular weight. 9. Transfer proteins to polyvinylidene difluoride (PVDF) membrane (Millipore Corporation) and block with 5% skimmed milk in TBST. 10. Probe membranes with primary antibody, followed by the incubation with horseradish peroxidase (HRP) conjugated secondary antibody. 11. Develop membranes with ECL kit (Pierce or Abcam). 12. Measure band intensities in Quantity One (Bio-Rad). 13. To assay the cytoskeletal degradation, measure normalized protein amounts (with respect to GAPDH) of alpha tubulin, smooth muscle actin (SMA) and vimentin in DMSO vs. Aβ1–42treated cells (see Note 5) (Fig. 1a, b). 14. Although Aβ1–42 is one of the methods of generating reproducible in vitro AD models, this method can be employed to other AD models as well. 15. Here, we have employed three representative proteins of the cytoskeleton, namely, the microtubule, intermediate filament, and microfilament. This assay can be used to probe for any cytoskeletal/microtubule proteins. Care must be taken to rigorously repeat the same using appropriate positive and negative controls.

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Fig. 1 Cytoskeletal degradation in Aβ1–42-treated cell models of AD. (a) Western blot (n = 3) showing the α-tubulin, SMA, vimentin and GAPDH levels in Aβ1–42-treated cell model. (b) Graph depicting the mean value of optical density of the α-tubulin, SMA, and vimentin bands, normalized against GAPDH. (c) Western blot (n = 3) showing the cleaved MAP2, SMA, vimentin, vinculin, and GAPDH levels in cells treated with DMSO (control), Aβ1–42 and ROR1+ Aβ1–42. (d) Graph depicting the mean value of optical density of cleaved MAP2 bands, normalized against GAPDH. (e) Graph depicting the mean value of optical density of SMA, vimentin, and vinculin bands, normalized against GAPDH. Error bars indicate ± SD. Significance level between different experimental pairs is shown (NS not significant, *p < 0.05, **p < 0.01, ***p < 0.001)

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3.2 Resisting Cytoskeletal Degradation by Overexpression of ROR1

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1. Procure human neuroblastoma cell lines SH-SY5Y from ATCC (USA) and culture routinely in DMEM-F12 (Gibco) supplemented with 10% (v/v) heat-inactivated FBS (Gibco), antibiotics penicillin/streptomycin PS 1% (v/v), and 400 μg/mL G418 (Invitrogen, USA) at 33 °C in humidified condition and 5% CO2. 2. Carry out all transfections on 70–80% confluent cells using Lipofectamine 2000 (Invitrogen) as per manufacturer’s protocol for 24–48 h. 3. Unless otherwise mentioned, for single transfection experiment, use 1 μg (30 mm plate), 2.5 μg (60 mm plate), or 5 μg (100 mm plate) of plasmid DNA (ROR1) constructs as well as 5, 10, or 15 μL of Lipofectamine 2000, respectively. 4. Normalize transfection using pEGFP-C1 (Clontech) and using the same protocol above followed by quantification of GFP positive cells using a microscope. 5. Leave cells 24–48 h after transfection and check for ROR1 expression and follow steps 3–12 as above. 6. To analyze if ROR1 hinders cytoskeletal degradation, measure normalized protein amounts (w.r.t. GAPDH) of cleaved MAP2, smooth muscle action (SMA), vinculin and vimentin in DMSO, Aβ1–42-treated and ROR1+ Aβ1–42-treated cells (Fig. 1c–e). 7. Although we have looked at the protective effects of ROR1 by assaying the protein levels of cytoskeletal components, this method can be easily modified to study the effects of other proteins and small molecules (inhibitors/activators) under similar conditions.

3.3 Probing Actin Dynamics by F/G Actin Assay

1. For the F/G actin assay, treat SH-SY5Y cells with Aβ1–42 as described above, in the presence and absence of ROR1 overexpression (24–48 h transfections). 2. Additional controls for the assay include Jasplakinolide (actin stabiliser) and Cytochalasin D (actin depolymeriser), untreated, GFP-expressed, and DMSO-treated cells (see Note 6). 3. After appropriate treatments, scrape cells from the petri-dishes and wash twice in PBS. 4. Centrifuge cells at 800 RCF for 3 min at 4 °C. 5. Resuspend the cell pellets in 200 μL PBS with 0.1% Triton-X100 (with protease inhibitors). 6. Incubate for 15 min with slight agitation, and centrifuge cells at 15,000 ×g RCF at 4 °C for 5 min.

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7. Separate the soluble supernatant (which contains G-actin) and the Triton-X-100 insoluble pellet (predominantly F-actin) and resuspend in 200 μL RIPA buffer. 8. Mix the soluble and insoluble fractions with 5× Loading dye, heat at 98 °C for 10 min, load equal volumes of the two fractions, and separate on a 12% SDS gel using the standard electrophoresis protocol. 9. Assay actin levels using the pan actin antibody (Clone C4) (Fig. 2a, b). 3.4 Qualitative Fluorescence Microscopic Estimation of Actin Network Disassembly in Aβ1–42-Treated Cell Models of AD

1. For the fluorescence-based actin assay, treat SH-SY5Y cells with Aβ1–42 treatment as described above. 2. Perform phalloidin staining on fixed cells grown on coverslips in 35 mm dishes, following Aβ1–42 treatment. 3. Fix cells using 4% paraformaldehyde in PBS (pH 7.4) for 12 min at room temperature. 4. Wash cells in chilled PBS (thrice). 5. Permeabilize cells with 0.1–0.25% Triton X-100 for 10 min at room temperature followed by PBS washes three times for 5 min. 6. Block fixed cells with 1% BSA, 22.52 mg/mL glycine in PBST (PBS+ 0.1% Tween 20) for 30 min to block unspecific binding. 7. Then, incubate the cells with Phalloidin-iFluor 594 Reagent for 3 h at room temperature. 8. Wash cells thrice with PBS. 9. Next, mount the coverslips on fresh, cleaned, and dried slides with a drop of mounting medium and seal with nail polish to prevent drying and movement under microscope. 10. Acquire images using a Zeiss LSM confocal microscope (1024 × 1024 pixels) using 63× oil immersion objective (filter at Ex/Em = 590/618 nm). 11. Capture Z stack images at 15 μm step size and represent final images as Maximum Intensity Projections. 12. Capture images of cell populations as well as individual cells to study the distinct mesh like actin network (in DMSO treated) or disrupted punctate structures (in Aβ1–42-treated cells) (Fig. 2c).

3.5 Quantitative Fluorescence-Based Microscopic Assay of ROR1-Induced Neurite Dynamics in Cell Models of AD

1. For the neurite dynamics assay, treat SH-SY5Y cells with Aβ1–42 treatment as described above, in the presence and absence of ROR1 overexpression (see Note 7). 2. After ROR1 overexpression and Aβ1–42 treatment, stain cells with phalloidin as above and add DRAQ5 to counterstain the nuclei followed by incubation in the dark at 37 °C for 15 min.

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Fig. 2 ROR1-mediated changes on actin dynamics in Aβ1–42-treated cell models of AD. (a) (i) Western blot (n = 3) showing the pan-actin levels in the F and G fractions of cells treated with Jasplakinolide, CytochalasinD, ROR1 + Aβ1–42, and Aβ1–42; and (ii) Western blot (n = 3) showing the pan-actin levels in the F and G fractions of untreated cells, GFP transfected cells, ROR1 transfected cells, and cells treated with DMSO. (b) Graph depicting the mean value of F: G actin ratio in Jasplakinolide, Cytochalasin-D, ROR1 + Aβ1–42, and Aβ1–42-treated cells, compared to their respective controls. In each case, F: G ratio (y-axis) > 1. Error bars indicate ± SD. (c) Confocal microscopy images of phalloidin-594 (actin) stained SH-SY5Y cells; DMSO treated (Panel i. 1× zoom), DMSO treated (Panel ii. and inset. 3× zoom), scale bars, 5 μm; Aβ1–42 treated (Panel iii. 1× zoom), Aβ1–42 treated (Panel iv. and inset. 3× zoom), scale bars, 5 μm; for each confocal experiment, images of at least 30 cells (or cell fields) were captured and the experiments were repeated thrice (n = 3). Significance level between different experimental pairs is shown (NS not significant, *p < 0.05, **p < 0.01, ***p < 0.001)

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3. Capture images as steps 10–12 above with additional lasers (647 DRAQ5, 488 GFP and associated filters) and use for neurite estimation. 4. Acquire Z stack images using ZEN 2015 (64 bit) software (Carl Zeiss). 5. Convert images to 16-bit grayscale and threshold. 6. Manually trace neurites, quantify by Sholl analysis in FIJI (ImageJ, NIH), and plot as neurite length. 7. Draw a series of concentric circles from the centre of the cell body at intervals of 20 μm, with the radius of the outermost circle set at 100 μm. 8. Calculate the maximum value of sampled intersections reflecting the highest number of processes/branches, and plot as the neurite number (Fig. 3a, b).

4

Notes 1. Although this study employed the cell lines Neuro-2A and SH-SY5Y, other cell lines like SK-N-SH and HeLa can also be used. However, neuronal cell lines are recommended to make results relevant to AD. Moreover, depending on the cell lines used, growth conditions and transfection efficiencies need to be optimized. 2. Lipofectamine-2000 gives lesser transfection efficiencies compared to Lipofectamine-3000, but transfection-related cytotoxicity is lesser with the former. 3. The time for Aβ1–42 was 24 h. It is however recommended to do a time course study starting from 12 to 48 h to look at cytoskeletal degradation in terms of protein markers, depending on the cell lines used (for example, other neuronal lines like Neuro-2A and SKN-SH). 4. Alternatively, protein amounts can also be quantified by Qubit protein assay. 5. For studying AD models, GAPDH serves as a better loading control, compared to beta-actin. However, the best method is to look at total protein load by Ponceau staining or stain free gels. 6. Cytochalasin D and Jasplakinolide are potent agents. Prior to actual experiments, it is recommended to optimize concentrations of both. A good starting concentration is 50 μM working but can be varied by cell type and cell numbers.

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Fig. 3 ROR1-induced neurite dynamics in cell models of AD. (a). Confocal microscopy images of SH-SY5Y cells transfected with ROR1-GFPSpark, treated with Aβ1–42 and stained with DAPI—panel i, ii, and inset (enrichment of ROR1 in neurite tips). ROR1 + Aβ1–42 leads to fewer but more elongated neurites which contacts adjacent cells (white wedge), scale bars, 10 μm; for each confocal experiment, images of at least 30 cells (or cell fields) were captured and the experiments were repeated thrice (n = 3). (b) Graph depicting the mean value of neurite length and neurite numbers in ROR1, Aβ1–42, and ROR1 + Aβ1–42 cells

7. For imaging studies, the empirical cell confluency of 70–80% prior to transfection can be altered. Lesser cell confluence helps in the imaging of single cells which are spatially separated. References 1. De Strooper B, Karran E (2016) The cellular phase of Alzheimer’s disease. Cell 164:603–615 2. Townsend M, Shankar GM, Mehta T, Walsh DM, Selkoe DJ (2006) Effects of secreted oligomers of amyloid beta-protein on hippocampal

synaptic plasticity: a potent role for trimers. J Physiol 572(Pt 2):477–492 3. Klein WL, Krafft GA, Finch CE (2001) Targeting small Abeta oligomers: the solution to an Alzheimer’s disease conundrum? Trends Neurosci 24:219–224

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4. Oishi I, Takeuchi S, Hashimoto R, Nagabukuro A, Ueda T, Liu ZJ, Hatta T, Akira S, Matsuda Y, Yamamura H, Otani H, Minami Y (1999) Spatio-temporally regulated expression of receptor tyrosine kinases, mRor1, mRor2, during mouse development: implications in development and function of the nervous system. Genes Cells 4:41–56 5. Al-Shawi R, Ashton SV, Underwood C, Simons JP (2001) Expression of the Ror1 and Ror2 receptor tyrosine kinase genes during mouse development. Dev Genes Evol 211:161–171

6. Paganoni S, Ferreira A (2005) Neurite extension in central neurons: a novel role for the receptor tyrosine kinases Ror1 and Ror2. J Cell Sci 118 (Pt 2):433–446 7. McMurray CT (2000) Neurodegeneration: diseases of the cytoskeleton? Cell Death Differ 7: 861–865 8. Paganoni S, Ferreira A (2003) Expression and subcellular localization of Ror tyrosine kinase receptors are developmentally regulated in cultured hippocampal neurons. J Neurosci Res 73:429–440

Chapter 20 microRNA Isolation, Expression Profiling, and Target Identification for Neuroprotection in Alzheimer’s Disease Saleem Iqbal and Debnath Pal Abstract Millions of people throughout the world are affected by neurodegenerative disorders like Alzheimer’s disease (AD), making them a major public health concern. To create successful medicines, early diagnosis and illness monitoring are required. Emerging as possible diagnostic and treatment tools for neurodegenerative illnesses are biomarkers such as microRNAs (miRNAs). In the realm of neuroscience, miRNAs have been discovered to function as essential regulators of gene expression, with roles spanning development, differentiation, and illness. Several neurodegenerative diseases, including AD, have been linked to miRNA dysregulation. As high-throughput methods have been developed for monitoring miRNA expression and identifying miRNA targets, miRNAs have become a prime candidate for use in diagnostics and therapy. The techniques for isolating miRNAs and the most up-to-date computational methods for finding miRNA target transcripts are both described in this chapter. This chapter will be a helpful reference for anyone investigating the role of miRNAs in AD and related neurodegenerative illnesses. Key words microRNA isolation, Expression profiling, Target identification, Neuroprotection, Alzheimer’s disease, Biomarker

1

Introduction MicroRNAs have emerged as potential biomarkers due to their stability and accessibility in various biological samples. miRNAs can be isolated from blood, cerebrospinal fluid (CSF), or tissues such as the brain or liver. Targeted gathering of tissues for miRNA profiling can be done by laser-capture microdissection (LCM) on the hippocampus or prefrontal cortex for Alzheimer’s disease (AD) and dementia. Another miRNA isolation method from brain tissues is RNA extraction from postmortem brain tissues. This technique involves the collection of brain tissue samples during the autopsy, followed by RNA extraction and miRNA profiling. Several studies [1] have shown that miRNAs can also be isolated from blood or CSF as potential biomarkers for AD. Microfluidics-based

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_20, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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approaches for exosome isolation and biosensors for exosomal miRNA detection offer exciting new opportunities for AD biomarker discovery. The protocol for miRNA profiling in the nervous system involves several steps, including tissue or cell disruption, total RNA extraction, isolation of miRNAs, quantification of total RNA, detection, quantification of individual miRNAs, and highthroughput expression miRNA profiling (Fig. 1) [2]. Cultured cell lysis or tissue lysis can be achieved using various methods such as homogenization, sonication, or enzymatic digestion. The total RNA can then be obtained from disrupted tissue or cells using organic extraction methods. The addition of phenol and chloroform to a sample, followed by centrifugation to separate the aqueous and organic phases, is a typical approach for miRNA isolation. After being rinsed with ethanol, the miRNAs are precipitated from the aqueous phase with isopropanol. Another method is column-based purification, which involves using a spin column with a silica-based membrane to bind miRNAs selectively. RNA-binding protein capture is another method for isolating miRNAs, which involves using antibodies that specifically recognize RNA-binding proteins associated with miRNAs. The extracted miRNAs are reverse-transcribed to cDNA, which is then used for miRNA detection and quantification across multiple platforms, including microarrays and quantitative polymerase chain reactions [2]. The isolated total RNA is quantified using several methods, viz., spectrophotometry or fluorometry. miRNA isolation from total RNA is also accomplished using commercial kits, which use specific capture probes to bind miRNAs selectively. The extracted miRNAs can then be reverse–transcribed to cDNA using specific primers and reverse transcriptase enzymes. The resulting cDNA can be used to detect and quantify individual miRNAs using quantitative PCR (qPCR) or for high-throughput expression profiling using microarrays or other platforms such as next-generation RNA sequencing. The use of specific isolation methods and detection platforms is critical for the accurate and reliable detection and profiling of miRNAs in the nervous system. Further research is needed to standardize miRNA isolation and profiling methods to ensure accurate and reproducible results. In silico analysis plays an important part in all miRNA discovery [3].

2

Materials

2.1 Tissue or Cell Culture Disruption and Total RNA Extraction

1. Fresh, frozen, or RNA later-stored mammalian tissue or cultured cells. 2. RNA later (Ambion). 3. RNaseZap solution (Ambion).

microRNA for Neuroprotection in Alzheimer’s Disease

Fig. 1 Well-established microRNA profiling approaches reviewed by Pritchard and co-workers [2]

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4. Trypsin (only required for adherent cell line samples). 5. mir Vana miRNA Isolation Kit Protocol (Ambion), 6. mir Vana miRNA Isolation Kit containing acid-phenol: chloroform (Ambion): miRNA wash solution 1, wash solution 2/3, collection tubes, filter cartridges, lysis/binding buffer, miRNA homogenate additive, acid-phenol: chloroform, gel loading buffer, and elution solution. 7. Nuclease-free 1.5 mL and 0.5 mL microfuge tubes and tips. 8. Mortar and pestle prechilled on dry ice or in liquid nitrogen. 9. Phosphate-buffered saline (PBS). 2.2 Isolation and Quantification

These are required for the isolation and quantification of miRNA containing total RNA: 1. mir Vana miRNA Isolation Kit Protocol (Ambion). 2. mir Vana miRNA Isolation Kit containing acid-phenol: chloroform (Ambion): miRNA wash solution 1, wash solution 2/3, collection tubes, filter cartridges, lysis/binding buffer, miRNA homogenate additive, acid-phenol: chloroform, gel loading buffer, and elution solution. 3. Nuclease-free 1.5 mL, 0.5 mL, and 0.2 mL microfuge tubes and tips. 4. 100% ethanol. 5. Nuclease-free water. 6. Ultraviolet (UV) spectrophotometer with cuvettes. These are required for working with individual miRNAs: 1. Nuclease-free 1.5 mL, and 0.2 mL microfuge tubes. 2. Nuclease-free water. 3. TaqMan Small RNA Assays Protocol (Applied Biosystems) (see Note 1). 4. Inventoried TaqMan microRNA Assay for miRNA of interest (Applied Biosystems) (see Note 2). 5. TaqMan microRNA Reverse Transcription Kit (Applied Biosystems). 6. TaqMan Universal PCR Master Mix (Applied Biosystems). 7. An endogenous control assay, e.g., Homo sapiens mature miRNA control (Applied Biosystems). 8. MicroAmp Optical 384-well plate (Applied Biosystems). 9. MicroAmp Optical Adhesive Film (Applied Biosystems). 10. Applied Biosystems 7900HT Thermal Cycler.

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1. Megaplex Pools for microRNA Expression Analysis Protocol (Applied Biosystems). 2. TaqMan microRNA Reverse Transcription Kit (Applied Biosystems). 3. Megaplex RT Primers, Human Pool Set (Pool A and Pool B) v3.0. 4. Nuclease-free 1.5 mL microfuge tubes and tips. 5. MicroAmp Optical Biosystems).

96-well

Reaction

Plates

(Applied

6. MicroAmp Clear Adhesive Film (Applied Biosystems). 7. Applied Biosystems 7900HT Thermal Cycler. 8. TaqMan Universal PCR Master Mix (Applied Biosystems). 9. Nuclease-free water. 10. TaqMan Arrays Human microRNA A and B Cards Set v3.0 (Applied Biosystems) (see Note 3).

3

Methods

3.1 Sample Preparation and Experimental Assays (See Note 4)

Follow these steps for disruption of tissue (see Note 5): 1. Collect tissue and discard any unnecessary parts. Red blood cells can be washed away with a cold PBS rinse. 2. Prepare the tissue for storage or disturbance by cutting it into smaller pieces. 3. RNases can be rendered dormant for long-term storage by flash-freezing the tissues in liquid nitrogen. The material can also be stored at -70 °C and added to RNA at a later time. 4. Find out how much tissue, either fresh or frozen, weighs. 5. Use a mortar and pestle chilled with dry ice to reduce frozen tissue to powder. Instead, you can insert the fresh tissue in a homogenization tube with lysis/binding buffer per tissue mass and thoroughly disrupt the sample while keeping it on ice. 6. To an ice-filled plastic weigh boat, add 10 L of lysis/binding buffer for every 0.1 grams of tissue. Using a cooled metal spatula, scrape the powdered frozen tissue into the lysis/binding buffer and mix rapidly until all clumps are dispersed. Follow these steps for the disruption of cells: 1. For suspension cells, centrifuge 102–107 cells at low speed and dispose of the supernatant. Spin the cells in 1 mL PBS and centrifuge. Throw away the supernatant and place the washed cells on ice.

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2. Trypsinizing and counting adherent cells is a common technique. Spin down the cell suspension in the centrifuge and then wash the pellet in 1 mL of PBS. The supernatant should be discarded, and the cells should be frozen after another centrifugation. 3. Add lysis/binding solution of 300–600 μL per 102–107 cells. 4. Pipette up and down vigorously or give the sample a good shake to lyse the cells. Follow these steps for organic extraction (see Note 6): 1. Add miRNA homogenate additive to the tissue or cell lysate at a ratio of 1/10 (e.g., 30 μL homogenate additive to 300 μL lysate). Use a vigorous vortex to combine. 2. Put the ingredients in an ice bath for 10 min. 3. When adding the miRNA homogenate, make sure to use an equal volume of acid-phenol: chloroform (e.g., if the lysate volume was 200 μL before the homogenate was added, use 200 μL of acid-phenol: chloroform). Give the sample 1 min in a vortex. 4. Centrifuge at room temperature at 10,000 ×g for 10 min. If the interphase layer is still lost, repeat the process. 5. Without disrupting the lower phase, carefully transfer the top aqueous phase to a new tube and record the volume lost. We shall isolate total RNA from the aqueous phase sample. Follow these steps for the isolation of total RNA (recovering complete RNA) (see Note 7): 1. To make the washing buffers, combine 21 mL of 100% ethanol with miRNA wash solution 1 and 40 mL with wash solution 2/3 from the mir Vana miRNA Isolation Kit, respectively (see Note 8). 2. To extract the eluate, heat 1 mL of nuclease-free water to 95 °C (see Note 9). 3. The aqueous phase needs 1.25 mL of 100% ethanol added to it. 4. Total RNA can be extracted by putting a filter cartridge in a sample collection tube. Fill the filter cartridge with the lysate/ ethanol solution. Apply the combination in stages to the same filter cartridge for samples larger than 700 μL. 5. Spin at 10,000 ×g for 15 s at room temperature, then send the waste through the centrifuge. It is possible to recycle the collection tube for further use. 6. Place 700 μL wash solution 1 in the filter cartridge and centrifuge for 10 s at 10,000 ×g at room temperature.

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7. Discard the flow-through and place the filter cartridge back into the same collection tube. 8. Add 500 μL wash solution 2/3 to the filter cartridge. 9. Centrifuge for 10 s at 10,000 ×g at room temperature. 10. Discard the flow-through. Add 500 μL wash solution 2/3 and centrifuge for 10 s at 10,000 ×g at room temperature. 11. Discard the flow-through. To remove any remaining fluid from the filter, place the filter cartridge back in the same collecting tube and centrifuge for 1 min at 10,000 ×g. Discard the flowthrough. 12. Transfer the filter cartridge to a new collection tube. Add 100 μL preheated (95 °C) nuclease-free water to the filter’s center and close the cap. 13. Total RNA can be extracted by centrifuging the sample at 10,000 ×g for 30 s. 14. Take 1–2 μL of the whole RNA sample and put it in a new, sterile, nuclease-free tube for quantification. 15. Store the remaining eluate at -70 °C until required for analysis. Quantifying and quality assessment of total RNA: 1. Dilute an aliquot of the total RNA sample 1:100 in nucleasefree water to determine the concentration of RNA isolated. 2. Measure the absorbance at 260 and 280 nm on a UV spectrophotometer, using a water sample as a blank. 3. Calculate the sample concentration using the equation: (Absorbance at 260 nm × 40 × dilution factor × 1000)/path length (μm). 4. Calculate the absorbance ratio at 260 and 280 nm (A260/280) to determine the quality of the preparation. A ratio between 1.8 and 2.1 indicates high-quality RNA suitable for use in microarray experiments. Detecting and quantifying specific miRNAs using individual TaqMan® miRNA assays by reverse transcription (RT): 1. Thaw the total RNA sample, miRNA-specific 5× RT primer (from TaqMan® miRNA assay), and the components of the TaqMan® microRNA Reverse Transcription Kit on ice (see Note 10). 2. Prepare the RT Master Mix by combining the components of the TaqMan® microRNA Reverse Transcription Kit (see Note 11).

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3. Mix 7 μL of the RT Master Mix, 5 μL of total RNA (containing 1–10 ng), and 3 μL of miRNA-specific 5× RT primer in a 0.2 mL tube. 4. Seal the tube and place it on ice for 5 min. 5. Run the RT reaction in a thermal cycler at 16 °C for 30 min, 42 °C for 30 min, 85 °C for 5 min, and 4 °C indefinitely. 6. Store the resulting RT product at -20 °C until further use. Detecting and quantifying specific miRNAs using individual TaqMan® miRNA assays by qPCR (see Note 12): 1. On the ice, defrost the RT product and the TaqMan® miRNA assay, and then mix the two using the inverted TaqMan® Universal PCR Master Mix. 2. Determine the total number of reactions for each test by factoring in replicates, exogenous controls, and negative controls (see Note 13). 3. Each assay requires a different number of qPCR reactions, which should be prepared in 1.5 mL tubes. 4. Seal a 384-well plate with adhesive film, centrifuge briefly, and load the plate into a thermal cycler after adding 20 μL of the qPCR reaction mix to each well. 5. Standard qPCR conditions include a volume of 20 μL, enzyme activation at 95 °C for 10 min, 40 PCR cycles at 95 °C for 15 s, and an annealing/extension step at 60 °C for 60 s (see Note 14). 6. Use the comparative Ct technique to examine differences in miRNA expression between samples (see Note 15). 3.2 High-Throughput Expression Profiling of microRNAs (See Note 16)

Reverse transcription of total RNA to single-strand cDNA: 1. Thaw the total RNA sample, TaqMan® microRNA Reverse Transcription Kit, and Megaplex™ primers on ice. Mix by flicking and briefly centrifuging the tubes. 2. Use 500 ng total RNA per RT reaction, with a final volume of 7.5 μL, consisting of 3 μL total RNA, and 4.5 μL RT reaction mix. Run two Megaplex™ RT reactions (pool A and B) and two TaqMan® miRNA arrays (array A and B) per sample for a full miRNA profile (see Note 17). 3. Calculate the number of RT reactions for each Megaplex™ RT primer pool based on the number of total RNA preparations. 4. Prepare a mix of RT reaction components in a 1.5 mL tube for every 10 reactions, including 12.5% excess for volume loss during pipetting. The components include Megaplex™ RT Primers, dNTPs with dTTP, MultiScribe™ Reverse Transcriptase, 10× RT buffer, MgCl2, RNase inhibitor, and nuclease-free water.

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5. Invert the tube to mix and centrifuge briefly. 6. Add 4.5 μL of RT mix to each well of a 96-well MicroAmp® reaction plate, followed by 3 μL (containing 500 ng) of total RNA. 7. Seal the plate using a MicroAmp® Adhesive Film. Invert the plate to mix the components and centrifuge briefly. 8. Place the plate on ice for 5 min. 9. Load the plate into the thermal cycler and run the RT with the following conditions: 16 °C for 2 min, 42 °C for 1 min, 50 °C for 1 s, 85 °C for 5 min, and 4 °C indefinitely. 10. Store the cDNA at -20 °C until further use. Real-time PCR and TaqMan® miRNA array (see Note 18): 1. Thaw the RT product and the PreAmp reagent on ice. 2. Invert the TaqMan® Universal PCR Master Mix. 3. Bring the TaqMan® miRNA Array Cards to room temperature. 4. Prepare the PCR reaction mix in a 1.5 mL tube, including 6 μL RT product, 450 μL TaqMan® Universal PCR Master Mix, and 440 μL nuclease-free water. 5. Invert the tube to mix and centrifuge briefly. 6. Add 100 μL of PCR reaction mix into each port of the TaqMan® miRNA Array. Centrifuge the array briefly and seal it. 7. Load the array into the thermal cycler and run the TaqMan low-density array default program on the machine. 8. Analyze the data for changes in miRNA expression using the comparative Ct method, as outlined in the Megaplex™ Pools for miRNA Expression Analysis protocol and http://www. affymetrix.com/dataassist. For further downstream analysis, Integromics StatMiner Software may be used (http://www. integromics.com) (see Note 19). 3.3 In Silico Analysis and Validation

Identification of miRNAs and their targets (see Note 20): 1. Use miTarget: It is a web-based tool for predicting miRNA targets in various species [4]. miTarget uses a combination of target prediction algorithms and is designed to be a nonspecies-specific tool. miTarget also allows users to input their miRNA sequences and generate lists of predicted targets based on these sequences. Use PicTar: It is a widely used tool for miRNA target prediction in various species [5]. PicTar uses a combination of seed sequence prediction and evolutionary conservation to identify potential miRNA targets. PicTar is a useful tool in identifying miRNA targets and is frequently used together with other bioinformatic tools [6].

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Use TargetScan: TargetScan is a widely used tool for predicting miRNA targets in vertebrates and is based on the seed sequence and conservation across species (https://targetscan.org, [7]). TargetScan has a database of conserved 3’ UTR sequences for various species and utilizes this information to predict miRNA targets. TargetScan is a useful tool for identifying miRNA targets and is frequently used in combination with other bioinformatic tools [8]. Sample protocol for use: (i) Input the miRNA sequence of interest into the TargetScan web tool, (ii) select the appropriate species for target prediction, (iii) analyze the results to identify potential miRNA targets, (iv) cross-reference the predicted targets with other bioinformatic tools and experimental validation data to identify potential miRNA targets with higher confidence, (v) and further validate potential miRNA targets using techniques such as qPCR and Western blotting. 2. Since the identification of the miRNAs involve in silico analysis, it is advisable to check the results through comparison with existing data. Use mirTools for multiple levels of computational analyses for characterizing the small-size RNA transcriptome [9]. miRbase (https://www.mirbase.org, [10]) is another good repository for exploring miRNAs and obtaining help with naming novel miRNA genes as it contains all known miRNA sequences, genomic locations, and their related annotation. In addition, miRNAMap (http://miRNAMap.mbc.nctu.edu.tw/, [11]) can be used to obtain link between the miRNAs and their target gene for regulation in humans, mice, rats, and dogs. Tarbase (http://www.diana.pcbi.upenn.edu/tarbase, [12]) is another collection of empirically verified targets described in the literature using a variety of experimental methods that can be looked into (see Note 21). Validation of miRNA Targets (see Note 22): There are several methods for validating miRNA targets, including: 1. qPCR: Measuring changes in mRNA levels after miRNA transfection or knockdown. 2. Western blotting: Measuring changes in protein levels after miRNA transfection or knockdown. 3. Reporter assays: Using reporter constructs containing the miRNA target site to measure changes in luciferase activity after miRNA transfection or knockdown. 4. CLIP-seq: Crosslinking immunoprecipitation followed by high-throughput sequencing allows for identifying miRNAmRNA interactions at a genome-wide scale.

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Notes 1. Applied Biosystems. (2017). TaqMan miRNA Assays Protocol. https://www.thermofisher.com/content/dam/LifeTech/ global/Forms/PDF/TaqMan-microRNA-Cells-to-CT-Kitprotocol.pdf 2. TaqMan miRNA assays are commonly used in quantitative polymerase chain reactions (qPCR); however, their specificity has not been validated. One way to do this is to run the PCR product on an agarose gel to verify its expected size, while another is to analyze its melting curve to make sure that just one product is amplified. 3. Mestdagh, P et al. [13]. 4. Working in an RNase-free environment is important for preventing RNA degradation and ensuring high-quality RNA isolation. Surfaces should be cleaned and treated with an RNase decontaminating spray, such as RNaseZap. Latex gloves should be worn during experiments, and gloves should be changed frequently. 5. To increase the output of high-quality RNA from tissue samples, it is crucial to minimize the period between collecting the samples and inactivating RNases. Direct immersion of samples in RNA later, the lysis buffer included in the miRNA isolation kit, or liquid nitrogen will inactivate RNases. Homogenizing tissue samples on ice should be done until no longer visible tissue clumps remain. 6. It is crucial to separate the acid-phenol: chloroform reagent during RNA isolation. The uppermost layer is a water-based buffer. It is crucial just to use the bottom layer, which includes the phenol: chloroform. Aspirate solely from the lowest layer by lacing the pipette tip there. 7. For best results when isolating RNA from cultivated cells, processing fresh cells is suggested. Instead of using frozen samples, cultured cells should be treated as soon as possible. Nevertheless, RNA isolated after miRNA isolation should be kept at -70 °C to preserve cells collected beforehand. 8. Qiagen. (2013). miR Vana miRNA Isolation Kit Handbook. https://www.qiagen.com/us/resources/resourcedetail?id=2 598f607-44d1-4c74-b696-0db2068f1a87&lang=en 9. Nuclease-free water or the 0.1 mM EDTA-containing elution buffer included in the kit can be used to elute the RNA. Water purified to remove nucleases is advised to avoid disrupting subsequent processes.

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10. Using a complete RNA preparation in the RT reaction, as opposed to a sample enriched for the miRNA fraction, is recommended for preserving endogenous control sequences. 11. To compensate for inevitable reagent loss during pipetting, the protocol’s indicated quantities for Master Mix should be increased by 20%. Each RT reaction needs 7.0 μL of Master Mix. To get the appropriate number of reactions, make enough Master Mix. 12. Variations in the amount of starting material, sample collection, RNA preparations, and the effectiveness of the RT reaction might lead to inaccurate results when performing qPCR; therefore, normalizing gene expression to endogenous control genes is essential. The expression of a strong endogenous regulatory gene is ubiquitous and high in all cell types and tissues. 13. Accuracy in qPCR tests can be ensured by including H2O as a template as a negative control. If any product is amplified in these wells, it is likely because of reagent contamination. To further prevent sample contamination, qPCR procedures should be performed in a clean, dedicated area away from any other amplification processes, such as siRNA transfections. It is advisable to keep sample preparation, PCR setup, and amplification in their own spaces with their own sets of equipment. Pipette tips and centrifuge tubes that are not susceptible to aerosols should be used before opening. 14. Applied Biosystems’ Megaplex PreAmp Primer Sets can be used to amplify miRNA cDNA targets in advance of qPCR when the yield of the total RNA starting material is low (300 ng). Including this procedure in your qPCR assay design can boost its sensitivity, allowing you to detect low-abundance miRNAs with greater precision. 15. It is crucial to employ suitable statistical approaches when examining changes in miRNA expression to establish their relevance. The delta-delta CT approach and the 2-CT method are two of the most popular options. 16. Microarray analysis is a powerful and high-throughput method for observing cleaved target mRNAs. However, no highthroughput techniques are available to verify the interaction with and regulation of a target [14]. Techniques used in other areas of biological research, such as Northern blot analysis [15], FRET-FISH [16], RT-PCR and qRT-PCR [17], microarray [18], and in situ hybridization [19] have been successfully used to validate miRNA identification data. 17. If other methods require tiny RNA molecules (200 nucleotides), they can be enriched from total RNA preparations. Before miRNA expression profiling using microarrays, it is

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recommended to synthesize total RNA containing the miRNA fraction for precise quantification and quality analysis. When doing microarrays, only high-quality RNA should be used. Before beginning array investigations, the 2100 Bioanalyzer is a helpful microfluidics-based platform that offers comprehensive information on the quality and amount of RNA samples. Labeling samples for miRNA microarray analysis requires first performing quality control, and then purifying the miRNA population according to the miRNA isolation kit procedure. 18. The TaqMan miRNA array can be prepared up to 48 h before the qPCR run and stored at 4 °C. It speeds up the experimental workflow by cutting down on the time spent preparing for large-scale experiments. However, the arrays should not be frozen, as this could damage the TaqMan probes. 19. Bustin, SA et al. [20]. 20. Although in silico methods have established miRNA-mRNA interaction rules to identify targets, they may potentially miss targets that do not conform to these rules. To maximize their dependability, most in silico methods require a fully complementary “seed” sequence in the 3′ UTR of the mRNA and conservation of this site across several species. 21. Most bioinformatics programs look for complementary sequences in the 3′ UTR of mRNA transcripts by analyzing the “seed” region, a 7–8 bp sequence beginning at the first or second base of the mature miRNA’s 5′ end. miRbase is a comprehensive database of all known miRNAs that is routinely updated and contains a database of projected miRNA target genes; there are several other in silico approaches for miRNAmRNA duplex prediction and databases of known miRNAs and their targets. 22. Finally, it is crucial to correctly interpret the data from miRNA expression analyses. Variables such as cellular setting, developmental phase, and environmental variables can all impact miRNA expression levels. Consequently, it is crucial to validate any findings through different experimental procedures and to take the biological context into account when interpreting the results. References 1. Lugli G, Cohen AM, Bennett DA, Shah RC, Fields CJ, Hernandez AG, Smalheiser NR (2015) Plasma exosomal miRNAs in persons with and without Alzheimer disease: altered expression and prospects for biomarkers. PLoS One 10:e0139233. https://doi.org/10. 1371/journal.pone.0139233

2. Pritchard CC, Cheng HH, Tewari M (2012) MicroRNA profiling: approaches and considerations. Nat Rev Genet 13:358–369. https:// doi.org/10.1038/nrg3198 3. Iqbal S, Malik MZ, Pal D (2021) Networkbased identification of miRNAs and transcription factors and in silico drug screening

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targeting delta-secretase involved in Alzheimer’s disease. Heliyon 7:e08502. https://doi. org/10.1016/j.heliyon.2021.e08502 4. Lin SY, Johnson SM, Abraham M, Vella MC, Pasquinelli A, Gamberi C, Gottlieb E, Slack FJ (2003) The C elegans hunchback homolog, hbl-1, controls temporal patterning and is a probable microRNA target. Dev Cell 4:639– 650. https://doi.org/10.1016/s1534-5807 (03)00124-2 5. Doench JG, Sharp PA (2004) Specificity of microRNA target selection in translational repression. Genes Dev 18:504–511. https:// doi.org/10.1101/gad.1184404 6. Wuchty S, Fontana W, Hofacker IL, Schuster P (1999) Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers 49:145–165. https://doi.org/10. 1002/(SICI)1097-0282(199902)49:23.0.CO;2-G 7. Chaudhuri K, Chatterjee R (2007) MicroRNA detection and target prediction: integration of computational and experimental approaches. DNA Cell Biol 26:321–337. https://doi.org/ 10.1089/dna.2006.0549 8. Watanabe Y, Tomita M, Kanai A (2007) Computational methods for microRNA target prediction. Methods Enzymol 427:65–86. https://doi.org/10.1016/S0076-6879(07) 27004-1 9. Zhu E, Zhao F, Xu G, Hou H, Zhou L, Li X, Sun Z, Wu J (2010) mirTools: microRNA profiling and discovery based on highthroughput sequencing. Nucleic Acids Res 38:W392–W397. https://doi.org/10.1093/ nar/gkq393 10. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157. https://doi.org/10.1093/nar/ gkq1027 11. Hsu SD, Chu CH, Tsou AP, Chen SJ, Chen HC, Hsu PW, Wong YH, Chen YH, Chen GH, Huang HD (2008) miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Res 36:D165–D169. https:// doi.org/10.1093/nar/gkm1012 12. Sethupathy P, Corda B, Hatzigeorgiou AG (2006) TarBase: a comprehensive database of experimentally supported animal microRNA

targets. RNA 12:192–197. https://doi.org/ 10.1261/rna.2239606 13. Mestdagh P, Hartmann N, Baeriswyl L, Andreasen D, Bernard N, Chen C, Cheo D, D’Andrade P, DeMayo M, Dennis L et al (2014) Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods 11:809– 815. https://doi.org/10.1038/nmeth.3014 14. Grimson A, Farh KK, Johnston WK, GarrettEngele P, Lim LP, Bartel DP (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27:91– 105. https://doi.org/10.1016/j.molcel. 2007.06.017 15. Wienholds E, Kloosterman WP, Miska E, Alvarez-Saavedra E, Berezikov E, de Bruijn E, Horvitz HR, Kauppinen S, Plasterk RH (2005) MicroRNA expression in zebrafish embryonic development. Science 309:310–311. https:// doi.org/10.1126/science.1114519 16. Kim J, Kang C, Shin S, Hohng S (2022) Rapid quantification of miRNAs using dynamic FRET-FISH. Commun Biol 5:1072. https:// doi.org/10.1038/s42003-022-04036-x 17. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR et al (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33: e179. https://doi.org/10.1093/nar/gni178 18. Muniategui A, Pey J, Planes FJ, Rubio A (2013) Joint analysis of miRNA and mRNA expression data. Brief Bioinform 14:263–278. https://doi.org/10.1093/bib/bbs028 19. Lyons SM, Gudanis D, Coyne SM, Gdaniec Z, Ivanov P (2017) Identification of functional tetramolecular RNA G-quadruplexes derived from transfer RNAs. Nat Commun 8:1127. https://doi.org/10.1038/s41467-01701278-w 20. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622. https://doi.org/10.1373/clinchem.2008. 112797

Chapter 21 Quantitative Measurement of Tau Aggregation in Genetically Modified Rats with Neurodegeneration YouJin Lee and Eric M. Morrow Abstract Animal models of neurodegenerative diseases have helped us to better understand the pathogenesis of neurodegenerative diseases. However, recent failure to translate pre-clinical model studies to the clinic urges us to develop more rigorous and faithful animal models in neurodegenerative diseases. As genetic manipulation of rats becomes much more accessible due to availability of CRISPR-Cas9 and other genomic editing toolboxes, rats have been emerging as a new model system for neurodegenerative diseases. Even though mouse models have been dominant over the last decades, rats may provide advantages over mice. Rats are more genetically and physiologically closer to humans than to mice. Also, certain rat models can represent deposition of tau, which is one of the key pathological features of Alzheimer’s diseases and tauopathies. However, there is an unmet need for standardized, rigorous testing in rat models. We adopted two commonly used biochemical and immunofluorescence methods from mice and human postmortem brains to measure tau aggregation. Due to the intrinsic differences between mice and rats, e.g., size of rat brains, certain equipment is required for rat models to study tau pathologies. Along with specific tools, here we describe the detailed methods for rat models of neurodegenerative diseases. Key words Tau, Neurodegeneration, Rat models, Immunoblotting, Sequential tau extraction, Immunofluorescence

1

Introduction Neurodegenerative diseases affect millions of people worldwide with huge economic burden [1, 2]. Currently, there is no therapeutic intervention curing neurodegenerative disorders. As life expectancy increases, prevalence of neurodegenerative diseases is expected to be increased. Along with aging, certain genetic variants are known for risk factors for neurodegenerative disorders [3]. However, the pathogenesis of many neurodegenerative diseases remains unclear. Thereby, it will be critical to generate and validate preclinical models based on genetic data to advance the identification of therapeutic targets in neurodegenerative diseases.

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_21, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Rodent models of neurodegenerative diseases have been developed to recapitulate human pathologies [4]. Mouse models of neurodegenerative diseases have been used and enhanced our understanding of the pathogenesis of diseases. The first mouse models of Alzheimer’s disease (AD) were generated with overexpression of genetic mutations in amyloid precursor protein (APP), presenilin 1 (PS1), and presenilin 2 (PS2). These mouse models (APP/PS1) present key aspects of AD pathologies such as amyloid accumulation, hyper-phosphorylation of tau, and gliosis [5– 8]. Although these mice and other mouse models have been more frequently used as preclinical models than rats, rats have been recently emerging as complementary models for neurodegenerative diseases due to CRISPR-Cas9 technologies available [9]. As rats are genetically and physiologically closer to human than to mice [10– 12], rat models may offer a few advantages over mouse models [13]. For example, rats have six isoforms of tau, similarly to human [14]. Also, transgenic Tg344-AD rats expressing APP and PS1 mutations more faithfully recapitulate key features of human AD, including accumulation of amyloid plaque, neuronal death, and tau pathologies, compared to corresponding APP/PS1 mice [15]. One of the hallmarks in many neurodegenerative disorders such as AD is the accumulation of microtubule-associated protein tau [16]. Hyper-phosphorylated tau fibrils are composed of neurofibrillary tangles, which are detected in various neurodegenerative diseases. Most of transgenic mice models presenting such tau pathologies are generated with overexpression of frontotemporal dementia mutation forms of human tau [4]. Many methodologies are available to study tau pathologies in mice models and those can be applied to the rat models. However, certain rat models of neurodegenerative diseases present tau pathologies without the overexpression of mutated human tau [15, 17]. Also, the size of rats and their brains are much larger than those of mice, which require a new set of experimental systems. Here, we describe the detailed methods for studying tau pathologies in rat brains, using biochemical assay and immunohistochemistry. We adopted two commonly used methods since these methods have been widely used in both mouse models and human postmortem brains. These methods can be used for cross-species comparison between different models of neurodegenerative diseases.

2

Materials Here we describe two commonly used methods for measuring tau proteins in rat brains: sequential tau extraction assay with epitopespecific tau antibodies (Fig. 1) and immunohistochemistry using microscope. Prepare all reagents using ultrapure water. All reagents

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Fig. 1 Main steps for tau sequential extraction in rat brains

are prepared and stored at room temperature if it is not indicated otherwise. 2.1 Materials for Sequential Tau Extraction

Detergents for sequential tau extraction: 1. Tris-buffered saline (TBS): 1× TBS is diluted from 10× TBS. cOmplete ULTRA Tablet Protease Inhibitor Cocktail (PIC), phenylmethylsulfonyl fluoride (PMSF), and phosSTOP are freshly added to 1× TBS before the procedure. 2. Sarkosyl solution: Dissolve 1% sarksosyl in 1× TBS. Freshly add PIC, PMSF, and phosSTOP to the solution before the procedure. 3. Urea buffer: 8 M Urea, 50 mM Tris, pH 7.5. Reagents for immunoblotting: 1. SDS-Page Gel: NuPage 4–12% Bis-Tris gel 1.0 mm. 2. SDS running buffer (NuPage MES SDS running buffer): 50 mM MES, 50 mM Tris Base, 0.1% SDS, 1 mM EDTA, pH 7.3. 3. Western blot transfer buffer: 25 mM Tris-HCl (pH 7.6), 192 mM glycine, 20% methanol. 4. Blocking buffer: Dissolve 0.01% Tween-20 and 5% bovine serum albumin in 1× TBS. Store it at 4 °C. 5. Nitrocellulose membranes, 0.2 μm pore size.

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6. 10× Reducing agent: 500 mM dithiothreitol (DTT) for 10× concentration. Store it at 4 °C. 7. 4× sample loading buffer: 988 mM Tris, 2.04 mM EDTA, 8% lithium dodecyl sulfate, 40% glycerol, 0.88% Commassie Brilliant Blue G250, 0.7 mM phenol red. Store it at 4 °C. 2.2

Equipment

1. Tissue Grinder: Duall Tissue Grinder with PTFE pestle. 2. Ultracentrifuge with fixed-angle rotor (Beckman) and polycarbonate tubes (Beckman). 3. Heat block for boiling samples. 4. Electrophoresis system for western blotting: XCell SureLock Mini and Xcell II Blot Module (Invitrogen).

2.3 Materials for Immunohistochemistry

Reagents for rat brain perfusion: 1. TBS. 2. Fixatives: Dilute 37% formaldehyde solution to 3.7% formaldehyde in 1×TBS. This solution is freshly prepared and ice-chilled before the procedure. 3. Thirty percent sucrose solution: Dissolve 300 g of sucrose in 1×TBS. Store in 4 °C. 4. 21 G Needle and 23G needle. 5. Cole-Parmer Peristaltic Pump. 6. Disposable plastic rat restraints. 7. Bone nipper (Fine Science Tools), Curved Kelly Hemostats (Fine Science Tools), Curved Fine Scissors (Fine Science), and Scalpel handle #4 and its blade #22.

2.4 Equipment and Reagents for Brain Sectioning and Staining

1. Cryoprotectant solution for long-term tissue section storage: Add 30% sucrose, 30% ethylene glycol, and 1% polyvinylpyrrolidone to 1× TBS. 2. Sliding microtome (Thermo Scientific). 3. TBS with 0.01% of Triton X-100 (TBS-X). 4. Goat serum blocking buffer: Add 10% of normal goat serum to TBS-X. Store in 4 °C.

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Methods

3.1 Sequential Tau Extraction

Rat brain homogenization and sequential ultracentrifugation: 1. Measure the weight of the brain and homogenize with 7 volumes (v/w) of TBS containing PIC, PMSF, and phosSTOP in the grinder (see Note 1).

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2. Spin down the lysate at 100,000 ×g at 4 °C for 30 min in an ultracentrifuge. 3. Collect the supernatant for TBS fraction. 4. Re-suspend the pellet with the sarkosyl solution supplemented with PIC, PMSF, and phosSTOP and incubate on ice for 30 min. Vortex the sample every 5 min during the incubation. 5. Spin down the sample at 100,000 ×g at 4 °C for 30 min in an ultracentrifuge. 6. Save the supernatant for sarkosyl-soluble fraction. 7. Re-suspend the pellet with the urea solution and incubate on ice for 30 min. Vortex the sample every 5 min during the incubation. 8. Spin down the sample at 100,000 ×g at 4 °C for 30 min in an ultracentrifuge. 9. Save the supernatant for sarkosyl-insoluble fraction. 10. Measure the protein concentration of the TBS and sarkosylsoluble fractions with the BSA assay. Immunoblotting: 1. Boil the samples mixed with a sample reducing agent and sample loading buffer at 95 °C for 5 min. 2. Load 20 μg of the TBS and sarkosyl-soluble fractions on NuPage 4–12% SDS-PAGE gel. Load the same volume of sarkosyl-insoluble fraction mixed with a sample reducing agent and sample loading buffer. 3. Transfer the gel onto the nitrocellulose membrane (see Note 2). 4. Block the membrane for 1 h at room temperature with the blocking buffer. 5. Incubate the membrane for overnight at 4 °C with TAU5 for total tau (see Note 3) staining and AT8 or PHF1 (gifted from Dr. Peter Davies, Albert Einstein College of Medicine for phosphorylated tau). 6. After three washes with TBS containing Tween 20 (TBS-T), incubate the membrane with IRDye 680 W and 800 W goat anti-rabbit and anti-mouse secondary antibodies for 1 h at room temperature. 7. Visualize the membranes with the LiCor Odyssey Clx Infrared Imaging System (Fig. 2). 8. Measure the signal intensity from the full lane of TAU5 and AT8 using ImageJ. Normalize the AT8 intensity to the TAU5.

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Fig. 2 Representative western blot images of sequential tau extraction from rat brains. Brain tissues from wildtype (WT) and mutant rats at 3 months and 18 months were homogenized and sequentially extracted in TBS, sarkosyl-containing buffer, and 8 M urea buffer. We measured the densitometry of the whole lanes of AT8 and TAU5 for quantification, using the signal present in the entire lane. The expression of AT8 was normalized to the expression of TAU5. In this example, the insoluble AT8 fraction in mutant rats was increased compared to WT at 18 months (P = 0.0027 for WT versus mutants at 18 months, P = 0.00034 for mutants at 3 vs. 18 months). Two-way ANOVA with Tukey’s HSD was performed (WT = 5, mutants = 5 for each time point) 3.2 ImmunofluorescenceImmunofluorescence for Tau

Rat perfusion and sectioning: 1. Rats are weighted to calculate the dose of pentobarbital for anesthesia. 2. Place a rat into a disposable plastic restraint and administer accurate dose of pentobarbital (100 mg/kg) via intraperitoneal injection using 21G needles. 3. Check the animal for complete anesthetic state such as toe-pinch reflex and corneal reflex. Once it is confirmed, place the subject on perfusion pan. 4. Cut through the abdominal wall using surgical scissors and keep it open with a Kelly hemostat. 5. Laterally cut the diaphragm to expose the heart using fine scissors. 6. Make a small incision in the left ventricle and insert the 23G needle into the ascending aorta.

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7. Transcardially perfuse a rat with ice-cold 1× TBS at a rate of 50–60 mL/min until the color of liver gets clear. 8. Switch to the fixatives. Once fixation begins, body twitching will be observed. End the fixation procedure after the body twitching is stopped. 9. Decapitate the head right behind the ears using a guillotine. 10. Use the scalpel to remove skin and open the skull with the bone nipper to harvest the brain. 11. Place the brain in 50 mL of the fixatives for 24 h at 4 °C. 12. Transfer the brain into a new container filled with 50 mL of 30% sucrose solution until the brain sinks to the bottom of the container. This procedure may take up to a week depending on the size and weight of the brain. 13. Freeze the sample on a block of dry ice and keep it at -80 °C until sectioning. 14. Section the brain on a freezing stage sliding microtome and collect tissues in a 24-well plate containing the cryoprotectant. Keep the plate at -80 °C until staining. Staining: 1. Mount the sections onto the slide and air dry them fully at room temperature. 2. Wash the slides with TBS for 5 min, three times. 3. Incubate the slides with TBS-X for 15 min. 4. Wash the slides with TBS for 5 min, three times. 5. Incubate the slides with the blocking buffer for 2 h at room temperature. 6. Wash the slides with TBS for 5 min, three times. 7. Incubate the slides with primary antibodies such as AT8 and PHF1 prepared in the goat serum blocking buffer for overnight at 4 °C. We used NeuN for a neuronal marker, and GFAP for astrocytic marker (Fig. 3). 8. Wash the slides with TBS for 5 min, three times. 9. Incubate the slides with secondary antibodies prepared in the goat serum blocking buffer for 2 h at room temperature. 10. Wash the slides with TBS for 5 min, three times. 11. Optional, add DAPI prepared in the goat serum blocking buffer to the slides for 10 min at room temperature. Wash the slides with TBS for 5 min, three times. 12. Add the mounting media to fully cover the section and cover the section with coverslip.

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Fig. 3 Representative fluorescence images of tau staining in rat brains. (a) Tau detection in neurons. We stained PHF1 (magenta) antibody along with NeuN (green, neuronal marker) in the substantia nigra region of WT and mutant rats at 18 months. Scale bar = 20 μm. (b) Tau detection in glia. We stained AT8 antibody (magenta) together with GFAP (yellow, astrocyte marker) in the corpus callosum region of WT and mutant rats at 18 months. Scale bar = 20 μm

Tissue visualization and analysis: 1. Place the slides onto the confocal such as Olympus FV3000 or fluorescence microscope. 2. Make sure to apply the same fluorescence of other imaging settings to all the samples. 3. Acquire five random fields within the region of interest per sample. 4. Analyze the data in ImageJ. We measured the % of AT8 covered area from each image.

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Notes 1. 1. It is critical to measure the weight of rat brain in order to add the proportional amount of detergents, as most rat models with neurodegeneration showed the loss of brain volume due to brain atrophy [15, 18–20]. 2. It is important to ensure thorough transfer of your sample onto nitrocellulose membrane with prolonged transfer time, e.g., 90 min in our XCell Blot module, to detect various tau species. 3. The reactivity of TAU5 includes humans, mice, rats, cows, and sheep. This antibody will detect both non-phosphorylated and phosphorylated forms of MAPT.

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Acknowledgments This work was supported by NIH/NIA grant (F32AG066372), NIH/NIA grant (K99AG076868), NIH/NINDS/NIA grant R01NS113141, and NIH/NINDS R01NS121618. References 1. Wong W (2020) Economic burden of Alzheimer disease and managed care considerations. Am J Manag Care 26:S177–S183 2. Yang W, Hamilton JL, Kopil C et al (2020) Current and projected future economic burden of Parkinson’s disease in the U.S. NPJ Parkinsons Dis 6:15 3. Sheppard O, Coleman M (2020) Alzheimer’s disease: etiology, neuropathology and pathogenesis. In: Huang X (ed) Alzheimer’s disease: drug discovery, Brisbane (AU) 4. Dawson TM, Golde TE, Lagier-Tourenne C (2018) Animal models of neurodegenerative diseases. Nat Neurosci 21:1370–1379 5. Hsiao KK, Borchelt DR, Olson K et al (1995) Age-related CNS disorder and early death in transgenic FVB/N mice overexpressing Alzheimer amyloid precursor proteins. Neuron 15:1203–1218 6. Quon D, Wang Y, Catalano R et al (1991) Formation of beta-amyloid protein deposits in brains of transgenic mice. Nature 352:239– 241 7. Games D, Adams D, Alessandrini R et al (1995) Alzheimer-type neuropathology in transgenic mice overexpressing V717F betaamyloid precursor protein. Nature 373:523– 527 8. Holcomb L, Gordon MN, Mcgowan E et al (1998) Accelerated Alzheimer-type phenotype in transgenic mice carrying both mutant amyloid precursor protein and presenilin 1 transgenes. Nat Med 4:97–100 9. Ellenbroek B, Youn J (2016) Rodent models in neuroscience research: is it a rat race? Dis Model Mech 9:1079–1087 10. Gibbs RA, Weinstock GM, Metzker ML et al (2004) Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428:493–521

11. Jacob HJ, Kwitek AE (2002) Rat genetics: attaching physiology and pharmacology to the genome. Nat Rev Genet 3:33–42 12. Francis C, Natarajan S, Lee MT et al (2014) Divergence of RNA localization between rat and mouse neurons reveals the potential for rapid brain evolution. BMC Genomics 15:883 13. Do Carmo S, Cuello AC (2013) Modeling Alzheimer’s disease in transgenic rats. Mol Neurodegener 8:37 14. Hanes J, Zilka N, Bartkova M et al (2009) Rat tau proteome consists of six tau isoforms: implication for animal models of human tauopathies. J Neurochem 108:1167–1176 15. Cohen RM, Rezai-Zadeh K, Weitz TM et al (2013) A transgenic Alzheimer rat with plaques, tau pathology, behavioral impairment, oligomeric abeta, and frank neuronal loss. J Neurosci 33:6245–6256 16. Hyman B (2023) All the tau we cannot see. Annu Rev Med 74:503–514 17. Lee Y, Miller MR, Fernandez MA et al (2021) Early lysosome defects precede neurodegeneration with amyloid-beta and tau aggregation in NHE6-null rat brain. Brain 145:3187 18. Agca C, Fritz JJ, Walker LC et al (2008) Development of transgenic rats producing human beta-amyloid precursor protein as a model for Alzheimer’s disease: transgene and endogenous APP genes are regulated tissuespecifically. BMC Neurosci 9:28 19. Pang K, Jiang R, Zhang W et al (2022) An App knock-in rat model for Alzheimer’s disease exhibiting Abeta and tau pathologies, neuronal death and cognitive impairments. Cell Res 32: 157–175 20. Leon WC, Canneva F, Partridge V et al (2010) A novel transgenic rat model with a full Alzheimer’s-like amyloid pathology displays pre-plaque intracellular amyloid-beta-associated cognitive impairment. J Alzheimers Dis 20:113–126

Chapter 22 Detection and Characterization of Apoptosis-Related Proteins in Hippocampal Neurodegeneration: From mRNA Expression to Protein Quantification Kajal Rawat, Vipasha Gautam, Arushi Sandhu, and Lekha Saha Abstract The involvement of apoptosis in neurodegeneration can be detected by quantifying the apoptotic proteins in hippocampal lysate. Apoptosis can occur due to the overproduction of apoptotic proteins under the influence of external trigger or due to the overexpression of the apoptotic genes. Thus, the imbalance in the production of apoptotic proteins can be quantified using the Western blotting technique and the overexpression of apoptotic genes in hippocampal DNA can be quantified using the real-time quantification of mRNA expression of the apoptotic proteins. Here we provide the methodology of detecting the apoptosisrelated proteins like Bax and Bcl-2 and their mRNA expression in hippocampal neurodegeneration. In this chapter, we have described the methodology for quantification of mRNA expression of these apoptosisrelated proteins in the hippocampal lysate using the real-time quantitative polymerase chain reaction (qPCR) technique and the methodology of detection and characterization of respective protein expression in the hippocampal lysate using the Western blotting technique. Key words Neurodegeneration, Apoptosis, Western blotting, Real-time qPCR, Immunoblot, mRNA quantification

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Introduction In neurodegenerative diseases, the increased expression of apoptotic genes leads to neuronal degeneration [1]. Here we describe the methodology for quantification and detection of mRNA expression and protein levels of apoptotic proteins, Bcl-2 and Bax. Bcl-2 is an anti-apoptotic protein and Bax is a pro-apoptotic protein, and imbalance between these leads to neurodegeneration. The overproduction of these proteins can occur due to alterations at the transcription level or at the translation level. The alterations at the transcription level can be assessed by quantifying mRNA expression using real-time quantitative polymerase chain reaction (qPCR) and

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_22, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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the alterations at the translation level can be assessed by quantifying the protein expression using Western blot technique. Kary Mullis invented the PCR method in the 1980s [2, 3]. It can detect and quantify the minute amount of specific nucleic acid sequence and hence has been used widely in biomedical research, e.g., diagnostic and food applications. For instance, the amount of messenger RNA (mRNA) can be quantified by combining reverse transcription with PCR; reverse transcription creates complementary DNA (cDNA) followed by PCR amplification to replicate the cDNA strands exponentially [4]. An auxiliary version of the technique that enables users to observe the progression of a PCR reaction in real time is known as real-time qPCR, which is described in this chapter. This method combines the chemistry of the PCR with the use of fluorescent reporter molecules to detect the amplification products generated during each cycle of the PCR reaction [5]. Real-time qPCR has become a preferred alternative to conventional PCR because of its excellent sensitivity and specificity, reliable data, low risk of contamination, and minimized hand-on time. Western blotting also known as protein immunoblot is a standard technique for detecting and measuring a particular protein in a complex mixture isolated from cell or tissue lysate [6]. It includes electrophoretic protein transfer to a microporous membrane support and consequent immunodetection [7], and it has made a significant impact on the domain of life sciences. Native and denatured proteins are separated on sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE) and are electrophoretically transferred to nitrocellulose or polyvinylidene difluoride membrane [6–9]. There are three main ways to transfer protein from gel to membrane: (a) simple diffusion [9], (b) vacuumassisted solvent flow [10, 11], and (c) electrophoretic elution [7, 12]. Among these methods, electrophoretic elution is convenient, accurate, and more efficient to transfer. Once the proteins are transferred to the membrane, primary and secondary antibodies specific to the protein of interest are used to bind the target protein and further visualized using chemiluminescence imaging system in the form of bands. The scientific applications of Western blot are: (a) detecting different isoforms of proteins, (b) detecting protein– protein interactions, (c) detecting protein–DNA interactions, (d) detecting post-translational modification, (e) detecting proteins’ subcellular localization, and (f) antibody development, characterization, and epitope mapping [8].

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Materials

2.1 Real-Time qPCR Materials

Prepare all the solutions in ultrapure deionized water and in nuclease-free water wherever indicated. Perform all the steps while handling nucleic acids in decontaminated area, preferably in a polymerase chain reaction (PCR) hood.

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1. TRIzol™ Reagent: A monophasic solution of phenol, guanidine isothiocyanate made specifically for isolation of a variety of RNA species. 2. Molecular grade chloroform and molecular grade iso-propanol. 3. 75% ethanol: for 50 mL, add 37.5 mL molecular grade absolute ethanol to 12.5 mL water. 4. Nuclease-free water: store at 4 °C.

2.1.2 cDNA Synthesis Reagents

1. RNA template (preferably use freshly isolated RNA or store at -20 °C for 1 week). 2. 5× reaction mixture: Mixture of oligonucleotides, random hexamer primers, and buffer. 3. Reverse transcriptase: Modified Moloney murine leukemia virus reverse transcriptase (RNase H+) enzyme. 4. Nuclease-free water: store at 4 °C.

2.1.3 Agarose Gel Electrophoresis Reagents

1. PCR product: 2× Taq mix consisting of ready-to-use solution of Taq DNA polymerase, dNTPs (oligonucleotides), magnesium, and reaction buffer; primers diluted in 1× Tris-EDTA (TE) buffer, cDNA template, and nuclease-free water. 2. 10× Tris-boric acid-EDTA (TBE): Add 10.8 g Tris base, 5.5 g boric acid and 750 mg EDTA to a beaker and add 75 mL water and dissolve these. Set the pH to 8.3 with 1 N hydrochloric acid and further make up the volume to 100 mL with water. For 0.5× TBE, add 5 mL 10× TBE to 95 mL water, and similarly for 1× TBE, add 10 mL 10× TBE to 90 mL water. 3. 2% agarose solution: Add 2 g agarose to 100 mL 0.5× TBE in a beaker and heat the beaker to dissolve agarose. 4. Ethidium bromide (5 mg/mL) solution. 5. 6× bromophenol blue: For 5 mL solution, add 12.5 mg bromophenol blue and 2 g sucrose in a 5 mL tube and add 5 mL water to it and mix.

2.1.4 Real-Time PCR Reagents

1. qPCR master mix: 2× formulation which included doublestranded DNA binding dye, a low level of carboxy-X-rhodamine reference dye, hot start polymerase, magnesium chloride, dNTPs (oligonucleotides), and reaction buffer. 2. cDNA template: Dilute in nuclease-free water and store at 20 °C and at -80 °C for long-term usage. 3. Primer mix: Primers should be dissolved in 1× TE buffer to obtain 100 μM concentration and stored at -20 °C. 4. Nuclease-free water: Store at 4 °C.

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2.2 Western Blotting Materials 2.2.1 Sample Preparation

Prepare all the solutions in ultrapure deionized water and follow proper waste disposal process, especially for acrylamide gel. Store all the solutions at room temperature unless otherwise specified. 1. 1× radioimmunoprecipitation assay (RIPA) buffer: For 100 mL RIPA buffer, add 0.6057 g tris hydrochloride, 0.877 g sodium chloride in 50 mL water and dissolve. Set the pH to 8 using 1 N hydrochloric acid. Then add 100 mg sodium dodecyl sulfate (SDS), 500 mg sodium deoxycholate, and 1 mL Triton-X and make up the volume to 100 mL with water (see Note 1). 2. 1 M sodium fluoride solution: For 10 mL solution, add 410 mg of sodium fluoride to the 10 mL water and dissolve it. This solution can be stored for up to 6 months at room temperature. 3. 100 mM sodium orthovendate solution: For 10 mL solution, add 183.91 mg sodium orthovendate to 8 mL water and set pH to 10 using 1 N hydrochloric acid. The solution then turns yellow. Boil the solution until it becomes colorless and make up the volume to 10 mL using water. 4. 200 mM ethylenediaminetetraacetic acid (EDTA) solution: Add 744.48 mg EDTA to 10 mL water and dissolve. 5. 100 mM phenylmethylsulfonyl fluoride (PMSF): add 87 mg PMSF to 5 mL iso-propyl alcohol and dissolve. PMSF stock solution can be stored at -20 °C. 6. 5× Laemmli buffer: For 50 ml 5× Laemmli buffer, add 760 mg tris hydrochloride to 40 mL water and set the pH to 6.8 using 1 N hydrochloric acid. To this add 1 g sodium dodecyl sulfate (SDS) and heat at 37 °C to dissolve the SDS and avoid frothing. Then add 5 mL glycerol and 10 mg brilliant blue dye and dissolve carefully. Make the final volume up to 50 mL using water (see Note 2).

2.2.2 SDS-Poly Acrylamide Gel Electrophoresis (SDSPAGE)

1. 1.5 M tris buffer: For 100 mL, add 18.2 g of tris base to 50 mL water and adjust the pH to 8.8 using 1 N hydrochloric acid. Make the final volume up to 100 mL using water and store at 4 °C. 2. 0.5 M tris buffer: For 100 mL, add 6.05 g of tris base to 50 mL water and adjust the pH to 6.8 using 1 N hydrochloric acid. Make the final volume up to 100 mL using water and store at 4 °C. 3. 30% acrylamide solution: For 100 mL, add 29.2 g of acrylamide monomer and 0.8 g of bis-acrylamide to 80 mL water and dissolve in the magnetic stirrer and then make up the volume to 100 mL using water. Filter the solution using micron filter paper with pore size 0.45 μm in a vacuum filter system and store the solution in amber-colored glass bottle at room temperature (see Note 3).

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4. 10% ammonium persulfate solution: For 0.5 ml solution, add 50 mg ammonium persulfate to 0.5 mL water and always use freshly prepared solution. 5. 10% SDS: For 10 mL solution, add 1 g of SDS to 10 mL water. 6. N,N,N,N′-Tetramethyl-ethylenediamine (TEMED): Store at 4 °C. 7. 1× running buffer: For 2 L running buffer, add 6.2 g tris base, 28.8 g glycine, and 2 g SDS to 1.5 L water. Dissolve on magnetic stirrer and then make up the volume to 2 L with water and store at room temperature. 2.2.3 Electrophoretic Transfer and Blocking

1. 1× transfer buffer: For 2 L transfer buffer, add 6.2 g of tris base, 28.8 g glycine to 1 L water and dissolve. Make the volume up to 1600 mL using water and then add 400 mL methanol to the mixture and store at 4 °C, as it should be chilled before using. 2. Nitrocellulose membranes. 3. 10× tris-buffered saline (TBS): For 1 L TBS 10×, add 24.22 g tris base and 87.66 g sodium chloride to 700 mL water and dissolve. Set the pH to 7.2 using 1 N hydrochloric acid and make the volume up to 1 L with water. Store the solution at 4 ° C. 4. 1× tris-buffered saline with Tween-20 (TBS-T): For 1 L TBS-T, add 100 mL TBS 10× to 800 mL water and then add 1 mL Tween-20 to it. Swirl the mixture and make up the volume to 1 L using water. 5. Blocking solution: For 100 mL blocking solution, add 5 g skim milk powder to 100 mL TBS-T and mix. Always use freshly prepared blocking solution. 6. Ponceau S stain: For 500 mL ponceau stain, add 0.5 g ponceau stain powder to 400 mL water and 25 mL glacial acetic acid. Mix the solution and make the volume up to 500 mL with water. Store the solution in an amber-colored bottle at room temperature.

2.2.4 Antigen Antibody Reaction and Chemiluminescence Imaging

1. Primary antibody: Anti-β-actin mouse monoclonal antibody, anti-Bcl2 mouse monoclonal antibody, and anti-Bax mouse monoclonal antibody diluted in TBS-T buffer (1:1000). 2. Secondary antibody: Horseradish peroxidase (HRP)conjugated secondary antibody diluted in TBS-T buffer (1–10,000). 3. Enhanced chemiluminescence reagent: Highly sensitive detection reagent consisting of peroxide reagent and luminol/ enhancer reagent.

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Methods Real-Time qPCR RNA Isolation

Perform all the steps at room temperature (20–25 °C) unless otherwise specified. 1. Perform the RNA isolation immediately after brain collection or quick freeze the brain tissue at -80 °C or liquid nitrogen for later experimentation. For best results, preserve the brain tissue in RNA later and then store at -80 °C or liquid nitrogen (see Note 4). 2. Use sterile and RNase-free tubes, pipettes and pipette tips, and gloves for RNA isolation. 3. RNA isolation is started with tissue homogenization in ice cold TRIzol™ reagent. Thaw the frozen brain tissue and weigh 100 mg of it and add to 2 mL tube. Add 1 mL TRIzol™ reagent to the 100 mg tissue and start homogenizing using tissue homogenizer. 4. Incubate the homogenate for 5–10 min at room temperature to settle down the frothing and to allow complete dissociation of the nucleoproteins complex (see Note 5). 5. Add 200 μL of chloroform to tissue homogenate and vortex for 15–20 s and then incubate the mixture for 10 min at room temperature. 6. Centrifuge the content at 12,000 ×g for 15 min at 4 °C. This separates the mixture into three layers: uppermost colorless aqueous, interphase, and lowermost red-phenol chloroform layers. 7. Pipette out uppermost aqueous layer containing RNA to the fresh sterile tube and add 500 μL of iso-propanol to the aqueous phase and incubate for 10 min at room temperature. This step will allow RNA to precipitate (see Note 6). 8. Centrifuge the mixture at 12,000 ×g for 10 min at 4 ° C. Total RNA is precipitated out as white translucent jelly at the bottom of the tube. Discard the supernatant using micropipette. 9. Suspend the RNA pellet in 1 mL 75% ethanol and centrifuge at 12,000 ×g for 5 min at 4 °C and discard the supernatant. Repeat the step by adding 30–50 μL 75% ethanol and vacuum or air dry the pellet for 10 min. 10. Resuspend the pellet in 50 μL nuclease-free water and check the purity and concentration of RNA using nanodrop (RNA with A260/280 ratio ~ 2 is considered pure) (see Note 7).

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cDNA Synthesis

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1. Use 1 μg RNA for cDNA synthesis with the help of commercially procured cDNA synthesis kit and perform all the steps in PCR hood to avoid contamination (see Note 8). 2. Add 4 μL of 5× reaction mix to the PCR tubes, followed by the addition of 1 μL reverse transcriptase enzyme to the PCR tubes. 3. Add the pre-calculated volume of RNA (1 μg) to the PCR tubes followed by addition of nuclease-free water to make up the total volume to 20 μL (see Note 9). 4. Incubate the complete reaction mix in a thermal cycler as per the provided conditions, for example, priming at 25 °C for 5 min, reverse transcription at 46 °C for 20 min, and RT inactivation at 95 °C for 1 min. 5. Check the quality and concentration of cDNA synthesized in the nanodrop or by agarose gel electrophoresis (singlestranded DNA with A260/280 ratio ~ 1.8 is considered pure).

3.1.3 Agarose Gel Electrophoresis

1. Agarose gel electrophoresis is used to check the quality of cDNA synthesized and to optimize the primer concentration to be used in real-time PCR. 2. Run a PCR reaction on 500 ng cDNA, add reverse and forward housekeeping gene (β-actin) primers at 1 μM concentration, 25 μL 1× Taq DNA mix, and make the total volume up to 50 μL with nuclease-free water. Simultaneously run one sample without template DNA as non-template control (NTC) to nullify the reading from other ingredients. Incubate the complete reaction mix in a thermal cycler as per provided conditions, initial denaturation for 3 min at 95 °C, 35 cycles of annealing, each cycle consisting of incubation at 94 °C for 30 s followed by 55–68 °C depending upon the melting temperature (Tm) of the primer for 30 s and extension at 72 °C for 1 min, and at the end final extension at 72 °C for 10 min. Load the final PCR product on 2% agarose gel, to check the quality of cDNA (see Note 10). 3. Add 10 μL of ethidium bromide (5 mg/mL stock) to the 100 mL 2% agarose gel solution and mix by swirling. 4. Allow the gel to cool to 55 °C and then cast in the casting apparatus. It takes 15–20 min for the casting. On the other hand, prepare the PCR products for loading on the gel by adding 2 μL bromophenol blue to the 3 μL sample (PCR product). 5. Set the gel on the tank and fill the tank with 1× TBE. Load the samples on the wells and along with DNA ladder on one side (see Note 11).

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Fig. 1 Representative picture of 2% agarose gel electrophoresis of β-actin cDNA. NTC 1 and NTC 2 indicate non-template control 1 and 2 in replicates without any template DNA. Sample 1 and sample 2 contain β-actin cDNA with product length 650 bp

6. Attach the assembly to the power supply, keep the current constant (at 400 mA), and set the voltage to 70 V (4–10 volts/cm gel). Allow the samples to run until the edges of the gel and at the end visualize the bands in the gel doc machine. 7. Single band on the gel against the DNA ladder determines the quality of the PCR product and the cDNA as well as the primer concentration to be used for real-time qPCR (Fig. 1). 3.1.4 Real-Time qPCR Steps

1. Dilute the cDNA synthesized to 100 ng concentration using nuclease-free water. 2. Prepare the qPCR mix in triplicates for each sample in the instrument-compatible PCR plate. Add 5 μL qPCR master mixes to the wells followed by the addition of pre-calculated volume of 100 ng cDNA template. Prepare one well for non-template control without using template DNA (see Note 12). 3. Add 0.1 μL each of 100 μM forward and reverse primer to the wells and make the volume of total reaction mix to 10 μL using nuclease-free water so that the final reaction mixture consists of 1 μM reverse and forward primer (see Note 13).

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Fig. 2 Amplification plot generated from real-time PCR assay for (a) β-actin, (b) Bcl-2, and (c) Bax. The X-axis of plot represents cycle threshold (Ct) value, i.e., the cycle at which the fluorescence is detected. The higher Ct value indicates less amount of mRNA detected in the sample and vice versa

4. Seal the plate using the sealer and centrifuge briefly to collect the contents of the wells at the bottom. Proceed the thermal cycling as per provided conditions consisting of holding stage (qPCR mix activation at 95 °C for 10 min), cycling stage (total 40 cycles, each cycle consisting of denaturation at 95 °C for 15 s, annealing at primer Tm for 30 s and extension at 72 °C for 15 s), and melt curve stage for melt curve analysis (15 s at 95 ° C, 1 min at 60 °C and 15 s at 95 °C) (see Note 14). 5. Note down the Ct values from the amplification plot obtained at the end of the run as shown in Fig. 2a–c. 6. Calculate the relative mRNA expression normalized against β-actin using the 2-ΔΔCt method. 3.2

Western Blotting

3.2.1 Sample Preparation

1. 100 mL cell lysis buffer is prepared by mixing 83.5 mL RIPA buffer, 10 mL 1 M sodium fluoride solution, 5 mL 200 mM EDTA solution, and 1 mL 100 mM sodium orthovandate solution; and store at 4 °C. 2. For 10% brain homogenate, add 100 mg brain tissue to the 1 mL cold cell lysis buffer and 10 μL 100 mM PMSF solution; then homogenize using the tissue homogenizer. 3. Incubate the homogenate in ice for 5 min and then sonicate each sample using probe sonicator or bath sonicator for 30 s. 4. Centrifuge the samples at 12,000 ×g for 15 min at 4 °C and pipette out the supernatant in fresh labeled tube and store at 80 °C for long-term storage. 5. Measure the protein concentration in tissue homogenate by performing the Lowry’s assay [13], using bovine serum albumin as reference standard.

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6. For sample preparation, add enough volume of β-mercaptoethanol to 3× Laemmli buffer (3 mL 5× Laemmli buffer mixed with 2 mL water) so that the final concentration of β-mercaptoethanol is 5%. Mix both the components and add 50 μg tissue homogenate to the laemmli buffer and heat at 95 ° C for 5 min. 3.2.2 15% SDS-PAGE Gel Electrophoresis

7. Bax and Bcl-2 are low-molecular-weight proteins (20 kDa and 26 kDa, respectively); therefore, prepare higher percentage (15%) acrylamide gel for protein separation (see Note 15). 8. For 15 mL of resolving gel solution, add 7.5 mL 30% acrylamide mix to the clean beaker followed by addition of 3.8 mL 1.5 M tris base (pH 8.8). Then add 150 μL each of 10% ammonium persulfate and 10% sodium dodecyl sulfate solution to the mixture and make up the volume to 15 mL with water. 9. Assemble the electrophoresis module using the glass plate cassette for gel casting and test the leakage by pouring the water in the cassette. 10. Just before adding the resolving gel solution into the glass plate cassette, add 6 μL TEMED to the mixture and swirl it (see Note 16). 11. Pour the mixture into the glass plate cassette carefully without leaving bubbles. Overlay each gel with a thin layer of isopropanol to avoid direct contact of resolving gel with oxygen and place the assembly in hot air oven at 37 °C for gel formation. 12. For stacking gel formation, add 830 μL of 30% acrylamide mix to the clean beaker followed by addition of 630 μL of 0.5 M tris base (pH 6.8). Then add 500 μL each of 10% ammonium persulfate and 10% sodium dodecyl sulfate solution to the mixture and make up the volume to 5 mL with water. 13. Check the resolving gel for solidification of gel, which generally takes 10–15 min at 37 °C. Once the gel solidifies, pour off the iso-propanol layer from the assembly and rinse the top of gel with distilled water. Insert the 10-well comb before adding stacking gel in the assembly. 14. Add 5 μL of TEMED to the stacking gel solution and swirl it and pour into the casting assembly carefully without leaving bubbles. 15. Prepare the 50 μg protein samples for equal loading by heating the mixture of protein homogenate and 3× Laemmli buffer in the boiling water for 5 min and then spin the samples in a spinner for 60 s.

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16. Set the vertical gel electrophoresis assembly by placing the modules cast with gel and fill the tank with running buffer after removing the combs. Load the 20 μL samples in the wells and load pre-stained protein marker in one lane. Run the experiment at 100 V and constant current (10–15 mA per gel) (see Note 17). 17. Check the progress by monitoring the position of tracking dye (brilliant blue) and turn off the power supply once the dye front reaches the bottom of gel. 18. Release the cassette from the module and remove the gel from cassette carefully using the scalpels. Rinse the gel with distilled water and place it in the transfer buffer. 19. Cut the nitrocellulose membrane to the size of the gel and activate it by immersing it in distilled water for 10 min and then place it in the chilled transfer buffer. 3.2.3 Electrophoretic Transfer and Blocking

1. Pre-wet the transfer pads and packing sponge in cold transfer buffer and let the gel settle in the transfer buffer for 10 min before preparing the transfer stack (see Note 18). 2. Prepare the transfer stack according to the instructions provided in the transfer module (Fig. 3). Start with placing the packing sponge on the cathode side followed by stacking the transfer pads on top of packing sponge and then equilibrated gel on top of transfer pad followed by placing the activated nitrocellulose membrane on top of gel and then covering it with transfer pad and then packing sponge. The stack is repeated in the similar order for another gel on top of the stack of first gel. A maximum of two gels can be transferred in a single blotting module. (see Note 19). 3. Tight the assembly and snap it to close the side clamps and place it in the tank and fill with chilled transfer buffer. Plug on the power supply and run the experiment at 00 mA current and 80–90 V for 70 min. 4. After electrophoretic transfer, turn off the power supply, lift out the transfer module, carefully remove the nitrocellulose membrane, and dip into the ponceau stain to visualize protein bands, as shown in Fig. 4 (see Note 20). 5. With the help of reference protein marker, identify the protein of interest on membrane and cut out the required portion and wash in TBST solution until the ponceau stain fades away. β-Actin (42 kDa) is commonly used as housekeeping protein for normalization of bands of protein of interest, i.e., Bax (20 kDa) and Bcl-2 (26 kDa). 6. Incubate the membrane with 5% skim milk blocking solution for 1 hour with continuous shaking and then wash the membrane with TBST, three times for 5 min each (see Note 21).

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Fig. 3 Electrophoretic transfer stack assembly where “a” indicates packing sponge, “b” indicates wet filter paper, “c” indicates gel, “d” indicates nitrocellulose membrane, “e” indicates wet filter paper, and “f” indicates packing sponge

Fig. 4 Representative picture of nitrocellulose membrane after electrophoretic transfer stained with Ponceau S stain. The protein bands transferred from gel to membrane can be visualized in this picture against the protein ladder. First lane consists of protein ladder with different molecular weight fragments and other lanes consist of samples 1–4 showing various protein bands transferred from gel 3.2.4 Antigen Antibody Reaction and Chemiluminescence Imaging

1. For antigen antibody complex formation, incubate the membranes with primary antibody of β-actin, Bax and Bcl-2 at 1: 1000 dilutions in TBST solution for 10–12 h at 4 °C with continuous shaking (see Note 22). 2. Wash the membrane in TBST solution three times for 5 min each and then incubate the membranes with secondary antibody at 1: 10,000 dilutions in TBST solution for 1 h (see Note 23).

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Fig. 5 Immunoblot of β-actin, Bcl-2, and Bax developed at the end of the procedure after antigen treatment and visualized using chemiluminescence imaging system

3. Wash the membrane in TBST solution three times for 5 min each and then proceed for the development of bands using luminol-H2O2-HRP chemiluminescence imaging system in the dim-lit room. Mix an equal amount of both the solutions, i.e., enhancer solution and luminol-H2O2 solution, and dip the membrane into it and visualize under the chemiluminescence imaging system. Protein bands will appear as shown in Fig. 5.

4 Notes 1. SDS and sodium deoxycholate tend to form lumps when added to cold solution and do not easily dissolve; therefore, heating the solution to 37 °C will help in dissolving both the components and will reduce the frothing. 2. 5× Laemmli buffer can be stored at -20 °C for future use. β-mercaptoethanol is added later to the Laemmli buffer while preparing the samples for SDS PAGE. Usually 5% β-mercaptoethanol is used in sample preparation. 3. Acrylamide is a potential neurotoxin; therefore, it should be handled carefully wearing laboratory gloves and mask. It is photosensitive and hence should be prepared in an ambercolored bottle and stored in dark place. 4. RNA later is essential for RNase inactivation which is essential for long-term storage of biological samples and helps to preserve RNA for further assessment of gene expression. For freshly extracted tissue samples, a part of tissue shall be immerse into the 5–10 volumes of RNA later prior to freezing for RNA preservation. For cultured cells, the cells should be first pelleted, then resuspended in a small volume of PBS and finally mixed with 5–10 volumes of RNA later for RNA preservation.

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5. After homogenization, the incubation time can be increased to 20–30 min to reduce the frothing, or the samples can be incubated at 37 °C in water bath for 5 min to get rid of frothing. 6. The RNA precipitation step is essential to obtain the sufficient yield of RNA from the aqueous phase. This step shall avoid vortex since RNA can be easily degraded. To expedite the RNA precipitation process, gently pipette up and down the mixture using 1 mL pipette for 10 s and then stand still the samples at room temperature. 7. RNA pellet is dissolved in nuclease-free water by incubating in water bath at 55–60 °C for 10–5 min. 8. RNA is highly unstable and could easily get degraded; therefore, prefer to proceed the cDNA synthesis immediately after RNA isolation and quantification. On the same day or it can be stored at -20 °C for up to 1 week under aseptic conditions. 9. The total volume of cDNA synthesis can be scaled up or scaled down depending upon the sample size. For this the amount of reaction mix, reverse transcriptase, RNA template, and nuclease-free water are also increased or decreased in multiples of original quantity. 10. The primers usually work best at 500 nM to 1 μM concentration and the concentration of primer at which the fluorescence intensity is high should be further used in real-time qPCR. 11. The DNA ladder and TBE buffer concentration are decided based on the nucleic acid fragment length obtained from PCR. For example, for the PCR product length of 500–10,000 bp 1× TBE buffer is used and 50 bp DNA ladder is used for the fragment range of 50 bp–1500 bp. 12. The reaction is performed in the aseptic area designated for handling nucleic acids, like PCR hood. It should be decontaminated with 70% ethanol and UV irradiation when not in work. The preparation of qPCR mix should be done in the dark without the direct exposure of light. 13. The final aliquots of reverse and forward primers can be pooled into one tube immediately before adding to the reaction mixture. This will help in reducing the pipetting errors and avoid variations among replicates. 14. Melt curve analysis is used to determine the specificity of the qPCR reaction, and it is based on the dissociation characteristics of double-stranded DNA during heating [14]. A single distinct peak in the curve indicates that the amplified doublestranded DNA products are a single species (Fig. 6a–c).

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Fig. 6 Melt curve generated from real-time PCR assay for (a) β-actin, (b) Bcl-2, and (c) Bax. Single peak indicates the single mRNA type detected in all the samples

15. Different percentages of gel can be used based on the molecular weight of protein you want to separate. The higher the percentage of gel, the lower will be the pore size; hence, the lower molecular weight proteins will be resolved. 16. TEMED is added immediately before pouring the mixture into gel casting cassette. It promotes polymerization in acrylamide solution and kick-starts the process of gel formation. 17. The current supply required for running two gels in vertical electrophoresis usually ranges from 20 to 40 mA, and this can be achieved at voltage ranging from 100 to 110 V. Depending upon the voltage supplied and the resistance provided by the gel matrix, the current fluctuates. The voltage can be increased to 120 V once the samples enter resolving gel. 18. Transfer the samples immediately after electrophoresis for the best results; else you can store the gel overnight in the transfer buffer at 4 °C. 19. While preparing the transfer stack, use the roller to set the gel on the transfer pad and membrane on top of the gel. The gentle rolling after every step will help in removing the air trapped in between the sandwich and will increase the efficiency of protein transfer. 20. The nitrocellulose membrane and PVDF membrane are very delicate to bare touch and hence should be handled carefully using blunt-end forceps. It should not be directly held with forceps; instead, it should be held in between the covers provided with the membrane. Before using the nitrocellulose membrane, it should be activated in water and PVDF membrane should be activated in methanol.

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21. The blocking solution is always prepared fresh before use. Use 5% skim-milk, while one can also use 5% bovine serum albumin solution in TBST. The percentage of blocking solutions can be decreased to 3%, but then the time duration of blocking shall be increased from 1 to 3 h. 22. The concentration and diluent to dilute primary antibody can be referred from the antibody specification sheet provided by the supplier. However, we suggest standardizing both the dilution and diluents because it varies for different tissue types. Some antibodies work well on dilution with TBST while some may work with 3–5% bovine serum albumin or skim-milk. 23. The secondary antibody used can be anti-mouse or anti-rabbit. It depends upon the animals used as hosts for the origin of primary antibody. References 1. Okouchi M, Ekshyyan O, Maracine M, Aw TY (2007) Neuronal apoptosis in neurodegeneration. Antioxid Redox Signal 9:1059–1096 2. Mullis KB (1990) The unusual origin of the polymerase chain reaction. Sci Am 262:56–65 3. Mullis KB, Faloona FA (1987) Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. In: Methods in enzymology. Elsevier, pp 335–350 4. Higuchi R, Fockler C, Dollinger G, Watson R (1993) Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Bio/Technology 11:1026–1030 5. Valasek MA, Repa JJ (2005) The power of realtime PCR. Adv Physiol Educ 29:151–159 6. Hirano S (2012) Western blot analysis. Nanotox Methods Protoc:87–97 7. Towbin H, Staehelin T, Gordon J (1979) Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc Natl Acad Sci 76: 4350–4354 8. Gershoni JM, Palade GE (1983) Protein blotting: principles and applications. Anal Biochem 131:1–15

9. Renart J, Reiser J, Stark GR (1979) Transfer of proteins from gels to diazobenzyloxymethylpaper and detection with antisera: a method for studying antibody specificity and antigen structure. Proc Natl Acad Sci 76:3116–3120 10. Kurien BT, Scofield RH (1997) Multiple immunoblots after non-electrophoretic bidirectional transfer of a single SDS–PAGE gel with multiple antigens. J Immunol Methods 205:91–94 11. Peferoen M, Huybrechts R, De Loof A (1982) Vacuum-blotting: a new simple and efficient transfer of proteins from sodium dodecyl sulfate – polyacrylamide gels to nitrocellulose. FEBS Lett 145:369–372 12. MacPhee DJ (2010) Methodological considerations for improving Western blot analysis. J Pharmacol Toxicol Methods 61:171–177 13. Sedlak J, Lindsay RH (1968) Estimation of total, protein-bound, and nonprotein sulfhydryl groups in tissue with Ellman’s reagent. Anal Biochem 25:192–205 14. Mergny J-L, Lacroix L (2003) Analysis of thermal melting curves. Oligonucleotides 13:515– 537

Chapter 23 Isolation and Detection of Pathological Tau Species in a Tauopathy Mouse Model Abhay Kumar Singh, Karthikeyan Selvarasu, and Siva Sundara Kumar Durairajan Abstract Tau protein in Alzheimer’s disease (AD) and tauopathies becomes insoluble due to hyperphosphorylation, conformational alterations, and aggregation. To analyze insoluble tau and pathological tau species, this study employs a methodology that utilizes wild-type and transgenic tau mice (P310S Tau) tissue extraction using 1% Sarkosyl or N-Lauroylsarcosine sodium salt and the radio immunoprecipitation assay (RIPA) buffer. However, the commonly used methods to study the insoluble tau fraction using detergents like Sarkosyl and RIPA require a large amount of homogenate, which can pose challenges when dealing with small tissue samples. Additionally, the study employs immunohistochemistry to visualize and quantify the pathological tau species in the brain tissue of transgenic mice, aiming to identify and analyze pathological tau species such as hyperphosphorylated tau to further our understanding of tauopathies such as Alzheimer’s disease. Key words Tauopathies, Transgenic tau mouse model, Insoluble phosphotau, Ultracentrifugation, Western blotting, Immunohistochemistry

1

Introduction Tau is a microtubule-associated protein typically localized to the axons of neurons within the central nervous system. It plays a crucial role in regulating microtubule stability and formation, which is essential for proper neural function [1]. Tau is involved in a variety of neurodegenerative diseases, such as Alzheimer’s disease, frontotemporal dementia, and chronic traumatic encephalopathy, which are collectively known as tauopathies [2]. In these diseases, tau protein becomes hyperphosphorylated, forming insoluble tau aggregates, or neurofibrillary tangles, within neurons [3]. The accumulation of these aggregates is believed to contribute to the cognitive decline and neuronal death observed in these diseases [4]. Due to the involvement of tau in so many

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_23, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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neurodegenerative diseases, there is a significant interest in studying its role in disease pathogenesis. In particular, understanding the mechanisms of tau aggregation and the formation of insoluble tau species is critical for developing targeted therapeutic interventions. Several studies have investigated the aggregation of tau and its pathological consequences. However, the methods used to extract, fractionate, and analyze tau have varied significantly, making it difficult to compare results across studies. This study aims to provide a standardized methodology for fractionating and analyzing insoluble and pathological tau species. Our study builds on previous work investigating tau aggregation in various tauopathy models. For example, wild-type and transgenic mouse models (P310S) [5] have been developed to investigate the role of tau in Alzheimer’s disease and frontotemporal dementia. Our methodology involves tissue extraction using 1% Sarkosyl or N-Lauroylsarcosine sodium salt and RIPA buffer, followed by SDS-PAGE and Western Blot analysis. Additionally, we employ immunohistochemistry to visualize and quantify the pathological tau species in the brain tissue of transgenic mice [6]. Our study aims to identify and analyze pathological tau species, such as hyperphosphorylated tau, to understand further the mechanisms of tauopathies such as Alzheimer’s disease. Identifying these species is challenging due to the lack of specific antibodies to detect and quantify them. Therefore, we use fractionation techniques, Western blot analysis, and immunohistochemistry to identify and quantify these species. The methodology we have developed for fractionating and analyzing insoluble tau and pathological tau species has several advantages over previous methods.

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Materials Ensure that all reagents are of analytical grade and that water is ultrapure. Store all reagents in a cool, dry place away from direct sunlight. Follow appropriate safety precautions when handling and disposing of waste materials.

2.1

Mice

1. Use wild-type (WT) mice (C57BL/6J) mouse strain, which is widely used in research and is known to have a normal expression of endogenous mouse tau protein. 2. Use the P310S Tau transgenic mice, established by Alle et al. overexpressing the human four-repeat tau isoform (0N4R) using the neuron-specific Thy-1.2 promoter element in the mixed background (CBA.C57BL/6) [5]. These mice demonstrate several hallmarks of human tauopathy, including tau hyperphosphorylation, filament production, neurodegeneration, and motor dysfunction, with widespread tau pathology in the central nervous system.

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1. N-Lauroylsarcosine sodium salt or 1% sarkosyl. 2. Radioimmunoprecipitation assay (RIPA) buffer: 50 mM TrisHCl (pH 7.4), 1% NP-40, 0.25% sodium deoxycholate, 150 mM NaCl, 1 mM EDTA, 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 mM sodium orthovanadate, 1 mM sodium fluoride (NaF), and 1 μL/mL protease inhibitor mix. 3. Tris-EDTA buffer: 10 mM Tris-HCl, 1 mM EDTA, pH 8.0.

2.3

SDS-PAGE

1. 30% acrylamide/bis solution (Dissolve 290 g acrylamide and 10 g N,N′-methylbisacrylamide in 600 mL H2O. Adjust volume to 1 L with H2O. Sterilize solution by 0.45 μm pore size filtration. Check pH (should be 7.0 or less). Store in dark bottles at room temperature). 2. 10% sodium dodecyl sulfate (SDS) (10 g in 100 mL H2O). 3. 10% ammonium persulfate (APS) (100 mg in 1 mL H2O). 4. TEMED (Bio-Rad). 5. NuPAGE LDS sample buffer (4×) (Thermo Scientific). 6. PageRule plus pre-stained protein ladder, 10–250 kDa (Thermo Scientific). 7. Tris-glycine running buffer: 0.025 M Tris–HCl, pH 8.3, 0.192 M glycine, 0.1% SDS.

2.4

Western Blotting

1. TBS (10×): 1.5 M NaCl, 0.5 M Tris–HCl, pH 7.4. 2. TBST: 1× TBS containing 0.05% Tween-20. 3. Blocking solution: 5% nonfat milk in TBST. 4. PVDF membranes (0.45 μm pore size). 5. Western blot transfer buffer: 0.025 M Tris–HCl, 0.192 M glycine, 20% methanol. 6. Pico PLUS chemiluminescent substrate (Thermo Scientific).

2.5 Solutions for Immunohistochemistry

Tissue preparation: 1. 4% paraformaldehyde (4 g of paraformaldehyde powder to 80 mL of 1× PBS. Stir the mixture at 60 °C in a well-ventilated space, ensuring it does not boil. Gradually raise the pH to create a clear solution by adding 5 N NaOH drop by drop). 2. Phosphate buffered saline (dissolve 8 g NaCl, 0.2 g KCl, 1.44 g Na2HPO4, and 0.24 g KH2PO4 in distilled water to make 1 L of PBS, adjusting the pH to 7.4 to maintain a stable buffer solution). 3. 30% sucrose solution. 4. Tissue-Tek® O.C.T. Compound (Sakura).

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5. Isopentane. 6. Liquid nitrogen. Sectioning: 1. To prepare a 0.1 M phosphate buffer solution with a pH of 7.4, dissolve 13.6 g of NaH2PO4·H2O (monobasic sodium phosphate monohydrate) and 28.4 g of Na2HPO4·7H2O (dibasic sodium phosphate heptahydrate) separately in 400 ml of distilled water. Then, mix both solutions, adjust the total volume to 1 L, and sterilize if necessary. 2. Cryoprotectant solution (150 g sucrose plus 300 mL ethylene glycol make up volume 1 L with 0.05 M phosphate buffer [pH 7.4]). Immunostaining: 1. Phosphate-buffered saline (PBS). 2. PBST: 0.05% Tween in PBS. 3. Triton X-100. 4. Hydrogen peroxide (H2O2). 5. Blocking solution (5–10 g BSA in 100 mL PBS). 6. Avidin/Biotin Blocking Kit (Vector Laboratories). 7. Vectastain Elite ABC-HRP Kit, Peroxidase, R.T.U. (Universal) (Vector Laboratories). 8. DAB Substrate Kit, Peroxidase (HRP), with Nickel, (3,3′-diaminobenzidine) (Vector Laboratories). 9. Permanent Mounting Medium (Vector Laboratories). 2.6

Antibodies

1. Tau monoclonal antibody (HT7) (Thermo Fisher Scientific), diluted 1:1000 in TBS for Western blotting. 2. Phospho-Tau (Thr212, Ser214) Monoclonal Antibody (AT100) (Thermo Fisher Scientific) diluted 1:1000 in TBS for Western blotting. 3. PHF1 Monoclonal Antibody (2G7) (Thermo Fisher Scientific), diluted 1:1000 in TBS for Western blotting. 4. Beta Actin Loading Control Monoclonal Antibody (BA3R) (Thermo Fisher Scientific) diluted 1:1000 in TBS for Western blotting. 5. Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8), (Thermo Fisher Scientific), diluted 1:1000 in TBS for Western blotting. 6. Goat anti-Mouse IgG (H + L) Secondary Antibody, HRP (Thermo Fisher Scientific), diluted 1:10,000 in TBST for Western blotting.

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7. Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8), Biotin (Thermo Fisher Scientific), diluted 1:500 in PBS for immunohistochemistry. 8. Biotinylated secondary antibody (goat anti-mouse biotinylated antibody from Vector Laboratories) diluted 1:1000 in PBS for immunohistochemistry.

3 3.1

Methods Tissue Extraction

1. To maintain the natural biochemical profiles of tau protein, use cervical dislocation to euthanize mice, which ensures the preservation of the brain’s metabolic environment and prevents any potential artifacts (Fig. 1). 2. Dissect the mouse brains. Use the left forebrain of WT and P301S mice and the right store for an Immunohistochemistry study. 3. Homogenize WT and P301S mice brains separately in cold RIPA buffer (10× volume/weight) with a mechanical homogenizer. 4. Centrifuge homogenates at 21,130 × g for 30 min at 4 °C. 5. Transfer the supernatant to a clean tube as a RIPA-soluble fraction.

Fig. 1 For biochemical and immunohistochemistry analysis, a mouse brain was dissected, and the left hemisphere was frozen and divided into three parts: olfactory bulb, anterior brain, and cerebellum. The cerebral cortex and the hippocampus of the forebrain were used to prepare sarosyl-soluble and insoluble fractions. The right hemisphere was reserved for immunohistochemistry assessment

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6. Discard the pellet. 7. At room temperature, incubate the RIPA-soluble fraction with 1% sarkosyl for 2 h. 8. Ultracentrifuge at 100,000 × g for 1 h at 4 °C to separate into sarkosyl-soluble (S2) and insoluble (pellet) fractions. 9. Resuspend the pellet in 50 μL of Tris-EDTA buffer. 10. Utilize the sarkosyl-soluble and insoluble fractions for the purpose of conducting Western blot analyses aimed at detecting phospho-tau epitopes (AT8, AT100, and PHF1) as well as total human tau (HT7) [6–8]. 11. Use appropriate antibodies and follow standard Western blotting procedures for detecting proteins of interest (Fig. 2). 3.2

Western Blotting

1. Clean and thoroughly dry the casting frames, stand, and glass plates. 2. Add water to the assembly to check for any leakage and ensure none. 3. Once you have confirmed, there is no water leakage. Prepare the running and stacking gel according to the required volumes. 4. Add the running gel to the gel cassette assembly and let it solidify. 5. Add the stacking gel to the running gel layer and insert a comb into the stacking gel without forming any bubbles. Allow the gel to polymerize. 6. Once the gel has polymerized, remove the gel cassette assembly from the stand and release the plates from the stand. 7. Keep the gel cassette sandwich in the slot of the electrode assembly. 8. Assemble the inner chamber of the mini-tank and fill it with a running buffer. 9. Remove the comb from the stacking gel and start loading. 10. Mix the protein sample and SDS sample buffer and denature the protein by keeping it in a 95 °C bath for 5 min. 11. Add the denatured protein sample to the wells; add a protein ladder in a well. 12. Close the lid of the mini-tank and start the electrophoresis process at 70–90 V until the protein crosses the stacking gel. 13. Gradually increase the voltage to 100–120 V. 14. Carefully remove the gel from the gel cassette after the completion of electrophoresis.

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Fig. 2 Isolation of soluble and insoluble tau fractions was carried out using RIPA and sarkosyl, following the protocol described by Selvarasu et al. [6]

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15. Cut the polyvinylidene difluoride (PVDF) membrane to the size of the gel and place it over the gel without forming any bubbles. 16. Assemble the transfer casting in the following order: Cathode—Filter pad—Filter paper—Gel—PVDF membrane—Filter paper—Filter pad—Anode. 17. Tighten the setup and place it in the transfer cassette. 18. Fill the tank with pre-cooled 1× transfer buffer and place the tank in a cold room. 19. Cover the mini tank completely with ice to avoid denaturation of proteins due to heat generation during transfer. 20. Start the transfer at 100 V or 350 mA for 60–90 min. 21. After the transfer, carefully disassemble the transfer casting and remove the PVDF membrane with the transferred protein. 22. Cut the membrane to the appropriate sites using a ladder and transfer it to a plastic box (see Note 1). 23. Add a blocking agent to the plastic box and keep it in a shaker for 1 h. 24. After blocking, wash the membrane three times with TBST for 5 min each to remove any unbound blocking agent. 25. Add the primary antibodies to the membrane and incubate it overnight in a 4 °C shaker. 26. After the overnight incubation, wash the membrane three times with TBST for 15 min each to remove the excess primary antibodies bound to the membrane. 27. Add the secondary antibodies to the membrane and incubate it in a shaker for 1–2 h. 28. After incubation with the secondary antibodies, wash the membrane three times with TBST for 15 min each to remove the excess secondary antibodies bound to the membrane. 29. Proceed with detecting the protein bands; prepare the chemiluminescent substrate according to the manufacturer’s instructions. 30. Incubate the membrane with the chemiluminescent substrate for the recommended time, typically for a few minutes. 31. Place the membrane into the imaging system and ensure it is correctly aligned with the camera lens and light source. 32. If the image quality is unsatisfactory, adjust the exposure time or other parameters and repeat the imaging process until you achieve a high-quality image. 33. Analyze the image using the image analysis software to obtain the desired data.

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Tissue preparation: 1. Dissect the tissue and place it in fresh 4% paraformaldehyde for 48 h at 4 °C (see Note 2). 2. Wash the tissue in 1× PBS for 5 min and repeat. 3. Transfer the tissue to 30% sucrose until it sinks. 4. Transfer the tissue through a 1:1 mixture of OCT: sucrose and then into OCT (see Note 3). 5. Place the tissue in the cryomold or cryostand, overlay with OCT, orient, and freeze quickly on the cryo-chamber. 6. Transfer the tissue block to a mixture of 20% sucrose and embedding media (such as OCT) at 4 °C for 1 h. Subsequently, move it to the final cryo-mold filled with neat (100%) embedding media and keep it at 4 °C for 1 h. 7. Prepare a metal container of isopentane and place it in liquid nitrogen so that the nitrogen almost reaches the top of the metal container. 8. After the isopentane becomes extremely cold and turns white and dense at the container’s base, gently immerse the tissue block/mold into the freezing isopentane and let it sit until the clear embedding media becomes opaque white (see Note 4). 9. Using forceps, carefully remove the mold from the cold isopentane and immediately transfer it to dry ice or the -80 °C freezer without delay. Sectioning: 1. Remove the frozen tissue block from the cryomold and allow it to equilibrate to the temperature of the vibratome chamber (usually between -15 and -20 °C). 2. Place the tissue block on the vibratome specimen holder and secure it in place using the appropriate mounting bracket or adhesive. 3. Fill the vibratome chamber with ice-cold 0.1 M phosphate buffer (pH 7.4) and adjust the blade position and speed according to the manufacturer’s instructions. 4. Cut sections of the desired thickness (30–40 μm) using the vibratome. 5. Collect the sections in a 24-well culture plate filled with 0.1 M phosphate buffer (pH 7.4). 6. If the sections are to be stored, transfer them to cryoprotectant solution and store them at -20 °C.

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Staining: 1. Collect the 30 μm free-floating brain sections in a 24-well plate with PBS/0.3% Triton X-100. Alternatively, incubate the sections with 0.3–0.4% Triton X-100 in PBS at room temperature for 30 min. 2. Rinse the sections three times for 3 min each with PBS. 3. Perform antigen retrieval by the heating method. Place the tissue sections from the anterior, medial, and posterior positions in preheated PBS 2 mL tubes and heat at 95 °C or boiling (97–99 °C) for 5–10 min. After heating, collect the sections and place them in their respective positions [6, 9]. 4. Rinse the sections once for 3 min with PBST. 5. Perform H2O2 blocking by incubating the sections with 3% H2O2 blocking solution for 5–10 min. 6. Rinse the sections once for 3 min with PBST. 7. Before applying the primary antibody, perform avidin-biotin blocking by adding four drops of Avidin D solution to each 1 mL of PBS buffer and incubating the sections with the Avidin D solution for 15 min Subsequently, rinse briefly with PBS buffer and incubate for another 15 min with the biotin solution (four drops in 1 mL of PBS buffer). 8. To perform serum blocking, incubate the sections with a blocking solution (5–10% BSA in PBS) or use normal blocking serum from the Vectastain Universal ABC Kit for 1 h. Alternatively, follow the manufacturer’s instructions to prepare the blocking serum (normal horse serum). 9. Incubate the sections with primary antibody (biotinylated AT8) diluted in diluent (5% BSA-PBS) at 4 °C (longer incubations may give better results). For each well, add 250–300 μL of primary antibody. 10. Rinse the sections three times for 5 min each with PBST. 11. Dilute the secondary antibody in a diluent such as 5% BSA or use the Biotinylated Universal secondary antibody from the kit. 12. Incubate the sample with the secondary antibody solution for 1–2 h at room temperature or as the manufacturer recommends. 13. Rinse the sample three times with PBST for 3 min each to remove unbound secondary antibodies. 14. Incubate the sample with the Vectastain ABC Reagent (prepare the Vectastain ABC Reagent according to the manufacturer’s instructions) for 30 min at room temperature or as the manufacturer recommends.

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15. Rinse the sample three times with PBST for 3 min each to remove unbound Vectastain ABC Reagent. 16. Place the sample in the DAB substrate solution and let it incubate for 2–10 min at room temperature (see Note 5). 17. Stop the reaction by rinsing the sample five times with 1 mL of PBS or water. 18. Spreading the sections: Using a paintbrush, take the sections and spread them evenly on a pre-coated slide. Ensure that the sections have the same position and orientation and are closely placed. You may drain the excess water or place it inside an oven for 5–10 min or until dry. 19. After drying, add mounting medium and place a coverslip to seal the sections.

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Notes 1. The apparent molecular weight (64 kDa) of the isolated sarkosyl-insoluble tau protein band was higher than the observable weights in the soluble fraction, suggesting the presence of hyperphosphorylated tau protein. 2. Proper safety measures must be taken while working with chemicals such as PFA. It is recommended to perform this protocol in a fume hood or with proper ventilation. 3. If you do not have enough OCT, skip the 1:1 mixture of OCT: sucrose step. 4. The brain tissue must be rapidly snap-frozen using liquid nitrogen prior to storage at -80 °C to avoid the development of large ice crystals that can harm cells and result in gaps. Furthermore, it is important to refrain from keeping the tissue at -20 ° C, which can lead to tissue deterioration. 5. Observe the tissues until they turn dark brown, and then stop the DAB substrate reaction immediately with water.

References 1. Barbier P, Zejneli O, Martinho M, Lasorsa A, Belle V, Smet-Nocca C et al (2019) Role of tau as a microtubule-associated protein: structural and functional aspects. Front Aging Neurosci 11:204 2. Wolfe MS (2012) The role of tau in neurodegenerative diseases and its potential as a therapeutic target. Scientifica (Cairo) 2012:796024 3. Huang Y, Li X, Luo G, Wang J, Li R, Zhou C, Wan T, Yang F (2022) Pyroptosis as a candidate

therapeutic target for Alzheimer’s disease. Front Aging Neurosci 14:996646 4. Rajmohan R, Reddy PH (2017) Amyloid-beta and phosphorylated tau accumulations cause abnormalities at synapses of Alzheimer’s disease neurons. J Alzheimers Dis 57:975–999 5. Allen B, Ingram E, Takao M, Smith MJ, Jakes R, Virdee K et al (2002) Abundant tau filaments and nonapoptotic neurodegeneration in transgenic mice expressing human P301S tau protein. J Neurosci 22(21):9340–9351

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6. Selvarasu K, Singh AK, Iyaswamy A, Gopalkrishnashetty Sreenivasmurthy S, Krishnamoorthi S, Bera AK, Huang JD, Durairajan SSK (2022) Reduction of kinesin I heavy chain decreases tau hyperphosphorylation, aggregation, and memory impairment in Alzheimer’s disease and tauopathy models. Front Mol Biosci 9:1050768 7. Iyaswamy A, Venkatasubramanian L, Singh N, Jagannathan R, Krishnamoorthy E (2021) Qingyangshen mitigates amyloid-β and tau aggregate defects involving PPARA-TFEB activation in transgenic mice of Alzheimer’s disease. Phytomedicine 91:153648

8. Myeku N, Clelland CL, Emrani S, Kukushkin NV, Yu WH, Goldberg AL, Duff KE (2015) Tau-driven 26S proteasome impairment and cognitive dysfunction can be prevented early in disease by activating cAMP-PKA signaling. Nat Med 22(1):46–53 9. Durairajan SS, Liu LF, Lu J, Tang SC, Viswanath A, Shanmugam MK et al (2012) Berberine ameliorates β-amyloid pathology, gliosis, and cognitive impairment in an Alzheimer’s disease transgenic mouse model. Neurobiol Aging 33(12):2903–2919

Chapter 24 Enzyme Inhibition Assays for Monoamine Oxidase Bijo Mathew, Jong Min Oh, Della Grace Thomas Parambi, Sachithra Thazhathuveedu Sudevan, Sunil Kumar, and Hoon Kim Abstract Monoamine oxidase (MAO) catalyzes the oxidative deamination of monoamines with two isoforms, namely, MAO-A and MAO-B, in mitochondrial outer membranes. These two types of MAO-A and MAO-B participate in changes in levels of neurotransmitter such as serotonin (5-hydroxytryptamine) and dopamine. Selective MAO-A inhibitors have been targeted for anti-depression treatment, while selective MAO-B inhibitors are targets of therapeutic agents for Alzheimer’s disease and Parkinson’s disease. For this reason, study on the development of MAO inhibitors has recently become important. Here, we describe methods of MAO activity assay, especially continuous spectrophotometric methods, which give relatively high accuracy. MAO-A and MAO-B can be assayed using kynuramine and benzylamine as substrates, respectively, at 316 nm and 250 nm, respectively, to measure their respective products, 4-hydroxyquinoline and benzaldehyde. Inhibition degree and pattern can be analyzed by using the Lineweaver-Burk and secondary plots in the presence of inhibitor, and reversibility of inhibitor can be determined by using the dialysis method. Key words Monoamine oxidase, MAO-A inhibitors, MAO-B inhibitors, Alzheimer’s disease, Parkinson’s disease, Continuous assay

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Introduction Flavin adenine dinucleotide (FAD)-dependent enzymes like monoamine oxidases (MAOs) are a privileged class of neuronal enzymes that have been well studied for their critical functions in the process of neurodegeneration targeted in drug therapy in neuroscience for more than 60 years. MAO enzymes are situated in the outer membrane of the mitochondria and catalyze the oxidative deamination of biological amines [1, 2]. Employing several complementary approaches, researchers understood their role in the brain’s metabolism of serotonin (5-hydroxy tryptamine, 5-HT), melatonin, and dopamine (DA) and the crucial role in regulating brain architecture and behavior [3]. In humans, MAO exists in two isoforms, MAO-A and MAO-B. Protein sequences of these isoenzymes are almost

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Fig. 1 MAOs in the outer membrane of mitochondria and their specificities to various neurotransmitters

73% similar, and they are required for the inactivation of neurotransmitters. But they have different substrate specificities and enzyme activity [4]. Especially serotonin, melatonin, epinephrine, and norepinephrine are metabolized by MAO-A, whereas MAO-B mainly degrades phenylethylamine (PEA) and benzylamine (BA) [5]. Furthermore, these isozymes occupy different areas in the brain: MAO-A isozyme is principally concentrated at the nigrostriatal DAergic axon terminals, whereas MAO-B isozyme is predominately found in serotonergic neurons of astrocytes [6–8]. MAO-A degrades 5-HT, while MAO-B shows high affinity toward PEA, BA, catecholamines (CAs) like adrenalin (epinephrine), noradrenaline (norepinephrine, NE), and DA. Tryptamine and tyramines are the neurotransmitters targeted by both isoforms. However, the metabolism of DA occurs in MAO-B in substantia nigra (Fig. 1) [9–12]. MAOs extend a significant role in controlling all neurological processes. Varied enzyme levels are associated with many neurological diseases like Brunner syndrome [13] and autism [14, 15], where abnormal MAO-A is observed. High MAO-B levels (~4 times) with aging are seen in Alzheimer’s disease (AD) and Parkinson’s disease (PD) patients [16, 17]. The clinical significance of MAOs is attributed to their different specificities for substrates, i.e., neurotransmitters. Most MAO-A inhibitors are used for treating psychological disorders, while selective MAO-B inhibitors are used for PD (Fig. 2) [12, 18–20]. A meta-analysis report by Henkel et al. revealed that MAO-A is more efficient in managing depression than tricyclic antidepressants [21]. Furthermore, rasagiline and selegiline, which are irreversible MAO-B inhibitors, had early acceptance for early PD or with levodopa as a combination in the later stages of PD [22, 23]. Reports from Kumar et al. illustrate that irreversible

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Fig. 2 MAO inhibitors used in treating neurological disorders

MAO inhibition can bring safety complications and adverse side effects [24]. Safinamide was approved in 2015 for mild- to late-stage early PD to treat motor complications [25]. The drug sembragiline (reversible MAO-B inhibitor) was patented but discontinued in clinical phase III to treat moderate AD [26, 27]. The new research of X-ray crystallography studies of both isozymes with

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several reversible and irreversible inhibitors brought about a potential therapeutic approach in neurological diseases [7, 28, 29]. X-ray crystallography also revealed that MAO-A crystallizes as monomer [30], whereas MAO-B crystallizes as dimer [27]. Furthermore, MAO-A in humans possesses a hydrophobic cavity with a volume measurement as 550 Å3, whereas the bipartite cavity of human MAO-B has a volume measurement as 700 Å3. MAO active site is further divided into FAD co-factor (400 Å3) and hydrophobic cavity (300 Å3). Irreversible inhibitor clorgyline is covalently bound to MAO-A and reversible inhibitor safinamide is non-covalently bound to MAO-B [29].

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Materials 1. Recombinant human MAO-A (hMAO-A) and hMAO-B (see Note 1), reversible inhibitors (toloxatone, lazabemide or safinamide), substrates (kynuramine and benzylamine), dimethyl sulfoxide (DMSO) and sodium phosphate monobasic, and sodium phosphate dibasic [31]. 2. The reference irreversible inhibitors (clorgyline and pargyline). 3. Dialysis kit for reversibility test with DiaEasy™ Dialyzer (6–8 kDa). 4. Make all solutions using sterilized distilled water (SDW); all reagents should be stored at 4 °C and placed at room temperature (25 ± 5 °C) prior to use. 5. Prepare reaction buffer to 10× (0.5 M sodium phosphate, pH 7.2) and substrates (5 mM kynuramine and 25 mM benzylamine).

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Methods

3.1 Principle of MAO Assay

1. MAO-A assay: When comparing the ultraviolet (UV) absorbances of 4-hydroxyquinoline and kynuramine, the unique maximum absorbance of 4-hydroxyquinoline is observed at ~320 nm, though both compounds commonly show maximum absorbances at ~250 nm [32]. Referring to the data, conduct measurement of MAO-A activity at 316 nm, which shows maximum absorbance difference between 4-hydroxyquinoline and kynuramine. 2. MAO-B assay: The maximum absorbance of benzaldehyde, a reaction product of benzylamine by MAO-B, is observed at ~250 nm [33]. However, benzylamine has a much lower absorbance than benzaldehyde. Referring to the data, measure MAO-B activity at 250 nm, which is the similar maximum absorbance wavelength of the product benzaldehyde.

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Assay Procedure

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1. Use kynuramine and benzylamine as substrates (see Note 2) for recombinant hMAO-A and hMAO-B, respectively. 2. Set the kinetic mode at 316 nm for 45 min for MAO-A or at 250 nm for 45 min for MAO-B, respectively. Express reaction velocity as ΔA/min. 3. Prepare standard reaction mixtures using 10× reaction buffer to be 0.06 mM kynuramine and 0.3 mM benzylamine for MAO-A and MAO-B, respectively. Use approximately 0.003 ~ 0.005 U MAO-A or MAO-B in 500 μL mixture in a cuvette. The enzymes should be kept on ice during the assay. 4. In standard assay, add the reaction solutions to the cuvette in order, i.e., SDW, buffer, substrate, and enzyme. 5. In inhibition assay, an inhibitor should be added prior to substrate addition. Most of inhibitors should be dissolved in dimethyl sulfoxide (DMSO). If preincubation is needed for an inhibitor and the enzyme, the substrate should be added last. 6. Measure the absorbances of the reaction mixtures.

3.3 Enzyme Kinetics and Ki Determination

1. Carry out enzyme kinetics at five different substrate concentrations, i.e., 0.0075, 0.015, 0.030, 0.060, and 0.12 mM of kynuramine for MAO-A; 0.0375, 0.075, 0.150, 0.30, and 0.60 mM of benzylamine for MAO-B [34, 35]. 2. Draw the Lineweaver-Burk (LB) plots in the absence of inhibitor and calculate the Km value. In LB plots, the X-axis is the reciprocal of the substrate concentrations (1/S), and the Y-axis is the reciprocal of the reaction rate (1/V) using the change in absorbance per minute (ΔA/min). Km can be determined using the X-axis intercept, -1/Km (Fig. 3).

Fig. 3 An example of Lineweaver-Burk (LB) plots for MAO-B inhibition in the absence of inhibitor

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Fig. 4 Examples of Lineweaver-Burk (LB) plots for MAO-B inhibition in the presence of inhibitor (a) and the secondary plot (b) of the slope vs. inhibitor concentration [34]

3. Carry out Inhibition kinetics for leading compounds at three concentrations, i.e., ~ 0.5×, 1.0×, and 2.0× IC50 values. 4. Construct LB plots and determine the inhibition pattern using the plots. If the lines meet at Y-axis, inhibition type is competitive (Fig. 4a); if the straight lines of concentrations meet on the Y-axis, it is a competitive inhibitor; if it meets on the X-axis, it is a non-competitive inhibitor; and if it meets in the second quadrant, it is a mixed-type inhibitor. 5. Construct the secondary plot by using the slopes of the lines from the LB plots on the Y-axis, and the inhibitor concentrations (~0.5×, 1.0×, and 2.0× IC50) on the X-axis (Fig. 4b). Ki value can be calculated by X-axis intercept, -Ki. 3.4 Reversibility Study

1. Evaluate the reversibility of inhibition relevantly using a dialysis method, by comparing residual activities before and after dialysis [34, 35]. 2. Measure the enzyme activity of MAO-A or MAO-B for the control (i.e., without inhibitor) and samples containing leading compound(s) or reference compound(s) at ~2× IC50 concentration. In this step, each reaction mixture contains an inhibitor and each enzyme without its substrate. 3. Deliver the reaction mixture into a 6–8 kDa DiaEasy™ Dialyzer and dialyze the mixture for 6 h with the buffer exchange (50 mM sodium phosphate, pH 7.2) for 3 h each. 4. After dialysis, add the substrate (kynuramine or benzylamine) to the mixture. 5. Measure the residual activity and compare to the control.

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Notes 1. MAO-A and MAO-B should be kept in ice, during the experiments. Other reagents can be located at room temperature. 2. Substrates for enzyme activity assay such as kynuramine and benzylamine should be added in the last order. 3. The total volume should be minimized. In this study, 0.5 mL was used in 1.0 mL cuvette. The assay was monitored for 45 min using minimal enzyme amount. During the assay, the slope of the absorbance was linear to the time. 4. In IC50 determination, substrate concentration should be used at ~2× Km. Therefore, Km value should be determined before the experiment for IC50 determination. 5. For Ki determination, three inhibitor concentrations should be used, i.e., at ~1/2×, 1×, and 2× IC50. 6. Reversibility can be analyzed accurately by using the dialysis method. Enzyme was preincubated with ~2× IC50 concentration of inhibitor. Buffer should be changed at least two times. If residual activity is much less than 50% after preincubation, time dependency of the inhibitor should be checked.

References 1. Shih JC, Chen K, Ridd MJ (1999) Role of MAO A and B in neurotransmitter metabolism and behavior. Pol J Pharmacol 51:25–29 2. Cai Z (2014) Monoamine oxidase inhibitors: promising therapeutic agents for Alzheimer’s disease (Review). Mol Med Rep 9:1533–1541 3. Bortolato M, Chen K, Shih JC (2008) Monoamine oxidase inactivation: from pathophysiology to therapeutics. Adv Drug Deliv Rev 60: 1527–1533 4. Edmondson DE, Binda C, Mattevi A (2007) Structural insights into the mechanism of amine oxidation by monoamine oxidases A and B. Arch Biochem Biophys 464:269–176 5. Brannan T, Prikhojan A, Martı´nez-Tica J, Yahr MD (1995) In vivo comparison of the effects of inhibition of MAO-A versus MAO-B on striatal L-DOPA and dopamine metabolism. J Neural Transm Park Dis Dement Sect 10:79– 89 6. Shih JC, Chen K, Ridd MJ (1999) Monoamine oxidase: from genes to behavior. Annu Rev Neurosci 22:197–217 7. Binda C, Newton-Vinson P, Huba´lek F, Edmondson DE, Mattevi A (2002) Structure of human monoamine oxidase B, a drug target for the treatment of neurological disorders. Nat Struct Biol 9:22–26

8. Castagnoli N, Petzer JP, Steyn S, Castagnoli K, Chen J-F, Schwarzschild MA, Van der Schyf CJ (2003) Monoamine oxidase B inhibition and neuroprotection: studies on selective adenosine A2A receptor antagonists. Neurology 61:S62– S68 9. Tong J, Meyer JH, Furukawa Y, Boileau I, Chang L-J, Wilson AA, Houle S, Kish SJ (2013) Distribution of monoamine oxidase proteins in human brain: implications for brain imaging studies. J Cereb Blood Flow Metab 33:863–871 10. Youdim MB, Bakhle Y (2006) Monoamine oxidase: isoforms and inhibitors in Parkinson’s disease and depressive illness. Br J Pharmacol 147:S287–S296 11. Jo G, Sung SH, Lee Y, Kim B-G, Yoon J, Lee HO et al (2012) Discovery of monoamine oxidase A inhibitors derived from in silico docking. Bull Korean Chem Soc 33:3841–3844 12. Tzvetkov NT, Stammler H-G, Neumann B, Hristova S, Antonov L, Gastreich M (2017) Crystal structures, binding interactions, and ADME evaluation of brain penetrant N-substituted indazole-5-carboxamides as subnanomolar, selective monoamine oxidase B and dual MAO-A/B inhibitors. Eur J Med Chem 127:470–492

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13. Brunner HG, Nelen MR, Van Zandvoort P, Abeling NG, Van Gennip AH, Wolters EC, Kuiper MA, Ropers HH, Van Oost BA (1993) X-linked borderline mental retardation with prominent behavioral disturbance: phenotype, genetic localization, and evidence for disturbed monoamine metabolism. Am J Hum Genet 52:1032–1039 14. Cohen IL, Liu X, Lewis M, Chudley A, Forster-Gibson C, Gonzalez M, Jenkins EE, Brown WT, Holden JJ (2011) Autism severity is associated with child and maternal MAOA genotypes. Clin Genet 79:355–362 15. Meyer JH, Ginovart N, Boovariwala A, Sagrati S, Hussey D, Garcia A, Young T, Praschak-Rieder N, Wilson AA, Houle S (2006) Elevated monoamine oxidase a level in the brain: an explanation for the monoamine imbalance of major depression. Arch Gen Psychiatry 63:1209–1216 16. Saura J, Luque J, Cesura A, Da Prada M, ChanPalay V, Huber G, Lo¨ffler J, Richards JG (1994) Increased monoamine oxidase B activity in plaque-associated astrocytes of Alzheimer brains revealed by quantitative enzyme radioautography. Neuroscience 62:15–30 17. Mallajosyula JK, Chinta SJ, Rajagopalan S, Nicholls DG, Andersen JK (2009) Metabolic control analysis in a cellular model of elevated MAO-B: relevance to Parkinson’s disease. Neurotox Res 16:186–193 18. Riederer P, Danielczyk W, Gru¨nblatt E (2004) Monoamine oxidase-B inhibition in Alzheimer’s disease. Neurotoxicology 25:271–277 19. Yamada M, Yasuhara H (2004) Clinical pharmacology of MAO inhibitors: safety and future. Neurotoxicology 25:215–221 20. Riederer P, Lachenmayer L, Laux G (2004) Clinical applications of MAO inhibitors. Curr Med Chem 11:2033–2043 21. Henkel V, Mergl R, Allgaier A-K, Kohnen R, Mo¨ller H-J, Hegerl U (2006) Treatment of depression with atypical features: a metaanalytic approach. Psychiatry Res 141:89–101 22. Lakhan SE (2007) From a Parkinson’s disease expert: rasagiline and the future of therapy. Mol Neurodegener 2:13 23. Fowler JS, Logan J, Volkow ND, Shumay E, Mccall-Perez F, Jayne M, Wang GJ, Alexoff DL, Apelskog-Torres K, Hubbard B, Carter P, King P, Fahn S, Gilmor M, Telang F, Shea C, Xu Y, Muench L (2015) Evidence that formulations of the selective MAO-B inhibitor, selegiline, which bypass first-pass metabolism, also inhibit MAO-A in the human brain. Neuropsychopharmacology 40:650–657 24. Deeks ED (2015) Safinamide: first global approval. Drugs 75:705–711

25. Borroni E, Bohrmann B, Grueninger F, Prinssen E, Nave S, Loetscher H, Chinta SJ, Rajagopalan S, Rane A, Siddiqui A, Ellenbroek B, Messer J, P€ahler A, Andersen JK, Wyler R, Cesura AM (2017) Sembragiline: a novel, selective monoamine oxidase type B inhibitor for the treatment of Alzheimer’s disease. J Pharmacol Exp Ther 362:413–423 26. Tzvetkov NT, Stammler H-G, Hristova S, Atanasov AG, Antonov L (2019) (Pyrrolo-pyridin5-yl) benzamides: BBB permeable monoamine oxidase B inhibitors with neuroprotective effect on cortical neurons. Eur J Med Chem 162:793–809 27. Binda C, Huba´lek F, Li M, Herzig Y, Sterling J, Edmondson DE, Mattevi A (2004) Crystal structures of monoamine oxidase B in complex with four inhibitors of the N-propargylaminoindan class. J Med Chem 47:1767–1774 28. Binda C, Wang J, Pisani L, Caccia C, Carotti A, Salvati P, Edmondson DE, Mattevi A (2007) Structures of human monoamine oxidase B complexes with selective noncovalent inhibitors: safinamide and coumarin analogs. J Med Chem 50:5848–5852 29. De Colibus L, Li M, Binda C, Lustig A, Edmondson DE, Mattevi A (2005) Threedimensional structure of human monoamine oxidase A (MAO A): relation to the structures of rat MAO A and human MAO B. Proc Natl Acad Sci U S A 102:12684–12689 30. Son S-Y, Ma J, Kondou Y, Yoshimura M, Yamashita E, Tsukihara T (2008) Structure of human monoamine oxidase A at 2.2-Åresolution: the control of opening the entry for substrates/inhibitors. Proc Natl Acad Sci U S A 105:5739–5744 31. Oh JM, Jang HJ, Kim WJ, Kang MG, Baek SC, Lee JP, Park D, Oh SR, Kim H (2020) Calycosin and 8-O-methylretusin isolated from Maackia amurensis as potent and selective reversible inhibitors of human monoamine oxidase-B. Int J Biol Macromol 151:441–448 32. Padiglia A, Medda R, Lorrai A, Murgia B, Pedersen JZ, Finazzi Agro´ A, Floris G (1998) Characterization of euphorbia characias latex amine oxidase. Plant Physiol 117(4): 1363–1371 33. www.webbook.nist.gov/chemistry 34. Lee HW, Ryu HW, Kang MG, Park D, Oh SR, Kim H (2017) Selective inhibition of monoamine oxidase A by purpurin, an anthraquinone. Bioorg Med Chem Lett 27(5): 1136–1140 35. Lee HW, Ryu HW, Kang MG, Park D, Oh SR, Kim H (2016) Potent selective monoamine oxidase B inhibition by maackiain, a pterocarpan from the roots of Sophora flavescens. Bioorg Med Chem Lett 26(19):4714–4719

Chapter 25 Role of Amyloid Beta in Neurodegeneration and Therapeutic Strategies for Neuroprotection Priyam Ghosh, Kavita Narang, and Parameswar Krishnan Iyer Abstract The gradual loss of neurons’ structure and function in the central nervous system is known as neurodegeneration. It is a defining feature of several incapacitating illnesses, such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. The buildup of amyloid beta (Aβ) protein in the brain is one of the several variables linked to neurodegeneration. We shall delve into the fascinating realm of Aβ in this chapter and examine its role in the etiology of neurodegenerative illnesses. Insights into the processes through which Aβ exerts its toxicity are crucial for the creation of therapeutic approaches to treat these lifethreatening diseases. Despite the presence of multiple obstacles, recent research shows promise for the development of some new anti-Aβ therapies that will help millions of people suffering from neurodegeneration. In this chapter, we discuss the role of Aβ in contributing to neurotoxicity and several anti-Aβ therapies for neuroprotection. Key words Amyloid beta (Aβ) protein, Neurodegeneration, Anti-Aβ therapies, Neuroprotection Alzheimer’s disease

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Introduction Millions of individuals throughout the world suffer from neurodegenerative illnesses, including Alzheimer’s, Parkinson’s, and Huntington’s disease, which are terrible disorders. Despite substantial investigation, the fundamental processes causing the pathophysiology of many illnesses are not fully known. One of the most popular theories in the area of neurodegeneration is the theory of the amyloid cascade. After being initially put forth in the 1990s, the theory has since been improved upon and revised considering new information. According to the amyloid cascade theory, Alzheimer’s disease (AD) and other neurodegenerative illnesses are primarily brought on by the buildup of amyloid beta protein in the brain [1]. The amyloid cascade theory and its connection to neurodegeneration will be covered in this chapter. Amyloid precursor protein (APP), which is processed proteolytically by the

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_25, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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enzymes like β- and γ-secretases, generates the amyloid beta peptide (Aβ), which leads to the development of the amyloid cascade hypothesis. Plaques, a distinctive feature of Alzheimer’s disease, are subsequently formed by the resultant Aβ peptides and are then stored in the brain. Aβ peptides come in many forms, but the most popular ones are Aβ40 and Aβ42. Aβ40 comprises 40 amino acids, whereas Aβ42 contains 42 amino acids [2]. The bulk of amyloid plaques identified in AD is made up of Aβ42 and Aβ40, which are more prone to aggregation. The lengthy, insoluble protein structures known as fibrils, a hallmark of amyloid disorders, are often formed by accumulating Aβ-peptides. These fibrils are made of flat, elongated structures called β-sheets that stack on top of one another to create a stiff, sturdy three-dimensional structure. Because of their strong resistance to deterioration, Aβ fibrils can build up over time and result in the development of amyloid plaques. These plaques have the potential to impair regular brain activity and cause brain cell death, which is a significant factor in the cognitive loss associated with AD [3]. The length of the peptide, the concentration of Aβ in the solution, and the presence of other molecules that can interact with Aβ all affect the complicated aggregation pattern of Aβ. Aβ-peptides can be found in low quantities as flexible and disordered monomers. The peptides start to assemble into tiny oligomers as the concentration of Aβ rises. As they may damage cell membranes and impair neural function, these oligomers are the most hazardous form of Aβ and may play a crucial role in the early stages of AD. In general, the characteristics of Aβ-peptides and their capacity to group together to form fibrils and oligomers are important contributors to the onset of AD. It is essential to comprehend these structures and features to create effective remedies for this terrible illness. The presence of other molecules in the solution is just one of several variables that affect how Aβ aggregates. For instance, metal ions, such as copper and zinc, can encourage the production of hazardous Aβ oligomers. Similarly, the pattern of Aβ’s aggregation can be affected by the presence of lipids or other proteins. Aβ is a beta-sheet structure in three dimensions, and Aβ’s aggregation pattern is intricate and dependent on several variables. The four different forms of Aβ peptides are monomers, oligomers, protofibrils, and fibrils, with oligomers and protofibrils being the most hazardous [4]. The concentration of Aβ in the solution and the presence of other molecules, such as metal ions, lipids, and proteins, affect the aggregation pattern of Aβ. For many years, the amyloid cascade hypothesis has been a major factor in the study of neurodegeneration. Although the hypothesis has drawn some criticism, it has also helped us understand the pathology of AD and other neurodegenerative diseases and how to treat them. The amyloid cascade theory is expected to develop and be refined as research goes on, offering fresh perspectives and potential treatments for these debilitating illnesses [3].

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Separation of Aβ and Characterization Techniques Aβ is separated by isolating and purifying the protein from a biological sample, such as blood, brain tissue, or cerebrospinal fluid. To accomplish this, a variety of methods can be utilized. Gel electrophoresis: This method uses an electric field to separate proteins according to their size and charge. Gel electrophoresis is a technique for separating Aβ from other proteins in a sample. In gel electrophoresis, the gel matrix comprises agarose or polyacrylamide. To make agarose or polyacrylamide gel, dilute the gel material in a buffer solution. The gel is subsequently placed into a casting tray, and a comb is inserted at one end to generate sample wells. Following that, the molecules to be separated are combined with a loading solution that contains a tracking dye to monitor the electrophoresis process and a denser component (e.g., glycerol) to ensure that the samples settle into the wells. The samples are then gently pipetted into the gel’s wells. After that, the gel is immersed in a buffer-filled chamber, with electrodes positioned at both ends. The negative electrode (cathode) is located at the end of the tube where the samples were loaded, while the positive electrode (anode) is located at the opposite end. When an electric current is applied, negatively charged molecules (such as DNA or proteins) migrate through the gel matrix toward the positive electrode. Smaller molecules move quicker and farther through the gel, while larger molecules move slower and stay closer to the source. After electrophoresis, the gel is treated with antibodies for protein separation. Ultraviolet (UV) light or other detection methods can be used to visualize the stained molecules. The separated molecules appear on the gel as distinct bands or spots, with each band representing a different size or charge. To determine the size of the molecules in the sample, the band spots can be compared to molecular weight or size markers that are run alongside the samples. Chromatography: Chromatography is a commonly used technique for the separation and analysis of different compounds, including proteins. It uses a stationary phase and a mobile phase to separate molecules according to their chemical properties. The stationary phase in protein chromatography is often a matrix or resin that interacts with proteins depending on specific characteristics such as size, charge, hydrophobicity, or affinity. Among the various types of chromatography are size exclusion chromatography, ion exchange chromatography, reversed-phase chromatography (RPC), hydrophobic interaction chromatography (HIC), immobilized metal affinity chromatography (IMAC) for purification of histidine-tagged

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proteins, dye-ligand affinity chromatography (DLAC), multicolumn chromatography systems, and affinity chromatography. Aβ can be separated from other proteins using any of these chromatographic methods. Immunoprecipitation: Immunoprecipitation (IP) is a protein separation and purification process that depends on the specific binding of an antibody to its target protein. It is particularly advantageous for isolating a specific protein from a complex protein mixture. The basic idea of immunoprecipitation is forming an antibody-antigen complex, which is subsequently captured using solid support material, such as protein A/G agarose beads or protein A/G coated magnetic beads. This method is used to isolate and purify the protein from a sample using an antibody that binds specifically to Aβ. Using methods like mass spectrometry, Western blotting, and ELISA, amyloid beta may then be further characterized after being isolated from other proteins in a sample [5]. Researchers may utilize these methods to measure the size, amount, and structure of amyloid beta to better understand how it affects disorders like AD. Characterizing Aβ involves a few processes intended to comprehend its structure, attributes, and behavior. Purification and isolation: Aβ is normally obtained from cultured or post-mortem brain cells that have produced the protein. After that, the protein is purified using strategies discussed earlier (such as chromatography), which divides proteins according to their physical and chemical characteristics. Analysis of the Aβ protein’s structure: After it has been purified, the Aβ protein’s structure is examined using methods like X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy. These methods reveal details about the protein’s threedimensional structure and the locations of its amino acid residues. Analysis of aggregates: Aβ is known to produce clumps or aggregates that are harmful to brain cells. Techniques like Thioflavin T fluorescence, which tracks the development of amyloid fibrils, or dynamic light scattering, which gauges the size and shape of Aβ particles, can be used to track the aggregation process. Aβ is a highly flexible protein that can take on a multitude of conformations, according to its biophysical characterization. The conformational dynamics of Aβ may be investigated using biophysical methods such as circular dichroism (CD spectroscopy), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance spectroscopy (NMR spectroscopy) [5].

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Tests conducted in vitro and in vivo: In vitro tests, such as western blotting, enzyme-linked immunosorbent assays (ELISAs), and cell viability assays, can be used to determine the biological activity of Aβ. The effects of Aβ on brain function and behavior may also be studied in vivo using animal models of AD. Overall, characterizing Aβ is a difficult task that requires a variety of methods and procedures. However, the creation of efficient treatments for AD depends on a thorough understanding of the characteristics and behavior of this protein.

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Structure of Aβ and It’s Nucleation Aβ peptides come mainly in two different forms: Aβ40 and Aβ42. The most prevalent type is Aβ40, while Aβ42 is more pathogenic and more prone to aggregation. The most thoroughly investigated type, Aβ42, has been associated with the beginning and development of AD through its aggregation. Aβ peptides can be found in monomers, oligomers, or fibrils at various states of aggregation. While oligomers and fibrils are more toxic to neurons than monomeric Aβ peptides, which are thought to be relatively safe. Since oligomers can compromise the integrity of neuronal membranes, which results in neuronal dysfunction and eventual cell death, they are thought to be particularly harmful. There are several methods for identifying Aβ peptides at various levels of aggregation. For instance, thioflavin T (ThT) fluorescence may be used to identify fibril formation, whereas circular dichroism (CD) spectroscopy can be used to examine the secondary structure of Aβ peptides in solution. Aβ fibrils and oligomers can be seen using atomic force microscopy (AFM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), respectively [6]. The process by which individual Aβ molecules join to form tiny clusters, or nuclei, which can ultimately develop into bigger aggregates, is known as the nucleation of Aβ. The concentration of Aβ in the solution and the existence of other molecules that can interact complicate the process of Aβ nucleation. The development of a critical nucleus, the smallest group of Aβ molecules that may continue to expand into bigger aggregates, is one of the crucial processes in the nucleation of Aβ. The creation of a critical nucleus can be facilitated by the recruitment of additional Aβ molecules using the critical nucleus as a template which causes the formation of bigger and more durable aggregates. Hence, it may be crucial to comprehend how Aβ nucleation works to create new disease therapies. Alzheimer’s patients’ brains have been found to accumulate the protein amyloid beta (Aβ). The process of Aβ nucleation and aggregation is complex and poorly understood. However, it is

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Fig. 1 Nucleation pathways of amyloid beta

thought that different stages can be involved in the nucleation of Aβ. Small oligomers are formed during the initial step of Aβ nucleation, which is thought to be the toxic species that cause neuronal injury. Through a process known as nucleation-dependent polymerization, which involves the formation of a nucleus that can expand by incorporating more monomers, these oligomers can be formed from monomers [7] (Fig. 1). The production of bigger aggregates, such as protofibrils and fibrils, marks the second stage of Aβ nucleation. Secondary nucleation, which entails the creation of a nucleus on the surface of an existing aggregate, is a technique used to create these aggregates from oligomers [8]. As a result, a chain reaction may occur, resulting in the production of even more aggregates. The production of amyloid plaques, which are large insoluble clumps and a defining feature of AD, is the last step of Aβ nucleation. β-sheet-rich oligomers and fibrils that are formed when Aβ-peptides spontaneously combine can eventually deposit in the brain as amyloid plaques. Nucleation, elongation, and fibril maturation are three separate steps in the complicated process of Aβ aggregation. Fibril elongation and branching, which entails the addition of additional monomers to the ends of pre-existing fibrils and the development of new branches, is a mechanism by which these plaques can be formed from fibrils. Although the mechanism of secondary nucleation is not fully understood, it is thought to involve the oligomer and fibril forming a stable complex. The development of a new fibril follows a structural modification of this complex. Research is currently being done to determine the precise molecular specifics of this process. A potential therapeutic target for the therapy of AD is secondary nucleation. The development of new amyloid beta aggregates might be stopped, and the disease’s progression slowed with the use of secondary nucleation inhibitors. Due to the intricacy of the process and the incomplete understanding of the underlying molecular principles, the creation of such inhibitors is difficult.

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Fig. 2 Schematic representation of amyloid beta aggregation

Overall, the production of Aβ may start with monomers and progress via tiny oligomers, bigger aggregates, and eventually amyloid plaques (Fig. 2). Understanding Aβ nucleation and aggregation is essential for creating effective therapies for AD, while the precise mechanisms behind these processes are currently under investigation. It is thought that the amyloid beta peptide undergoes a conformational shift that causes hydrophobic areas that are typically hidden inside the peptide’s structure to become exposed. Then, because of interactions between these exposed hydrophobic regions, small aggregates or oligomers are created. After that, the oligomers continue to expand by incorporating new peptides, which eventually results in the development of more durable amyloid fibrils. The number of amyloid beta peptides, the existence of other molecules or ions that may affect the aggregation process, and the physical characteristics of the surrounding environment are all important elements in the complicated process of amyloid beta nucleation and fibril formation. Existing amyloid fibrils may function as templates for the growth of new fibrils. Small oligomers of amyloid beta attach to the surfaces of pre-existing fibrils in this route, causing conformational changes that cause the emergence of new fibrils. The development of amyloid beta aggregates in vivo is significantly influenced by this mechanism.

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The Role of Aβ in Neurodegeneration Neurodegeneration, or the gradual loss of structure or function of brain neurons, is known to be brought on by these Aβ plaques. According to theory, the amyloid beta buildup causes neurodegeneration by interfering with neurons’ ability to communicate, which in turn causes them to malfunction and eventually die. Cognitive decline, memory loss, and other symptoms of AD are therefore brought on by this. According to studies, the buildup of amyloid beta can also result in an inflammatory reaction in the brain, which aggravates the neurodegenerative process. It is essential to comprehend the underlying mechanics of this process to create effective therapies for these disorders. The gradual loss of a neuron’s structure or function is known as neurodegeneration; and it can cause death, mobility abnormalities, and cognitive decline. The buildup of amyloid beta (Aβ) proteins in the brain, which can result in the onset of AD, is one of the most frequently occurring causes of neurodegeneration. The bigger protein known as amyloid precursor protein (APP), which is present in the brain, is where Aβ proteins originate. Enzymes normally break down APP in a manner that does not result in the production of Aβ proteins. Aβ disruption in this mechanism causes Aβ proteins to build up in the brain in AD. Plaques, which are sticky protein aggregates that can interfere with normal cellular function, can develop neuronal death because of the buildup of Aβ proteins. In addition to causing inflammation and immune cell activation in the brain, plaques have the potential to further harm neurons and accelerate the process of dementia. Brain tissue decreases when neurons die, impairing cognition and motor function. Memory loss, problems speaking and communicating, and modifications in mood and behavior are all symptoms of AD. AD and other neurodegenerative conditions brought on by Aβ protein buildup are now incurable. Treatments, however, can help control symptoms and halt the spread of the illness. These include therapies that target the underlying causes of the disease, such as inflammation and oxidative stress, as well as drugs that target Aβ protein accumulation [9] (Fig. 3). A crucial nucleus of Aβ peptides occurs at the nucleation stage, and this nucleus can later be used as a template for the fibril’s further development. Since the nucleation process is thought to be the rate-limiting step in Aβ fibrillogenesis, it is essential to comprehend the variables that affect nucleation when creating new treatments to halt or slow the progression of AD. The creation of a compact, somewhat ordered nucleus that is rich in β-sheet structure is believed to be a key step in the production of the tertiary structure of Aβ during nucleation, albeit this is not yet fully understood. The length, sequence, and solution circumstances under which aggregation of the Aβ peptide takes place, as well as the precise shape of the nucleus, may all affect these factors.

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Fig. 3 Schematic of neurodegeneration by Aβ

Recent research has examined the early phases of Aβ aggregation using a mix of experimental methods, including nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics simulations. The structural alterations that take place during nucleation and the interactions between A peptides that encourage aggregation have been clarified by these investigations. It is believed that amyloid beta has a variety of neurotoxic consequences. The production of toxic oligomers is one of the main ways Aβ is thought to be harmful. These tiny collections of Aβ molecules can affect synaptic activation and normal cellular processes, which impairs neural transmission. Amyloid beta may also cause inflammation in the brain. Aβ may trigger immune cells called microglia, which release inflammatory chemicals when it builds up in the body. This inflammatory reaction may also accelerate neuronal dysfunction and damage. The propensity of Aβ to cause oxidative stress is another component of its neurotoxicity. Reactive oxygen species (ROS) produced by amyloid beta have the potential to harm cellular constituents such as proteins, lipids, and DNA. The cellular activity might be hampered by

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this oxidative damage and promote neuronal death. Aβ has also been shown to interfere with the calcium homeostasis of neurons. Normal neuronal signaling and operation require calcium. However, high intracellular calcium levels, which amyloid beta can cause, can result in mitochondrial malfunction, the activation of cell-damaging enzymes, and finally neuronal death [10]. Mitochondria are referred to as the cell’s powerhouses because they produce most of the cellular energy in the form of adenosine triphosphate (ATP) via oxidative phosphorylation. But in AD, mitochondrial activity is compromised, which results in less ATP generation. This energy shortage has an impact on neuronal activity and speeds up neurodegeneration. Reactive oxygen species (ROS) are mostly produced by mitochondria in cells. Amyloid beta peptides, particularly those found in the electron transport chain, can interact with mitochondrial elements in AD, increasing the generation of ROS. Excessive ROS can result in oxidative stress, which damages DNA, proteins, and lipids in cells and eventually leads to neuronal malfunction and death. To maintain calcium homeostasis in cells, mitochondria are required. Amyloid beta has been demonstrated to cause aberrant calcium signaling in neurons, and disturbed calcium regulation is seen in AD. Increased mitochondrial calcium uptake can cause mitochondrial malfunction and start the production of ROS. Fission and fusion activities occur continuously in healthy mitochondria to preserve their structural integrity and functional efficiency. Amyloid beta buildup in AD disturbs mitochondrial mechanics, causing an unbalanced ratio of fusion to fission. Excessive fission can lead to defective and fragmented mitochondria, reducing their ability to produce energy and aggravating brain damage. The inner mitochondrial membrane has a channel known as the mitochondrial permeability transition pore (mPTP). In AD, amyloid beta may cause the mPTP to open, releasing cytochrome c and other pro-apoptotic components into the cytoplasm. In AD, this stimulation of apoptotic pathways results in the death of neuronal cells. The deliberate eradication and recycling of damaged or malfunctioning mitochondria is known as mitophagy. The buildup of damaged mitochondria can exacerbate mitochondrial dysfunction and oxidative stress in AD due to poor mitophagy pathways. Overall, mitochondrial dysfunction, oxidative stress, and neuronal death are all caused by the aberrant interactions between amyloid beta and mitochondria in AD [11]. Overall, it is thought that the increasing neurodegeneration seen in AD is a result of amyloid beta’s neurotoxicity. It is essential to comprehend the processes underlying this toxicity to create new treatments that target amyloid beta and diminish its negative effects.

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Neuronal Cell Death Mechanisms The process leading to a cell’s demise is referred to as cell death. Cellular structures are disassembled throughout the process. By definition “Cell Death is irreversible decline in crucial cellular processes, including ATP synthesis and the maintenance of redox balance, which results in cellular integrity loss (permanent plasma membrane permeabilization or cellular fragmentation).” Understanding how cells die is crucial since it forms the basis of most pathological processes and plays a crucial role in the strategy for the regulation of healthy tissues. Cell death promotes removal of unwanted cells [12]. Cells that fail to die or die when they may trigger or worsen several disorders.

5.1 Cell Death in Neuronal Disease

Most studies suggested that there is little evidence linking amyloid plaques and clinical symptoms or neuronal death. Studies have shown that Aβ causes stress on neurons in a variety of ways, despite the limitations of the cell death pathology associated with Aβ at the molecular level. Defects in the pre- and post-synaptic membrane have been associated with Aβ-induced disease [13]. It has been suggested that neurons exposed to Aβ oligomers over extended periods of time can accumulate glutamate in synaptic clefts and stabilize NMDA receptors. It is therefore likely that Aβ oligomers can act similarly to Tau protein in promoting excitotoxicity and consequent neuronal death [14]. However, it is currently unclear whether synaptic abnormalities are primarily brought on by the expression of amyloid precursor protein (APP), Aβ monomers, specific Aβ plaques, or a combination of these factors. Furthermore, the precise mechanism underlying Aβ-mediated synaptic dysfunctions needs to be clarified. On the other hand, an in vitro investigation revealed that in short-term co-cultures of Aβ40 or Aβ42 with hippocampus neurons, the neuronal cell membrane flexibility can decrease by 30% and display evidence of the presence of aged neurons. These biomechanical alterations in the neurons may be linked to a variety of functional alterations, such as those in vesicle transport and ion-channel activity, making the neurons more vulnerable to neuronal cell death [15].

5.2 Classification of Cell Death Mechanisms

There are three main categories of morphologically distinct cell death: Apoptosis (Type I), Autophagy (Type II), and Necrosis (Type III). Apoptosis coined by Kerr et al. 1972, Apoptosis is the process through which a cell ceases developing and dividing and instead initiates a process that ultimately leads to the cell’s controlled death without releasing its contents into the environment [16]. The process of Autophagy involves the sequestration of cellular constituents, such as macro-proteins or even entire organelles, into lysosomes for breakdown. Once these substrates have been

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digested by the lysosomes, their parts can either be reused to build new organelles and/or cellular structures or they can be further processed and used as a source of energy [17]. Necrosis, in contrast to apoptosis, is a different uncontrollable type of cell death that is brought on by external harm, such as hypoxia or inflammation [18]. This process involves a rupture of the cell membrane, which releases the contents of the cell into the surrounding tissues, setting off a chain reaction of inflammation. Unlike Apoptosis, Necrosis is an energy-independent kind of cell death in which the cell experiences a sudden shock that renders it incapable of functioning due to extreme damage (radiation, heat, chemicals, hypoxia, etc.) [18]. Apoptosis Apoptosis in AD is driven by the accumulation of amyloid-beta peptides and other harmful proteins. Here are some details on how accumulation of Aβ triggers apoptosis: Aβ-induced oxidative stress: Reactive oxygen species (ROS) are produced because of the accumulation of Aβ and can damage cellular components like lipids, proteins, and DNA through oxidative stress. By triggering pro-apoptotic signaling pathways, this oxidative stress may cause the death of neurons [19]. Mitochondrial dysfunction: Accumulation of Aβ can disrupt mitochondrial function, causing the release of cytochrome c and other pro-apoptotic elements into the cytoplasm. The caspases protein family, which is crucial in apoptosis, can then be activated because of these stimuli [20]. Activation of pro-Apoptotic signaling pathways: Aβ accumulation can activate several pro-apoptotic signaling pathways, including the death receptor pathway, the p38 MAPK (mitogen-activated protein kinase) system, and the JNK (c-Jun N-terminal kinase) route. Caspases and other pro-apoptotic proteins may be activated by these pathways, resulting in neuronal cell death [21]. Disruption of calcium homeostasis: Calcium homeostasis in neurons can be altered by Aβ accumulation, which increases intracellular calcium levels excessively high. This may lead to the activation of pro-apoptotic proteins. In conclusion, the buildup of Aβ protein in the brain can set off a complex cascade of occurrences that ultimately result in neuronal death in AD. Necrosis Aβ has a variety of pathways via which it might induce necrosis. One way is to interfere with mitochondria’s ability to produce energy, which is a function of these cellular organelles. Aβ may interact with mitochondrial membranes, causing pores to develop that interfere with the electrochemical gradient across the membrane. This may cause the release of the protein cytochrome C, which in turn initiates a series of enzymes that ultimately cause neuronal cell death. Aβ may also cause necrosis by triggering inflammatory reactions in the brain. Pro-inflammatory cytokines may be induced by

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Aβ, and these cytokines may then attract immune cells to the location of Aβ deposition. These immune cells have the capacity to emit toxins that can damage neighboring neurons and harm nearby neurons. Aβ may also cause necrosis by elevating oxidative stress. Reactive oxygen species (ROS) are extremely reactive molecules that can degrade biological components like DNA, proteins, and lipids. Aβ can produce these ROS. ROS can build up over time and exceed a cell’s antioxidant defenses, causing oxidative damage and ultimately cell death. Autophagy Aβ cellular process called autophagy is in command of disposing of malfunctioning or damaged parts of cells, including misfolded proteins like amyloid beta (Aβ). For cellular health to be preserved and to stop the buildup of harmful proteins like Aβ, autophagy is crucial. Moreover, the buildup of Aβ can activate the unfolded protein response (UPR), a cellular stress response mechanism designed to reestablish protein homeostasis. Yet, ongoing UPR activation may result in dysregulated autophagy, which may further increase Aβ accumulation [22]. Pyroptosis Pyroptosis is a form of pro-inflammatory response-driven programmed cell death that is brought on by inflammasome activation. As amyloid beta (Aβ) can activate the inflammasome and cause pyroptotic cell death in neurons, pyroptosis has been linked to Alzheimer’s disease [23]. Aβ is one of many stimuli that can activate a protein complex called the inflammasome. Inflammasome activation stimulates caspase-1 activation, which cleaves pro-inflammatory cytokines like interleukin-1 (IL-1) and interleukin-18 (IL-18). These cytokines can generate pro-inflammatory reactions, which can lead to pyroptotic cell death. Both in vitro and in vivo models of Alzheimer’s disease have shown evidence of pyroptosis, and it has been demonstrated that inhibiting it can lower neuronal cell death and enhance cognitive performance [24] (Fig. 4). Excito-cytotoxicity Excessive stimulation of neurons by excitatory neurotransmitters, notably glutamate, results in excitotoxicity, a kind of neuronal cell death. Amyloid beta (Aβ) buildup in the brain, which can impair typical synaptic function and increase glutamate release, can lead to excitotoxicity in AD [25]. Aβ may influence synaptic plasticity and neurotransmitter release by interfering with the function of pre-synaptic and post-synaptic neurons. The effects of Aβ have been particularly demonstrated to increase glutamate release from pre-synaptic neurons and decrease the expression of glutamate transporters, which oversee clearing extra glutamate from the synaptic cleft [26]. The N-methyl-D-aspartate (NMDA) receptor, for

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Fig. 4 Schematic representation of cell death mechanism in Alzheimer’s disease

instance, can be activated by excessive glutamate, which causes an influx of calcium ions into the cell. This can trigger a cascade of events that eventually leads to the death of neuronal cells, including the activation of enzymes that break down cellular components and the production of reactive oxygen species, which can harm DNA and cellular membranes. Excitotoxicity has been linked to the neurodegeneration found in Alzheimer’s disease, especially in the areas of the brain responsible for learning and memory, such as the hippocampus. Excitotoxicity can contribute to the loss of synapses and neuronal death, impairing cognition and memory [27].

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Strategies for Neuroprotection in AD When it comes to AD, neuroprotection refers to the employment of remedies or measures intended to safeguard brain tissue and stop or delay the disease’s progression. The accumulation of betaamyloid plaques and neurofibrillary tangles in the brain, which result in the loss of brain cells, is the fundamental cause of AD. For neuroprotection in AD, both pharmaceutical and non-pharmacological therapies have been studied [28]. Some strategies are as follows.

6.1 Anti-Aβ Therapies

The accumulation of beta-amyloid in the brain is the focus of numerous medications. These medications include monoclonal antibodies that attach to and remove beta-amyloid as well as medications that block the enzymes responsible for producing beta-

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amyloid [29]. Many approaches have been used for anti-amyloid therapy: Monoclonal antibody: Monoclonal antibodies are manufactured in laboratories and target proteins in the body, such as beta-amyloid. Solanezumab, Acuranumab, and Gantenerumab are a few monoclonal antibodies that have been created for the treatment of AD. These medications attach to beta-amyloid and eliminate it from the brain, possibly reducing the disease’s progression [30]. β-secretase inhibitor: An enzyme called beta-secretase contributes to the creation of beta-amyloid. As prospective antiamyloid treatments, beta-secretase inhibitors like verubecestat and lanabecestat have been developed [31]. Gamma secretase inhibitor: Another enzyme responsible for the formation of beta-amyloid is gamma-secretase. Semagacestat and other gamma-secretase inhibitors have been studied as potential anti-amyloid treatments [32]. Anti-aggregation agent: It is hypothesized that beta-amyloid clumps and aggregates in the brain contribute to the development of AD. Potential anti-amyloid treatments include medications like tramiprosate and scyllo-inositol that prevent or interfere with the production of these aggregates [33]. The effectiveness of these medications has been demonstrated in clinical trials in a variety of ways, with some showing considerable increases in cognitive function and decreases in beta-amyloid levels, while others have failed to show any appreciable improvements. Further study is required to completely understand the safety and efficacy of these medications because they may also have side effects. However, for the development of neuroprotective AD treatments, anti-amyloid medicines continue to be a crucial area of research [34]. 6.2 Antioxidant Therapies

An increasing amount of research indicates that oxidative stress may have an impact on brain tissues as AD progresses in individuals. AD development is closely linked to oxidative stress or damage caused by processes, including protein oxidation, lipid oxidation, DNA oxidation, and glycoxidation. Age-related neurodegeneration and cognitive decline are thought to be significantly influenced by oxidative stress, which is generally characterized by an imbalance in the production of reactive oxygen species (ROS) and the antioxidative defense system that removes ROS. Antioxidant treatment has been investigated for years as one of the possible therapeutic approaches for AD. According to studies, antioxidants like lipoic acid (also known as thioctic acid), vitamin E, vitamin C, and alphacarotene may aid in the breakdown of intracellular and extracellular superoxide radicals and H2O2-cell-damaging compounds that are waste products of normally functioning cells before these radicals’ damage cells or activate microglia through their action as intracellular second messengers. Several medications that have antioxidant characteristics have been investigated as potential neuroprotective remedies [35].

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6.3 Antiinflammatory Therapy

Although the association between AD and neuroinflammation was found more than 30 years ago, it is still unclear whether the inflammation is a cause or symptom of the illness. Numerous neurodegenerative diseases, including AD, have been linked to chronically excessive pro-inflammatory responses. Numerous sources of research show that neuroinflammation plays a role in the etiology of AD despite that inflammatory marker detection has not yet been proven to be a useful method for AD diagnosis or monitoring. According to several epidemiological studies, those who have taken non-steroidal anti-inflammatory medicines (NSAIDs) can avoid developing AD. Several anti-inflammatory medications NSAIDs, such as meclofenamate, diclofenac, flurbiprofen, ibuprofen, indomethacin, piroxicam, and flurbiprofen, have been researched as potential neuroprotective remedies. More research is required to determine a better course of treatment for preventing the progression of the disease, including persistent neuroinflammation, at the most appropriate time [36].

6.4

Around 5 million people worldwide every year die from noncommunicable diseases because of physical inactivity. A physically active lifestyle has been linked to a lower risk of cognitive deterioration in numerous studies. Both forced and voluntary exercise therapies have been shown to reduce Ab plaques and NFTs in transgenic mice used as AD models. Improvements in memory and learning have sometimes been observed in conjunction with these findings. Regular physical activity has been proven to be beneficial for lowering inflammatory markers like C-reactive protein, IL-6, and TNF-a, especially in elderly individuals [37].

Exercise

6.5 Cognitive Training

Cognitive training is a non-pharmacological intervention strategy that seeks to improve cognitive capacities in people with AD, such as memory, focus, and problem-solving skills. It entails performing structured mental workouts and activities that focus on cognitive areas to preserve or improve cognitive abilities. Maintaining cognitive function and reducing cognitive decline in people with AD is the major objective of cognitive training. To make up for cognitive impairments, it focuses on activating brain networks and encouraging neuroplasticity. Depending on the program and the individual, cognitive training’s frequency and duration can change. According to research, cognitive training can help people with AD make small improvements in their daily functioning and cognitive health. It is crucial to remember that cognitive training should be a component of an all-encompassing AD treatment plan and should be overseen by medical specialists with expertise in dementia care. It is important to keep in mind that there is no known cure for AD currently, and many of the above-mentioned treatments are still in the research and development phase. To effectively reduce the disease’s progression and enhance the quality of life for persons with AD, early intervention and treatment may be helpful.

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Conclusions The process of neuronal cell death, its effects on neurological disease, and methods of prevention are all covered in this book chapter. Understanding cell death is essential since it serves as the foundation for most pathological processes and is key to the technique used to regulate healthy tissues. Apoptosis, autophagy, and necrosis are the three basic types of morphologically different cell death. Necrosis is an uncontrollable type of cell death, whereas apoptosis is a controlled type. Apoptosis is brought on by the buildup of amyloid-beta peptides and other damaging proteins in AD. A protein called Aβ protein buildup in the brain can cause a complex chain of events that ultimately leads to neuronal death. Aβ might potentially result in necrosis by causing inflammatory responses in the brain. The chapter emphasizes the need of understanding how to prevent neuronal cell death to create plans for controlling healthy tissues and preventing neurodegenerative diseases.

References 1. Barage SH, Sonawane KD (2015) Amyloid cascade hypothesis: pathogenesis and therapeutic strategies in Alzheimer’s disease. Neuropeptides 52:1–18 2. Riek R (2017) The three-dimensional structures of amyloids. Cold Spring Harb Perspect Biol 9:a023572 3. Chen GF, Xu TH, Yan Y, Zhou YR, Jiang Y, Melcher K, Xu HE (2017) Amyloid beta: structure, biology and structure-based therapeutic development. Acta Pharmacol Sin 38:1205– 1235 4. Chatani E, Yamamoto N (2018) Recent progress on understanding the mechanisms of amyloid nucleation. Biophys Rev 10:527–534 5. Ghosh P, Roy H, Mukhopadhyay S, Mondal S, Ghosh SS, Iyer PK (2023) Analytical techniques in identifying and purifying the proteins. In: Joshi S, Kar RK, Lahiri D, Nag M (eds) Lantibiotics as alternative therapeutics. Elsevier, San Diego, pp 473–489 6. Mondal S, Kumar V, Roy Chowdhury S, Shah M, Gaur A, Kumar S, Iyer PK (2019) Template-mediated detoxification of lowmolecular-weight amyloid oligomers and regulation of their nucleation pathway. ACS Appl Bio Mater 2:5306–5312 7. Barz B, Liao Q, Strodel B (2018) Pathways of amyloid-β aggregation depend on oligomer shape. J Am Chem Soc 140:319–327

8. Srivastava AK, Pittman JM, Zerweck J, Venkata BS, Moore PC, Sachleben JR, Meredith SC (2019) β-Amyloid aggregation and heterogeneous nucleation. Protein Sci 28:1567–1581 9. Ramesh M, Govindaraju T (2022) Multipronged diagnostic and therapeutic strategies for Alzheimer’s disease. Chem Sci 13:13657– 13689 10. Savelieff MG, DeToma AS, Derrick JS, Lim MH (2014) The ongoing search for small molecules to study metal-associated amyloid-β species in Alzheimer’s disease. Acc Chem Res 47:2475–2482 11. Mondal S, Vashi Y, Ghosh P, Roy D, Barthakur M, Kumar S, Iyer PK (2020) Amyloid targeting “artificial chaperone” impairs oligomer mediated neuronal damage and mitochondrial dysfunction associated with Alzheimer’s disease. ACS Chem Neurosci 11:3277– 3287 12. Moujalled D, Strasser A, Liddell JR (2021) Molecular mechanisms of cell death in neurological diseases. Cell Death Differ 28:2029– 2044 13. Wyllie AH (1987) Cell death in cytology and cell physiology. In: Cell death in cytology and cell physiology, pp 755–785 14. Marsh J, Alifragis P (2018) Synaptic dysfunction in Alzheimer’s disease: the effects of amyloid beta on synaptic vesicle dynamics as a novel

354

Priyam Ghosh et al.

target for therapeutic intervention. Neural Regen Res 13:616–623 15. Ungureanu A-A, Benilova I, Krylychkina O, Braeken D, De Strooper B, Van Haesendonck C, Dotti CG, Bartic C (2016) Amyloid beta oligomers induce neuronal elasticity changes in age-dependent manner: a force spectroscopy study on living hippocampal neurons. Sci Rep 6:25841 16. Kerr JF, Wyllie AH, Currie AR (1972) Apoptosis: a basic biological phenomenon with wideranging implications in tissue kinetics. Br J Cancer 26:239–257 17. Shintani T, Klionsky DJ (2004) Autophagy in health and disease: a double-edged sword. Science 306:990–995 18. Elmore S (2007) Apoptosis: a review of programmed cell death. Toxicol Pathol 35:495– 516 19. Kadowaki H, Nishitoh H, Urano F, Sadamitsu C, Matsuzawa A, Takeda K, Masutani H, Yodoi J, Urano Y, Nagano T, Ichijo H (2005) Amyloid beta induces neuronal cell death through ROS-mediated ASK1 activation. Cell Death Differ 12:19–24 20. Moreira PI, Carvalho C, Zhu X, Smith MA, Perry G (2010) Mitochondrial dysfunction is a trigger of Alzheimer’s disease pathophysiology. Biochim Biophys Acta 1802:2–10 21. Redza-Dutordoir M, Averill-Bates DA (2016) Activation of apoptosis signalling pathways by reactive oxygen species. Biochim Biophys Acta 1863:2977–2992 22. Zhang Z, Yang X, Song Y-Q, Tu J (2021) Autophagy in Alzheimer’s disease pathogenesis: therapeutic potential and future perspectives. Ageing Res Rev 72:101464 23. Huang Y (2022) Pyroptosis as a candidate therapeutic target for Alzheimer’s disease. Front Aging Neurosci 4:996646 24. Tan M-S, Tan L, Jiang T, Zhu X-C, Wang H-F, Jia C-D, Yu J-T (2014) Amyloid-β induces NLRP1-dependent neuronal pyroptosis in models of Alzheimer’s disease. Cell Death Dis 5:e1382 25. Esposito Z, Belli L, Toniolo S, Sancesario G, Bianconi C, Martorana A (2013) Amyloid β, glutamate, excitotoxicity in Alzheimer’s disease: are we on the right track? CNS Neurosci Ther 19:549–555

26. Wang R, Reddy PH (2017) Role of glutamate and NMDA receptors in Alzheimer’s disease. J Alzheimers Dis 57:1041–1048 27. Liu J, Chang L, Song Y, Li H, Wu Y (2019) The role of NMDA receptors in Alzheimer’s disease. Front Neurosci 13:43 28. Niikura T, Tajima H, Kita Y (2006) Neuronal cell death in Alzheimer’s disease and a neuroprotective factor, humanin. Curr Neuropharmacol 4:139–147 29. Karran E, Hardy J (2014) Antiamyloid therapy for Alzheimer’s disease--are we on the right road? N Engl J Med 370:377–378 30. Shi M, Chu F, Zhu F, Zhu J (2022) Impact of anti-amyloid-β monoclonal antibodies on the pathology and clinical profile of Alzheimer’s disease: a focus on aducanumab and lecanemab. Front Aging Neurosci 14:870517 31. Yan R, Vassar R (2014) Targeting the β secretase BACE1 for Alzheimer’s disease therapy. Lancet Neurol 13:319–329 32. Hur J-Y (2022) Γ-secretase in Alzheimer’s disease. Exp Mol Med 54:433–446 33. Pasieka A, Panek D, Szałaj N, Espargaro´ A, Wie˛ckowska A, Malawska B, Sabate´ R, Bajda M (2021) Dual inhibitors of amyloid-β and tau aggregation with amyloid-β disaggregating properties: extended in cellulo, in silico, and kinetic studies of multifunctional anti-Alzheimer’s agents. ACS Chem Neurosci 12:2057– 2068 34. Huang L-K, Chao S-P, Hu C-J (2020) Clinical trials of new drugs for Alzheimer disease. J Biomed Sci 27:18 35. Collins AE, Saleh TM, Kalisch BE (2022) Naturally occurring antioxidant therapy in Alzheimer’s disease. Antioxidants (Basel) 11:213 36. Rivers-Auty J, Mather AE, Peters R, Lawrence CB, Brough D (2020) Anti-inflammatories in Alzheimer’s disease-potential therapy or spurious correlate? Brain Commun 2:fcaa109 37. De la Rosa A, Olaso-Gonzalez G, Arc-Chagnaud C, Millan F, Salvador-Pascual A, Garcı´a-Lucerga C, Blasco-Lafarga C, GarciaDominguez E, Carretero A, Correas AG, ˜a J, Gomez-Cabrera MC (2020) Physical Vin exercise in the prevention and treatment of Alzheimer’s disease. J Sport Health Sci 9: 394–404

Chapter 26 Amyloid Beta–Mediated Neurovascular Toxicity in Alzheimer’s Disease Sayani Banerjee and Sugato Banerjee Abstract The brain vascular system receives one-fifth of the total oxygen from the cardiac output, and this transport system is highly dependent on blood-brain barrier (BBB) integrity. The cerebral blood flow is controlled by neurovascular coupling through neurovascular units (NVUs). The NVU includes different types of cells, such as mural cells, astrocytes, pericytes, endothelial cells (ECs), and vascular smooth muscle cells (VSMCs). The cellular composition of NVU varies throughout the vascular tree. Amyloid β (Aβ) is abundantly present in the central nervous system, but the pathological accumulation of misfolded Aβ protein causes vascular damage, resulting in neurovascular dysfunction. Aβ aggregation can activate the astrocytes and endothelial cells. It is followed by pericyte degeneration which results in dysregulation of cerebral blood flow (CBF), neurovascular uncoupling, and BBB breakdown. Thus, understanding the cellular and molecular mechanisms of Aβ-induced neurovascular toxicity is crucial for determining normal and diseased brain function. This chapter discusses the components of NVU, neurovascular uncoupling, Aβ-induced cerebrovascular reactivity, and cerebral blood flow reduction in neurodegenerative disorders, with special emphasis on Alzheimer’s disease. Key words Alzheimer’s disease, Amyloid β (Aβ), Blood-brain barrier (BBB), Vascular smooth muscle cells (VSMCs), Neurovascular units (NVUs), Neurodegeneration

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Introduction The human brain has 86 billion neurons. These high-functioning cells need an adequate blood supply to achieve their required nutrient demand. Proper blood supply throughout the brain is one of the most vital requirements, and a well-regulated and elaborate vascular network adequately maintains that function. Components of the vascular network are mainly arteries, arterioles, capillaries, venules, and veins, which create a complex network throughout the brain atlas. A countless number of cells are elaborately involved in the regulation of cerebral blood flow and BBB integrity. A collection of cells directly regulating cerebral blood flow is called a neurovascular unit (NVU). NVU mainly has endothelial

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_26, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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cells that form the vascular wall and mural cells that control the vascular tone. The cellular composition of NVU changes throughout the vascular tree. Smooth muscle cells (SMCs) act like a rubber band around the arteries and arterioles which controls the cerebral blood flow (CBF). While pericyte wraps around the capillary and “stellate” smooth muscle cells [1]. Many neurodegenerative disorders like Alzheimer’s disease are due to microvascular dysfunction. The blood-brain barrier dysfunction is mainly hypothesized as a two-hit hypothesis theory; this two-hit hypothesis connects the bi-directional relationship between blood-brain barrier dysfunction and amyloid beta production [2]. Amyloid beta (Aβ) is processed by the enzymatic cleavage of amyloid precursor protein APP by β and γ secretase, respectively, and produces amyloid beta fragment. The misfolded protein accumulation causes cascades of downstream effects and microvascular dysfunction. Aβ production and assembly directly induce massive reactive oxygen species (ROS) production and immune response activation. This further leads to functional hyperemia, vascular inflammation, and endothelial dysfunction. The impaired microvascular function causes poor cerebral blood flow, which could be lethal to the neurons. CBF dysregulation affects the primary nutrient supply along with oxygen transport. Aβ clearance also gets affected by impaired CBF, which causes further toxicity in neurons [3]. In the later stage of AD, the accumulation of Aβ is reportedly found inside the vascular artery and causes microbleeds. Aβ accumulation in the brain eventually disrupts the integrity of tight junction proteins and initiates several pathways, which leads to vasoconstriction and damage to the cerebral endothelial cell. Advanced technology in imaging and detailed knowledge of molecular pathways is needed to advance this particular field of research in cerebrovascular pathology and dysfunction. In this chapter, we critically review various important factors related to BBB function, disruption, and dysfunction of NVU related to Aβ.

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Blood-Brain Barrier Neurons are high energy-demanding cells and have an essential need for adequate blood supply. A well-organized vascular network of arteries, arterioles, capillaries, and veins shares a fair role in the blood supply from heart to brain. Various cell types regulate cerebral blood flow by maintaining blood-brain barrier integrity. Endothelial cells, pericytes, mural cells, and vascular smooth muscles are collectively called neurovascular units (NVUs) [4]. Each portion of the vascular tree differs depending on the location and cell types. The microvasculature systems are arranged with post-capillary and capillary venules and have different properties to perform their

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specific functions. There are three main structural classes in capillaries in the physiological system, which are mainly based on the diverse adaptability of solute movements. The continuous non fenestrated capillaries are present in the skin and lungs; there is lack of fenestra in the plasma and basement membranes. One of the unique microvasculature systems of the central nervous system is blood-brain barrier (BBB). CNS vessels are non-fenestrated vessels designed to regulate the movements of molecules, ions, and cells between blood and CNS [5, 6]. This regulated circulation is essential for retaining CNS homeostasis by performing the normal neuronal functions while protecting the CNS from pathogens, toxins, injury, inflammation, and diseases. Partial or complete loss of these barrier properties during various neurological disorders like stroke, brain trauma, and Alzheimer’s disease is considered one of the major factors of disease progression and pathophysiology. The BBB dysfunction directly leads to altered homeostasis of signals, ionic dysregulation, and impaired cell transport, which further become a causative factor of neuronal dysfunction and degeneration [7]. 2.1 Components of BBB

Two main cell types are the main components of blood vessels: endothelial cells and mural cells. ECs mainly form the blood vessel walls, whereas the mural cells are designed to sit on the outer face of the EC layer. The BBB properties primarily depend on the ECs, and their critical interaction with the mural cells, immune cells, and glial and neuronal cells. BBB also has a tight regulating system for restricted entry to the CNS. The BBB permeability is a function of TJ proteins specific transporters and transcytosis across the barrier (Fig. 1) [8]. Endothelial cells: Endothelial cells are altered simple squamous epithelial cells which are derived from mesoderm. Dozens of ECs are involved in constructing arteries and veins with larger diameters. Along with this, the smallest capillaries are formed with a single folding pattern to itself. These further form the lumen of the vessels. Cerebral endothelial cells have distinct properties which are different from the other endothelial cells in the body. Cerebral endothelial cells are considered one of the tightest barriers in our body, which has low paracellular permeability and high endothelial electrical resistance. These special properties are needed for the restricted exchange of molecules and ions between blood and brain. Cerebral endothelial cells are connected with (TJs), and this formation limits the para cellular solute flux. Luminal and abluminal membrane compartments contain polarized cells present as paracellular and transcellular barriers to control the tight regulation of cellular transport [9]. Two main categories of transporters in CNS are efflux transporter and specific nutrient transporters. The efflux transporters transport lipophilic molecules across the cell membrane into systemic circulation, whereas the nutrient

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Fig. 1 Components of blood-brain barrier (BBB). Endothelial cells are connected with tight junctions and are surrounded by a basement membrane, pericytes, and astrocytes foot process. Pericytes are mainly localized at the capillaries

transporter helps the nutrients in crossing the BBB into the CNS and plays a vital role in waste removal from CNS [10]. Pericyte: Pericytes are located around the abluminal surface of the microvascular endothelial tube and surround the vascular basement membrane [11]. Pericytes construct long cellular processes from the surface of the endothelium to the EC bodies. These cells process contractile proteins and control the diameter of the capillary [12, 13]. Anatomically, these cells are not directly attached to the endothelium but are separated by the basement membrane and are embedded within it. PCs in CNS express different properties compared to other PCs in other tissues. CNS PCs originate from the neural crest, whereas peripheral PCs are derived from mesoderm. Pericyte-endothelial adhesions are present at the cellular level, including gap junctions, adhesion plaques, and tight junctions [14]. One of the most crucial roles of pericytes is regulating the vascular diameter and controlling the cerebral blood flow. It also governs angiogenesis, wound healing, and immune cell infiltration regulation, influencing stem cell activity [10]. Pericytes are essential in BBB development and maintenance [15]. Due to the availability of specific markers of PCs, it is often misrecognized with other perivascular cells. The most widely established molecular markers of CNS pericytes are PDGFR-B and NG2. Other markers like Anpep (CD13), desmin, Rgs5, Abcc9, Kcnj8, Dlk, and Zic1 are also used to identify them [16]. Basement membranes: There are two types of basement membranes (BM): inner vascular BM and outer parenchymal BM. The vascular BM is an extracellular matrix formed by ECs and PCs whereas parenchymal BM is made up of astrocytes and lengthens

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to the vasculature [17]. The BM is an anchor for various signaling molecules at the vascular level. It also acts as an additional barrier for cells and molecules from reaching the neurons. The matrix metalloproteinase disrupts the BMs, which is an important factor in BBB breakdown and causes leukocyte infiltration in many neurological disorders [8]. Astrocytes: Astrocytes are considered as a major glial cell type playing a major role in the neuronal and the vascular system [18]. The basal process of the end-feet covers the vascular tube and contains a family of proteins like dystroglycan, dystrophin, and aquaporin 4. The linkage between the end-feet cytoskeleton and the BM is established by the dystroglycan-dystrophin complex, which is further bound by arain [19]. This aquaporin 4 assembled into an orthogonal array is crucial for maintaining the water homeostasis in CNS. The primary function of astrocytes is to provide a cellular link between blood vessels and neurons. Neurovascular coupling is a process through which astrocytes transmit signals for regulating blood flow as a response to neuronal function. This process includes the control of vascular diameters, diameters of arterioles as well as capillaries surrounded by PCs. Apart from this, astrocytes also contribute to BBB formation and function [20]. Tight junction: CNS and ECs are packed together by tight junctions (TJs), which create a highly selective paracellular sieve for molecules and ions. Most of the tight junction works on ECs. The cellular adhesions are found at the apical part of the lateral membrane by heterotypic and homotypic interactions with the cytoskeleton-linked transmembrane molecules and cytoplasmic adaptors. The junction strength varies depending upon the location of the tissue. Cell culture studies also found that they have sizeselective permeability for up to 4 nm for uncharged molecules whereas lower permeability to larger molecules [21]. The transmembrane proteins which cause pore formation are claudins, occludins, and JAMS. These proteins also maintain the barrier properties of BBB [22]. Claudins are tetra spanins characterized by a W-GLW-C-C domain in their first extracellular loop [23]. Claudins mainly construct the Tj strands in fibroblast, and the disruption of this eventually decreases the paracellular barrier properties. Amino acid residues in the first extracellular loop of claudins define the pore size and charge selectivity Thus, the claudin composition within a specific cell determines the permeability of the para cellular barrier. Mouse knock-out model shows that specific claudin (cldn) family members are important for different epithelial barriers in various tissues like cldn 1 in epidermal barrier, cldn 16 in kidney epithelia, cldn11 in CNS myelin, and cldn 19 in peripheral myelin [24]. Cldn-5 is reported to be expressed in CNS, and Cldn-5 knock-out mice showed the size-selective leaky BBB [25]. On the other hand, occludin, which is a tetra protein, plays a vital role in barrier resistance. This protein is highly expressed in CNS

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compared to any other peripheral tissue. Occludin-deficient mice show higher calcification in CNS, suggesting that occludin may be related to calcium transport. Another protein JAMs plays a vital role in BBB function and stability. JAMs belong to the immunoglobulin superfamily, which is involved in homotypic interactions to form tight junctions in ECs. JAM is involved in leukocyte extravasation and maintenance of paracellular permeability [26].

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Molecular Pathways for Maintaining BBB Integrity BBB functions primarily depend on the tight junction proteins and further their adhesion capabilities along with the appropriate transendothelial transcytosis. The gap junctions between endothelial cells, pericytes, and astrocytes maintain cerebrovascular integrity. Pericyte survival depends on the Notch 3 receptor activation and PDGF-BB. The Notch receptors present in endothelial cells (Notch 1) get activated by pericyte-derived Notch ligands such as delta-like ligands (DLL) or jagged ligands (JAG), which in turn cleaves the Notch receptor at and transcriptionally activate intracellular domain of Notch which acts as a key activator for Notch pathway. In endothelial cells, shear stress causes the activation of Notch 1, which further results in the Rac1-induced cortical actin assembly at vascular endothelial cells. Cadherin at the cell-cell junction regulates important functions to reduce vascular permeability [27]. Studies showed that the loss of Notch3 (present in mural cells) causes loss of vascular smooth muscle coverage on the large vessels and arterioles. Notch3 phenotypes reportedly affect small vessel where the pericytes are primarily localized [28]. In addition to homotopic cell-cell interaction, endothelial cells also interact with mural cells in heterotopic cell-cell interaction. Studies have shown that several molecular signaling pathways are involved in this cell-to-cell heterotopic interaction, such as Ang1/Tie2 and PDGFRβ. The platelet-derived growth factor-BB binds to PDGFRβ, which causes pericyte survival, migration, and proliferation [29]. The ligands of the (PDGF) family are categorized into four subunits (A–D), and the receptors are specifically two types, α and β. The PDGF receptor is a receptor tyrosine kinase that has three active conformations αα, ββ, and αβ. The PDGF ligands bind with PDGFRs differentially. The ligand of PDGFRαα is PDGF-AA and so on. In vitro studies showed that the retention motif is responsible for precise spatial and temporal regulation of PDGFBB signaling. A positively charged amino acid at the C terminus binds with the extracellular matrix’s negatively charged heparin sulfate proteoglycan motif. The binding of PDGF-BB to PDGFRβ results in non-covalent dimerization and receptor autophosphorylation of 13 cytoplasmic tyrosine residues that further activate PDGFRβ. RasGAP further binds to the phosphorylated PDGFRβ,

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which causes Grb2 binding along with the son of sevenless homolog 1 (SOS1) complex followed by Ras/ERK2pathway activation [30]. In vitro studies in human pericyte and endothelial cell cocultures showed that pericytes and endothelial cells secrete VEGF-A and activate the VEGF receptor-2 (VEGFR2) pathway, which increases X-linked inhibitor of pre-apoptotic and Bcl-2 expression. Upregulating VEGF-A pericyte secretes vitronectin, in a PDGFBB– PDGFRβ-dependent manner for activating integrin αV–NFκB signaling. The primary function of this VEGF-A–VEGFR2 signaling was to promote angiogenesis, cell survival, and vascular permeability [31]. Astrocytes release APOE2 and APOE3, which bind at pericytic lipoprotein LRP1 receptor to further inhibit the downstream CypA–NFκB–MMP-9 pathway. APOE4 feebly binds to LRP1, thus activating CypA–NFκB–MMP-9-mediated proinflammatory cascades, leading to the BBB breakdown [32]. Neuronal factors such as glutamate increase astrocytic Ca2+ levels, which initiates phospholipase A2 (PLA2)-dependent generation of arachidonic acid (AA). In astrocyte cyclooxygenase-1 (Cox1) and cytochrome (Cyt), P450 metabolizes AA into PGE2 and epoxyeicosatrienoic acids (EET), respectively. This AA metabolite causes pericyte contraction [33]. PGE2 secreted from astrocytes binds to EP4 receptor on pericyte, causing altered conductance of K+ which further causes pericytic relaxation. The nitric oxide synthase (nNOS) from neurons causes NO generation and inhibits Cyt P450 in astrocytes. Increased cAMP signals can also prevent pericytic contraction by inhibiting the phosphorylation of the myosin light chain (Fig. 2) [34].

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Neurovascular Unit and Transport through BBB The neurovascular unit contains vascular cells like endothelial cells, pericytes, vascular smooth muscle cells, glial cells, and neurons [35]. A highly specialized membrane of endothelial cells forms around the blood vessels. This membrane acts as a sieve to limit the entry of red blood cells, plasma components, and leukocytes into the brain. For proper neuronal and synaptic function, BBB plays a vital role in regulating metabolites, nutrients, and energy circulation. Neurons and non-neuronal cells act together to control the BBB permeability along with CBF. Glia and vascular cells are primarily responsible for preserving the chemical homeostasis of the blood-spinal cord barrier (BSCB) and ISF. The endothelial cells, which are the components of the BBB, are linked by adherent and tight junctions. These tight junctions control the paracellular permeability of the blood-brain barrier. Across the endothelial cells, within the BBB, the smaller lipophilic molecules, such as carbon dioxide and oxygen, diffuse freely. The fenestrae are absent in normal brain endothelium, and the vesicular transport is limited

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Fig. 2 Molecular pathways of BBB stability. The adherent junctions (AJ) and tight junction (TJ) proteins help maintain BBB’s integrity. The Notch3 receptor signaling regulates the survival of pericytes. The plateletderived growth factor-BB binds on PDGFRβ and helps in the survival of pericytes, their proliferation, and migration. The signaling of TGFβR2 receptor and the transforming growth factor (TGF-β) occurs between endothelial cells and pericytes in a bidirectional manner. Pericyte-secrete angiopoietin-1 (Angpt1) binds to receptors of Tie2 on the endothelium cells to increase its proliferation. APOE2 and APOE3 secreted by astrocytes, as opposed to APOE4, downregulate the proinflammatory signaling of Cyclophilin (Cyp A) NFkB-matrix metalloproteinase-9 (MMP9) pathway within the pericytes for maintaining the BBB integrity. Similarly, laminin, which is produced by the astrocytes, maintains the stability of BBB. The interaction of astrocyte-secreted sonic hedgehog (Shh) with patched-1 (PTCH1) is further helpful in promoting the BBB integrity at the endothelium. BBB stability on the endothelium is also mediated by ephrin B2 (EphB2). The binding of PDGF-BB increases the survival and migration of SMCs with PDGFRβ. The SMC maturation and survival is also maintained by binding endothelial-secreted jagged-1 (Jag-1) to Notch3

[4]. Endothelial cells have many mitochondria, indicating the high energy demand. The reason behind this high energy demand is its highly selective transport mechanisms like ATP-dependent transport and ATP-binding cassette (ABC) efflux transporters. Na+/K+ ATPase regulates sodium influx and potassium efflux through the abluminal side of BBB. This sodium and potassium level variation in ISF directly influences the action potential generation and synaptic function [5]. Glucose transporter 1 or (GLUT-1) a solute carrier family 2 transporter is a specialized BBB transporter expressed in endothelial cells and facilitates nutrient transport down their concentration gradient. This transporter plays a vital role in brain energy supply because glucose is one of the key factors for neuronal energy processing [6]. Other transporters like monocarboxylate transporter 1 (MCT1) and y + amino acid transporters are generally expressed in abluminal and luminal membrane of BBB. One of the most critical transporters for maintaining neuronal homeostasis and excitotoxicity is sodium-dependent amino acid transporter 1 (EAAT1), EAAT2, and EAAT3. The primary

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function of these transporters is to remove excess glutamate and reduce neuronal toxicity. ABC transporter, also known as ATP-binding cassette subfamily B member 1, influences the drug entry mechanism into the brain, such as the rapid removal of lipophilic metabolites. Additionally, the ABC transporter facilitates nutrient efflux from endothelium to ISF [7]. Organic anion transporter family member 1CT (OATP1C1) transfer thyroid hormones inside the brain whereas MCT8-dependent thyroid hormone transportation happens from blood to endothelium cell [8]. Nutrient transportation is faster than peptide circulation through BBB [9]. There are some proteins like insulin, and insulin-like growth factor (IGF1), which cross the BBB through the process called transcytosis.

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Amyloid Precursor Protein and Aβ Processing on Endothelial Cells Amyloid precursor protein and amyloid precursor-like protein (APLP1 and APLP2) are highly expressed transmembrane glycoproteins in the brain along with kidney and platelets. The APP processing causes the production of misfolded Aβ protein, which is considered one of the most important factors for neurodegeneration and endothelial dysfunction. From invertebrates to humans, the APP sequence is conserved due to its important cellular functions. APP and APLPs are mainly localized in somatodendritic neurons and axons. The transportation process of APP is mainly through vesicles anchored at the appropriate positions. In physiological conditions, APP cleaves by α-secretase, and the soluble APP enters the endosomes. In endothelial cells, APP751 and APP770 (number denotes the amino acid sequence in APP protein) have higher expression whereas comparatively peripheral arteries have a lesser expression of these amino acids. At the surface of the endothelial cell, the APP is first cleaved by α-secretase by the non-amylogenic pathway, which results in soluble APP or (sAPP) at the ectodomain of the endothelial lumen releasing the C83 fragment. This C83 domain is further cleaved by γ-secretase complex (presenilin, nicastrin, anterior pharynx-defective 1, and presenilin enhancer). It creates an intracellular domain (AICD) along with a carboxyl-terminal fragment, also called P3 domain [10]. At the same time, the β-secretase cleavage causes the formation of toxic Aβ. The sAPP performs physiological functions like neuroprotection and synaptogenesis.

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Amyloid Beta and Membrane Toxicity as a Downstream Effect Accumulation of misfolded proteins like the amyloid beta leads to development of AD. Molecular mechanisms which influence the Aβ

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aggregation involve in the formation of various soluble oligomeric intermediates with different structures and sizes. These soluble oligomeric species initiate a cascade of downstream effects, which eventually leads to neurotoxicity by disrupting the permeability of the cellular membrane, followed by calcium dysregulation, mitochondrial dysfunction, inflammasome activation, and oxidative stress. The formation of aggregated amyloid oligomers is considered highly heterogeneous and has a variety of structures, sizes, and shapes with different surface hydrophobicity and β-sheet content. Different types of oligomers produce differential cytotoxicity, so it is important to know toxicity-related, specific structural features. Aβ oligomer-mediated toxicity originates from the cleavage of amyloid precursor protein (APP) along with the successive deposition of excess Aβ on the surface of the membrane. Peptide fragments that are not released from the membrane into the extracellular space cause the formation of neurofibrillary tangles [11]. Collective evidence shows that elevated cholesterol level in the membrane plays a vital role in AD. Some evidence has already supported that Aβ production increases in the cholesterol-rich areas by forming lipid rafts associated with ganglioside and cholesterol [12]. Aβ produces membrane toxicity by disrupting the membrane integrity and altering the dielectric property of membrane receptor binding and activation. They can also initiate the Ca2+ influx, leading to excitotoxicity [13]. Images collected from solid-state NMR and atomic force microscopy (AFM) confirmed the presence of hexagonal annular channels in Aβ-containing membrane with a six-subunit pore-like assembly [14]. The toxic peptide binds directly to lipids and causes the extraction of the lipid fragment. Studies have shown that large and small spherical Aβ42 oligomers extract the membrane bilayer, while detergent may facilitate this process [10]. In the amyloid channel hypothesis, Aβ oligomers are inserted directly into the lipid layer and eventually form the calcium-permeable channels, disrupting calcium homeostasis. This calcium homeostasis disruption eventually causes excitotoxicity, mitochondrial dysfunction, and neuroinflammation [15]. Calcium overloading forms the mitochondrial permeability shift by opening the mitochondrial permeability transition pore (MPTP). The MPTP channel is a nonselective channel that increases inner mitochondrial membrane permeability for water and solutes with lower molecular weight. This irreversible MPTP activation causes mitochondrial dysfunction [16].

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Two-Hit Hypothesis for a Correlation of Aβ and Cerebral Vasculature Cardiovascular circulation delivers blood to the brain, which is controlled by the heart rate. The blood supply to the occipital lobe, brainstem, and cerebellum is through vertebral arteries. The

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carotid arteries transport blood to the frontal, parietal, and lateral temporal lobes. Under normal conditions, the brain’s systemic blood flow stays the same throughout the branches of the vascular tree [17]. Large deep brain arteries diverge into arterioles to pericapillary arterioles, forming the capillaries. As previously discussed, neurons, astrocytes VSMCs, pericytes, and endothelial cells work hand in hand to construct the neurovascular unit (NVU). Depending upon the brain activity, the NVU controls local cerebral blood flow which is also called neurovascular coupling or functional hyperemia. Neuronal hyperactivity causes an increase in cerebral blood flow (CBF) which can be suppressed by the feed-forward hypothesis, which involves vasoactive messengers like NO [18]. Astrocytes release vasoactive components and metabolic products like PGE2 and epoxyeicosatrienoic acids to regulate CBF. Due to the active participation of astrocytes in CBF regulation, it is also called a neovascular unit. The causal relationship between AD and cerebrovascular dysfunction is still being explored. The progressive nature of AD causes homeostatic and hemodynamic vascular damage and disrupts the macromolecular regulation important for maintaining neuronal activity. The neurovascular hypothesis and pathogenic vascular component together form the two-hit hypothesis of AD [19]. This hypothesis is based on bidirectional causal effects, such as disruption of BBB causing impaired Aβ clearance (hit 1), while this BBB disruption further causes Aβ accumulation in the brain and results in vasculotoxicity (hit 2). Recent studies on rodents showed that peripheral Aβ is an important precursor for brain Aβ. Reduced Aβ burden has been observed by the presence of soluble LRP1 anti-Aβ antibodies, gelsolin, and ganglioside GM1 and systematic expression of neprilysin (Aβ degrading enzyme) [20]. Receptors for advanced glycation end product (RAGE)-dependent Aβ transport can also affect downstream toxicity. Increased expression of RAGE in brain endothelium causes Aβ influx and inspires the movement of Aβ-loaded monocytes across BBB. Disrupted clearance of Aβ in brain tissue is one of the vital causative factors for AD. Endothelium LRP1 binds with brain-derived Aβ at the abluminal portion of BBB, which initiates the clearance through blood [21]. APOE4 blocks LRP1-mediated Aβ clearance from the brain and facilitates Aβ accumulation. At the same time, apolipoprotein j (APOJ) increases the Aβ clearance across BBB [22]. Disrupted Aβ clearance causes further accumulation of Aβ along the walls of cerebral blood vessels. Recent studies also suggested that vascular changes occur at the first stage, which later causes Aβ accumulation. MRI results detected CBF changes long before AD symptoms appeared [23].

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Amyloid Beta and the Disruption of the Cerebral Endothelial Cell Layer One of the critical mediators of endothelial dysfunction is reactive oxygen species (ROS) which is generated as an influential factor of Aβ accumulation. The toxic effect of ROS causes functional hyperemia and vascular inflammation, followed by endothelial dysfunction [24]. It has also been found in studies that peroxynitrite triggers APPP overexpression and causes DNA double-strand break due to oxidative and nitrosative stress. Postmortem data showed that Aβ immune activation increases the DNA damage marker phospho-Ch2AX [25]. Aβ-induced peroxynitrite formation and DNA damage can be linked by activating PARP-1. PARP-1 is involved in the repair mechanism of oxidative stress–induced DNA damage whereas excessive activation of PARP-1 has a lethal effect on cells. Recent studies also suggested that Aβ-mediated PARP-1 activation plays a crucial role in cerebrovascular dysfunction by altering the endothelial-dependent vasodilation while PARP-1 inhibitor PJ-34 could successfully reverse it [26]. BBB dysfunction can also be regulated through PARP-1 activation, followed by neuroinflammation [27]. Poly (ADP-ribose) glycohydrolase (PRAG) is a catalytic enzyme that cleaves ADPR polymer into ADPR. The cerebrovascular dysfunction observed in APP-muted mice and Aβ-treated wild-type mice can be altered by inhibiting the PRAG inhibition. ADPR is considered a potent factor of TRPM2 channel activation and increase of intracellular Ca2+ and other positive ions, which further causes excitotoxicity and neuronal death [28]. The adhesive capability of Aβ acts as a toxic component on endothelial cells. It is also reported that the endothelial cells try to escape this Aβ entrapment using cellular movement. The Aβ aggregation eventually immobilizes the endothelial cell activity and causes cellular rupture. The organization of the actin cytoskeleton was found to be abnormal. The actin dots were co-localized with the Aβ aggregates, and many non-colocalized dots were also observed. Myosin II is reported to show high affinity to actin in a tensile situation where, as cofilin does not bind to it. These binding proteins play a vital role in cytoskeleton organization. Two main types of Aβ which contribute to vascular deposits are Aβ40 and Aβ42, but in soluble oligomeric Aβ42 has a more toxic effect than Aβ40. This toxic oligomer is also reported to produce endothelial cytotoxicity and increased permeability of endothelial cells [29]. In recent studies, soluble factors secreted from astrocytes cause endothelial disturbance and BBB damage. Astrogliosis and swollen astrocytic end feet cause detrimental effects on the cerebral endothelial cellular lining [30].

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Amyloid Beta and Cerebrovascular Reactivity Cerebrovascular reactivity is the capability of the blood vessels to dilate in response to the increased demand for blood supply received by the tissue. It can be scrutinized by analyzing the change in (CBF) or cerebral blood volume (CBV) induced by vasodilation. It is a helpful tool in evaluating vascular health in steno-occlusive diseases and pathologies and with some more subtle impairments in CVR detected in the case of Alzheimer’s disease and diseases of small cerebral vessels. Three key elements are essential for measuring CVR: the signal acquisition, the vasodilatory stimulus, and the processing [31]. Using the transcranial Doppler along with the blood oxygenation level–dependent (BOLD) and functional MRI (fMRI), it was found that in early AD, as compared to cognitively normal controls, there is impaired cerebrovascular reactivity which reflects reduced vasodilation in the brain areas in response to a challenge of CO2 inhalation. Cerebrovascular reactivity is the capability of a blood vessel to dilate. It is comprehensible that these effects could also be a downstream signaling process to Aβ-mediated vasculopathy [32]. The CAA is distinguished by the presence of Aβ within the wall of small to mid-sized cerebral arteries, veins, and cerebral capillaries of the leptomeninges, which is related to AD70. The reactivity of the cerebral blood vessels has been shown to be impaired in response to CO2 in the areas of the hippocampus. By using BOLD-fMRI in response to a memory task, it was visible that CBF velocity is reduced in young APOE4 carriers in the medial cerebral artery with the use of transcranial doppler. To overcome the increased demand, vasodilation is needed to supply blood to specific brain tissues. This can be investigated by induction of vasodilation and measuring cerebral blood flow (CBF) or cerebral blood volume (CBV). It is a valuable tool for assessing vascular health in pathologies, including stenoocclusive diseases. At the same time, more subtle CVR impairments have been found in Alzheimer’s and cerebral small vessel disease [33]. The measurement of CVR relies on three key elements: the vasodilatory stimulus, the signal acquisition, and the processing method.

10

Reduction of Cerebral Blood Flow One of the most visible conditions of early-stage AD is reduced cerebrovascular reactivity and weakened hemodynamic responses. Reduced CBF is considered a useful biomarker in preclinical AD. Transcranial Doppler measurement showed that an individual with greater CBF velocity is less prone to develop dementia—in the arterial spin labeling (ASL) MRI region [34]. Pericyte is a

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tissue-specific, multipotent, and heterogeneous cell attached to endothelium. It has cytoskeletal proteins such as α-smooth muscle actin (α-SMA), vimentin, desmin, myosin, and nestin functioning as contractile proteins in pericyte. The cytoplasm of pericytes winds around the endothelial cellular lining of pre- and post-capillary venules [35]. The cerebral blood flow is initially controlled by vascular smooth muscle, but contractile pericytes are warped around the vascular system and play vital roles in fine capillaries. AD patients are reported to have anatomic capillary constriction. Although the role of pericytes and AD pathology is unclear, its CBF reduction function is well documented. The pericyte dysfunction leads to BBB/NVU dysfunction [34]. Short-term Aβ-treated rodents showed capillary constriction, although long-term pericyte-mediated vascular constriction in the human AD brain is still under study. Studies found that Aβ-mediated pericyte-dependent vasoconstriction is caused by the generation of reactive oxygen species (ROS), activating endothelin A (ETA) receptors. ROS generated by microglia and pericyte causes ETA receptor activation that further causes elevated levels of extracellular endothelin-1 [36]. Aβ causes the release of inflammatory mediators, which causes CBF to decrease. Previously it was observed that AD causes changes in gray matter, but recent studies also observed that Aβ also causes changes the white matter. The decrease in CBF causes white matter dysfunction by slowing the action potential propagation [37]. There are two mechanisms through which complete occlusion of blood vessels is observed in AD. Cell movement imaging in cerebral capillaries showed that the capillaries get clogged by neutrophil adhesion and aggregation, eventually decreasing blood flow. It was also hypothesized that the reduced diameter of capillaries causes neutrophil aggregation and vice versa. The neutrophil structure is bigger than red blood cells, so it causes vascular clogs easily. It was also reported that capillary shrinkage occurs predominantly in aged mice more than in wild-type mice [38]. It was observed that treatment with neutrophil surface marker antibody (Ly6G) resulted in reliving the capillary block, and blood flow got increased by 26–32% and showed improved cognitive function. Long-term anticoagulant treatment showed improved CBF, and excess fibrin deposition was also observed.

11

Hypoperfusion and Hypoxia-Mediated Downstream Toxicity Neurovascular coupling is known to regulate CBF depending on neuronal activity and metabolism. Pial and intracerebral arteries control local CBF in case of brain activation, termed functional hyperemia. Intact pial circulation, VSMCs, and proper pericytemediated response are necessary for neurovascular coupling to vasoactive stimuli. Severe-to-moderate CBF reduction causes

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hypoxia that affects the synthesis of ATP, which further diminishes Na+/K+ ATPase action and directly affects the ability to generate the action potential [39]. The CBF reduction further affects pH homeostasis and electrolyte balance, eventually developing edema followed by white matter lesions. This detrimental factor causes glutamate accumulation and further Aβ formation in the brain [40]. It has already been proven in animal models that hypoperfusion causes neuronal dysfunction and neuropathological changes, which in turn leads to AD-like cognitive decline and symptoms along with Aβ accumulation [41]. Mice with impaired neurovascular coupling showed excess APP production and expressed transforming growth factor β1 (TGFβ1). Along with that, it shows cholinergic deprivation and age-dependent cognitive decline [42]. Hypoxia-ischemia directly influences APP processing and influences the amylogenic pathway of Aβ processing by β/γ secretase. Hypoxia-inducible factor 1α (HIFI α) acts as a transcriptional modulator of β-secretase and increases its expression [43]. Hypoxia increases the tau phosphorylation through mitogen-activated protein kinase (MAPK) and also through the extracellular signaling kinase (ERK) pathway [44]. Aβ degrading enzyme neprilysin downregulation causes variations in vascular-specific factors in brain endothelial cells which includes homeobox protein MOX2 gene mesenchyme homeobox 2 (MEOX2) reduction [45]. Because of impaired clearance of Aβ, the accumulation of Aβ is observed at the wall of cerebral vessels and in the brains of AD patients. In the familial mouse model expressing the Swedish mutation in the Aβ-precursor protein (APPsw±) of AD, a dense plaque of Aβ is observed at the vascular wall as the clearance was impaired through BBB and Virchow-Robin arterial spaces. One of the major Aβ clearing receptors is lipoprotein receptor-related protein 1 (LRP) in cerebral vascular smooth muscle [46]. Hypoxia initiates alternative splicing of EAAT2 mRNA, which suppress glutamate reuptake by astrocyte and facilitates excitotoxicity [47].

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Conclusions and Future Directions Neurodegenerative disease research is still trying to associate brain vasculature function and the pathogenesis of neurodegeneration. Exploring the importance of a healthy vascular network for normal brain functioning is opening new direction to research on ageing brain. One recent study of single-cell transcriptomics for determining the zonation at the A-V axis of the brain showed some promising concepts [48]. The single-cell RNA-seq method is proven to be useful in designing the base of BBB disruption and CBF reduction. Whereas the molecular atlas of BBB and cerebral blood vessels are already designed in a rodent model, the detailed molecular pathways of human NVU, BBB, and vasculature

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function are being explored. Regional change in the brain vascular system has been associated with phenotypic change in neurological disorders. Advanced neuroimaging system holds the potential for mapping changes in regional vascular pathology. Biomarkers, mainly focusing on specific cell types such as SMCs, pericyte endothelial cells, vascular zonation, etc., can unveil new molecular pathways. PET imaging is used in leukocyte infiltration in an in vivo model of BBB. Endothelial adhesion molecules causing inflammatory phenotype, which can be detected through MRI imaging, can be further understanding of neurodegenerative processes. Detailed studies are needed on the RAGE-mediated Aβ vascular influx which can be tracked through specialized probes [49]. The advancement in imaging techniques and improved probes for tracking BBB breakdown will help our future understanding of cerebral blood flow and neurodegeneration. References 1. Sweeney MD, Kisler K, Montagne A, Toga AW, Zlokovic BV (2018) The role of brain vasculature in neurodegenerative disorders. Nat Neurosci 21(10):1318–1331 2. Cai Z, Qiao PF, Wan CQ, Cai M, Zhou NK, Li Q (2018) Role of blood-brain barrier in Alzheimer’s disease. J Alzheimers Dis 63(4): 1223–1234 3. Deane R, Bell RD, Sagare A, Zlokovic BV (2009) Clearance of amyloid-β peptide across the blood-brain barrier: implication for therapies in Alzheimer’s disease. CNS Neurol Disord Drug Targets 8(1):16–30 4. Jiao X, He P, Li Y, Fan Z, Si M, Xie Q, Chang X, Huang D (2015) The role of circulating tight junction proteins in evaluating blood brain barrier disruption following intracranial hemorrhage. Dis Markers 2015: 860120 5. Neuwelt EA, Bauer B, Fahlke C, Fricker G, Iadecola C, Janigro D, Leybaert L, Molna´r Z, O’Donnell ME, Povlishock JT, Saunders NR (2011) Engaging neuroscience to advance translational research in brain barrier biology. Nat Rev Neurosci 12(3):169–182 6. Redzic Z (2011) Molecular biology of the blood-brain and the blood-cerebrospinal fluid barriers: similarities and differences. Fluids Barriers CNS 8(1):1–25 7. ElAli A, Hermann DM (2011) ATP-binding cassette transporters and their roles in protecting the brain. Neuroscientist 17(4):423–436 8. Visser WE, Friesema EC, Visser TJ (2011) Minireview: thyroid hormone transporters:

the knowns and the unknowns. Mol Endocrinol 25(1):1–4 9. Dogrukol-Ak D, Kumar VB, Ryerse JS, Farr SA, Verma S, Nonaka N, Nakamachi T, Ohtaki H, Niehoff ML, Edwards JC, Shioda S (2009) Isolation of peptide transport system-6 from brain endothelial cells: therapeutic effects with antisense inhibition in Alzheimer and stroke models. J Cereb Blood Flow Metab 29(2):411–422 10. Mrdenovic D, Pieta IS, Nowakowski R, Kutner W, Lipkowski J, Pieta P (2022) Amyloid β interaction with model cell membranes– what are the toxicity-defining properties of amyloid β. Int J Biol Macromol 200:520 11. Miyashita N, Straub JE, Thirumalai D, Sugita Y (2009) Transmembrane structures of amyloid precursor protein dimer predicted by replicaexchange molecular dynamics simulations. J Am Chem Soc 131(10):3438–3439 12. Kakio A, Nishimoto SI, Kozutsumi Y, Matsuzaki K (2003) Formation of a membrane-active form of amyloid β-protein in raft-like model membranes. Biochem Biophys Res Commun 303(2):514–518 13. Lashuel HA, Hartley D, Petre BM, Walz T, Lansbury PT Jr (2002) Amyloid pores from pathogenic mutations. Nature 418(6895):291 14. Lin HA, Bhatia R, Lal R (2001) Amyloid β protein forms ion channels: implications for Alzheimer’s disease pathophysiology. FASEB J 15(13):2433–2444 15. Madhu P, Mukhopadhyay S (2021) Distinct types of amyloid-β oligomers displaying diverse

Amyloid β in Neurovascular Toxicity in AD neurotoxicity mechanisms in Alzheimer’s disease. J Cell Biochem 122(11):1594–1608 16. Jia K, Du H (2021) Mitochondrial permeability transition: a pore intertwines brain aging and Alzheimer’s disease. Cell 10(3):649 17. Lecrux C, Hamel E (2011) The neurovascular unit in brain function and disease. Acta Physiol 203(1):47–59 18. Lourenc¸o CF, Santos RM, Barbosa RM, Cadenas E, Radi R, Laranjinha J (2014) Neurovascular coupling in hippocampus is mediated via diffusion by neuronal-derived nitric oxide. Free Radic Biol Med 73:421–429 19. Arvanitakis Z, Capuano AW, Leurgans SE, Bennett DA, Schneider JA (2016) Relation of cerebral vessel disease to Alzheimer’s disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol 15(9):934–943 20. Sagare A, Deane R, Bell RD, Johnson B, Hamm K, Pendu R, Marky A, Lenting PJ, Wu Z, Zarcone T, Goate A (2007) Clearance of amyloid-β by circulating lipoprotein receptors. Nat Med 13(9):1029–1031 21. Yamada K, Hashimoto T, Yabuki C, Nagae Y, Tachikawa M, Strickland DK, Liu Q, Bu G, Basak JM, Holtzman DM, Ohtsuki S (2008) The low-density lipoprotein receptor-related protein 1 mediates uptake of amyloid β peptides in an in vitro model of the blood-brain barrier cells. J Biol Chem 283(50): 34554–34562 22. Zlokovic BV (2011) Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci 12(12):723–738 23. Wierenga CE, Hays CC, Zlatar ZZ (2014) Cerebral blood flow measured by arterial spin labeling MRI as a preclinical marker of Alzheimer’s disease. J Alzheimers Dis 42(s4):S411– S419 24. Niwa K, Carlson GA, Iadecola C (2000) Exogenous Aβ1–40 reproduces cerebrovascular alterations resulting from amyloid precursor protein overexpression in mice. J Cereb Blood Flow Metab 20(12):1659–1668 25. Garwood CJ, Simpson JE, Al Mashhadi S, Axe C, Wilson S, Heath PR, Shaw PJ, Matthews FE, Brayne C, Ince PG, Wharton SB (2014) DNA damage response and senescence in endothelial cells of human cerebral cortex and relation to A lzheimer’s neuropathology progression: a population-based study in the Medical Research Council Cognitive Function and Ageing Study (MRC-CFAS) cohort. Neuropathol Appl Neurobiol 40(7):802–814

371

26. Park L, Wang G, Moore J, Girouard H, Zhou P, Anrather J, Iadecola C (2014) The key role of transient receptor potential melastatin-2 channels in amyloid-β-induced neurovascular dysfunction. Nat Commun 5(1):5318 27. Rom S, Zuluaga-Ramirez V, Dykstra H, Reichenbach NL, Ramirez SH, Persidsky Y (2015) Poly(ADP-ribose) polymerase-1 inhibition in brain endothelium protects the blood—brain barrier under physiologic and neuroinflammatory conditions. J Cereb Blood Flow Metab 35(1):28–36 28. Sumoza-Toledo A, Penner R (2011) TRPM2: a multifunctional ion channel for calcium signalling. J Physiol 589(7):1515–1525 29. Take Y, Chikai Y, Shimamori K, Kuragano M, Kurita H, Tokuraku K (2022) Amyloid β aggregation induces human brain microvascular endothelial cell death with abnormal actin organization. Biochem Biophys Rep 29: 101189 30. Yue Q, Zhou X, Zhang Z, Hoi MP (2022) Murine beta-amyloid (1–42) oligomers disrupt endothelial barrier integrity and VEGFR signaling via activating astrocytes to release deleterious soluble factors. Int J Mol Sci 23(3): 1878 31. Pillai JJ, Mikulis DJ (2015) Cerebrovascular reactivity mapping: an evolving standard for clinical functional imaging. Am J Neuroradiol 36(1):7–13 32. Al-Bachari S, Vidyasagar R, Emsley HC, Parkes LM (2017) Structural and physiological neurovascular changes in idiopathic Parkinson’s disease and its clinical phenotypes. J Cereb Blood Flow Metab 37(10):3409–3421 33. Richiardi J, Monsch AU, Haas T, Barkhof F, Van de Ville D, Radu¨ EW, Kressig RW, Haller S (2015) Altered cerebrovascular reactivity velocity in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging 36(1): 33–41 34. Alcendor J, Interactions D (2020) Between amyloid-β proteins and human brain pericytes: implications for the pathobiology of Alzheimer’s disease. J Clin Med 9(5):1490 35. Hays CC, Zlatar ZZ, Wierenga CE (2016) The utility of cerebral blood flow as a biomarker of preclinical Alzheimer’s disease. Cell Mol Neurobiol 36:167–179 36. Nortley R, Korte N, Izquierdo P, Hirunpattarasilp C, Mishra A, Jaunmuktane Z, Kyrargyri V, Pfeiffer T, Khennouf L, Madry C, Gong H (2019) Amyloid β oligomers constrict human capillaries in

372

Sayani Banerjee and Sugato Banerjee

Alzheimer’s disease via signaling to pericytes. Science 365(6450):eaav9518 37. Ji F, Pasternak O, Ng KK, Chong JS, Liu S, Zhang L, Shim HY, Loke YM, Tan BY, Venketasubramanian N, Chen CL (2019) White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer’s disease continuum. Sci Rep 9(1):4749 38. Schager B, Brown CE (2020) Susceptibility to capillary plugging can predict brain region specific vessel loss with aging. J Cereb Blood Flow Metab 40(12):2475–2490 39. Kalaria RN (2010) Vascular basis for brain degeneration: faltering controls and risk factors for dementia. Nutr Rev 68(suppl_2):S74–S87 40. Moskowitz MA, Lo EH, Iadecola C (2010) The science of stroke: mechanisms in search of treatments. Neuron 67(2):181–198 41. Koike MA, Green KN, Blurton-Jones M, LaFerla FM (2010) Oligemic hypoperfusion differentially affects tau and amyloid-β. Am J Pathol 177(1):300–310 42. Gordon-Krajcer W, Kozniewska E, Lazarewicz JW, Ksiezak-Reding H (2007) Differential changes in phosphorylation of tau at PHF-1 and 12E8 epitopes during brain ischemia and reperfusion in gerbils. Neurochem Res 32: 729–737 43. Zhang X, Zhou K, Wang R, Cui J, Lipton SA, Liao FF, Xu H, Zhang YW (2007) Hypoxiainducible factor 1α (HIF-1α)-mediated

hypoxia increases BACE1 expression and β-amyloid generation. J Biol Chem 282(15): 10873–10880 44. Fang H, Zhang LF, Meng FT, Du X, Zhou JN (2010) Acute hypoxia promote the phosphorylation of tau via ERK pathway. Neurosci Lett 474(3):173–177 45. Wang Z, Yang D, Zhang X, Li T, Li J, Tang Y, Le W (2011) Hypoxia-induced down-regulation of neprilysin by histone modification in mouse primary cortical and hippocampal neurons. PLoS One 6(4):e19229 46. Bell RD, Deane R, Chow N, Long X, Sagare A, Singh I, Streb JW, Guo H, Rubio A, Van Nostrand W, Miano JM (2009) SRF and myocardin regulate LRP-mediated amyloid-β clearance in brain vascular cells. Nat Cell Biol 11(2): 143–153 47. Dallas M, Boycott HE, Atkinson L, Miller A, Boyle JP, Pearson HA, Peers C (2007) Hypoxia suppresses glutamate transport in astrocytes. J Neurosci 27(15):3946–3955 48. Vanlandewijck M, He L, M€ae MA, Andrae J, Ando K, Del Gaudio F, Nahar K, Lebouvier T, ˜ a B, Gouveia L, Sun Y (2018) A molecuLavin lar atlas of cell types and zonation in the brain vasculature. Nature 554(7693):475–480 49. Sweeney MD, Sagare AP, Zlokovic BV (2015) Cerebrospinal fluid biomarkers of neurovascular dysfunction in mild dementia and Alzheimer’s disease. J Cereb Blood Flow Metab 35(7):1055–1068

Chapter 27 Fecal Microbiota Transplantation in Amyotrophic Lateral Sclerosis: Clinical Protocol and Evaluation of Microbiota Immunity Axis Elena Niccolai, Ilaria Martinelli, Gianluca Quaranta, Giulia Nannini, Elisabetta Zucchi, Flavio De Maio, Giulia Gianferrari, Stefano Bibbo`, Giovanni Cammarota, Jessica Mandrioli, Luca Masucci, and Amedeo Amedei Abstract The fecal microbial transplantation (FMT) is a therapeutic transplant of fecal microbiota from healthy donors to patients. This practice is aimed at restoring eubiosis and rebalancing the enteric and systemic immune responses, and then eliminating pathogenic triggers of multiple disease, including neurodegenerative diseases. Alterations of gut microbiota (GM) affect the central nervous system (CNS) health, impacting neuro-immune interactions, synaptic plasticity, myelination, and skeletal muscle function. T-regulatory lymphocytes (Treg) are among the most important players in the pathogenesis of amyotrophic lateral sclerosis (ALS), altering the disease course. Along with circulating neuropeptides, other immune cells, and the gut-brain axis, the GM influences immunological tolerance and controls Treg’s number and suppressive functions. A double-blind, controlled, multicenter study on FMT in ALS patients has been designed to evaluate if FMT can modulate neuroinflammation, by restoring Treg number, thus modifying disease activity and progression. Key words Microbiota, Amyotrophic lateral sclerosis, T-regulatory, Inflammation, Clinical trial, Flow cytometry, Culturomics

1

Introduction The fecal microbial transplantation (FMT) consists of the infusion of feces from a healthy donor to the gastrointestinal tract of a recipient patient to treat a specific disease associated with alteration of gut microbiota (GM) [1]. Spilling as a fourth-century Chinese custom [2], the FMT actually represents an approved treatment for the management of recurrent, and possibly refractory, Clostridium difficile infection (CDI) by restoring gut microbial diversity [1, 3,

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_27, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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4]. Although the spread of FMT in clinical practice is restricted by local regulatory and bureaucratic issues, FMT is booming, ranging from highly organized frozen stool banking programs to personalized treatments with patient-identified direct donors [5]. Since a disrupted GM composition is associated with several neurological diseases, research into its role in modulating brain function has rapidly increased over the past 10 years, along with the theory that microbiota restoration therapies could be crucial in their management [6]. Therefore, growing clinical and preclinical data postulated that the GM could represent a possible pathomechanistic target for different neurodegenerative diseases, including Alzheimer’s disease (AD), autism spectrum disorder, multiple sclerosis, amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and stroke [7]. As an example, FMT is now underway (ClinicalTrials.gov NCT03671785 and NCT04854291) to study its action on constipation in patients with PD but also to explore its effects on motor and non-motor symptoms [8]. In ALS field, some preliminary data provide the theoretical basis for a GM role in ALS pathogenesis because the immune system, a known key player in ALS progression, can be modulated through gut-brain axis [9]. Indeed, the gut environment promotes the production of autoreactive T-cells, threatening CNS autoimmunity [10]. Some commensal bacteria can induce Treg generation and FMT determine Treg’ increase [11]. Moreover, data from two preclinical studies in ALS mouse models seem to confirm the pathological consequences of a disrupted interplay between gut dysbiosis, altered intestinal permeability, and enteric inflammatory/neurogenic responses [12]. Wu et al. found an augmented gut permeability due to impairment of the intestinal tight junction structure and related protein expression in a SOD1 G93A transgenic mouse compared to wild-type mice [13]. In a similar way, morphofunctional alterations of intestinal permeability in the same animal model, since the earliest stages of the disease, were confirmed by Zhang et al. [14]. Furthermore, the investigators demonstrated that the treatment with 2% butyrate (a natural bacterial product able to restore the intestinal microbial homeostasis) restored GM balance and intestinal epithelial barrier integrity in G93A mouse model, but also improved central and peripheral symptoms of the disease, prolonged survival, and slowed weight loss [14]. A recent study with SOD1-Tg mice prone to ALS showed that a pre-symptomatic dysbiosis with altered configuration of metabolites, occurring with a disease worsened under conditions of germfree or broad-spectrum antibiotic treatment, could be potentially targeted in order to change mice phenotype [15]. Finally, Burberry et al. found that an environment with reduced abundance of immune-stimulating bacteria protects C9orf72mutant mice from premature mortality and significantly

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ameliorates their underlying systemic inflammation and autoimmunity [16]. Reducing the microbial burden in mutant C9orf72 mice with broad-spectrum antibiotics—as well as transplanting gut microflora from a protective environment—attenuated inflammatory phenotypes, even after their onset [16]. The way through which microbiota might influence ALS onset or progression could be represented by a direct route of communication through the vagus nerve, since bacterial-derived neurotransmitters and neuropeptides could activate directly myenteric neurons, which, through vagal nerve ascending fibers, might deliver nerve inputs to the brain [12, 17]. In addition, the intestinal epithelium regulates the spread of specific bacterial products (e.g., short-chain fatty acids, vitamins, or neurotransmitters, such as acetylcholine, dopamine, noradrenaline, gamma aminobutyric acid, or serotonin) into the circulatory system, which, in turn, may arrive to the CNS [7, 12, 18]. In this context, FMT could represent a strategy against ALS progression, by influencing the mutual signaling between gut microflora and CNS [18], employing bidirectional communication (via neuronal, hormonal, immunologic, and toxic signaling) [19, 20]. Individual case reports of ALS patients documented positive results because of FMT [21], opening the possibility of a transition from observational to interventional study designs. Based on these premises, we perform a clinical trial to treat GM dysbiosis through microbiota restoration with FMT, evaluating the biological basis of dampening ALS progression.

2

Materials

2.1 Biological Specimens’ Preparation and Collection

1. MACS® tissue storage solution (Miltenyi Biotec). 2. NucleoProtect RNA solution (MACHEREY-NAGEL). 3. Blood collection tubes: silicon-coated and lithium-heparin BD vacutainer collection tubes. 4. T-PER™ tissue protein extraction reagent (Thermo Scientific). 5. Halt™ protease Scientific™).

inhibitor

cocktail

(100×)

(Thermo

6. gentleMACS™ C tubes and M tubes for gentleMACS dissociator (Miltenyi Biotec). 7. gentleMACS™ octo dissociator with heaters (Miltenyi Biotec). 8. Tumor dissociation kit, human (Miltenyi Biotec) containing enzyme H, enzyme R, and enzyme A. 9. RPMI medium (EuroClone). 10. Phosphate-buffered saline (PBS), pH 7.4).

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11. Deionized water. 12. 2 mL cryovials. 13. 40 μm diameter filters. 14. Lymphoprep™ (STEMCELL Technologies). 15. 5polypropylene, conical, printed graduated, centrifuge tubes. RNase-free water. 16. NucleoZOL one phase RNA purification (MACHEREYNAGEL). 17. NucleoSpin RNA Set for NucleoZOL (MACHEREYNAGEL). 18. iScript cDNA synthesis kit (Bio-Rad). 19. GoTaq qPCR Master Mix (Promega). 20. qPCR primers: b2M 5′-AGTATGCCTGCCGTGGAAC-3′ forward and 5′-GCGGCATCTTCACAAACCT-3′reverse; FoxP3 Hs_FOXP3_1_SG Quantitect Primer Assay (Qiagen). 21. T-PER tissue protein extraction reagent (Invitrogen). 22. Halt Protease Inhibitor Cocktail 100× (Thermo Scientific). 2.2

Flow Cytometry

1. REAfinity recombinant human antibodies fluorochromeconjugated (Miltenyi Biotec). 2. MACSQuant running buffer (PBS, EDTA, stabilizer, and 0.09% sodium azide) (Miltenyi Biotec). 3. Tregs’ detection kit, human (Miltenyi Biotec) containing: antiCD45-VioBlue, anti-CD4-VioGreen, anti-CD25-VioBright, anti-CD127-PE, anti-FoxP3-Vio667, anti-CD4-VioGreen, anti-CD25-VioBright antibodies, anti-CD127-PE, Tandem Signal Enhancer, permeabilization buffer, Fixation Solution 1 and Fixation Solution 2. 4. 5 mL round-bottom polystyrene tube. 5. Running buffer (Miltenyi Biotech). 6. Antihuman antibodies (Miltenyi Biotech): CD45-PercP Vio700, CD3-VioBlue, CD8-APC Vio770, CD4-VioGreen, CCR10-PE, CD183-VioBright FITC, CD194-PE Vio770, CD196-APC, CD45-VioBlue. 7. Zombie NIR™ Fixable Viability Kit (BioLegend).

2.3 Luminex Screening Assay

1. Adjustable pipettes. 2. Multichannel pipettes capable of delivering 5–200 μL. 3. Reagent reservoirs. 4. Polypropylene microfuge tubes. 5. Aluminum foil.

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6. Absorbent pads. 7. Vortex mixer. 8. Plate shaker. 9. Magnetic separation block for magnetic beads. 10. Human cytokine/chemokine magnetic bead panel kit. 2.4 Fecal Microbiota Transplantation (FMT)

1. Sterile spatulas. 2. STR strainer bags (Seward). 3. 0.9% NaCl, 10% glycerol. 4. Stomacher® 400 circulator (Seward). 5. Sterile gauzes (20 cm × 20 cm). 6. A funnel. 7. Sterile plastic bottles.

2.5

Culturomics

1. Blood culture bottles. 2. Rumen fluid. 3. Brucella broth. 4. Agar growth media: tryptic soy agar (TSA), Schaedler agar (SCH), Columbia agar (CAN), and chocolate agar (PVX). 5. Anaerobic sachet AnaeroGen™ 2.5 L (Thermo Fisher). 6. Microaerophilic sachet CampyGen™ 3.5 L (Thermo Fisher). 7. MALDI steel plate (Bruker Daltonics). 8. MALDI-TOF mass spectrometer (Bruker Daltonics). 9. α-Cyrano-4-hydroxycinnamic acid (Sigma-Aldrich). 10. Bacterial test standard (Bruker Daltonics).

2.6

Metagenomics

1. Fecal DNA extraction: DANAGENE MICROBIOME fecal DNA kit (DanaGen-Bioted) containing proteinase K, disinhibition buffer, wash buffer, and elution buffer. 2. Saliva DNA extraction: DANAGENE MICROBIOME saliva DNA kit (DanaGen-Bioted) containing CTAB extraction buffer, binding buffer, disinhibition buffer, wash buffer, elution buffer, bead microtubes, proteinase K, microbial DNA columns, and collection tubes. 3. Biopsies DNA extraction: QIAamp DNA mini kit (Qiagen) containing QIAamp mini spin columns, collection tubes, buffer AL, buffer ATL, buffer AW1, buffer AW2, buffer AE, and proteinase K. 4. Library preparation: Agencourt® AMPure beads XP, ethanol (80% solution in water), and resuspension buffer (10 mM Tris–HCl, 1 mM EDTA, pH 8.0).

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5. DNA sequencing: Illumina MiSeq reagent kit v2 (500 cycles) and PhiX control v3. 6. Data analysis: Microbiota analysis tool (MicrobAT) system and R studio and R software.

3 3.1

Methods Clinical Protocol

1. We design a randomized, double-blind, multicenter study on FMT in ALS patients. The study is planned to include 42 ALS patients with 2:1 allocation in two groups of subjects (28 FMT vs. 14 controls (no treatment)). Treatment is double-blinded to patients and neurologists, but not to the endoscopist and microbiologist. Randomization is done using a computer-generated list of random number that is centrally generated in the statistical unit. Given the heterogeneous ALS progression, patients are stratified by progression rate, calculated at randomization considering the monthly decline of ALSFRS-R total score at screening and a total score of 48 at onset, and using a cutoff of 0.7 [22]. 2. Endoscopic FMT must be performed within 21 days from randomization. The trial includes, according to the revised El Escorial criteria [23], patients with sporadic or familial probable laboratory-supported, clinically probable, or definite ALS that can be subjected to FMT treatment. Female or male patients aged between 18 and 70 years old can be included if disease duration from symptom onset is no longer than 18 months at the screening visit; patients are treated with a stable dose of riluzole (100 mg/days) for at least 30 days prior to screening; patients’ weight is >50 kg and BMI ≥18; patients have a FVC equal or more than 70% predicted normal value for gender, height, and age at the screening visit; patients are able and willing to comply with study procedures as per protocol; patients are able to understand and capable of providing informed consent at screening visit prior to any protocolspecific procedures; and patients use effective contraception. 3. We exclude from procedure the patients with known organic gastrointestinal disease; history of gastrointestinal malignancy; ongoing malignancies; use of immunosuppressive or chemotherapy within the past 2 years; celiac disease and/or food (e.g., lactose) intolerance; previous gastrointestinal surgery; any condition that would make endoscopic procedures contraindicated; acute infections requiring antibiotics; antimicrobial treatment or probiotics 4 weeks prior to screening; severe comorbidities (heart, renal, liver failure); severe renal (eGFR < 30 mL/min/1.73 m2) or liver failure or liver aminotransferase (ALT/AST > 2× upper limit of normal);

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autoimmune diseases, inflammatory disorders (SLE, rheumatoid arthritis, connective tissue disorder) or chronic infections (HIV, hepatitis B or C infection, tuberculosis); abuse of alcohol or drugs; participation in clinical trials Z-project > Maximum Intensity (Fig. 1b). 16. Perform proper adjustment of brightness and contrast from Image > Adjust > Brightness/Contrast to distinctly visualize aggregates. Insert scale bars in raw images (Fig. 1c) using FiJi or the Nikon Imaris software. 17. Next, use 3D object counter v 2.0 for estimating aggregate numbers based on voxel intensity from Analyze > 3D Objects Counter. Adjust threshold to locate punctate structures as per the image intensity and check only objects, statistics, and summary boxes (Fig. 2a). 18. This gives an output file of object distributions by volume and their sizes (Fig. 2b). For Huntingtin aggregates (perinuclear), there is no universal size and they can vary from cell types and depend on experimental conditions. For better estimates, consider most size distributions and those which occur across all replicates. 19. For better visualization of Huntingtin aggregates around the nucleus, create 3D movies in the Imaris software with default parameters. 20. Although this method is used to visualize and quantify HTT aggregates, the same can be easily modified to study any aggregate prone molecules. 3.2 Alteration of HTT Dynamics by Perturbing Expression of lncRNAs Meg3 and Neat1

For a quantitative assay for measuring changes in mutant HTT aggregates upon transient knockdown of lncRNAs Meg3 and Neat1, in both Neuro-2a and SHSY-5Y cells (seeded on coverslips), follow steps 1–3 as above. 1. Transfect cells with HTT-83Q-DsRed only or with HTT-83QDsRed+ siRNA against Meg3 or Neat1 (see Note 7). 2. Following transfections, process cells for image acquisition as steps 5–9 as above. 3. Capture Z stack images in A1 laser scanning confocal mode with 60×/1.27 (WD 0.17 mm) SR Plan Apo water immersion objective with correction collar, Nikon LU-NV Solid State Lasers 405 (DAPI), 561 (DsRED), with appropriate filters, at pixel parameters of 1024 × 1024. 4. For counting aggregates under the three conditions in step 1 above, follow steps 12–18 of the previous section. 5. Perform separate sets of experiments for aggregate quantification in Dsred only cells (Fig. 3) as well as cells with Dsred and counterstained with DAPI (Figs. 4 and 5).

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Fig. 1 Visualizing HTT aggregates in cell models of HD by super resolution microscopy. (a) Raw unprocessed z-stacks of Neuro-2a cells transfected with HTT-83Q-Dsred. (b) Maximum intensity projected image from (a). (c) Threshold corrected and scaled super resolution image depicting distinct HTT aggregates separate from the nucleus

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Fig. 2 Quantitative estimation of Huntingtin aggregates in cell models of HD by super resolution microscopy (a) and steps for image preprocessing, intensity thresholding, and counting numbers of 3D aggregates by 3D object counter version 2.0 in FiJi (b)

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Fig. 3 Alteration of HTT dynamics by perturbing expression of lncRNAs Meg3 and Neat1 (a. i) and (b. i) HTT-83Q-DsRed aggregates in Neuro-2A and SHSY-5Y cells, respectively, (a. ii) and (b. ii) HTT-83Q-DsRed aggregates in Neuro-2a and SHSY-5Y cells, respectively, co-transfected with siRNA against Neat1 (a. iii) and (b. iii) HTT-83Q-DsRed aggregates in Neuro-2a and SHSY-5Y cells, respectively, co-transfected with siRNA against Meg3. All representative images are acquired 24 h post-transfection. Scale bars = 10 μm

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Fig. 4 Alteration of HTT dynamics by perturbing expression of lncRNAs Meg3 and Neat1 (continued). (a) Panel i: HTT-83Q-DsRed aggregates in Neuro-2A cells; Panel ii: HTT-83Q-DsRed aggregates in Neuro-2a cells transfected with siRNA against Neat1; and Panel iii: HTT-83Q-DsRed aggregates in Neuro-2a cells transfected with siRNA against Meg3. Scale bars = 10 μm

4 Notes 1. Although this study employed the cell lines Neuro-2A and SHSY-5Y, other cell lines like SK-N-SH and HeLa can also be used. However, neuronal cell lines are recommended to make results relevant to HD. Moreover, depending on the cell lines

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Fig. 5 Alteration of HTT dynamics by perturbing expression of lncRNAs Meg3 and Neat1 (continued). (a) Panel i: HTT-83Q-DsRed aggregates in SHSY-5Y cells; Panel ii. HTT-83Q-DsRed aggregates in SHSY-5Y cells transfected with siRNA against Neat1; and Panel iii: HTT-83Q-DsRed aggregates in SHSY-5Y cells transfected with siRNA against Meg3. Scale bars = 10 μm

used, growth conditions and transfection efficiencies need to be optimized. 2. Neuro-2a cells grow better in DMEM only, but for SHSY-5Y cells, DMEM-F12 gives the best growth results. 3. Lipofectamine 2000 gives lesser transfection efficiencies compared to Lipofectamine 3000, but transfection-related cytotoxicity is lesser with the former. This is of importance, considering HTT constructs are themselves cytotoxic to cells.

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4. Instead of DsRed clones, the GFP clones of HTT (both 16Q and 83Q) can be used. However, for imaging, DsRed variants give better and brighter aggregates. 5. siRNAs used for this study were ones which were readily available from Qiagen. The protocol can be modified to use custom siRNAs also. However, in that case, it is recommended to check the knockdown efficiency of the siRNAs by qPCR. 6. For imaging studies, the empirical cell confluency of 70–80% prior to transfection can be altered. Lesser cell confluence helps in the imaging of single cells which are spatially separated. 7. For double transfection experiments, the DNA, siRNA, and Lipofectamine amounts need to be optimized. A good starting point for siRNA amounts can be obtained from the Invitrogen Lipofectamine 2000 protocol booklet. It is recommended to perform a qPCR validation of siRNA knockdown in double transfection experiments at least once. References 1. Huntington’s Disease Collaborative Research Group (1993) A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 72: 971–983 2. Roos RA (2010) Huntington’s disease: a clinical review. Orphanet J Rare Dis 5:40 3. Kopp F, Mendell JT (2018) Functional classification and experimental dissection of long noncoding RNAs. Cell 172:393–407

4. Bo¨hmdorfer G, Wierzbicki AT (2015) Control of chromatin structure by long noncoding RNA. Trends Cell Biol 25:623–632 5. Wan P, Su W, Zhuo Y (2017) The role of long noncoding RNAs in neurodegenerative diseases. Mol Neurobiol 54:2012–2021 6. Kerschbamer E, Biagioli M (2016) Huntington’s disease as neurodevelopmental disorder: altered chromatin regulation, coding, and non-coding RNA transcription. Front Neurosci 9:509

Chapter 30 Astrocyte Activation and Drug Target in Pathophysiology of Multiple Sclerosis Preeti Bisht, Charul Rathore, Ankit Rathee, and Atul Kabra Abstract Multiple sclerosis (MS) is a neurodegenerative disease, which is also referred to as an autoimmune disorder with chronic inflammatory demyelination affecting the core system that is the central nervous system (CNS). Demyelination is a pathological manifestation of MS. It is the destruction of myelin sheath, which is wrapped around the axons, and it results in the loss of synaptic connections and conduction along the axon is also compromised. Various attempts are made to understand MS and demyelination using various experimental models out of them. The most popular model is experimental autoimmune encephalomyelitis (EAE), in which autoimmunity against CNS components is induced in experimental animals by immunization with self-antigens derived from basic myelin protein. Astrocytes serve as a dual-edged sword both in demyelination and remyelination. Various drug targets have also been discussed that can be further explored for the treatment of MS. An extensive literature research was done from various online scholarly and research articles available on PubMed, Google Scholar, and Elsevier. Keywords used for these articles were astrocyte, demyelination, astrogliosis, and reactive astrocytes. This includes articles being the most relevant information to the area compiled to compose a current review. Key words Multiple sclerosis (MS), Astrocytes, Demyelination, Remyelination, Astrogliosis, Reactive astrocytes

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Introduction The myelin revolves around axons; it is a specialized plasma membrane, and it forms a fatty lining that helps the fast transmission of electrical impulses so that the flow of information can be transmitted easily from one axon to another. The term myelin has a Greek origin from the word marrow (myelos), and Rudolf Virchow first used the term myelin in 1854 to characterize the structure that is particularly prevalent in the brain’s core [1]. Myelin sheaths are un-organized and are composed of several layers of modified plasma membrane that enhance the conduction action potential velocity along the axon [2]. The transverse capacitance of the axonal membrane and the axon’s size both negatively affect the

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_30, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Fig. 1 Demyelination of the neurons

speed of neural impulse conduction along the axon [3]. As the myelin has major functions in the nervous system’s physiology, there are various neurological defects observed in the living beings that are attributed to defects in the myelination. There are various myelin diseases that are represented by large, heterogenous groups in terms of expression, pathophysiology, and etiology. Acquired and genetic pathologies can be separated, the most common of which can be inflammatory, infectious, toxic, and metabolic [1]. Numerous studies prove that there is interdependence among oligodendrocytes, microglia, astrocytes, and axons indicating myelin disfunction. The myelin sheath is destroyed in demyelination (Fig. 1), conduction alongside the axon is also compromised, and synaptic connections are severed [2]. Numerous central nervous system disorders, including acute disseminated encephalomyelitis (ADEM), MS, Balo’s disease, and neuromyelitis optica, are caused by demyelination [1]. MS is one of these demyelinating disorders and is also one of the most common.

2 Multiple Sclerosis (MS) One of the main causes of neurological impairment is MS [4]. The severity of MS predominates in progressive forms of the disease. The central nervous system (CNS) is chronically inflamed in MS, which is classified as an autoimmune illness with demyelinating features [5–7]. It is attributed to several clinical manifestations such as fatigue, pain, depression, and anxiety. MS was first described in the 1800s as a malaise that resulted in increasing functional impairment of the CNS [2]. Relapsing–remitting MS (RRMS), which typically begins as a clinically isolated disease, is characterized

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by a series of alternating periods of remission and aggravation. Even while patients frequently regain near-normal neurologic function after each episode, failure of the CNS to remyelinate MS lesions and grow axons can eventually cause secondary progressive MS (SPMS), which is an irreversible progression of clinical disability. Additionally, 10–15% of MS individuals will experience primary progressive MS (PPMS), which is a clinical progression without remissions from the time of onset. The primary cause of MS is the loss of oligodendrocytes, which is followed by demyelination. 2.1 Relapsing– Remitting Multiple Sclerosis (RRMS)

Most MS patients initially experience relapsing–remitting symptoms. Acute exacerbations are used to describe it, and it typically recovers fully or partially with a somewhat clinically stable interval in between. The International Panel on the Diagnosis of Multiple Sclerosis defines exacerbations, also known as recurrences or seizures, as previous or present patient-reported symptoms or reliably discovered indicators typical of acute inflammatory demyelinating episodes in the central nervous system. It endures at least 24 h without getting sick or infected [8]. Diagnostic standards have modified through the years primarily based on the studies and the combination of technologies that support the diagnostic procedure, which include research displaying using magnetic resonance imaging (MRI) to increase diagnostic sensitivity while maintaining specificity [9]. Applying the latest criteria, commonly referred to as “McDonald’s 2010,” seems to provide an earlier diagnosis of multiple sclerosis in comparison to previous standards [10].

2.2 Clinically Isolated Syndrome (CIS)

The group of CIS has been introduced to the current classification scheme, but the terminology has been used for years in both clinical practice and studies. Clinically isolated syndromes characterize the first symptoms of a patient with clinical symptoms typical of a demyelination event. Patients are categorized as having CIS if there may be single clinical proof of exacerbation and MRI does not fulfill the RRMS criteria. If there is even a single clinical indication of an exacerbation and the MRI does not meet the RRMS criteria, the patient is diagnosed with CIS. Studies have shown that individuals with CIS, especially those who have MRI findings of brain lesions compatible with MS, are more likely to eventually fulfill RRMS requirements and should receive treatment right once [9, 11]. According to several recent studies, the presence of oligoclonal bands is crucial for prognosis, and additional cerebrospinal fluid (CSF) biomarkers may also be used to predict the conversion of CIS to RRMS [12, 19]. In addition, there is an opposite correlation between ergosterol levels during CIS and the possibility of meeting RRMS criteria [13], which may be a marker of other factors or may be overcome by vitamin D. Optical coherence tomography (OCT) may also serve as a predictor [9].

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2.3 Radiologically Isolated Syndrome (RIS)

Patients who had never before had any clinical symptoms of MS have been found to have abnormalities suggestive of MS as magnetic resonance imaging (MRI) is used increasingly frequently to diagnose headaches, shock, and other illnesses. The term RIS, which was first used to describe primary progressive multiple sclerosis in 2009, has just been included to the new MS classification scheme [14].

2.4 Primary Progressive Multiple Sclerosis (PPMS)

PPMS class refers to victim with progressively impaired neurological role from the duration of symptom. Clinically, patients primarily exhibit progressive myelopathy, but as will be discussed further, they may also exhibit progressive cerebellar syndrome or other progressive symptoms. The McDonald’s 2010 criteria call for evidence of DIS in the brain (at least one T2 lesion periventricular, close to the cortex, or under the tent), evidence of DIS in the spinal cord, and at least 1 year of clinical illness progression, either positive CSF (isoelectric focusing of oligoclonal bands and/or raised immunoglobulin G index) or the spinal cord (at least two T2 lesions of the spinal cord). Like RRMS, the number of MRI DIS lesions does not include symptomatologic lesions [9].

2.5 Secondary Progressive Multiple Sclerosis (SPMS)

Up to 40% of patients with SPMS, which is characterized by persistent advancement after the first course of recurrence, are diagnosed 20 years after the initial incidence [15]. It is typically characterized by a gradual decline in brain function, frequently involving CNS regions that were formerly active throughout the recurrence process. A transition to SPMS can be difficult to pinpoint, and it frequently takes a few years from the first, imperceptible signs of advancement to be realized [16]. Studies on potential imaging and laboratory biomarkers that could discriminate between SPMS and RRMS and more accurately describe the change from RRMS to SPMS are currently being conducted.

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Symptoms of MS The clinical manifestations of MS include mononuclear painful vision loss, hemiparesis, hypoesthesia, dysesthesia, paresthesia, vertigo, reduced motor function, and sexual dysfunction [17–19]. The emergence of multifocal inflammatory lesions is the basic pathological mechanism in relapsing–remitting MS. Furthermore, the array of disability that develops over time and characterizes progressive MS appears to be caused primarily by neurodegeneration and diffuse immunological processes. Autoreactive T-cells (CD4+ T cells), which are triggered by an unidentified mechanism, are involved in the immune-pathogenesis of MS. This mechanism is likely related to activation of molecular mimicry or bystander. Through an interaction between integrins on the surface of these

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Table 1 Typical and atypical presentations of MS patients Typical presentations

Atypical or red flag presentations

Acute unilateral optic neuritis

Bilateral optic neuritis or unilateral optic neuritis

Double vision

Complete gaze palsy or fluctuating ophthalmoparesis

Facial sensory loss or trigeminal neuralgia

Intractable nausea, vomiting, or hiccups

Cereballar ataxia and nystagmus

Complete transverse myelopathy with bilateral motor

Partial myelopathy

Sensory involvement

Sensory symptoms in a CNS pattern

Encephalopathy

Lhermitte’s symptom

T-cells and adhesion molecules on the blood–brain barrier (BBB) endothelium, stimulation of these T-cells has the potential to cross the BBB. Thereafter, the BBB is degraded because of the secretion of matrix metalloproteases. The mechanisms behind the treatments currently in use are varied, but only a small number of them are nonspecific. They also vary in terms of administration method, effectiveness, and side effect profile. While there is a growing consensus in favor of early intervention [2, 20], which is made possible by more widely available and precise diagnostic criteria, the methodology and access to illness management also differ significantly. These findings unequivocally show that the pathophysiology of MS is complicated [4]. These observations clearly demarcate the fact that the pathophysiology of MS is complex (Table 1). 3.1

Optic Neuritis

Optic neuritis is present in roughly 20% of patients and affects about 50% of MS patients over the course of the disease [21]. The patient may experience blurred or lost vision, which is typically accompanied by eye pain. Low-contrast vision and color vision such as red desaturation are also impacted.

3.2

Myelitis

Patients with MS may develop either transverse myelitis or partial myelitis. Transverse myelitis is a spinal cord condition that affects the motor, sensory, bowel, and bladder tracts. One or more of these functional spinal tract deficits, but not all, are involved in partial myelitis. An uncomfortable band-like sensation around the chest or abdomen is a common MS symptom [22, 23].

3.3 Brainstem Syndromes

In patients with MS, the brainstem is commonly affected. There can be various clinical syndromes that can be observed like double vision, internuclear ophthalmoplegia, weakness in the face or myokymia, dizziness, and several bulbar symptoms including tongue paralysis, dysarthria, and dysphagia. Hearing loss is less frequent [24].

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Motor Symptoms

At some course of disease, weakness affects about 89% of the patients. Due to the involvement of the corticospinal tract, upper motor neuron syndrome symptoms can include limb weakness in addition to other symptoms. Even in the absence of paralysis, spasticity can cause stiffness, cramping, spasms, and a change in stride [24].

3.5 Sensory Impairment

At some course of disease, sensory impairment affects about 87% of the patients. MS patients can experience numbness and paresthesias. These symptoms can be transient lasting for few seconds to minutes or in severe cases can last from hours to days [24].

3.6

Many patients with MS often suffer from being off-balance, unsteady, or uncoordinated. In these cases, a thorough neurologic evaluation is necessary to pinpoint the issue because the symptoms could be caused by cerebellar dysfunction, sensory impairment, or vestibular dysfunction, spasticity, or weakness [24].

Imbalance

3.7 Cognitive Impairment

At some course of disease, cognitive impairment affects about 40–70% of the patients [25]. Patient can experience dementia. Additionally, the patient may develop executive dysfunction, a slowing of information processing, and deterioration of long-term verbal and visual memory [25].

3.8

Depression

About 30–45% of MS patients faces major depression [26, 27].

3.9

Fatigue

In nearly 83% of patients, fatigue is common in MS. Fatigue is associated with chronic CNS inflammation [24].

3.10 Bladder and Bowel Dysfunction

An important cause of disability in MS is bladder and lower urinary tract impairment [28]. Detrusor hyperreflexia is prevalent in around two thirds of MS patients. In MS, bladder involvement is more common than gastrointestinal dysfunction, and constipation is a typical symptom.

3.11 Sexual Dysfunction

One third or so of patients report having sexual problems. The most prevalent pathology in men is erectile dysfunction, whereas in women it is loss of libido and/or exhaustion [24, 29].

3.12

Heat can worsen the symptoms of MS and can also slowly permit the old symptoms that that have been remitted. An exposure in sun on a hot day, heavy exercise, or a hot shower can be among the reasons to heat exposure. Symptoms due to heat are often transient and can be ameliorated by cooling down the body temperature [24].

Heat Sensitivity

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Headache

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Headache in MS patients is attributed to migraine and is reported in about two thirds of the patients [30]. It’s critical to distinguish between migraine and MS symptoms as it is important for the appropriate treatment [24].

Epidemiology The epidemiology of the disease varied worldwide. The prevalence of the disease is based on the geographical area and the race. Nearly about 33 cases per 100,000 people are observed worldwide. It is highly prevalent in white population of temperate region [17]. It is more common among the young adults aged between 20 and 40 years. Compared to men, women are more prone to the disease’s progression. Nearly three to one is the ratio of female to male. A significant impact is the genetic component; roughly one in eight individuals has a family history of the condition. Monozygotic twins are additionally more vulnerable [31]. MS comes in two main kinds. A SPMS is reached after a RRMS for most MS patients [6]. RRMS affects women twice as frequently as males, with a prevalence of 85–90% [32]. Most RRMS patients go on to develop SPMS in the future. PPMS, which has an insidious disease onset and consistent progression, affects 10–15% of individuals. About 10–15% of RRMS patients have a less severe illness history and can exhibit clinical stability for many years (benign MS). Most times, clinically isolated syndrome (CIS), a demyelinating disease that can affect the CNS in either focal or multifocal fashion and most usually affects the optic nerve, brainstem, or spinal cord, is followed by the start of MS [32]. Early MS symptoms include white matter lesions that are new and signal inflammation with blood–brain barrier disruption. While rarely new lesions, especially enhancing lesions, occur as patients begin the progressive phase [33].

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Factors in the Pathophysiology of Demyelination-Induced Neurodegeneration Although the disease’s etiology is not fully understood, there are a number of risk factors connected to it. Vitamin D deficiency, lifestyle factors like cigarette smoking, overweight or obesity, high salt level in diet, and gut microbiota are the factors responsible for development of the disease. Caffeine may reduce the risk of the disease [34]. Figure 2 briefly describes the various factors responsible for demyelination which may result into MS. The etiology is not well established, and multiple sclerosis is thought to arise from a breakdown in regulation of immune system which can be a result from genetic predisposition or environmental factors that possibly include viral infections like Epstein–Barr virus [31]. In Multiple sclerosis’ immune-pathogenesis there is a role for

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Fig. 2 Factors associated with astrogliosis leading to neurodegeneration

autoreactive T-cells (CD4+ T cells) in the background, which are triggered by an unidentified mechanism, possibly bystander or molecular mimicry activation. When T-cells are activated, they gain the ability to cross the BBB through interaction with adhesion molecules on the endothelium of the BBB. Thereafter, the BBB degrades as a result of the secretion of matrix metalloproteases. T-cells in the CNS become reactivated when they interact with macrophages, microglia, or B-cells, which are antigen-presenting

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cells that have come into contact with local CNS antigens. T-cells’ release of inflammatory cytokines and plasma cells’ production of myelin-specific antibodies cause myelin sheath destruction. Inflammation encourages the recruitment of additional inflammatory cells into the CNS. Proteases, nitric oxide, and free radicals are released when microglia are activated, causing tissue damage and axon loss [2]. The discovery that inflammation may be diminishing in tandem with neurodegeneration at very late stages of the disease, to levels seen in age-matched controls, supports the assertion that inflammation is the primary cause of neurodegeneration in all levels of MS [6].

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MS Related to Autoimmunity Autoimmunity is widely known to be a major cause for MS. Various data have been collected from experimental autoimmune encephalomyelitis, a disease that leads to the pathological events of the CNS, which is characterized by many aspects of MS [35]. This aspect is further backed by the immunological studies that clearly demonstrate that in MS patients, there is the appearance of autoantibodies and autoreactive T-cells [36]. From MS lesions, extraction of antibodies like anti-myelin oligodendrocyte [36], myelin basic protein peptides and MS lesion class II major histocompatibility complex have also been found in antigen-presenting cells. However, no distinct autoimmune response to MS has been found. Summarizing these data reveals that in the pathogenesis of the disease, autoimmune responses are involved. It is still unexplored whether the autoimmune response is the major factor for MS or it provides a secondary mechanism for enhancing the lesion formation. The fact that only class II major histocompatibility complex (MHC)-restricted CD4+ T lymphocytes have been identified as the cells that start the CNS autoimmune is another significant element of MS autoimmunity. However, a fresh experimental paradigm currently shows that class I MHC-restricted CD8+ T cells can also cause inflammation in the brain and organ-specific autoimmunity [37, 38]. As was already established, CD8+ T-cells predominate the T-cell infiltration in all MS lesions, regardless of the clinical subtype of the illness or the lesion’s stage, and these cells show dominating clone proliferation. Additionally, such CD8+ T-cell clones in the patient’s immune system may remain stable for several years [39], and autoreactive class I MHC-restricted T-cells have been seen in the circulation of MS patients [40]. Such T-cell-mediated autoimmunity may be more MS-specific than class II MHC-restricted CD4+ T-cell-mediated autoimmunity but is currently unknown.

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MS Related to Infection and Environmental Factors Major efforts have been made to identify potential triggers for the transmission of the disease [41]. So far, no MS-specific infectious agent has been identified, but several candidates have been proposed. It is in good agreement that the disease could not be transmitted to laboratory animals and that epidemiological studies did not produce evidence of transmission of the disease among individuals living in the same environment. However, this does not preclude foreign substances or endogenous viruses from being present in the brain and initiating or transmitting an inflammatory response. Epstein–Barr virus (EBV) is currently considered a potential candidate due to the association between MS susceptibility and EBV infection and sufficient evidence of a T-cell and antibody-mediated immune response against EBV in patients [42]. The presence of EBV-infected B-cells in MS lesions has been reported, suggesting that they participate in promoting the inflammatory process by specific T-cell immune responses or innate immune activation [43]. However, the presence of EBV-infected B-cells in the MS brain has not been confirmed in other studies [44, 45]. These discrepancies may be due in part to technical problems in the detection of EBV infections and their interpretation in human biopsy and autopsy tissues [8]. In addition to classical productive infections, activation of endogenous viral antigen expression may also be involved in the pathogenesis of multiple sclerosis [46]. Endogenous retrovirus products are easily detected in MS lesions and are found primarily in actively demyelinated plaques. However, their expression in the brain is not specific to MS and is also found in various other pathological conditions of the nervous system. They may be involved in the transmission of tissue damage, as some of them have the potential for cytotoxicity or cause cellular stress. Alternatively, it may function as a target for immune responses mediated by T-cells and B-cells or may promote inflammation nonspecifically [46]. Another interesting observation that may correlate the pathogenesis of MS with infection is that bacterial peptidoglycan is detectable in some lesions. These molecules may stimulate the innate immune response through toll-like receptor-mediated activation. It is not yet clear how such antigens access brain lesions in MS patients, but may reflect systemic peripheral infections that are not uncommon in patients with chronic disease or severe neuropathy. When they reach the brain via the impaired BBB, they can locally activate effector cells such as microglia and propagate the inflammatory response. Therefore, peripheral infections can cause recurrence of MS not only by activating peripheral immune but also by providing pro-inflammatory molecules directly to established lesions. In summary, MS is not an infectious disease in the classical sense, and so far, there is no

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conclusive evidence that the brain is infected with exogenous substances. However, this does not necessarily mean that MS is an autoimmune disease [46]. Whether the disease is initiated or caused by an endogenous self-antigen or an exogenous drug, the quest is still open.

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Neurodegeneration MRI shows that neurodegeneration occurs in the MS brain in the absence of inflammation, detected by gadolinium–diethylenetriamine pentaacetic acid (Gd–DTPA) leakage. Additionally, immunosuppressive, immunomodulatory, or anti-inflammatory therapies decrease the frequency of newly developed localized white matter lesions, but have little effect on progressive neurodegenerative events. Based on these results, it had been proposed that MS brain injury is facilitated by two presumably autonomous events: an inflammatory response that promotes the development of lesions of localized white matter and diffuse and progressive brain neurodegeneration that causes injury [47, 48]. Research on early MS lesions in patients who passed just hours after the beginning of clinical disease due to lesions in the brainstem further corroborated this observation. In this case, microglial activation has been linked to oligodendrocyte death and early demyelination, but these studies found no infiltration of T-cell lesion parenchyma, MS may be caused by oligodendrocytes and myelin degradation, and after that there is an inflammatory response that can enhance tissue damage. Such concepts are partially supported by experimental evidence. Some inflammations are common in mice with genetically determined demyelinating disease, and mating such mice with immunodeficient animals reduces demyelinating and tissue damage. Similar observations have been made with autoimmune encephalomyelitis. Induction of demyelination in these animals, for example, by antibody demyelination, results in further activation of microglia, exacerbation of inflammation, and increased axonal damage [49]. Release of myelin degradation products may be one of the stimuli that exacerbates microglial activation and inflammation, perhaps in combination with radical-mediated myelin damage. In humans, a subset of patients with an inflammatory demyelinating disease that bears just a passing resemblance to MS exhibits a genetically determined abnormality in lipid metabolism.

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Current Treatment Strategies At present, there is no exact treatment for MS that can completely cure MS. The treatment is primarily based on immunosuppressive and immune-modulating agents. The treatment of multiple

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sclerosis can be categorized into three classes: treatment of exacerbations, symptomatic therapies, and disease-modifying therapies (DMTs). Depending on clinical manifestations, different interventions can be prescribed. Comprehensive treatment can be enclosed in three areas: relapse managing, modification of disease, and management of symptoms. One of the components of comprehensive treatment of multiple sclerosis is the relapses control, which can be described as spontaneous episodes of novel or recurrent neurological dysfunction; they need to be detached from unrelated neurologic symptoms due to contagion, feverish, or other traumas, which is named as pseudo-relapses. Mainly true relapses last for 24 h at least (though the typical total duration is 3 months approx.) and are related with new symptom emergence not earlier observed by the patient, yet in few cases, old symptoms may re-emerge. The managing of multiple sclerosis has become very complicated with the expansion of additional DMTs. For the prophylaxis of relapsing–remitting multiple sclerosis, vast DMTs have been planned, it aims to minimize the attack rate and also to postpone the progression of the disease, and it mainly targets inflammation in RRMS patients [50]. Although significant developments have happened in the therapy of multiple sclerosis, consideration for progressive disability rate and early mortality is required. Lately, three monoclonal antibodies, namely, ocrelizumab, rituximab, and ofatumumab, have been accepted for multiple sclerosis treatment; among them, ocrelizumab has demonstrated beneficial effects in primary progressive MS clinical trials. Moreover, new treatment approaches that focus on neuroprotection or remyelination are under assessment [51, 52]. 9.1 Treatment of Exacerbations

The problem related with relapse controlling comes from describing if the episode of MS is true relapse or fluctuation or exacerbation because of presence of already existing demyelinating lesion [53]. The prime concern is to treat and exclude any associated contagion like urinary tract infections (UTIs), which may be responsible for such agitations. If there is no certainty, gadolinium MRI can be supportive and can show new growing lesions till 6 weeks after any relapse starting. High-dose methylprednisolone therapy must be considered. When the relapse is of reasonable functional severity or worse, a dose of 500–1000 mg/day, as per local norms, should be taken for 3–5 days. Plasma exchange is occasionally used as an additional therapy, or it might be used exclusively if the relapse is progressing or severe. To increase the recovery rate, therapeutic interventions like physiotherapy can be introduced earlier [54].

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9.2 DiseaseModifying Treatments

In the previous 20 years, an uprising is observed in the management of MS, especially in the previous 5 years with the appearance of new potent disease-modifying treatments (DMTs). The main motive of DMT is the amalgamation of clinical and MRI factors, condensed as “no evidence of disease activity” (NEDA). In the United Kingdom, generally disease-modifying therapy is recommended and examined by neurologists with a main interest in multiple sclerosis, as per guidelines issued by the National Institute for Health and Care Excellence (NICE) [53].

9.3 Symptomatic Treatments

MS patients experience a large array of remarkable and immobilizing symptoms. All the symptoms don’t have effective therapies. The key is to remove coexisting underlying causes, such as illness or anemia, that deal with provoking factors like insomnia and use the existing treatments sensibly and persistently, in a manner such that it relieves the symptoms [53, 54].

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Emerging Treatments for MS

10.1 Bruton’s Tyrosine Kinase (BTK)

The inhibition of BTK is an emerging therapy for relapsing–remitting MS and secondary progressive MS. It mainly works by regulating immune cells (B-cells and microglia) of CNS [55].

10.2 Stem Cell Transplantation

In this therapy, the immune cells of patient with MS are destroyed and then these are replaced by the healthy one. Studies are still undergoing to find out if this can inhibit the inflammation in patient with multiple sclerosis to increase the immune system [56].

10.3 DiseaseModifying Injectable Therapies

Ofatumumab (monoclonal antibody): It was approved by FDA in 2020. It mainly targets the cells, i.e., B-cells, that damage the nervous system. It inhibits MS brain lesions and worsens symptoms. Mode of drug administration: It is given via subcutaneous route. Possible side effects include the local reactions to injection, infections, and headaches [57]. Interferons: These drugs mainly work by reducing the inflammation and increasing the growth of nerves. Mode of drug administration is via subcutaneous and intramuscular injections. Possible side effects include reactions at site of injection, anemia, flu-like symptoms, etc. [58].

10.4

Fingolimod was approved by FDA in 2010 and was the first FDA-approved oral DMT. The discovery of fingolimod was groundbreaking because of its working and oral route [59]. Siponimod was approved by FDA in 2019 for both forms of MS, relapsing–remitting and secondary progressive. This is an immune-modulating therapy [60].

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Cladribine was approved by FDA in 2019 for both forms of MS, relapsing–remitting and secondary progressive. In clinical trials, it inhibits disability progression and relapse rate [61]. Ozanimod was approved by FDA in 2020. It inhibits the rate of relapse in MS. It may show side effects like elevated blood pressure, infections, and liver inflammation [62]. Ponesimod was approved by FDA in 2021 and this medicine has to be taken once a day by gradually increasing dose schedule. It has lesser rate of relapse and shows few brain lesions than other medicines used for MS. Possible side effects may be respiratory tract infections, high BP, liver irritation, etc. [63]. 10.5

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Ocrelizumab was given approval in 2017 by the US Food and Drug Administration (FDA). It inhibits the rate of relapse and disability development in relapsing–remitting MS. Additionally, this one is the first DMT to halt the advancement of MS’s primary progressive form [64]. Alemtuzumab was given approval in 2014 by the FDA. It is a monoclonal antibody that shows the advantages of brain MRI and reduces the rate of relapse annually. Low-platelet counts and thyroid problems may be its negative consequences [65, 66].

Significance of Astrogliosis in the Pathogenesis of MS There are two types of each glial cell, namely, microglial cells and macroglial cells, in the brain. Microglial cells originate from the mesoderm, whereas macroglial cells originate from the embryonic ectoderm. Astrocytes and oligodendrocytes are the two types of macroglial cells [67]. Astrocytes are small cells that are radially organized and show diversity based on molecule, structure, and function. Morphologically, astrocytes are star-shaped and are plentiful in CNS glial cell types. They play various important roles like in BBB maintenance, in survival of neurons, and in construction of synapse, strength, and turnover. They also manage the flow of blood and inflammatory processes by releasing the signal mediators. They play multiple functions in the CNS of the adults. Earlier astrocytes were attributed to be only a structural support to axonal function, but Santiago Ramon Y and Camillo Golgi put forward its functional capacity. The molecular and functional changes in astrocytes may graduate to astrogliosis. It is a process in which reactive astrocytes envelop around the injury sites to reduce the tissue damage [68, 69]. Combining the various studies that have been done, it can be said that the astrocytes are the active regulators of processing the information in CNS. In the normal central nervous system, both steady-state microglia and inactive astrocytes can increase or prevent the distinction of oligodendrocyte progenitor cells into grown myelinating oligodendrocytes [70]. Astrocytes

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combine activity of neurons by enhancing their intracellular calcium levels, and this leads to the release of neuroactive substances which regulate the synaptic communication. In the pathological process of MS lesions, the role that was originally assigned to astrocytes was the development of the glial scar the inflammation has descended. They are now considered as the key to provide peripheral immune cell access to the CNS [71]. Astrocytes play various functions in the development of MS lesion; they not only recruit leukocytes and contribute damage to tissues damage, but they also constrict swelling and promote lesion repair. Along with this, astrocytes themselves bear considerable damage during the inflammation process. They are also involved in the neurosteroid production like allopregnanolone, estrogen, and dehydroepiandrosterone (DHEA), which are produced in the nervous system, and then they perform various functions like modulating neuron excitability, promoting myelination, and decreasing pro-inflammatory responses in astrocytes. Within healthy CNS, astrocytes make contribution to an anti-inflammatory condition via secreting low level of the anti-inflammatory cytokines TGF-β and IL-10, expressing Fas ligand, and triggering the upregulation of the co-inhibitory receptor of CTLA-4 on helper T cells [71, 72]. Astrocytes are being known as cells that help in the progress of multiple sclerosis lesions. Earlier, astrocytes were supposed to respond only at later, post-inflammatory stages by the formation of glial scar, but with advancements they are now measured earlier and active players in lesion pathology. In active lesions, astrocytes are assumed to have a hypertrophic morphology, which is described by huge cell enlargement and reduction in process density. Astroglial hypertrophy is an indicator of injury in tissues and may be initiated in MS lesions by loss of oligodendrocytes and results in interruption of astrocyte–oligodendrocyte networks. Furthermore, hypertrophic astrocytes can themselves undergo damage that causes recantation or loss of glia limitans from the basal lamina surrounding blood vessels, which may further increase the access of immune cells to the CNS [73]. The role of astrocytes has been established in healthy as well as diseased CNS. Astrocytes not only play a vital role in supporting element in neurons, but it also has functions like synaptic transmission and processing [57]. Astrocyte is also a key structural component of the BBB; it extends and wraps its feet around the cerebral vasculature, hence controlling the movement of small soluble cells and molecules inside the CNS [74]. Connexin 43 (Cx43), a protein that forms the junction gap, aquaporin-4 (AQP4), kir4.1, and other specific proteins of channel formation are among the molecules that are expressed at the glia limitans by the astrocytic end-feet. These are in charge of enabling astrocytes and endothelial cells to interact directly, giving them the capacity to regulate the diffusion and redistribution of water, ions, and other soluble

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substances across the BBB [75]. Astrocytes regulate the flow of blood through the CNS by the production of various local vasoactive molecules like nitric oxide, arachidonic acid, and prostaglandins [76]. They also control the extracellular fluid’s pH and composition through specific water and ion channels [77]. Astrocytes are located at the point where blood arteries meet neurons; as a result, they give neurons energy by removing glucose from the bloodstream and serving as a source of glucose for the central nervous system [78]. In the myelin synthesis, CNS cholesterol is needed which is fulfilled by lipid metabolism that is regulated by astrocytes [79, 80]. Astrocytes play a key role in the innate immune response; in addition to their capacity to create cytokines and chemokines and take part in immunomodulatory activities, they also express MHC-II molecules [81, 82]. In the neurological domain, astrocytes play both positive and negative roles known as astrogliosis. In response to the CNS disease and trauma, astrocytes activate and integrate cell proliferation, morphological changes like increased branching, elongation of cells, augmented size of cells, and functional modification [83]. Glial fibrillary acidic protein (GFAP), a marker of astrogliosis, is upregulated in the astrocytic reactivity or astrogliosis in CNS disease and stress. Reactives have two phenotypes: A1 and A2. A1 phenotype of astrocyte is related to neurotoxicity and is induced by neuroinflammatory microglia in diseased CNS. On the contrary to A1, A2 phenotype is attributed to anti-inflammation and neuroprotection [84]. It has been recognized that the response of astroglia is regionand disease-specific. It has been established that in CNS autoimmunity, the reactive astrocytes play a dual role, i.e., they can be reparative or may be detrimental. The gain or loss of the functional property of astrocytes is determined by the molecules released by the damaged CNS cells, neighboring glial cells, and the immune cells like T-cells [85]. It is widely known that astrocytes in MS create and react to immunomodulatory cytokines that have both pro- and antiinflammatory characteristics. Tumor necrosis factor (TNF) is elevated in the astrocytes found at the margin of active and chronic lesions [86, 87]. There is a correlation established between lesion severity in MS and astroglial TNF expression. In chronic silent lesions, TNF is not detected in astrocytes but few TNF+ cells display macrophage morphology. In MS, TNF is not only produced by astrocytes, but it is also produced by other cells like neurons, immune cells, and microglia. TNF can be both beneficial and harmful in neurological illnesses like MS [88]. Through activation of TNFR1, the soluble form of TNF (solTNF) participates in pro-apoptotic and pro-inflammatory activities. TNF2 is activated by the other form, membrane-bound form (memTNF), which is in charge of the healing and anti-inflammatory activities [78].

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Role of Astrocytes in Demyelination Astrocytes have a dual role in autoimmune demyelination. The demyelinating action of astrocytes is attributed to their enhanced immune response in the CNS. Autoreactive T-cells are activated by astrocytes via the expression of co-stimulatory molecules, MHC-II, and adhesion molecules [89]. The cytokines derived by astrocytes are IL-23 and IL-12 which promote responses of TH17 and TH1, respectively. In demyelination induced by a virus, production of IL-6 and type I interferons (IFN-a/b) is stimulated and induced by PKR (dsRNA-dependent protein kinase) and TLR3 (toll-like receptor 3). Cell-mediated autoimmune response is initiated with the help of these innate responses. Chemokines are produced by astrocytes to recruit microglia, macrophages, and T-cells for inflammatory lesions. Also, the inhibition of aspects of immune response by astrocytes helps in inhibition of demyelination. By producing anti-inflammatory cytokines (IL-4, IL-10, IL-5, IL-27, and TGF-b) and expressing CTLA-4, astrocytes reduce autoreactive Th17 and Th1 responses. TLR3 activation causes the release of anti-inflammatory cytokines to be activated and the pro-inflammatory cytokines IL-23 and IL-12 to be inhibited. Finally, TIMPs (tissue inhibitors of metalloproteinases) block the activity of matrix metalloproteinases (MMPs), which reduces the ability of autoreactive T-cells to move to demyelination sites [71].

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Role of Astrocytes in Remyelination Astrocytes act on OPCs (oligodendrocyte progenitor cells) and axon regeneration to modulate remyelination. Axon growth due to injuries is inhibited by Nogo-A receptor (NgR) and chondroitin sulfate proteoglycans (CS-PGs). Glial scar, i.e., the physical barrier around demyelinated lesions, is the point of association of astrocytes with NgR and CS-PGs. Binding of axon receptors by ephrins released by astrocytes induces collapse of growth cones on the regenerating axons. Modulation of astrocytes target OPCs. Remyelination is inhibited by preventing OPC maturation which is caused by a potent mitogen for OPCs known as fibroblast growth factor2 (FGF-2). OPC proliferation is essential for remyelination, as is their migration to demyelination sites and maturation into myelinating oligodendrocytes. Accodingly chemokines (IL-6, IL-11, IGF-I), plorifeartion and maturation are all ways that astrocytederived substances encourage OPC migration. OPC survival is promoted by release of neurotrophic factors CNTF, NT-3, and BDNF that are the result of TLR3 activation. Myelin damage is limited by astrocytes due to their ability to take up extracellular glutamate that induces excitotoxicity in oligodendrocytes [90].

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Pharmacological Targets in the Astrogliosis-Dependent MS Till date, the available treatments for demyelination are only partially capable of preventing the deterioration in CNS and have no potential to reverse it. Targeting astrogliosis can be one of the novel strategies that can be adopted in order to find a treatment for the demyelination and related CNS disorders as astrogliosis is linked to both neuroinflammation and neurodegeneration in demyelinating diseases. Melanocortin receptor agonists are one such class of compounds that have shown to have beneficial effects in demyelination through astrocytes. A highly conserved neuropeptide group, melanocortins are sliced in the pituitary gland with the help of a precursor known as pro-opiomelanocortin (POMC) and show their action by activating the G-coupled protein melanocortin receptors (MC1R-5R) [91]. Alpha-melanocyte-stimulating hormone (α-MSH) is one of the melanocortins that has been found to lower the disease in MS animal model experimental autoimmune encephalomyelitis (EAE). It elicits this property by limiting the inflammation in CNS and also in periphery [92]. MC4R is one of the melanocortin receptors expressed in various brain regions like in the hypothalamus, thalamus, cortex, brainstem, and hippocampus [93, 94]. There are various evidences that show that MC4R activation in astrocyte can have potent neuroprotective effects and anti-inflammatory effects [91, 95]. Both MC5R and MC1R are expressed in the periphery for which α-MSH is a full agonist. These studies propose that astrocytic MC4R is a potential target for inflammation of amelioration and degeneration of neurons in demyelinating diseases like MS. However, it has not been yet studied in detail neither in human astrocyte nor in astrocytic tissues [96]. Various research advocate that targeting of MC4R on astrocytes provides the chances for the improvement of new treatments for MS. Reactive astrocytes are sources of production of high levels of endothelin-1 (ET-1) which are found in both cerebrospinal fluid and plasma of multiple sclerosis patients [97, 98]. In the pathology of MS, the ET-1 produced in response to astrogliosis results in interruption of BBB and enhancement of responses due to inflammation, promotes toxic action of excitatory neurotransmitters, and lowers cerebral blood flow (CBF) [99]. From reactive astrocytes ET-1 is released which acts as a negative regulator of oligodendrocyte progenitor cells and remyelination differentiation [89]. Various drugs have shown to affect ET-1 synthesis by acting at various levels of ET-1 expression. Rolipram, simvastatin, fenofibrate, resveratrol, fluoxetine, prucalopride, and daglutril are some of the drugs that act on ET-1 expression produced by reactive astrocytes [98]. Dimethyl fumarate (DMF) is a first-line medication for RRMS. DMF contains anti-inflammatory and antioxidant properties, albeit its exact mechanism is still unknown. With fewer negative effects, a

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newly developed fumarate isosorbide di-(methyl fumarate) (IDMF) can partially mimic the activities of DMF. ICAM1, a gene associated with proliferative reactive astrocyte phenotype, and genes encoding NF-kappa-B subunits (NFKB2, RELA, RELB) and NF-kappa B targets were suppressed, according to a specific transcriptome, IDMF (NCAPG, CXCL1, OAS3). These findings demonstrate that astrocyte-centered processes underlie the demonstrated efficacy of DMF as an RRMS therapy. Additionally, a number of findings support the idea that IDMF is a novel fumarate that could be used in future research with various oppressive effects on glial scar formation and astrocyte reactivity [101]. It has been shown that the reactive astrocyte mouse model of multiple sclerosis has elevated levels of S1P receptor 1 (S1PR1). For the loss of demyelination and oligodendrocytes, a modification in selective S1PR1 is sufficient. In cuprizone-exposed animals, therapy with a nonselective S1PR modulator or a short-lived S1PR1-specific modulator, CYM5442, significantly reduced the induction of Il-1, Il-6, Cxcl10, and Cxcl3, which suppressed reactive gliosis and demyelination. Involvement of protein αB-crystallin (CRYAB) leads to the protection of cells from stress and necrobiosis, and it’s also formed in the brain lesions of multiple sclerosis patients. CRYAB plays a role in converting astrocytes into activated form which worsens injury. Also, modification of specific stress-induced CRYAB carries its role in the initiation of astrocyte and disturbs the intracellular signaling pathways it regulates. CRYAB phosphorylation has been linked to different neurodegenerative diseases like Alzheimer disease, Down syndrome, and Alexander’s disease. Phosphorylation of CRYAB can be further investigated as one of the main regulators of reactive astrogliosis and can be proved as one of those important factors that control the pathogenic astrocyte response [102]. In reactive astrocytes, endothelin-1 (ET-1) is extremely expressed which considerably reduces the remyelination rate. It has been discovered that Notch activation is promoted by ET-1 in OPCs during remyelination via stimulation of Jagged1 in reactive astrocytes. Notch activation can be prevented in demyelinated lesions through pharmacological inhibition of ET signaling and remyelination can be activated. Studies reveal that the negative regulator of OPC differentiation and remyelination is ET-1 and is a potential therapeutic target for lesion repair in demyelinated tissue [100]. In MS brain, the primary cellular source of elevated ceramide production is reactive astrocytes. In astrocytes isolated from multiple sclerosis lesions, the level of mRNA in enzyme-producing ceramide acid sphingomyelinase (ASM) is enhanced. In MS, reactive astrocytes represent a potential extracellular target for fingolimod by directly dipping the pro-inflammatory production and limiting the migration of subsequent trans-endothelial leukocyte [103].

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Conclusions Demyelination is the deterioration of the myelin sheath which is responsible for the proper flow of information from one neuron to another. Demyelination of the CNS leads to many CNS complications and disorders. MS is one of the prevalent demyelinating white matter diseases. It has been prevalent in many populations. There are numerous factors that lead to the progression of MS. Autoimmunity, environmental factors, and various gene-related factors are responsible for the disease. Up-to-date treatment strategies available for MS do not provide the cure and are only responsible for symptomatic relief of the disease. Different glial cells are present in the lesions of the MS brain. Astrocytes are one of the glial cells that have a wide role to offer in the MS. These star-shaped cells play a dual role in the disease, and they also have protective as well as destructive role both in demyelination and remyelination. Although reactive astrocytes or astrogliosis often plays a protective role, under some circumstances, they release neurotoxic substances, disrupt intercellular communication, and accelerate the vicious cycle. Astrocytes should therefore be considered as a potential therapeutic target for MS. Numerous targets have been proposed as potential targets for cell-specific therapy, which could lead to new approaches to the treatment or even prevention of neurodegenerative diseases.

References 1. Stadelmann C, Timmler S, Barrantes-Freer A, Simons M (2019) Myelin in the central nervous system: structure, function, and pathology. Physiol Rev 99(3):1381–1431 2. Robert H, Fyffe-Maricich MS, Caprariello AC (2017) Animal models for the study of human disease. In: Animal models for the study of human disease. Academic, pp 967–988 3. Michelle M (2018) Myelin plasticity and nervous system function. Annu Rev Neurosci 41: 61–76 4. Miller DH, Thompson AJ (2017) Advancing trial design in progressive multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England) 23(12):1571–1572 5. Hafler DA, Slavik JM, Anderson DE, O’Connor KC, DeJager P, Baecher-Allan C (2005) Multiple sclerosis. Immunol Rev 204:208– 231 6. Montalban X (2005) Primary progressive multiple sclerosis. Curr Opin Neurol 18: 261–266

7. Procaccini C, Rosa V, Pucino V, Formisano L, Matarese G (2015) Animal models of multiple sclerosis. Eur J Pharmacol:1–10 8. Thompson AJ, Banwell BL, Barkhof F et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17(2):162–173 9. Katz Sand I (2015) Classification, diagnosis, and differential diagnosis of multiple sclerosis. Curr Opin Neurol 28(3):193–205 10. Brownlee WJ, Swanton JK, Altmann DR, Ciccarelli O, Miller DH (2015) Earlier and more frequent diagnosis of multiple sclerosis using the McDonald criteria. J Neurol Neurosurg Psychiatry 86(5):584–585 11. Kuhle J, Disanto G, Dobson R, Adiutori R, Bianchi L, Topping J, Bestwick JP, Meier UC, Marta M, Dalla Costa G, Runia T, Evdoshenko E, Lazareva N, Thouvenot E, Iaffaldano P, Direnzo V, Khademi M, Piehl F, Comabella M, Sombekke M, Giovannoni G (2015) Conversion from clinically

Astrocyte Activation and Drug Target in MS isolated syndrome to multiple sclerosis: a large multicentre study. Mult Scler (Houndmills, Basingstoke, England) 21(8):1013–1024 12. Dobson R, Ramagopalan S, Davis A, Giovannoni G (2013) Cerebrospinal fluid oligoclonal bands in multiple sclerosis and clinically isolated syndromes: a meta-analysis of prevalence, prognosis and effect of latitude. J Neurol Neurosurg Psychiatry 84(8):909–914 13. Martinelli V, Dalla Costa G, Colombo B, Dalla Libera D, Rubinacci A, Filippi M, Furlan R, Comi G (2014) Vitamin D levels and risk of multiple sclerosis in patients with clinically isolated syndromes. Mult Scler (Houndmills, Basingstoke, England) 20(2): 147–155 14. Okuda DT, Mowry EM, BeheSshtian A, Waubant E, Baranzini SE, Goodin DS, Hauser SL, Pelletier D (2009) Incidental MRI anomalies suggestive of multiple sclerosis: the radiologically isolated syndrome. Neurology 72(9):800–805 15. Rovaris M, Confavreux C, Furlan R, Kappos L, Comi G, Filippi M (2006) Secondary progressive multiple sclerosis: current knowledge and future challenges. Lancet Neurol 5(4):343–354 16. Katz Sand I, Krieger S, Farrell C, Miller AE (2014) Diagnostic uncertainty during the transition to secondary progressive multiple sclerosis. Mult Scler (Houndmills, Basingstoke, England) 20(12):1654–1657 17. Oh J, Vidal-Jordana A, Montalban X (2018) Multiple sclerosis: clinical aspects. Curr Opin Neurol 31(6):752–759 18. Norbye AD, Midgard R, GyrdThrane (2019) Spasticity, gait, and balance in patients with multiple sclerosis: a cross-sectional study. Physiother Res Int:1–9 19. Drulovic J, Kisic-Tepavcevic D, Pekmezovic T (2020) Epidemiology, diagnosis and management of sexual dysfunction in multiple sclerosis. Belgian Neurological Society, pp 1–7 20. Giovannoni G, Butzkueven H, Dhib-Jalbut S, Hobart J, Kobelt G, Pepper G, Sormani MP, Thalheim C, Traboulsee A, Vollmer T (2016) Brain health: time matters in multiple sclerosis. Mult Scler Relat Disord 9(Suppl 1):S5– S48 21. Balcer LJ (2006) Optic neuritis. N Engl J Med 354(12):1273–1280 22. Cordonnier C, De Seze J, Breteau G, Ferriby D, Michelin E, Stojkovic T, Pruvo JP, Vermersch P (2003) Prospective study of patients presenting with acute partial transverse myelopathy. J Neurol 250(12): 1447–1452

451

23. Bourre B, Ze´phir H, Ongagna JC, Cordonnier C, Collongues N, Debette S, Fleury MC, Outteryck O, Hannequin D, Vermersch P, de Seze J (2012) Long-term follow-up of acute partial transverse myelitis. Arch Neurol 69(3):357–362 24. Gelfand JM (2014) Multiple sclerosis: diagnosis, differential diagnosis, and clinical presentation. Handb Clin Neurol 122:269–290 25. Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7(12):1139–1151 26. Patten SB, Beck CA, Williams JV, Barbui C, Metz LM (2003) Major depression in multiple sclerosis: a population-based perspective. Neurology 61(11):1524–1527 27. Jones KH, Ford DV, Jones PA, John A, Middleton RM, Lockhart-Jones H, Osborne LA, Noble JG (2012) A large-scale study of anxiety and depression in people with multiple sclerosis: a survey via the web portal of the UK MS Register. PLoS One 7(7):e41910 28. De Ridder D, Van Der Aa F, Debruyne J, D’hooghe MB, Dubois B, Guillaume D, Heerings M, Ilsbroukx S, Medaer R, Nagels G, Seeldrayers P, Van Landegem W, Willekens B, Zicot AF (2013) Consensus guidelines on the neurologist’s role in the management of neurogenic lower urinary tract dysfunction in multiple sclerosis. Clin Neurol Neurosurg 115(10):2033–2040 29. Minden SL, Frankel D, Hadden L, Perloffp J, Srinath KP, Hoaglin DC (2006) The Sonya Slifka Longitudinal Multiple Sclerosis Study: methods and sample characteristics. Mult Scler 12(1):24–38 30. Kister I, Caminero AB, Monteith TS, Soliman A, Bacon TE, Bacon JH, Kalina JT, Inglese M, Herbert J, Lipton RB (2010) Migraine is comorbid with multiple sclerosis and associated with a more symptomatic MS course. J Headache Pain 11(5):417–425 31. Dobson R, Giovannoni G (2019) Multiple sclerosis – a review. Eur J Neurol 26(1):27–40 32. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, Lublin FD, Metz LM, McFarland HF, O’Connor PW, Sandberg-Wollheim M, Thompson AJ, Weinshenker BG, Wolinsky JS (2005) Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol 58(6):840–846 33. Wiendl H, Hohlfeld R (2009) Multiple sclerosis therapeutics: unexpected outcomes clouding undisputed successes. Neurology 72(11):1008–1015

452

Preeti Bisht et al.

34. Amato MP, Derfuss T, Hemmer B, Liblau R, Montalban X, Soelberg Sørensen P, Miller DH, 2016 ECTRIMS Focused Workshop Group (2018) Environmental modifiable risk factors for multiple sclerosis: report from the 2016 ECTRIMS focused workshop. Mult Scler 24(5):590–603 35. Storch MK, Stefferl A, Brehm U, Weissert R, Wallstro¨m E, Kerschensteiner M, Olsson T, Linington C, Lassmann H (1998) Autoimmunity to myelin oligodendrocyte glycoprotein in rats mimics the spectrum of multiple sclerosis pathology. Brain Pathol 8(4): 681–694 36. O’Connor KC, Appel H, Bregoli L, Call ME, Catz I, Chan JA, Moore NH, Warren KG, Wong SJ, Hafler DA, Wucherpfennig KW (2005) Antibodies from inflamed central nervous system tissue recognize myelin oligodendrocyte glycoprotein. J Immunol 175(3): 1974–1982 37. Huseby ES, Liggitt D, Brabb T, Schnabel B, Ohle´n C, Goverman J (2001) A pathogenic role for myelin-specific CD8(+) T cells in a model for multiple sclerosis. J Exp Med 194(5):669–676 38. Sun D, Whitaker JN, Huang Z, Liu D, Coleclough C, Wekerle H, Raine CS (2001) Myelin antigen-specific CD8+ T cells are encephalitogenic and produce severe disease in C57BL/6 mice. J Immunol 166(12): 7579–7587 39. Skulina C, Schmidt S, Dornmair K, Babbe H, Roers A, Rajewsky K, Wekerle H, Hohlfeld R, Goebels N (2004) Multiple sclerosis: braininfiltrating CD8+ T cells persist as clonal expansions in the cerebrospinal fluid and blood. Proc Natl Acad Sci U S A 101(8): 2428–2433 40. Friese MA, Fugger L (2005) Autoreactive CD8+ T cells in multiple sclerosis: a new target for therapy? Brain 128(Pt 8):1747–1763 41. Steiner I, Nisipianu P, Wirguin I (2001) Infection and the etiology and pathogenesis of multiple sclerosis. Curr Neurol Neurosci Rep 1(3):271–276 42. Pender MP (2011) The essential role of Epstein-Barr virus in the pathogenesis of multiple sclerosis. Neuroscientist 17(4):351–367 43. Tzartos JS, Khan G, Vossenkamper A, CruzSadaba M, Lonardi S, Sefia E, Meager A, Elia A, Middeldorp JM, Clemens M, Farrell PJ, Giovannoni G, Meier UC (2012) Association of innate immune activation with latent Epstein-Barr virus in active MS lesions. Neurology 78(1):15–23

44. Willis SN, Stadelmann C, Rodig SJ, Caron T, Gattenloehner S, Mallozzi SS, Roughan JE, Almendinger SE, Blewett MM, Bru¨ck W, Hafler DA, O’Connor KC (2009) EpsteinBarr virus infection is not a characteristic feature of multiple sclerosis brain. Brain 132 (Pt 12):3318–3328 45. Peferoen LA, Lamers F, Lodder LN, Gerritsen WH, Huitinga I, Melief J, Giovannoni G, Meier U, Hintzen RQ, Verjans GM, van Nierop GP, Vos W, Peferoen-Baert RM, Middeldorp JM, van der Valk P, Amor S (2010) Epstein Barr virus is not a characteristic feature in the central nervous system in established multiple sclerosis. Brain 133(Pt 5): e137 46. Perron H, Lang A (2010) The human endogenous retrovirus link between genes and environment in multiple sclerosis and in multifactorial diseases associating neuroinflammation. Clin Rev Allergy Immunol 39(1):51–61 47. Burt RK, Cohen BA, Russell E, Spero K, Joshi A, Oyama Y, Karpus WJ, Luo K, Jovanovic B, Traynor A, Karlin K, Stefoski D, Burns WH (2003) Hematopoietic stem cell transplantation for progressive multiple sclerosis: failure of a total body irradiation-based conditioning regimen to prevent disease progression in patients with high disability scores. Blood 102(7): 2373–2378 48. Coles AJ, Cox A, Le Page E, Jones J, Trip SA, Deans J, Seaman S, Miller DH, Hale G, Waldmann H, Compston DA (2006) The window of therapeutic opportunity in multiple sclerosis: evidence from monoclonal antibody therapy. J Neurol 253(1):98–108 49. Aboul-Enein F, Weiser P, Ho¨ftberger R, Lassmann H, Bradl M (2006) Transient axonal injury in the absence of demyelination: a correlate of clinical disease in acute experimental autoimmune encephalomyelitis. Acta Neuropathol 111(6):539–547 50. Tanasescu R, Ionete C, Chou IJ, Constantinescu CS (2014) Advances in the treatment of relapsing-remitting multiple sclerosis. Biom J 37(2):41–49 51. Singer BA (2016) Parenteral treatment of multiple sclerosis: the advent of monoclonal antibodies. Semin Neurol 36(2):140–147 52. Gholamzad M, Ebtekar M, Ardestani MS, Azimi M, Mahmodi Z, Mousavi MJ, Aslani S (2019) A comprehensive review on the treatment approaches of multiple sclerosis:

Astrocyte Activation and Drug Target in MS currently and in the future. Inflamm Res 68(1):25–38 53. Perry M, Swain S, Kemmis-Betty S, Cooper P, Guideline Development Group of the National Institute for Health and Care Excellence (2014) Multiple sclerosis: summary of NICE guidance. BMJ 349:g5701 54. Doshi A, Chataway J (2017) Multiple sclerosis, a treatable disease. Clin Med (Lond) 17(6):530–536 55. Dolgin E (2021) BTK blockers make headway in multiple sclerosis. Nat Biotechnol 39(1): 3–6 56. Miller AE, Chitnis T, Cohen BA, Costello K, Sicotte NL, Stacom R, National Medical Advisory Committee of the National Multiple Sclerosis Society (2021) Autologous hematopoietic stem cell transplant in multiple sclerosis: recommendations of the National Multiple Sclerosis Society. JAMA Neurol 78(2):241–246 57. Kang C, Blair HA (2022) Ofatumumab: a review in relapsing forms of multiple sclerosis. Drugs:1–8 58. Dhib-Jalbut S (2002) Mechanisms of action of interferons and glatiramer acetate in multiple sclerosis. Neurology 58(8 suppl 4):S3–S9 59. Kappos L, Radue EW, O’Connor P, Polman C, Hohlfeld R, Calabresi P et al (2010) A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. N Engl J Med 362(5):387–401 60. Scott LJ (2020) Siponimod: a review in secondary progressive multiple sclerosis. CNS Drugs 34:1191–1200 61. Deeks ED (2018) Cladribine tablets: a review in relapsing MS. CNS Drugs 32:785–796 62. Lassiter G, Melancon C, Rooney T, Murat AM, Kaye JS, Kaye AM et al (2020) Ozanimod to treat relapsing forms of multiple sclerosis: a comprehensive review of disease, drug efficacy and side effects. Neurol Int 12(3): 89–108 ´ , Freedman 63. Olsson T, Boster A, Ferna´ndez O MS, Pozzilli C, Bach D et al (2014) Oral ponesimod in relapsing–remitting multiple sclerosis: a randomised phase II trial. J Neurol Neurosurg Psychiatry 85(11):1198–1208 64. Syed YY (2018) Ocrelizumab: a review in multiple sclerosis. CNS Drugs 32(9): 883–890 65. Cohen JA, Coles AJ, Arnold DL, Confavreux C, Fox EJ, Hartung HP et al (2012) Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing-remitting multiple sclerosis: a

453

randomised controlled phase 3 trial. Lancet 380(9856):1819–1828 66. Cossburn M, Pace AA, Jones J, Ali R, Ingram G, Baker K et al (2011) Autoimmune disease after alemtuzumab treatment for multiple sclerosis in a multicenter cohort. Neurology 77(6):573–579 67. Rose J, Brian C, Woods J, Pappa A, Panayiotidis MI, Powers R, Franco R (2017) Mitochondrial dysfunction in glial cells: implications for neuronal homeostasis and survival. Toxicology 391:109–115 68. Barres BA (2008) The mystery and magic of glia: a perspective on their roles in health and disease. Neuron 60(3):430–440 69. Sofroniew MV, Vinters HV (2010) Astrocytes: biology and pathology. Acta Neuropathol 119(1):7–35 70. Domingues HS, Portugal CC, Socodato R, Relvas JB (2016) Oligodendrocyte, astrocyte, and microglia crosstalk in myelin development, damage, and repair. Front Cell Dev Biol 4:71 71. Ponath G, Park C, Pitt D (2018) The role of astrocytes in multiple sclerosis. Front Immunol 9:217 72. John GR, Lee SC, Brosnan CF (2003) Cytokines: powerful regulators of glial cell activation. Neuroscientist 9(1):10–22 73. Brosnan CF, Raine CS (2013) The astrocyte in multiple sclerosis revisited. Glia 61(4): 453–465 74. Sinclair C, Kirk J, Herron B, Fitzgerald U, McQuaid S (2007) Absence of aquaporin-4 expression in lesions of neuromyelitis optica but increased expression in multiple sclerosis lesions and normal-appearing white matter. Acta Neuropathol 113(2):187–194 75. Keaney J, Campbell M (2015) The dynamic blood-brain barrier. FEBS J 282(21): 4067–4079 76. Koehler RC, Roman RJ, Harder DR (2009) Astrocytes and the regulation of cerebral blood flow. Trends Neurosci 32(3):160–169 77. Simard M, Nedergaard M (2004) The neurobiology of glia in the context of water and ion homeostasis. Neuroscience 129(4):877–896 78. Brambilla R (2019) The contribution of astrocytes to the neuroinflammatory response in multiple sclerosis and experimental autoimmune encephalomyelitis. Acta Neuropathol 137(5):757–783 79. Camargo N, Brouwers JF, Loos M, Gutmann DH, Smit AB, Verheijen MH (2012) tHighfat diet ameliorates neurological deficits caused by defective astrocyte lipid metabolism. FASEB J 26(10):4302–4315

454

Preeti Bisht et al.

80. Camargo N, Goudriaan A, van Deijk AF, Otte WM, Brouwers JF, Lodder H, Gutmann DH, Nave KA, Dijkhuizen RM, Mansvelder HD, Chrast R, Smit AB, Verheijen MHG (2017) Oligodendroglial myelination requires astrocyte-derived lipids. PLoS Biol 15(5): e1002605 81. Chastain EM, Duncan DS, Rodgers JM, Miller SD (2011) The role of antigen presenting cells in multiple sclerosis. Biochim Biophys Acta 1812(2):265–274 82. Colombo E, Farina C (2016) Astrocytes: key regulators of neuroinflammation. Trends Immunol 37(9):608–620 83. Pekny M, Pekna M, Messing A, Steinh€auser C, Lee JM, Parpura V, Hol EM, Sofroniew MV, Verkhratsky A (2016) Astrocytes: a central element in neurological diseases. Acta Neuropathol 131(3):323–345 84. Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, Bennett ML, Mu¨nch AE, Chung WS, Peterson TC, Wilton DK, Frouin A, Napier BA, Panicker N, Kumar M, Buckwalter MS, Rowitch DH, Dawson VL, Dawson TM, Stevens B, Barres BA (2017) Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541(7638):481–487 85. Sofroniew MV (2009) Molecular dissection of reactive astrogliosis and glial scar formation. Trends Neurosci 32(12):638–647 86. Hofman FM, Hinton DR, Johnson K, Merrill JE (1989) Tumor necrosis factor identified in multiple sclerosis brain. J Exp Med 170(2): 607–612 87. Selmaj K, Raine CS, Cannella B, Brosnan CF (1991) Identification of lymphotoxin and tumor necrosis factor in multiple sclerosis lesions. J Clin Invest 87(3):949–954 88. Probert L (2015) TNF and its receptors in the CNS: the essential, the desirable and the deleterious effects. Neuroscience 302:2–22 89. Cornet A, Bettelli E, Oukka M, Cambouris C, Avellana-Adalid V, Kosmatopoulos K, Liblau RS (2000) Role of astrocytes in antigen presentation and naive T-cell activation. J Neuroimmunol 106(1–2):69–77 90. Williams A, Piaton G, Lubetzki C (2007) Astrocytes – friends or foes in multiple sclerosis? Glia 55(13):1300–1312 91. Kamermans A, Verhoeven T, van Het Hof B, Koning JJ, Borghuis L, Witte M, van Horssen J, de Vries HE, Rijnsburger M (2019) Setmelanotide novel, selective melanocortin receptor-4 agonist exerts antiinflammatory actions in astrocytes and

promotes an anti-inflammatory macrophage phenotype. Front Immunol 10:2312 92. Mykicki N, Herrmann AM, Schwab N, Deenen R, Sparwasser T, Limmer A, Wachsmuth L, Klotz L, Ko¨hrer K, Faber C, Wiendl H, Luger TA, Meuth SG, Loser K (2016) Melanocortin-1 receptor activation is neuroprotective in mouse models of neuroinflammatory disease. Sci Transl Med 8(362): 362ra146 93. Kishi T, Aschkenasi CJ, Lee CE, Mountjoy KG, Saper CB, Elmquist JK (2003) Expression of melanocortin 4 receptor mRNA in the central nervous system of the rat. J Comp Neurol 457(3):213–235 94. Mountjoy KG (2010) Distribution and function of melanocortin receptors within the brain. Adv Exp Med Biol 681:29–48 95. Carniglia L, Durand D, Caruso C, Lasaga M (2013) Effect of NDP-α-MSH on PPAR-γ and -β expression and anti-inflammatory cytokine release in rat astrocytes and microglia. PLoS One 8(2):e57313 96. Lock C, Hermans G, Pedotti R, Brendolan A, Schadt E, Garren H, Langer-Gould A, Strober S, Cannella B, Allard J, Klonowski P, Austin A, Lad N, Kaminski N, Galli SJ, Oksenberg JR, Raine CS, Heller R, Steinman L (2002) Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat Med 8(5):500–508 97. D’haeseleer M, Beelen R, Fierens Y, Cambron M, Vanbinst AM, Verborgh C, Demey J, De Keyser J (2013) Cerebral hypoperfusion in multiple sclerosis is reversible and mediated by endothelin-1. Proc Natl Acad Sci U S A 110(14):5654–5658 98. Hostenbach S, D’Haeseleer M, Kooijman R, De Keyser J (2020) Modulation of cytokineinduced astrocytic endothelin-1 production as a possible new approach to the treatment of multiple sclerosis. Front Pharmacol 10:1491 99. Hostenbach S, D’haeseleer M, Kooijman R, De Keyser J (2016) The pathophysiological role of astrocytic endothelin-1. Prog Neurobiol 144:88–102 100. Hammond TR, Gadea A, Dupree J, Kerninon C, Nait-Oumesmar B, Aguirre A, Gallo V (2014) Astrocyte-derived endothelin-1 inhibits remyelination through notch activation. Neuron 81(3):588–602 101. Swindell WR, Bojanowski K, Chaudhuri RK (2020) A novel fumarate, isosorbide di-(methyl fumarate) (IDMF), replicates astrocyte transcriptome responses to dimethyl fumarate (DMF) but specifically down-

Astrocyte Activation and Drug Target in MS regulates genes linked to a reactive phenotype. Biochem Biophys Res Commun 532(3):475–481 102. Kuipers HF, Yoon J, van Horssen J, Han MH, Bollyky PL, Palmer TD, Steinman L (2017) Phosphorylation of αB-crystallin supports reactive astrogliosis in demyelination. Proc Natl Acad Sci U S A 114(9):E1745–E1754

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103. van Doorn R, Nijland PG, Dekker N, Witte ME, Lopes-Pinheiro MA, van het Hof B, Kooij G, Reijerkerk A, Dijkstra C, van van der Valk P, van Horssen J, de Vries HE (2012) Fingolimod attenuates ceramideinduced blood-brain barrier dysfunction in multiple sclerosis by targeting reactive astrocytes. Acta Neuropathol 124(3):397–410

Chapter 31 Promises of Lipid-Based Nanocarriers for Delivery of Dimethyl Fumarate to Multiple Sclerosis Brain Sreya Subhash, Nishtha Chaurawal, and Kaisar Raza Abstract Multiple sclerosis (MS) is a neurodegenerative autoimmune disorder of the central nervous system (CNS) infecting 2.5 million people worldwide. It is the most common nontraumatic neurological impairment in young adults. The blood–brain barrier rupture for multiple sclerosis pathogenesis has two effects: first, during the onset of the immunological attack, and second, for the CNS self-sustained “inside–out” demyelination and neurodegeneration processes. In addition to genetic variations, environmental and lifestyle variables can also significantly increase the risk of developing MS. Dimethyl fumarate (DMF) and sphingosine-1-phosphate (S1P) receptor modulators that may pass the blood–brain barrier and have positive direct effects in the CNS with quite diverse mechanisms of action raise the possibility that a combination therapy could be successful in treating MS. Lipid nanocarriers are recognized as one of the best drug delivery techniques to the brain for effective brain delivery. Numerous scientific studies have shown that lipid nanoparticles can enhance the lipid solubility, oral bioavailability, and brain availability of the drugs. Nanolipidic carriers for DMF delivery could be derived through vitamin D, tocopherol acetate, stearic acid, quercetin, cell-mimicking platelet-based, and chitosan–alginate core–shell–corona-shaped nanoparticles. Clinical and laboratory diagnosis of MS can be performed mainly through magnetic resonance imaging. The advancements in nanotechnology have enabled the clinicians to cross the blood–brain barrier and to target the brain and central nervous system of the patient with multiple sclerosis. Key words Multiple sclerosis (MS), Targeted brain delivery, Lipid nanocarriers, Blood–brain barrier (BBB), Dimethyl fumarate (DMF), Pharmacokinetics

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Introduction According to the World Health Organization (WHO), neurological diseases are the third most common disease burden worldwide. A neurological disorder caused by an autoimmune process called multiple sclerosis (MS) affects 2.5 million people worldwide [1]. The disease primarily affects young people, and there has not been any mention of gender differences. The majority of MS patients first show symptoms between the ages of 20 and 40 [1]. The several clinical forms of MS are relapsing/remitting,

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_31, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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primary progressive, and secondary progressive forms. The substantial pathophysiological disease mechanisms are related to these forms of MS [2]. The disease typically targets the central nervous system’s (CNS) brain, spinal cord, and optic nerves while sparing the peripheral nervous system’s nerve roots and peripheral nerves which causes axonal degradation, demyelination, and finally neurodegeneration due to inflammation and oxidative stress, which lead to the demyelination of neurons and exacerbate the symptoms. There is evidence linking oxidative stress, which causes apoptosis, to neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), MS, Alzheimer’s disease (AD), and Parkinson’s disease (PD) [3]. Intermittent neurological disturbance caused by the interaction of inflammatory and neurodegenerative processes in MS is typically followed by progressive disability accumulation [4]. Myelin is destroyed in the spinal cord and brain after inflammation in specific CNS white matter regions during an MS attack, which results in progressive disability. Nystagmus, numbness, loss of balance, tremor, motor dysfunction, disturbance in speech and vision, acute paralysis, cognitive impairment, and exhaustion are some of the clinical symptoms of MS. Inflammatory autoimmune condition in MS having one of the major symptoms is malfunction of the blood–brain barrier (BBB). One of the initial cerebrovascular abnormalities observed in MS brains is the dysregulation of the BBB, which is followed by the trans-endothelial migration of activated leukocytes. The autoaggressive T cells cross the BBB and disrupt the myelination of axons in MS considered it as an immune-mediated CNS disease leading to progressive disability [2]. There are numerous relapsing MS treatment medicines on the market, such as dimethyl fumarate (DMF) [5]. Since 1990, fumaric acid esters such as monomethyl fumarate (MMF) and DMF have been used in Germany to treat plaque psoriasis [6]. Then, DMF, a fumaric acid ester, was recently authorized for oral therapy of MS by several federal agencies. In the past 20 years, DMF’s long-term safety studies have been associated with anti-inflammatory effects in Europe. Several investigations have demonstrated that the gastrointestinal tract’s esterase enzyme quickly breaks down DMF into its primary metabolite, i.e., MMF. Although the proper mechanism of action of DMF is still unclear, it is thought to include many pathways, including Kelch-like erythroid cell-derived (ECH)-associated protein-1 (KEAP-1) pathway and nuclear factor (erythroid derived 2)-like 2 (NRF2) [7]. Due to related issues, such as instability in the gastric tract, dosing frequency, high dose, decreased permeability in the brain, financial barriers, and poor patient compliance, DMF, despite being orally efficacious, also presents a significant research opportunity with the use of several lipoidal nanocarriers that incorporate neuroprotective ingredients such as fat-soluble vitamins. For once-daily nano-formulations of DMF the

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neuroprotectives used are retinol acetate, tocopherol acetate, and cholecalciferol of which cellular uptake, physicochemical, stability, pharmacokinetic, and drug release evidences have been described [8, 9]. The current preclinical evaluation looks at how the DMF-designed nano-systems affect mice receiving the demyelinating drug cuprizone’s body weight, locomotor activity, performance, motor coordination, and body weight on a functional observation battery [10]. Neuronal circuit damage or inflammation is a definition of a brain illness. Parkinson’s disease (PD), Alzheimer’s disease (AD), depression, multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), epilepsy, migraine, epilepsy disorders of motor nerve cells, brain cancer, stroke, and brain infection are the most common conditions covered under the heading of brain disorders. The BBB protective system suffers severe damage in brain diseases. The scientific community has identified a variety of medications for the treatment of brain illnesses, but due to subpar physicochemical properties, most pharmaceuticals do not cross the BBB to provide targeted drug delivery in the brain [11].

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Etiology of MS Age, sex, race, genetics, geography, and diseases like herpes simplex, chlamydia, and rabies are some of the possible factors that contribute to the development of MS. MS is the result of complex interactions among genetics, nutrition, and environment. Due to hyper-immunity, the primary cause of MS is an autoimmune attack on the brain. Although there are many postulated pathways that suggested “outside–in” mechanism is CD4+ proinflammatory T cells. The BBB is believed to be crossed because of an unspecified antigen that enhances and activates T-helper 17 (Th17) and T-helper 1 (Th1) cells which then allows for an immune attack through cross-reactivity. Vitamin D deficiency has been proposed as a potential cause of the susceptibility seen in populations residing in higher latitudes. The likelihood of contracting the disease is significantly higher in people with relatives. Heritability is predicted to fall between 35 and 75 percent. Significant evidence links human leukocyte antigen DRB11501 to MS [12].

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Blood–Brain Barrier (BBB) A complex network of vasculature is represented by the blood– cerebrospinal fluid barrier (BCB) which together form a continuous cellular blockade between the systemic circulation and CNS. In contrast with this carefully controlled network, most significant metabolic exchanges necessary for maintaining brain homeostasis

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take place. The location of BCB is the choroid plexus adding to meninges arachnoid layers. The interface between the blood vessels and brain consists of the BBB. The BBB is a membranous barrier which keeps the circulating blood away from the tissues of the brain. The blood capillaries in the CNS have a different structural make-up from capillaries in different tissues and are protected by unique endothelial cells containing no pores that are joined together by tight junctions [13]. 3.1 BBB Disruption Process in MS

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The recurrence in MS may be seen in the same or other areas over the course of weeks, months, or even years. The breakdown of the BBB is assumed to be transitory. Axonal transection to varying degrees, new phases of BBB leaking, and immunologically induced demyelination are all part of the future development and progression of the lesion. Although BBB degradation has lately been linked to the onset and maintenance of MS, an intact BBB represents a significant barrier to the CNS medication delivery process [14].

Risk Factors in MS

4.1 Deficiency of Vitamin D

The activity of vitamin D in stimulating lymphocytes and controlling immune response and growth suggests that it contributes significantly to the pathogenesis of MS. Moreover, the immunological activity of innate immune system is boosted in response. The synthesis of Th1-mediated proinflammatory cytokines is decreased by vitamin D. Interleukin-17 and interleukin-10 levels were significantly changed by vitamin D administration in numerous trials. Those who live farther south or north of the equator are more likely to have MS. Near the equator, the prevalence rate is nonexistent, but it rises to 50 instances per one lac people who live 45° north or south. This intriguing regional distribution is probably a result of vitamin D deficiency in MS patients [15].

4.2

Family History

There is evidence that certain people are more susceptible to MS because of their ancestry. As there is no gene specifically for MS, this genetic predisposition cannot be passed down via the family. There is a relationship between first relatives, second relatives, and third distant relatives, according to genetic studies [16].

4.3

Diseases

According to a theory, viral or bacterial infections may encourage the onset of MS in people with certain genetic predispositions. Childhood disorders may introduce unknown antigens which initiate Th1 cells and cause the autoimmune response that is characteristic of MS [17].

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Investigations into suspected MS triggers have focused on severe accidents that cause direct damage to the brain or spinal cord. Trauma makes the BBB more permeable, which makes it easier for Th1 cells to enter the brain and work as a catalyst for the infection that causes the myelin to be destroyed and developed MS lesions [18].

Injury

Smoking is linked to an increased chance of developing MS. In comparison to nonsmokers, MS smokers had a dreadful extended prognosis and a higher rate of brain atrophy. Moreover, smokers are more prevalent among MS patients than in the overall population. In comparison to the normal people, MS patients are more prone to have coexisting illnesses that are linked to lower life expectancy, enhanced disability, and high mortality rates [18].

4.5 Cigarette Smoking

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Pathophysiology In addition to demyelination, inflammation, axonal loss, and damage, the brain develops plaque, which is referred to as MS. These plaques are seen in white matter adjoining the ventricles, cerebellar peduncles, wide tracts, corpus callosum, brainstem, and subpial region of the spinal cord along with gray matter in the brain and spinal cord. They have spoken out regarding all types of MS, i.e., primary, secondary, and relapsing–remitting MS (RRMS). The dendrocyte degeneration and demyelination patterns exhibit considerable difference between the relapsing–remitting forms and the progressive forms of the MS, as seen by their variable expression over time [19, 20]. MS is viewed as an autoimmune condition brought on by the immune cells that cross the BBB and target the brain. Figure 1 shows the pathophysiology of MS by disrupting

NEUROVASCULAR UNIT

CIRCULATING TH LYMPHOCYTES

PERIPHERY AUTO-REACTIVE TH LYMPHOCYTES TETHERING

AUTO-REACTIVE TH LYMPHOCYTES

ADHESION

ROLLING PSOL-1

PARACELLULAR DIAPEDESIS VLA-4

LFA-1

VCAM-1 ICAM-1

TJ

TRANSCELLULAR DIAPEDESIS

GPCR

CCL19 CCL21

E-selectin P-selectin

CENTRAL NERVOUS SYSTEM

Fig. 1 A schematic diagram of pathophysiology of multiple sclerosis by disrupting the BBB. (Reproduced as per Creative Commons Attribution License from Balasa et al. [21])

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the BBB. The autoreactive immune cells are regularly deleted during growth in the bone marrow or thymus via central B cells. Despite the possibility that some may evade this procedure and enter the bloodstream, peripheral mechanisms often stop them from spreading disease. Peripheral tolerance may be compromised by two mechanisms: regulatory T cells with reduced function and autoreactive T cells resistant to suppression. The stimulation and activity of these autoreactive cells may be impacted by a composite interplay between environmental and genetic factors, influencing disease development. The CD4+ Th1 cells, CD8+ T cells, and Th17 cells are the main T-cell subsets linked to MS. Autoreactive T cells can release the cytokines i1L-17 and granulocyte–macrophage colony-stimulating factors, which may aid in the pathogenesis of MS [22]. The elevated levels of immunoglobulin in cerebral fluid imply that B cells play a part in the disease. MS can be identified by the intrathecal synthesis of immunoglobulins, commonly known as oligoclonal bands (OCBs). The bulk of B cells in the brain parenchyma and cerebrospinal fluid (CSF) in MS are CD27+ memory B cells. In the CSF and brain parenchyma, memory B cells are clonally enlarged and show somatic hypermutation and class-switched immunoglobulin transcripts. Additionally, the OCBs that are present in CSF manifested that the cells secreting from antibody are derived from the clonally enlarged B cells in the brain and are a substantial source in the production of high amount of intrathecal clonal immunoglobulin, which is supported by the overlap of the B-cell immunoglobulin transcriptomes and CSF immunoglobulin proteomes. The B-cell infiltrates are more prevalent in the meninges of MS patients which contribute to the severity of clinical disability, cortical lesions, and neurodegeneration. The Epstein–Barr virus (EBV) reservoirs could be B cells. The B cells change into antigen-processing cells after contracting EBV, which enhances the antigen presentation. It has been demonstrated that EBV-infected B cells internalize and cross-present recombinant human myelin oligodendrocyte glycoprotein, which is quickly recognized by CD8+ T cells [20, 22]. Additional evidence showed that B cells convey antigens more efficiently, which comes from the fact that B cells taken from MS patients have increased CD40 on their surface [23]. A high degree of neurodegeneration was associated with enhanced expression of B-cell activation markers in people with RRMS, as shown by a decline in the brain volume and an increase in frequency of T1 hyperintense lesions. Loss of normal function in the effector T-cell population can affect the development of MS in addition to B-cell-related disorders [23]. EBV infection is controlled in healthy individuals by CD8+ cytotoxic T lymphocytes that destroy lymphoblastoid cell lines that are EBV-infected. The term “latency-specific T cells” will henceforth be used to refer to specific CD8+ cells with cytotoxicity that are primed to recognize and eliminate infected cells that produce

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EBV latent proteins. Exacerbations of MS are accompanied by increases in latency-specific CD8+ T-cell activity and the EBV-specific T-cell population. But as MS worsens, latency-specific CD8+ T cells have a worn-out phenotype and are unable to stop the spread of latently infected cells. The increased number of infected cells that come from this suppresses the autoregulatory mechanism, further depleting T cells. Poor EBV reactivation management may contribute to recurrent relapses by increasing naive B-cell infection and virus production. The substance release which is harmful for oligodendrocytes and antigen presentation to T cells are also involve in B cells in pathogenic processes of MS. Many cytokines, such as tumor necrosis factor (TNF)- and interleukin (IL)-1, are released by macrophages and microglia. These cytokines can cause cell death, impede glutamate absorption in astrocytic cells, and cause abnormal ribonucleic acid-binding proteins. Also capable of releasing glutamate, macrophages and microglia may contribute to neurodegeneration and glutamate excitotoxicity. Reactive oxygen/ nitrogen species are produced by macrophages and microglia, and they can cause mitochondrial damage and oxidative stress, two factors that can lead to dementia. Moreover, microglia have antiinflammatory traits that aid in remyelination [22].

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Clinical and Laboratory Diagnosis of MS The first method used to diagnose MS is magnetic resonance imaging (MRI), which shows multiple lesions, white matter lesions, lesions perpendicular to the ventricular surface, and juxtacortical lesions. The second method used to diagnose MS is cerebrospinal fluid, which shows mild inflammation and oligoclonal immunoglobulin [24]. The third method used to diagnose MS is evoked potentials, which shows conduction delay in auditory, sensory, and visual pathways. The two principal kinds of MS are relapsing–remitting MS (RRMS), which affects about 85% of patients, and primary progressive MS (PPMS), which affects around 10% of cases (Fig. 2) [25]. Secondary progressive MS can develop in 50% of RRMS patients. Discrete episodes of neurological dysfunction that persist for a few hours to days are referred to as relapses, flares, attacks, or exacerbations. Even while most people bounce back quickly, attacks can be quite disastrous. Attacks that go untreated, especially those that affect the brainstem or spinal cord, can occasionally be lifethreatening. Myelin can become inflamed during a severe exacerbation, which can harm the underlying axons and result in lifelong disability and sluggish recovery. Despite the probability of functional disability following MS attacks in patients, there is not an increase in attacks containing independent disability [26]. Initial prognostic characteristics can offer some insight into the course of the disease; initial sensory symptoms are typically positive

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Fig. 2 Types and phases of MS in patients. (Reproduced as per Creative Commons Attribution License from Freeman et al. [25])

prognosis, while early relapse, cerebellar and motor symptoms, and onset after 40 years of age are prognostically negative and indicative of an extremely aggressive and quickly incapacitating course. Unlike RRMS, 100–102 PPMS affects both genders equally and frequently worsens with age. Some symptoms, like ocular neuritis, are more prevalent in RRMS compared to PPMS. A male patient over the age of 40 with a progressing myelopathy who finally develops upper limb involvement, paraparesis, and only a few additional impairments is the typical presentation. Symptomatic lesions in the cerebral subcortical white matter are another frequent manifestation. The presence of two distinct episodes of neurologic impairment that happen at least 30 days apart in various regions of the CNS or, in the case of one relapse, the presence of dissemination in time (DIT) and dissemination in space (DIS) on a MRI serve as the basis for the diagnosis of MS [26, 27]. After comparing a radiographically isolated syndrome (RIS), which is defined as an inadvertently discovered MS via imaging, to a clinically isolated syndrome (CIS), which does not fulfill the criteria for MS due to single attack, the MS diagnosis is obtained by the presence of multiple lesions in DIS and DIT after excluding out alternative illnesses using clinical, radiological, and laboratory procedures. The various symptoms of MS and their treatment have been shown in Table 1 [28].

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Table 1 Symptoms of multiple sclerosis and their treatments Symptoms

Treatments

Fatigue

SSRIs, amantadine, and stimulants

Walking difficulty

Dalfampridine

Depression

SSRIs, SNRIs, bupropion, and psychotherapy

Pseudobulbar palsy

Dextromethorphan or quinidine (Nuedexta)

Nystagmus

Clonazepam, memantine, gabapentin

Spasticity

Baclofen, benzodiazepines, Zanaflex, botulinum toxin

Sexual dysfunction

Sildenafil

Pain or paresthesias

NSAIDs, surgery, anticonvulsants, and antidepressants

Bladder dysfunction

Oxybutynin, intravesicular botulinum toxin type A, desmopressin, terazosin, self-catheterization

7

Treatment Options for MS For the treatment of MS, the European Medicines Agency and the US Food and Drug Administration (USFDA) have approved a number of disease-modifying treatments (Table 2) [25]. Diseasemodifying drugs, such as fumarates, CD52-directed cytolytic monoclonal antibodies, purine antimetabolites, integrin receptor antagonists, etc., are the mainstay of MS treatment. Once MS has been identified, therapy should start right away. For the treatment of RMS, including secondary progressive disease, relapsing–remitting disease, and clinical syndrome, dimethyl fumarate is advised. In fumarates, dimethyl fumarate is used orally along with sphingosine1-phosphate receptor modulators such as fingolimod and ponesimod. The first approved oral RMS therapy was fingolimod [29]. The adhesion protein 41 integrin, which lymphocytes produce on their surface and participates in transmigration to the CNS through endothelial cells, is inhibited by natalizumab. In real-world studies, natalizumab significantly lowers relapse rates and slows the development of the disease in individuals with RMS benefits that last over time. Natalizumab is given as an intravenous infusion once every month [30]. A recently approved selective receptor modulator named ozanimod showed effectiveness and safety in RRMS. Due to their safety characteristics, cladribine and alemtuzumab can be administered intravenously with CD52-directed cytolytic monoclonal antibody for the management of SPMS and RRMS [31].

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Table 2 Treatment options for multiple sclerosis and route of administration

Drug name

Route of Brand administration name

Mechanism of action

US approved year

Approved US indication

Alemtuzumab IV

Lemtrada

CD52-directed cytolytic mAb 2014

RMS

Cladribine

Oral

Mavenclad

Purine antimetabolite

2019

RMS

Dimethyl fumarate

Oral

Tecfidera

2013 Type II myeloid cell and Th2 cell differentiation and neuroprotection

RMS

Fingolimod

Oral

Gilenya

S1P receptor modulator

2010

RMS

Glatiramer

SC

Unknown

2017

RMS

Mitoxantrone IV

Novantrone Synthetic antineoplastic anthracenedione

2000

RMS

Natalizumab

IV

Tysabri

Integrin receptor antagonist

2004

RMS

Ozanimod

Oral

Zeposia

S1P receptor modulator

2019

RMS

Ponesimod

Oral

Ponvory

S1P receptor modulator

2021

RMS

8

Need of Brain Delivery and Its Challenges Due to the restricted access to brain areas that are shielded by physical barriers like the BBB, neurological illnesses are challenging to treat. The BBB serves as a defense mechanism to maintain CNS homeostasis and keep harmful chemicals out of the brain [32]. The monoendothelial layer of cells, joined by tight junctions, pericytes, and perivascular glial processes, all of which are encircled by the basal lamina, are the primary cellular components of this barrier. The BBB’s endothelial cells are distinguished by their sparse fenestrations and pinocytotic vesicles, which further prevent molecules from crossing the barrier and entering the brain [33]. If molecules are tiny enough or soluble in lipids, they can passively cross the BBB. They might cross by way of active absorption by BBB cellexpressed receptors, transporters, or carriers. As every brain cell is within 20 mm of a blood capillary, the highly arborized network of blood capillaries in the brain permits global perfusion of the whole brain parenchyma. The challenges for brain delivery in MS are to repair and enhance the transport function of a disease-induced leak in the BBB and to reduce local inflammatory responses and leukocyte infiltration into the CNS [34]. Anti-inflammatory, immunosuppressive, or immunomodulating drugs make up most current MS treatments. For instance, the humanized monoclonal antibody natalizumab targets the leukocyte

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VLA-4 (VCAM-1 ligand). Effectively binding the a4 integrin on the surface of activated lymphocytes and monocytes, natalizumab prevents these cells from adhering to the MAdCAM-1 and VCAM1 and on the surface of activated endothelium. As a result, natalizumab prevents immune cells from migrating across the BBB-CEC in a VLA-4 dependent manner. Despite natalizumab’s effectiveness in controlling MS symptoms and signs, its usage is constrained by the emergence of clinically significant viral brain infection. The corticosteroids, MMP inhibitors, and interferons are some of the current treatments for MS that affect the BBB. There are several research examining fish oils in MS and immunomodulatory effects of long-chain omega-3 fatty acids in brain disorders. For instance, MS patients who took fish oil (8 g/day) for 3 months proclaimed a substantial reduction in MMP-9 levels [35]. The BBB allows only certain compounds to enter the brain. Extremely lipophilic chemicals directly pass the membranes to enter the brain. Most nutrients pass across the BBB by promoting diffusion, frequently via the movement of the nutrient with the movement of an ion traveling down its concentration gradient. The brain is a sensitive organ; therefore, growth devised incredibly effective safeguards to keep it safe. Hence, the same defense systems that shield it from harmful chemicals and cells can also shield it from therapeutic therapies [36, 37]. In fact, because we are unable to distribute and maintain numerous medications successfully inside the brain, including neuropeptides, proteins, and anticancer drugs, they are ineffectual in the treatment of cerebral illnesses. As a result, obtaining access to the brain is crucial for the creation of medications for the management of MS, and the BBB opening should be as short as possible to minimize edema and other adverse effects. Many methods are being researched right now to improve distribution of drug along the BBB [38].

9

Importance of Nanotechnology in MS The administration of drugs for neurological illnesses is typically constrained by the BBB and BCB. These defenses are designed to preserve homeostasis and shield the brain from poisons. Due to endothelial efflux of molecules, targeting of chemicals to the brain is challenging. Several approaches have been used to overcome these obstacles; most of them are based on nano-based drug delivery systems. For efficient brain delivery, a wide range of lipid-based nanocarriers including liposomes, lipoidal micelles, nanostructured lipid carriers (NLCs), solid lipid nanoparticles (SLNs), and other nanocarriers such as polymeric nanoparticles, dendrimers, organic nanoparticles, and carbon nanotubes have been reported (Fig. 3). Due to their biocompatibility, scalability, and capacity to avoid first-pass

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Fig. 3 Schematic representation showing different drug delivery systems for the management of MS

metabolism, SLNs are considered as one of the best delivery systems for drugs to the brain. Many scientific investigations have demonstrated that SLNs are known to have several benefits, including regulated drug release, passive targeting capability, improved drug/carrier stability, and improved brain permeability. Recent developments have shown that lipid-based nanotherapy has a substantial therapeutic promise to treat and cure a number of neurological illnesses, including MS [39].

10

Lipid-Based Nanocarriers Delivery to MS Brain Lipoidal nanocarriers are one of the greatest drug delivery vehicles for the brain delivery because of their biocompatibility, scalability, and ability to escape first-pass metabolism. Numerous scientific studies have shown that lipid nanocarriers can enhance the lipid solubility, brain availability, and oral bioavailability of the drugs. Multiple sclerosis (MS) and other neurological diseases should be treated with fat-soluble vitamins. In MS, vitamins A, E, and D are utilized specifically as immunomodulators [40]. Kumar et al. (2017) combined retinol acetate and cholecalciferol for the first time in the SLNs to enhance DMF delivery to the brain. For better in vitro and in vivo brain transport, DMF–cholecalciferol solid lipid nanoparticles (SLNs) and DMF-loaded retinol acetate-based SLNs have now been created. The created

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formulations improved oral bioavailability and biological half-life, two requirements for once-daily oral formulation development. The prepared effective nanoparticles with the ability to enhance the bioavailability, Cmax, and biological residence time as well as the CNS provide the evidence for an effective once in a daily product of DMF for the management of MS. SLNs loaded with DMF–retinol acetate and DMF–cholecalciferol have been used to establish the once-daily dosage concept for neurological diseases. The developed new study opens the door for enhanced DMF delivery in the future when combined with neuroprotectants like cholecalciferol and retinol acetate [41]. Raza et al. (2017) developed the nanolipid carriers (NLCs) for the improved brain delivery using DMF. The findings showed an ameliorated pharmacokinetic parameter and significantly better DMF therapeutic levels in the brain, indicating increased brain delivery of DMF—and that too via oral route. The results are in line with cellular permeability experiments done on Caco-2 and SH-SY5Y cells. The results show that DMF–tocopherol acetate NLCs are effective at avoiding the conventional oral pathways of DMF absorption, resulting in much higher plasma and brain levels of the medication. The greater efficacy and less frequent dosing are feasible because of the higher concentrations made possible by DMF–tocopherol acetate NLCs, which are constructed of triedand-true biocompatible excipients. DMF–tocopherol acetate NLCs can have positive biological effects to better control a variety of neurological diseases, including MS [42]. For improving brain transport of DMF, Kaisar Raza et al. prepared stearic acid-based, methodically constructed oral lipid nanoparticles. DMF-O-SLNs produced in a systematic manner demonstrated considerable Caco-2 cellular uptake, an enhanced pharmacokinetic profile, significant brain bioavailability, and postponed drug elimination. The studies that have been submitted have provided evidence for the idea that brain delivery over a longer time span is possible after a single dosage administration. The outcomes suggest a once-daily oral administration alternative for DMF based on a safe, effective, and scalable nanotechnology approach that enhances DMF’s bioavailability. The systematic approach for O-SLNs has produced results that have been amply demonstrated, and the technology now permits future study of DMF-loaded O-SLNs utilizing more sophisticated nanocarriers that employ additional neuroprotectants. A fumaric acid ester called dimethyl fumarate (DMF) lowers inflammation and guards against neuronal damage. It is frequently used to treat neurological issues, especially for multiple sclerosis. It just won approval for the treatment of multiple sclerosis and exerts its neuroprotective effect through unidentified mechanisms [43]. Raza et al. prepared SLNs for oral administration by transferring methylthioadenosine (MTA) to the CNS for the treatment of

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MS. It was discovered that the mice’s normal metabolism, locomotor activity, balance, motor coordination, metabolism, and grip strength were all maintained by the MTA-loaded SLNs better than by plain MTA. Histopathological analyses showed the potential of MTA-loaded SLNs in the remyelination of neurons. The evidence from the pharmacokinetic tests for better bioavailability and bioresidence was consistent with the findings of the pharmacodynamic trials. According to the studies, SLN-encapsulated MTA can effectively remyelinate neurons and transport a sizable amount of MTA to the brain. It is an oral medication that treats multiple sclerosis-like symptoms safely and efficiently. The study demonstrates that lipid-based nanoparticles are capable of orally delivering nucleosides to the brain. The plain MTA and the SLN form of this nucleoside to a great extent noticed the clinical signs in the animals, which were successfully replicated by the demyelination resembling MS induced by a chelating agent of copper. The considerable increase in remyelination with MTA-loaded SLNs compared to free MTA is a clear indication of the benefits of lipid-based carriers in the administration of bioactives taken orally to the brain. The pharmacokinetics improved not just the Cmax and bioavailability but also the half-life enhanced the drug’s ability to interact at the therapeutic site and supported the pharmacodynamic results. These results provide preliminary evidence in favor of SLNs produced by simple techniques for delivering nucleosides to the brain [44]. Hajar Ashrafi et al. prepared lipid-based nanoparticles for DMF delivery to cross BBB for the treatment of MS. The novel component of this report is the development of a biomimetic carrier for targeted medicine delivery to the inflamed area in the periphery and CNS. DMF-loaded platelet-based nanoparticles were used to develop a cell-based drug delivery system, and it was compared to chitosan-based nanogel and chitosan nanogel with platelet membrane coating. The produced nanoparticles were characterized using criteria related to particle size, shape, drug release properties, and loading of drug. To the best of their abilities, each nanoparticle was produced with the specified nano-size and loading specifications. The pharmacokinetic assessment was completed by estimating the brain cells’ uptake of DMF and drug clearance for passing through BBB. An in vivo experiment revealed that the CNS had higher concentration of nanoparticles as compared to the free DMF. The platelet membrane-coated chitosan nanogel was compared with the chitosan-loaded nanogel; DMF-loaded platelet nanoparticle showed improved brain uptake clearance. According to the report, platelet nanoparticles might be a great option as a lipid carrier to treat MS [45]. Ojha et al. prepared DMF-loaded SLNs using hot emulsion ultrasonication and optimized by Box–Behnken design. The oral pharmacokinetic results showed that DMF-loaded SLNs had a better bioavailability than free DMF, which may be attributable to

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the SLNs’ small particle size. The increment of the SLNs’ Cmax values suggests that the current optimized formulation would be useful in decreasing the dose of DMF. According to the bioavailability results, the current optimized preparation might have increased efficacy and allow for a lower dose of DMF. A controlled release rate profile of SLNs can explain the delayed Tmax value. Additionally, it was discovered that MRT and half-life had risen, which might aid to decrease dosage frequency and negative effects. According to the findings of the in vitro investigation, the cumulative drug release rate was 82%, and 40% of the drug was released in the first 3 h. A stability trial that lasted 90 days revealed that the formulation can retain its original property with very minor alterations. The findings of the current study suggested that DMF-loaded SLNs might be a promising carrier for improving MS disease. In the future, the in vivo research should be done to assess the formulation’s efficacy in treating the disease [46]. Rohit Dutt et al. developed chitosan–alginate core–shell nanoparticles. Chitosan–alginate core–shell–corona-shaped nanoparticles of DMF in orodispersible films improved bioavailability for treatment of multiple sclerosis. To boost oral bioavailability in the treatment of MS, a biocompatible and biodegradable oral film containing DMF nanoparticles constructed of chitosan–alginate was created. Investigations were conducted on the impacts of different concentrations of independent variables on significant film quality parameters, and experiment designs with full factorial designs were used to optimize the film formulations incorporating DMF. The ionotropic pre-gelation of the alginate core was followed by chitosan polyelectrolyte complexation to produce the orodispersible film of DMF chitosan–alginate core–shell shaped nanoparticles. After a straightforward process integration step, the created colloidal nanosuspension was added to the optimized polymer matrix composition, after which it was solvent cast into films. The films underwent testing for critical quality factors such as in vitro drug release, tensile strength, and ex vivo penetration through the buccal mucosa of porcine and in vivo pharmacokinetic study. In contrast to oral film of DMF, which released about 80% of the DMF in 15 min, the in vitro drug release profile from chitosan– alginate nanoparticles with core–shell–corona shapes showed a sustained release with up to 18.39% release in half an hour and controlled release up to the next 6 h. The in vivo pharmacokinetic analysis revealed that DMF nanoparticles in orodispersible films showed 0.6-fold more bioavailability than the more conventional oral film formulation, which contained 30 mg of drug/film, even at low drug concentrations (2 mg/film). According to the study, the new dosage form has increased bioavailability, allowing for dose reduction and fewer side effects [47].

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Clinical Trials The several clinical trials for the treatment of multiple sclerosis are completed or ongoing and in distinct phases. The list of some clinical trials with their interventions and outcomes is shown in Table 3 [48].

12

Conclusions DMF has shown promise as a disease-modifying agent for the treatment of MS and psoriasis. Both Nrf2-dependent and Nrf2independent molecular pathways, which oversee the immunomod-

Table 3 The clinical trials on multiple sclerosis Sr. no. Title

Conditions

Interventions

Phases Outcome measures

1.

Dimethyl Fumarate Treatment of Primary Progressive MS

Primary progressive MS

DMF Placebo

II

Neurofilament light chain in the CSF

2.

Monotherapy Safety and Efficacy Extension Study in MS

RRMS

DMF Placebo

III

Annualized relapse rate (ARR)

3.

Phase IIa Study of Ublituximab in Participants with Relapsing Forms of MS

MS

Ublituximab

II

Number of new gadolinium (Gd)enhancing T1 lesions at weeks 24 and 48

4.

Oral Ponesimod Versus Teriflunomide in Relapsing MS

MS

Ponesimod III Teriflunomide

Annualized Confirmed Relapse Rate Questionnaire-Relapsing Multiple Sclerosis (FSIQ-RMS) Score to week 108

5.

Safety and Immunologic MS and Effect of Low Dose Versus vitamin D High Dose vitamin D3 in deficiency MS

Cholecalciferol I

Assess clinical effects of vitamin D supplementation in patients with MS

6.

Pharmacokinetics of Vitamin RRMS D in MS and in Health

Vitamin D3

Change in percentages of T-cell subsets (IFNγ+ and IL-17+) Gene expression microarray

NA

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ulatory and neuroprotective actions of DMF, participate in its mode of action. One of DMF’s immunomodulatory effects is a change in the phenotypic and migration of immune cells to the CNS. DMF also inhibits microglia and astrocyte activation, preventing CNS disease and neuronal death. Although additional research is needed to fully understand DMF’s immunomodulatory, neuroprotective, and adverse effects, the literature points to it as a promising drug for the treatment of Th1- and Th17-mediated autoimmunity. Systematically created DMF-O-SLNs showed notable Caco-2 cellular uptake, an improved pharmacokinetic profile, significant brain bioavailability, and significantly delayed drug clearance. The submitted investigations have demonstrated the concept that brain delivery is possible over a longer period following single dosage administration. The results show promise for a DMF nanotechnology-based solution that is safe, efficient, and scalable and that increases DMF’s bioavailability as well as shows promise as a once-daily oral delivery option. The results of the systematic approach for O-SLNs have been clearly shown, and the technology opens the door to further investigation of DMF-O-SLNs using more advanced nanocarriers that employ additional neuroprotectants. References 1. Torkildsen O, Myhr KM, Bø L (2016) Diseasemodifying treatments for multiple sclerosis - a review of approved medications. Eur J Neurol 23 Suppl 1:18–27. https://doi.org/10.1111/ ENE.12883 2. Lassmann H, Bru¨ck W, Lucchinetti C, Rodriguez M (1997) Remyelination in multiple sclerosis. Mult Scler 3:133–136. https://doi.org/ 10.1177/135245859700300213 3. Kumar P, Sharma G, Kumar R et al (2016) Promises of a biocompatible nanocarrier in improved brain delivery of quercetin: biochemical, pharmacokinetic and biodistribution evidences. Int J Pharm 515:307–314. https:// doi.org/10.1016/j.ijpharm.2016.10.024 4. Ortiz GG, Pacheco-Moise´s FP, Macı´as-Islas ´ et al (2014) Role of the blood-brain barMA rier in multiple sclerosis. Arch Med Res 45: 687–697. https://doi.org/10.1016/j. arcmed.2014.11.013 5. Nicholas JA, Lee Boster A, Imitola J et al (2014) Design of oral agents for the management of multiple sclerosis: benefit and risk assessment for dimethyl fumarate. Drug Des Devel Ther 8:897–908. https://doi.org/10. 2147/DDDT.S50962 6. Meissner M, Valesky EM, Kippenberger S, Kaufmann R (2012) Dimethyl fumarate - only

an anti-psoriatic medication? J Dtsch Dermatol Ges 10:793–801. https://doi.org/10.1111/J. 1610-0387.2012.07996.X 7. Deshmukh P, Unni S, Krishnappa G, Padmanabhan B (2017) The Keap1-Nrf2 pathway: promising therapeutic target to counteract ROS-mediated damage in cancers and neurodegenerative diseases. Biophys Rev 9:41–56. https://doi.org/10.1007/S12551-0160244-4 8. Kumar P, Sharma G, Kumar R et al (2017) Enhanced brain delivery of dimethyl fumarate employing tocopherol-acetate-based nanolipidic carriers: evidence from pharmacokinetic, biodistribution, and cellular uptake studies. ACS Chem Neurosci 8:860–865. https://doi. org/10.1021/ACSCHEMNEURO.6B00428 9. Kumar P, Sharma G, Kumar R et al (2017) Stearic acid based, systematically designed oral lipid nanoparticles for enhanced brain delivery of dimethyl fumarate. Nanomedicine 12: 2607–2621. https://doi.org/10.2217/nnm2017-0082 10. Franco-Pons N, Torrente M, Colomina MT, Vilella E (2007) Behavioral deficits in the cuprizone-induced murine model of demyelination/remyelination. Toxicol Lett 169:205–

474

Sreya Subhash et al.

213. https://doi.org/10.1016/j.toxlet.2007. 01.010 11. Brain targeting drug delivery systems for the management of brain disorders: molecular targets and nanotechnological strategies - Google Search 12. Dighriri IM, Aldalbahi AA, Albeladi F et al (2023) An overview of the history, pathophysiology, and pharmacological interventions of multiple sclerosis. Cureus 15. https://doi. org/10.7759/CUREUS.33242 13. Wong AD, Ye M, Levy AF et al (2013) The blood-brain barrier: an engineering perspective. Front Neuroeng 6:7. https://doi.org/ 10.3389/FNENG.2013.00007/ABSTRACT 14. Spencer JI, Bell JS, DeLuca GC (2018) Vascular pathology in multiple sclerosis: reframing pathogenesis around the blood-brain barrier. J Neurol Neurosurg Psychiatry 89:42–52. h t t p s : // d o i . o r g / 1 0 . 1 1 3 6 / J N N P 2017-316011 15. Charabati M, Wheeler MA, Weiner HL, Quintana FJ (2023) Multiple sclerosis: neuroimmune crosstalk and therapeutic targeting. Cell 186:1309–1327. https://doi.org/10.1016/J. CELL.2023.03.008 16. Merjane J, Chung R, Patani R, Lisowski L (2023) Molecular mechanisms of amyotrophic lateral sclerosis as broad therapeutic targets for gene therapy applications utilizing adenoassociated viral vectors. Med Res Rev. https:// doi.org/10.1002/MED.21937 17. de Se`ze J, Maillart E, Gueguen A et al (2023) Anti-CD20 therapies in multiple sclerosis: from pathology to the clinic. Front Immunol 14. https://doi.org/10.3389/FIMMU.2023. 1004795 18. Graham EL (2023) Neuroimmunological disorders: the gender effect. Neurol Clin 41. https://doi.org/10.1016/J.NCL.2022. 10.004 19. Lassmann H, Van Horssen J, Mahad D (2012) Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol 8:647–656. https://doi.org/10.1038/nrneurol.2012.168 20. Ze´phir H (2018) Progress in understanding the pathophysiology of multiple sclerosis. Rev Neurol (Paris) 174:358–363. https://doi.org/ 10.1016/j.neurol.2018.03.006 21. Balasa R, Barcutean L, Mosora O, Manu D (2021) Reviewing the significance of blood– brain barrier disruption in multiple sclerosis pathology and treatment. Int J Mol Sci 22: 8 3 7 0 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / IJMS22168370 22. Goldman MD, Ward M, Goldman MD (2022) Epidemiology and pathophysiology of multiple

sclerosis. Contin Lifelong Learn Neurol 28: 988–1005. https://doi.org/10.1212/CON. 0000000000001136 23. Ward M, Goldman MD (2022) Epidemiology and pathophysiology of multiple sclerosis. Continuum (Minneap Minn) 28:988–1005. 1 - Google Search 24. Maier S, Barcutean L, Andone S et al (2023) Recent progress in the identification of early transition biomarkers from relapsing-remitting to progressive multiple sclerosis. Int J Mol Sci 2 4 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / IJMS24054375 25. Freeman L, Longbrake EE, Coyle PK et al (2022) High-efficacy therapies for treatmentNaı¨ve individuals with relapsing-remitting multiple sclerosis. CNS Drugs 36:1285– 1299. https://doi.org/10.1007/S40263022-00965-7 26. Ransohoff RM (2023) Multiple sclerosis: role of meningeal lymphoid aggregates in progression independent of relapse activity. Trends Immunol 44. https://doi.org/10.1016/J.IT. 2023.02.002 27. Gilli F, Ceccarelli A (2023) Magnetic resonance imaging approaches for studying mouse models of multiple sclerosis: a mini review. J Neurosci Res. https://doi.org/10.1002/JNR. 25193 28. Leray E, Moreau T, Fromont A, Edan G (2016) Epidemiology of multiple sclerosis. Rev Neurol (Paris) 172:3–13. https://doi. org/10.1016/J.NEUROL.2015.10.006 29. Pilotto S, Zoledziewska M, Fenu G et al (2023) Disease-modifying therapy for multiple sclerosis: implications for gut microbiota. Mult Scler Relat Disord 73. https://doi.org/10.1016/J. MSARD.2023.104671 30. El Ouaamari Y, Van den Bos J, Willekens B et al (2023) Neurotrophic factors as regenerative therapy for neurodegenerative diseases: current status, challenges and future perspectives. Int J Mol Sci 24. https://doi.org/10.3390/ IJMS24043866 31. Furman MJ, Meuth SG, Albrecht P et al (2023) B cell targeted therapies in inflammatory autoimmune disease of the central nervous system. Front Immunol 14. https://doi.org/ 10.3389/FIMMU.2023.1129906 32. Spuch C, Navarro C (2012) Transport mechanisms at the blood-cerebrospinal-fluid barrier: role of megalin (LRP2). Recent Pat Endocr Metab Immune Drug Discov 4:190– 2 0 5 . h t t p s : // d o i . o r g / 1 0 . 2 1 7 4 / 1872214811004030190 33. Kolbe SC, Garcia LM, Yu N et al (2022) Lesion volume in relapsing multiple sclerosis is

Delivery of Dimethyl Fumarate to Multiple Sclerosis Brain associated with perivascular space enlargement at the level of the basal ganglia. AJNR Am J Neuroradiol 43:238–244. https://doi.org/10. 3174/AJNR.A7398 34. Ndemazie NB, Inkoom A, Morfaw EF et al (2022) Multi-disciplinary approach for drug and gene delivery systems to the brain. AAPS PharmSciTech 23. https://doi.org/10.1208/ s12249-021-02144-1 35. Herna´ndez-Pedro NY, Espinosa-Ramirez G, De La Cruz VP et al (2013) Initial immunopathogenesis of multiple sclerosis: innate immune response. Clin Dev Immunol 2013. https://doi.org/10.1155/2013/413465 36. Masserini M (2013) Nanoparticles for brain drug delivery. ISRN Biochem 2013:1–18. https://doi.org/10.1155/2013/238428 37. Upadhyay RK (2014) Drug delivery systems, CNS protection, and the blood brain barrier. Biomed Res Int 2014. https://doi.org/10. 1155/2014/869269 38. Ortiz GG, Pacheco-Moise´s FP, Macı´as-Islas ´ et al (2014) Role of the blood–brain barMA rier in multiple sclerosis. Arch Med Res 45: 687–697. https://doi.org/10.1016/J. ARCMED.2014.11.013 39. Pena SA, Iyengar R, Eshraghi RS et al (2020) Gene therapy for neurological disorders: challenges and recent advancements. J Drug Target 28:111–128. https://doi.org/10.1080/ 1061186X.2019.1630415 40. Teixeira MI, Lopes CM, Amaral MH, Costa PC (2023) Surface-modified lipid nanocarriers for crossing the blood-brain barrier (BBB): a current overview of active targeting in brain diseases. Colloids Surf B Biointerfaces 221. https://doi.org/10.1016/J.COLSURFB. 2022.112999 41. Kumar P, Sharma G, Kumar R et al (2017) Vitamin-derived nanolipoidal carriers for brain delivery of dimethyl fumarate: a novel approach with preclinical evidence. ACS Chem Neurosci.

475

acschemneuro.7b00041. https://doi.org/10. 1021/acschemneuro.7b00041 42. Kumar P, Sharma G, Kumar R et al (2017) Enhanced brain delivery of dimethyl fumarate employing tocopherol-acetate-based nanolipidic carriers: evidence from pharmacokinetic, biodistribution, and cellular uptake studies. ACS Chem Neurosci 8:860–865. https://doi. org/10.1021/acschemneuro.6b00428 43. Kumar P, Sharma G, Kumar R et al (2017) Stearic acid based, systematically designed oral lipid nanoparticles for enhanced brain delivery of dimethyl fumarate. Nanomedicine (Lond) 12:2607–2621. https://doi.org/10.2217/ NNM-2017-0082 44. Kumar P, Sharma G, Gupta V et al (2019) Oral delivery of methylthioadenosine to the brain employing solid lipid nanoparticles: pharmacokinetic, behavioral, and histopathological evidences. AAPS PharmSciTech 20:74. https:// doi.org/10.1208/s12249-019-1296-0 45. Mehdi-alamdarlou S, Ahmadi F, Azadi A et al (2022) A cell-mimicking platelet-based drug delivery system as a potential carrier of dimethyl fumarate for multiple sclerosis. Int J Pharm 625:122084. https://doi.org/10. 1016/J.IJPHARM.2022.122084 46. Ojha S, Kumar B (2018) Preparation and statistical modeling of solid lipid nanoparticles of dimethyl fumarate for better management of multiple sclerosis. Adv Pharm Bull 8:225– 233. https://doi.org/10.15171/APB. 2018.027 47. Sinha S, Garg V, Sonali, et al (2021) Chitosanalginate core-shell-corona shaped nanoparticles of dimethyl fumarate in orodispersible film to improve bioavailability in treatment of multiple sclerosis: preparation, characterization and biodistribution in rats. J Drug Deliv Sci Technol 64:. https://doi.org/10.1016/j. jddst.2021.102645 48. clinical trials - Google Search

Chapter 32 Chrysin for Neurotrophic and Neurotransmitter Balance in Parkinson’s Disease Alagudurai Krishnamoorthy, Riddhi Upadhyay, and Murugan Sevanan Abstract 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) has a direct impact on the dopaminergic neurons in the substantia nigra pars compacta (SNpc), dopamine in the striatum (ST), homovanillic acid (HVA), neurotrophic factors of the SNpc, and ST regions leading to Parkinson’s disease (PD). Dopaminergic neuron atrophy in the SNpc and dopamine degradation in the ST have an explicit link to disrupted homeostasis of the neurotrophic factor brain-derived neurotrophic factor (BDNF) of the SNpc and ST regions. Chrysin is a flavonoid with a pharmacological potential that directly influences neurotrophic levels as well as neurotransmitters. As a result, analysis of the altering levels of neurotransmitters such as dopamine and its metabolites, 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA), are observed via high-performance liquid chromatography (HPLC) and the confirmation of the influential role of BDNF and glial-derived neurotrophic factor (GDNF) in the homeostasis of dopamine, DOPAC, and HAV via examination of gene expression. The observation confirmed that chrysin balances the altering levels of neurotransmitters as well as neurotrophic factors. The protocols for reverse transcription–polymerase chain reaction (RT–PCR) and HPLC analysis for neurotransmitter levels from the SNpc and ST regions of acute PD mice brain-induced MPTP are described in this chapter. Key words Neurotropic factor, RT-PCR, HPLC, Neurotransmitter, MPTP-induced Parkinson’s disease

1

Introduction Parkinson’s disease (PD) is a neurodegenerative disorder marked by dopaminergic neuron loss, resulting in reduced dopamine concentrations and Lewy body aggregation in the substantia nigra pars compacta (SNpc) and striatum (ST) [1]. The depletion of neurons, approximately 70% of them being dopaminergic neurons, is followed by apoptosis in the substantia nigra of microglia and astrocytes [2]. Brain-derived neurotrophic factor (BDNF) is the most extensively circulated neurotrophic factor in the central nervous system, and it is crucial for synaptic plasticity and cell survival [3]. One of the most often utilized models to investigate the

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_32, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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development of Parkinson’s disease (PD) is 1-methyl-1,2,3,6-tetradihydropyridine (MPTP) [4]. Chrysin (5,7-dihydroxyflavone) is an ubiquitous flavonoid found in propolis, honey, and plant extracts [5]. Chrysin has numerous pharmacological features that make it useful in the treatment of various diseases and disorders [6]. Recent research has demonstrated that chrysin protects mice against localized ischemic/cerebral reperfusion damage [7] and has neurological regenerative and neuroprotective properties against 6-hydroxydopamine-induced Parkinson’s disease [8]. This phytochemical is naturally found in several fruits and vegetables, including blue passionflowers (Oroxylum indicum, Passiflora incarnate, and Passiflora caerulea), Scutellaria baicalensis, mushrooms, and honey [9]. In vitro, studies have also explored the potential of chrysin in the context of Parkinson’s disease. One such study conducted on human neuroblastoma cells treated with 6-hydroxydopamine (6-OHDA), a commonly used toxin to induce Parkinson’s-like cellular damage, demonstrated that chrysin treatment protected against 6-OHDA-induced cytotoxicity and apoptosis [10]. The study suggested that chrysin exerted its protective effects through antioxidant and anti-inflammatory mechanisms. Another in vitro study using a cell model of Parkinson’s disease investigated the neuroprotective effects of chrysin against mitochondrial dysfunction and oxidative stress. Chrysin treatment was found to attenuate mitochondrial damage, enhance antioxidant defenses, and reduce reactive oxygen species levels in the cells [7]. The studies on mice model of Parkinson’s disease induced by 1-methyl-4-phenyl1,2,3,6-tetrahydropyridine (MPTP) showed that chrysin treatment improved motor function and [7] exerted its neuroprotective effects through antioxidant and anti-inflammatory mechanisms. Mice are widely used as animal models for studying PD due to several reasons. Here are some key points explaining their utility as an animal model. The similarity in the dopaminergic system: Mice share similarities in the dopaminergic system with humans, which are primarily affected by Parkinson’s disease. The nigrostriatal pathway, responsible for dopamine production and regulation of movement, is highly conserved between mice and humans [11]. Genetic manipulation: Mice allow for genetic manipulation, enabling the creation of transgenic and knockout models to investigate the role of specific genes and pathways involved in Parkinson’s disease. This approach helps in understanding the underlying molecular mechanisms and testing potential therapeutic interventions [12]. Behavioral assessments: Mice offer the ability to perform behavioral assessments that mimic motor dysfunctions seen in Parkinson’s disease, such as impaired locomotion, bradykinesia, and tremors. Various tests, including the pole test, rotarod test, and cylinder test, can be conducted to evaluate motor function [13]. Mice models can develop pathological hallmarks observed

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in Parkinson’s disease, including the accumulation of alphasynuclein, formation of Lewy bodies, and degeneration of dopaminergic neurons. These features allow researchers to investigate disease progression and test potential therapeutic strategies [14].

2 2.1

Materials Neurotoxin

MPTP is a neurotoxic agent that is commonly used in PD research due to its ability to induce Parkinsonism in experimental animals. In humans, MPTP exposure resembles comparative and specific dopaminergic neurodegenerative symptoms. Before handling MPTP, use personal protective equipment (PPE) such as chemical goggles, a lab coat, and two pairs of chemical-resistant gloves (e.g., nitrile). Take the following steps into consideration for MPTP preparation: 1. Purchase MPTP in hydrochloride or tartrate salt form rather than as a free base (see Note 1). Store MPTP and MPTP solutions in labeled tightly capped containers. 2. Place the primary container for MPTP in a sealed, leak-proof, unbreakable secondary container, which must also be labeled. 3. Prepare MPTP solutions in a certified chemical fume hood (see Note 2). 4. Use disposable lab ware when preparing MPTP solutions. If non-disposable glassware is used, single-rinsing in 0.1 N HCl before washing is required (see Note 3). The rinsate was collected and disposed of as chemical waste. 5. Before using MPTP, pre-treat it in saline (see Note 4). In an amber vial, weigh MPTP and dissolve it in a minuscule portion of normal saline. The final volume is made up of saline.

2.2

Treatment Agent

Chrysin is primarily available as a dietary supplement rather than an approved pharmaceutical drug. Chrysin, like many other compounds, has specific storage requirements to maintain its stability and quality over time. Here are some key considerations for storing chrysin: 1. Chrysin should be stored at a cool temperature to prevent degradation. It is generally recommended to store chrysin at temperatures below 25 °C. 2. Chrysin is sensitive to light and can undergo degradation when exposed to excessive light. It is advisable to store chrysin in a dark or opaque container that provides protection against light. 3. Also, moisture can lead to the degradation of chrysin and potential loss of potency (see Note 5). The shelf life of chrysin can vary depending on factors such as purity, storage conditions, and formulation.

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1. Use male C57BL/6 J mice weighing 18 and 22 g which are housed in separate groups, with cages containing five animals (see Note 6).

Animals

2. House animals in a well-maintained environmental conditions according to CCSEA guidelines (see Note 7). 3. The mice are being provided with rodent feed and purified water ad libitum (see Note 8), meaning they have unrestricted access to food and water. 4. Before the experiment, acclimatize the animals for a 7-day acclimatization period to adjust to the laboratory conditions.

3

Methods

3.1 MPTP-Induced PD Models

Use the prepared MPTP to induce acute and chronic PD in mice models. 1. MPTP-induced acute PD model: Administer vehicle (2% DMSO + saline) (see Note 9) in animals for 5 days in a row. On day 5, administer MPTP intraperitoneally in separate dosages 40–50 min following vehicle delivery [15] (Table 1). 2. MPTP-induced chronic PD model: Administer vehicle (2% DMSO + saline) animals. Pair MPTP probenecid (see Note 10) or saline combined with probenecid 3.5 days apart (Table 1). The clinical symptoms and motor activity are assessed in the animals once every 10 days. After the experiment, sacrifice animals using the cervical dislocation method as per the guidelines of CCSEA and surgically remove their brains for neurochemical examination [15].

3.2 Administration of Neurotoxin and Treatment

1. For five consecutive days, pre-treat animals with a vehicle and provide the MPTP (250 mg/kg; 10 × 25 mg/kg) mix with probenecid (250 mg/kg) or saline mix with probenecid (250 mg/kg) (see Note 11).

Table 1 Chrysin administration at different concentrations for neuroprotective effects Sr no.

Group

Administration

Concentration

1

Group I

2% DMSO + saline

10 mL/kg

2

Group II

MPTP + probenecid + 2% DMSO vehicle

10 mL/kg

3

Group III

MPTP + probenecid + chrysin

50 mg/kg

4

Group IV

MPTP + probenecid + chrysin

100 mg/kg

5

Group V

MPTP + probenecid + chrysin

200 mg/kg

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2. Until the experiment is over, administer chrysin daily for 1 h before the first injection of MPTP. 3. Observation should be made for any clinical signs and motor activity consecutively for 10 days [16]. 4. Groups can be formed for the drug administration as shown in Table 1. 3.3 Preparation of Tissue Samples

After administering the drug, the animals are observed for 10 days. At the end of the experiment, the animals are sacrificed and isolated to a specific brain region called the striatum. The isolation of the striatum from the brain tissue is performed by removing the hippocampus. 1. Place the brain with the dorsal side facing a metal plate and make a coronal section, revealing the prefrontal cortex and striatum at different levels. 2. Using a sharp razor blade, create three sections. The first section contains the motor cortex, the second section contains the anterior forceps of the corpus callosum and the medial prefrontal cortex (mPFC), and the third section contains the genu of the corpus callosum and both the dorsal and ventral striatum. 3. From the third section, separate the striatum from the genu of the corpus callosum using a sharp razor blade. 4. Then add 0.2 M ice-cold perchloric acid with 100 μg/mL isoproterenol (see Notes 12 and 13) to the dissected striatum region and homogenize it for analysis of BDNF, GDNF, dopamine, DOPAC, and HAV.

3.4 Reverse Transcription–Polymerase Chain Reaction (RT–PCR)

The expression levels of DAT, SYN, BAX, Bcl2, caspase-3, caspase9, GFAP, and neurotrophic factors (BDNF and GDNF) are determined using RT–PCR. To isolate RNA and synthesize cDNA for further analysis, the following steps are performed: 1. Homogenize the dissected striatum brain regions using TRIzol reagent (Sigma). Add 1 mL of TRIzol per 50–100 mg of tissue and centrifuge for 5 min at 1500 rpm. 2. Add an equal volume of chloroform to the supernatant and centrifuge for 15 min at 12,000 rpm. 3. To precipitate the total RNA, supplement the supernatant with isopropyl alcohol and centrifuge for 15 min at 12,000 rpm. 4. Carefully discard the supernatant and wash the pellet by centrifuging it three times with 75% ethanol. 5. Air-dry the pellet, resuspend it, and store it at -80 °C in RNase-free water.

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Table 2 Primer sequences for mouse mRNA expression Product

Forward primer

Reverse primer

GAPDH

5′-TTCACCACCATGGAGAGGC-3′

5′-TCATGACCACAGTCCATGCC-3′

β-Actin

5′-CCTCTATGCCAACACAGTGC-3′

5′-GTACTCCTGCTTGCTGATCC-3′

DAT

5′-AGATCTGCCCTGTCCTGAAAG-3′

5′-ATCGATCCACACAGATGCCTC-3′

TH

5′-GCTTCAGAAGAGCCGTCTCAG-3′

5′-CTTGTATTGGAAGGCAATCTC-3′

SYN

5′-GATCCTGGCAGTGAGGCTTA-3′

5′-GCTTCAGGCTCATAGTCTTGG-3′

BDNF

5′-GAAGAGCTGCTGGATGAGGAC-3′

5′-TTCAGTTGGCCTTTTGATACC-3′

Bax

5′-TTCATCCAGGATCGAGCAGA-3′

5′-GCAAAGTAGAAGGCAACG-3′

Bcl-2

5′-CTGGTG GAC AAC ATC GCT CTG-3′ 5′-GGTCTGCTGACCTCACTTGTG-3′

Caspase3

5′-TCTGACTGGAAAGCCGAAACTC-3′

5′-TCCCACTGTCTGTCTCAATGCCAC3′

Caspase9

5′-TCCCAGGTTTTGTCTCCTGG-3′

5′-CAAGCCATGAGAGCTTCGGA-3′

6. Use PCR master cycler gradient and reverse-transcribe the isolated RNA to obtain cDNA (Genet Bio). 7. Use the primer sequences (Table 2) for the expression levels of DAT, SYN, Bax, Bcl2, caspase-3, caspase-9, GFAP, and neurotrophic factors (BDNF and GDNF) which were determined by RT–PCR. 8. Confirm gene expression and capture it using agarose gel electrophoresis and a gel documentation unit (VilberLaumar, Germany). 9. Quantify the gene expression using ImageJ software. 3.5 Quantification by ImageJ Software

For the measurement, analysis, processing, and editing of image data, download the ImageJ software from the provided link (https://imagej.nih.gov/ij/), which is freely available. 1. We import the image data into the program by either dragging and dropping the image or using the “Open” option in the software’s navigator (Fig. 1). 2. Once the image is imported, we determine the scale for image analysis. Select the straight-line option from the toolbox and adjust the length accordingly (Figs. 2 and 3). 3. From the top menu, access to various analysis options such as measuring, analyzing particles, labeling, summarizing, calibration, and histogram, based on our specific requirements.

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Fig. 1 ImageJ software navigator

Fig. 2 Image analysis toolbox of ImageJ software

Fig. 3 Measuring scale menu of ImageJ software

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Fig. 4 Top menu of ImageJ software

4. To measure the image, choose the wand option from the toolbox and select a specific area of the image. Then, under the “Analyse” menu in the top menu, we click on the “Measure” option (Fig. 4). 5. A result box appears, providing data such as area, mean, minimum, and maximum values. Record this data and repeat the same process for all the images in the dataset. 3.6

HPLC

To quantify dopamine and its metabolites such as 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA), using high-performance liquid chromatography (HPLC) with an electrochemical detector [17], follow the steps below: 1. Prepare the tissue samples by utilizing 0.2 M perchloric acid that is kept ice-cold. 2. Add 100 μg/mL of isoproterenol to the perchloric acid solution (see Note 14). 3. Homogenize the brain region of interest thoroughly. 4. Proceed with the HPLC analysis, and calculate the concentration of dopamine and its metabolism in the tissue sample using the obtained chromatographic peaks. Express the quantification results in μg/g tissue.

3.7 Statistical Analysis

Perform statistical analysis using GraphPad Prism 5 software, with a predetermined level of statistical significance set at p < 0.05. The data is presented as the mean ± standard error of the mean ( SEM), and the analysis involves conducting a one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test as the post hoc analysis to determine the statistical differences between the groups. The steps for performing the ANOVA analysis using GraphPad Prism 5 software are as follows: 1. Download the GraphPad Prism 5 software from https://www. graphpad.com/features and open it, which directs to a welcome dialogue page. 2. On the left side of the page, there are six different graph formats available for analysis, depending on the specific requirements. Below that, there is an option to select a file or duplicate it, based on needs. Choose the appropriate graph

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format, which, in this case, is column graphs, for the experiment at hand. 3. After selecting the column graph format, the dialogue page displays two options: “Enter/Import data” and “Use sample data.” Choose the “Enter the plot error values already calculated elsewhere” option from the Enter/Import data dialogue box. From the sample data dialogue box, select the “One-way ANOVA ordinary” option. Then, click on the “Create” button. 4. This action opens a data sheet where the group names and corresponding values for each parameter are entered. To create the graph, select the “green +” icon from the toolbox above. The analysis of the table can be observed in the provided photo. Upon opening the “Create New Graph” dialogue box, choose the column bar graph design and select the “Mean SDM” option from the plot options. After clicking “Ok,” the graph is generated and displayed on the screen. 5. Adjust the graph’s size, color, and column bar appearance pattern by clicking on specific bars. Based on the prepared graph, assess the data and draw conclusions. 3.8 Assessment of Treatment Outcomes

To analyze the effects of chrysin supplementation on dopamine, DOPAC, and homovanillic acid levels in different groups, follow the instructions given below: 1. Measure the levels of dopamine, DOPAC, and homovanillic acid in each group using an appropriate quantification method, such as high-performance liquid chromatography (HPLC) with an electrochemical detector (see Note 15). 2. Compare the levels of dopamine, DOPAC, and homovanillic acid in the different groups to draw conclusions about the effects of chrysin supplementation. 3. Assess the statistical significance of the observed differences by conducting appropriate statistical tests (ANOVA). 4. Present the results in figures, indicating the respective changes in dopamine (Fig. 5), DOPAC (Fig. 6), and homovanillic acid (Fig. 7) levels among the different groups. 5. The positive control group, which receives 2% DMSO, displays normal levels of dopamine (Fig. 5), DOPAC (Fig. 6), and homovanillic acid (Fig. 7). In contrast, the negative control group treated with Probi + MPTP + 2% DMSO will exhibit low dopamine (Fig. 5), DOPAC (Fig. 6), and homovanillic acid (Fig. 7) levels. 6. Group 1, supplemented with Probi + MPTP + chrysin (50 mg/ kg), will show a slight increase in dopamine (Fig. 5), DOPAC

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Fig. 5 Effect of chrysin (50, 100, 200 mg/kg) on the alerted levels of dopamine due to MPTP-induced PD in mice brain tissues

Fig. 6 Effect of chrysin (50, 100, 200 mg/kg) on the alerted levels of DOPAC due to MPTP-induced PD in mice brain tissues

Fig. 7 Effect of chrysin (50, 100, 200 mg/kg) on the altered levels of homovanillic acid due to MPTP-induced PD in mice brain tissues

(Fig. 6), and homovanillic acid (Fig. 7) levels compared to the negative control group. 7. In Group 2, the mice are supplemented with Probi + MPTP + chrysin (100 mg/kg), which will result in moderately increased

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levels of dopamine (Fig. 5), DOPAC (Fig. 6), and homovanillic acid (Fig. 7) compared to the negative control group and Group 1. 8. In Group 3, the mice are supplemented with Probi + MPTP + chrysin (200 mg/kg), which will show significantly higher levels of dopamine (Fig. 5), DOPAC (Fig. 6), and homovanillic acid (Fig. 7) compared to the negative control group, Group 1, Group 2, and even the positive control group.

4

Notes 1. MPTP in salt form enhances its stability and helps to prevent degradation over time, solubility, safety, and compatibility, making it more suitable for experimental use and ensuring accurate and reliable results. MPTP can be stored as a phosphate salt, mesylate salt, sulfate salt, and citrate salt forms. 2. Using a certified chemical fume hood when preparing MPTP solutions is crucial for the safety of researchers and for preventing the release and spread of the substance in the laboratory environment. It helps mitigate potential health risks associated with MPTP and ensures compliance with safety standards and guidelines. 3. Performing a single-rinse in 0.1 N HCl before washing non-disposable lab ware helps to remove contaminants, neutralize alkalinity, and prepare the lab ware for subsequent cleaning steps. This helps maintain the integrity of experiments, prevents cross-contamination, and ensures accurate and reliable results in the laboratory. H2SO4, HNO3, H2O2, NaOH, and KOH are some alternatives that can be used for singlerinse. Saline serves as a vehicle for dissolving MPTP and facilitating its administration to mice. 4. Saline is a widely used and biologically compatible solution that provides a suitable medium for MPTP delivery. Alternative solutions to saline for pre-treating MPTP depend on the specific requirements of the experiment or administration route like phosphate-buffered saline (PBS): PBS is a commonly used buffer solution that maintains pH stability and physiological compatibility and water for injection (WFI)—WFI is commonly used as a solvent or vehicle for various pharmaceutical preparations. 5. It is important to store chrysin in a dry environment and protect it from exposure to excessive humidity. Consider using moisture-absorbing packets or desiccants in the storage container to help maintain a dry atmosphere. Also, chrysin can

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be susceptible to oxidation in the presence of air and oxygen, which can lead to a decrease in its stability. It is recommended to store chrysin in a tightly sealed container to minimize contact with air and reduce the risk of oxidation. Consider using containers that offer airtight seals or use sealing techniques such as vacuum sealing if possible. 6. Determining the appropriate sample size for studying several factors such as effect size, significance level (alpha), statistical power, power analysis using statistical methods (ANOVA, correlation, regression), and attrition rate measurement is essential. Several software options available can be used to estimate sample size in studies involving mice such as GPower (for power analysis and sample size determination), PS (power and sample size calculation), and nQuery Advisor and OpenEpi (for sample size calculations and epidemiological statistics). 7. The housing conditions for test animals, such as mice, are carefully controlled and maintained by providing them with social interaction to avoid social isolation stress, ventilation and better air quality in a cage, temperature of 22 ± 3 °C and relative humidity of 40–60% to provide them optimal thermal comfort, and 12-h light/dark photoperiod to regulate the animals’ circadian rhythms. The specific water requirements for mice may vary based on factors such as strain, age, and experimental conditions. 8. Providing purified water ad libitum is a standard practice to ensure mice have continuous access to clean and uncontaminated water, promoting their overall health and supporting reliable research outcomes. If purified water is not available or feasible tap water (with certain quality standards), filtered water and sterilized water in some cases can be provided. 9. DMSO is considered to be relatively well-tolerated by mice and has a low toxicity profile at appropriate concentrations. A 2% DMSO solution is often considered a safe concentration for administration to mice. 10. By using a 2% DMSO + saline vehicle, it serves as a control group where the mice receive the vehicle without the active compound being tested. This allows us to differentiate the effects of the compound from any potential effects of the vehicle. If 2% DMSO + saline is not suitable or desired as the administered vehicle for mice, saline solution, propylene glycol, corn oil, and PEG (polyethylene glycol) can be used as an alternative. 11. MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) is often paired with probenecid for administration in mice due to its pharmacokinetics, increased toxicity, and pharmacological interaction. Probenecid inhibits the renal excretion of

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MPTP and its metabolite MPP+, which increases the activity of the neurotoxin. 12. Perchloric acid is a strong acid that is commonly used for the extraction of soluble compounds, such as neurotransmitters, from tissue samples. It also induces protein precipitation in the sample, and ice-cold perchloric acid helps to preserve the tissue and prevents enzymatic activity that could degrade the compounds of interest. 13. Instead of perchloric acid, other acidic extraction solvents such as sulfuric acid or hydrochloric acid can be used. These acids can facilitate the extraction of soluble compounds from brain tissues. Also, organic solvents like methanol, acetonitrile, or ethanol can be used for extracting compounds from brain tissues. 14. Isoproterenol is often added to the perchloric acid solution as an internal standard. 15. The reverse transcription–polymerase chain reaction (RT– PCR) and HPLC are done to analyze the gene expression of these assays as part of a comprehensive research study aiming to investigate specific aspects of the animal’s physiology and behavior. References 1. Sawada H, Hishida R, Hirata Y, Ono K, Suzuki H, Muramatsu S, Nakano I, Nagatsu T, Sawada M (2007) Activated microglia affect the nigro-striatal dopamine neurons differently in neonatal and aged mice treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. J Neurosci Res 5(8):1752–1761 2. Chen K, Tan Y, Lu Y, Wu J, Liu X, Zhao Y (2020) Effect of exercise on quality of life in Parkinson’s disease: a systematic review and meta-analysis. Parkinsons Dis 2020:3257623 3. Binder DK, Scharfman HE (2004) Brainderived neurotrophic factor. Growth Factors 22(3):123–131 4. Cheng L, Quek CY, Hung LW, Sharples RA, Sherratt NA, Barnham KJ, Hill AF (2016) Gene dysregulation is restored in the Parkinson’s disease MPTP neurotoxic mice model upon treatment of the therapeutic drug Cu (II) (atsm). Sci Rep 6:22398 5. Pietta PG (2000) Flavonoids as antioxidants. J Nat Prod 3(7):1035–1042 6. Lee BK, Lee WJ, Jung YS (2017) Chrysin attenuates VCAM-1 expression and monocyte adhesion in lipopolysaccharide-stimulated brain endothelial cells by preventing NF-κB signaling. Int J Mol Sci 18(7):1424

7. Yao Y, Chen L, Xiao J, Wang C, Jiang W, Zhang R, Hao J (2014) Chrysin protects against focal cerebral ischemia/reperfusion injury in mice through attenuation of oxidative stress and inflammation. Int J Mol Sci 15(11): 20913–20926 8. Zhang Z, Li G, Szeto SS, Chong CM, Quan Q, Huang C, Chu IK (2015) Examining the neuroprotective effects of protocatechuic acid and chrysin on in vitro and in vivo models of Parkinson’s disease. Free Radic Biol Med 4:331– 343 9. Dou W, Zhang J, Zhang E, Sun A, Ding L, Chou G, Wang Z, Mani S (2013) Chrysin ameliorates chemically induced colitis in the mouse through modulation of a PXR/NF-κB signaling pathway. J Pharmacol Exp Ther 345(3):473–482 10. Dinda B, Dinda S, DasSharma S, Banik R, Chakraborty A, Dinda M (2017) Therapeutic potentials of baicalin and its aglycone, baicalein against inflammatory disorders. Eur J Med Chem 131:68–80 11. Bezard E, Yue Z, Kirik D, Spillantini MG (2013) Animal models of Parkinson’s disease: limits and relevance to neuroprotection studies. Mov Disord 28(1):61–70

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12. Gonza´lez-Cuello A, Meza-Aguilar D, FuentesMera L (2019) The use of animal models in Parkinson’s disease research: review of scientific evidence from 2000-2020. Neurol Int 11(2): 70–83 13. Blesa J, Trigo-Damas I, Quiroga-Varela A, Jackson-Lewis VR (2015) Oxidative stress and Parkinson’s disease. Front Neuroanat 9:91 14. Marques O, Outeiro TF (2012) Alphasynuclein: from secretion to dysfunction and death. Cell Death Dis 3(7):e350 15. Luchtman DW, Shao D, Song C (2009) Behavior, neurotransmitters and inflammation in

three regimens of the MPTP mouse model of Parkinson’s disease. Physiol Behav 98(1–2): 130–138 16. Ali BH, Adham SA, Al Za’abi M, Waly MI, Yasin J, Nemmar A, Schupp N (2015) Ameliorative effect of chrysin on adenine-induced chronic kidney disease in rats. PLoS One 10: e0125285 17. Araki T, Kumagai T, Tanaka K, Matsubara M, Kato H, Itoyama Y, Imai Y (2001) Neuroprotective effect of riluzole in MPTP-treated mice. Brain Res 918:176–118

Chapter 33 Establishment of a 6-OHDA Induced Unilaterally Lesioned Male Wistar Rat Model of Parkinson’s Disease Namrata Kumari and Pratibha Mehta Luthra Abstract Robust preclinical models of Parkinson’s disease (PD) are valuable tools for understanding the biology and treatment of this complex disease. 6-Hydroxydopamine (6-OHDA) is a selective catecholaminergic drug injected into the substantia nigra pars compacta (SNc), medial forebrain bundle (MFB), or striatum, which is then metabolized to induce parkinsonism. Unilateral injection of 6-OHDA produces loss of dopaminergic (DAergic) neurons on the injected side with a marked motor asymmetry known as hemiparkinsonism, typically characterized by a rotational behavior to the impaired side. The present work describes a stable unilateral 6-OHDA-lesioned rat model of PD. 6-OHDA was administered into the MFB, leading to the consistent loss of striatal dopamine (DA) and behavioral imbalance in unilateral 6-OHDA-lesioned rats to establish the model of PD. This model of PD is a valuable tool for understanding the mechanisms underlying the generation of parkinsonian symptoms. Key words Parkinson’s disease (PD), 6-Hydroxydopamine (6-OHDA), Dopaminergic neuron, Medial forebrain bundle, Unilateral, Rat, Animal model

1

Introduction Animal models of Parkinson’s disease (PD) have proved highly effective in the search of clues to the underlying cause of the illness. Models based on specific pathogenic mechanisms may subsequently lead to the development of novel treatments that may stop or slow the disease progression in the therapy of PD. Large array of rodent models are available. The acute pharmacological models include the reserpine or haloperidol, which induce one or more parkinsonian symptoms. The classical 6-hydroxydopamine (6-OHDA)-induced rat and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse models are well-characterized models of PD and are adopted to test the new molecules for treating the motor symptoms of PD. Systemic injection of pesticides like rotenone and paraquat, are another toxin-based model to study the nigrostriatal neurodegeneration, but it is not employed

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_33, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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for drug development. The most widely used rodent models of PD are the 6-OHDA-treated rat and the MPTP-treated mouse. However, 6-OHDA model has been extensively used to test novel agents and study the mechanism of pathogenesis for assessing potential neuroprotective and neurorepair strategies in PD [1]. 6-OHDA is a selective catecholaminergic drug, which is metabolized to a neurotoxin, preferentially, damaging dopaminergic (DAergic) and norepinephrinergic (NEergic) neurons. 6-OHDA does not cross the blood–brain barrier (BBB) and must be injected intracranially to produce catecholamine depletions in the brain. Desipramine, a NE reuptake inhibitor, is usually given before 6-OHDA injections to protect NEergic neurons and produce selective dopamine (DA) depletion. 6-OHDA is injected into the substantia nigra pars compacta (SNc), medial forebrain bundle (MFB), or striatum leading to the depletion of nigrostriatal DA, a pathological characteristic of PD. Bilateral injections of 6-OHDA easily induce dysphagia and high mortality, therefore, 6-OHDA is, preferentially, applied only into one hemisphere. Unilateral injections of 6OHDA into the nigrostriatal tract cause turning asymmetry, i.e., the animal shows significantly more rotations toward the side of the lesion [2–4]. An indirect DA agonist, e.g., amphetamine, acts presynaptically to stimulate DA release and/or block DA reuptake; when administered the lesioned rats show noticeable rotations toward the lesioned side. However, a direct DA agonist, e.g., apomorphine, is administered to induce rotation in the opposite contralateral direction, i.e., away from the lesioned side [4]. Amphetamine stimulates release of endogenous DA primarily on the intact side of the brain, apomorphine causes direct activation of supersensitive DA receptors on the lesioned side acting postsynaptically to trigger hyperstimulation of supersensitive DA receptors in the denervated striatum. These drug-induced rotations have the potential to serve as a behavioral measure for determining the degree of striatal DA depletion [4, 5]. We developed a sustained unilateral 6-OHDA-lesioned animal model of PD in male Wistar rats by directly administering 6-OHDA into the MFB. This caused a constant loss of 70% of striatal dopamine, resulting in behavioral abnormalities. This model of PD is a valuable tool in understanding the mechanisms underlying generation of parkinsonian symptoms.

2

Materials

2.1 Procuring, Housing, and Preparation of Rats

1. Healthy adult male Wistar albino rats weighing around 250–270 g were procured from All India Medical Sciences (AIIMS), New Delhi (see Note 1).

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2. Divide the animals according to the experimental groups— taking eight animals for each group (keep four animals in each cage). 3. Acclimatize the animals for 3 days. 4. Keep animals under the standard laboratory conditions (room temperature, 23 ± 2 °C; relative humidity, 60 + 5%; illumination, 12 h-light/dark cycle) and freely access to food and fresh water. 5. Monitor animals daily for access to food and water. 6. Weigh each animal and write the animal weight in the notebook for record before 30 min prior to surgery. 2.2 Chemical Procurement and Preparation

1. Anesthesia used: ketamine, xylazine, and pentobarbital (see Note 2). 2. Preparation of desipramine solution (desipramine hydrochloride): Prepare stock solution of 10 mg/mL of desipramine in sterile water. Correct for the weight of the HCl salt in desipramine hydrochloride: molecular weight (MW) of desipramine HCl, 302.84; MW of HCl, 36.46; MW of free base, 266.38. Correction factor: 302.84/266.38 = 1.137. For preparation of 10 mL of desipramine solution: 10 mg/mL desipramine × 10.0 mL solvent × correction factor = 10 mg/ mL × 10.0 mL solvent × 1.137 = 113.7 mg of desipramine hydrochloride. The stock solution can be stored at -80 °C until use. 3. Preparation of 6-OHDA solution (6-OHDA hydrobromide): 6-OHDA solutions must be prepared immediately prior to surgery (see Note 3). Dissolve 6-OHDA hydrobromide (6-OHDA.Br) in a vehicle 0.9% sterile saline and 0.02% ascorbic acid (pH 7.4). Make a 5.0 mg/mL stock solution (administer 10 μg of 6-OHDA in each rat). Correct for the weight of the HBr salt in 6-OHDA.HBr: MW of 6-OHDA.HBr, 250.09; MW of HBr, 79.9; MW of free base, 170.19. Correction factor: 250.09/170.19 = 1.47. For 0.5 mL of solution, weigh out: 5.0 mg/mL 6-OHDA × 0.5 mL × correction factor = 5.0 mg/ mL 6-OHDA × 0.5 mL × 1.47 = 3.675 mg of 6-OHDA.HBr. Weigh 6-OHDA.HBr into a sterile 1.0 mL tube that has been wrapped in aluminum foil, and then add 0.5 mL of the vehicle. Vortex until the mixture is dissolved, label and place immediately on ice until use. Inject vehicle only in sham-operated group. 4. Surgical instrumentation setup: All Surgical tools such as scissor, forceps and scalpel must be sterilized using an autoclave prior to surgery. Between each operation, surgical tools must be sterilized in 95% ethanol. Clean all surfaces of stereotaxic table with isopropyl alcohol prior to setting up the equipment.

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Wear sterile gloves when handling clean surgical equipment. Set up a rat recovery cage with paper towels on the base. Place the cage under a heating lamp or on top of a heated pad to warm up. Set up the anesthesia. Keep sterile surgical blade, Hamilton syringe (10 μL syringe with a 26-gauge needle), sutures, cotton pad, ear buds, Betadine, 70% ethanol, saline and lidocaine in separate tray. To eliminate obstructions in the Hamilton syringe, draw and expel 100% acetone five times, and then inject sterile PBS five times to eliminate the acetone.

3

Methods

3.1 Surgery Procedure and Anesthesia Administration

1. Administer desipramine (25 mg/kg) 30 min before 6-OHDA infusion. Warm up the surgical area by using heating lamp or disc. After 30 min of administration of the desipramine, anesthetize the animals by using sodium pentobarbital (40 mg/kg) or ketamine + xylazine mixture (75 + 10 mg/kg) via intraperitoneal (IP) injection (Table 1). The animal is sufficiently anesthetized when it shows no response to hind leg pinch and no blink reflex. Place the animal in a stereotaxic frame adapted for rats. 2. Set up the stereotaxic surgical instrument according to the manufacturer instructions. Place the animal into the incisor bars with the anesthesia mask (inhaled anesthesia) placed over the face of the animal. Carefully place one ear bar in the ear canal, fasten it, and then hold the animal while you place and secure the other. Animals should not move laterally. Next, place the mouth in the stereotaxic instrument’s incisor adapter without pinching the tongue or restricting the airway. To secure the animal’s head, gently tighten the nose clamp. Check the head and adjust the incisor adapter pitch to level it. 3. Shave the top of the rat’s head and apply the topical analgesic lidocaine directly to the skin using cotton. Five minutes following lidocaine application, sterilize the head of the animal with Betadine solution. Using a scalpel blade, make a cut along the midline of the skin on the top of the head, and then retract the skin. Dry the surface of the skull using ear buds. 4. Make a small incision (1–2 cm approx.) medial to the eyes and beginning at the posterior region of the eyes. Identify the place of Bregma on brain map of rat. 5. The precise placement of neuroanatomical structures is described by a set of three coordinates in the X (medial lateral, ML), Y (anterior-posterior, AP), and Z (dorsal-ventral, DV) planes using either the bregmatic or lambdoidal sutures of the skull as a reference point (Fig. 1) [6].

6-OHDA Unilateral Lesion Male Rat Model of PD

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Table 1 Dose and duration of anesthesia

Drug

Dose

Ketamine + xylazine

75 mg/kg ketamine 10 mg/kg xylazine

Duration of Route anesthesia Comments IP

Pentobarbital 40–60 mg/kg IP

45–90 min Combinations are mixed in the syringe immediately prior to use. If additional anesthetic is needed, use 1/3 dose of ketamine. Xylazine is not re-dosed 80–90 min Dose sufficient to produce surgical anesthesia may cause severe respiratory depression and death. Drug for administration is diluted in saline (50% of low-threshold spike (LTS) amplitude in the subthalamic nucleus (in a current clamp method), and decrease in >60% of rebound burst activity in STN neurons on the application of 3 μM ML218 [91]. In a rat model, TTCC blockers like NNC-55-0396 and mibefradil (now withdrawn from the market) were found to block the burst-firing activity in STN [92]. Together, this data suggests that T-type Ca2+ channels could also be an attractive therapeutic target for PD to regulate the abnormal burst firing in the STN. 6.2.2 Potassium Channels

There are various types of potassium channels located in the substantia nigra, striatum, and other parts of the brain affected by PD like voltage-gated K+ channel, Kir channel with rectified inward activities such as Kir1–7, and ATP-sensitive potassium (KATP) channel and K2P channels with two pores such as K2p15-K2P18, K2p9K2p10, KCNK, K2P12-K2P13, and K2p1-K2P7 (small-conductance calcium-activated potassium (SK) channel), which are key players in the attenuation of neuronal survival and cell death associated with PD [93]. Recently, it has been found that blockers of ATP-sensitive potassium channels like glibenclamide improved the behavioral parameters and accorded neuroprotection in the 6-OHDA model in PD [94]. Moreover, transient potassium channel openers are also known to show improvements in the rotenone model of PD [95]. Different research groups have used behavioral and molecular techniques to show that mRNA expression of different subunits of ATP-sensitive potassium channels is increased in 6-OHDA and the haloperidol model of PD. It has been concluded that this is the result of a compensatory self-protective reaction of the body to these exogenous insults [96]. Moreover, MPP+, which is used to induce PD, is known to elevate inflammatory mediators like Interferon gamma (IFN-γ), Tumor necrosis factor alpha (TNF-α), and interleukin-1 β (IL-1β) in microglia, and this elevation was greatly suppressed by potassium channel opener pinacidil [97]. Another agent that has been recently tried in the PD models is H2S. It exerts its anti-apoptotic effect on the dopamineproducing neurons of the substantia nigra by the opening of KATP

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channels in the mitochondria, which in turn plays a role in the maintenance of the mitochondrial integrity [98]. SK channels are known to exhibit a neuroprotective effect in PD by suppressing the NMDA channel-mediated excitotoxicity. It has been shown that these channels when activated cause a reduction in the mitochondrial membrane potential, maintain the ATP levels in the cell, and increase cell viability and survival [99]. The role of SK channels in cell viability has also been confirmed with the help of electrophysiological measurements done on SNpc neurons in the 6-OHDA model. Additionally, these channels relieved the cells of the oxidative stress changes associated with the administration of PD-inducing agents like 6-OHDA by inhibiting ROS generation by mitochondria and the NADPH pathway [100]. Therefore, modulation of these potassium channels may be a useful option in the treatment of PD. Various drugs targeting different potassium channels are there in the clinical trials as shown in Table 3. Hence based on preclinical and clinical data available, potassium channels may be the future of PD therapy. 6.2.3 Hyperpolarizationactivated Cyclic Nucleotide-gated (HCN) Channels

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are voltage-gated channels that are widely expressed in peripheral as well as CNS and are involved in the pathology of numerous neurological disorders like pain, epilepsy, and PD [101]. In CNS, HCN channels are generally expressed in the hippocampus and midbrain region specifically multiple basal ganglia such as SNpc, globus pallidus, and STN. HCN2 and HCN4 isoforms play a central role in the regulation of the electrical activities of dopaminergic neurons in the midbrain under normal physiological conditions by regulating network spontaneous activity [102].

6.2.4 Voltage-gated Proton (Hv1) Channels

Voltage-gated proton (Hv1) channels are selectively expressed in microglia (often referred to as resident macrophage of the brain) but not in neurons or astrocytes and modulate intracellular pH as well as facilitate the production of ROS with the help of NADPH oxidase enzyme [103]. Moreover, Neal et al. recently reported that striatal Hv1 proton channel mRNA expression is higher in postmortem PD patients than in age-matched controls suggesting the important role of Hv1 proton channels in PD [104].

6.2.5 Voltage-Gated Sodium Channels (VGSCs)

Voltage-gated sodium channels (VGSCs) are found in every neuron in the central and peripheral nervous systems, and their prime role is to generate action potentials for cellular communication [105]. In PD, voltage-gated sodium channels are responsible for aberrant electrical activity of neurons in the GP and the STN. Zhu and his coworkers demonstrated the role of VGSCs in motor disabilities in PD upon injecting a sodium channel modulator, Bmk I, into the GP region in rats [106]. The expression of different VGSC

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isoforms, such as Nav 1.1, Nav 1.2, and Nav 1.6, was examined by Wang Z et al. in the hippocampus of rats treated with 6-OHDA at various time points. Also, they administered phenytoin, a VGSC blocker, to assess the rats’ learning and memory, concluding that VGSCs may be crucial in the cognitive impairment associated with PD [107]. According to research by Liu et al., a change in Nav 1.1 expression in the basal ganglia (BG) may have an impact on the motor impairments in the MPTP-treated mice model of PD, the SN area, and the basal ganglia of C57BL/6 mice [108]. Interestingly, re-expression of Nav 1.3 in the SN area in male SD rats 49 days after 6-OHDA injection has also been documented [109]. This suggests the intriguing role of voltage-gated sodium channels in PD pathology.

7

Role of Metal Ions in PD Neurodegenerative diseases have been associated with brain metal accumulation, which produces oxidative stress, matrix metalloproteinase induction, and neuronal cell death. Several metals have been reported to downregulate both the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway and the antioxidant enzymes regulated by it, mediating oxidative stress induction and neurodegeneration [110]. Heavy metal pollution is one of the major environmental issues in the world. Metal pollution is involved in many pathologies from neurodegenerative diseases like PD and other metabolic diseases. Metal ions’ role is important for biological and cellular function, primarily zinc and copper in homeostasis, regulation of the biosynthesis, and activity of zinc and most notably zinc-dependent transcription factors, etc. [111]. Industrial exposure to precise metals, viz., mercury, zinc, copper, iron, manganese, calcium, lead, and aluminum, seems to be a major risk factor for neurodegenerative diseases like PD based on epidemiological studies. Elevated levels of several of these metals have also been reported in the substantia nigra of PD patients [112]. Postmortem analysis of brain tissues from PD patients gives further confirmation for the involvement of heavy metals in this disorder, in that a considerable increase in total iron, zinc, and aluminum content of the parkinsonian substantia nigra was observed when compared with control tissues [113]. The role of different metals in PD has also been shown in Fig. 2.

7.1

Mercury

Being one of the most toxic heavy metals, mercury has a detrimental effect on the CNS including alteration of synaptic transmission, NMDA receptor over-activation, etc. Several reports have suggested that the extent of mercury toxicity depends on types of mercuric isoforms, dose, and duration of exposure to the subject. Bjorklund et al. reported a significant resemblance of pathologies

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Fig. 2 Disbalance in metal ion concentration could result in loss of dopaminergic (DAergic) neurons via a variety of pathological reasons, including increased α-synuclein formation, oxidative stress, and elevated glutamate level, which leads to PD

between mercury exposure and PD including dopamine receptor loss, glutathione depletion in the substantia nigra, elevated glutamate level, and mitochondrial dysfunction [114]. Recently it has been reported that mercury was found in neurons as well as oligodendrocytes and co-localizes with α-synuclein aggregates in the brain of PD-affected people [115]. 7.2

Zinc

In the brain, Zn2+ is concentrated inside the presynaptic vesicles at the glutamatergic terminal and released after neuronal stimulation [116]. Zn2+-containing neurons are present primarily in the cortical and limbic areas of the brain; therefore, Zn2+ is supposed to play a vital role in behavior and emotion [117]. Zn2+ is necessary for maintaining normal brain functions, but excessive Zn2+ causes cell death, leading to various CNS disorders. The imbalance of Zn2+

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content is undoubtedly involved in the pathogenesis of PD, but the available data are showing uncertain and contradictory results. Some reports say that levels of Zn2+ are increased in the brain of PD patients [118–120]. However, some other reports found decreased levels of Zn2+ in blood serum [121] and cerebrospinal fluid [122] of patients with PD. 7.3

Copper

Copper is a trace element that has been linked to the pathogenesis of PD. Copper is often found as a cofactor in many CNS enzymes like tyrosinase, ceruloplasmin, cytochrome c oxidase, amine oxidases, and superoxide dismutase (SOD). It also interferes with dopamine metabolism, oxidative stress, and α-synuclein aggregation, which plays a prime role in PD pathogenesis [123]. Cu2+ can generate free radicals via the Haber-Weiss reaction and causes DNA breakage, mitochondrial damage, and neuronal injury. It has been shown that the loss of tyrosine hydroxylase (TH) expression in SNpc, ventral tegmental area (VTA) and loss of striatal outputs subsequently results in the loss of locomotor performance, after the acute Cu2+ intoxication in rats [124].

7.4

Iron

Iron is a necessary micronutrient in the human body, but it is also harmful when found in elevated concentration because of redoxactive properties and produces free radicals during its conversion between ferrous (Fe2+) and ferric (Fe3+) forms [125]. There are many studies that show a significant increase in iron content in the SNpc, SNpr, putamen, globus pallidus, red nucleus, and caudate nucleus of the brain of PD patients [125, 126]. Shi et al. reported an elevated level of iron in the SNpc, as well as DA degradation and motor abnormalities in MPTP/MPP+-induced nonhuman primate models with the most severe PD pathology and behavioral deficits [127].

7.5

Manganese

Manganese functions as a cofactor for many enzymes and is required for normal growth and development. Intoxication of manganese particulates is very high in mining and industries, which results in its accumulation in selected brain regions like basal ganglionic (substantia nigra, striatum, subthalamic nucleus, and globus pallidus) region, which is involved in the control of motor and non-motor functions and causing an extrapyramidal motor disorder, called manganism [128, 129]. Mn2+ ion occupies the Ca2+-binding sites within the mitochondria and disrupts the Ca2+ homeostasis by elevating the mitochondrial Ca2+ level that affects oxidative respiration and causes oxidative stress and induces various neurological disorders including PD [125]. Moreover, the Mn2+ ion also promotes α-synuclein accumulation by inducing protein phase transition [130].

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7.6

Calcium

Calcium ions play an important role in the functioning of the nervous system including as the second messenger in neurotransmitter synthesis, release, and signaling. It also plays a central role in the control of synaptic plasticity [131]. Neuronal calcium signaling involves various ion channels and a large number of calciumdependent proteins or enzymes [132]. An imbalance in calcium homeostasis could be a well-known cause of many neurodegenerative disorders. James Surmeier and team proposed that the influx of calcium to dopaminergic neurons of SNpc via LTCCs increases cellular vulnerability to toxins and, as a result, a minute change in calcium homeostasis may lead to neuronal degeneration [83, 133].

7.7

Lead

Lead has a number of direct neurotoxic effects, including apoptosis, excitotoxicity, oxidative stress, and mitochondrial dysfunction in neurons [134]. Epidemiological studies have found a link between lead exposure and an increased risk of PD, with a two–three-fold increase in PD risk upon exposure to lead [135]. Rogers et al. reported the effect of lead on iron homeostasis and modulate the amyloid precursor protein (APP) level using human dopaminergic SH-SY5Y neuroblastoma cells [136].

8

Conclusions and Future Perspectives Currently, different research groups all over the world are looking for alternate strategies for the treatment and management of PD and the symptoms associated with it. Ion channels are involved in the maintenance of cellular homeostasis and hence abnormalities in their signaling mechanism have been associated with various pathological events in PD. Going through the pipeline of drugs in the clinical trials for PD, it is evident that very few of them target the ion channels. One of the reasons for this slim number is the lack of knowledge about the complete signaling mechanisms of these ion channels. Moreover, the inability of the disease models to replicate the abnormalities linked to the dysfunction of the ion channels is another reason for the translational failure associated with drugs targeting the ion channels.

References 1. Sheerin UM, Houlden H, Wood NW (2014) Advances in the genetics of Parkinson’s disease: a guide for the clinician. Mov Disord Clin Pract 1(1):3–13 2. Galvan A, Wichmann T (2008) Pathophysiology of parkinsonism. Clin Neurophysiol 7(119):1459–1474 3. Parkinson J (2002) An essay on the shaking palsy. 1817. J Neuropsychiatry Clin Neurosci 2(14):223–236; discussion 2

4. Lees AJ (2007) Unresolved issues relating to the shaking palsy on the celebration of James Parkinson’s 250th birthday. Mov Disord S17 (22 Suppl 17):S327–S334 5. Thapak P, Vaidya B, Joshi HC, Singh JN, Sharma SS (2020) Therapeutic potential of pharmacological agents targeting TRP channels in CNS disorders. Pharmacol Res 159: 105026

Role of Ion Channels and Metal Ions in Parkinson’s Disease 6. Duda J, Potschke C, Liss B (2016) Converging roles of ion channels, calcium, metabolic stress, and activity pattern of substantia nigra dopaminergic neurons in health and Parkinson’s disease. J Neurochem Suppl Suppl 1(139 Suppl 1):156–178 7. Vaidya B, Roy I, Sharma SS (2022) Neuroprotective potential of HC070, a potent TRPC5 channel inhibitor in Parkinson’s disease models: a behavioral and mechanistic study. ACS Chem Neurosci 18(13): 2728–2742 8. Wang S, Wang B, Shang D, Zhang K, Yan X, Zhang X (2022) Ion channel dysfunction in astrocytes in neurodegenerative diseases. Front Physiol 13:814285 9. Neve KA, Seamans JK, Trantham-Davidson H (2004) Dopamine receptor signaling. J Recept Signal Transduct Res 3(24):165–205 10. Beaulieu JM, Espinoza S, Gainetdinov RR (2015) Dopamine receptors–IUPHAR review 13. Br J Pharmacol 1(172):1–23 11. Levey AI, Hersch SM, Rye DB, Sunahara RK, Niznik HB, Kitt CA et al (1993) Localization of D1 and D2 dopamine receptors in brain with subtype-specific antibodies. Proc Natl Acad Sci U S A 19(90):8861–8865 12. Franco R, Reyes-Resina I, Navarro GJB (2021) Dopamine in health and disease: much more than a neurotransmitter. Biomedicines 2(9):109 13. Kant R, Meena MK, Pathania M (2021) Dopamine: a modulator of circadian rhythms/biological clock. J Adv Med Med Res 2(8):316–324 14. Bezard E, Przedborski S (2011) A tale on animal models of Parkinson’s disease. Mov Disord 6(26):993–1002 15. Bjo¨rklund A, Dunnett SB (2007) Dopamine neuron systems in the brain: an update. Trends Neurosci 5(30):194–202 16. Farde L, Nordstrom AL, Wiesel FA, Pauli S, Halldin C, Sedvall G (1992) Positron emission tomographic analysis of central D1 and D2 dopamine receptor occupancy in patients treated with classical neuroleptics and clozapine. Relation to extrapyramidal side effects. Arch Gen Psychiatry 7(49):538–544 17. Money KM, Stanwood GD (2013) Developmental origins of brain disorders: roles for dopamine. Front Cell Neurosci 7:260 18. Stevens JR (1979) Schizophrenia and dopamine regulation in the mesolimbic system. Trends Neurosci 2:102–105 19. Habibi M (2010) Dopamine receptors. In: Kompoliti K, Metman LV (eds) Encyclopedia

551

of movement disorders. Academic Press, Cambridge, Massachusetts, pp 326–329 20. Muller EE, Locatelli V, Cella S, Penalva A, Novelli A, Cocchi D (1983) Prolactinlowering and -releasing drugs. Mechanisms of action and therapeutic applications. Drugs 4(25):399–432 21. Escande MV, Taravini IR, Zold CL, Belforte JE, Murer MG (2016) Loss of homeostasis in the direct pathway in a mouse model of asymptomatic Parkinson’s disease. J Neurosci 21(36):5686–5698 22. Katzung BG, Masters SB, Trevor AJ (2004) Basic & clinical pharmacology. 8. McGrawHill Medical, New York, pp 483–493 23. Schneider SA, Obeso JA (2014) Clinical and pathological features of Parkinson’s disease. In: Behavioral neurobiology of Huntington’s disease and Parkinson’s disease. Springer, Heidelberg, pp 205–220 24. Brunton L, Knollman B, Chabner B (2011) Goodman & Gilman’s the pharmacological basis of therapeutics, 12th edn. McGraw-Hill Medical, New York, pp 609–628 25. Klein C, Schlossmacher MG (2006) The genetics of Parkinson disease: implications for neurological care. Nat Clin Pract Neurol 3(2):136–146 26. Trojanowski JQ, Lee VM (1998) Aggregation of neurofilament and alpha-synuclein proteins in Lewy bodies: implications for the pathogenesis of Parkinson disease and Lewy body dementia. Arch Neurol 2(55):151–152 27. Poewe W, Seppi K, Tanner CM, Halliday GM, Brundin P, Volkmann J et al (2017) Parkinson disease. Nat Rev Dis Primers 3:17013 28. Mullin S, Schapira A (2015) The genetics of Parkinson’s disease. Br Med Bull 1(114): 39–52 29. Klein C, Westenberger A (2012) Genetics of Parkinson’s disease. Cold Spring Harb Perspect Med 1(2):a008888 30. Li J, Yang D, Li Z, Zhao M, Wang D, Sun Z et al (2023) PINK1/Parkin-mediated mitophagy in neurodegenerative diseases. Ageing Res Rev 84:101817 31. Hirsch EC, Jenner P, Przedborski S (2013) Pathogenesis of Parkinson’s disease. Mov Disord 1(28):24–30 32. Zimprich A, Benet-Pages A, Struhal W, Graf E, Eck SH, Offman MN et al (2011) A mutation in VPS35, encoding a subunit of the retromer complex, causes late-onset Parkinson disease. Am J Hum Genet 1(89):168–175 33. Vila M, Przedborski S (2004) Genetic clues to the pathogenesis of Parkinson’s disease. Nat Med 10 Suppl:S58–S62

552

Bhupesh Vaidya et al.

34. Elbaz A, Carcaillon L, Kab S, Moisan F (2016) Epidemiology of Parkinson’s disease. Rev Neurol (Paris) 1(172):14–26 35. Chen H, Zhang SM, Hernan MA, Willett WC, Ascherio A (2002) Diet and Parkinson’s disease: a potential role of dairy products in men. Ann Neurol 6(52):793–801 36. Ascherio A, Chen H, Weisskopf MG, O’Reilly E, McCullough ML, Calle EE et al (2006) Pesticide exposure and risk for Parkinson’s disease. Ann Neurol 2(60):197–203 37. Caudle WM, Guillot TS, Lazo CR, Miller GW (2012) Industrial toxicants and Parkinson’s disease. Neurotoxicology 2(33):178–188 38. Vellingiri B, Suriyanarayanan A, Abraham KS, Venkatesan D, Iyer M, Raj N et al (2022) Influence of heavy metals in Parkinson’s disease: an overview. J Neurol 11(269): 5798–5811 39. Przedborski S (2017) The two-century journey of Parkinson disease research. Nat Rev Neurosci 4(18):251–259 40. Reeve A, Simcox E, Turnbull D (2014) Ageing and Parkinson’s disease: why is advancing age the biggest risk factor? Ageing Res Rev 100(14):19–30 41. Davis KL, Charney D, Coyle JT, Nemeroff C (2002) Neuropsychopharmacology: the fifth generation of progress. American College of Neuropsychopharmacology: Lippincott Williams & Wilkins, Philadelphia, Pennsylvania 42. Vaidya B, Sharma SS (2020) Transient receptor potential channels as an emerging target for the treatment of Parkinson’s disease: an insight into role of pharmacological interventions. Front Cell Dev Biol 8:584513 43. Daniel NH, Aravind A, Thakur PJN (2021) Are ion channels potential therapeutic targets for Parkinson’s disease? Neurotoxicology 87: 243–257 44. Majla´th Z, Ve´csei L (2014) NMDA antagonists as Parkinson’s disease therapy: disseminating the evidence. Neurodegener Dis Manag 4(1):23–30 45. Ravenscroft P, Brotchie J (2000) NMDA receptors in the basal ganglia. J Anat Pt 4(196 (Pt 4)):577–585 46. Gan J, Qi C, Mao L-M, Liu Z (2014) Changes in surface expression of N-methylD-aspartate receptors in the striatum in a rat model of Parkinson’s disease. Drug Des Devel Ther 8:165 47. Ulas J, Weihmuller FB, Brunner LC, Joyce JN, Marshall JF, Cotman CW (1994) Selective increase of NMDA-sensitive glutamate binding in the striatum of Parkinson’s disease, Alzheimer’s disease, and mixed Parkinson’s disease/Alzheimer’s disease patients: an

autoradiographic study. J Neurosci 11 Pt 1(14):6317–6324 48. Xu H, Liu X, Xia J, Yu T, Qu Y, Jiang H et al (2018) Activation of NMDA receptors mediated iron accumulation via modulating iron transporters in Parkinson’s disease. The FASEB Journal fj. 201800060RR 32:6100 49. Mellone M, Stanic J, Hernandez LF, Iglesias E, Zianni E, Longhi A et al (2015) NMDA receptor GluN2A/GluN2B subunit ratio as synaptic trait of levodopa-induced dyskinesias: from experimental models to patients. Front Cell Neurosci 9:245 50. Iderberg H, Rylander D, Bimpisidis Z, Cenci MA (2013) Modulating mGluR5 and 5-HT1A/1B receptors to treat l-DOPAinduced dyskinesia: effects of combined treatment and possible mechanisms of action. Exp Neurol 250:116–124 51. Chan H, Paur H, Vernon AC, Zabarsky V, Datla KP, Croucher MJ et al (2010) Neuroprotection and functional recovery associated with decreased microglial activation following selective activation of mGluR2/3 receptors in a rodent model of Parkinson’s disease. Parkinsons Dis 2010:1 52. Bennouar K-E, Uberti MA, Melon C, Bacolod MD, Jimenez HN, Cajina M et al (2013) Synergy between L-DOPA and a novel positive allosteric modulator of metabotropic glutamate receptor 4: implications for Parkinson’s disease treatment and dyskinesia. Neuropharmacology 66:158–169 53. Ahmed I, Bose SK, Pavese N, Ramlackhansingh A, Turkheimer F, Hotton G et al (2011) Glutamate NMDA receptor dysregulation in Parkinson’s disease with dyskinesias. Brain 4(134):979–986 54. Dong XX, Wang Y, Qin ZH (2009) Molecular mechanisms of excitotoxicity and their relevance to pathogenesis of neurodegenerative diseases. Acta Pharmacol Sin 4(30):379–387 55. Olivares D, Deshpande VK, Shi Y, Lahiri DK, Greig NH, Rogers JT et al (2012) N-methyl D-aspartate (NMDA) receptor antagonists and memantine treatment for Alzheimer’s disease, vascular dementia and Parkinson’s disease. Curr Alzheimer Res 6(9):746–758 56. Chen LW, Wei LC, Lang B, Ju G, Chan YS (2001) Differential expression of AMPA receptor subunits in dopamine neurons of the rat brain: a double immunocytochemical study. Neuroscience 1(106):149–160 57. Sobolevsky AI, Rosconi MP, Gouaux E (2009) X-ray structure, symmetry and mechanism of an AMPA-subtype glutamate receptor. Nature 7274(462):745–756

Role of Ion Channels and Metal Ions in Parkinson’s Disease 58. Lo¨schmann P-A, Lange KW, Kunow M, Rettig K-J, J€ahnig P, Honore T et al (1991) Synergism of the AMPA-antagonist NBQX and the NMDA-antagonist CPP with L-dopa in models of Parkinson’s disease. J Neural Transm Park Dis Dement Sect 3(3): 203–213 59. Konitsiotis S, Blanchet PJ, Verhagen L, Lamers E, Chase TN (2000) AMPA receptor blockade improves levodopa-induced dyskinesia in MPTP monkeys. Neurology 8(54): 1589–1595 60. Pinheiro P, Mulle C (2006) Kainate receptors. Cell Tissue Res 2(326):457–482 61. Jane DE, Lodge D, Collingridge GL (2009) Kainate receptors: pharmacology, function and therapeutic potential. Neuropharmacology 1(56):90–113 62. Lerma J (2006) Kainate receptor physiology. Curr Opin Pharmacol 1(6):89–97 63. Petralia RS, Wang YX, Wenthold RJ (1994) Histological and ultrastructural localization of the kainate receptor subunits, KA2 and GluR6/7, in the rat nervous system using selective antipeptide antibodies. J Comp Neurol 1(349):85–110 64. Kieval JZ, Hubert GW, Charara A, Pare JF, Smith Y (2001) Subcellular and subsynaptic localization of presynaptic and postsynaptic kainate receptor subunits in the monkey striatum. J Neurosci 22(21):8746–8757 65. Chagnac-Amitai Y, Connors BW (1989) Horizontal spread of synchronized activity in neocortex and its control by GABA-mediated inhibition. J Neurophysiol 4(61):747–758 66. Walls AB, Waagepetersen HS, Bak LK, Schousboe A, Sonnewald U (2015) The glutamine-glutamate/GABA cycle: function, regional differences in glutamate and GABA production and effects of interference with GABA metabolism. Neurochem Res 2(40): 402–409 67. Angelotti TP, Macdonald RL (1993) Assembly of GABAA receptor subunits: alpha 1 beta 1 and alpha 1 beta 1 gamma 2S subunits produce unique ion channels with dissimilar single-channel properties. J Neurosci 4(13): 1429–1440 68. Olsen RW, DeLorey TM (1999) GABA receptor physiology and pharmacology. In: GJ S, BW A, RW A (ed) Basic neurochemistry: molecular, cellular and medical aspects. Lippincott-Raven, Philadelphia 69. Munoz MD, de la Fuente N, Sanchez-Capelo A (2020) TGF-β/Smad3 Signalling modulates GABA neurotransmission: implications

553

in Parkinson’s disease. Int J Mol Sci 2(21): 590 70. Munoz MD, Antolin-Vallespin M, TapiaGonzalez S, Sanchez-Capelo A (2016) Smad3 deficiency inhibits dentate gyrus LTP by enhancing GABAA neurotransmission. J Neurochem 2(137):190–199 71. Chen XY, Xue Y, Wang H, Zhu SH, Hao XM, Chen L (2013) Modulation of firing activity by endogenous GABAA receptors in the globus pallidus of MPTP-treated parkinsonian mice. Neurosci Bull 6(29):701–707 72. NCT03000569. A study to evaluate SAGE217 in participants with Parkinson’s disease: Sage Therapeutics. Available from: https:// clinicaltrials.gov/ct2/show/NCT03000569 73. Mograbi KDM, de Castro ACF, de Oliveira JAR, Sales PJB, Covolan L, Del Bel EA et al (2017) Effects of GABAa receptor antagonists on motor behavior in pharmacological Parkinson9s disease model in mice. Physiol Rep 6(5):e13081 74. Boyes J, Bolam JP (2007) Localization of GABA receptors in the basal ganglia. Prog Brain Res 160:229–243 75. Daniele A, Panza F, Greco A, Logroscino G, Seripa D (2016) Can a positive allosteric modulation of GABAergic receptors improve motor symptoms in patients with Parkinson’s disease? The potential role of zolpidem in the treatment of Parkinson’s disease. Parkinson’s Dis 2016:1 76. Lloyd K, Shemen L, Hornykiewicz O (1977) Distribution of high affinity sodiumindependent [3 H] gamma-aminobutyric acid ([3 H] GABA) binding in the human brain: alterations in Parkinson’s disease. Brain Res 2(127):269–278 77. Calon F, Goulet M, Blanchet P, Martel J, Piercey M, Be P et al (1995) Levodopa or D2 agonist induced dyskinesia in MPTP monkeys: correlation with changes in dopamine and GABA A, receptors in the striatopallidal complex. Brain Res 1(680):43–52 78. Capiod T (2016) Extracellular calcium has multiple targets to control cell proliferation. In: Calcium entry pathways in non-excitable cells. Springer, Cham, pp 133–156 79. La Rovere RM, Roest G, Bultynck G, Parys JB (2016) Intracellular Ca(2+) signaling and Ca (2+) microdomains in the control of cell survival, apoptosis and autophagy. Cell Calcium 2(60):74–87 80. Pchitskaya E, Popugaeva E, Bezprozvanny I (2018) Calcium signaling and molecular

554

Bhupesh Vaidya et al.

mechanisms underlying neurodegenerative diseases. Cell Calcium 70:87–94 81. Ertel EA, Campbell KP, Harpold MM, Hofmann F, Mori Y, Perez-Reyes E et al (2000) Nomenclature of voltage-gated calcium channels. Neuron 3(25):533–535 82. Catterall WA, Perez-Reyes E, Snutch TP, Striessnig J (2005) International Union of Pharmacology. XLVIII. Nomenclature and structure-function relationships of voltagegated calcium channels. Pharmacol Rev 4(57):411–425 83. Chan CS, Guzman JN, Ilijic E, Mercer JN, Rick C, Tkatch T et al (2007) ‘Rejuvenation’protects neurons in mouse models of Parkinson’s disease. Nature 7148(447):1081–1086 84. Wang QM, Xu YY, Liu S, Ma ZG (2017) Isradipine attenuates MPTP-induced dopamine neuron degeneration by inhibiting up-regulation of L-type calcium channels and iron accumulation in the substantia nigra of mice. Oncotarget 29(8):47284–47295 85. Kupsch E-M, Aubel D, Gibbs CP, Kahrs AF, Rudel T, Meyer TF (1996) Construction of hermes shuttle vectors: a versatile system useful for genetic complementation of transformable and non-transformable Neisseria mutants. Mol Gen Genet MGG 5(250): 558–569 86. Ilijic E, Guzman J, Surmeier D (2011) The L-type channel antagonist isradipine is neuroprotective in a mouse model of Parkinson’s disease. Neurobiol Dis 2(43):364–371 87. Kang S, Cooper G, Dunne SF, Dusel B, Luan C-H, Surmeier DJ et al (2012) CaV1. 3-selective L-type calcium channel antagonists as potential new therapeutics for Parkinson’s disease. Nat Commun 3:1146 88. Hurley MJ, Gentleman SM, Dexter DT (2015) Calcium CaV1 channel subtype mRNA expression in Parkinson’s disease examined by in situ hybridization. J Mol Neurosci 3(55):715–724 89. Devergnas A, Chen E, Ma Y, Hamada I, Pittard D, Kammermeier S et al (2016) Anatomical localization of Cav3.1 calcium channels and electrophysiological effects of T-type calcium channel blockade in the motor thalamus of MPTP-treated monkeys. J Neurophysiol 1(115):470–485 90. Poetschke C, Dragicevic E, Duda J, Benkert J, Dougalis A, DeZio R et al (2015) Compensatory T-type Ca2+ channel activity alters D2-autoreceptor responses of Substantia nigra dopamine neurons from Cav1.3 L-type Ca2+ channel KO mice. Sci Rep 5:13688

91. Xiang Z, Thompson AD, Brogan JT, Schulte ML, Melancon BJ, Mi D et al (2011) The discovery and characterization of ML218: a novel, centrally active T-type calcium channel inhibitor with robust effects in STN neurons and in a rodent model of Parkinson’s disease. ACS Chem Neurosci 12(2):730–742 92. Brustovetsky T, Brittain MK, Sheets PL, Cummins TR, Pinelis V, Brustovetsky N (2011) KB-R7943, an inhibitor of the reverse Na+/Ca2+ exchanger, blocks N-methyl-Daspartate receptor and inhibits mitochondrial complex I. Br J Pharmacol 1(162):255–270 93. Chen X, Xue B, Wang J, Liu H, Shi L, Xie J (2018) Potassium channels: a potential therapeutic target for Parkinson’s disease. Neurosci Bull 2(34):341–348 94. Piri H, Haghdoost-Yazdi H, Fraidouni N, Dargahi T, Yaghoubidoust M, Azadmehr A (2017) The anti-parkinsonism effects of K (ATP) channel blockade in the 6-hydroxydopamine-induced animal model: the role of oxidative stress. Basic Clin Neurosci 3(8): 183–192 95. Tai KK, Truong DD (2002) Activation of adenosine triphosphate-sensitive potassium channels confers protection against rotenone-induced cell death: therapeutic implications for Parkinson’s disease. J Neurosci Res 4(69):559–566 96. Wang S, Hu LF, Yang Y, Ding JH, Hu G (2005) Studies of ATP-sensitive potassium channels on 6-hydroxydopamine and haloperidol rat models of Parkinson’s disease: implications for treating Parkinson’s disease? Neuropharmacology 7(48):984–992 97. Ren Y, Ye M, Chen S, Ding J (2016) CD200 inhibits inflammatory response by promoting KATP channel opening in microglia cells in Parkinson’s disease. Med Sci Monit 22:1733 98. Cao X, Cao L, Ding L, Bian J-s (2017) A new hope for a devastating disease: hydrogen sulfide in Parkinson’s disease. Mol Neurobiol 55(5):1–11 99. Dolga AM, de Andrade A, Meissner L, Knaus HG, Hollerhage M, Christophersen P et al (2014) Subcellular expression and neuroprotective effects of SK channels in human dopaminergic neurons. Cell Death Dis 1(5):e999 100. Wang Y, Qu L, Wang X-L, Gao L, Li Z-Z, Gao G-D et al (2015) Firing pattern modulation through SK channel current increase underlies neuronal survival in an organotypic slice model of Parkinson’s disease. Mol Neurobiol 1(51):424–436

Role of Ion Channels and Metal Ions in Parkinson’s Disease 101. DiFrancesco JC, DiFrancesco D (2015) Dysfunctional HCN ion channels in neurological diseases. Front Cell Neurosci 6:174 102. Chu HY, Zhen X (2010) Hyperpolarizationactivated, cyclic nucleotide-gated (HCN) channels in the regulation of midbrain dopamine systems. Acta Pharmacol Sin 9(31): 1036–1043 103. Wu LJ (2014) Voltage-gated proton channel HV1 in microglia. Neuroscientist 6(20): 599–609 104. Neal ML, Beier EE, Hossain MM, Boyle A, Zheng J, Kim C et al (2023) Voltage-gated proton channel Hv1 regulates neuroinflammation and dopaminergic neurodegeneration in Parkinson’s disease models. Antioxidants (Basel) 12(3):582 105. Black JA, Waxman SG (2012) Sodium channels and microglial function. Exp Neurol 2(234):302–315 106. Zhu H, Wang Z, Jin J, Pei X, Zhao Y, Wu H et al (2016) Parkinson’s disease-like forelimb akinesia induced by BmK I, a sodium channel modulator. Behav Brain Res 308:166–176 107. Wang Z, Lin Y, Liu W, Kuang P, Lao W, Ji Y et al (2019) Voltage-gated sodium channels are involved in cognitive impairments in Parkinson’s disease- like rats. Neuroscience 418: 231–243 108. Liu W, Lao W, Zhang R, Zhu H (2021) Altered expression of voltage gated sodium channel Nav1.1 is involved in motor ability in MPTP-treated mice. Brain Res Bull 170: 187–198 109. Wang Z, Kuang P, Lin Y, Liu W, Lao W, Ji Y et al (2018) Re-expression of voltage-gated sodium channel subtype Nav1.3 in the substantia nigra after dopamine depletion. Neurosci Lett 687:146–152 110. Moyano P, Vicente-Zurdo D, BlazquezBarbadillo C, Menendez JC, Gonzalez JF, Rosales-Conrado N et al (2021) Neuroprotective action of multitarget 7-Aminophenanthridin-6(5H)-one derivatives against metalinduced cell death and oxidative stress in SN56 cells. ACS Chem Neurosci 18(12): 3358–3372 111. Moustakas M (2021) The role of metal ions in biology, biochemistry and medicine. Materials (Basel, Switzerland) 3(14):549 112. Pyatha S, Kim H, Lee D, Kim K (2022) Association between heavy metal exposure and Parkinson’s disease: a review of the mechanisms related to oxidative stress. Antioxidants (Basel, Switzerland) 12(11):2467

555

113. Uversky VN, Li J, Fink AL (2001) Metaltriggered structural transformations, aggregation, and fibrillation of human alphasynuclein. A possible molecular NK between Parkinson’s disease and heavy metal exposure. J Biol Chem 47(276):44284–44296 114. Bjorklund G, Stejskal V, Urbina MA, Dadar M, Chirumbolo S, Mutter J (2018) Metals and Parkinson’s disease: mechanisms and biochemical processes. Curr Med Chem 19(25):2198–2214 115. Pamphlett R, Bishop DP (2022) Mercury is present in neurons and oligodendrocytes in regions of the brain affected by Parkinson’s disease and co-localises with Lewy bodies. PLoS One 1(17):e0262464 116. Sørensen JC, Slomianka L, Christensen J, Zimmer J (1990) Zinc-containing telencephalic connections to the rat striatum: a combined Fluoro-Gold tracing and histochemical study. Exp Brain Res 3(105):370–382 117. Bitanihirwe BK, Cunningham MG (2009) Zinc: the brain’s dark horse. Synapse 11(63):1029–1049 118. Dexter DT, Wells FR, Lees AJ, Agid F, Agid Y, Jenner P et al (1989) Increased nigral iron content and alterations in other metal ions occurring in brain in Parkinson’s disease. J Neurochem 6(52):1830–1836 119. Dexter DT, Jenner P, Schapira AH, Marsden CD (1992) Alterations in levels of iron, ferritin, and other trace metals in neurodegenerative diseases affecting the basal ganglia. The Royal Kings and Queens Parkinson’s Disease Research Group. Ann Neurol S1(32 Suppl): S94–S100 120. Jenner P, Dexter D, Sian J, Schapira A, Marsden C (1992) Oxidative stress as a cause of nigral cell death in Parkinson’s disease and incidental Lewy body disease. Ann Neurol S1(32):S82 121. Brewer GJ, Kanzer SH, Zimmerman EA, Molho ES, Celmins DF, Heckman SM et al (2010) Subclinical zinc deficiency in Alzheimer’s disease and Parkinson’s disease. Am J Alzheimers Dis Other Dement 7(25): 572–575 122. Ahmed SS, Santosh W (2010) Metallomic profiling and linkage map analysis of early Parkinson’s disease: a new insight to aluminum marker for the possible diagnosis. PLoS One 6(5):e11252 123. Bisaglia M, Bubacco L (2020) Copper ions and Parkinson’s disease: why is homeostasis so relevant? Biomol Ther 2(10):195

556

Bhupesh Vaidya et al.

124. Abbaoui A, El Hiba O, Gamrani H (2016) Copper poisoning induces neurobehavioral features of Parkinson’s disease in rat: alters dopaminergic system and locomotor performance. Parkinsonism Relat Disord 22:e188 125. Eid R, Arab NT, Greenwood MT (2017) Iron mediated toxicity and programmed cell death: a review and a re-examination of existing paradigms. Biochim Biophys Acta, Mol Cell Res 2(1864):399–430 126. Takahashi H, Watanabe Y, Tanaka H, Mihara M, Mochizuki H, Takahashi K et al (2018) Comprehensive MRI quantification of the substantia nigra pars compacta in Parkinson’s disease. Eur J Radiol 109:48–56 127. Shi L, Huang C, Luo Q, Rogers E, Xia Y, Liu W et al (2019) The association of iron and the pathologies of Parkinson’s diseases in MPTP/ MPP(+)-induced neuronal degeneration in non-human primates and in cell culture. Front Aging Neurosci 11:215 128. Kwakye GF, Paoliello M, Mukhopadhyay S, Bowman AB, Aschner M (2015) Manganeseinduced Parkinsonism and Parkinson’s disease: shared and distinguishable features. Int J Environ Res Public Health 7(12): 7519–7540 129. Guilarte TR, Gonzales KK (2015) Manganese-induced parkinsonism is not idiopathic Parkinson’s disease: environmental and genetic evidence. Toxicol Sci 2(146): 204–212 130. Xu B, Huang S, Liu Y, Wan C, Gu Y, Wang D et al (2022) Manganese promotes alphasynuclein amyloid aggregation through the induction of protein phase transition. J Biol Chem 1(298):101469 131. Berridge MJ (1998) Neuronal calcium signaling. Neuron 1(21):13–26 132. Brini M, Calı` T, Ottolini D, Carafoli E (2013) Intracellular calcium homeostasis and signaling. In: Metallomics and the cell. Springer, Dordrecht, pp 119–168 133. Surmeier DJ, Guzman JN, Sanchez J, Schumacker PT (2012) Physiological phenotype and vulnerability in Parkinson’s disease. Cold Spring Harb Perspect Med 7(2):a009290 134. Neal AP, Guilarte TR (2013) Mechanisms of lead and manganese neurotoxicity. Toxicol Res 2(2):99–114 135. Weisskopf MG, Weuve J, Nie H, Saint-Hilaire MH, Sudarsky L, Simon DK et al (2010) Association of cumulative lead exposure with Parkinson’s disease. Environ Health Perspect 11(118):1609–1613

136. Rogers JT, Venkataramani V, Washburn C, Liu Y, Tummala V, Jiang H et al (2016) A role for amyloid precursor protein translation to restore iron homeostasis and ameliorate lead (Pb) neurotoxicity. J Neurochem 3(138):479–494 137. Biglan KM, Oakes D, Lang AE, Hauser RA, Hodgeman K, Greco B et al (2017) A novel design of a phase III trial of isradipine in early Parkinson disease (STEADY-PD III). Ann Clin Transl Neurol 6(4):360–368 138. Murata M, Hasegawa K, Kanazawa I, Fukasaka J, Kochi K, Shimazu R et al (2015) Zonisamide improves wearing-off in Parkinson’s disease: a randomized, double-blind study. Mov Disord 10(30):1343–1350 139. NCT01491022. A randomized trial to evaluate Ampyra for gait impairment in Parkinson’s disease: University of Miami. Available from: https://clinicaltrials.gov/ct2/show/results/ NCT01491022 140. NCT01341080. Varenicline for gait and balance impairment in Parkinson disease (Chantix-PD): Rush University Medical Center. Available from: https://clinicaltrials.gov/ ct2/show/NCT01341080 141. Surges R, Volynski KE, Walker MC (2008) Is levetiracetam different from other antiepileptic drugs? Levetiracetam and its cellular mechanism of action in epilepsy revisited. Ther Adv Neurol Disord 1(1):13–24 142. Yamamoto S, Takahashi N, Mori Y (2010) Chemical physiology of oxidative stressactivated TRPM2 and TRPC5 channels. Prog Biophys Mol Biol 1(103):18–27 143. Jankovic J (2000) Complications and limitations of drug therapy for Parkinson’s disease. Neurology 12 Suppl 6(55):S2–S6 144. NCT00163085. The effects of an NR2B NMDA antagonist, CP-101,606, in patients with Parkinson’s disease: Pfizer. Available from: https://clinicaltrials.gov/ ct2/show/NCT00163085 145. NCT00296959. Topiramate as a treatment for Levodopa-induced dyskinesia in Parkinson’s disease: University Health Network, To r o n t o . A v a i l a b l e f r o m : h t t p s : // clinicaltrials.gov/ct2/show/NCT00296959 146. NCT00004576. Study of LY300164 for the treatment of Parkinson’s disease. Available from: https://clinicaltrials.gov/ ct2/show/NCT00004576 147. NCT00001929. Treatment of Parkinson’s disease with eliprodil: National Institute of Neurological Disorders and Stroke

Role of Ion Channels and Metal Ions in Parkinson’s Disease ( N I N D S ) . A v a i la b l e f r o m : https:// clinicaltrials.gov/ct2/show/NCT00001929 148. NCT00630500. Efficacy and safety of memantine for Parkinson’s Disease Dementia (PDD) and Dementia with Lewy Bodies (DLB) (MEMPDD): Helse Stavanger HF. Available from: https://clinicaltrials. gov/ct2/show/NCT00630500 149. NCT01474421. Safety and efficacy of AQW051 in L-dopa induced dyskinesias in

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patients with Parkinson’s disease: Novartis Pharmaceuticals. Available from: https:// clinicaltrials.gov/ct2/show/NCT01474421 150. NCT00001365. Dextromethorphan for the treatment of Parkinson’s disease and similar conditions of the nervous system: National Institute of Neurological Disorders and Stroke (NINDS). Available from: https:// clinicaltrials.gov/ct2/show/NCT00001365

Chapter 37 Creating a Reproducible Model of Spinal Cord Injury in Rats: A Contusion Approach Syed Shadab Raza Abstract Spinal cord injury (SCI) is a devastating clinical condition that affects millions of people worldwide. SCI primarily affects males in younger age groups. It is characterized by a complex of neurological dysfunctions that can lead to permanent disability. We describe an adapted technique for SCI, i.e., a contusion model of SCI, in this chapter. This model is widely used to study the pathology of SCI and test potential therapies. The experimental contusion is performed by using a compression device, which allows the creation of a reproducible injury animal model through the definition of specific injury parameters. A detailed methodology has been developed and described here that utilizes a stereotactic frame and impactor to produce reproducible injuries. Key words Spinal cord injury (SCI), Contusion injury, Trauma, Impactor, Wistar rats

1

Introduction Animal models are essential for assessing the effectiveness of therapeutic approaches designed to mitigate neurological disorders, including spinal cord injury (SCI). To provide reliable results, these models must produce consistent deficits in locomotor and sensory behaviors, allow for adjustable injury severity, and demonstrate a correlation between injury severity and neurological deficits [1–3]. SCI can be classified into three types: transection, contusion, and compression. Selecting an appropriate injury model is crucial for effectively evaluating new treatments for SCI. Recent surveys have revealed that, when compared to the hemisection or total transection models, the contusion model is widely acknowledged as clinically relevant [4–6]. Contusion injuries are usually caused by specialized equipment and mimic human SCI caused by spinal column impaction [7, 8].

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_37, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Outmost care should be taken while performing SCI surgery. Variability in functional recovery remains a challenge with contusion models, which can be addressed by using a computercontrolled impactor and stabilizing the spine before impact for uniform force delivery. It should be noted that recovery after SCI is primarily driven by plasticity and collateral contributions from surviving cells, so even minor variations in contusion technique can yield significantly different results.

2

Materials

2.1 Surgical Preparations

Use the following surgical instruments for the procedure: 1. Self-retaining retractors. 2. Ocular forceps. 3. Ocular iris and Hard Age Vannas Micro Scissors, angled 8 CM/3 1/8″. 4. Absorbable sutures. 5. Autoclave. 6. Cotton thread. 7. Cotton-tip applicators. 8. Pointed, sharp-edged scissors. 9. Scalpel blades. 10. Cleaning wipes. 11. Syringe (5 mL). 12. Kidney tray. 13. Syringe discarder. 14. Heating pad (Fig. 1). 15. Rectal probe thermometer (Fig. 2). 16. Rongeurs (Fig. 3). 17. Stereo-zoom microscope (Fig. 4). 18. Stereotaxic (Fig. 5). 19. Impactor (Fig. 6). 20. Betadine. 21. Ethanol. 22. Anesthesia: ketamine and xylazine hydrochloride. 23. Analgesic: buprenorphine. 24. Antibiotic (baytril or enrofloxacin). 25. Sodium chloride. 26. Potassium chloride.

Reproducible Contusion Model of SCI

Fig. 1 It depicts a surgical heating pad

Fig. 2 It illustrates a rectal thermometer

27. Potassium phosphate monobasic anhydrous. 28. pH meter. 29. pH paper. 30. Weighing scale. 31. Measuring cylinder.

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Fig. 3 It shows a rongeurs

Fig. 4 It displays a surgical stereo-zoom microscope

3 Methods 3.1 Preparation of 1× PhosphateBuffered Saline (PBS)

1. Weigh out the following amounts of reagents: 8.00 g of NaCl, 0.20 g of KCl, and 0.24 g of KH2PO4. 2. Add the reagents to a mixing container containing 800 mL of distilled or deionized water. 3. Stir the mixture using a stirring rod until the reagents are completely dissolved.

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Fig. 5 It portrays a stereotaxic

4. Check the pH of the solution using a pH meter or pH paper. Adjust the pH to 7.4 by adding small amounts of hydrochloric acid (HCl) or sodium hydroxide (NaOH) as needed. 5. If desired, sterilize the solution using an autoclave. 6. Label the container as 1× PBS and include the date of preparation. 7. Store the 1× PBS in a tightly sealed container at room temperature. 3.2 Preparation of 70% Alcohol

1. Measure 70 mL of 100% ethanol using a measuring cylinder (see Notes 1 and 2). 2. Pour the 70 mL of ethanol into a mixing container. 3. Add 30 mL of distilled or deionized water to the mixing container. 4. Mix the ethanol and water together thoroughly until they are completely blended. 5. Label the container as 70% ethanol and include the date of preparation. 6. Store the 70% ethanol in a tightly sealed container away from heat and flame.

3.3 Animal Preparation

1. Preoperative care: The rats should be housed under standard conditions and should be acclimatized to the housing conditions for at least 3–4 days prior to surgery.

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Fig. 6 It features an impactor manufactured by Kopf Instruments. (Adapted from https://kopfinstruments.com/product/model-990-spinal-compression-device/)

2. Stop feeding the rats (male Wistar rats, weighing 180–220 g) the night before surgery (see Note 3). 3. Administer pain medication at least 1 h before the SCI procedure (typically buprenorphine, 2 mL of 0.004 mg/mL subcutaneous). 4. Administer preoperative antibiotics (typically baytril 4 mg/kg or enrofloxacin 5–10 mg/kg body weight or any suitable antibiotic subcutaneously) prior to surgery.

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5. Anesthetize the rats using 100 mg/kg of ketamine and 10 mg/ kg of xylazine hydrochloride (see Note 4). 6. Transfer the rat to the stereotaxic apparatus with a heating pad placed on it, where surgery has to be performed (see Note 5). 7. Move the animal into position on the stereotaxic platform. Use ear clamps to immobilize the animal. 8. Insert a rectal temperature probe to monitor the core temperature and adjust the heating accordingly to keep the animal’s temperature as close to normal (~37 °C) as possible. 9. Apply ophthalmic lubricant to each eye, e.g., Systane Ultra Ophthalmic Solution, to prevent eye dryness (see Note 6). 10. Use a betadine solution to clean the skin in the area where SCI surgery will be performed. 11. Put a dot (with a marker) on the skin where the target level should be in relation to T10. 12. Shave a rectangle longitudinally and center it at the T10 level. 3.4 Surgical Procedure

1. Make a 2–3 cm incision using a scalpel blade centered on the T10 mark (see Note 7). 2. Dissect the fascia and muscle layers away from the T9-T11 spinous processes and laminae (see Note 8). 3. Divide the interspinous ligament between T9-T10 and T10-T11 using fine scissors. 4. Use fine tip rongeurs to carefully perform piece-meal laminectomy bilaterally at T10. 5. Use an impactor tip of 2.0 mm with a depth of 1 mm (see Note 9). 6. Using a micro-rongeur, remove the T10 spinous process and lamina (laminectomy), exposing the dura mater covering the spinal cord. 7. Consistently cleanse the exposed area using a cotton-tip applicator soaked in 1× PBS solution. 8. Add a 10 g weight onto an impounder positioned on the exposed dura at spinal level T10 and drop it from a height of 2 cm (see Notes 10–12). 9. Use a cotton-tip applicator to clean the surgical spinal area following damage (see Note 13). 10. Suture muscle and fascia layers using absorbable sutures. 11. Close the skin with a cotton thread (see Note 14).

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3.5 Postoperative Care

1. After suturing, place the rats in a warm environment for 24 h. Use a heating pad and infrared light while they are unconscious. 2. Once the rats are fully awake, administer subcutaneously 2–3 mL of saline, 2 mL (0.004 mg/mL) of buprenorphine, and 4 mg/kg of baytril or 5–10 mg/kg body weight of enrofloxacin. 3. Continue administering buprenorphine subcutaneously twice a day for the first 3 days and once a day for the next 4 days. 4. Continue administering baytril or enrofloxacin subcutaneously once a day for 4 days (see Note 15). 5. Until the rats’ bladder function restores, manually express the animals’ bladders five times daily for urine (see Note 16). 6. To encourage eating, provide mashed food in a Petri dish due to the prevalence of postoperative weight loss. 7. Weigh the rats daily beginning the day after surgery to assess their recovery.

3.6

4

Sham Animals

The sham group animals should also get the same treatment as the surgery group animals but without the contusive injury.

Notes 1. Always use caution when handling reagents and sterilization equipment. Make sure to work in a clean, sterile environment to prevent contamination of the solution. 2. Always use caution when handling ethanol, as it is flammable and can be harmful if ingested or inhaled. To sterilize the area for surgery, stereotaxic, etc., 70% ethanol might be used. 3. The rats should be fasted for 12 h prior to surgery, and they were given access to water ad libitum. 4. Post-anesthesia, prior to surgery; wait until there is no toe-pinch response. 5. Clean the surgical area and apparatus with alcohol (70% ethanol) wipes. 6. To prevent dryness during the surgical operation, normal saline can be applied to the eyes a few times. 7. Use a separate set of instruments and supplies for each animal. 8. Retractors can be used to retract the muscle and fascia away from the bone. 9. Based on our experience, the Kopf Instruments Model 990 Spinal Compression Device is among the most cost-effective choices for inducing reproducible spinal compression injuries.

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10. At vertebral level T10, a 10 g weight dropped from a height of 2 cm onto an impounder positioned on the exposed dura can cause a moderate spinal cord contusion injury. 11. For mice, use a weight of 5 g. 12. Open the individual instruments sterilely and carefully place them in the surgical field. 13. If the spinal dura mater appears to be dry, moisten it with 1× PBS using a cotton-tipped applicator. 14. Be careful not to pull the stitch too tight while suturing the muscle and fascia layers. 15. If an infection is present, consult with local veterinarians to increase the dosage (or restart) of antibiotics, or else discard the animal from the study. 16. Ensure that the rat urinates early in the morning upon the experimenters’ arrival at the lab and late in the evening before their departure. References 1. Alizadeh A, Dyck SM, Karimi-Abdolrezaee S (2019) Traumatic spinal cord injury: an overview of pathophysiology, models and acute injury mechanisms. Front Neurol 10:282 2. Ahuja CS, Wilson JR, Nori S, Kotter MRN, Druschel C, Curt A, Fehlings MG (2017) Traumatic spinal cord injury. Nat Rev Dis Primers 3: 17018 3. McDonough A, Monterrubio A, Ariza J, Martı´nez-Cerdeno V (2015) Calibrated forceps model of spinal cord compression injury. J Vis Exp 98:52318 4. Sharif-Alhoseini M, Khormali M, Rezaei M, Safdarian M, Hajighadery A, Khalatbari MM, Safdarian M, Meknatkhah S, Rezvan M, Chalangari M, Derakhshan P, Rahimi-Movaghar V (2017) Animal models of spinal cord injury: a systematic review. Spinal Cord 8:714–721

5. Cheriyan T, Ryan DJ, Weinreb JH, Cheriyan J, Paul JC, Lafage V, Kirsch T, Errico TJ (2014) Spinal cord injury models: a review. Spinal Cord 52:588–595 6. Lee JH, Streijger F, Tigchelaar S, Maloon M, Liu J, Tetzlaff W, Kwon BK (2012) A contusive model of unilateral cervical spinal cord injury using the infinite horizon impactor. J Vis Exp 65:3313 7. Stokes BT, Jakeman LB (2002) Experimental modelling of human spinal cord injury: a model that crosses the species barrier and mimics the spectrum of human cytopathology. Spinal Cord 40:101–109 8. Krishna V, Andrews H, Jin X, Yu J, Varma A, Wen X, Kindy M (2013) A contusion model of severe spinal cord injury in rats. J Vis Exp 78: 50111

Chapter 38 Weight-Drop Method for Inducing Closed Head Diffuse Traumatic Brain Injury Bhagawati Saxena, Bhavna Bohra, and Krishna A. Lad Abstract Traumatic brain injury (TBI) is one of the foremost causes of disability and death globally. Prerequisites for successful therapy of disabilities associated with TBI involved improved knowledge of the neurobiology of TBI, measurement of quantitative changes in recovery dynamics brought about by therapy, and the translation of quantitative methodologies and techniques that were successful in tracking recovery in preclinical models to human TBI. Frequently used animal models of TBI in research and development include controlled cortical impact, fluid percussion injury, blast injury, penetrating blast brain injury, and weight-drop impact acceleration models. Preclinical models of TBI benefit from controlled injury settings and the best prospects for biometric quantification of injury and therapy-induced gradual recovery from disabilities. Impact acceleration closed head TBI paradigm causes diffuse TBI (DTBI) without substantial focal brain lesions in rats. DTBI is linked to a significant rate of death, morbidity, and long-term disability. DTBI is difficult to diagnose at the time of hospitalization with imaging techniques making it challenging to take prompt therapeutic action. The weight-drop method without craniotomy is an impact acceleration closed head DTBI model that is used to induce mild/moderate diffuse brain injuries in rodents. Additionally, we have characterized neuropathological and neurobehavioral outcomes of the weight-drop model without craniotomy for inducing closed head DTBI of graded severity with a range of mass of weights (50–450 gm). This chapter also discusses techniques and protocols for measuring numerous functional disabilities and pathological changes in the brain brought on by DTBI. Key words Traumatic brain injury (TBI), Weight-drop model, Closed head injury, Diffuse axonal injury, Brain edema, Blood-brain barrier, Motor dysfunction

1

Introduction Traumatic brain injury (TBI) is a severe neurological condition caused by an external mechanical head impact such as a collision, jolt, blow, or penetrating head injury that impairs brain function. TBI affects millions of people worldwide. According to estimates, 69 million people worldwide experience TBI each year [1]. TBI is a silent public health crisis that causes mortality, morbidity, and functional impairment, burdening the global healthcare system

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_38, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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financially [2]. Unfortunately, except for ordinary medical care and intervention, there is currently no effective treatment to support functional rehabilitation [3, 4]. Hence, the development of new treatment methods would have huge clinical and financial advantages. Novel TBI treatments depend on animal screening models that elucidate trauma’s cellular and molecular underpinnings. Over the years, researchers studying TBI in animal models have created a plethora of knowledge that has helped them understand the processes before, during, and after injury. However, the complexity of the pathophysiology and biomechanical components of progressive brain damage followed by mechanical trauma makes establishing therapeutically relevant animal models of TBI challenging [5, 6]. Numerous animal models of TBI have been established due to the variable clinical state of TBI patients. Fluid percussion injury (FPI), controlled cortical impact (CCI), penetrating ballistic-like brain injury (PBBI), blast TBI, weight-drop impact acceleration models, etc. are frequently utilized TBI models in research (Fig. 1) [7–11]. All models are ambiguous due to the inherent variation in neurological prognosis and damage severity. These constraints create obstacles in undertaking translational investigations. The majority of models available are unable to accurately represent typical damage mechanisms or the full range of actual TBI. Certain models mimic very specific types of TBI.

Fig. 1 Experimental settings in various animal models of TBI: fluid percussion injury (FPI), controlled cortical impact (CCI) injury model, penetrating ballistic-like brain injury (PBBI), blast brain injury, Feeney’s weight-drop model

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Biomechanics of PBBI, blast TBI model, and mild TBI model mimics human ballistic TBI, military TBI, and sports TBI, respectively [9, 10, 12]. Weight-drop models are gaining momentum even though it is a relatively nascent area of study because this model is relatively simple and easy to perform, inexpensive, and reproducible and generates injuries with characteristics that are comparable to those generated in clinical instances. Models of weight drop can imitate all types of TBI, varying from mild concussions to severe TBI. On the other hand, typical TBI models like FPI and CCI, although reproducible, required a sophisticated machine that is not economically efficient and results in focal brain contusion with minimal axonal damage. Weight-drop models mimic diffuse TBI (DBTI). Weight-drop models have been modified in different ways including Feeney’s, Marmarou’s, Shohami’s, and Maryland’s methods [6, 13]. Marmarou’s approach is the most popular of all types of weight-drop models of TBI and mimics closed head “impact acceleration” type of DTBI (Fig. 2). Pathologically DTBI that diffusely scattered throughout the brain stem and cerebrum [14] is majorly induced by falls and motor vehicle accidents in the human

Fig. 2 Closed head diffuse TBI setup. The apparatus and protocol [26] produce graded, repeatable, impact acceleration closed head diffuse TBI

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population. DTBI is linked to significant rates of death and morbidity. The expected neurobehavioral, neurochemical, and histopathological outcomes in closed head impact accelerated model of DTBI include loss of consciousness as measured by the time to right (TTR) reflex and absence of structural brain damage such as contusions or hemorrhages, but the existence of blood-brain barrier (BBB) disruption, edema, oxidative damage, and axonal injury resulted in neuronal cell death [15, 16]. Many TBI patients do not exhibit signs of injury on CT scans, while MRI offers higher sensitivity for detecting tiny, focal, localized traumatic cerebral lesions [17, 18]. DTBI patients often had diffuse brain damage without a focal lesion, making it difficult to identify with routine CT scans and take immediate preventive action [19]. Our lab findings show that in the weight-drop model of TBI without craniotomy, none of the traumatized rats suffered skull fractures, extensive structural brain injury, or intraparenchymal hemorrhages. Therefore, this model mimics gross pathological changes associated with closed head DTBI. DTBI induces motor and cognitive dysfunction [20, 21]. This weight-drop model of closed head DTBI also resulted in impaired locomotor activity and motor coordination as depicted by the reduced performance of rats in the beam-walking paradigm (Fig. 3a) as well as negatively affected sensorimotor function and balance of rats in pole test (Fig. 3b). The severity of motor dysfunction induced by this DTBI model was also increased with an increase in mass of weights and found maximum with 450 gm in both of the functional tests (Fig. 3). In our lab we observed that when 450 gm weight was dropped from 1 m height in this impact accelerated closed head DTBI, it resulted in the loss of memory in Y-maze test and increased unconsciousness time (TTR response) [22]. Thus, this model is useful for identifying how injury severity affects motor and cognitive impairments, and it may be used to assess how well therapy is working by looking at these functional outcomes. BBB disruption, cerebral edema, and the ensuing increased intracranial pressure are linked to an unfavorable outcome in TBI [23, 24]. Traumatized animals in this paradigm showed a considerable increase in BBB breakdown, which was anticipated by a significant rise in Evans blue concentration in the brains of traumatized rats (Fig. 4a). Our lab findings show that closed head DTBI induced by a weight-drop model without craniotomy can cause brain edema (increased % brain water content/100 g animal body weight) when a weight (50 to 450 gm) is dropped from a height of 1 m, without causing any mortality (Fig. 4b). The severity of BBB damage, as well as brain edema, also increased with an increase in mass of weights and maximum with 450 gm (Fig. 4).

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Fig. 3 Effect of different weights in a weight-drop-induced rat model of DTBI on the performance of rats in beam-walking test (a) and pole test (b). Data were expressed as mean ± standard error of the mean (SEM) (n = 6). ***p < 0.001, ****p < 0.001 compared to normal, using one-way analysis of variance or ANOVA (Tukey’s multiple comparisons test)

The development and progression of neurobehavioral impairments, as well as the pathogenesis of TBI, are both significantly influenced by oxidative stress [25]. Oxidative stress is an imbalance between generated free radicals and antioxidative mechanisms. Our laboratory findings show that this weight-drop-induced closed head DTBI resulted in increased oxidative stress including lipid peroxidation (malondialdehyde) and generation of nitric oxide (nitrite) (Fig. 5) as well as attenuated levels of antioxidant parameters, including superoxide dismutase (SOD), catalase, and reduced glutathione (Fig. 6). Lower weight of 50 gm is capable of inducing a mild form of DTBI, i.e., concussion where the functional disability occurs, but pathological changes are difficult to observe, while increasing weight to 100, 200, and 450 gm increases the severity of DTBI. Thus, an utmost important advantage of this weight-drop model is that adjusting the mass of various weights can mimic all types of DTBI ranging from mild concussion

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Fig. 4 Effect of different weights in a weight-drop-induced rat model of DTBI on the brain BBB permeability (a) and brain edema (b). Data were expressed as mean ± SEM (n = 6). *p < 0.05, ****p < 0.001 compared to normal, using one-way ANOVA (Tukey’s multiple comparisons test)

to severe closed head DTBI. This chapter focuses on adjusting the severity of closed head DTBI by varying the mass of the weight to 50, 100, 200, and 450 gm and fixing the drop height of these weights to 1 m. It also describes models and protocols used for measuring DTBI-induced functional disabilities and brain pathological changes.

2 2.1

Materials Animals

1. Use male Sprague Dawley rats weighing 300 ± 50 gm (see Notes 1–3). 2. Keep rats in clean polypropylene cages with husk bedding at the institutional animal home.

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Fig. 5 Effect of different weights in a weight-drop-induced rat model of DTBI on the levels of malondialdehyde (a) and nitrite (b) in the brain. Data were expressed as mean ± SEM (n = 6). *p < 0.05, ***p < 0.001, ****p < 0.001 compared to normal, using one-way ANOVA (Tukey’s multiple comparisons test)

3. Before starting the experiments, allow animals to acclimatize to the laboratory conditions for 2 weeks and have access to water ad libitum and a normal pellet diet. 2.2

Anesthesia

2.3 Equipment for Anesthesia Setup

Use isoflurane as the anesthesia of choice to measure consistent outcomes and replicate the surgical procedure (see Note 4). 1. Matrix VIP 3000 Corporation).

Isoflurane

Vaporizer

(MIDMARK

2. Ventilated anesthesia induction chamber (1500 cc). 3. Rodent intubation stand. 4. Intubation tube. 5. Cone mask.

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Fig. 6 Effect of different weight in a weight-drop-induced rat model of DTBI on the levels of SOD (a), catalase (b), and reduced glutathione (c). Data were expressed as mean ± SEM (n = 6). *p < 0.05, ****p < 0.001 compared to normal, using one-way ANOVA (Tukey’s multiple comparisons test) 2.4 Surgical Instruments

1. Small fine blades of spring scissors. 2. Tissue forceps. 3. Forceps fine tipped. 4. Scissors with fine tips. 5. Dissector. 6. Scalpel handle and blades.

2.5 General Surgical Supplies

1. Surgical gloves with latex. 2. Surgical absorbent under pads. 3. Needle of 20-gauge. 4. 1 and 5 mL syringes

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5. Nylon spool suture with 3-0 black monofilament (Ethilon). 6. Sterile suture with the needle of 3-0 black monofilament. 2.6 Intraoperative Surgical Supplies

1. Ophthalmic ointment (Neosporin). 2. Anesthetic agent to apply topically (lidocaine). 3. Antiseptic (betadine). 4. Antibacterial agent (bacitracin). 5. Normal sterile saline solution (0.9% NaCl). 6. Sterilized normal saline solution (0.9% NaCl) with 0.2% heparin.

2.7 Equipments for Closed Head DTBI Induction

Impact acceleration DTBI model assembly is depicted in Fig. 2. This contains the following parts: 1. Metal stand with 2–3 clamps (1 metal base without wheel). 2. Brass weights of 50, 100, 200, and 450 gm (2.5 cm diameter) (see Notes 5 and 6). 3. Plexiglass tube (3 cm diameter and 100 cm height) (see Notes 5–7). 4. A metallic helmet (disc cap) of 2 mm thickness and 5 mm diameter. 5. Sponge pad (18 cm width, 25 cm length, and 6 cm thickness). 6. Rectangular wooden platform support (20 cm width, 25 cm length, and 2.5 cm thickness).

2.8 Equipments for Estimating Locomotor Disabilities

1. A wooden beam with a surface area of 2.5 × 112 cm square elevated 60 cm above the ground by wooden support. 2. A darkened goal box (20 × 25 × 24 cm cube with a 10 cm aperture). 3. A round pole with a diameter of 2.5 cm and a height of 100 cm and a rough surface.

2.9 Equipments for Estimation of Biochemical Parameters

1. Tissue homogenizer. 2. Refrigerated centrifuge. 3. Water bath. 4. Vortex mixer. 5. UV-visible spectrophotometer (see Note 8). 6. Oven with temperature control.

2.10 Chemicals for Estimation of BBB Integrity and Brain Edema

1. Evans blue (2%, w/v). 2. Heparinized saline (ice-cold). 3. Potassium hydroxide (KOH) (0.5 N).

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4. Mixture of acetone and 4 N phosphoric acid (H3PO4) (15:3). 5. Absolute alcohol. 2.11 Chemicals for Lipid Peroxidation (Malondialdehyde) Estimation

1. Hanks’ Balanced Salt Solution (HBSS). 2. Sodium hydroxide (NaOH) (1 M). 3. Sodium dodecyl sulfate (SDS) (8.1%). 4. Acetic acid (20%). Adjust the pH with 1 M NaOH to 3.5. 5. Thiobarbituric acid (TBA) (0.8%). 6. MilliQ water. 7. Mixture of pyridine and n-butanol (1:15). 8. Standard 1,1,3,3-tetramethoxypropane (TMP) (ranging from 0 to 100 μM).

2.12 Chemicals for Nitric Oxide (Nitrite) Estimation

1. Phosphate buffer (ice-cold, 0.2 M, pH 7.6). 2. Phosphoric acid (5%). 3. Griess reagent (prepare by mixing 1% sulfanilamide and 1% naphthylethylenediamine dihydrochloride in 5% phosphoric acid). 4. Standard solution of sodium nitrite (NaNO2) in phosphate buffer (ice-cold, 0.2 M, pH 7.6) with known concentration, ranging from 0 to 100 μM.

2.13 Chemicals for Superoxide Dismutase Estimation

1. 2 mM ethylenediaminetetraacetic acid (EDTA) 2. Tris buffer (chilled, 50 mM, pH 8.2, prepare with 2 mM EDTA). 3. Triton X-100 (1%). 4. Pyrogallol (0.2 mM).

2.14 Chemicals for Catalase Estimation

1. Phosphate buffer (50 mM, pH 7.0).

2.15 Chemicals for Reduced Glutathione Estimation

1. Phosphate buffer (ice-cold, 0.2 M, pH 7.6).

2. Hydrogen peroxide (H2O2) (100 mM).

2. Trichloroacetic acid (25%). 3. DTNB (5,5′-dithiobis(2-nitrobenzoic acid)). 4. Standard solution of reduced glutathione in phosphate buffer.

3

Methods

3.1 Experimental Design

1. Use 30 male Sprague Dawley rats and group them into 5, with 6 rats in each group (see Notes 1 and 2).

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2. Use Group 1 to serve as the normal control while subjecting the remaining four groups to closed head DTBI by using the weight-drop method without craniotomy with various weights. Specifically, use Group 2 for inducing TBI with a weight mass of 50 gm, Group 3 with a weight mass of 100 gm, Group 4 with a weight mass of 200 gm, and Group 5 with a weight mass of 450 gm. 3.2

Anesthesia

1. Induction of anesthesia: Administer a combination of 4% of isoflurane and oxygen into animals for 4 min in a ventilated anesthesia induction chamber. 2. Maintenance of anesthesia: Maintain anesthesia with 1.5–2% of isoflurane during the pre-injury surgical procedure and then at 0.5–1.5% during the post-surgery procedure in a mixture of oxygen.

3.3 Induction of Graded Closed Head DTBI

Marmarou and his group suggested that the weight-drop model simulate closed head “impact acceleration” type of DTBI in rats [26, 27]. In contrast to other TBI models that only generate focal brain contusion, this one generates diffuse axonal injury (DAI) and shear stress. According to data from the model’s initial account, when a 450 g weight is dropped from a height of 2 m, there is a 12.5% incidence of skull fractures and a 44% death rate. Seizures, apnea, and hypertension occur along with brain damage. The use of mechanical ventilation dramatically increased survival. However, when a 450 g weight is dropped from a height of 1 m in this model, it dramatically reduces the mortality rate to zero [26, 28]. The severity of closed head DTBI can be modified by changing the weight’s mass to 50, 100, 200, and 450 gm and fixing the fall height of 1 m. Induction of graded DTBI consists of the following steps: 1. Position the anesthetized animals on a surgical plate in a prone posture and then maintained the anesthesia via a nasal cone (see Subheading 3.2). 2. Frequently check the rats for adequate depth of anesthesia by the absent response to tail pinch. 3. Throughout the procedure, maintain core body temperature on a thermal pad. 4. Apply Neosporin eye ointment in both eyes of animals to prevent drying during the surgical procedure. 5. After shaving the animal’s head, use 95% ethanol to sterilize the surgical site and the top of the animal’s head. 6. Make a 20 mm midline incision on the head using a scalpel for exposing the skull.

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7. Carefully press the skull’s periosteum to the incision’s most lateral margins using two cotton-tipped applicators to expose the skull. Apply mild pressure to stop bleeding and dry the exposed cranium. 8. Recognize the coronal and lambdoidal sutures and attach a helmet (metallic disc cap) with dimensions of 2 mm in thickness and 5 mm in diameter to the midline of the skull and two cranial sutures using bone wax. 9. Maintain anesthesia and place the anesthetized rats on the sponge bed (6 cm thickness) in a prone position under the hollow plexiglass tube. 10. Induce trauma by releasing various weights with varying masses, such as 50, 100, 200, and 450 gm, through the plexiglass tube and onto the helmet fastened to the animal’s cranial vault from a height of 1 m. 11. After the impact, take out the rat and remove the metallic cap. Administer topical lidocaine to the rat’s skull after cleaning the surgical wound and suturing the skin. 12. Finally, put the rat subsequently in a cage in supine posture. 13. Record TTR or the amount of time it took the rat to flip from the supine position to the prone position after waking up from the anesthesia. TTR is an index of loss of consciousness (see Note 9). 3.4 Assessment of Locomotor Defect

The methods for assessment of locomotor defects (beam-walking and pole test) have been optimized and modified in our labs from several different works of literature [29–31]. The beam-walking test is useful for assessing rats’ functional locomotor recovery after damage in the sensorimotor cortex region of the brain [30]. The pole test was first reported to measure bradykinesia in a rodent model of Parkinson’s disease [29]. Further, it has been reported to be an approach to measure impairment in motor coordination after brain damage [31, 32]. 1. For the beam-walking test, take a wooden beam (dimension as mentioned in Subheading 2.8, item 1). 2. Keep a darkened goal box (dimension as mentioned in Subheading 2.8, item 2) at one of the beam’s ends. 3. Train rats to enter a darkened goal box that is positioned at the opposite end of the beam by crossing the narrow beam by stimulation with a loud noise. 4. Perform two trials on each animal, 10 min apart. 5. Note the outcome after two trials. 6. Score the performance of rat’s walking on the narrow beam on a 7-point scale (Table 1) (see Note 10).

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Table 1 Scoring of the performance of rat on the beam Score

Observation

1

Rat fails to position its injured hind paw on the beam’s horizontal plane.

2

Rat balances itself by placing its injured hind paw on the beam’s horizontal surface, but incapable of moving across it.

3

Rat drags its injured hind paw along the beam while crossing it.

4

Rat moves along the beam, placing its injured hind paw on the beam.

5

Rat traverses the beam and rests the injured hind paw on the horizontal surface of the beam to assist fewer than half of its steps.

6

Rat takes more than half of its steps with assistance from the injured hind paw.

7

Rat moves no more than twice over the beam.

7. For the pole test, use a round upright pole (as mentioned in Subheading 2.8, item 3). 8. Habituate the animal to the pole. 9. Conduct four training trials a day before the test with 15-min intervals. 10. Place the animal at the top of a pole in a head-up/upright position. 11. Train the animal to turn its body and feet completely downward (T-turn) and descend to the floor. 12. Conduct five test runs on the day of the test with 15-min intervals. 13. Note the total amount of time each animal takes to take a T-turn and turn their body and feet downward and reach the bottom of the pole in each test run. 14. Consider 120 s as the cutoff time. 15. Calculate the average duration that each animal takes to take a T-turn and descend from the pole to the floor in five test runs. 3.5 Assessment of Blood-Brain Barrier (BBB) Permeability and Brain Edema

Based on previously published studies [33, 34], we use the procedures below to quantify BBB permeability and cerebral edema in the lab. 1. Take the body weight of all the animals. 2. Administer Evans blue (2 mL/kg, i.p., 2% w/v) to animals from all the groups. 3. Anesthetized all the animals (as mentioned in Subheading 3.2).

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4. Expose the animals of all groups except the control group to trauma (as mentioned in Subheading 3.3) after 30 min of Evans blue administration. 5. Perfuse animal transcardially ice-cold heparinized saline. 6. Euthanize all the rats by cervical dislocation and excise their brains. 7. Utilize the right lobe to measure BBB permeability and the left lobe to quantify cerebral edema. 8. For measuring the BBB permeability, incubate the brain sample at 37 °C overnight in 0.5 N KOH. 9. Next day, add 2.5 mL of a mixture of acetone and 4 N H3PO4 (15:3) to each tube. 10. Vortex the tubes for 1 min and then centrifuge them at 3000 rpm for 15 min at 25 °C. 11. Measure the absorbance of the extracted dye by using a UV-visible spectrophotometer at a wavelength of 620 nm. 12. Calculate the amount of dye in the brain sample by using the standard curve of Evans blue. 13. Expressed the concentration of the Evans blue as μM per 100 mg of brain tissue. 14. For accessing the brain edema, take out the left lobe of the brain and weigh it. 15. Dip the brain in absolute alcohol for 30 min. 16. Dry the brain sample in an oven at 55 ± 5 °C for 24 h until a constant weight is achieved. 17. Reweigh the brain dried in the previous step. 18. Calculate the percentage of water content in the brain tissue by using the following formula: %Brain water content = ½ðwet weight–dry weightÞ=wet weight] × 100 where wet weight is the weight of the brain tissue immediately after removal and dry weight is the weight of the brain tissue after drying in the oven. 19. Express the brain edema (brain water content) as % brain water content per 100 gm body weight of the animal. 3.6 Assessment of Oxidative and Antioxidative Parameters

The etiology of TBI and eventually the emergence and progression of neuropathological impairments are greatly influenced by augmented oxidative stress and the weakening of endogenous antioxidant defense systems [35]. Lipid peroxidation and nitric oxide formation are well-established markers of oxidative stress and can serve as a useful tool for assessing the severity of TBI in experimental animal models. Lipid peroxidation (malondialdehyde) was

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measured as per Draper and Hadley [36], while nitric oxide (nitrite level) was measured as per Hevel and Marletta [37]. Antioxidant parameters include measurement of SOD [38], catalase [39], and reduced glutathione [40]. Assessment of malondialdehyde levels as a marker of lipid peroxidation: 1. Take 100 mg of the brain cortex section. 2. Use a tissue homogenizer and homogenize the brain sample in 5 mL of HBSS buffer in three cycles at 3000 rpm of 30 s each with a 30-s gap. 3. Centrifuge the homogenate for 10 min at 3000 rpm at room temperature. 4. Collect the cell pellet while discarding the supernatant. 5. Transfer the cell pellet into a clean glass tube and resuspend it into 1 mL of HBSS. 6. Add 1.5 mL of 0.8% aqueous solution of TBA, 1.5 mL of 20% acetic acid, 0.2 mL of 8.1% SDS, and 0.7 mL of MilliQ water to the 0.1 mL of tissue homogenate obtained in the above step. 7. Use 0.1 mL HBSS, instead of homogenate in the control tubes. 8. Mix well and heat for 1 h at 95 °C in a water bath. 9. Cool the reaction mixture at room temperature and then add 1 mL of distilled water and 5 mL of a mixture of pyridine and n-butanol (1:15) in it. 10. Shake vigorously the mixture on a vortex mixer for 5 min and centrifuge at 3000 rpm for 10 min. 11. Collect the upper organic layer and measure the amount of malondialdehyde in it by recording the absorbance at 532 nm using a UV-visible spectrophotometer. 12. Prepare the standard curve of 1,1,3,3-tetraethoxypropane (TEP). 13. Calculate the levels of malondialdehyde in each sample using TEP standard curve and express them as μM/mg tissue weight. Assessment of nitrite levels as a marker of nitric oxide: 1. Dissect 100 mg cortex region of the brain. 2. Homogenize the 100 mg brain sample in 4 mL of 0.2 M ice-cold phosphate buffer (pH 7.6) in three cycles at 3000 rpm of 30 s each with a 30-s gap. 3. Transfer 100 μL of Griess reagent and 2.6 mL of phosphate buffer (pH 7.6) in a tube containing 300 μL of the homogenized sample from the previous step. 4. For blank solution, mix 100 μL Griess reagent and 2.9 mL phosphate buffer (0.2 M, pH 7.6).

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5. Incubate the above mixtures at room temperature for about 30 min. 6. Record the absorbance of all the samples at 548 nm using a UV-visible spectrophotometer. 7. Prepare a series of sodium nitrite (NaNO2) standard solutions in ice-cold phosphate buffer (0.2 M pH 7.6) with known concentration, ranging from 0 to 100 μM. 8. Calculate the concentration of nitrite in μM/mg of tissue weight using a standard curve of sodium nitrite. Assessment of superoxide dismutase (SOD): 1. Take 100 mg of brain cortex. 2. Homogenize brain cortex tissue (100 mg) in 4 mL of chilled Tris-EDTA buffer (50 mM, pH 8.2) in three cycles at 13,000 rpm of 30 s each with a 30-s gap. 3. Add 1 mL of 1% Triton X-100 to this homogenate, thoroughly vortex it, and incubate at 4–8 °C for 20 min. 4. Transfer the content to microcentrifuge tubes and then centrifuge them at 4 °C at 10,000 rpm for 30 min. 5. Separate the supernatant and keep it in the refrigerator for 45 min. 6. Mix supernatant and pyrogallol (0.2 mM) and then record the absorbance of the reaction mixture at 420 nm by using a UV-visible spectrophotometer for 10 min at every 60 s. 7. For control, take the reaction mixture without the brain homogenate and record the absorbance readings every 60 s for a total duration of 10 min. 8. Evaluate the rate of rise in absorbance units per minute (ΔA420 nM/min) for the test sample(s) and control. 9. Use the following formulae and compute the percentage inhibition for the test(s) sample: %inhibition = f½ðΔA420 nM= min Þ control–ðΔA420 nM= min Þtest]=ðΔA420 nM= min Þcontrolg × 100

10. Express the SOD activity as the quantity of enzyme needed to inhibit 50% of the pyrogallol reduction and then express the activity in units per g of brain tissue. Assessment of catalase (CAT): 1. Use 100 mg of the brain (cortex region) for the estimation of catalase.

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2. Homogenize the isolated cortex region using a tissue homogenizer in 5 mL of phosphate buffer (50 mM, pH 7.0) in three cycles for 30 s at 1800 rpm with a 30-s gap. 3. Dilute 1 mL tissue homogenate up to 5 mL using phosphate buffer. 4. Mix 1 mL of diluted homogenate with 2 mL of hydrogen peroxide (H2O2) (100 mM). 5. Note the absorbance for 0–10 min at 240 nm using a UV-visible spectrophotometer. 6. Calculate the catalase activity and express it in μM of H2O2 decomposes per min per mg of tissue weight using a standard curve of H2O2. Assessment of reduced glutathione: 1. Separate the 100 mg of the cortex region of brain tissue. 2. Homogenize the isolated brain cortex in 5 mL of ice-cold phosphate buffer (0.2 M, pH 7.6) in three cycles for 30 s at 3000 rpm with a 30-s gap. 3. Add 0.1 mL of 25% trichloroacetic acid to tissue homogenate. 4. Centrifuge the treated tissue homogenate at 3900 rpm for 10 min at a temperature of 25 °C. 5. Collect the supernatant after centrifugation, which will contain the desired soluble fraction. 6. Mix 1 mL of supernatant with 1 mL of 5,5-dithiobis-(2-nitrobenzoic acid) (DTNB) and 1 mL of phosphate buffer (pH 7.6). 7. Vortex the reaction mixtures for 1 min and then incubate them for 5 min at room temperature. 8. Take the absorbance at 412 nm using a UV-visible spectrometer against a blank in the reference cell. 9. For blank, take a mixture of 2 mL phosphate buffer and 1 mL DTNB. 10. Prepare a standard curve by using different concentrations of standard reduced glutathione. 11. Determine the levels of reduced glutathione in units μM/mg of tissue weight using the standard curve of reduced glutathione.

4

Notes 1. Rats and mice are commonly employed species in scientific study due to their many genetic, anatomical, and physiological traits that are parallel to humans. Rat has benefits, such as its excellent responsiveness and performance in complex

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neurobehavioral studies. Rats are generally inexpensive to maintain and easy to handle and offer simple intravenous drug injection line insertion. Rats have been extensively studied and have a well-characterized genome, which makes them valuable for studying specific genes and pathways. Antibody and PCR tools that detect a variety of rat proteins and RNA species are readily available. Additionally, rats are larger than mice, making them more suitable for some types of surgeries and physiological measurements. Given that most magnet imaging techniques can effectively include coils to accommodate a rat’s head. Thus, the large size of the rat head is a clear benefit when considering imaging approaches [13]. 2. Male and female rats both are equally susceptible to developing weight-drop-induced TBI [41]. However, few studies show that gender did not affect MWM performance followed by trauma; however, females outperformed males on both motor tasks [42, 43]. Further, rodent gender selection should take the objectives of certain investigations into account. 3. It is essential to use male and female rats that are of the same age and weight since the lighter-weight rat may sustain less inertial damage from a hit. The mortality rate in younger (adolescent) rodents may be higher, but that should also be calibrated in each lab to reduce the variability. 4. Several anesthetic substances, including inhalation (halothane, sevoflurane, and isoflurane) or intravenous (thiopentone sodium, propofol barbiturates, and ketamine/xylazine), and other substances are utilized in experimental models of TBI. Nearly all anesthetics offer neuroprotective advantages in models of brain injury. Nevertheless, none of the anesthetic medications provides a distinct edge over others in terms of neuroprotection that may hamper the development of the model [44]. We selected isoflurane/oxygen for rapid anesthesia recovery and precise measurement of the time of loss of consciousness. The choice of anesthetic in the TBI model is important as it may alter the pathogenesis of TBI concussion and will certainly restrict its applicability to human patients. In one of the earlier studies, we have chosen thiopentone sodium as anesthetics in experimental TBI models. Its recovery time is also short. However, we observed a lot of variability in TTR responses (loss of consciousness time) among animals [22]. 5. Increasing the falling weight and raising the drop height are the two strategies to raise the severity of the injury. 6. For inducing concussion each lab should calibrate the falling weight’s mass and height to induce both a strong cognitive and functional disability with a lack of anatomical brain injury.

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7. Plastic tubes or tubes of any other non-transparent material can also be used; however, it is having the limitation of visibility across the tube. 8. A microplate reader can also be used. The advantage of a microplate reader in the place of a UV-visible spectrophotometer is that it required a very small amount of brain tissue. So, by using a microplate reader, numerous parameters can be done simultaneously and accordingly quantity of other chemicals can be adjusted for estimating various parameters. 9. One of the DTBI end criteria is indicated to be the unconsciousness time, which is measured by TTR (the righting reflex). 10. The time to transverse the beam can also be used as an end parameter in the beam-walking test for accessing the impairment in locomotor activity [22]. References 1. Dewan M, Rattani A, Gupta S et al (2018) Estimating the global incidence of traumatic brain injury. J Neurosurg 130:1–18 2. Coburn K (1992) Traumatic brain injury: the silent epidemic. AACN Adv Crit Care 3:9–18 3. Narayan RK, Michel ME, Ansell B et al (2002) Clinical trials in head injury. J Neurotrauma 19: 503–557 4. Beauchamp K, Mutlak H, Smith WR et al (2008) Pharmacology of traumatic brain injury: where is the “golden bullet”? Mol Med 14:731–740 5. Briones TL (2015) Chapter 3 animal models of traumatic brain injury: is there an optimal model that parallels human brain injury? Annu Rev Nurs Res 33:31–73 6. Xiong Y, Mahmood A, Chopp M (2013) Animal models of traumatic brain injury. Nat Rev Neurosci 14:128–142 7. Mcintosh TK, Noble L, Andrews B, Faden AI (1987) Traumatic brain injury in the rat: characterization of a midline fluid-percussion model. Cent Nerv Syst Trauma 4:119–134 8. Lighthall JW (1988) Controlled cortical impact: a new experimental brain injury model. J Neurotrauma 5:1–15 9. Williams AJ, Hartings JA, Lu X-CM et al (2006) Penetrating ballistic-like brain injury in the rat: differential time courses of hemorrhage, cell death, inflammation, and remote degeneration. J Neurotrauma 23:1828–1846 10. Cheng J, Gu J, Ma Y et al (2010) Development of a rat model for studying blast-induced traumatic brain injury. J Neurol Sci 294:23–28

11. Feeney DM, Boyeson MG, Linn RT et al (1981) Responses to cortical injury: I. Methodology and local effects of contusions in the rat. Brain Res 211:67–77 12. DeFord SM, Wilson MS, Rice AC et al (2002) Repeated mild brain injuries result in cognitive impairment in B6C3F1 mice. J Neurotrauma 19:427–438 13. Kalish BT, Whalen MJ (2016) Weight drop models in traumatic brain injury BT. In: Kobeissy FH, Dixon CE, Hayes RL, Mondello S (eds) Injury models of the central nervous system: methods and protocols. Springer, New York, pp 193–209 14. Laurer HL, Lenzlinger PM, McIntosh TK (2000) Models of traumatic brain injury. Eur J Trauma 26:95–110 15. Bales JW, Bonow RH, Ellenbogen RG (2018) Closed head injury. In: Principles of neurological surgery, pp 366–389.e4 16. Rungruangsak K, Poriswanish N (2021) Pathology of fatal diffuse brain injury in severe non-penetrating head trauma. J Forensic Legal Med 82:102226 17. Jeret JS, Mandell M, Anziska B et al (1993) Clinical predictors of abnormality disclosed by computed tomography after mild head trauma. Neurosurgery 32:9–16 18. Lee H, Wintermark M, Gean AD et al (2008) Focal lesions in acute mild traumatic brain injury and neurocognitive outcome: CT versus 3T MRI. J Neurotrauma 25:1049–1056 19. Marshall LF, Marshall SB, Klauber MR et al (1991) A new classification of head injury

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based on computerized tomography. J Neurosurg 75:S14–S20 20. Walker WC (2007) Motor impairment after severe traumatic brain injury: a longitudinal multicenter study. J Rehabil Res Dev 44:975– 982 21. Beaumont A, Marmarou A, Czigner A et al (1999) The impact-acceleration model of head injury: injury severity predicts motor and cognitive performance after trauma. Neurol Res 21:742–754 22. Barot J, Saxena B (2021) Therapeutic effects of eugenol in a rat model of traumatic brain injury: a behavioral, biochemical, and histological study. J Tradit Complement Med 11:318– 327 23. Barzo´ P, Marmarou A, Fatouros P et al (1997) Acute blood-brain barrier changes in experimental closed head injury as measured by MRI and Gd-DTPA. In: Brain edema X. Springer Vienna, Vienna, pp 243–246 24. Zusman BE, Kochanek PM, Jha RM (2020) Cerebral edema in traumatic brain injury: a historical framework for current therapy. Curr Treat Options Neurol 22:9 25. Ikeda Y, Long DM (1990) The molecular basis of brain injury and brain edema: the role of oxygen free radicals. Neurosurgery 27:1–11 26. Marmarou A, Foda MAA-E, Brink W van den et al (1994) A new model of diffuse brain injury in rats: Part I: pathophysiology and biomechanics. J Neurosurg 80:291–300 27. Marmarou CR, Prieto R, Taya K et al (2009) Marmarou weight drop injury model BT. In: Chen J, Xu ZC, Xu X-M, Zhang JH (eds) Animal models of acute neurological injuries. Humana Press, Totowa, pp 393–407 28. Abd-Elfattah Foda MA, Marmarou A (1994) A new model of diffuse brain injury in rats: Part II: morphological characterization. J Neurosurg 80:301–313 29. Ogawa N, Hirose Y, Ohara S et al (1985) A simple quantitative bradykinesia test in MPTPtreated mice. Res Commun Chem Pathol Pharmacol 50:435–441 30. Goldstein LB, Davis JN (1990) Beam-walking in rats: studies towards developing an animal model of functional recovery after brain injury. J Neurosci Methods 31:101–107 31. Leconte C, Benedetto C, Lentini F et al (2020) Histological and behavioral evaluation after traumatic brain injury in mice: a ten months follow-up study. J Neurotrauma 37:1342– 1357

32. Lad KA, Maheshwari A, Saxena B (2019) Repositioning of an anti-depressant drug, agomelatine as therapy for brain injury induced by craniotomy. Drug Discov Ther 13:189–197 33. Katayama S, Shionoya H, Ohtake S (1978) A new method for extraction of extravasated dye in the skin and the influence of fasting stress on passive cutaneous anaphylaxis in guinea pigs and rats. Microbiol Immunol 22:89–101 34. Shigeno T, Brock M, Shigeno S et al (1982) The determination of brain water content: microgravimetry versus drying-weighing method. J Neurosurg 57:99–107 35. Palmieri M, Frati A, Santoro A et al (2021) Diffuse axonal injury: clinical prognostic factors, molecular experimental models and the impact of the trauma related oxidative stress. An extensive review concerning milestones and advances. Int J Mol Sci 22:10865 36. Draper HH, Hadley M (1990) Malondialdehyde determination as index of lipid peroxidation. In: Methods in enzymology. Elsevier, pp 421–431 37. Hevel JM, Marletta MA (1994) Nitric-oxide synthase assays. In: Methods in enzymology. Elsevier, pp 250–258 38. Marklund S, Marklund G (1974) Involvement of the superoxide anion radical in the autoxidation of pyrogallol and a convenient assay for superoxide dismutase. Eur J Biochem 47: 469–474 39. Sinha AK (1972) Colorimetric assay of catalase. Anal Biochem 47:389–394 40. Ellman GL (1959) Tissue sulfhydryl groups. Arch Biochem Biophys 82:70–77 41. Tucker LB, Fu AH, McCabe JT (2016) Performance of male and female C57BL/6J mice on motor and cognitive tasks commonly used in pre-clinical traumatic brain injury research. J Neurotrauma 33:880–894 42. Wagner AK, Kline AE, Ren D et al (2007) Gender associations with chronic methylphenidate treatment and behavioral performance following experimental traumatic brain injury. Behav Brain Res 181:200–209 43. Wagner AK, Willard LA, Kline AE et al (2004) Evaluation of estrous cycle stage and gender on behavioral outcome after experimental traumatic brain injury. Brain Res 998:113–121 44. Schifilliti D, Grasso G, Conti A, Fodale V (2010) Anaesthetic-related neuroprotection: intravenous or inhalational agents? CNS Drugs 24:893–907

Chapter 39 iDISCO Tissue Clearing Whole-Brain and Light Sheet Microscopy for High-Throughput Imaging in a Mouse Model of Traumatic Brain Injury Hannah Flinn, Leonardo Cruz-Pineda, Laura Montier, Philip J. Horner, and Sonia Villapol Abstract Immunolabeling-enabled imaging of solvent-cleared organs (iDISCO) (Renier N, Wu Z, Simon DJ, Yang J, Ariel P, Tessier-Lavigne M, Cell 159:896–910, 2014) aims to match the refractive index (RI) of tissue to the surrounding medium, thereby facilitating three-dimensional (3D) imaging and quantification of cellular points and tissue structures. Once cleared, transparent tissue samples allow for rapid imaging with no mechanical sectioning. This imaging technology enables us to visualize brain tissue in situ and quantify the morphology and extent of glial cell branches or neuronal processes extending from the epicenter of a traumatic brain injury (TBI). In this way, we can more accurately assess and quantify the damaging consequences of TBI not only in the impact region but also in the extended pericontusional regions. Key words Immunolabeling-enabled imaging of solvent-cleared organs (iDISCO), Whole-brain clearing, Tissue clearing, 3D imaging, Light sheet microscopy

1

Introduction Traumatic brain injury (TBI) is a leading cause of mortality worldwide. TBI leads to devastating impairment of normal brain function as a result of both short-term and long-term physiologic and biochemical injury to neural tissue [2]. After the immediate induction of TBI, secondary sequelae, such as vascular injury and neuroinflammation, continue to damage the brain. Neuroinflammation has also been associated with increased neuronal loss and dendritic spine loss, creating behavioral and memory deficits. Acute and chronic neuroinflammatory processes are mediated by glial cell activation, macrophage and inflammatory cell aggregation, cytokine secretion, diverse cascade activation, and tissue structural transformation that differ between sex and age [3]. While TBI may be treated, currently available treatments are unable to

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_39, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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completely reverse the effects of TBI. To find an effective treatment, we need to better measure the changes in cell signaling and structure that occur throughout the many complex cellular networks of the brain. New imaging tools are needed to allow us to better characterize these neuropathological processes. Recently, new imaging techniques such as tissue clearing for three-dimensional (3D) visualization have expanded the way that neurological diseases are understood at an anatomical level. Tissue clearing was first developed by Werner Spalteholz in 1914 [4] when he homogenized the refractive index (RI) of a tissue with a mixture of methyl salicylate/benzyl benzoate by dehydration (5:3 volume), making the tissue more transparent compared to control tissue [5]. At present, the clearing process has evolved into standardized protocols that allow us to clear entire organisms without having to separate them from their original state. Clearing technique defines the process in which a tissue can reach transparency through light. Transparency is achieved by decreasing the RI between two objects, in this case, variable regions of the brain’s tissue. The RI indicates how the speed of light changes as it passes through a material [6]. This process is classified based on the solute’s chemistry characteristics: hydrophobic, hydrophilic, and hydrogel-based and a subclassification center on the reagents used and approaches depending on the tissue or labeling method [5]. Organic solventbased methods (a.k.a. hydrophobic) use clearing components with high tissue RI (RI > 1.5) that translate to better transparency, but significant fluorescence loss. It can be highlighted such as low pH value, protein denaturation, and presence of free radicals within the solvent. Aqueous reagent-based methods (a.k.a. hydrophilic) are meant for lower RI (RI < 1.49) and are amenable for fluorescent proteins [7]. Ultimately, there is a wide variety of protocols for tissue clearing, but all of them share similar steps. Importantly, clearing methods and their resulting tissue RI must be matched to the optics of the microscopy platform. The immunolabeling-enabled three-dimensional imaging of solvent-cleared organs (iDISCO) is a method for immunolabeling and volume imaging of large biological samples. Notably, iDISCO is one of the most broadly used protocols for tissue clearing. The process is divided into the following steps: (a) perfusion and fixation, (b) decolorization, (c) decalcification, (d) permeabilization, (e) staining, (f) dehydration, (g) delipidation, and (h) RI matching [8] (Fig. 1). Perfusion and fixation (a) prepare the tissue for the process by stopping any normal biochemical internal reactions. The perfusion enables a better circulation of liquids in the body and the fixation keeps tissue’s mechanical strength and stability. Overfixation of an organ is to be avoided because it renders the tissue less transparent and may limit antibody penetration or specificity. Decolorization (b) eliminates any pigment from the body, specifically heme groups. Quadrol is one of the most used solutions for

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Fig. 1 Schematic of the iDISCO whole-brain clearing protocol detailing timepoints of the 25-day procedure followed by clearing solution descriptions

this. Decalcification (c) is the process in which bones become more permeable to clearing solutions. Shrinkage is the main concern at this point. Permeabilization (d) removes lipids and extracellular matrices, enabling antibodies to label tissues [9]. The variation in tissue staining (e) depends greatly on the depth and extent of the organ. Tissue clearing, on the other hand, involves removing water and lipids from the organ through dehydration (f). The RI matching (h) is commonly achieved with FluorClearBABB solution [10]. It is important to know how compatible the tissue labeling technique is with the RI matching solutions. The protocol described in this chapter allows for substitutions in steps, depending on tissue type and other uses. The organic solvent-based method known as PEGASOS [10] was created to decrease fluorescent loss. Ultimately, there exist a complete body process and a passive immersion method for clearing an individual organ or small body parts, depending on the amount and type of tissue. For example, 7 days are required to clear an individual soft tissue (e.g., the brain) [11]. Various clearing techniques, such as PEGASOS, CLARITY, iDISCO, SHIELD, and SWITCH [11], have been used to clear brain tissue. Still, concerns exist for each protocol, mainly related to transparency success, fluorescence preservation, and tissue applicability. Of note, PEGASOS standardizes the process for both high-density and low-density organs without compromising endogenous fluorescence as a solvent-based clearing method. Distinctively, it achieves this through the addition of a polyethylene glycol (a.k.a. a polyether component), which provides endogenous fluorescents with

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prolonged protection and clears almost all tissues with some concerns in pigmented epithelium due to pure benzyl benzoate immediately quenched green fluorescent protein (GFP) fluorescence. The only issue in this process is tissue shrinkage, but it is anisotropic and does not cause detectable distortion of internal structures [7]. This technique has been previously used to determine amyloid precursor protein (APP) accumulation, axonal disconnection, and axon degeneration in brain injury [12]. Also, some studies have presented the distribution of axonal-axonal loss of connection in patients after a stroke [13]. Still, until recently most studies used conventional imaging techniques wherein the brain is dissected and mechanically sliced to be analyzed, which limits our understanding of 3D tissue microenvironment. Thus, the main advantage of using tissue clearing for 3D tissue imaging in TBI is the ability to visualize the distribution, structure, and cell-to-cell interactions of activated glia and damaged neuronal processes over large, contiguous regions of the neuropil, across brain regions and in serial timepoints post-TBI (Fig. 2). Tissue clearing will allow us to look at the conformation and extension of the lesion volume, relationship between astrocyte and microglial cells, and/or the tissue healing structural organization following TBI (Fig. 3). A reconstruction of the contiguous cellular structures across large regional boundaries will likely produce new functional insights and hypotheses not easily obtained from traditional thin tissue sectioning and sparse stereological quantification.

Fig. 2 Morphological effects of the iDISCO clearing protocol on the mouse brain. Pre-cleared brain measuring 2 × 1.5 cm was reduced with dehydration and dilapidation to approximately 1 × .5 cm, with a changed coloration and texture

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Fig. 3 Light-sheet imaging of the mouse brain after whole-brain clearing procedure. (a) Single axis optical compressed stack image (23.8X objective) of cleared mouse brain and high magnification of vessel morphology (arrow). (b) Three-color single axis optical stack showing astroglia (GFAP) and vessels (lectin) in the border of lesion. (c) Interaction of glial processes with vessels within the injured cortex and corpus callosum. (d) 3D morphological structure of microglia/macrophage cells (Iba-1). (e) Interaction of vascular and astroglial processes in the injured cortex

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Materials Prepare all solutions prior to the start of the clearing process under a ventilated chemical hood. Store all clearing reagents at 4 °C (unless indicated otherwise). Follow all waste disposal regulations when disposing of chemical materials and wear proper protective equipment when handling chemicals.

2.1

iDISCO

1. Quadrol: ethylenediamine).

(N,N,N′,N′-Tetrakis(2-hydroxypropyl)

2. Tert-Butanol: 2-methyl-2-propanol. 3. PEG-MMA500: methacrylate.

Poly(ethylene

glycol)

methyl

ether

4. Benzyl benzoate: benzoic acid benzyl ester. 5. EDTA: ethylenediaminetetraacetic dehydrates.

acid

disodium

6. Ammonia solution: 25% solution in water. 7. 0.01 M phosphate-buffered saline (PBS): solution in water 8. Sucrose solution: 800 mL PBS, 300 g sucrose. 9. Paraformaldehyde solution: 4% in PBS. 10. 25 mL or 50 mL conical tubes.

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2.2 Clearing Solutions

Prepare all clearing solution prior to starting iDISCO procedure. 1. Decolorization solution: 25% quadrol, 7% ammonia, MilliQ water. Measure out 250 g of quadrol in a 1 L glass beaker using a weighted scale (see Note 1). Using a glass pipette, add 28 mL of 25% ammonia solution into the beaker. Lastly, add MilliQ water until the desired total amount of 400 mL is reached. Add a stir bar to the solution. Allow solution to completely dissolve before storing it in a closed container at room temperature (RT) (see Note 2). 2. Gradient tB dilapidation solution: 30% Tert-Butanol, 3% quadrol, 67% MilliQ water. Weigh out 15 g of quadrol in a 1 L glass beaker using a weighted scale (see Note 1). Using a glass pipette, add 150 mL of Tert-Butanol into the beaker. Lastly, add MilliQ water until the desired total amount of 500 mL is reached. Add a stir bar and allow solution to completely dissolve before storing it in a closed container at 4 °C (see Note 2). 3. Gradient tB dilapidation solution: 50% Tert-Butanol, 3% quadrol, 47% MilliQ water. Weigh out 15 g of quadrol in a 1 L glass beaker using a weighted scale (see Note 1). Using a glass pipette, add 250 mL of Tert-Butanol into the beaker. Lastly, add MilliQ water until the desired total amount of 500 mL is reached. Add a stir bar and allow solution to completely dissolve before storing it in a closed container at 4 °C (see Note 2). 4. Gradient tB dilapidation solution: 70% Tert-Butanol, 3% quadrol, 27% MilliQ water. Weigh out 15 g of quadrol in a 1 L glass beaker using a weighted scale (see Note 1). With a glass pipette, add 350 mL of Tert-Butanol into the beaker. Lastly, add MilliQ water until the desired total amount of 500 mL is reached. Add a stir bar and allow solution to completely dissolve before storing it in a closed container at 4 °C (see Note 2). 5. tB-PEG dehydration solution: 70% Tert-Butanol, 30% PEG-MMA-500, 3% quadrol. Weigh out 15 g of quadrol in a 1 L glass beaker using a weighted scale (see Note 1). Using a glass pipette, add 350 mL of Tert-Butanol into the beaker. Lastly, pipette 150 mL of PEG-MMA 500 solution in the same beaker to reach the desired amount of 500 mL total. Add a stir bar and allow solution to completely dissolve before storing it in a closed container at 4 °C (see Note 2). 6. BB-PEG clearing medium: 75% benzyl benzoate, 25% PEG-MMA-500, 3% quadrol. Weigh out 15 g of quadrol in a 1 L glass beaker using a weighted scale (see Note 1). Using a glass pipette, add 375 mL of benzyl benzoate into the beaker. Lastly, pipette 125 mL of PEG-MMA 500 solution in the same beaker to reach the desired amount of 500 mL total. Add a stir bar and allow solution to completely dissolve before storing it in a closed container at 4 °C (see Note 2).

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1. Blocking solution: 10% DMSO, 0.5% IGEPAL CA-630, 1× casein solution, 1× PBS. Pipette 20 mL of DMSO into a glass 500 mL storage container. Add 10 mL of IGEPAL CA-630 into the same container followed by 10 mL of 1× casein solution. Lastly, add 160 mL of 0.01 M PBS solution into the glass container to reach a total solution amount of 200 mL. Store solution at 4 °C (see Note 3). 2. 0.01 M PBS. 3. DAPI solution (1:50,000). 4. Post-fixed samples should be placed in 25–50 mL conical tubes for treatment in each step (see Note 4).

2.4

Antibodies

1. GFAP (glial fibrillary acidic protein (1:1000)). 2. Anti-rabbit Iba-1 antibody (1:500). 3. DyLight 488-conjugated tomato lectin (1:1000). 4. Alexa Fluor 647-conjugated IgG secondary antibodies (1: 1000). 5. Alexa Fluor 568-conjugated IgG secondary antibodies (1: 1000).

3

Methods

3.1 Immunohistochemistry

1. Take fixated brain samples and completely remove storage solution from tubes. Wash samples 3× with PBS, 10 min per wash on shaker. 2. Remove remaining PBS solution, and add 3 mL decolorization solution to samples (brains must be fully submerged) under a chemical hood. 3. Place samples in an optimizing chamber or shaker, settings at 37 °C, 100 RPM, for 24 h. 4. Remove decolorization solution under a chemical hood, depositing waste solution into a labeled chemical waste bottle. 5. Wash samples 3× with PBS, 10 min per wash on shaker. 6. Add approximately 2 mL (brains must be fully submerged) of blocking solution and block at room temperature for 1–7 days (5 days preferred) in 4 °C cold room, on shaker. 7. Prepare primary antibody solution by dilution of primary antibodies in pre-made blocking solution. 8. Apply 1 mL of primary antibody solution per sample (brains must be fully submerged), and incubate at 4 °C on shaker for 7 days. In accounting for a small solution error, apply 975 μL of primary antibody solution.

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9. Wash samples 3× with PBS, 10 min per wash on shaker. 10. After final wash, submerge samples in PBS at RT for 24 h on a shaker. 11. Prepare secondary antibody solution using selected secondary antibodies diluted in pre-made blocking solution. 12. Apply 2 mL of secondary antibody solution per sample, and store for 3 days at 4 °C, on rotor. Avoid contact of light with samples via application of an aluminum foil cover or other cover types. 13. Wash samples 3× with PBS, 10 min per wash on shaker. 14. Apply 2 mL of DAPI solution diluted with 0.01 M PBS, and incubate overnight at 4 °C in a dark room. 3.2

iDISCO Clearing

1. Carry out the following steps under the ventilation of a chemical hood. All solutions should be discarded in a labeled chemical waste disposal container (see Note 4). 2. Following the end of immune staining, wash samples 3× with PBS, 10 min per wash on shaker (see Note 5). 3. Apply 2 mL of 30% gradient tB dilapidation solution to each sample. Place samples in an optimizing chamber or shaker, settings at 37 °C, 100 RPM for 4 h. 4. Apply 2 mL of 50% gradient tB dilapidation solution. Place samples in an optimizing chamber or shaker, settings at 37 °C, 100 RPM for 6 h. 5. Apply 2 mL of 70% gradient tB dilapidation solution. Place samples in an optimizing chamber or shaker, settings at 37 °C, 100 RPM for 24 h. 6. Apply 2 mL of tB-PEG dehydration solution. Place samples in optimizing chamber or shaker for 2 days at 37 °C, 100 RPM, and change out solution every day. 7. Apply 6 mL of BB-PEG clearing medium (approximately a quarter amount of conical tube size). Place samples in an optimizing chamber or shaker at 37 °C, 100 RPM, for 4 days. 8. Brains have been fully cleared and should appear completely transparent. Store samples at RT and avoid light exposure (see Note 6).

4

Notes 1. Quadrol is very viscous, and it is recommended to weight amounts out using a scale. When dissolving, use a stir rod to help break up the solution to allow the stir bar to move more

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easily. Use a glass pipette or other glassware to measure out all chemicals when making solutions. 2. All clearing solutions (decolorization solution, gradient tB dilapidation solution, tB-PEG dehydration solution, BB-PEG clearing medium) can be stored at 4 °C for up to a year. All solutions were prepared at a desired amount of 500 mL; however, amounts can be adjusted based on sample size. 3. Blocking solution should be calculated for use in blocking, primary antibody solution, and secondary antibody solution. It is recommended to store blocking solution at 4 °C for 3–4 months. 4. When adding new solvents and solutions between 0.01 M PBS washes, it is recommended to use a suction pipette to completely remove the remaining solution. Brain samples become very delicate and are prone to damage or lacerations with pipette tips, and handle gently. 5. With the use of alcohol-based solutions, label samples with pencil or alcohol-based pens to avoid loss of sample labels. 6. Fully cleared brains can be stored in BB-PEG clearing medium for 3–12 months at RT. Avoid light exposure to samples. References 1. Renier N, Wu Z, Simon DJ, Yang J, Ariel P, Tessier-Lavigne M (2014) iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging. Cell 159:896–910 2. Peterson AB, Thomas KE (2021) Incidence of nonfatal traumatic brain injury-related hospitalizations – United States, 2018. MMWR Morb Mortal Wkly Rep 70:1664– 1668 3. Villapol S, Loane DJ, Burns MP (2017) Sexual dimorphism in the inflammatory response to traumatic brain injury. Glia 65:1423–1438 4. Williams DJ (1999) The history of Werner Spalteholz’s Handatlas der Anatomie des Menschen. J Audiov Media Med 122:164–170 5. Molbay M, Kolabas ZI, Todorov MI, Ohn TL, Erturk A (2021) A guidebook for DISCO tissue clearing. Mol Syst Biol 17:e9807 6. Liu AK, Hurry ME, Ng OT, DeFelice J, Lai HM, Pearce RK et al (2016) Bringing CLARITY to the human brain: visualization of Lewy pathology in three dimensions. Neuropathol Appl Neurobiol 42:573–587 7. Jing D, Zhang S, Luo W, Gao X, Men Y, Ma C et al (2018) Tissue clearing of both hard and soft tissue organs with the PEGASOS method. Cell Res 28:803–818

8. Kirchner KN, Li H, Denton AR, Harrod SB, Mactutus CF, Booze RM (2020) A hydrophobic tissue clearing method for rat brain tissue. J Vis Exp 2020:10.3791/61821 9. Vigouroux RJ, Belle M, Chedotal A (2017) Neuroscience in the third dimension: shedding new light on the brain with tissue clearing. Mol Brain 10:33 10. Jing D, Men Y, Zhao H (2021) Tissue clearing and 3-D visualization of vasculature with the PEGASOS method. Methods Mol Biol 2319: 1–13 11. Parra-Damas A, Saura CA (2020) Tissue clearing and expansion methods for imaging brain pathology in neurodegeneration: from circuits to synapses and beyond. Front Neurosci 14: 914 12. Yin X, Zhang X, Zhang J, Yang W, Sun X, Zhang H et al (2022) High-resolution digital panorama of multiple structures in whole brain of Alzheimer’s disease mice. Front Neurosci 16:870520 13. Weber MT, Arena JD, Xiao R, Wolf JA, Johnson VE (2019) CLARITY reveals a more protracted temporal course of axon swelling and disconnection than previously described following traumatic brain injury. Brain Pathol 29:437–450

Chapter 40 Insights from Rodent Models for Improving Bench-to-Bedside Translation in Traumatic Brain Injury Tulasi Pasam and Manoj P. Dandekar Abstract Road accidents, domestic falls, and persons associated with sports and military services exhibited the concussion or contusion type of traumatic brain injury (TBI) that resulted in chronic traumatic encephalopathy. In some instances, these complex neurological aberrations pose severe brain damage and devastating long-term neurological sequelae. Several preclinical (rat and mouse) TBI models simulate the clinical TBI endophenotypes. Moreover, many investigational neuroprotective candidates showed promising effects in these models; however, the therapeutic success of these screening candidates has been discouraging at various stages of clinical trials. Thus, a correct selection of screening model that recapitulates the clinical neurobiology and endophenotypes of concussion or contusion is essential. Herein, we summarize the advantages and caveats of different preclinical models adopted for TBI research. We suggest that an accurate selection of experimental TBI models may improve the translational viability of the investigational entity. Key words Traumatic brain injury (TBI), Controlled-cortical impact (CCI), Fluid percussion injury (FPI), Weight-drop (WD) impact, Blast injury (BI) model

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Introduction Traumatic brain injury (TBI) is characterized as a blow to the head, eliciting the brain to rotate or penetrate inside the skull. This damage occurs every 15 s, accounting for more than 1.7 million new cases yearly [1]. It is a global health crisis that affects children and adults during their productive years and accounts for major disabilities in people. It has been noted that TBI survivors suffer from serious brain damage and also show mild or moderate wounds [2–4]. TBI is a complex and diverse disease cascade [5], resulting in primary and secondary insults linked with structural damage and functional deficits [6]. Primary insult is always incurred by external force of mechanical disruption in the brain tissue leading to contusion and hemorrhage [7, 8]. A series of metabolic, physiological, and molecular events result in secondary damage, ranging from

Swapan K. Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6_40, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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minutes to months following primary injury. The end outcome is cell death in brain tissue, neuronal damage, and atrophy [9– 11]. External mechanical forces such as direct impact or penetration by the projectile, blast injuries, and acceleration or deceleration play a main role in causing damage to the brain leading to impairment of cognitive, physical, and psychosocial functions that may be transient or persistent [12]. The utility of animal models has always played an integral role in understanding the complex etiology of TBI making it stay skeptical [13–15]. Animal injuries are categorized as closed head injury (CHI), which does not require a fractured skull, and penetrating brain injury (PBI), which involves a fractured skull from a gunshot wound or being struck by a sharp object. The most recurrent kind of TBI is induced by an outside force or impact from a quick, intense signal without a fractured skull. CHI is more frequently observed among members of the military services and athletes [16–18]. CHI mainly constitutes the contusive and concussion type of damage involving diffuse axonal injury (DAI) and intracranial hematoma [19]. Mild TBI is often manifested with fainting, throbbing head, daze, confusion, and amnesia [20]. Mild TBI patients recover within a span of weeks, but some of them (10–15%) exhibit chronic incapacitating post-concussive symptoms including dementia, Alzheimer’s, and Parkinson’s diseases [21]. Severe brain trauma is associated with prolonged unconsciousness and may leave a person permanently disabled. Ventricular drainage, surgical hematoma excision, and hyperosmotic agents were used to halt further neurological worsening, which acts as mainstays of contemporary therapeutic methods [22]. Conventional models adopted for TBI include fluid percussion (FP) injury and controlled-cortical impact injury (CCI) model [7]. Four major animal models have been extensively utilized in investigational research such as FP injury [23] (Fig. 1), CCI injury [24, 25] (Fig. 2), weight-drop (WD) impact acceleration injury [26] (Fig. 3), and blast injury [27, 28] (Fig. 4). Recently, a closed-head impact model of engineered rotational acceleration (CHIMERA) has been developed for inducing brain injury [29]. The translational failure of investigational molecules from preclinical to clinical may be ascribed to (1) a lack of viable therapeutic approaches that specifically target the pathways of brain damage and (2) a lack of understanding of the fundamental processes of TBI that can be applied to human brain injury mechanisms. In many models, mechanical input is regulated where the results produced are reproducible, quantifiable, and clinically relevant. However, no single paradigm accurately simulates the complete range of human TBI. Herein, we describe the advantages and limitations of different preclinical models adopted for TBI research.

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Fig. 1 Fluid percussion model (FPI) induces injury through a craniectomy by applying a momentary fluid pressure pulse onto the exposed dura

Fig. 2 Controlled-cortical impact (CCI) is a commonly used model of brain trauma that uses an electromagnetically controlled piston to induce regulated injury

2 Animal Models of TBI Multiple TBI models have been established for recapitulating the diverse clinical endophenotypes in TBI. While larger animals size and physiology are more similar to humans, rodents are primarily utilized in TBI research because of their low cost and easy availability [30]. Earlier induction methods addressed the biomechanics of brain injury [31–33], while current models targeted on improving our understanding of the detrimental, complex molecular cascades initiated by head trauma. TBI-generated cognitive deficits [34], and mental instability [35–40], have been noticed in CCI, FPI, and blast models based on their impact of insult (Fig. 5). Two categories of brain injury models have been defined: acceleration and percussion concussions [23]. Investigational methods employ mechanical force to produce either static or dynamic brain injuries

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Fig. 3 Weight-drop (WD) models are easily used to produce graded pattern of injury using guided weight

Fig. 4 Blast injury (BI) is a complex physical trauma that results from direct or indirect exposure to an explosive event

(Fig. 6) [7, 24]. Static models rely on the strength and duration of mechanical energy to have morphological and functional deficits brought on by trauma [7, 24, 25]. On the other hand, mechanical force with a specific and defined velocity, amplitude, duration, and acceleration can cause a vibrant injury where the movement of the head is either restricted or unrestricted during trauma [7]. TBI can cause focal (limited to a single, specific location of the brain) or widespread brain injury (spread in more than one brain area). These focal and diffuse TBIs are prevalent among athletes and armed veterans. Brain injury induction can be performed using focal,

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Fig. 5 Common models used in neurotrauma research explaining the focal, diffuse, and mixed categories of injuries

Fig. 6 Schematic representation of animal models of neurotrauma illustrating the static and dynamic mechanical forces

diffuse, and non-impact TBI. Despite numerous animal injury methods, none of the individual brain trauma models could replace or replicate the human spectra of TBI [41–47]. Hence, the approach for revisiting the current models makes the need of the hour to contribute to better clinical translational benefits. 2.1 Fluid Percussion Injury (FPI) Model

Percussion injuries were initially developed in sheep, cats, dogs, pigs, and rabbits [48–52] and were further adopted for rodents [23, 53]. These models mimic the clinical TBI [54], reproducing the pathophysiological similarity in producing intracranial hemorrhage, edema of the brain, and gray matter degeneration [55]. Lindgren and Rinder created a mechanical percussion concussion model employing hydraulic pressure induction at the

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University of Goteborg in the late 1960s [56–58]. A similar injury model was developed in cats at Virginia medical college [59]. The device is made from a cylinder-shaped pool with a sterile isotonic solution. The impact is produced by discharging a pendulum from a certain height and striking a piston to create a pulse pressure for the intact dura [7, 11, 54, 60, 61]. Stalhammer and colleagues developed a cat model that enables accurate regulation of the fluid volume even when the researcher cannot precisely regulate the volume of fluid delivered into the skull [49]. LFPI produces continuous long-standing neurological deficits and superior injury severity compared to the midline model [11, 55, 62–64]. This model’s advantage is that it pretends several aspects of clinical contusive TBI [7, 55, 60, 65]. Percussion models involve a craniotomy procedure in the middle of bregma and lambda to ensure midline impact or laterally above the parietal bone between bregma and lambda to produce the lateral FPI (LFPI). This creates a fluid pressure pulse on the complete dura [60]. FPI models have been categorized into midline, parasagittal, and lateral injury based on the craniotomy position away from the sagittal suture. Midline percussions were reformed to provide LFPI in rodents [54, 60], where LFPI was a frequently used TBI model [54]. Percussions often yield displacement and deformities in the brain tissue, where the pulse pressure strength decides the injury severity [60]. Within minutes of the assault, LFPI in rats causes a blend of focal cortical contusion and diffusional subcortical neuronal injury, including damage to the thalamus and hippocampus with a complete loss of neurons by 12 h [66]. Due to ongoing cell death, the region of the contusional cortex beneath the injury site cultivates over several weeks to become a void lined with glia, which continuously expands up to a year after the injury [67]. Progressive degenerative cascades persist throughout days to months in specific vulnerable brain regions, occurring at sites of the thalamus and ipsilateral hippocampus, striatum, septum, and amygdala regions [66, 68]. Patients with TBI frequently experience neurobehavioral and cognitive abnormalities following LFPI, including issues with movement and memory [62]. Following severe LFPI, neurological and mental deficits also stay for over a year [64]. In contrast, FPI models had higher mortality rates than other models, likely due to the prolonged apnea that compromises the brain stem. To make this model more reliable and reproducible, craniotomy is to be made as midline and parasagittal FPI generates two-sided cortical changes linked to direct axial movement of the lower brain stem. LFPI typically causes unilateral cortical injury, infrequently involving the contralateral cortices and brain stem [7]. The location of craniotomy also influences the degree and position of tissue injury produced by LFPI in rats [69]. The grade of cortical deformity

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dramatically depends on both the craniotomy site and the severity of the damage [70]. With the height of the pendulum being the only mechanical variable that can be changed, the FPI model only permits modest biomechanical control of the insult. To increase reproducibility and handle operational issues with typical FPI devices in rats, a pneumatically operated and regulated microprocessor instrument was established [64]. Dwell time and the impact pressure may be accurately managed with this new instrument, minimizing variation across trials. According to measurements made by histopathological deviations, structural changes diagnosed by MRI, and long-term behavioral patterns, this technique produces acute and chronic TBI features like those described in the LFPI models. Despite the LFPI model’s popularity in researching neuronal cell death pathways in TBI, interest in midline FPI has recently surged due to the growing awareness of diffusional brain injury brought on by blasts and sports [71, 72]. Advantages of FPI Method FPI model is now the well-known and widely used model of experimental TBI [73]. In this percussion method, damage severity can be precisely managed and produced as mild, moderate, or severe to estimate either individual diffusional and focal injuries or both. These benefits have allowed investigators to define potential behavioral outputs and conduct extensive research to determine statistically significant results, giving them the platform to study TBI. Percussion research also aided in the detailed understanding of brain impact mechanisms like those seen in humans. Limitations of FPI Model Inaccuracy in ensuring the exact position of craniotomy makes a significant drawback in this model. Complexity in the experimental setup and procedures needed for craniotomy and lack of reproducibility makes it inappropriate as a CHI model. Another limitation is creating damage to the skull, thus decreasing its efficiency in mimicking moderate and severe traumatic happenings [30]. Further, the mortality rate in FPI in animals is often higher than in other models of TBI, owing to its compromised brain stem and apnea formation [74]. 2.2 ControlledCortical Impact (CCI) Injury Model

CCI models are often described as rigid percussion models [74, 75] performed in ferrets [25], rats [24, 76], mice [77], swine [78], and monkeys [79]. This approach requires anesthesia and craniotomy surgery, to fetch contact for the animal brain for delivering the impact through a pneumatic or electromechanical CCI device [80]. A pneumatic impactor was developed in the 1980s by Lighthall and his colleagues to simulate TBI in ferrets [25, 44]. This impactor ensures the deformity of the underlying cortex, making better control to regulate the deformation depth and impact

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velocity [24, 81]. To study focal injuries, CCI stands the best where the hit is given by direct brain trauma. This method involves a craniotomy to expose the dura, which is subjected to blunt force where the depth and velocity of the impact are measured. In this method, the cortical segment is commonly deformed by the controlled impact that frequently occurs between bregma and lambda [24]. Hemodynamic responses like a rise in intracranial pressure (ICP), the decline in cerebral and blood perfusion pressures [81], histological changes [44, 76], and alterations at the cellular level [82–84] along with functional deficits [79, 85] are related with the depth of deformity and the velocity of the impact. Extensive acute and chronic neuronal injury is caused by effects with speeds of more than 4.3 m/s and depths of cortical deformation beginning at 1.0 mm [26, 44, 86, 87]. This assault results in similar cortical area loss, immediate subdural hemorrhage, axonal injury, concussion, and distortion of the blood-brain barrier (BBB) to that of human TBI [14, 15, 41–44]. A detailed pathological examination of neurological abnormalities revealed that the extensive damage was caused by acute cortical, hippocampal, and thalamic degeneration [88]. Advantages of CCI Model Compared to the FPI model, CCI involves less invasive surgery [80]. An additional asset of CCI is its adaptability to diverse species such as mice [40, 77], rats [24], swine [78], and primates [79]. Compared to lateral FPI, CCI injury typically results in a more targeted injury, which may have repercussions for behavioral suppression or functional changes that resemble coma [89]. The real benefit of this model over fluid percussion models is its feasibility in controlling mechanical parameters such as impact duration, speed, and depth [45, 46]. Another aspect of this model is the lack of risk of rebound injury. In contrast, the cortical deformation increases depending on the severity of the injury driven, permitting the adjustment of impact during experimental procedures [47, 74]. As the rate of brain deformation, neurodegeneration, cognitive difficulties, and a decrease in cerebral blood flow is determined by depth and speed of impact, which can last for up to a year, researchers can alter the dwell time, shape, and size of the tip, as well as the impact velocity, depth, and severity, to provide the most accurate and reproducible set of injury data [75]. In this way, CCI model replicates the human TBI parameters such as cortical deformities and compression, which are comprehensively used to evaluate the critical molecular [37, 90–92] and genetic [93, 94] pathways underlying the neuronal cell death and neurological deficits following TBI [86, 87, 95]. This model offers enormous importance for gathering physiological data from animal experiments like clinical TBI.

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Fig. 7 Feeney’s model of brain injury delivers the weight directly onto the exposed dura through craniotomy

Limitations of CCI Model Craniotomy produces abnormalities such as BBB distortion brought by tissue shearing, which rapidly changes neuropathological variations and causes hemorrhage linked to disruption of tight junctions and activation of astrocytes and microglia neurons, ending with acute or chronic neurodegenerative conditions contributing to cognitive dysfunctions [30, 31]. After impact, even a slight rise in ICP could cause increased axonal damage and atrophy linked to the development of brain injury mechanisms [76]. Fewer modifications may make it a reliable induction procedure as CCI injury gives focal injury, while human TBI involves diffuse injury. 2.3 Weight-Drop (WD) Model of TBI

In this model an impact is produced by a free-falling mass directed in a tube, being exposed directly to the skull with or without a craniotomy, resulting in a contusional diffuse injury to the brain. Fenney’s WD model was developed to overcome the drawbacks of the CCI model [11] (Fig. 7). However, like the CCI model, the height and mass of the object that falls on the intact dura can be changed to produce a collision that causes a cerebral contusion [96]. This model uses a brass weight dropped from a specified height onto a foam-bedded animal. The animals are impacted through a midline incision amid the bregma and the lambda and a mountable disc with adhesive to prevent skull fracture. Creating artificial ventilation after a severe injury can reduce mortality in this model [26, 97]. Due to the creation of necrotic cavities, hemorrhage results in the early hours of the contused region of white matter, which spreads over the following days to cause even more destruction [96, 98]. However, most neurological regeneration and repair occur in the first 2 weeks following damage in rodents, where deficits might persist for up to 90 days [96, 99].

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Fig. 8 Shohami’s model creates the impact by dropping the weight on one side of the unprotected skull, to mimic the focal injury

Fig. 9 Marmarou’s model of injury mimics diffusional injury using a metal disc being placed over the skull to protect bone fractures

Moreover, Shohami (Fig. 8) and his associates developed a model for CHI in rats utilizing weights that are made to fall on the exposed side of the skull while the head of the animals is exposed to the hard surface [90, 91] on mice [37, 92, 93]. Outcomes of the impact have shown distortion and BBB breakdown, which causes neurobehavioral impairments, activation of microglia and astrocytes, and morphological abnormalities in mice [94], mimicking the clinical TBI [37]. To connect the acceleration force equivalent to human TBI brought on by falls or car accidents, Marmarou (Fig. 9) has also constructed a model of DAI impact [8],

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even though TBI in humans and experimental animals frequently results in this form of axonal puncture [88, 100]. In the brain stem, Marmarou’s model results in bilateral destruction to neurons in the regions of the corpus callosum and optic tracts [97] and is also associated with motor and cognitive deficits [101, 102] comparable to those witnessed after FPI and CCI, and the intensity of these deficits always correlates with severity of injury [26, 90, 103]. The Sprague Dawley (SD) rat model developed by Marmarou is comparable to the Shohami model, which uses a mass being directed to fall through a plexiglass tube from a specific height. Downward rotational movement of the skull during impact produces better disseminated axonal damage [26]. Advantages of WD Method Creating a diffusional pattern of injury, these models are used to reproduce mild injury; the primary advantage of the WD technique is that it is reasonably simple to execute and inexpensive. Moreover, controlled force and acceleration can be regulated by guided mass and distance to travel to cause tissue injury [104]. Depending on the energy essential to cause ICP, subarachnoid and ventricular hemorrhage, persistent vasoconstriction of cerebral micro-arteries, and hypoperfusion of the cerebral microcirculation, different degrees of DAI can be ensured [105]. Limitations of WD Method A significant caveat of this paradigm is the unpredictability of injury. However, Marmarou’s method is inexpensive, practical, and easy to employ and can cause diffusional damage that remarkably resembles the clinical outcomes of TBI in humans. Previous models do not replicate the frontal impact seen in vehicles and sports accidents. A new rat Maryland model reforming Marmarou’s impacts was chosen to understand this scenario where the skull and sagittal brain acceleration was done within the crown [103]. Despite many benefits, there is uncertainty that individual acceleration of the head alone cannot induce DAI [106]. Other demerits include the risk of second rebound injury and inappropriate impact sites [107, 108]. Other concerns include the inability to appropriately mimic clinical TBI due to differences in anatomical relativeness between rodents and a human skull, the shortfall in its inefficiency to replicate, and the improved death rate in patients who lack ventilator assistance [106]. Producing accurate results in tissue deformation by this model has also been examined based on the variable rebound impact and velocity owing to its variances in the experimental setup. Further studies are granted, which will be needed to characterize this model further.

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2.4 Penetrating Ballistic-Like Brain Injury (PBBI) Models

High energy projectile diffusion with a shock wave-producing cavity in the brain is utilized in these models [109], reconstructing the cognitive impairment [109, 110] inducing seizures, edema of the brain, cortical degeneration, and neuroinflammation associated with sensorimotor alteration [111, 112]. Rats were utilized in a novel method with low-velocity penetrating blast injuries [113]. This model has been developed to simulate the ballistic scene and characterize acute and subacute variations in ICP [114]. In contrast, permeability in the BBB region, brain edema formation, and motor and cognitive deficits are identified in a unilateral frontal PBBI in rats [36, 115]. The occurrence of hemispheric edema, elevated ICP, slight white matter damage, and neuroinflammation are the pathophysiological features of PBBI observed in various brain trauma models [112]. Moreover, compared with other TBI models, PBBI results in widespread intracerebral hemorrhage and offers numerous unique temporal aspects of a ballistic brain injury as a highly significant model of moderate-tosevere brain trauma for mechanistic studies and for the evaluation of therapeutic interventions. Models have been utilized to specify the appropriate intimacy of blast exposure to the animals.

2.5 Rodent Model of Blast Traumatic Brain Injury (bTBI)

Blast injury can be brought by using live-fire, combusted, and compressed gas shock tubes and minor explosion shock tubes for small animals, such as rodents, and larger animals, including pigs producing primary and secondary effects [35, 116, 117]. Several shock tube models for bTBI in rodents have been developed employing one or a set of repeated blast intensities ranging between 10 and 20 pounds per square inch (PSI) to complete a “mild-moderate severe” brain injury spectrum. It is challenging to reproduce a clinical blast scenario produced by the explosive device causing a primary, secondary, and tertiary cascade of bTBI. Despite recent advancements in bTBI in animal models, there needs to be more consent and evidence about the rationale for selecting the range of blast injuries. Recently, mouse and rat models that experienced an overpressure blast to the brain were described [118]. Additionally, this method employs blast pressure to target CHI of mild TBI. Usage of higher PSI blasts causes more significant damage, which ensures neurological impairments. A diaphragm separates the compression chamber from the expansion chamber in the shock tube model, which has two sections. The membrane bursts when the compression chamber is pressured, sending high-speed pressure waves into the expansion chamber. Injury is quite reproducible since the peak overpressure is determined by the membrane’s thickness [119]. In a CHI, the brain’s rotation within the skull is brought on by quick spinning of the head, which serves as the basis for the damage mechanism [120]. The mechanical loading varies depending on where the animal is inside the shock tube. This model elicits DAI, edema, and tissue ischemia in a diverse group of animals comprising swine, primates, and rodents [121, 122].

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Advantages of Blast Injury Method Considering the protection issues, shock tube is an acceptable model for blast injury [123]. Precise control over blast waves can be done, gaining more reproducibility [124, 125]. Unlike the other CHI models, the blasting technique precludes the head acceleration effects. It also recreates emotional, sensory, and motor deficits, like mild bTBI in persons. Multifaceted cellular and molecular changes and axonal pathologies are associated with blast models [126]. Limitations of Blast Injury Method The major limitations are brain surface area, geometric status, and white-to-gray matter ratio, and the size of rodents and humans is very different. The bTBI model exhibited less severity of impact and intensity of injury delivered to the animal brain [126]. Methods for using shock tubes vary depending on the type of explosive utilized, tube’s design, species and placement, body protection, and head mobility [86]. Hypoxia, blood pressure spikes caused by the pressure of the aorta, heart, and lungs; and subsequent injury to blood vessels are seen in shock tube models. Blast models perhaps occur alone; instead, they frequently coexist with other traumas such as burns, amputations of limbs, and shock [127]. As a result, there has been an increase in interest in constructing polytrauma models that replicate bTBI and have the potential to simulate the injury cascade while considering the relevant comorbidities like seizures, and post-traumatic stress disorder, depression, and polytrauma models become complex. Focusing on the physical and neurobehavioral problems related to TBI, only limited knowledge of human neuropathology exists. To induce repeated mild bTBI, one must consider the temporal disparities in animal disease versus human pathology. This highlights other discrepancies between rodent injury models and human injury. For instance, rodents enhanced vulnerability period is measured in hours instead of days in humans [128]. Additionally, there is a decreased precision in categorizing mild, moderate, and severe type of injuries. [129]. Also, since anesthetics employed in rodent surgeries such as isoflurane and ketamine showed neuroprotective activity, they may interfere with functional and histological outcomes of postTBI pathology [130–132]. 2.6 Closed-Head Impact Model of Engineered Rotational Acceleration (CHIMERA)

To examine the behavioral and neuropathological effects of mild TBI, models of brain injury always play a crucial role, and a recent biomechanical model with clinical applicability known as the CHIMERA was recently developed [133, 134]. It is a non-invasive method that enables the study of “milder” repetitive injuries, adopted for mice, rats, and ferrets. It also provides the platform for testing and comparison across commonly used species. These models are of great concern as they can be utilized to create injuries that can be regulated and enable kinematic measurement of head

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motion during the time of impact, which is associated with behavioral, histological, and biochemical results [134]. These models can also be appropriately modified for divergent animal models providing comparison in models of head injury by defining variations in the severity of impact, velocity, and acceleration of impact [133]. In addition to scaling issues between preclinical animal models and human TBI, making the models fit exactly to clinical injuries presents a challenge. So, in contrast to other animal models, it is necessary to balance the intensity of the injury within a range that causes tissue damage from acceleration. Namjoshi and colleagues explained a scaling process in that individuals could utilize the concept of equal stress/equal velocity [133, 135]. However, variations in anatomy could violate predictions because abrupt changes in rotation or velocity without a linear component cause the brain to accelerate and decelerate, possibly resulting in the deformation of the skull. Rodents are more flexible than humans considering the bone structure, where velocity and damage intensity changes could be more rigid in the human head [134, 136–138]. This method enables the researchers to evaluate the neurological severity scores and long-term changes in behavior, which offers additional benefits of this model as it requires only isoflurane as an anesthetic agent [139], making studies feasible for investigating various impacts as well as the persistent consequences of TBI [133]. The utilization of this model has broadened the platform to understand the biomechanisms of TBI [129]. Despite being a new animal model, functional deficits following CHIMERA injury have been evaluated across several behavioral domains, especially those most frequently examined after injury motor, cognitive, and neuropsychiatric symptoms. Classical TBI models like CCI, FPI, repetitive concussive brain injuries (rCBI), and blast damage have been used for several decades to highlight the motor and cognitive deficits shown in CHIMERA models. CHIMERA also generated anxiety- and depression-like phenotypes on different post-injury time points.

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Limitations of Existing Animal Models Several caveats have been identified in the experimental model such as differences in the structure and operations of the human brain compared to those of rodents [11, 140]. CCI and WD models of rats and mice could generate cortical deformity, loss of tissue, and axonal damage followed by hemorrhage, concussion, contusion, and BBB dysfunction analogous to as seen in TBI patients [37, 72, 141, 142]. Among all, CCI has measured a beneficial model for creating focal impact as it provides better regulation in duration,

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the velocity of influence, and the depth of resulting damage in the brain. It also eliminates the risk of a rebound injury [107, 108]. A standard limitation of all these TBI models is injury induced by direct contact where the position of the animal’s head is arrested. These situations are not seen in human TBI. Many researchers have experimented with overcoming these demerits by developing the following closed-head impact model of engineered rotational acceleration (CHIMERA). The anatomical differences related to white-to-gray matter ratio, craniospinal angle, and gyral complexity could be a factor in how differently various species respond differentially to the severity of injury [76]. There are differences between histopathological and behavioral responses to TBI among rat [143, 144] and mouse strains [145]. Sex differences also contributed to TBI outcomes in clinical and preclinical models [146, 147]. However, there is often controversy in stating the potential aspects of sex differences on clinical TBI outcomes [147, 148]. Several other differences between the sexes exist beyond sex hormones, such as variations in pre-injury comorbidities, brain function, and metabolism, which may impact the outcomes [148]. Further, research on the differences in sex responses to TBI is necessary because most experimental TBI investigations have been conducted in male animals. Several researchers investigating TBI models must carefully examine physiological factors such as changes in pH levels and partial pressures of oxygen and carbon dioxide, mainly the blood pressure during preand post-TBI period. These variables are essential in defining the pathophysiological responses to injury and treatment. Given the importance of these variables on short- and longterm outcomes, this is one of the significant TBI research flaws that needs to be reinforced. Besides abovementioned limitations, current lacunae in animal models of brain injury include a limited understanding of long-term effects and their applicability to human TBI, an incomplete version of individual variability, a lack of knowledge of sex differences, and, finally, ethical concerns. Considering all the pros and cons in existing animal models, few experimental voids are seen in common as follows: 1. Variability: Because the severity and location of the injury might vary based on several parameters, for instance, velocity and angle of impact, it can be challenging to compare the findings from different results across the studies. 2. Limited application: Brain injury models are limited in their relevance to human TBI as the injury produced differs from those seen in the clinical scenario. 3. Animal models: Existing models may not accurately reflect the complex physiology of the human brain. Additionally, the use of animals raises ethical concerns.

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4. Lack of understanding: Complex mechanisms that occur in the brain after injury cascade are yet to be understood, for developing potent neuroprotective agents. 5. Translation to clinical setting: Any intervention, if successful, may not be necessarily effective in humans. Translation findings from animal models to clinical settings require additional testing and validation. Many potential treatments that have shown promising results in animal models have failed to show significant benefits in clinical trials, highlighting the challenges of translating preclinical research to human patients. 6. Differences between animal and human brains: The size, structure, and function of the brain can differ substantially between species, which can affect how brain injuries are manifested and how they respond to treatment. 7. Inadequate recapitulation of injury mechanisms: Some animal models may not fully recapitulate the mechanisms of human brain injury. For example, animal models may not fully capture the complexity of the pathophysiology of human traumatic brain injury, which can make it difficult to translate findings to humans. 8. Limited ability to assess cognitive deficits: Many animal models of brain injury focus on measuring neurological deficits, such as motor functions, but may not fully capture cognitive deficits that are common in human brain injury. This can make it difficult to evaluate the effectiveness of potential treatments for cognitive deficits in humans. 9. Ethical concerns: The use of animal models in research raises ethical concerns, particularly in cases where the animals are subjected to painful or stressful procedures. While efforts are made to minimize pain and distress in animal models of brain injury, these concerns must be carefully weighed against the potential benefits of the research. 10. Long-term consequences: Even relatively mild brain injuries can have long-term consequences, including increased risk of developing dementia, depression, and other cognitive and emotional disorders. More severe injuries can result in lifelong disability, requiring ongoing support and care. 11. Limited access to care: Access to specialized care for brain injury can be limited, particularly in rural or low-income areas. This can lead to delays in diagnosis and treatment, as well as disparities in outcomes for individuals with brain injury. 12. Lack of effective treatments: There are currently no widely accepted pharmacological treatments for TBI or stroke, and the effectiveness of rehabilitation and other non-pharmacological interventions can vary widely depending

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on the individual and the severity of their injury. There is a need for more effective treatments to improve outcomes for individuals with brain injury. Apart from the common flaws, specific model issues are also taken into consideration, such as for WD models for its inconsistent injury produced when guided masses are being dropped of various shapes and sizes of weight and height and the orientation of the importance at the time of impact making it challenging to compare results across studies. Whereas in blast models, heterogeneity of injury makes it difficult to study and understand the injury cascade mechanisms as they range from primary to tertiary injuries. Also, its lack of controlled environments makes collecting accurate and comprehensive data on blast injury and its effects difficult. The availability of limited animal models to study blast injury animal models makes it difficult to replicate the complex pressure wave and blast conditions as seen in human injury. Other significant limitations include the unavailability of documented evidence of longterm effects and the limited treatment options. Finally, considering factors like reproducibility, ability to study complex injuries, better translation to human TBI, reduction in animal size, and potential for personalized medicine, recent advances bought the use of the CHIMERA model of brain injury, which creates a more accurate and reproducible way to study TBI.

4

Conclusions Brain injury remains a significant public health challenge, and there is a need for continued research and innovation to improve our understanding and treatment for brain injury. In this chapter, we summarize different experimental TBI models and their merits and demerits. Importantly, experimental TBI models help to decipher the major underlying neuropathology of TBI. Indeed, rodent studies that focused on molecular mechanisms improved the understanding of post-TBI neuropathophysiology such as glial response to injury and neurogenesis [149]. Many investigators used a CCI model to assess the cognitive, behavioral, and sensorimotor changes that correlate with DAI. Several physiological and biochemical variations are ascribed to species-specific characteristics, which can produce differential responses following brain trauma [150, 151]. Therefore, inclusion of two models/two-species approach may improve the consistency and reliability of the results. Although using two models will undoubtedly provide an accurate assessment of the severity of the impact, the model should also replicate the cascades of primary and secondary injury pathways. Since the acute and chronic consequences of TBI are different, a similar type of experimental design in the TBI model may impart a

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better therapeutic intervention. Thus, selecting a precise model relies on the specific goals and objectives of the study [74, 152], and combined neurotrauma models can be beneficial. We suggest that an accurate selection of experimental TBI models may improve the translational viability of the investigational entity. References 1. Prins M, Greco T, Alexander D, Giza CC (2013) The pathophysiology of traumatic brain injury at a glance. Dis Model Mech 6: 1307–1315 2. Reilly P, Bullock R (1997) Head injury: pathophysiology and management of severe closed injury. Chapman Hall 3. Graham DI, Lantos PL, Greenfield JG (1997) Greenfield’s neuropathology. No Title 4. Kraus JF, McArthur DL, Silberman TA (1994) Epidemiology of mild brain injury. In: Seminars in neurology, vol 14, No. 01. © 1994 by Thieme Medical Publishers, Inc., pp 1–7 5. Masel BE, DeWitt DS (2010) Traumatic brain injury: a disease process, not an event. J Neurotrauma 27(8):1529–1540 6. Ng SY, Lee AYW (2019) Traumatic brain injuries: pathophysiology and potential therapeutic targets. Front Cell Neurosci 13:528 7. Cernak I (2005) Animal models of head trauma. NeuroRx 2(3):410–422 8. Gaetz M (2004) The neurophysiology of brain injury. Clin Neurophysiol 115(1):4–18 9. Bramlett HM, Dietrich WD (2007) Progressive damage after brain and spinal cord injury: pathomechanisms and treatment strategies. Prog Brain Res 161:125–141 10. Marklund N, Bakshi A, Castelbuono DJ, Conte V, McIntosh TK (2006) Evaluation of pharmacological treatment strategies in traumatic brain injury. Curr Pharm Des 12(13): 1645–1680 11. Morales DM, Marklund N, Lebold D, Thompson HJ, Pitkanen A, Maxwell WL et al (2005) Experimental models of traumatic brain injury: do we really need to build a better mousetrap? Neuroscience 136(4): 971–989 12. Sullivan DR, Miller MW, Wolf EJ, Logue MW, Robinson ME, Fortier CB et al (2021) Cerebral perfusion is associated with blast exposure in military personnel without moderate or severe TBI. J Cereb Blood Flow Metab 41(4):886–900 13. Smith DH, Uryu K, Saatman KE, Trojanowski JQ, McIntosh TK (2003) Protein

accumulation in traumatic brain injury. NeuroMolecular Med 4:59–72 14. Blumbergs PC, Jones NR, North JB (1989) Diffuse axonal injury in head trauma. J Neurol Neurosurg Psychiatry 52(7):838–841 15. Graham DI, Ford I, Adams JH, Doyle D, Lawrence AE, McLellan DR, Ng HK (1989) Fatal head injury in children. J Clin Pathol 42(1):18–22 16. Silverberg ND, Iverson GL (2013) Is rest after concussion “the best medicine?”: recommendations for activity resumption following concussion in athletes, civilians, and military service members. J Head Trauma Rehabil 28(4):250–259 17. Theadom A, Mahon S, Hume P, Starkey N, Barker-Collo S, Jones K et al (2020) Incidence of sports-related traumatic brain injury of all severities: a systematic review. Neuroepidemiology 54(2):192–199 18. Missliwetz J, Wieser I (1989) Gunshot wounds of the head in soldiers wearing military helmets--general aspects and experiments and observations on the biomechanics and wound morphology. Zeitschrift fur Rechtsmedizin. J Legal Med 102(1):41–54 19. Hoogenboom WS, Branch CA, Lipton ML (2019) Animal models of closed-skull, repetitive mild traumatic brain injury. Pharmacol Ther 198:109–122 20. Silverberg ND, Duhaime AC, Iaccarino MA (2020) Mild traumatic brain injury in 2019-2020. JAMA 323(2):177–178 21. Graham NS, Sharp DJ (2019) Understanding neurodegeneration after traumatic brain injury: from mechanisms to clinical trials in dementia. J Neurol Neurosurg Psychiatry 90(11):1221–1233 22. Nortje J, Menon DK (2004) Traumatic brain injury: physiology, mechanisms, and outcome. Curr Opin Neurol 17(6):711–718 23. Dixon CE, Lyeth BG, Povlishock JT, Findling RL, Hamm RJ, Marmarou A et al (1987) A fluid percussion model of experimental brain injury in the rat. J Neurosurg 67(1):110–119 24. Dixon CE, Clifton GL, Lighthall JW, Yaghmai AA, Hayes RL (1991) A controlled

Animal Models of TBI cortical impact model of traumatic brain injury in the rat. J Neurosci Methods 39(3): 253–262 25. Lighthall JW (1988) Controlled cortical impact: a new experimental brain injury model. J Neurotrauma 5(1):1–15 26. Marmarou A, Foda MAAE, Van Den Brink W, Campbell J, Kita H, Demetriadou K (1994) A new model of diffuse brain injury in rats: part I: pathophysiology and biomechanics. J Neurosurg 80(2):291–300 27. Cernak I, Savic J, Malicevic Z, Zunic G, Radosevic P, Ivanovic I, Davidovic L (1996) Involvement of the central nervous system in the general response to pulmonary blast injury. J Trauma Acute Care Surg 40(3S):100S–104S 28. Leung LY, VandeVord PJ, Dal Cengio AL, Bir C, Yang KH, King AI (2008) Blast related neurotrauma: a review of cellular injury. Mol Cell Biomech 5(3):155 29. Petersen A, Soderstrom M, Saha B, Sharma P (2021 Oct) Animal models of traumatic brain injury: a review of pathophysiology to biomarkers and treatments. Exp Brain Res 239: 2939–2950 30. Xiong Y, Mahmood A, Chopp M (2013) Animal models of traumatic brain injury. Nat Rev Neurosci 14(2):128–142 31. Gurdjian ES, Lissner HR, Webster JE, Latimer FR, Haddad BF (1954) Studies on experimental concussion: relation of physiologic effect to time duration of intracranial pressure increase at impact. Neurology 4(9):674–674 32. Walker AE (1994) The physiological basis of concussion: 50 years later. J Neurosurg 81(3): 493–494 33. Denny-Brown DE, Russell WR (1941) Experimental concussion:(section of neurology). Proc R Soc Med 34(11):691 34. Levin HS, Eisenberg HM, Wigg NR, Kobayashi K (1982) Memory and intellectual ability after head injury in children and adolescents. Neurosurgery 11(5):668–673 35. Rubovitch V, Ten-Bosch M, Zohar O, Harrison CR, Tempel-Brami C, Stein E et al (2011) A mouse model of blast-induced mild traumatic brain injury. Exp Neurol 232(2): 280–289 36. Shear DA, Lu XCM, Bombard MC, Pedersen R, Chen Z, Davis A, Tortella FC (2010) Longitudinal characterization of motor and cognitive deficits in a model of penetrating ballistic-like brain injury. J Neurotrauma 27(10):1911–1923 37. Chen YUN, Constantini S, Trembovler V, Weinstock M, Shohami E (1996) An

617

experimental model of closed head injury in mice: pathophysiology, histopathology, and cognitive deficits. J Neurotrauma 13(10):557–568 38. Hamm RJ, Dixon CE, Gbadebo DM, Singha AK, Jenkins LW, Lyeth BG, Hayes RL (1992) Cognitive deficits following traumatic brain injury produced by controlled cortical impact. J Neurotrauma 9(1):11–20 39. Smith DH, Okiyama K, Thomas MJ, Claussen B, McIntosh TK (1991) Evaluation of memory dysfunction following experimental brain injury using the Morris water maze. J Neurotrauma 8(4):259–269 40. Fox GB, Faden AI (1998) Traumatic brain injury causes delayed motor and cognitive impairment in a mutant mouse strain known to exhibit delayed Wallerian degeneration. J Neurosci Res 53(6):718–727 41. Gennarelli TA, Adams JH, Graham DI (1981) Acceleration induced head injury in the monkey. I. The model, its mechanical and physiological correlates. In: Experimental and clinical neuropathology: proceedings of the first European neuropathology meeting, Vienna, May 6–8, 1980. Springer, Berlin Heidelberg, pp 23–25 42. Gennarelli TA, Thibault LE (1983) Experimental production of prolonged traumatic coma in the primate. Advances in neurotraumatology. Excerpta Medica, Amsterdam, pp 31–33 43. Lighthall JW, Dixon CE, Anderson TE (1989) Experimental models of brain injury. J Neurotrauma 6(2):83–97 44. Lighthall JW, Goshgarian HG, Pinderski CR (1990) Characterization of axonal injury produced by controlled cortical impact. J Neurotrauma 7(2):65–76 45. Park HK, Fernandez I, Dujovny M, Diaz FG (1999) Experimental animal models of traumatic brain injury: medical and biomechanical mechanism. Crit Rev Neurosurg 9(1):44–52 46. Povlishock JT (1996) An overview of brain injury models. Chapter 97. In: Narayan RK, Wilberger JE Jr, Povlishock JT (eds) Neurotrauma. 1325 47. Povlishock JT, Becker DP, Sullivan HG, Miller JD (1978) Vascular permeability alterations to horseradish peroxidase in experimental brain injury. Brain Res 153(2):223–239 48. H€artl R, Medary MB, Ruge M, Arfors KE, Ghajar J (1997) Early white blood cell dynamics after traumatic brain injury: effects on the cerebral microcirculation. J Cereb Blood Flow Metab 17(11):1210–1220

618

Tulasi Pasam and Manoj P. Dandekar

49. Hayes RL, Stalhammar D, Povlishock JT, Allen AM, Galinat BJ, Becker DP, Stonnington HH (1987) A new model of concussive brain injury in the cat produced by extradural fluid volume loading: II. Physiological and neuropathological observations. Brain Inj 1(1):93–112 50. Millen JE, Glauser FL, Fairman RP (1985) A comparison of physiological responses to percussive brain trauma in dogs and sheep. J Neurosurg 62(4):587–591 51. Pfenninger EG, Reith A, Breitig D, Gru¨nert AD, Ahnefeld FW (1989) Early changes of intracranial pressure, perfusion pressure, and blood flow after acute head injury: part 1: an experimental study of the underlying pathophysiology. J Neurosurg 70(5):774–779 52. Thibault LE, Meaney DF, Anderson BJ, Marmarou A (1992) Biomechanical aspects of a fluid percussion model of brain injury. J Neurotrauma 9(4):311–322 53. Carbonell WS, Maris DO, McCall TODD, Grady MS (1998) Adaptation of the fluid percussion injury model to the mouse. J Neurotrauma 15(3):217–229 54. Thompson HJ, Lifshitz J, Marklund N, Grady MS, Graham DI, Hovda DA, McIntosh TK (2005) Lateral fluid percussion brain injury: a 15-year review and evaluation. J Neurotrauma 22(1):42–75 55. Graham DI, Raghupathi R, Saatman KE, Meaney D, McIntosh TK (2000) Tissue tears in the white matter after lateral fluid percussion brain injury in the rat: relevance to human brain injury. Acta Neuropathol 99(2):117–124 56. Lindgren S, Kinder L (1969) Production and distribution of intracranial and intraspinal pressure changes at sudden extradural fluid volume input in rabbits. Acta Physiol Scand 76(3):340–351 57. Lindgren S, Rinder L (1966) Experimental studies in head injury: II. Pressure propagation in “percussion concussion”. Biophysik 3(2):174–180 58. Lindgren S, Rinder L (1965) Experimental studies in head injury. I. Some factors influencing results of model experiments. Biophysik 2(5):320–329 59. Sullivan HG, Martinez J, Becker DP, Miller JD, Griffith R, Wist AO (1976) Fluidpercussion model of mechanical brain injury in the cat. J Neurosurg 45(5):520–534 60. McIntosh TK, Vink R, Noble L, Yamakami I, Fernyak S, Soares H, Faden AL (1989) Traumatic brain injury in the rat: characterization

of a lateral fluid-percussion model. Neuroscience 28(1):233–244 61. McIntosh TK, Noble L, Andrews B, Faden AI (1987) Traumatic brain injury in the rat: characterization of a midline fluid-percussion model. Cent Nerv Syst Trauma 4(2):119–134 62. Hamm RJ (2001) Neurobehavioral assessment of outcome following traumatic brain injury in rats: an evaluation of selected measures. J Neurotrauma 18(11):1207–1216 63. Cernak I, Vink R, Zapple DN, Cruz MI, Ahmed F, Chang T et al (2004) The pathobiology of moderate diffuse traumatic brain injury as identified using a new experimental model of injury in rats. Neurobiol Dis 17(1): 29–43 64. Pierce JES (1996) Prolonged neurobehavioral impairments and progressive neurodegeneration following experimental brain injury: implications for traumatic brain injury and Alzheimer’s disease. University of Connecticut 65. Graham DI, Maxwell WL, Adams JH, Jennett B (2005) Novel aspects of the neuropathology of the vegetative state after blunt head injury. Prog Brain Res 150:445–455 66. Hicks R, Soares H, Smith D, McIntosh T (1996) Temporal and spatial characterization of neuronal injury following lateral fluidpercussion brain injury in the rat. Acta Neuropathol 91:236–246 67. Bramlett HM, Dietrich DW (2002) Quantitative structural changes in white and gray matter 1 year following traumatic brain injury in rats. Acta Neuropathol 103:607–614 68. Liu MC, Akinyi L, Scharf D, Mo J, Larner SF, Muller U et al (2010) Ubiquitin C-terminal hydrolase-L1 as a biomarker for ischemic and traumatic brain injury in rats. Eur J Neurosci 31(4):722–732 69. Vink R, Nimmo AJ, Cernak I (2001) An overview of new and novel pharmacotherapies for use in traumatic brain injury. Clin Exp Pharmacol Physiol 28(11):919–921 70. Floyd CL, Golden KM, Black RT, Hamm RJ, Lyeth BG (2002) Craniectomy position affects morris water maze performance and hippocampal cell loss after parasagittal fluid percussion. J Neurotrauma 19(3):303–316 71. Kabadi SV, Hilton GD, Stoica BA, Zapple DN, Faden AI (2010) Fluid-percussion– induced traumatic brain injury model in rats. Nat Protoc 5(9):1552–1563 72. Lifshitz J, Kelley BJ, Povlishock JT (2007) Perisomatic thalamic axotomy after diffuse traumatic brain injury is associated with

Animal Models of TBI atrophy rather than cell death. J Neuropathol Exp Neurol 66(3):218–229 73. Lyeth BG (2016) Historical review of the fluid-percussion TBI model. Front Neurol 7: 217 74. Povlishock JT, Hayes RL, Michel ME, McIntosh TK (1994) Workshop on animal models of traumatic brain injury. J Neurotrauma 11(6):723–732 75. Gennarelli TA (1994) Animate models of human head injury. J Neurotrauma 11(4): 357–368 76. Goodman JC, Cherian L, Bryan RM Jr, Robertson CS (1994) Lateral cortical impact injury in rats: pathologic effects of varying cortical compression and impact velocity. J Neurotrauma 11(5):587–597 77. Smith DH, Soares HD, Pierce JS, Perlman KG, Saatman KE, Meaney DF et al (1995) A model of parasagittal controlled cortical impact in the mouse: cognitive and histopathologic effects. J Neurotrauma 12(2):169–178 78. Manley GT, Rosenthal G, Lam M, Morabito D, Yan D, Derugin N et al (2006) Controlled cortical impact in swine: pathophysiology and biomechanics. J Neurotrauma 23(2):128–139 79. King C, Robinson T, Dixon CE, Rao GR, Larnard D, Nemoto CEM (2010) Brain temperature profiles during epidural cooling with the ChillerPad in a monkey model of traumatic brain injury. J Neurotrauma 27(10): 1895–1903 80. Kline AE, Yu J, Horvath E, Marion DW, Dixon CE (2001) The selective 5-HT1A receptor agonist repinotan HCl attenuates histopathology and spatial learning deficits following traumatic brain injury in rats. Neuroscience 106(3):547–555 81. Cherian LEELA, Robertson CS, Contant CF Jr, Bryan RM Jr (1994) Lateral cortical impact injury in rats: cerebrovascular effects of varying depth of cortical deformation and impact velocity. J Neurotrauma 11(5):573–585 82. Clark RS, Schiding JK, Kaczorowski SL, Marion DW, Kochanek PM (1994) Neutrophil accumulation after traumatic brain injury in rats: comparison of weight drop and controlled cortical impact models. J Neurotrauma 11(5):499–506 83. Palmer AM, Marion DW, Botscheller ML, Swedlow PE, Styren SD, DeKosky ST (1993) Traumatic brain injury-induced excitotoxicity assessed in a controlled cortical impact model. J Neurochem 61(6): 2015–2024

619

84. Fox GB, Fan LEI, Levasseur RA, Faden AI (1998) Sustained sensory/motor and cognitive deficits with neuronal apoptosis following controlled cortical impact brain injury in the mouse. J Neurotrauma 15(8):599–614 85. Scheff SW, Baldwin SA, Brown RW, Kraemer PJ (1997) Morris water maze deficits in rats following traumatic brain injury: lateral controlled cortical impact. J Neurotrauma 14(9): 615–627 86. Albert-Weißenberger C, Va´rrallyay C, Raslan F, Kleinschnitz C, Sire´n AL (2012) An experimental protocol for mimicking pathomechanisms of traumatic brain injury in mice. Exp Transl Stroke Med 4(1):1–5 87. Christman CW, Grady MS, Walker SA, Holloway KL, Povlishock JT (1994) Ultrastructural studies of diffuse axonal injury in humans. J Neurotrauma 11(2):173–186 88. Hall ED, Sullivan PG, Gibson TR, Pavel KM, Thompson BM, Scheff SW (2005) Spatial and temporal characteristics of neurodegeneration after controlled cortical impact in mice: more than a focal brain injury. J Neurotrauma 22(2):252–265 89. Dunn-Meynell AA, Levin BE (1997) Histological markers of neuronal, axonal and astrocytic changes after lateral rigid impact traumatic brain injury. Brain Res 761(1): 25–41 90. Shapira YORAM, Shohami ESTHER, Sidi A, Soffer D, Freeman SHARON, Cotev SHAMAY (1988) Experimental closed head injury in rats: mechanical, pathophysiologic, and neurologic properties. Crit Care Med 16(3): 258–265 91. Shohami E, Shapira Y, Cotev S (1988) Experimental closed head injury in rats: prostaglandin production in a noninjured zone. Neurosurgery 22(5):859–863 92. Stahel PF, Shohami E, Younis FM, Kariya K, Otto VI, Lenzlinger PM et al (2000) Experimental closed head injury: analysis of neurological outcome, blood–brain barrier dysfunction, intracranial neutrophil infiltration, and neuronal cell death in mice deficient in genes for pro-inflammatory cytokines. J Cereb Blood Flow Metab 20(2):369–380 93. Flierl MA, Stahel PF, Beauchamp KM, Morgan SJ, Smith WR, Shohami E (2009) Mouse closed head injury model induced by a weight-drop device. Nat Protoc 4(9): 1328–1337 94. Goldstein LE, Fisher AM, Tagge CA, Zhang XL, Velisek L, Sullivan JA et al (2012) Chronic traumatic encephalopathy in blast-

620

Tulasi Pasam and Manoj P. Dandekar

exposed military veterans and a blast neurotrauma mouse model. Sci Transl Med 4(134): 134ra60-134ra60 95. Shohami E, Shapira Y, Sidi A, Cotev S (1987) Head injury induces increased prostaglandin synthesis in rat brain. J Cereb Blood Flow Metab 7(1):58–63 96. Feeney DM, Boyeson MG, Linn RT, Murray HM, Dail WG (1981) Responses to cortical injury: I. Methodology and local effects of contusions in the rat. Brain Res 211(1):67–77 97. Foda MAAE, Marmarou A (1994) A new model of diffuse brain injury in rats: part II: morphological characterization. J Neurosurg 80(2):301–313 98. Dail WG, Fenney DM, Murray HM, Linn RT, Boyeson MG (1981) Responses to cortical injury: II. Widespread depression of the activity of an enzyme in cortex remote from a focal injury. Brain Res 211(1):79–89 99. Gasparovic C, Arfai N, Smid N, Feeney DM (2001) Decrease and recovery of N-acetylaspartate/creatine in rat brain remote from focal injury. J Neurotrauma 18(3): 241–246 100. Johnson VE, Stewart W, Smith DH (2013) Axonal pathology in traumatic brain injury. Exp Neurol 246:35–43 101. Heath DL, Vink R (1995) Impact acceleration-induced severe diffuse axonal injury in rats: characterization of phosphate metabolism and neurologic outcome. J Neurotrauma 12(6):1027–1034 102. Schmidt RH, Schoten KJ, Maughan PH (2000) Cognitive impairment and synaptosomal choline uptake in rats following impact acceleration injury. J Neurotrauma 17(12): 1129–1139 103. Kilbourne M, Kuehn R, Tosun C, Caridi J, Keledjian K, Bochicchio G et al (2009) Novel model of frontal impact closed head injury in the rat. J Neurotrauma 26(12):2233–2243 104. Ma X, Aravind A, Pfister BJ, Chandra N, Haorah J (2019) Animal models of traumatic brain injury and assessment of injury severity. Mol Neurobiol 56:5332–5345 105. Logsdon AF, Lucke-Wold BP, Turner RC, Huber JD, Rosen CL, Simpkins JW (2015) Role of microvascular disruption in brain damage from traumatic brain injury. Compr Physiol 5(3):1147 106. Li Y, Zhang L, Kallakuri S, Zhou R, Cavanaugh JM (2011) Quantitative relationship between axonal injury and mechanical response in a rodent head impact acceleration model. J Neurotrauma 28(9):1767–1782

107. Rostami E, Davidsson J, Ng KC, Lu J, Gyorgy A, Walker J et al (2012) A model for mild traumatic brain injury that induces limited transient memory impairment and increased levels of axon related serum biomarkers. Front Neurol 3:115 108. Briones TL (2015) Animal models of traumatic brain injury: is there an optimal model that parallels human brain injury? Annu Rev Nurs Res 33(1):31–73 109. Williams AJ, Hartings JA, Lu XCM, Rolli ML, Dave JR, Tortella FC (2005) Characterization of a new rat model of penetrating ballistic brain injury. J Neurotrauma 22(2): 313–331 110. Davis AR, Shear DA, Chen Z, Lu XCM, Tortella FC (2010) A comparison of two cognitive test paradigms in a penetrating brain injury model. J Neurosci Methods 189(1): 84–87 111. Williams AJ, Hartings JA, Lu XCM, Rolli ML, Tortella FC (2006) Penetrating ballistic-like brain injury in the rat: differential time courses of hemorrhage, cell death, inflammation, and remote degeneration. J Neurotrauma 23(12):1828–1846 112. Williams AJ, Wei HH, Dave JR, Tortella FC (2007) Acute and delayed neuroinflammatory response following experimental penetrating ballistic brain injury in the rat. J Neuroinflammation 4(1):1–12 113. Plantman S, Ng KC, Lu J, Davidsson J, Risling M (2012) Characterization of a novel rat model of penetrating traumatic brain injury. J Neurotrauma 29(6):1219–1232 114. Wei SC, Ulmer S, Lev MH, Pomerantz SR, Gonza´lez RG, Henson JW (2010) Value of coronal reformations in the CT evaluation of acute head trauma. Am J Neuroradiol 31(2): 334–339 115. Shear DA, Tate CC, Tate MC, Archer DR, LaPlaca MC, Stein DG, Dunbar GL (2011) Stem cell survival and functional outcome after traumatic brain injury is dependent on transplant timing and location. Restor Neurol Neurosci 29(4):215–225 116. Axelsson H, Hjelmqvist H, Medin A, Persson JK, Suneson A (2000) Physiological changes in pigs exposed to a blast wave from a detonating high-explosive charge. Mil Med 165(2):119–126 117. Yarnell AM, Shaughness MC, Barry ES, Ahlers ST, McCarron RM, Grunberg NE (2013) Blast traumatic brain injury in the rat using a blast overpressure model. Curr Protoc Neurosci 62(1):9–41

Animal Models of TBI 118. Bu W, Ren H, Deng Y, Del Mar N, Guley NM, Moore BM et al (2016) Mild traumatic brain injury produces neuron loss that can be rescued by modulating microglial activation using a CB2 receptor inverse agonist. Front Neurosci 10:449 119. Long JB, Bentley TL, Wessner KA, Cerone C, Sweeney S, Bauman RA (2009) Blast overpressure in rats: recreating a battlefield injury in the laboratory. J Neurotrauma 26(6): 827–840 120. Elder GA, Cristian A (2009) Blast-related mild traumatic brain injury: mechanisms of injury and impact on clinical care. Mount Sinai J Med. J Transl Personal Med. J Transl Personal Med 76(2):111–118 121. Garman RH, Jenkins LW, Switzer RC III, Bauman RA, Tong LC, Swauger PV et al (2011) Blast exposure in rats with body shielding is characterized primarily by diffuse axonal injury. J Neurotrauma 28(6):947–959 122. Ryu J, Horkayne-Szakaly I, Xu L, Pletnikova O, Leri F, Eberhart C et al (2014) The problem of axonal injury in the brains of veterans with histories of blast exposure. Acta Neuropathol Commun 2:1–14 123. Agoston DV, Kamnaksh A (2015) Modeling the neurobehavioral consequences of blastinduced traumatic brain injury spectrum disorder and identifying related biomarkers. In: Brain Neurotrauma: molecular, neuropsychological, and rehabilitation aspects. CRC Press/Taylor & Francis, Boca Raton 124. Reneer DV, Hisel RD, Hoffman JM, Kryscio RJ, Lusk BT, Geddes JW (2011) A multimode shock tube for investigation of blastinduced traumatic brain injury. J Neurotrauma 28(1):95–104 125. Zhu F, Chou CC, Yang KH, King AI (2013) Some considerations on the threshold and inter-species scaling law for primary blastinduced traumatic brain injury: a semianalytical approach. J Mech Med Biol 13(04):1350065 126. Margulies SS, Thibault LE, Gennarelli TA (1990) Physical model simulations of brain injury in the primate. J Biomech 23(8): 823–836 127. Earle SA, de Moya MA, Zuccarelli JE, Norenberg MD, Proctor KG (2007) Cerebrovascular resuscitation after polytrauma and fluid restriction. J Am Coll Surg 204(2):261–275 128. Povlishock JT (2013) The window of risk in repeated head injury. J Neurotrauma 30(1):1 129. Bodnar CN, Roberts KN, Higgins EK, Bachstetter AD (2019) A systematic review of closed head injury models of mild traumatic

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brain injury in mice and rats. J Neurotrauma 36(11):1683–1706 130. Rowe RK, Harrison JL, Thomas TC, Pauly JR, Adelson PD, Lifshitz J (2013) Using anesthetics and analgesics in experimental traumatic brain injury. Lab Anim 42(8): 286–291 131. Statler KD, Kochanek PM, Dixon CE, Alexander HL, Warner DS, Clark RS et al (2000) Isoflurane improves long-term neurologic outcome versus fentanyl after traumatic brain injury in rats. J Neurotrauma 17(12): 1179–1189 132. Statler KD, Alexander H, Vagni V, Holubkov R, Dixon CE, Clark RS et al (2006) Isoflurane exerts neuroprotective actions at or near the time of severe traumatic brain injury. Brain Res 1076(1):216–224 133. Namjoshi DR, Cheng WH, McInnes KA, Martens KM, Carr M, Wilkinson A et al (2014) Merging pathology with biomechanics using CHIMERA (Closed-Head Impact Model of Engineered Rotational Acceleration): a novel, surgery-free model of traumatic brain injury. Mol Neurodegener 9:1–18 134. Namjoshi DR, Cheng WH, Bashir A, Wilkinson A, Stukas S, Martens KM et al (2017) Defining the biomechanical and biological threshold of murine mild traumatic brain injury using CHIMERA (Closed Head Impact Model of Engineered Rotational Acceleration). Exp Neurol 292:80–91 135. Viano DC, Hamberger A, Bolouri H, S€aljo¨ A (2009) Concussion in professional football: animal model of brain injury—part 15. Neurosurgery 64(6):1162–1173 136. Cullen DK, Harris JP, Browne KD, Wolf JA, Duda JE, Meaney DF et al (2016) A porcine model of traumatic brain injury via head rotational acceleration. In: Injury models of the central nervous system: methods and protocols, pp 289–324 137. Cairns H, Holbourn H (1943) Head injuries in motor-cyclists: with special reference to crash helmets. Br Med J 1(4297):591 138. Ommaya AK, Hirsch AE, Flamm ES, Mahone RH (1966) Cerebral concussion in the monkey: an experimental model. Science 153(3732):211–212 139. Tucker LB, Fu AH, McCabe JT (2021) Hippocampal-dependent cognitive dysfunction following repeated diffuse rotational brain injury in male and female mice. J Neurotrauma 38(11):1585–1606 140. Laurer HL, McIntosh TK (1999) Experimental models of brain trauma. Curr Opin Neurol 12(6):715–721

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141. Schmidt RH, Grady MS (1993) Regional patterns of blood–brain barrier breakdown following central and lateral fluid percussion injury in rodents. J Neurotrauma 10(4): 415–430 142. Whelan-Goodinson R, Ponsford J, Scho¨nberger M (2009) Validity of the Hospital Anxiety and Depression Scale to assess depression and anxiety following traumatic brain injury as compared with the structured clinical interview for DSM-IV. J Affect Disord 114(1–3): 94–102 143. Reid WM, Rolfe A, Register D, Levasseur JE, Churn SB, Sun D (2010) Strain-related differences after experimental traumatic brain injury in rats. J Neurotrauma 27(7): 1243–1253 144. Tan AA, Quigley A, Smith DC, Hoane MR (2009) Strain differences in response to traumatic brain injury in Long-Evans compared to Sprague-Dawley rats. J Neurotrauma 26(4):539–548 145. Fox GB, LeVasseur RA, Faden AI (1999) Behavioral responses of C57BL/6, FVB/N, and 129/SvEMS mouse strains to traumatic brain injury: implications for gene targeting approaches to neurotrauma. J Neurotrauma 16(5):377–389 146. Roof RL, Hall ED (2000) Estrogen-related gender difference in survival rate and cortical

blood flow after impact-acceleration head injury in rats. J Neurotrauma 17(12): 1155–1169 147. Berry C, Ley EJ, Tillou A, Cryer G, Margulies DR, Salim A (2009) The effect of gender on patients with moderate to severe head injuries. J Trauma Acute Care Surg 67(5): 950–953 148. Farace E, Alves WM (2000) Do women fare worse: a metaanalysis of gender differences in traumatic brain injury outcome. J Neurosurg 93(4):539–545 149. Teo L, Rosenfeld J, Bourne J (2012) Models of CNS injury in the nonhuman primate: a new era for treatment strategies. Transl Neurosci 3(2):181–195 150. Johansson CB, Lothian C, Molin M, Okano H, Lendahl U (2002) Nestin enhancer requirements for expression in normal and injured adult CNS. J Neurosci Res 69(6): 784–794 151. Yoburn BC, Lutfy K, Candido J (1991) Species differences in μ-and δ-opioid receptors. Eur J Pharmacol 193(1):105–108 152. McPherson RW, Kirsch JR, Salzman SK, Traystman RJ (1994) The neurobiology of central nervous system trauma. Oxford University Press, Oxford

Chapter 41 Rat Model of Middle Cerebral Artery Occlusion Syed Shadab Raza Abstract Stroke is the third-leading cause of death and the leading cause of acquired adult disability worldwide. Several ischemic stroke models are currently available. However, mimicking focal cerebral ischemia (FCI) is the most common. The formation of an embolic or thrombotic occlusion at or near the middle cerebral artery causes most events in FCI. The current protocol closely mimics the etiology of human stroke and ensures that the results obtained are highly relevant. The method described in this protocol yields reproducible results. The success of this model in ischemic research can be examined through the utilization of Doppler blood flow imaging equipment. Key words Ischemic stroke, Ischemia, Reperfusion, Animal models of ischemic stroke, Middle cerebral artery (MCA) occlusion, Doppler blood flow imaging

1

Introduction Stroke is the world’s leading cause of morbidity and mortality [1, 2]. Ischemic strokes account for more than 85% of all strokes [3]. There are no approved treatments besides recombinant tissue plasminogen activator (rtPA) and endovascular stroke therapy [4]. Along these lines, numerous animal stroke models have been developed during the past several years with the aim of understanding the mechanisms underlying cerebral ischemia and creating new medications for stroke therapy [5, 6]. Animal stroke models are a crucial tool for several reasons [7], including the fact that experimental ischemic stroke is highly reproducible, well controllable, and standardized, allowing for more accurate analysis of stroke pathophysiology and drug effects. Additionally, pathophysiological events that take place in the first minute of an ischemic stroke are frequently not visible using imaging techniques used to diagnose stroke in humans and can only be examined in an animal model.

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Herein, we present the transient middle cerebral artery (MCA) occlusion procedure in rats. Previous studies have reported that the physical properties of the suture used to occlude the MCA, such as tip diameter, length, shape, and flexibility, are critical for the reproducibility of the infarct volume [8]. To rule out the abovementioned possibilities, in this protocol we define the use of commercial silicon-coated monofilaments. The advantage is that this monofilament reduces the risk of inducing subarachnoid hemorrhages [9].

2

Materials

2.1 Surgical Preparations

Presurgical requirements: 1. Wistar rats (adult male weighing 250–280 g) 2. Rat cage bins

2.2 Surgical Requirements

1. Heating pad (Fig. 1) 2. Rectal probe thermometer (Fig. 2) 3. Doppler blood flow meter (Fig. 3) 4. Stereo-zoom microscope (Fig. 4) 5. Autoclave

Fig. 1 A heating pad is depicted in this image

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Fig. 2 A picture of thermometer with rectal probe

Fig. 3 A picture of Doppler blood flow imaging meter with optic probe

6. Ocular forceps (Fig. 5) 7. Ocular iris (Fig. 6) and Hard Age Vannas Micro Scissors angled 8 CM/3 1/8″ 8. Pointed sharp-edge scissor (Fig. 7) 9. Surgical blades

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Fig. 4 A stereo-zoom microscope image

Fig. 5 A picture of ocular forceps

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Fig. 6 A picture of ocular iris

10. Silicon sutures (Fig. 8) 11. Vascular clip (Fig. 9) and Debakey Atrauma Bulldog Clamp curved 7.5 CM/3″ 12. Betadine 13. Ethanol 14. Anesthesia: ketamine and xylazine hydrochloride 15. Analgesic: buprenorphine 16. Cleaning wipes 17. Syringe (5 mL) 18. Kidney tray 19. Syringe discarder

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Fig. 7 A visual image of blunt scissor

Fig. 8 A visual image of a silicon suture

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Fig. 9 A picture of vascular clip

20. Petri dish 21. Normal suture 22. Cotton-tip applicator 23. Cotton thread 2.3 Postsurgical Requirements

1. Suture needle 2. Skin suture 3. Infrared heating lamp 4. Rat cage bins 5. Analgesic: buprenorphine

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Autoclave

Autoclave all the surgical instruments listed in the preceding lines.

2.5 Preparation of 1× phosphatebuffered saline (PBS)

Add 8 g sodium chloride, 0.20 g potassium chloride, and 0.24 g potassium phosphate monobasic anhydrous in a biker initially with 70 mL of MilliQ water and stir well. Once 70% of the ingredients have been dissolved in the beaker, increase the volume to 100 mL. Store at room temperature.

2.6 Preparation for 0.9% Normal Saline (NaCl)

To make 0.9% normal saline, dissolve 0.9 g of sodium chloride (Fisher Scientific, Mumbai, India; cat. no.) in 70 mL of MilliQ water and stir well until the NaCl is nearly dissolved. Fill with 30 mL MilliQ water to make a total volume of 100 mL.

2.4

3

Methods

3.1 Middle Cerebral Artery (MCA) Occlusion

1. Preoperative care: Prior to surgery, the rats should be housed in standard settings and acclimatized to the housing conditions for at least 3–4 days. 2. The night before surgery, stop feeding male Wistar rats weighing 250–280 g (see Note 3). 3. Next morning, give pain medicine at least 1 h before the procedure (usually buprenorphine, 2 mL of 0.004 mg/mL subcutaneous). 4. Deeply anesthetize the rats with ketamine (50 mg/kg bd. wt.) and xylazine (10 mg/kg bd. wt.) (see Note 1). 5. Before surgery, administer preoperative antibiotics (usually baytril 4 mg/kg or enrofloxacin 5–10 mg/kg body weight or any acceptable antibiotic subcutaneously). 6. Place an anesthetized rat in the prone position on a heating blanket to maintain body temperature and use a rectal probe to measure the rat’s temperature. 7. Apply a small amount of ophthalmic ointment, such as Systane Ultra Ophthalmic Solution, to both eyes to prevent dryness (see Note 2). 8. Using a sterile pad, disinfect the fur and skin with betadine or 70% ethanol. 9. Remove the fur with hair removal cream (see Note 3). 10. Make a 1–1.5 cm-long skin incision on the skull (extending from the lateral canthus of the left eye to the base of the left ear) and dissect the left temporalis muscle to expose the skull. Then, create a small burr hole 5 mm lateral to and 1 mm posterior to the bregma to ease placement of the tip of the Doppler flow meter probe [10].

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11. Next a blunt dissection is carried out under a stereo microscope (Olympus Corporation, Japan) to expose the trachea and withdraw the muscles to locate the carotid artery. 12. Make a midline incision in the neck and gently pull the soft tissues apart (see Note 4). 13. Carefully dissect out the right common carotid artery (CCA) from surrounding tissue. Assess the intersection of the internal carotid artery (ICA) and the external carotid artery (ECA). A permanent suture is placed as far away from the ECA as possible, and a temporary suture is placed slightly tight on the ECA distal to the bifurcation [6, 11] (see Note 5). 14. Using 1× PBS, clean the arteries and adjacent muscles during the surgical operations (see Note 6). 15. Make a small incision in the ECA. Gently advance the 4 0 silicon suture through the ICA toward the MCA until resistance is felt (see Note 7) (Fig. 10) [12]. At this point, there should be around 80% reductions in Doppler blood flow readings (see Notes 8 and 9).

Fig. 10 Model of a middle cerebral artery occlusion (MCAO) with intraluminal suture method. CCA common carotid artery, ECA external carotid artery, ICA internal carotid artery, MCA middle cerebral artery, OA occipital artery, PCA posterior cerebral artery, PComA posterior communicating artery, PPA pterygopalatine artery, ST superior thyroid artery. (Adapted from Zhang et al. [6])

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16. After 1 h, allow monofilament withdrawal (see Note 10) and blood reperfusion by slightly opening the temporary suture onto the ECA (see Note 11). To prevent blood loss, permanently tie off the temporary suture on the ECA. 3.2 Postoperative Care

1. After the surgical operations are completed, seal the wound, and place the rat under the infrared heating light for 30–60 min. 2. After ensuring that the animal’s mobility has been restored, return the animals to their cages, and provide wet food (see Note 12). 3. Once the rats are fully awake, administer subcutaneously 2–3 mL of saline, 2 mL (0.004 mg/mL) of buprenorphine, and 4 mg/kg of baytril or 5–10 mg/kg body weight of enrofloxacin. 4. Continue administering buprenorphine subcutaneously twice a day for the first 3 days and once a day for the next 4 days. 5. Continue administering baytril or enrofloxacin subcutaneously once a day for 4 days.

3.3

4

Sham Surgery

All procedures are identical for sham operations except that the occlude (suture) is not inserted.

Notes 1. Instead of using ketamine-xylazine mixture, isoflurane at 5% flow can also be used. In rodent surgery, isoflurane is preferred over a ketamine-xylazine cocktail as an anesthetic agent. Although the cocktail works well in most situations, it has a high mortality rate in some vulnerable strains. 2. 0.9% NaCl can be applied to the eyes a few times to prevent dryness during the surgical procedure. 3. Shaving fur may result in the production of hair fragments, micro-abrasions, and inflammation, which may influence stroke pathogenesis; therefore, avoid shaving and instead use a commercially available hair removal cream to remove the fur. 4. At this point, it is crucial to exercise extreme caution to avoid damaging the vagal nerve. 5. It is best to use a vascular clip to clamp the CCA just before its division into the ICA and ECA to prevent undesired blood flow or seeping from the ECA while placing the suture. Additionally, another vascular clip should be used to clamp the ICA to minimize excessive blood flow that may occur when placing the suture into the ECA, as blood can backflow during the procedure.

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6. Cleaning the surgical region with 1× PBS not only keeps it clean but also moist. Use a cotton-tip applicator for this purpose. 7. It is critical not to pierce the pterygopalatine artery. If you feel resistance while inserting silicon suture at a point where only half of the suture is inserted, immediately pull back the suture and try again. 8. Record the baseline cerebral blood flow (CBF) of each animal and consider it to be 100%. Notably, when a decrease in CBF of more than 80% is detected, middle cerebral artery occlusion (MCAO) is initiated. 9. Animals with a decrease in Doppler blood flow flux of less than 80% should be excluded from the study. 10. The suture can be reused by soaking it in 70% ethanol and then reusing it. 11. Cotton thread can be used to seal the ECA cut. 12. Because postoperative weight loss is common, mashed food is placed in a Petri dish to stimulate eating. References 1. Bonkhoff AK, Grefkes C (2022) Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 45:457–475 2. Prakash R, Fauzia E, Siddiqui AJ, Yadav SK, Kumari N, Shams MT, Naeem A, Praharaj PP, Khan MA, Bhutia SK, Janowski M, Boltze J, Raza SS (2022) Oxidative stress-induced autophagy compromises stem cell viability. Stem Cells 40:468–478 3. Chen CJ, Ding D, Starke RM, Mehndiratta P, Crowley RW, Liu KC, Southerland AM, Worrall BB (2015) Endovascular vs. medical management of acute ischemic stroke. Neurology 85:1980–1990 4. Prabhakaran S, Ruff I, Bernstein RA (2015) Acute stroke intervention: a systematic review. JAMA 313:1451–1462 5. Raza SS, Khan MM, Ahmad A, Ashafaq M, Khuwaja G, Tabassum R, Javed H, Siddiqui MS, Safhi MM, Islam F (2011) Hesperidin ameliorates functional and histological outcome and reduces neuroinflammation in experimental stroke. Brain Res 1420:93–105 6. Raza SS, Khan MM, Ahmad A, Ashafaq M, Islam F, Wagner AP, Safhi MM, Islam F (2013) Neuroprotective effect of naringenin is mediated through suppression of NF-κB signaling pathway in experimental stroke. Neuroscience 230:157–171

7. Fluri F, Schuhmann MK, Kleinschnitz C (2015) Animal models of ischemic stroke and their application in clinical research. Drug Des Devel Ther 9:3445–3454 8. Liu S, Zhen G, Meloni BP, Campbell K, Winn HR (2009) Rodent stroke model guidelines for preclinical stroke trials (1st edition). J Exp Stroke Transl Med 2:2–27 9. Rousselet E, Kriz J, Seidah NG (2012) Mouse model of intraluminal MCAO: cerebral infarct evaluation by cresyl violet staining. J Vis Exp 69:4038 10. Panahpour H, Farhoudi M, Omidi Y, Mahmoudi J (2018) An in vivo assessment of blood-brain barrier disruption in a rat model of ischemic stroke. J Vis Exp 133:57156 11. Raza SS, Khan MM, Ashafaq M, Ahmad A, Khuwaja G, Khan A, Siddiqui MS, Safhi MM, Islam F (2011) Silymarin protects neurons from oxidative stress associated damages in focal cerebral ischemia: a behavioral, biochemical and immunohistological study in Wistar rats. J Neurol Sci 309:45–54 12. Zhang Y, Pan S, Zheng X, Wan Q (2016) Cytomembrane ATP-sensitive K+ channels in neurovascular unit targets of ischemic stroke in the recovery period. Exp Ther Med 12:1055– 1059

INDEX A Acrylamide......................................... 140, 146, 233, 247, 304, 310, 313, 315, 319 Actin cytoskeleton......................231, 232, 245, 252, 366 Actin dynamics ....................................231, 246, 271–273 Actin-remodeling ....................... 246, 248, 252, 257–265 Acute disseminated encephalomyelitis (ADEM)......... 432 Adenosine triphosphate (ATP)............................... 50, 51, 54, 55, 346, 347, 369, 546 Adenovirus..................................................................... 393 Advantages................................................ 17, 69, 82, 136, 211, 212, 292, 318, 400, 444, 523, 570, 573, 586, 587, 592, 600, 604, 606, 608, 611, 624 Agarose gel electrophoresis ................................ 3, 14, 18, 21, 84, 303, 307, 308, 482 α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)....................................193, 538, 540–542 α-linolenic acid (ALA) ........................213, 246, 248–254 α-smooth muscle actin (α-SMA).................................. 368 α-synuclein........................... 82, 533, 534, 543, 548, 549 Alzheimer’s disease (AD)........................... 4, 28, 67, 172, 182, 192, 215, 217, 220, 223, 231, 232, 246, 257, 258, 267–270, 272–275, 277, 278, 292, 330, 331, 337, 338, 341, 346, 350–352, 355–370, 374, 458, 459, 516, 529, 542 Amino acid transporter 1 (EAAT1) ............................. 362 7-amino-4-methylcoumarin-3-acetic acid (AMCA) ....................................... 2, 3, 5, 8, 17, 19 Amyloid beta (Aβ)..................................4, 172, 220, 246, 257, 267, 337–353, 355–370 Amyloid precursor protein (APP) ...................... 292, 337, 344, 347, 356, 363, 364, 369, 550, 592 Amyotrophic lateral sclerosis (ALS)....................... 4, 164, 373–395, 397–416, 458, 459 Anaesthesia .........................................382, 493–497, 560, 575, 579, 580, 586, 627 Angiogenic factors .......................................................... 67 Animal models.......................................... 1–22, 137, 162, 186, 215, 220, 341, 369, 374, 448, 478, 491, 492, 511, 512, 515, 517, 559, 570, 582, 600–615, 623 Anterior temporal lobe (ATL) .........................58, 59, 62, 377, 384, 394

Anti-Aβ therapies ................................................. 350–351 Anxiety .................................................... 93–96, 186, 187, 192, 432, 518, 524, 536, 538 Apical dendrites .........................................................62–64 Apoptosis .................................................. 1–22, 137, 149, 161, 168, 169, 172, 190, 192, 193, 195, 215, 301–316, 347, 348, 353, 458, 477, 478, 539, 543, 550 Aquaporin4 (AQP4) ........................................... 121–125, 128, 130, 131, 145, 445 Astrocyte activation.............................................. 145, 473 Astrocytes ..........................................................2, 4, 9, 11, 27, 144, 182, 192, 193, 195, 298, 330, 358, 359, 361, 362, 365, 366, 369, 432, 477, 546, 607, 608 Astrogliosis ................................................. 366, 438, 444, 446, 448–450 Atomic force microscopy (AFM) ........................ 341, 364 ATP flux........................................................................... 55 Autoantibody serostatus ...................................... 121–132 Autoimmunity ..................................................... 374, 375, 439, 446, 450, 473 Autophagy .......................................................97, 99, 102, 114–118, 137, 347, 349, 353, 533, 534 Axon......................................................62, 104, 109, 317, 330, 363, 431–433, 439, 447, 458, 463, 512, 592 Aβ40.....................................................338, 341, 347, 366 Aβ42............................................338, 341, 347, 364, 366

B Basal dendrites...........................................................57–65 B cells ..........................................188, 191, 443, 462, 463 Behavioural test ...................................................... 93, 505 Bench-to-bedside translation .............................. 599–616 Benzylamine .................................................330, 332–335 β-mercaptoethanol ................................37, 140, 310, 313 Bioavailability ...................................................... 211–212, 218, 468–471, 473 Bioinformatics .....................................285, 286, 289, 417 Biopsies DNA extraction .............................................. 377 Bipolar disorder (BD) ......................................... 220, 222, 223, 375, 392, 393 Blast injury (BI) model ....................................... 600, 602, 610, 611, 615

Swapan Ray (ed.), Neuroprotection: Method and Protocols, Methods in Molecular Biology, vol. 2761, https://doi.org/10.1007/978-1-0716-3662-6, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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636 Index

Blood-brain barrier (BBB)......................... 27–29, 34, 35, 182, 183, 194, 355–363, 365, 366, 368–370, 435, 438, 440, 444–446, 448, 458–462, 466, 467, 470, 492, 572, 574, 577, 581, 582, 606–608, 610, 612

C C57BL/6 mice.................................................11, 29, 547 Capillaries .................................................................27–37, 355–359, 365, 367, 368, 460, 466 Capillary isolation ................................. 28, 29, 31–33, 36 Catalase (CAT) .................................................... 144, 508, 573, 578, 583–585 Catecholamine (CA) ............................................ 330, 492 CD8+ T cells ............................................... 439, 462, 463 Cell-based assay (CBA)....................................... 122, 124, 125, 129, 130, 318 Cell death ......................................................1–3, 15, 127, 144, 161, 193, 195, 214, 246, 338, 341, 347–350, 353, 463, 512, 534, 540, 545, 547, 548, 572, 600, 604–606 Cell proliferation assay .................................................. 141 Central nervous system (CNS)..................................1–22, 27–29, 49, 67, 68, 98, 121, 137, 149–156, 161, 182–184, 186–196, 209, 218, 221, 231, 317, 318, 357–360, 374, 375, 432–434, 436–439, 443–448, 450, 458–460, 464–466, 469, 470, 473, 477, 535, 537, 539, 543, 546–549 Centrifugation ............................................ 28, 32, 39–47, 143, 147, 278, 282, 393, 394, 413, 507, 585 Ceramide .................................... 149, 152, 154–156, 449 Cerebral blood flow (CBF) ................................ 224, 355, 356, 358, 361, 365, 367–370, 448, 606, 633 Cerebrospinal fluid (CSF) .......................................67–78, 277, 339, 380, 433, 434, 448, 462, 463, 472, 549 Chemiluminescence imaging system .................. 302, 313 Chloroform .....................................................6, 9, 18, 20, 89, 139, 143, 150, 151, 153–155, 278, 280, 282, 287, 303, 306, 481 Chromatin immunoprecipitation (ChIP) ...............82–84, 88–89, 399, 402 Chromatographic separation ........................................ 151 Chrysin ....................................... 478–481, 485, 486, 488 Clinically isolated syndrome (CIS) ............ 433, 437, 464 Clinical trial .......................................... 68, 222–224, 351, 375, 379, 380, 442, 444, 472, 540–542, 544, 546, 550, 614 Closed head injury (CHI) .................................. 600, 604, 608, 610, 611 Cognition ...............................................93, 95, 161, 182, 186, 187, 214–215, 219, 221, 223, 344, 350, 531, 539

Column chromatography ...................151, 152, 154, 156 Complementary DNA (cDNA)83, 89, 90, 143, 278, 282, 285, 288, 302, 303, 307, 308, 314, 376, 391, 481, 482 Confocal microscopy .......................................... 127–128, 232, 236–238, 273, 275 Controlled cortical impact (CCI) ...................... 570, 571, 600, 601, 604–607, 609, 612, 615 Contusion injury .................................................. 559, 567 CRISPR-Cas9 technologies.......................................... 292 CRISPR/dCas9-SunTag ................................................ 82 CRISPR Toolkit .............................................................. 98 Culturomics..................................................377, 387–388 Cytokines ............................................67–71, 76, 77, 144, 182, 187–190, 192, 194–196, 215, 221, 224, 348, 349, 380, 388, 392, 439, 445–447, 460, 462, 463, 589 Cytoskeleton............................................... 235, 258, 267, 269, 359, 366, 533

D DAPI.................................................. 233, 235, 247, 249, 253, 259, 260, 262, 275, 297, 422, 424, 595 Demyelination ..................................................... 149, 214, 221, 432, 433, 437, 441, 447–450, 458, 460, 461, 470 Depression .................................................... 67, 183, 186, 187, 192, 193, 211, 215–223, 330, 432, 436, 459, 465, 495, 497, 538, 611, 614 Dietary intake ......................................210–213, 218, 221 Digoxigenin (DIG) ........................ 2, 5, 7, 8, 17, 19, 155 Dihydroceramide (dhCer) ................................... 149, 152 Dihydrosphingosine (dhSph) .............149, 150, 153–156 Dimethyl fumarate (DMF) .................448, 449, 457–473 Diseases...................................................1, 28, 49, 67, 97, 128, 136, 159, 183, 210, 246, 257, 291, 301, 317, 330, 337, 357, 373, 399, 421, 432, 457, 478, 491, 511, 529, 590, 599 DNA laddering.............................................3, 6, 9–14, 22 DNA sequencing.................................................. 378, 387 Dopamine (DA) .................................................. 186, 211, 213, 217, 220, 329, 330, 375, 477, 478, 481, 483, 485–487, 492, 530–532, 535–537, 543, 548, 549 Dopaminergic neuron......................................... 145, 217, 220, 477, 479, 500, 511, 534, 535, 543, 546, 550 Doppler blood flow imaging ........................................ 625 Double immunofluorescent labeling (DIFL)....... 2, 8, 19 Dounce homogenizer ..................................................... 52 Drosophila neuromuscular junctions (NMJs) .......97–119 Drug target...................................................431–450, 538

NEUROPROTECTION: METHOD E Electron transport chain (ETC) ......................50, 52, 346 Elevated plus maze.......................................................... 94 Endocytic internalization..................................... 252–253 Endocytosis ......................................................... 232, 245, 246, 249, 253, 257, 258 Endosomal trafficking .......................................... 245–254 Endothelial cells ............................................27, 355–358, 361–363, 365, 366, 369, 370, 445, 460, 465, 466 Enzyme-linked immunosorbent assay (ELISA) ........... 69, 122, 340 Epidemiology ................................................................ 437 Epigallocatechin-3-gallate (EGCC) ............................. 136 Epigenetic architecture ................................................... 82 Epigenetic marks .......................................................81–91 Epinephrine .......................................................... 330, 507 Epstein Barr virus (EBV)............................ 440, 462, 463 Essential fatty acids (EFAs) ........................ 209, 213, 214 Essential transition metal (ETM) ................................. 136 EV isolation ...............................................................39, 40 Excitotoxicity.............................................. 161, 347, 349, 350, 362, 364, 366, 369, 447, 463, 533, 539, 540, 546, 550 ExoEasy Kit ...............................................................43, 46 Extracellular matrix (ECM)..................27, 194, 258, 358 Extracellular signaling kinase (ERK).................. 170, 172, 185, 232, 369 Extracellular tau .................................. 231–241, 245–254 Extracellular vesicles (EVs).......................................39–47

F Fecal DNA extraction .......................................... 377, 383 Fecal microbiota ...........................................377, 379–381 Fibrils .......................................... 246, 292, 338, 340–344 Filter retardation assay ................................ 402, 413, 414 Flow cytometry .......................... 142, 376, 389–391, 411 Fluid percussion injury (FPI) ............................. 570, 571, 601, 603–606, 609, 612 Fluorescein isothiocyanate (FITC) ........................ 2, 3, 5, 8, 11, 17, 19, 100, 107, 109, 110, 262, 376, 390 Fluorescence-based immunoprecipitation assay (FIPA) ................................................................ 122 Fluorescence microscopy .................................... 232, 236, 237, 248, 252, 253 Forelimb locomotor scale (FLS) ................ 501, 503, 504 Forelimb step alternation test (FSAT) ................ 501, 504 Functional MRI (fMRI) ............................................... 367

G GABAergic transmission ............................................... 543 Gangliosides ...................... 149, 150, 153–156, 364, 365 Gas chromatography (GC) .................150, 152–154, 156

AND

PROTOCOLS Index 637

Gene expression omnibus (GEO)...............................397, 398, 401, 402, 404 Gene set enrichment analysis (GSEA) ........................398, 399, 401, 404, 405, 415, 416 Glial cells...........................................1–22, 144, 182–184, 193–195, 359, 361, 444, 446, 450, 513, 589 Glial fibrillary acidic protein (GFAP)..........................2, 5, 8, 9, 19, 144, 297, 298, 446, 481, 482, 593, 595 Glioma-associated neuroinflammation ........................ 194 Glutamate .......................................... 4, 51, 53, 347, 349, 350, 361, 363, 369, 447, 463, 538, 540–543, 548 Glutathione (GSH) .................................... 221, 508, 548, 573, 578, 583, 585 Glycosphingolipids........................................................ 150 Golgi-cox staining.....................................................57–65 Green fluorescent protein (GFP) .................87, 114–116, 118, 119, 123, 128, 142, 271, 273, 274, 430, 592 Grip strength test ................................................. 523–525 Gut microbiota (GM) ..................................373–375, 437

H Health ................................................ 9, 28, 68, 105, 135, 136, 161, 209–224, 349, 352, 367, 443, 472, 486, 488, 499, 542, 569, 599, 615 Healthy sleep cycle ............................................... 215, 216 Heavy metals .......................................135–147, 535, 547 Helicobacter pylori ......................................................... 393 High performance liquid chromatography (HPLC)........................................... 150, 152–156, 483, 485, 489 High-throughput expression............................... 278, 281 High-throughput fluidics (HTF) ................................... 74 High-throughput imaging .................................. 589–597 Hippocampus ........................................... 32, 58, 59, 277, 321, 347, 350, 367, 448, 481, 543, 546, 547, 604 Histone modifications...............................................81, 87 Huntingtin (HTT) Protein ................................. 421–430 Huntington’s disease (HD).................................. 67, 164, 421–423, 425, 426, 428 6-hydroxydopamine (6-OHDA)........................ 221, 478, 491–497, 511, 535, 543, 545–547 4-hydroxyquinoline and benzaldehyde........................ 332 5-hydroxy tryptamine (5-HT) ............................ 329, 330 Hypoxia-inducible factor 1α (HIFI α)......................... 369

I Imaging......................................................... 8, 19, 57, 61, 65, 98, 108, 116, 128, 142, 236, 241, 249, 250, 262, 265, 275, 298, 324, 356, 368, 370, 402, 412, 413, 415, 416, 421–430, 434, 464, 543, 586, 593, 623, 625 Immunity ........................... 187, 191, 211, 373–395, 459

NEUROPROTECTION: METHOD AND PROTOCOLS

638 Index

Immunofluorescence ................................. 121, 127–128, 233, 235–236, 248, 259–261, 296–298 Immunohistochemistry .......................................... 16, 99, 107, 115, 292, 318–321, 325–327 Immunolabeling-enabled imaging of solvent-cleared organs (iDISCO)..............................590–594, 596 Impactor ...................................................... 560, 564, 604 Inflammasome ...................................................... 349, 364 Initial transfer latency (ITL).....................................95, 96 Injuries ............................................. 1, 67, 144, 161, 182, 210, 342, 357, 441, 461, 549, 559, 589, 600 In silico analysis .................................................... 278, 286 Internucleosomal DNA fragmentation.... 3, 9–14, 20, 22 Ion channels ............................................... 185, 187, 347, 446, 530, 535–547, 550 Ischemia.................................................29, 369, 610, 623 Ischemic stroke............................. 28, 169, 170, 172, 623 Isoflurane .................. 497, 575, 579, 586, 611, 612, 631

K Kainate receptors.................................................. 538, 540 Ketamine.............................................4, 15, 51, 493–495, 506, 560, 565, 586, 611, 627, 630 Kynuramine .......................................................... 332–335

L Laemmli buffer......................................42, 304, 310, 313 Ligand-gated ion channels .................................. 538, 543 Light sheet microscopy........................................ 589–597 Limitations ............................................... 28, 29, 68, 347, 400, 535, 587, 600, 604, 607, 608, 611–615 Lipid-based nanocarriers............................................... 467 Lipid extraction ............................................................. 151 Long non-coding RNAs (IncRNAs) .................. 421–430 Long-term consequences ............................................. 614 Luminex screening assay...................................... 376, 392

M Magnetic resonance imaging (MRI).................. 223, 365, 367, 370, 433, 434, 441, 442, 444, 463, 464, 572, 605 MALDI-TOF mass spectrometer................................. 377 Malondialdehyde (MDA) ................................... 144, 507, 573, 578, 582, 583 MAO inhibitors............................................................. 331 Mass spectrometry ...................................... 340, 388, 394 Medial forebrain bundle (MFB).......................... 492, 495 Meg3....................................................422, 424, 427–429 Melatonin ............................................................ 137, 184, 189, 192, 216, 329, 330 Memory .................................................. 93–96, 161, 186, 214, 215, 224, 257, 344, 350, 352, 367, 436, 462, 516, 537, 543, 547, 572, 589, 604

Metabolite addition experiments ................................. 415 Metal ions .....................................................338, 529–550 1-methyl-4-phenylpyridinium ion (MPP+) ..................... 4 1-methyl-1, 2, 3, 6-tetra-dihydropyridine (MPTP)..................................................9, 11, 364, 478–481, 485–488, 491, 511, 535, 543, 549 Microarray .................................................. 168, 278, 283, 288, 289, 397, 402–404, 406 Microglia................................................... 2, 4, 9, 11, 144, 145, 182, 192, 194, 195, 231, 232, 235–239, 245–254, 257, 258, 345, 351, 368, 432, 438–441, 444, 446, 447, 463, 473, 477, 545, 546, 593, 607, 608 MicroRNA (miRNA) isolation............................ 277–289 Middle cerebral artery occlusion (MCAO) ........ 623–633 miRbase ................................................................ 286, 289 miRNA-mRNA duplex prediction ............................... 289 Mitochondrial function ................................... 49–55, 348 Mitochondrial permeability transition pore (mPTP) ..................................................... 346, 364 Mixed culture .............................................................. 1–22 Molecular docking study ..................................... 168, 217 Monoamine oxidase (MAO) ............................... 329–335 Monoamine oxidase-A (MAO-A) ...................... 184, 188, 189, 191, 329, 330, 332–335 Monoamine oxidase-B (MAO-B) ...............329–335, 536 Monoglycosylceramides (MGCs)........................ 151–155 Monomers .................................................. 234, 236, 247, 248, 258, 304, 332, 338, 341–343, 347 Motor dysfunction .............................................. 318, 458, 499–509, 511–526, 572 MPTP-induced PD ....................................................... 486 mRNA quantification.................................................... 301 Multi-omics .......................................................... 397–416 Multiple sclerosis (MS) ......................................... 4, 9, 13, 28, 67, 149, 156, 164, 182, 183, 192, 221, 223, 374, 432–437, 439–446, 448–450, 457–473, 541 Multiplex immunoassay (MIA) ......................... 69, 72, 77 Myelin basic protein (MBP) ..................... 2, 5, 8, 19, 439 Myelitis .......................................................................... 435

N Nanotechnology................................................... 467–469 Nano-tracking analysis (NTA)........................... 40, 42, 44 Neat1 ...................................................422, 424, 427–429 Necrosis .............................................................3, 14, 223, 347–349, 353, 392, 446, 463 Network pharmacology (NP).............................. 159–173 Neuro2a cells........................................................ 258, 259 Neurobehavioral analysis ..................................... 514, 515 Neurochemical disorders ..................................... 221–223 Neurodegeneration ............................................. 161, 162, 291–297, 301–316, 318, 329, 337–353, 363,

NEUROPROTECTION: METHOD 369, 370, 434, 437–439, 441, 448, 458, 462, 463, 501, 530, 535, 543, 545, 547, 606 Neurodegenerative............................................49, 67, 68, 97–99, 136, 160–162, 187, 210, 216, 218–224, 246, 257, 291, 292, 301, 317, 337, 338, 344, 352, 353, 356, 369, 370, 374, 400, 421, 441, 449, 450, 458, 477, 479, 500, 511, 512, 516, 529, 538, 547, 550, 607 Neurofibrillary tangles (NFTs)........................... 220, 267, 292, 317, 350, 352, 364 Neuroinflammation................................................. 67, 68, 135–147, 161, 182–184, 192–197, 246, 352, 364, 366, 448, 513, 530, 589, 610 Neuroinflammatory markers ....................................67–78 Neurological disorders...................................... 28, 67–69, 136, 170, 171, 173, 182, 217, 218, 331, 357, 359, 370, 457, 542, 546, 549, 559 Neuromuscular function ..................................... 523–525 Neuromuscular junction............................................... 108 Neuromyelitis optica spectrum disorder (NMOSD) ................................................ 121–132 Neuronal apoptosis ......................................................... 12 Neuronal autophagy ...............................................97–119 Neuronal dendritic spine density .............................57–65 Neuronal Nuclei (NeuN) ................................. 2, 5, 8–10, 12, 19, 29, 35, 297, 298 Neurons .................................................. 1–22, 49, 57, 58, 62–65, 97–99, 102, 111, 112, 114, 144, 145, 149, 161, 182, 184, 186, 187, 232, 257, 298, 317, 330, 341, 344, 346–349, 355, 356, 359, 361, 363, 365, 375, 432, 436, 444–446, 448, 450, 458, 470, 477, 492, 512, 513, 531–533, 539, 543, 545, 546, 548, 550, 604, 607, 609 Neuroprotection .......................................... 3, 12, 14, 98, 99, 161, 214, 277–289, 337–353, 442, 466, 512, 545, 586 Neuroprotective compounds............................... 159–173 Neuropsychiatric disorders ................................... 67, 217, 219–220, 222, 224 Neurotoxicity ...................................................... 135–147, 345, 346, 364, 446, 500 Neurotoxin ............................................... 4, 15, 313, 479, 480, 489, 492, 497, 511, 512 Neurotransmitter ......................................... 28, 111, 144, 182, 186, 187, 214, 215, 330, 349, 375, 448, 477–489, 530–533, 543, 550 Neurotrophic factor ...................215, 447, 477, 481, 482 Neurovascular toxicity ......................................... 355–370 Neurovascular units (NVUs).............................. 355, 356, 361–363, 365, 368, 369 NLRP3 inflammasome ................................................. 137 N-methyl-D-aspartate (NMDA) receptor ................... 349 Norepinephrine ........................................... 186, 211, 330 Norovirus....................................................................... 393 Nucleation ......................................................62, 341–345

AND

PROTOCOLS Index 639

O Olfactory bulb ............................................. 321, 512, 513 Oligodendrocytes ....................................... 2, 4, 121, 149, 432, 433, 439, 441, 444, 445, 447–449, 462, 463, 548 Oligomers ...........................................232, 338, 341–343, 345, 347, 364, 366 Omega-3 fatty acids ............................209–224, 246, 467 Optic neuritis................................................................. 435 Optimal cutting temperature (OCT)..........................4, 7, 16, 325, 326, 433 Oxidative phosphorylation (OXPHOS) ....................... 50, 53–55, 346 Oxidative stress.................................................... 136, 144, 161, 216, 220, 344–346, 348, 349, 351, 364, 458, 463, 478, 499–509, 530, 533, 534, 538, 546–550, 573, 582

P Paraformaldehyde (PFA) .................................... 100, 107, 123, 127, 131, 233, 235, 240, 247, 248, 259, 260, 319, 325, 326, 423, 593 Parkinson’s disease (PD) ................................4, 9–11, 28, 172, 182, 220, 221, 223, 330, 331, 374, 458, 459, 477–489, 491–497, 500, 501, 511–526, 529–550 Pathophysiology ........................................... 93, 171, 182, 219, 221, 337, 357, 432, 435, 461–463, 530–532, 538, 570, 614, 623 Penetrating ballistic-like brain injury (PBBI) .............570, 571, 610 Phagocytosis ...................... 145, 191, 220, 231–241, 246 Phalloidin.................................................... 233, 235–237, 239, 247, 248, 259, 272 Pharmacokinetics ................................459, 469–473, 488 Phenol..............................................................6, 9, 17, 18, 20, 89, 278, 287, 303 Phenylethylamine (PEA) .............................................. 330 Phenylmethylsulfonyl fluoride (PMSF) ................. 51, 70, 74, 268, 293–295, 304, 309, 319, 402 Phosphate-buffered saline (PBS) ....................... 4, 5, 7, 8, 15, 16, 43, 44, 70, 84, 88, 99, 100, 104, 107, 122–125, 127, 128, 131, 139–142, 145, 233–235, 246–249, 253, 258–260, 269, 271, 272, 280–282, 319–321, 325–327, 375, 376, 389, 392, 411, 423, 486, 494, 562–564, 567, 593, 595–597, 630, 631, 633 Phospho-tau ......................................................... 320, 321 Physiology .................................... 58, 432, 489, 601, 613 p38 mitogen-activated protein kinase (MAPK) .........168, 170, 172, 185, 192, 193, 348, 369 Podosomes ........................................................... 257–265 Pole test ......................................478, 521, 572, 573, 580 Poly (ADP-ribose) glycohydrolase (PRAG) ................ 366

NEUROPROTECTION: METHOD AND PROTOCOLS

640 Index

Poly unsaturated fatty acids (PUFA) ................. 209–213, 219, 220, 222, 224 Postural stability test (PST) ................................. 501, 505 Presenilin 1 (PS1) ......................................................... 292 Presenilin 2 (PS2) ......................................................... 292 Proteinase K ....................................................6, 9, 17, 84, 377, 383, 384 Protein-protein interaction.................167, 399, 403, 404 Protofibrils............................................................ 338, 342 Psychosine ...........................................150, 153, 155, 156 Pyroptosis ...................................................................... 349

Q Quantitative polymerase chain reaction (qPCR).......... 89, 90, 278, 284, 286–289, 301–303, 306–309, 314, 376, 391, 430

R Rab7.....................................................247, 248, 250–253 Radio immunoprecipitation assay (RIPA) buffer ......... 42, 139, 272, 304, 309, 318, 319, 321 Radiologically isolated syndrome (RIS).............. 434, 464 Rat models.........................................................9, 10, 292, 297, 491–497, 545, 573–576, 609, 610, 623–633 Reactive astrocytes ..................... 144, 444, 446, 448–450 Reactive oxygen species (ROS) ................... 67, 139, 142, 144, 169, 189, 193, 215, 345, 346, 348–351, 356, 366, 368, 478, 534, 540, 546 Real-time polymerase chain reaction (RT-PCR) .......... 89, 143, 168, 288 Receptors for advanced glycation end products (RAGE) ..................................................... 172, 365 Receptor tyrosine kinase (RTK)................................... 358 Receptor tyrosine kinase-like orphan receptor 1 (ROR1) ..................................................... 267–275 Relapsing remitting MS (RRMS)....................... 432–434, 437, 442, 448, 449, 461–465, 472 Remyelination ................... 221, 442, 447–450, 463, 470 Reperfusion ................................................. 172, 478, 632 Respiratory control ratio (RCR) ....................... 51, 53–55 Respirometry .............................................................49–55 Retention transfer latency (RTL) .............................95, 96 Rodents..........................................................2, 15, 49–55, 93–96, 188, 189, 292, 365, 368, 369, 480, 491, 492, 500, 512, 513, 515–518, 520, 524, 575, 580, 586, 599–616, 631 Rotavirus........................................................................ 393 Rotenone ...................................... 4, 50, 53, 54, 491, 545

S Saccharomyces cerevisiae............................... 405, 412, 415 Saline-sodium citrate (SSC) buffer .................................. 5 Saliva DNA extraction ......................................... 377, 383

Scanning electron microscopy (SEM) ......................9, 11, 341, 483, 573–576 Secondary progressive MS (SPMS).................... 433, 434, 437, 443, 463, 465 Sembragiline .................................................................. 331 Serotonergic system ............................................. 182–198 Serotonin ............................................................. 182–195, 197, 211, 329, 375 Serotonin in brain cancer..................................... 192–193 Serotonin receptors.................... 183, 185–186, 188–193 SH-SY5Y cells......................................82, 83, 87–89, 469 Silicic acid column chromatography ................... 150, 151 Small molecules ...................................135–147, 232, 271 Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE)...........................146, 233, 234, 295, 302, 304, 310, 318, 319 Sphingolipids ........................................................ 149–156 Sphingosine .........................................149, 150, 154, 156 Spinal cord injury (SCI) ................ 4, 9, 12, 14, 559–567 Spinocerebellar ataxia type 3 (SCA3) ............................ 99 Sprague-Dawley rats ......................................58, 574, 578 STRING database ......................................................... 167 Substantia nigra pars compacta (SNpc) ......................477, 492, 511, 512, 531, 532, 535, 543, 546, 549, 550 Substantia nigra................................................... 217, 220, 298, 330, 477, 513, 530, 532, 534, 535, 538, 543, 545, 547, 548 Subthalamic nucleus (STN)................532, 545, 546, 549 Super oxide dismutase enzyme 1 (SOD1) ..............4, 374 Surgery.......................................................... 15, 378, 465, 493–496, 560, 563–566, 586, 604, 606, 611, 630, 631 Synaptopathies.........................................................97–119

T TargetScan ..................................................................... 286 Tau .............................................................. 220, 231–239, 246–253, 257, 291–297, 317, 318, 320, 321, 323, 326, 347, 369 Tau aggregation assay ................................................... 247 Tauopathy mouse model ..................................... 317–326 Tauopathy...................................................................... 318 Tau species........................................................... 233, 234, 248, 252, 254, 297, 317–326 TBE buffer..................................................................... 314 TBST............................................................. 42, 146, 268, 269, 311–313, 316, 319, 320, 324, 402, 414 Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) ...................... 2, 3, 5, 7–10, 12 Tetramethylethylenediamine (TEMED).....................140, 146, 233, 247, 305, 310, 315, 319 Texas Red (TR) ................................ 2, 3, 5, 8, 11, 17, 19 Tg344-AD rats .............................................................. 292 Thin-layer chromatography (TLC).............150, 154–156

NEUROPROTECTION: METHOD Thiobarbituric acid (TBA).......................... 507, 578, 583 3D imaging.................................................................... 238 Three dimensions (3D) ............................................3, 338 Tissue clearing ...................................................... 589–597 Tissue Freezing Medium (TFM) .......................... 4, 7, 16 Transcriptomics ................................................... 369, 400, 404, 409, 415, 416 Transfection ................................................ 123–125, 127, 131, 268, 271, 274, 275, 286, 288, 422–424, 429, 430 Transformation....................................401, 410, 416, 589 Transforming growth factor β (TGF-β)....................... 543 Transgenic mice..........................220, 292, 318, 352, 374 Transmission electron microscopy (TEM) ........ 234, 235, 247–249, 341 Transplantation ............................................373–395, 443 Trauma..................................................29, 357, 446, 461, 570, 580, 582, 586, 600–603, 606, 610, 615 Traumatic brain injury (TBI) ............................... 28, 182, 192, 569–587, 589–597, 599–616 T regulatory lymphocytes (Treg) ................................374, 380, 390, 391 Tricarboxylic acid (TCA) cycle .................................50, 52 Trichloroethylene (TCE)..................................... 499–509 Tris-acetate EDTA (TAE) buffer ...................... 6, 21, 139 TUNEL-n-DICL method .............................................. 20 TUNEL-n-DIFL method.................2, 3, 6, 9–12, 19, 20 Two-hit hypothesis ......................................356, 364–365

AND

PROTOCOLS Index 641

U Ultracentrifugation ..........................................44, 46, 294 Unilateral .............................................491–497, 604, 610

V Vascular smooth muscle cells (VSMCs)......................192, 361, 365, 368 Vibratome sectioning...................................................... 60 Vitamin C ...................................................................... 351 Vitamin E.............................................................. 223, 351 Voltage-gated calcium channels (VGCCs) .................. 543

W Weight drop method ........................................... 569–587 Western blotting.......................................................30–31, 34–35, 40, 42, 44, 46, 53, 122, 140, 146, 168, 269, 286, 294, 302, 304–305, 309–313, 319, 320, 322–324, 340, 341 Whole brain clearing ............................................ 589–597 Wistar rats ............................................................ 491–497, 499–509, 564, 624, 630

X Xylazine..................................................... 4, 15, 493–495, 560, 565, 586, 627, 630