Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols (Methods in Molecular Biology, 2138) 1071604708, 9781071604700

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Methods in Molecular Biology 2138

Paul C. Guest Editor

Clinical and Preclinical Models for Maximizing Healthspan 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.

Clinical and Preclinical Models for Maximizing Healthspan Methods and Protocols

Edited by

Paul C. Guest Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil

Editor Paul C. Guest Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology University of Campinas (UNICAMP) Campinas, SP, Brazil

ISSN 1064-3745 ISSN 1940-6029 (electronic) ISBN 978-1-0716-0470-0 ISBN 978-1-0716-0471-7 (eBook) https://doi.org/10.1007/978-1-0716-0471-7 © Springer Science+Business Media, LLC, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved 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.

Preface The health span of most living organisms is limited by physical decline and an increase in chronic diseases during later life. It has been an ongoing quest of mankind to understand this process and apply this knowledge towards maximizing the amount of time that we can spend free of illness during our lives. This is important as most chronic diseases in the world are intertwined with old age. According to the World Health Organization, noncommunicable diseases affect mainly adults and aged individuals, and this imposes the greatest burden on global health with staggering associated costs to the healthcare services. People worldwide can now expect to live well beyond their 60th birthday due to advances in public health which began in the nineteenth century. The members of the population that comprise this super-60-year-old group have been projected to double from what it is today and reach a total of 2.1 billion people by the year 2050. Thus far, this increase in life span has not been accompanied by an increase in the health span. Aging is still the biggest risk factor for deficits in cellular, tissue, and organ function, which set the stage for diseases that ultimately lead to death. Thousands of studies have now been carried out with the aim of testing special diets, additives, and even ancient remedies from the Orient. These have led to new insights into the physiological and molecular aspects of health and disease using both epidemiological and model organism approaches and, as a result, significant advancements have been made in maximizing health and the overall health span. This book presents reviews and a series of protocols in multiple disease areas affected by the aging process along with several methods which have shown progress in nutrient- or intervention-based approaches to maximize the health span. The authors in this series come from 5 of the 6 habitable continents from countries such as Australia, China, Germany, India, Indonesia, Iran, Italy, Japan, Russia, South Africa, Thailand, Turkey, the United Kingdom, and the United States. This underscores the keen interest in this topic throughout the world. It is hoped that further preclinical and clinical research efforts in this area will eventually lead to a world society in which individuals are not only living longer lives, but also more productive and healthier ones. The book will be of high interest to researchers in the areas of chronic disease, gerontology, physical exercise, and nutrition as well as to clinical scientists, physicians, and the major drug companies since it gives insights into the latest ideas and technologies enabling progress in this area. It will also be of high interest to both technical and bench scientists as it gives detailed instructions on how to carry out the various presented methods along with important notes which give insights beyond the traditional protocols. Lastly, it will provide important information on disease mechanisms and novel drug targets as each protocol will be presented in the context of specific chronic diseases or different therapeutic areas. Campinas, Brazil

Paul C. Guest

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

PART I

REVIEWS

1 The Impact of New Biomarkers and Drug Targets on Age-Related Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest 2 Vitamin D and Muscle Sarcopenia in Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behnaz Abiri and Mohammadreza Vafa 3 Quantitative Assays of Plasma Apolipoproteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anne Poljak, Mark W. Duncan, Tharusha Jayasena, and Perminder S. Sachdev 4 Protocol for the Use of the Ketogenic Diet in Preclinical and Clinical Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ann-Katrin Kraeuter, Paul C. Guest, and Zolta´n Sarnyai

PART II

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5 An In Vivo/Ex Vivo Study Design to Investigate Effects of Chronic Conditions and Therapeutic Compounds on Adipose Stem Cells in Animal Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hane´l Sadie-Van Gijsen, Liske Kotze´-Ho¨rstmann, and Barbara Huisamen 6 Model for Studying the Effects of Chronic Metabolic Disease on Endogenous Bone Marrow Stem Cell Populations . . . . . . . . . . . . . . . . . . . . . . . Yashar Mehrbani Azar, Maria Jacoba Kruger, Dalene de Swardt, Michelle Maartens, Ascentia Mathapelo Seboko, William Frank Ferris, and Mari van de Vyver 7 Investigating Alcohol Behavior and Physiology Using Drosophila melanogaster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aliza K. De Nobrega, Kristine V. Luz, Katherine N. Lyons, and Lisa C. Lyons 8 Using Genome-Editing Tools to Develop a Novel In Situ Coincidence Reporter Assay for Screening ATAD3A Transcriptional Inhibitors. . . . . . . . . . . . . Liwei Lang and Yong Teng 9 Visualizing and Evaluating Cancer Cell Growth and Invasion by a Novel 3D Culture System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chloe Shay, Liwei Lang, and Yong Teng

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Exploiting Plug-and-Play Electrochemical Biosensors to Determine the Role of FGF19 in Sorafenib-Mediated Superoxide and Nitric Oxide Production in Hepatocellular Carcinoma Cells . . . . . . . . . . . . . . . . . . . . . . . Lixia Gao and Yong Teng Proteomic Analysis of the Anoikis-Resistant Human Breast Cancer Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chareeporn Akekawatchai, Sittiruk Roytrakul, Narumon Phaonakrop, Janthima Jaresitthikunchai, and Sarawut Jitrapakdee Real-Time PCR Analysis of Metabolism-Related Genes in a Long-Lived Model of C. elegans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sumino Yanase Clinical Assessment of the NADome as Biomarkers for Healthy Aging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tharusha Jayasena, Sonia Bustamante, James Clement, Robert Welschinger, Gideon A. Caplan, Perminder S. Sachdev, and Nady Braidy Two-Dimensional Gel Electrophoresis Combined with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Analysis of Eye Lens to Identify Biomarkers of Age-Related Cataract . . . . . . . . . . Paul C. Guest Testing the Effects of Dietary Seafood Consumption on Depressive Symptoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maximus Berger, G. Paul Amminger, Robyn McDermott, Paul C. Guest, and Zolta´n Sarnyai Assays for Monitoring the Effects of Nicotinamide Supplementation on Mitochondrial Activity in Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . . . . . . . Ivan Orlandi and Marina Vai Measurement of a Surrogate Biomarker for Arginine Vasopressin Secretion in Association with Physiometric and Molecular Biomarkers of Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest, Hans-Gert Bernstein, Henrik Dobrowolny, Katrin Borucki, Sabine Westphal, and Johann Steiner Liquid Chromatography Tandem Mass Spectrometry Analysis of Proteins Associated with Age-Related Disorders in Human Pituitary Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest and Daniel Martins-de-Souza Coenzyme Q10 Assessment and the Establishment of a Neuronal Cell Model of CoQ10 Deficiency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robert Heaton, Kate Duberley, and Iain P. Hargreaves Analyzing Mitochondrial Function in Brown Adipocytes with a Bioenergetic Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Justin Darcy, Chih-Hao Wang, and Yu-Hua Tseng Quantitative Analysis of DNA Methylation by Bisulfite Sequencing . . . . . . . . . . . Vasily V. Ashapkin, Lyudmila I. Kutueva, and Boris F. Vanyushin Histomorphometric Analysis of Anti-Aging Properties on Rat Skin . . . . . . . . . . . Idha Kusumawati, Kresma Oky Kurniawan, Subhan Rullyansyah, and Eka Pramyrtha Hestianah

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Integration of qRT-PCR and Immunohistochemical Techniques for mRNA Expression and Localization of m1AChR in the Brain of Aging Rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Asha Devi and S. Abhijit Randomized Study Design to Test Effects of Vitamin D and Omega-3 Fatty Acid Supplementation as Adjuvant Therapy in Colorectal Cancer Patients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatemeh Haidari, Behnaz Abiri, Masood Iravani, Kambiz Ahmadi-Angali, and Mohammadreza Vafa Effect of Vitamin D Supplementation on Muscle Strength, Muscle Function, and Body Composition in Vitamin D-Deficient Middle-Aged Women . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behnaz Abiri, Mohsen Dehghani, and Mohammadreza Vafa The Association of Food Intake and Physical Activity with Body Composition, Muscle Strength, and Muscle Function in Postmenopausal Women. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammadreza Vafa, Behnaz Abiri, and Mohsen Dehghani Absolute Quantification of Plasma Apolipoproteins for Cardiovascular Disease Risk Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Betul Ozdemir, Zeliha Selamoglu, and Nady Braidy Multiplex Analysis of Circulating Hormone Levels in Rat Models of Age-Related Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest Proteomic Analysis of Brain Tissue from a Chronic Model of Stress Using a Combined 2D Gel Electrophoresis and Mass Spectrometry Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest Proteomic Analysis of Rat Hippocampus for Studies of Cognition and Memory Loss with Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest, Hassan Rahmoune, and Daniel Martins-de-Souza Brain Proteomic Analysis on the Effects of the Antidepressant Fluoxetine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest MK-801 Treatment of Oligodendrocytes as a Cellular Model of Aging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest

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

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Contributors S. ABHIJIT • Laboratory of Gerontology, Department of Zoology, Bangalore University, Bangalore, India BEHNAZ ABIRI • Department of Nutrition, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Department of Nutrition, School of Public Health, Iran; University of Medical Sciences, Tehran, Iran KAMBIZ AHMADI-ANGALI • Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran CHAREEPORN AKEKAWATCHAI • Faculty of Allied Health Sciences, Department of Medical Technology, Thammasat University, Pathumtani, Thailand G. PAUL AMMINGER • Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia VASILY V. ASHAPKIN • Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia MAXIMUS BERGER • Laboratory of Psychiatric Neuroscience, Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia; Discipline of Biomedicine, College of Public Health, Medicine and Veterinary Sciences, James Cook University, Townsville, QLD, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, University of Melbourne, Parkville, VIC, Australia HANS-GERT BERNSTEIN • Department of Psychiatry, University of Magdeburg, Magdeburg, Germany; Laboratory of Translational Psychiatry, University of Magdeburg, Magdeburg, Germany KATRIN BORUCKI • Institute of Clinical Chemistry and Pathobiochemistry, University of Magdeburg, Magdeburg, Germany NADY BRAIDY • Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia SONIA BUSTAMANTE • Mark Wainwright Analytical Centre, University of New South Wales, Sydney, NSW, Australia GIDEON A. CAPLAN • Department of Geriatric Medicine, Prince of Wales Hospital, Sydney, NSW, Australia JAMES CLEMENT • Better Humans Inc., Gainesville, FL, USA JUSTIN DARCY • Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA ALIZA K. DE NOBREGA • Program in Neuroscience, Department of Biological Science, Florida State University, Tallahassee, FL, USA DALENE DE SWARDT • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa MOHSEN DEHGHANI • Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran S. ASHA DEVI • Laboratory of Gerontology, Department of Zoology, Bangalore University, Bangalore, India

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HENRIK DOBROWOLNY • Department of Psychiatry, University of Magdeburg, Magdeburg, Germany; Laboratory of Translational Psychiatry, University of Magdeburg, Magdeburg, Germany KATE DUBERLEY • Institute of Neurology, London, UK MARK W. DUNCAN • Target Discovery Inc., Mountain View, CA, USA WILLIAM FRANK FERRIS • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa LIXIA GAO • Department of Oral Biology and Diagnostic Sciences, Dental College of Georgia, Augusta University, Augusta, GA, USA; Chongqing University of Arts and Sciences, Chongqing, People’s Republic of China PAUL C. GUEST • Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil FATEMEH HAIDARI • Nutrition and Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran IAIN P. HARGREAVES • School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK ROBERT HEATON • School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK EKA PRAMYRTHA HESTIANAH • Veterinary Anatomy Department, Faculty of Veterinary, Airlangga University, Surabaya, East Java, Indonesia BARBARA HUISAMEN • Centre for Cardio-metabolic Research in Africa (CARMA), Division of Medical Physiology, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Parow, South Africa MASOOD IRAVANI • Tehran University of Medical Sciences, Tehran, Iran JANTHIMA JARESITTHIKUNCHAI • National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumtani, Thailand THARUSHA JAYASENA • Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia SARAWUT JITRAPAKDEE • Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, Thailand LISKE KOTZE´-HO¨RSTMANN • Centre for Cardio-metabolic Research in Africa (CARMA), Division of Medical Physiology, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Parow, South Africa ANN-KATRIN KRAEUTER • Laboratory of Psychiatric Neuroscience, Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia; Discipline of Biomedicine, College of Public Health, Medicine and Veterinary Sciences, James Cook University, Townsville, QLD, Australia MARIA JACOBA KRUGER • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa KRESMA OKY KURNIAWAN • Pharmacognosy and Phytochemistry Department, Faculty of Pharmacy, Airlangga University, Surabaya, East Java, Indonesia IDHA KUSUMAWATI • Pharmacognosy and Phytochemistry Department, Faculty of Pharmacy, Airlangga University, Surabaya, East Java, Indonesia LYUDMILA I. KUTUEVA • Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia LIWEI LANG • Department of Oral Biology and Diagnostic Sciences, Dental College of Georgia, Augusta University, Augusta, GA, USA

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KRISTINE V. LUZ • Program in Neuroscience, Department of Biological Science, Florida State University, Tallahassee, FL, USA KATHERINE N. LYONS • Program in Neuroscience, Department of Biological Science, Florida State University, Tallahassee, FL, USA LISA C. LYONS • Program in Neuroscience, Department of Biological Science, Florida State University, Tallahassee, FL, USA MICHELLE MAARTENS • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa DANIEL MARTINS-DE-SOUZA • Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil; Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Cientı´fico e Tecnologico, Sa˜o Paulo, Brazil; UNICAMP Neurobiology Center, Campinas, SP, Brazil ROBYN MCDERMOTT • Centre for Chronic Disease Prevention, Australian Institute of Tropical Health and Medicine (AITHM), College of Public Health, Medical and Veterinary Sciences, James Cook University, Cairns, QLD, Australia; School of Health Sciences, University of South Australia, Adelaide, SA, Australia YASHAR MEHRBANI AZAR • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa IVAN ORLANDI • SYSBIO Centre for Systems Biology, Milan, Italy; Dipartimento di ` di Milano-Bicocca, Milan, Italy Biotecnologie e Bioscienze, Universita BETUL OZDEMIR • Department of Cardiology Faculty of Medicine, Nigde Omer Halisdemir University, Nigde, Turkey NARUMON PHAONAKROP • National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumtani, Thailand ANNE POLJAK • Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia; Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia HASSAN RAHMOUNE • Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK SITTIRUK ROYTRAKUL • National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumtani, Thailand SUBHAN RULLYANSYAH • Pharmacognosy and Phytochemistry Department, Faculty of Pharmacy, Airlangga University, Surabaya, East Java, Indonesia PERMINDER S. SACHDEV • Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia HANE´L SADIE-VAN GIJSEN • Centre for Cardio-metabolic Research in Africa (CARMA), Division of Medical Physiology, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Parow, South Africa ZOLTA´N SARNYAI • Laboratory of Psychiatric Neuroscience, Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia; Discipline of Biomedicine, College of Public Health, Medicine and Veterinary Sciences, James Cook University, Townsville, QLD, Australia ASCENTIA MATHAPELO SEBOKO • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

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¨ mer Halisdemir ZELIHA SELAMOGLU • Department of Medical Biology, Faculty of Medicine, O University, Nigde, Turkey CHLOE SHAY • Department of Pediatrics, Emory Children’s Center, Emory University, Atlanta, GA, USA JOHANN STEINER • Department of Psychiatry, University of Magdeburg, Magdeburg, Germany; Laboratory of Translational Psychiatry, University of Magdeburg, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany YONG TENG • Department of Oral Biology and Diagnostic Sciences, Dental College of Georgia, Augusta University, Augusta, GA, USA; Georgia Cancer Center, Department of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, Augusta, GA, USA; Department of Medical Laboratory, Imaging and Radiologic Sciences, College of Allied Health, Augusta University, Augusta, GA, USA YU-HUA TSENG • Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA; Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA MOHAMMADREZA VAFA • Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran; Pediatric Growth and Development Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran MARINA VAI • SYSBIO Centre for Systems Biology, Milan, Italy; Dipartimento di ` di Milano-Bicocca, Milan, Italy Biotecnologie e Bioscienze, Universita MARI VAN DE VYVER • Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa BORIS F. VANYUSHIN • Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russia CHIH-HAO WANG • Section on Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA ROBERT WELSCHINGER • Department of Geriatric Medicine, Prince of Wales Hospital, Sydney, NSW, Australia SABINE WESTPHAL • Institute of Clinical Chemistry and Pathobiochemistry, University of Magdeburg, Magdeburg, Germany SUMINO YANASE • Department of Health Science, Daito Bunka University School of Sports and Health Science, Higashi-Matsuyama, Saitama, Japan; Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa, Japan

Part I Reviews

Chapter 1 The Impact of New Biomarkers and Drug Targets on Age-Related Disorders Paul C. Guest Abstract The increase in the human lifespan has not been paralleled by an increase in healthy life. With the increase in the proportion of the aged population, there has been a natural increase in the prevalence of age-related disorders, such as Alzheimer’s disease, type 2 diabetes mellitus, frailty, and various other disorders. A continuous rise in these conditions could lead to a widespread medical and social burden. There are now considerable efforts underway to address these deficits in preclinical and clinical studies, which include the use of better study cohorts, longitudinal designs, improved translation of data from preclinical models, multi-omics profiling, identification of new biomarker candidates and refinement of computational tools and databases containing relevant information. Such efforts will support future interdisciplinary studies and help to identify potential new targets that are amenable to therapeutic approaches such as pharmacological interventions to increase the human healthspan in parallel with the lifespan. Key words Longevity, Age-related disorders, Dementia, Diabetes, Heart disease, Sarcopenia, Cancer, Biomarkers, Drug targets

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Introduction People worldwide can now expect to live longer than 60 years of age. This has been largely due to advances in public health beginning in the nineteenth century, which resulted in a rise in life expectancy [1]. By the year 2050, the members of the population that make up the age group of 60 year olds and older are expected to be double from what it is today and reach a total of two billion people [2]. In Japan, 30% of the people are already over 60 years old. Furthermore, the group of people worldwide aged 80 years and older will almost triple in number to reach a staggering 434 million. Unfortunately, this aging of the population has not been accompanied by an increase in the healthspan [3]. This is due to the fact that aging is the biggest risk factor for deficits in cellular, tissue and organ function, which often set the stage for diseases that ultimately kill the organism [4]. Such diseases include

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_1, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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neurodegenerative disorders, type 2 diabetes mellitus, heart disease, sarcopenia, and cancers. Therefore, the increase in longevity could lead to a widespread medical and social burden [5]. Healthy aging is often defined as freedom from specific age-related disorders and/or moderate to good performance levels upon functional testing in such areas as cognition and mobility [5]. Now we are living much longer than our ancestors mostly due to reduced early mortality from infectious diseases, accidents, and malnutrition [6]. However, with the present trend toward an increased lifespan, we are now contracting a completely different set of diseases related to advanced age and marked by chronic degeneration, such as those mentioned above. This could lead to an epidemic of the age-related disorders in just a few years time. The expected increase in these diseases is expected to have a critical impact for society including greater needs for long-term care, increased financial burdens for families and the healthcare systems, increased insurance premiums, along with the need to revise labor markets, monetary savings, retirement funding, housing, and transport Schemes [7, 8]. Lifespan and healthspan are related since people who live longer lives tend to do so because they are healthier. One study which compared young controls with those aged 90–99 years (nonagenarians), 100–104 years (centenarians), 105–109 years (semisupercentenarians) and 110–119 years (supercentenarians) found that the older age groups had a greater delay in the onset of the major diseases like cancer, cardiovascular disease, dementia, hypertension, osteoporosis and stroke [9]. However, this study was enriched for centenarians with a family history of longevity and the findings may therefore not be translatable to all centenarians. Another study carried out in the United States found that although mortality rates have declined, the prevalence of the disease has increased [10]. Therefore, there is a need for further studies addressing this issue. This review describes the latest research on the link between aging and disease, and it discusses the basic nature of these diseases, including the burdens to health, society and finance and the steps being taken to identify newer and more effective treatment strategies. It is hoped that further preclinical and clinical research efforts in this area will eventually lead to a world society in which individuals are not only living longer lives, but more productive and healthier ones.

2

Factors Affecting Aging Although some of the factors that affect health in old age are genetic, others have been linked to their physical and social environments. This includes gender, dietary patterns, physical exercise

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5

level, tobacco and alcohol consumption, as well as home and social life, and socioeconomic status. Furthermore, at the molecular level, the length of the telomeric repeat sequences that protect the ends of chromosomes during replication is intimately linked with age-related diseases and mortality. 2.1

Gender

Being born female is a factor that affects aging as most women live longer lives than men in most countries of the world [11]. For children born in 2019, it is estimated that boys will live 69.8 years and girls 74.2 years. In line with this, the number of years lived in good health was 64.8 years for women and 62.0 years for men between the years 2000 and 2016. However, the number of years lost through poor health was higher in women (9.5 years) compared to men (7.8 years). In line with these trends, the sex ratio changes with advancing age in favor of more females compared to males. Also, as women are living longer than men, specific diseases occur more frequently in women, such as Alzheimer’s disease. Furthermore, some diseases occur only in females such as breast and cervical cancer [12]. Conversely, X-linked factors can lead to enhanced immune responses in girls under 5 years of age, leading to a reduction in disease and mortality. Differences in lifestyle can also be a factor. For example, Beltran-Sanchez et al. showed that the greatest causes of death that contribute to the shorter lifespan of males in order of number of years lost are ischaemic heart disease, road accidents, lung cancer, stroke, liver cirrhosis, tuberculosis, prostate cancer, violence, liver cancer, stomach cancer lower respiratory infections, self-harm, oesophagus cancer, HIV/AIDS, and kidney diseases [12].

2.2

Diet

A typical factor that can affect robust aging is diet. A study published in 2018 revealed intake of high protein (
1.0 g/kg/day) results in better lower limb physical function and walking speed compared to individuals who took in lower protein levels (300 million are obese. It is estimated that more than half of type 2 diabetes cases could be avoided if weight gain is prevented or corrected

Reduction of smoking behavior

People with diabetes have a high risk of coronary heart disease, stroke and peripheral vascular disease compared to healthy controls. Studies have shown that smoking magnifies these risks

known to occur in the early disease stages [104, 105]. Therefore, the availability of reliable biomarker tests that could be used to identify susceptible individuals even before onset would allow these individuals to adopt some or all of the lifestyle changes highlighted in Table 2, which may delay the onset or even stop disease development [97, 98, 119]. This is an urgent need considering the devastating effects of diabetes to individual health, and to societies and the healthcare services throughout the world. 4.3 The Need for Biomarkers

Given that there is no cure for type 2 diabetes, reliable biomarker tests are urgently needed to enable better prediction of disease development and for assessing the risk of developing co-morbidities.

4.3.1 Biomarkers for Disease Risk

β-Cell dysfunction leads to impaired insulin production and insulin resistance, making these established risk factors for type 2 diabetes. In addition, insulin is known to be involved in the regulation of metabolism and/or growth in most cells in the body, leading to the idea that other proteins and metabolites associated with these processes can be detected as biomarkers in the circulation. A study carried out in 2006 using surface-enhanced laser desorption/ionization time-of-flight (SELD-TOF) mass spectrometry identified four proteins which were present at different concentrations between diabetic and control groups, and these proteins were identified as albumin, apolipoprotein C3, transferrin and transthyretin [120]. A comparative two-dimensional gel electrophoresis study (2DGE) found decreased levels of apolipoprotein A1

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(ApoA1) and increased levels of ApoE, leptin and C-reactive protein in patients with type 2 diabetes compared to controls [121]. Another 2DGE study found altered levels of fibrinogen, haptoglobin, α1-antitrypsin, ApoA1, transthyretin, and vitamin D binding protein in plasma from diabetic individuals compared to controls [122]. Finally, a study using on-chip liquid chromatography-nanoelectrospray ionization mass spectrometry found that simultaneous measurement of glucose and glycated forms of hemoglobin (HbA1c), albumin, cysteinylated albumin, S-nitrosylated hemoglobin, ApoA1, and methionine-oxidized ApoA1, provided a blood test that was capable of distinguishing type 2 diabetes patients from healthy controls with good accuracy [123]. Metabolomic studies have shown that increased levels of branched-chain and aromatic amino acids may occur several years before the clinical diagnosis of type 2 diabetes [124–127]. Other metabolomic profiling studies have found increased levels of α-hydroxybutyric acid, β-hydroxybutyrate, acetone, and acetoacetate prior to manifestation of type 2 diabetes [128, 129]. Finally, biomarker panels assessing fasting glucose, HbA1c, metabolites and the Diabetes Risk Score, found a power of 0.912 for prediction of type 2 diabetes [124]. These findings highlight the potential of combining molecular, dietary, lifestyle, and anthropomorphic data into predictive algorithms for type 2 diabetes. 4.3.2 Biomarkers for Cardiovascular Disease Risk

Diabetes is a risk factor for other disorders, such as cardiovascular disease, stroke and peripheral vascular disease [130]. Thus, biomarker tests can be used to predict and monitor associated co-morbidities and outcomes. Proteomic profiling studies have shown that there is an association between type 2 diabetes and inflammation, and the latter leads to increased coagulation and damaging effects on the vasculature, atherosclerosis, and formation of arterial thrombi [107]. This is associated with changes in the circulating levels of von Willebrand factor, interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α) and plasminogen activator inhibitor-1, which may serve as potential biomarkers for predicting the risk of cardiovascular and renal perturbations in diabetic patients. A study which carried out vascular assessments and biomarker measurements found that new cardiovascular events in type 2 diabetes patients with cardiovascular disease were associated with high baseline levels of IL-6, chemokine ligand 3, pentraxin 3, C-reactive peptide, hepatocyte growth factor, and vascular endothelial growth factor A [131]. Cardiovascular events in subjects with type 2 diabetes without cardiovascular disease were associated with more severe baseline atherosclerosis. Finally, a recent study found that serum cystatin C was also useful for the prediction of adverse cardiovascular outcomes in type 2 diabetes patients [132].

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4.3.3 Biomarkers for Diabetic Nephropathy

5

Diabetic nephropathy results from microvascular damage in diabetes patients and this is associated with an increase in the circulating levels of albumin [133, 134]. Proteomic and metabolomic studies have also led to the identification of potential biomarkers for prediction of onset of neuropathy and these include circulating asymmetric dimethylarginine (ADMA) and IL-18 and urinary ceruloplasmin, immunoglobulin G and transferrin [135–139]. In addition, biomarkers useful for predicting neuropathy progression have been identified including intercellular cell adhesion molecule 1, IL-6, ADMA, vascular cell adhesion molecule 1 and von Willebrand factor [139, 140]. Multiplex immunoassay analyses found that the urinary levels of eotaxin, granulocyte colony-stimulating factor, interferon-γ-inducible protein 10, IL-8, monocyte chemoattractant protein-1, RANTES and TNF-α were elevated in diabetic patients with micro-albuminurea compared to diabetic patients with normal albumin levels and controls [141]. A metabolomic profiling study found lower levels of 13 metabolites in urine that were mainly associated with mitochondrial function in diabetic patients with chronic renal disease compared to those without kidney disease [142]. Another metabolomic study of plasma found increased levels of uremic solutes and acylcarnitines, and decreased levels of branched chain and aromatic amino acids and the derivative α-hydroxybutyric acid that occurred before end-stage kidney disease in type 2 diabetes patients, compared to control patients with no renal dysfunction [143]. A longitudinal study of patients with type 2 diabetes identified 12 candidate biomarkers in urine that were associated with glomerular filtration rate decline, although the power of this prediction was low [144]. A recent study used a machine learning algorithm combining baseline values for glomerular filtration rate (GFR), systolic blood pressure, fasting plasma glucose (FPG) and potassium as early predictors and then GFR, FPG, and triglycerides as late predictors of diabetic nephropathy [145]. Taken together, these findings indicate that multiple parameters can be used for the prediction of diabetic nephropathy, such as physiological and biomarker readings.

Frailty Frailty has been defined as a clinically recognizable state in elderly people with increased vulnerability that results from an age-associated decline in physiological function in multiple systems, such that the ability to cope with everyday tasks or stressful situations is weakened [146]. A clinical study carried out in 2001 proposed the concept of a frailty syndrome that occurs when at least three of the following criteria are met [147]:

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1. Weakness as measured by low grip strength. 2. Slowness as measured by decreased walking speed. 3. Low physical activity. 4. Low energy or feelings of exhaustion. 5. Unintended weight loss. The same researchers proposed a pre-frail stage comprised of two of these criteria and a robust stage in which none of these criteria are met. Thus, this idea proposes that frailty is a clinical entity distinct from both disability and co-morbidity, although all three conditions can occur at the same time. A frailty index has also been proposed based on different principles [148]. The frailty index is calculated as the ratio between the deficits and number of deficits following a comprehensive clinical evaluation. Two cohort studies in the United Kingdom investigated the epidemiology of frailty [149, 150]. One of these studies assessed individuals with an average age of 69 years, and found that 65 years) in Taiwan, the prevalence of sarcopenia differed from 3.9% to 7.3% with prevalence reaching 13.6% among older men aged 75 years and older [48]. Much of the variations in these estimates may be owing to the lack of uniform criteria to diagnose

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sarcopenia. A recent systematic review reported that the prevalence of sarcopenia, operationalized based on EWGSOP criteria [24], was 1–29% in the community, 14–33% in long-term care, and 10% in acute hospital care, with notable differences depending on age and geographic area [49].

6

Sarcopenia Histopathology Early sarcopenia is characterized by a decline in the size of muscle. Over time, a decrease in muscle tissue quality also occurs. This is characterized by the replacement of muscle fibers with fat, an elevation in fibrosis, alterations in muscle metabolism, oxidative stress, and degeneration of the neuromuscular junction. This finally results in progressive loss of muscle performance and to frailty [50]. Studies evaluating the histological alterations in muscle fibers indicate that sarcopenia predominantly influences the type II (fasttwitch) muscle fibers, whereas type I (slow-twitch) fibers are much less influenced [51]. The size of type II fibers can be decreased by up to 50% in sarcopenia. However, such declines are only moderate when compared to overall declines in muscle mass. This raises the probability that sarcopenia causes both a reduction in muscle fiber number and decreased fiber size. Histological studies comparing muscle cross sections of older with those of younger individuals reported at least 50% fewer type I and type II fibers by the ninth decade of life [52]. Results from anatomic and electrophysiological studies indicated loss of anterior horn cells and ventral root fibers with advancing age [53, 54]. The mechanism of these histological alterations may propose that a chronic neuropathic process contributes to a loss of motor neurons that result in reduced muscle mass. Other factors including lifestyle, hormones, inflammatory cytokines, and genetic factors also impact these histological alterations.

7

Vitamin D Metabolism and Functions Vitamin D is a fat-soluble vitamin, and has an important role in calcium homeostasis and keeping normal bone metabolism [7]. The major pathway for vitamin D obtainment in humans is through cutaneous synthesis, under the action of ultraviolet rays. Vitamin D production in the skin provides 80–100% of the body needs [55]. However, factors including season of the year, regional latitude, daytime of sun exposure, sunscreen use, clothing, ethnicity, and age may affect the vitamin D synthesis rate [56]. The precursor, 7-dehydrocholesterol, synthesized by the liver from cholesterol, is turned in the skin as individuals are exposed to solar radiation [57]. By thermal isomerization, pro-vitamin D turns to vitamin D, and in the circulation it binds to the vitamin

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7-dehydrocholesterol

HO In skin cholecalciferol (vitamin D3)

In liver

HO

25-hydroxycholecalciferol (25-hydroxy vitamin D) OH

In kidney

HO

1,25-dihydroxycholecalciferaol (1,25-dihydroxy vitamin D) OH

Active form of vitamin D HO

OH

Fig. 2 Vitamin D metabolism

D-binding protein (DBP) and moves to the liver, where a hydroxyl group binds to the carbon atom 25 to produce 25-hydroxyvitamin D ¼ 25 (OH) D (calcidiol). Following these stages, the 25 (OH) D is secreted into the circulation and undergoes a new step of activation in the kidneys, where 1-alpha-hydroxylase turns 25 (OH) D to 1,25dihydroxyvitamin D ¼ 1,25 (OH) D (calcitriol), thus altering it into its active form. This is then released to various body tissues, consisting of the bone, muscular tissue, intestine, and others [56, 57] (Fig. 2). Data propose that vitamin D condition is critical for the normal function of various organs and cells, including osteoblasts, neurons, pancreatic β-cells, vascular endothelial cells, immune cells, and

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myocytes, where VDRs are expressed [58]. Moreover, vitamin D deficiency is related to decline of skeletal muscle mass and performance [58] and high risk of falls in elderly adults [59], although some researchers have produced inconsistent results [60]. Vitamin D deficiency is prevalent in the older population because of decreased exposure to sunlight, low intake of oral vitamin D, intestinal malabsorption, and reduced vitamin D hydroxylase activity in the kidneys [61]. Vitamin D deficiency is generally characterized as a 25-OH vitamin D level Lys, Arg-145-- > Cys);two rare point mutations associated with hyperlipoproteinemia type 3 Tandem repeat of residues 121–127 (E3-Leiden) O-glycosylation thr212 and 307 and ser308; O-glycosylation found in CSF, so may have relevance to CNS function Phosphorylation ser-147 Glycation lys-93; glycation impairs heparin binding Uniprot reports 33 natural variants (mostly amino acid substitutions or extensions) S-nitrosylation of E2 and E3 [65]

Lipid transport in blood and CNS ε2 ! longevity and survival ε4 ! mortality and disease Hyperlipoproteinemia type 3 (characterized by lipid deposition, E4 allele assoc. with coronary artery disease) Late onset AD (associated with E4 allele, and homozygous E4 is close to sufficient for AD by 80 years) Sea-blue histiocyte disease (lipid metabolism disorder) Lipoprotein glomerulopathy (lipid metabolism disorder, causing kidney failure)

apoH 71–280 mg/L [64] (uniprot P02749)

N-glycosylation asn-162, 183, 193, 253 O-glycosylation Uniprot reports six natural variants (mostly amino acid substitutions)

Biological functions include lipid binding, platelet agglutination and coagulation Anti-apoH antibodies are implicated in several diseases and disorders, including antiphospholipid syndrome (APS), Sjogren’s syndrome, transverse myelitis, (continued)

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Table 1 (continued)

Apolipoprotein and reference concentrations [45]

Post-translational variants

Salutary associations and diseases associated with apolipoprotein defects polymyositis and these antibodies exacerbate AD in an animal model ApoJ is an extracellular chaperone which prevents aggregation of non-native proteins and stress-induced aggregation of plama proteins. ApoJ inhibits amyloidosis of a variety of proteins including APP Chaperone, lipid transport, complement regulator, apoptosis protease inhibitor Found in association with amyloid plaques in AD For review see [4]

apoJ (uniprot P10909)

Phospho-thr-393 Phospho-ser-394, 396 N-glycans 86, 103, 145, 291, 317, 354, 374 Uniprot reports seven natural variants (mostly amino acid substitutions or alternative sequences)

apoSAA (uniprot P02735)

Methylation [66] has been ApoSAA is an acute phase reported reactant. Secondary No experimental evidence is amyloidosis disrupting tissue available for other PTMs. structure and function ModPreda has high confidence ApoSAA elevation in serum may be associated with cancer novel predictions for acetylation K52, ubiquitination K64, proteolytic cleavage H112 Uniprot reports ten natural variants (mostly amino acid substitutions or alternative sequences) and eight different chain lengths

apoF (associates predominantly with LDL in blood)

N-glycosylation [67] and phosphoserine [68] PTMs reported

This protein is also called lipid transfer inhibitor protein, since it inhibits lipid transfer between lipoproteins. Its movement from a plasma 470 kDa inactive complex to LDL was shown to be dependent upon the lipid composition of LDL [69]. ApoF is higher in plasma of females than males, correlates positively with HDL cholesterol and apoA-I and is lower in hypertriglyceridemic patients than in healthy controls [70] (continued)

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Table 1 (continued)

Apolipoprotein and reference concentrations [45]

Post-translational variants

Salutary associations and diseases associated with apolipoprotein defects

apoM (associates predominantly N-linked glycosylation [71] with HDL in blood) reported No experimental evidence is available for other PTMs. ModPreda has high confidence novel predictions foramidation Q142, E155, and N-linked glycosylation N188

ApoM may be involved in HDL metabolism. May have antiatherosclerotic functions, since gene polymorphism associates with coronary heart disease, diabetes, and immuneassociated diseases. For review see [72]

Apo(a) LPA 8.7–267 nM [73]

Apolipoprotein(a) associates with a specific kind of plasma lipoprotein particle. Apo (a) has variable numbers of kringle IV motifs. High blood levels of lipoprotein(a) Lp (a) are a risk factor for cardiovascular disease, atherosclerosis and stroke. For review see [74]

Uniprot reports 28 N-linked glycosylation sites and 11 natural variants/isoforms (amino acid substitutions)

apoO (associates predominantly O-glycosylation [75] The physiological function of with HDL in blood) No experimental evidence is apoO is not well understood. available for other PTMs, and However it is a mitochondrial ModPreda has only a single high protein, and it is also found in glycosylated/secreted form. scoring hit for proteolytic Evidence suggests that it is an cleavage at Y43 independent indicator of inflammation, being increased in plasma of patients with acute coronary syndrome [76], and in diabetic cardiomyopathy [77] apoL1 (associates predominantly N-linked glycosylation [78] and with HDL in blood) phosphoserine [79] modifications reported

a

Sequence variants of the APOL1 gene are associated with nondiabetic nephropathy [80], cancer and chronic kidney disease [81], cardiovascular disease and hypertension [82]

ModPred is a WEB based software tool which predicts post-translational modification sites on proteins, based on sequence motifs, provides scores for low, medium or high confidence and also provides information on whether the prediction is novel, or whether there is independent experimental evidence. The tool uses protein sequence data in fasta format, and can be found at: http://montana.informatics.indiana.edu/ModPred/index.html

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Table 2 Molecular weights and primary sequence identities of the human apolipoproteins

Sequence length (amino acid residues)

Avg. mass (Da)

Percent sequence identity to other apolipoproteins

apoAl

Preproprotein ¼ 1–267 Mature chain ¼ 19–267

30,778 28,962

apoA4 (25%), apoA5 (25%), apoE (27%)

apoA2

Preproprotein ¼ 1–100 Mature chain ¼ 19–100

11,175 9304

apoD (100%)

apoA4

Preproprotein ¼ 1–396 Mature chain ¼ 21–396

45,399 43,403

apoA5 (29%), apoA1 (25%), apoE (23%)

apoA5

Preproprotein ¼ 1–366 41,213 Mature chain ¼ 24–366 38,905

apoA4 (29%), apoA1 (25%)

apoB100 Preproprotein ¼ 1–4563 515,623 Mature chain ¼ 28–4563 512,875

apoC1

Preproprotein ¼ 1–83 Mature chain ¼ 27–83

apoC2

Percent sequence identity to other proteins

Putative uncharacterized apoB48 (100%). protein LOC400499 (23%), ApoB48 represents KIAA1620 (29%), periaxin the N-terminal (29%) 48% of the apoB100 sequence

9332 6631

No other sequences produce significant alignments in protein BLAST

Preproprotein ¼ 1–101 Mature chain ¼ 23–101

11,284 8915

No other sequences produce significant alignments in protein BLAST

apoC3

Preproprotein ¼ 1–99 Mature chain ¼ 21–99

10,852 8765

No other sequences produce significant alignments in protein BLAST

apoD

Preproprotein ¼ 1–189 Mature chain ¼ 21–189

21,276 19,303

apoE

Preproprotein ¼ 1–317 Mature chain ¼ 19–317

36,154 34,237

Retinol binding protein 4 (30%), neutrophil gelatinase-associated lipocalin (25%), protein AMBP(43%), hCG1795014, partial (26%) No other sequences produce significant alignments in protein BLAST

apoH

Preproprotein ¼ 1–345 Mature chain ¼ 20–345

38,298 36,255

Selectin P (granule membrane protein 140 kDa, antigen CD62) (30%), sushi, von Willebrand factor type A, EGF and pentraxin domaincontaining protein 1 precursor (25%), CCP module-containing protein (25%), complement factor H (26%), complement factor 4 binding protein (28%), and several other proteins (continued)

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Table 2 (continued)

Sequence length (amino acid residues)

Avg. mass (Da)

Percent sequence identity to other apolipoproteins

Percent sequence identity to other proteins

apoJ

Preproprotein ¼ 1–449 Mature chain ¼ 23–449

52,495 50,063

No other sequences produce significant alignments in protein BLAST. However, apoJ goes by several alternative names including clusterin, complement lysis inhibitor, SP-40, 40, sulfated glycoprotein 2 and testosterone-repressed prostate message 2. All of which are identified in the protein BLAST search

apoSAA

Preproprotein ¼ 1–122 Mature chain ¼ 19–122

13,532 11,683

No other sequences produce significant alignments in protein BLAST

apoF

Preproprotein ¼ 1–326 Mature chain ¼ 165–326

35,400 17,425

No other sequences produce significant alignments in protein BLAST

apoM

Mature chain ¼ 1–188

21,253

G3a protein (99%), HSPC336, partial (99%)

Apo(a)

Preproprotein ¼ 1–2040 226,546 Mature chain ¼ 20–2040 224,412

Plasminogen (63%), hCG2029799, isoform CRA_c (54%), plasmin (85%), macrophage stimulating 1 (hepatocyte growth factor-like) (38%), brain-rescue-factor-1 (31%), angiostatin (42%), hyaluronan-binding protein (34%) and several other proteins

apoO

Preproprotein ¼ 1–198 Mature chain ¼ 26–198

22,285 19,578

MIC26 precursor (100%), MIC27 precursor (37%), brain my025 (86%), hypothetical protein MGC4825 (100%), protooncogene tyrosine-protein kinase receptor ret (31%), hCG1646259 (93%)

apoL1

Preproprotein ¼ 1–398 Mature chain ¼ 28–398

43,974 41,129

No other sequences produce significant alignments in protein BLAST

Primary sequences were obtained from NCBI PubMed Proteins (https://www.ncbi.nlm.nih.gov/protein/), and sequence identities (preproprotein sequence used in each case) determined with the protein BLAST sequence alignment tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM¼blastp&PAGE_TYPE¼BlastSearch&LINK_ LOC¼blasthome)

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Fig. 1 The apolipoprotein family members vary greatly in their tertiary structures and molecular weights and, with a few exceptions, have surprisingly little primary structural homology either to other family members or to other proteins (see Table 2). The majority of apolipoproteins are relatively small (6–36 kDa), which makes them particularly amenable to mass spectrometric analysis. The exceptions are apoB and apo(a), which are 512.9 and 224.4 kDa, respectively. The tertiary structures of the majority of apolipoproteins have been elucidated, and the 3D structures in this schematic were taken from NCBI PubMed Structure (https://www. ncbi.nlm.nih.gov/structure/) [5–15]

In this chapter, we will focus on the background and protocols for three methods of targeted quantification of apolipoproteins: (1) immunoassay-based multiplexed method; (2) direct (or intact) mass spectrometry-based assays, specifically MALDI immunoassay (MALDI-MSIA); and (3) multiple reaction monitoring (MRM) and/or parallel reaction monitoring (PRM) mass spectrometric assays.

2

Pre-analytical Considerations in the Determination of Apolipoproteins Several publications outline pre-analytical considerations for bloodbased assays of the lipoproteins [16, 17]. Below, we summarize and discuss some of the primary concerns for maintaining consistency and reproducibility, both within and across laboratories.

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2.1 Nature of the Sample 2.1.1 Example Considerations

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Should serum or plasma samples be collected? If plasma, should it include addition of ethylenediaminetetraacetic acid (EDTA) or heparin?

2.1.2 Recommendations

Serum is often a good choice for analytes that are not part of, or affected by, the coagulation system. Serum is a “cleaner” matrix, and this simplifies mass spectrometric analysis and often results in better sensitivity. However, for the higher abundance apolipoproteins (see Table 1 for reference concentration ranges) sensitivity is not limiting. Apart from being a common anticoagulant, EDTA offers the advantage that it chelates divalent metals. This minimizes unwanted oxidative modifications related to Fenton chemistry. Heparin is a common anticoagulant, but at higher concentrations it can interfere with some ELISA assays.

2.2

Are there diurnal variations in analyte levels and, therefore, should the blood be collected in the AM or PM? How do these diurnal and circadian rhythms affect levels of each apolipoprotein? Is fasting required? Method of venipuncture (e.g., needle gauge) can affect factors, such as hemolysis. How does hemolysis affect apolipoprotein assay results (i.e., may be impacted by which assay type is to be employed, which antibodies, etc.)?

Sampling

2.2.1 Example Considerations

2.2.2 Recommendations

Diurnal and circadian variations in plasma levels of some apolipoproteins have been reported [18, 19]. Other studies have not considered the impact of these variables. Until such time as their influence is defined, standardization/control is recommended (e.g., collect a fasting sample in the AM). Blood hemolysis should always be avoided because: (a) it adds to matrix complexity, (b) heme iron may cause undesirable oxidative modifications to blood proteins, and (c) hemolysis may cause interference in some assays. Needle gauge is an important factor in minimizing hemolysis. A high gauge/wider bore needle is, therefore, a better choice to minimize the likelihood of hemolysis.

2.3

For how long and under what conditions should samples be stored? These are important concerns if biobanked samples are used in the study. Biobank samples may have been in storage for years or even decades. How many freeze-thaw cycles can be tolerated without significant changes in measured target analyte levels?

Sample Storage

2.3.1 Example Considerations

2.3.2 Recommendations

If assays will be performed immediately on sample collection (i.e., within 24 h), storage at 4 C may be acceptable for apolipoprotein assays. If storage is for weeks–months, 20  C to 30  C is common practice. For long-term storage (years–decades; e.g., biobank samples, longitudinal cohorts, etc.), 80  C or even nitrogen

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gas phase storage (i.e., 196  C) is advisable. Some studies indicate that quantification of some apolipoproteins may not be impacted by storage temperature or the number of freeze-thaw cycles [20], whereas separate studies suggest otherwise. Multiple freeze-thaw cycles (>5) reportedly decrease recoveries of many analytes [21], so it is better to prepare aliquots and minimize sample reuse following storage. There are limited data on the impact of extended storage on protein recovery/abundance or, more specifically, on apolipoprotein levels. For some study types (e.g., longitudinal studies) there is no option but to store samples for years/decades. In this case, the lower the storage temperature, the greater the sample stability. For years of storage time a minimum storage temperature of 80  C is advisable. 2.4

Demographics

Are apolipoprotein levels affected by age, sex, or genetic variations?

2.4.1 Example Considerations 2.4.2 Recommendations

Some apolipoprotein levels are affected by these factors. For example, APOE allele variants affect plasma apolipoprotein levels. Ageand sex-related changes are also observed for most, if not all, apolipoproteins [1, 22]. Whether or not to include or exclude demographic variety relates to study design, statistical power, and the broad relevance of the study outcomes (see Subheading 2.6 for a discussion of this topic).

2.5

Do changes in diet, exercise, nutrient supplements, and similar variables affect apolipoprotein levels?

Lifestyle

2.5.1 Considerations 2.5.2 Recommendations

These lifestyle variations are typically difficult to assess and/or control in population-based studies. In some studies these variables may need to be accepted as part of population variability and managed statistically (e.g., increased subject numbers (n) and statistical correction for confounders).

2.6 Experimental Design

Known demographic and genetic variations should ideally be managed by using well-defined subject categories. Confounding factors, which may bias results, can be managed by elimination, randomization, and/or use of matched controls. In laboratory science, particularly where animal models are used, elimination is preferred because population genetic, phenotypic and lifestyle characteristics are more easily kept homogeneous. In genetically, phenotypically, and lifestyle diverse human populations, management of variables by the elimination approach is difficult, if not impossible, if sufficient subject numbers are to be retained to maintain adequate statistical power. Randomization and use of matched

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controls are alternative options in this case, but these approaches may also restrict subject numbers and ultimately reduce statistical power. An alternative approach is post-analysis statistical correction for confounding variables. This is a common and acceptable compromise. Within the field of epidemiology, it can also be argued that restricting study groups by elimination of specific variables—even if adequate numbers are available—may have the undesirable consequence of rendering the results applicable to only a subset of the whole population. This argument also drives the need for inclusivity in population studies if the experimental results are to be broadly applicable (i.e., no control of variables, such as sex, race, and age).

3

Apolipoprotein Assay Methods

3.1 Antibody-Based Multiplex Apolipoprotein Assays

The LuminexTMxMAP bead-based technology is the best known and most commonly utilized antibody-based system for quantification of specific proteins [1]. The beads are manufactured by Myriad Rules Based Medicine (Myriad-RBM) and distributed both through Myriad-RBM and several commercial distributors (Table 3). Custom-constructed kits are also available, but their use will likely increase the cost, complexity, and duration of a study. There are several important considerations in the design of studies utilizing antibody-based multiplex apolipoprotein assays. The first of these relates to reagent supply. Kits are manufactured in batches/lots, and it is never certain that a study can be completed by utilizing a single batch/lot of reagents unless that study is small and can be completed at a single point in time. For large or longterm studies requiring multiple kits, users should be aware that batches/lots may vary in buffer composition, antibody specificity, and/or other kit components. Antibodies can be sourced from monoclonal cell lines or raised in a variety of animals. Even when all care is taken to maintain consistency, if new batches/lots are manufactured, biological variables, beyond the manufacturer control, can introduce changes to antibody specificity. Consequently, to minimize technical variability, users planning larger studies should request that the manufacturer provide sufficient kits with the same batch/lot number to complete the full experiment. For studies extending over long durations (e.g., where kits may be required over several years), continuity of kit manufacture should be discussed in advance with the kit provider. But discontinuation of kit supply is not uncommon and can happen without warning. While intra-assay quality control (QC) standards are often provided within kits, for large studies, in-house QC standards should be prepared and included in the study design to check for inter-assay variability. Such standard(s) should contain the analytes of interest and could be serum, plasma, or an in-house manufactured cocktail. There should be sufficient quantity of these

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Table 3 Commercially available apolipoprotein multiplex assays Apolipoprotein multiplex bead based assay, and quantitative range Assay platform

Company

Human apolipoprotein (Apo) panel (11-plex); apolipoproteins and minimum detectable concentration (MDC) Apo A1 (0.2 ng/mL), AII (0.3 ng/mL), B100 (1.0 ng/mL), CII (0.3 ng/mL), CIII (0.7 ng/mL), D (0.3 ng/mL), E (1.4 ng/mL), E4 (0.1 ng/mL), H (0.3 ng/mL), J (0.2 ng/mL), and M (0.2 ng/mL)

Flow cytometer together with LEGENDplex™ data analysis software

BioLegend

Milliplex MAP human apolipoprotein magnetic bead panel; apolipoproteins and minimum detectable concentration (minDC) Apo A1 (0.86 ng/mL), AII (0.38 ng/mL), B (1.91 ng/mL), CII (0.26 ng/mL), CIII (0.57 ng/mL) and E (0.49 ng/mL)

Luminex™-based instrument

Merck/sigma

Bio-Plex pro human apolipoprotein 10-plex assay; apolipoproteins, lower and upper limits of quantification (LLOQ and ULOQ) Apo A1 (0.059–70 ng/mL), A2 (0.032–36 ng/mL), B (0.41–360 ng/mL), C1 (0.030–17 ng/mL), C3 (0.023–28 ng/mL), D (0.055–30 ng/mL), E (0.021–12 ng/mL), H (0.15–210 ng/mL), J (0.12–170 ng/mL) and CRP (0.019–11 ng/mL)

Luminex™-based instrument

Bio-rad

ProcartaPlex human apolipoprotein panel 5-plex; Luminex™-based instrument apolipoproteins, lower and upper limits of quantification (LLOQ and ULOQ) Apo A1 (0.73–3000 ng/mL), A2 (0.02441–25 ng/mL), B (4.88–20,000 ng/mL), C2 (0.56–575 ng/mL), E (0.22–900 ng/mL)

ThermoFisher

standards to allow inclusion in the entire study and, typically, these should include low, medium and high concentration QC standards. A large batch (e.g., several 100 mL) can be prepared, aliquoted to assay sized volumes (e.g., 20–50 μl) and stored at 80  C until needed. Several platforms are available for fluorescent- and flow cytometry-based detection, including MAGPIX; Luminex 100/200™; FLEXMAP 3D™ from ThermoFisher (Waltham, MA, USA); Bio-Plex 200 and Bio-Plex 3D from BioRad (Hercules, CA, USA).

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Commercially available multiplex kits conveniently come with calibration standards, assay and wash buffers, multiplex beads pre-conjugated with specific antibodies, ELISA plates and covers. Several additional items are required, however, to perform these assays. These include the following: l

Multiplex plate reader platform suitable for fluorescent and flow cytometry-based detection, such as MAGPIX, Luminex 100/200™, FLEXMAP 3D™ from ThermoFisher; Bio-Plex 200, Bio-Plex 3D from BioRad.

l

Platform-specific wash buffer and calibration kits.

l

Microtiter plate shaker/vortexer.

l

Magnetic plate washer.

l

Pipettes (single and multichannel) with disposable tips, and reservoir for dispensing solutions/buffers.

l

Deionised water (e.g., glass distilled or MilliQ™).

Each of the manufacturers provide detailed procedures, but these vary with regards to wash steps, incubation times, specific volumes of buffers/solution to be dispensed, etc. In general, however, the following steps are common to most procedures [1]: l

Dilute kit calibration standards, QC standards and samples using the buffers and procedures provided.

l

Rinse 96 well microtiter plate with wash buffer for the specified time and at the specified temperature.

l

Add beads and then remove the liquid (the mode of liquid removal may be either by filtration for non-magnetic beads, or for magnetic beads, by magnetic hand washer and plate inversion.).

l

Dispense all standards and samples onto the provided 96 well plate and add assay diluent, if required. Typically, serum and plasma samples will be diluted several 1000-folds for apolipoprotein assay. The plates should be covered with disposable microtiter plate covers for all incubation steps, to avoid contamination and evaporative losses.

l

Incubate for the specified time and at the specified temperature. This step is normally performed on a microtiter plate shaker to keep the beads in suspension and is typically of 2 h duration.

l

After the incubation, the beads are typically washed 2–3 times with wash buffer.

l

The detection antibody is then added, mixed and the plate is incubated again, typically for 30 min to 1 h.

l

The beads are then washed 2–3 times with wash buffer.

l

A “development compound”—the specific nature of this depends on how the secondary antibody has been conjugated—is then added, for example, streptavidin-PE, biotin, etc.

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MALDI/ESI-MSIA

3.2.1 Materials

l

After mixing, the plate is incubated again, typically for 30 min to 1 h.

l

The beads are washed again (2–3 times) with wash buffer.

l

The beads are then resuspended in wash buffer; the plate is shaken and incubated again, typically for 5–10 min.

l

The data are then acquired on the appropriate platform.

Given the availability of sufficiently specific antibodies and detection methods, ELISA-based assays can accurately and sensitively quantify the total apolipoprotein level. However, functionally important post-translationally modified forms are typically indiscernible by this approach. To determine the full molecular heterogeneity of specific apolipoproteins requires more selective approaches. One such approach is mass spectrometry immunoassay (MSIA), which combines immunoaffinity capture of a specific apolipoprotein (and its variants) with MALDI mass spectrometry to separate, detect, and quantify each variant apolipoprotein form separately [23]. MSIA can be used to determine the amount and form of multiple apolipoproteins, that is, serum amyloid A (saa), apoAI, apoA2, apoCI, apoC2, apoC3, apoD, apoE, apoH, apoJ, and apoM. Quantification of the proteins and their modified forms is based on the ratio of targeted protein forms to an intentionally introduced internal standard(s). In the most common manifestation of MSIA, analytes are extracted from a biological milieu (e.g., plasma) by antibodies immobilized within a pipette tip—sometimes referred to as a MSIA tip or affinity pipette tip. After thorough washing, the bound target analyte(s) are eluted from the tip by the addition of MALDI matrix solution and the sample is analyzed by mass spectrometry. The specificity of this approach comes from a combination of antibody selectivity paired with medium-to-high resolution mass spectrometry-based protein identification and quantification. With this approach, single amino acid and post-translational variants can be determined and quantified. MSIAs measure intact proteins in contrast to other mass spectrometry approaches that measure proteolytic fragments. Consequently, unanticipated modifications are evident, even when present on multiple variant forms, and each can be quantified independently. l

MALDI system: A range of MALDI-MS systems are available through several manufacturers, most of which are suitable for MALDI-MSIA applications. Key manufacturers include Bruker, ABSciex, Shimadzu, and SimulTOF.

l

MALDI matrix: For intact protein work, sinapinic acid is a popular matrix. It is normally prepared at a concentration of

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10 mg/mL in 0.1% trifluoroacetic acid in acetonitrile: water (80:20). l

Apolipoprotein-specific antibodies immobilized in tips: These can either be prepared in house or are available commercially from ThermoFisher Scientific.

l

Internal Standards: For MALDI-MSIA of apolipoproteins, structural analogs are typically incorporated as internal standards.

For manual assay set-up hand-held pipettes with disposable tips are used, for example, Gilson/Eppendorf, 200 μl. For automated assay setup, robotic platforms are available from several manufacturers for example, BioSpot (from Tech-Matic, Loves Park, IL), Fluent® and Freedom EVO series® (Tecan Group Ltd., M€annedorf, Switzerland), and AssayMAP Bravo (Agilent, Santa Clara, CA). In MSIA, antibodies (e.g., to apoE) are immobilized on a solid support retained within pipette tip. The plasma is then drawn up and expelled multiple times to selectively retain apoE and its variants. The tip is then washed, and the apoE eluted (see Fig. 2 for the typical MALDI-MSIA workflow). Although MSIA is typically

3.2.2 Protocol

Immobilised antibody to target apolipoprotein

Plasma containing native and variant forms of target apolipoprotein

Apolipoproteinantibody complex

Native protein Intensity

Laser

Protein isoform

11,100

11,300

11,500 11,700 11,900 m/z

Fig. 2 Schematic of a typical MALDI-MSIA workflow

MALDI matrix and captured apolipoproteins

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combined with MALDI mass spectrometry, and in this example the apoE would be eluted directly onto a MALDI target (using acidic matrix solution), it is also possible to elute each sample in a single well of a 96-well microtiter plate. These wells can then by sampled and analyzed by electrospray ionization (ESI) mass spectrometry (using an acidic buffer containing additives to prevent oxidative degradation and adsorptive losses). Analysis of the intact proteins by mass spectrometry shows all of the variant forms, or at least all those that are retained, and their relative amounts can be determined. With the addition of an internal standard to the original sample, it is possible to quantify total apoE, together with separate determinations of each of the variants. MSIA assays can be developed for a wide range of peptides and proteins, and these assays are high throughput, sensitive, precise and accurate [24]. At the heart of MSIA, solid supports are built into pipette tips for protein isolation [24]. Immunoaffinity capture is greatly enhanced by immobilizing antibodies onto small, porous microcolumns that are fitted at the entrance of a pipettor tip. The advantage of tip-fitted microcolumns (as opposed to flat surfaces or beads) is in the sample processing. The sample can be aspirated and dispensed (multiple times) through the tip to expose the immobilized antibody to the antigen present in the sample. Once the antigen is captured, the affinity pipette is washed with buffer and water to remove any loosely associated sample components and salts. For MALDI-based analyses a small volume of MALDI matrix (~ 5 μl, pH ~ 2) is aspirated into the affinity pipette, and the antigen-containing eluent is deposited directly onto a MALDI target plate ready for MS analysis. For ESI-based configurations, an acidic buffer (pH ~ 2) is used and the eluent is deposited into microtiter plates for downstream mass spectrometry analysis. Processing of 96 affinity pipettes in parallel (using 96-well parallel robotics) is the preferred approach for routine applications, and here about 1 h is needed for preparation of 96 samples. Mass spectrometric analysis adds from about 1 h to 1 day to the analysis time for 96 samples, depending on the mass spectrometry system used. Multiple proteins can be analyzed with MSIA by immobilizing multiple antibodies in a single affinity pipette (i.e., multiplexing). The resulting mass spectra contain distinct signals for the target proteins and (oftentimes) additional signals due to their variant (i.e., protein-specific microheterogeneity). The approach is sensitive, offering limits of quantification in the low picogram/ milliliter range. Repeatability and reproducibility are good: that is, coefficients of variation (CVs) of 10,000 laser shots (MALDI) or >30 s worth of spectra (ESI)] relative to the sum of the integrals of all target protein peaks. In most cases, standards of variant protein forms will not be available, and, therefore, only precision will be evaluated when it comes to the relative abundance of variant protein forms. Precision can be evaluated according to standards delineated by the National Council of Clinical Laboratory Services (NCCLS, now known as the Clinical and Laboratory Standards Institute, CLSI) [27].

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Estimates of between-day, between-run, within-day standard deviations and within-run and total standard deviations can be determined by standard methods—for example, the repeat analysis of preidentified stock plasma samples (e.g., one male and one female). For example, relevant data can be obtained following the analysis of two replicates per specimen, per run, and two runs per day for 20 days. Typically, MSIA delivers percent CV values of less than 20% for any variant. To evaluate the accuracy of quantitative protein assays, protein standards (along with a suitably determined molecularly analogous internal standard) are fortified at known concentrations into either animal plasma (to which the immobilized antibody is not crossreactive) or CST Technologies, Inc.’s SeraSub (a human serum substitute lacking the target protein). An 8-point standard curve is run in triplicate, covering a concentration range slightly greater than that anticipated in the human population. The standard error across the concentration range can be estimated from these data. In addition, QC samples at low, mid, and high-range concentrations are prepared from a separate lot of protein standard. These quantitative sets are analyzed as described above for precision—that is, as two replicates per specimen per run and two runs per day for 20 days. Inaccuracy >20% would be deemed unacceptable. Limit of Detection (LOD) and Limit of Quantification (LOQ): The LOQ—the lowest concentration of analyte that can be measured with a precision and accuracy of C (rs662799) on fasting plasma lipids and risk of metabolic syndrome: evidence from a casecontrol study in China and a meta-analysis. PLoS One 8(2):e56216. https://doi.org/10. 1371/journal.pone.0056216 54. Smit M, van der Kooij-Meijs E, Frants RR, Havekes L, Klasen EC (1988) Apolipoprotein gene cluster on chromosome 19. Definite localization of the APOC2 gene and the polymorphic Hpa I site associated with type III hyperlipoproteinemia. Hum Genet 78 (1):90–93 55. Lee CJ, Choi S, Cheon DH, Kim KY, Cheon EJ, Ann SJ et al (2017) Effect of two lipidlowering strategies on high-density lipoprotein function and some HDL-related proteins: a randomized clinical trial. Lipids Health Dis 16 (1):49. https://doi.org/10.1186/s12944017-0433-6 56. Barber MJ, Mangravite LM, Hyde CL, Chasman DI, Smith JD, McCarty CA et al (2010) Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS One 5(3):e9763. https://doi.org/ 10.1371/journal.pone.0009763 57. van den Broek I, Sobhani K, Van Eyk JE (2017) Advances in quantifying apolipoproteins using LC-MS/MS technology: implications for the clinic. Expert Rev Proteomics 14(10):869–880 58. Chun EM, Park YJ, Kang HS, Cho HM, Jun DY, Kim YH (2001) Expression of the apolipoprotein C-II gene during myelomonocytic differentiation of human leukemic cells. J Leukoc Biol 69(4):645–650 59. Kinnunen PK, Jackson RL, Smith LC, Gotto AM Jr, Sparrow JT (1977) Activation of lipoprotein lipase by native and synthetic fragments of human plasma apolipoprotein C-II. Proc Natl Acad Sci U S A 74(11):4848–4851 60. Kumpusalo E, Karinp€a€a A, Jauhiainen M, Laitinen M, Lappetel€ainen R, M€aenp€a€a PH (1990) Multivitamin supplementation of adult omnivores and lactovegetarians: circulating levels of vitamin a, D and E, lipids, apolipoproteins and selenium. Int J Vitam Nutr Res 60 (1):58–66

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61. Breckenridge WC, Little JA, Steiner G, Chow A, Poapst M (1978) Hypertriglyceridemia associated with deficiency of apolipoprotein C-II. N Engl J Med 298:1265–1273 62. Cox DW, Breckenridge WC, Little JA (1978) Inheritance of apolipoprotein C-II deficiency with hypertriglyceridemia and pancreatitis. N Engl J Med 299(26):1421–1424 63. Wolska A, Dunbar RL, Freeman LA, Ueda M, Amar MJ, Sviridov DO et al (2017) Apolipoprotein C-II: new findings related to genetics, biochemistry, and role in triglyceride metabolism. Atherosclerosis 267:49–60 64. Rezeli M, Vegvari A, Fehniger TE, Laurell T, Marko-Varga G (2011) Moving towards high density clinical signature studies with a human proteome catalogue developing multiplexing mass spectrometry assay panels. J Clin Bioinform 1(1):7. https://doi.org/10.1186/20439113-1-7 65. Abrams AJ, Farooq A, Wang G (2011) S-nitrosylation of ApoE in Alzheimer’s disease. Biochemistry 50(17):3405–3407 66. Ducret A, Bruun CF, Bures EJ, Marhaug G, Husby G, Aebersold R (1996) Characterization of human serum amyloid a protein isoforms separated by two-dimensional electrophoresis by liquid chromatography/ electrospray ionization tandem mass spectrometry. Electrophoresis 17(5):866–876 67. Halim A, Nilsson J, Ru¨etschi U, Hesse C, Larson G (2012) Human urinary glycoproteomics; attachment site specific analysis of Nand O-linked glycosylations by CID and ECD. Mol Cell Proteomics 11(4): M111.013649. https://doi.org/10.1074/ mcp.M111.013649 68. Kumar A, Gangadharan B, Cobbold J, Thursz M, Zitzmann N (2017) Absolute quantitation of disease protein biomarkers in a single LC-MS acquisition using apolipoprotein F as an example. Sci Rep 7(1):12072. https://doi. org/10.1038/s41598-017-12229-2 69. Morton RE, Greene DJ (2011) Conversion of lipid transfer inhibitor protein (apolipoprotein F) to its active form depends on LDL composition. J Lipid Res 52(12):2262–2271 70. Kujiraoka T, Nakamoto T, Sugimura H, Iwasaki T, Ishihara M, Hoshi T et al (2013) Clinical significance of plasma apolipoprotein F in Japanese healthy and hypertriglyceridemic subjects. J Atheroscler Thromb 20 (4):380–390 71. Chen R, Jiang X, Sun D, Han G, Wang F, Ye M et al (2009) Glycoproteomics analysis of human liver tissue by combination of multiple

enzyme digestion and hydrazide chemistry. J Proteome Res 8(2):651–661 72. Huang LZ, Gao JL, Pu C, Zhang PH, Wang LZ, Feng G et al (2015) Apolipoprotein M: research progress, regulation and metabolic functions (review). Mol Med Rep 12 (2):1617–1624 73. Lassman ME, McLaughlin TM, Zhou H, Pan Y, Marcovina SM, Laterza O et al (2014) Simultaneous quantitation and size characterization of apolipoprotein(a) by ultraperformance liquid chromatography/mass spectrometry. Rapid Commun Mass Spectrom 28(10):1101–1106 74. Vasquez N, Joshi PH (2019) Lp(a): addressing a target for cardiovascular disease prevention. Curr Cardiol Rep 21(9):102. https://doi.org/ 10.1007/s11886-019-1182-0 75. Lamant M, Smih F, Harmancey R, PhilipCouderc P, Pathak A, Roncalli J et al (2006) ApoO, a novel apolipoprotein, is an original glycoprotein up-regulated by diabetes in human heart. J Biol Chem 281 (47):36289–36302 76. Yu BL, Wu CL, Zhao SP (2012) Plasma apolipoprotein O level increased in the patients with acute coronary syndrome. J Lipid Res 53 (9):1952–1957 77. Turkieh A, Caubere C, Barutaut M, Desmoulin F, Harmancey R, Galinier M et al (2014) Apolipoprotein O is mitochondrial and promotes lipotoxicity in heart. J Clin Invest 124(5):2277–2286 78. Liu T, Qian WJ, Gritsenko MA, Camp DG 2nd, Monroe ME, Moore RJ et al (2005) Human plasma N-glycoproteome analysis by immunoaffinity subtraction, hydrazide chemistry, and mass spectrometry. J Proteome Res 4 (6):2070–2080 79. Tagliabracci VS, Wiley SE, Guo X, Kinch LN, Durrant E, Wen J et al (2015) A single kinase generates the majority of the secreted phosphoproteome. Cell 161(7):1619–1632 80. Freedman BI, Kopp JB, Langefeld CD, Genovese G, Friedman DJ, Nelson GW et al (2010) The apolipoprotein L1 (APOL1) gene and nondiabetic nephropathy in African Americans. J Am Soc Nephrol 21(9):1422–1426 81. Hu CA, Klopfer EI, Ray PE (2012) Human apolipoprotein L1 (ApoL1) in cancer and chronic kidney disease. FEBS Lett 586 (7):947–955 82. Patel N, Nadkarni GN (2019) Apolipoprotein L1, cardiovascular disease and hypertension: more questions than answers. Cardiol Clin 37 (3):327–334

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Chapter 4 Protocol for the Use of the Ketogenic Diet in Preclinical and Clinical Practice Ann-Katrin Kraeuter, Paul C. Guest, and Zolta´n Sarnyai Abstract Many age-related diseases are associated with metabolic abnormalities, and dietary interventions may have some benefit in alleviating symptoms or in delaying disease onset. Here, we review the commonly used best practices involved in applications of the ketogenic diet to facilitate its translation into clinical use. The findings reveal that better education of physicians is essential for applying the optimum diet and monitoring its effects in clinical practice. In addition, investigators should carefully consider potential confounding factors prior to commencing studies involving a ketogenic diet. Most importantly, current studies should improve their reporting on ketone levels as well as on the intake of both macro- and micronutrients. Finally, more detailed studies on the mechanism of action are necessary to help identify potential biomarkers for response prediction and monitoring, and to uncover new drug targets to aid the development of novel treatments. Key words Age-related disease, Ketogenic diet, Glucose, Insulin, Preclinical studies, Clinical trials, Biomarkers, Novel treatments

1

Introduction Lifespan and healthspan have been repeatedly demonstrated to be linked with metabolism, and diet is a key regulatory factor affecting metabolism. Therefore, the improvement of the healthy lifespan through dietary interventions has been a popular topic of research over several decades. One method, popularly known as calorie restriction, causes a shift from carbohydrate to fat metabolism in cells and thereby increases the defenses of the organism against agerelated diseases such as arthritis, cardiovascular diseases, type 2 diabetes mellitus, and various cancers, and it delays the onset of sarcopenia, osteoporosis, and cognitive decline [1]. In a similar manner, the ketogenic diet induces changes in metabolic pathways from carbohydrate toward fat metabolism and causes increased levels of ketone bodies [2].

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_4, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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One study applied a ketogenic diet in mice and found that this led to increased lifespan with increased motor and memory functions and decreased incidence of tumor formation, via regulation of mTOR (mammalian target of rapamycin) signaling [3]. These findings were consistent with those of another study which found that the ketogenic diet reduced midlife mortality and improved memory functions in aged mice [4]. In addition, a high-fat diet was found to prevent premature aging in a mouse model of Cockayne syndrome (an advanced aging disease), through activation of the NAD+dependent deacetylase Sirt1 [5]. The ketogenic diet has also shown promise in cancer therapy as it appears to have antitumor effects in some patients with stomach or liver cancer and patients with other cancers show better recovery when administered the diet as an adjuvant therapy [6]. The ketogenic diet has been described as a modern form of fasting [7] and by the Merriam-Webster Medical dictionary as a diet supplying a large amount of fat and minimal amounts of carbohydrate and protein. It has been used formerly to control seizures in epilepsy. Most commonly in human practice, a 4:1 fat:carbohydrate ratio has been suggested [8]. During the ketogenic diet, carbohydrate consumption should be limited to less than 50 g/day [9]. The standard ketogenic diet is defined as having a very low-carbohydrate (10%), moderate-protein (20%), and high-fat (70%) composition (Fig. 1) [9]. Other commonly used ketogenic diets are: the cyclical ketogenic diet; intermittent high carbohydrate consumption; targeted ketogenic diet; a diet comprising more carbohydrate while performing intense exercise; and the highprotein ketogenic diet (reducing the fat percentage to 60%, while increasing protein to 35% with only 5% carbohydrates) [9].

USDA recommended diet

Ketogenic diet Carbohydrate (10%)

Fat (30%)

Protein (20%) Carbohydrate (55%)

Protein (15%)

Fat (70%)

Fig. 1 Pie chart showing the composition of the USDA recommended and ketogenic diets, with regards to carbohydrate, protein and fat content

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Glucose

Fatty acids travel to liver Insulin released

Insulin increases glucose transport into cells Ketones

Energy

Fig. 2 Predominant metabolic pathways for energy production in the presence of low (down arrow) and high (up arrow) circulating glucose

The diet is based on the principle of inducing a metabolic shift from glycolysis to fatty acid utilization, by producing ketone bodies as an alternative fuel source to glucose [8, 10, 11]. As described above, this diet is typically high in fat, with moderate protein and low carbohydrate, which causes fat to be used as an alternative fuel to glucose. This causes a reduction in circulating insulin levels, resulting in reduced insulin signaling and increased utilization of fatty acids which, in turn, leads to increased production of ketones as an alternative fuel source to glucose (Fig. 2) [8, 10, 11]. This occurs by the transport of the free fatty acids into liver mitochondria for the generation of acetyl-coenzyme A via the ß-oxidation pathway [8]. Under conditions of ketosis, acetyl-coenzyme A is converted to oxaloacetate which is depleted and drives the gluconeogenesis pathway for the purpose of maintaining blood glucose levels. This leads to an excess in acetyl-coenzyme A which is converted to ketone acetoacetate, and this is transformed into acetone or ß-hydroxybutyrate which are released into the circulation [10]. The ketogenic diet was first introduced in the 1920s for the management of medically refractory childhood epilepsy [8]. More recently, the therapeutic potential of the ketogenic diet has been trialed in a wide variety of diseases such as type II diabetes, brain cancer [12], brain trauma [13], migraine [14, 15], and in

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neurodegenerative, neurodevelopmental, and psychiatric disorders [16–22]. In this review, we describe the commonly used practices involving the application of the ketogenic diet to aid in translatability between preclinical and clinical use.

2

Best Practices It is currently hard to define the best practice of ketogenic diet implementation in preclinical and clinical studies, as studies vary in the initiation of the diet, length, and composition of the diet. These variables might influence the effect of the ketogenic diet in the above-mentioned conditions and the resultant health benefits.

2.1 Physiological vs. Pathological Ketosis

The ketogenic diet has not yet been accepted into everyday practice, potentially due to the lack of education of physicians for the potential therapeutic applications [23]. Ketosis is most commonly known to physicians in the form of diabetic ketoacidosis (pathological ketosis) [23]. Diabetic ketoacidosis is a serious potentially fatal acute metabolic complication of diabetes mellitus [24, 25], which is characterized by a biochemical shift to hyperglycemia, ketonemia, and metabolic acidosis. This biochemical shift is due to insulin deficiencies, while the counter-regulatory hormones are increased, such as glucagon, catecholamines, cortisol, and growth hormone [24]. To challenge the common misconception that the ketogenic diet induces ketoacidosis, we have to distinguish between physiological and pathological ketosis. Hans Krebs was the first to distinguish between these two states [26]. In the physiological ketosis that occurs while eating a ketogenic diet, the maximal levels of ketonemia do not exceed 7–8 mM without changes in blood pH, glucose levels are reduced or stable and insulin levels range between 6.6 and 9.4 (control values 6–23 μU/L). In contrast, in the state of pathological ketosis, ketone levels within the blood exceed 20 mM, resulting in a reduction of the blood pH, a rise in glucose levels and insulin levels are reduced to minimum levels [23].

2.2 Prior to Starting the Ketogenic Diet

No standard procedures for the ketogenic diet were described until 2009, although this protocol only described the use of the diet in the case of childhood epilepsy [27]. Currently, no guidelines for the use of this diet in the case of other disorders, or for adults, have been described. Potential considerations prior to commencing a ketogenic diet are hospitalization of patients, medical examination and medication status, socioeconomic, cultural, and religious factors, selection of the type of ketogenic diet, the use of potential supplements, nutritional education of the patient and caregivers, as well as the use of equipment such as ketone and glucose monitors [28].

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Hospitalization is recommended, when starting a classical 4:1 ratio ketogenic diet, whereas more conservative ratios might not require this [29]. A 10-year single-center experience of the ketogenic diet incorporated hospitalization of its patients before commencing the diet [30], which can aid in increasing our understanding of the nutritional requirements. However, hospitalization might not be feasible for all individuals. If the diet is initiated outside of a hospital environment, clear instructions need to be given to caregivers and patients. Other modified ketogenic diet regimes such as the modified Atkins diet, the low glycemic index treatment diet, and the ketogenic diet with more conservative fat:carbohydrate ratios do not require hospitalization [29]. Particularly in children on the ketogenic diet, the ratios might be gradually increased to avoid initial hospitalization and side effects. A study in infants started with a 2:1 ratio, which was gradually increased, as tolerated, to achieve optimal ketosis [31]. Similarly, other studies gradually have increased the ratio to achieve a final ratio of 3:1–4:1 [30]. The final ratio of the dietary composition should be adjusted in accordance with age, diet tolerance, and ketone levels [30]. Medical assessment before the commencement of the ketogenic diet is essential as the diet should not be implemented in individuals with primary carnitine deficiency, carnitine palmitroyltransferase I and II deficiency, carnitine translocase deficiency, betaoxidation defects, pyruvate carboxylate deficiency, and porphyria [29]. Other potential medical concerns include extreme dyslipidemia, cardiomyopathy, renal disease, liver disease, and baseline metabolic acidosis [29]. These conditions do not necessarily prohibit the diet from being initiated but they should be cleared and monitored. Gastrointestinal disturbances are a common side effect of the ketogenic diet [32, 33], which might be prevented or decreased with prior medical examination and taking the appropriate steps [28]. During discussions with an experienced dietician, the recommended calorie and fluid intake should be calculated, as well as any food aversions and allergies noted [28]. In children, it is mandatory to supplement a ketogenic diet with carbohydrate-free multivitamins containing minerals, calcium, and vitamin D. Other supplements may also be considered such as magnesium, selenium, carnitine, laxatives, probiotics, and exogenous ketones [28]. In a case report, a standard ketogenic diet was substituted with aspirin (81 mg daily), lutein esters (20 mg daily), astaxanthin (4 mg daily), ubiquinol (100 mg daily), bingko biloba (60 mg daily), multivitamins and vitamin D (5000 IU daily) during the winter months [34]. Most importantly, patients and potential caregivers need to be fully educated on how to identify sources of fat, protein, and carbohydrates [28]. Socioeconomic, cultural, and religious factors should also be assessed to address the feasibility of ketogenic diet within the family

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environment. These factors should include cost, additional meal preparation, and increased time commitment, as the patients will require alternative meals compared to the other family members, and cultural differences might hinder the successful administration of the diet [27–29]. Social constraints might include food availability, support of caregivers, and stability of the home environment [29]. Before starting changes in the diet, baseline measurements of height, body weight, and body mass index (BMI) should be taken, and these should be monitored throughout the diet. As stated by Roehl and Sewak, 2017, the success of starting a ketogenic diet is dependent on the interaction between the health professionals, the patients, and their support system, since poor communication and lack of understanding will likely result in discontinuation and reduced compliance [29]. Overall, these guidelines could be applied in the case of children with epilepsy and could also be implemented in the other disease areas and special situations in which the ketogenic diet is already showing promising therapeutic potential. 2.3 Monitoring the State of Ketosis

Ketosis sets in after prolonged fasting (longer than 72 h) or following a ketogenic diet after several days to weeks [35]. The time period to achieve ketosis can vary between individuals depending on macronutrient intake, age, BMI, body fat percentage, and basal metabolic rate [36]. For example, a study demonstrated that obese subjects were more resistant to ketosis compared to non-obese ones [37]. For individuals on a standard diet, the ketone levels are usually less than 0.3 mM with glucose levels at around 4 mM [38]. During a standard diet, the brain uses between 100 and 150 g of glucose per day [39]. On the other hand, starvation causes a rapid change in ketone levels. A comprehensive review summarized that the main rise in ketone bodies appears within the first 10 days after the last meal intake [23], resulting in 150 g of ketone bodies being produced daily [40]. At the same time, beta-hydroxybutyrate (BHB) levels can rise to around 4.5 mM and, after 40 days of starvation, the BHB levels can increase to 6 mM [23]. Other ketone bodies such as acetoacetate and acetone were found to be increased after 10 days of starvation to 1 mM [23]. After 3–4 days on a ketogenic diet, or fasting, the central nervous system (CNS) requires alternative fuels to glucose and switches to physiological ketosis [41–43]. Ketone bodies can be used as a fuel source by the brain at concentrations above 4 mM [44]. Overall, during ketosis more ATP is being produced as 100 g of acetoacetate yields 9400 g of ATP and 100 g of BHB yields 10,500 g of ATP, whereas 100 g of glucose produces only 8700 g of ATP [36]. Considering the physiological importance, the means of measuring ketone bodies at the preclinical and clinical levels must be

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tightly monitored to ensure a state of ketosis. Current practice assesses ketones in the breath [45], blood [21, 45], and urine [21, 34, 46]. In addition, daily self-monitoring can be achieved using urine dipsticks. In the guidelines for the implementation of the ketogenic diet in childhood epilepsy, it has been recommended to follow each individual up at 1, 3, 6, 9, and 12 months. At followups, overall health, nutrition status, and blood BHB levels should be assessed and the diet adjusted if necessary [27]. In addition, studies should report food intake and body weight. Some current preclinical and clinical studies assessing the effects of the ketogenic diet do not report ketone levels, which decreases the interpretability of the data. Therefore, future studies should assess ketone levels to ensure a state of ketosis and to ensure the safest possible intervention.

3 3.1

Macronutrients Preclinical

As stated previously the main definition of a ketogenic diet is a low carbohydrate content in combination with a relatively high fat content. However, between preclinical and clinical studies, macronutrients can vary greatly. Although initiation of a ketogenic diet in preclinical setting does not pose issues, macronutrient composition might vary greatly between preclinical studies. Different suppliers provide different compositions of the ketogenic diet (Table 1). Interestingly, within suppliers, the composition of ketogenic diet might also differ as Bioserve F3666 had three different macronutrient contents (Table 1). No guidelines are currently available to describe which dietary composition to use; however, the question remains if the macronutrients should be different since these preclinical studies

Table 1 Examples of differences within and between suppliers of the ketogenic diet in preclinical studies Supplier

Fat (%)

Carbohydrates (%)

Protein (%)

Reference

Bio-Serv Inc. F3666

79 75.1 69

1 11 5.22

8 8.6 18.1

[58–60] [61–66] [67]

Harlan Teklad TD 96355

90.7 80 67.4

0.3 3.4 0.6

9 16.6 15.3

[68] [69, 70] [71]

Research diet, Inc. New Brunswick, N

60 80

20 0

20 20

[72] [73]

Specialty feeds

77.6

9.4

9.5

[50, 51]

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investigated different disorders. It is important to note that every season and year the source of grains and other ingredients can vary, which reduces the reproducibility of studies [47]. Secondly, nutritional and contaminant contents may vary due to different geographical sources [47]. Thirdly, the quality of ingredients may fluctuate due to conditions such as drought, flood, mold, and insect infestations. Manufacturers testing for nutritional composition and contaminants should take the above-mentioned issues into account. In addition, the diets can differ between manufacturers due to varying sterilization techniques, as well as to differences in formulation and shelf-life [48]. Overall, the dietary composition might be variable at this point in time, although researchers should provide the macronutrient composition of the diet. Also, in addition to the focus on the ketogenic diet, the same concerns can be raised for standard diets as these may vary between manufacturers and therefore macronutrient content should be reported. 3.2

Clinical

In clinical practice, the ketogenic diet composition needs to be tailored to the individuals due to age, cultural, religious, ethical, tolerability, and potential allergen susceptibility differences [27, 29]. This individualized approach may create difficulties in comparing one study with another. However, programs such as the Charlie Foundation have produced the keto diet calculator (www.ketodietcalculator.org) to assist professionals, individuals, and caregivers in managing the application of this diet [27]. This program is set-up with the help of a licensed nutritionist to individualize the potentially therapeutic diet. Other support programs and mobile applications are available to aid in the management and administration of the correct macronutrient composition [27]. A main concern with the ketogenic diet is compliance, which often results in participant changes in the macronutrient content. In adults, compliance rates are limited to around 45% [49]. A study by Palmer in 2017 [20] demonstrated that the ketogenic diet was effective in treating the schizo-effective disorder; however, compliance issues resulted in a lack of ketosis and diminished symptom control. This demonstrated that a state of ketosis was vital for symptom control. This raises the important point that compliance can be improved with education and nutritional advice of caretakers. In clinical research studies and publications, macronutrients should be supplied in g/day or %/day for the carbohydrates, proteins, and fats consumed. Furthermore, measurements should be made daily with urinary ketone sticks to confirm ketosis and allow for potential adjustment of the macronutrients. Finally, compliance issues should be reported within publications, as well as the effects of non-compliance on symptom control.

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Micronutrients: Is It Just Fat? The question remains if only macronutrients contribute to the beneficial effects of ketogenic diet or micronutrients also have an essential role. Micronutrients potentially determining beneficial effects of the therapeutic ketogenic diet include the composition of total minerals, amino acids, vitamins, and fatty acids. Comparing the standard diet (Goldmix Stockfeeds, Norco, Lismore, NSW, Australia) and the ketogenic diet (SF14-063 Specialty Feeds: WA, Australia) used in Kraeuter et al. [50, 51], it was evident that the micronutrient composition differs greatly between these two diets (Table 2). The most evident differences were that polyunsaturated fatty acids (PUFAs) and most vitamins were present at higher levels in that particular ketogenic diet composition.

Table 2 Comparison of standard diet (Goldmix Stockfeeds, Norco, Lismore, NSW, Australia) and ketogenic diet (SF14-063 Specialty Feeds, WA, Australia) used in Kraeuter et al. [50, 51] Standard diet

Ketogenic diet

Protein (%)

20

9.5

Total fat (%)

3.081

77.6

Complex carbohydrates (%)

62.8

4.7

Crude fiber (%)

4.058

4.7

Digestible energy (MJ/kg)

12.87

30.8

Saturated fats (%)

0.781

36.19

Monounsaturated fats (% ω 7 and 9 and 11)

~0.564 (calculated)

29.78

Polyunsaturated fats (% ω 3 and 6)

~1.482

12.67

Total unsaturated (% all ω)

2.225

42.45

Linoleic acid C18: 2n ω-6

1.031

9.65

EPA C20:5n-3 and DHA C22:6n-3 ω-3

0.101

No data

Alpha-linolenic acid C18:3n-3 ω-3

0.350

2.43

Oleic acid C18:1 ω-9

0.564

27.57

Palmitic acid C16:0 saturated

0.272

19.70

Stearic acid C18:0 saturated

0.425

10.63

Saturated fats C12 or less

No data

1.91

Myristic acid 14:0 (tetradecanoic acid) saturated

No data

3.17

Nutritional parameter

Fatty acid composition

(continued)

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Table 2 (continued) Standard diet

Ketogenic diet

Gadoleic acid 20:1 (9-eiosenoic acid) ω-11 unsaturated

No data

0.47

Total n3

~0.451

2.67

Total n6

~1.031

9.74

Ratio ω-6/ω-3

2.29:1

3.65:1

Methioine

0.292

0.65

Isoleucine

0.625

0.41

Leucine

1.157

0.86

Threonine

0.572

0.38

Tryptophan

0.194

0.13

Valine

0.809

0.60

Arginine

1.024

No data

Lysine

1.066

0.71

Phenylalanine

No data

0.47

Tyrosine

No data

0.50

Histidine

No data

0.29

Vitamin A (retinol)

14,000 IU/kg

7000 IU/kg

Vitamin D (cholecalciferol)

3000 IU/kg (D3)

1350 IU/kg

Vitamin E (α tocopherol acetate)

40.19 mg/kg

108 mg/kg

Vitamin K (menadione)

2.000 mg/kg (K3)

1.4 mg/kg

Vitamin C (ascorbic acid)

100.00 mg/kg

None added

Vitamin B1 (thiamine)

0.220 mg/kg

8.1 mg/kg

Vitamin B2 (riboflavin)

4.160 mg/kg

8.3 mg/kg

Niacin (nicotinic acid)

21.80 mg/kg

40 mg/kg

Vitamin B6 (pryridoxine)

2.320 mg/kg

9.6 mg/kg

Pantothenic acid

18.73 mg/kg

22 mg/kg

Biotin

16 μg/kg

270 ug/kg

Folic acid

0.478 mg/kg

2.7 mg/kg

Vitamin B12 (cyancobalamin)

0.020 mg/kg

136 ug/kg

Choline

1103 mg/kg

1930 mg/kg

Amino acids

Vitamins

(continued)

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Table 2 (continued) Standard diet

Ketogenic diet

Calcium

1.364%

0.91%

Phosphorous

0.907%

0.29%

Magnesium

0.127%

0.07%

Sodium

0.437%

0.15%

Chloride

0.592%

0.22%

Potassium

0.651%

0.50%

Sulfur

0.143%

0.19%

Iron

120.0 mg/kg

64 mg/kg

Copper

16.00 mg/kg

8.7 mg/kg

Iodine

1.881 mg/kg

0.3 mg/kg

Manganese

80.00 mg/kg

16 mg/kg

Cobalt

0.400 mg/kg

No data

Zinc

200.0 mg/kg

Molybdenum

No data

Selenium

0.200 mg/kg

Cadmium

No data

No data

Chromium

No data

1.4 mg/kg

Fluoride

No data

1.4 mg/kg

Lithium

No data

0.1 mg/kg

Boron

No data

2.1 mg/kg

Nickel

No data

0.5 mg/kg

Vanadium

No data

0.1 mg/kg

Total minerals

55 mg/kg 0.2 mg/kg 0.3 mg/kg

Italic values reduced, bold values increased in the ketogenic diet

PUFAs, particularly docosahexaenoic acid and arachnoid acid, are reduced in the cell membranes of individuals with schizophrenia [52, 53] and treatment with PUFAs ameliorates symptoms of schizophrenia [52, 54]. PUFAs have been shown to have important biological roles such as neuroprotection, as well as in dopaminergic, serotonergic, and glutamatergic neurotransmission [54]. Therefore, the potential increase in PUFAs in the ketogenic diet group may have contributed to the improved symptom control.

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Vitamins B12 and B6 are important for neuronal function since deficiencies in these have been linked to an increased risk of neurodevelopmental disorders, psychiatric disease, and dementia [55]. Both vitamins were increased with the ketogenic diet and may contribute to improved symptom control. These examples demonstrate that micronutrients of ketogenic diet might add to the beneficial effects of the ketogenic diet. However, current preclinical and clinical studies have thus far neglected this issue.

5

Conclusions Here, we reviewed the commonly used best practices of the ketogenic diet to aid in translatability between preclinical and clinical use. We identified that better education of current and future physicians is important for the application of the ketogenic diet in clinical practice. Furthermore, when conducting research, investigators should carefully consider potential cofounders prior to commencing a ketogenic diet, which should also be outlined in detail in the associated publications. Most importantly, current studies should improve reporting on ketone levels to confirm ketosis and food intake of macronutrients, but should not neglect the potential influence of micronutrients. Taken together, the evidence that has accumulated from both preclinical and clinical studies indicates that the ketogenic diet could be an efficacious therapeutic option for individuals suffering from age-related disorders with a metabolic component. However, randomized controlled trials are still lacking in order to confirm the best practices and safeguards that must be taken [56]. Likewise, the precise mechanism of action on how this diet works in the case of specific diseases is still lacking [57]. However, considering that it has fewer side effects than most existing treatment options, it appears to present a safer alternative in delaying the onset or minimizing the effects of certain age-related diseases. It should be noted that a major drawback of the ketogenic diet appears to be non-compliance. This is because the diet must be strictly maintained with the appropriate composition and calorie content of foods. Obstacles to this could include: (1) eating too much protein; (2) eating too many junk foods; (3) consuming too few calories; (4) not properly balancing macronutrient intake; and (5) not drinking enough water. Therefore, close monitoring and management of these components as well as any adverse effects is important for a successful outcome. It should also be acknowledged that not all individuals will respond in the same way to the diet. For this reason, it will be important to carry out further research to identify biomarkers that can be used to stratify individuals ahead of time who are most likely

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to benefit from the treatment as well as those that are likely to suffer adverse effects. Such studies should also take into account ketogenic diets of different fat, protein and carbohydrate compositions. Finally, further research is also needed to fully characterize the mechanistic targets of the ketogenic diet, as this could lead to the identification of novel biomarkers and treatment options. References 1. Balasubramanian P, Howell PR, Anderson RM (2017) Aging and caloric restriction research: a biological perspective with translational potential. EBioMedicine 21:37–44 2. Meidenbauer JJ, Ta N, Seyfried TN (2014) Influence of a ketogenic diet, fish-oil, and calorie restriction on plasma metabolites and lipids in C57BL/6J mice. Nutr Metab (Lond) 11:23. https://doi.org/10.1186/1743-7075-11-23 3. Roberts MN, Wallace MA, Tomilov AA, Zhou Z, Marcotte GR, Tran D et al (2017) A ketogenic diet extends longevity and healthspan in adult mice. Cell Metab 26 (3):539–546.e5. https://doi.org/10.1016/j. cmet.2017.08.005 4. Newman JC, Verdin E (2017) β-Hydroxybutyrate: a signaling metabolite. Annu Rev Nutr 37:51–76 5. Scheibye-Knudsen M, Mitchell SJ, Fang EF, Iyama T, Ward T, Wang J et al (2014) A highfat diet and NAD+ activate Sirt1 to rescue premature aging in cockayne syndrome. Cell Metab 20(5):840–855 6. Weber DD, Aminazdeh-Gohari S, Kofler B (2018) Ketogenic diet in cancer therapy. Aging (Albany NY) 10(2):164–165 7. Seyfried TN (2014) Ketone strong: emerging evidence for a therapeutic role of ketone bodies in neurological and neurodegenerative diseases. J Lipid Res 55(9):1815–1817 8. Bough KJ, Rho JM (2007) Anticonvulsant mechanisms of the ketogenic diet. Epilepsia 48(1):43–58 9. Shilpa J, Mohan V (2018) Ketogenic diets: boon or bane? Indian J Med Res 148 (3):251–253 10. Hartman AL, Gasior M, Vining EP, Rogawski MA (2007) The neuropharmacology of the ketogenic diet. Pediatr Neurol 36(5):281–292 11. Paoli A, Rubini A, Volek JS, Grimaldi KA (2013) Beyond weight loss: a review of the therapeutic uses of very-low-carbohydrate (ketogenic) diets. Eur J Clin Nutr 67 (8):789–796

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Ketogenic Diet 48. Franklin CL, Ericsson AC (2017) Microbiota and reproducibility of rodent models. Lab Anim (NY) 46(4):114–122 49. Ye F, Li XJ, Jiang WL, Sun HB, Liu J (2015) Efficacy of and patient compliance with a ketogenic diet in adults with intractable epilepsy: a meta-analysis. J Clin Neurol 11(1):26–31 50. Kraeuter AK, Loxton H, Lima BC, Rudd D, Sarnyai Z (2015) Ketogenic diet reverses behavioral abnormalities in an acute NMDA receptor hypofunction model of schizophrenia. Schizophr Res 169(1–3):491–493 51. Kraeuter AK, van den Buuse M, Sarnyai Z (2019) Ketogenic diet prevents impaired prepulse inhibition of startle in an acute NMDA receptor hypofunction model of schizophrenia. Schizophr Res 206:244–250 52. Peet M (2008) Omega-3 polyunsaturated fatty acids in the treatment of schizophrenia. Isr J Psychiatry Relat Sci 45(1):19–25 53. Alqarni A, Mitchell TW, McGorry PD, Nelson B, Markulev C, Yuen HP et al (2019) Comparison of erythrocyte omega-3 index, fatty acids and molecular phospholipid species in people at ultra-high risk of developing psychosis and healthy people. Schizophr Res 2019:pii: S0920-9964(19)30241-5. https:// doi.org/10.1016/j.schres.2019.06.020 54. Schlo¨gelhofer M, Amminger GP, Schaefer MR, Fusar-Poli P, Smesny S, McGorry P et al (2014) Polyunsaturated fatty acids in emerging psychosis: a safer alternative? Early Interv Psychiatry 8(3):199–208 55. Mitchell ES, Conus N, Kaput J (2014) B vitamin polymorphisms and behavior: evidence of associations with neurodevelopment, depression, schizophrenia, bipolar disorder and cognitive decline. Neurosci Biobehav Rev 47:307–320 56. Feinman RD, Pogozelski WK, Astrup A, Bernstein RK, Fine EJ, Westman EC et al (2015) Dietary carbohydrate restriction as the first approach in diabetes management: critical review and evidence base. Nutrition 31 (1):1–13 57. Barzegar M, Afghan M, Tarmahi V, Behtari M, Rahimi Khamaneh S, Raeisi S (2019) Ketogenic diet: overview, types, and possible antiseizure mechanisms. Nutr Neurosci 26:1–10 58. Van der Auwera I, Wera S, Van Leuven F, Henderson ST (2005) A ketogenic diet reduces amyloid beta 40 and 42 in a mouse model of Alzheimer’s disease. Nutr Metab (Lond) 2:28. https://doi.org/10.1186/1743-7075-2-28 59. Ruskin DN, Ross JL, Kawamura M Jr, Ruiz TL, Geiger JD, Masino SA (2011) A ketogenic diet delays weight loss and does not impair

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working memory or motor function in the R6/2 1J mouse model of Huntington’s disease. Physiol Behav 103(5):501–507 60. Beckett TL, Studzinski CM, Keller JN, Paul Murphy M, Niedowicz DM (2013) A ketogenic diet improves motor performance but does not affect beta-amyloid levels in a mouse model of Alzheimer’s disease. Brain Res 1505:61–67 61. Kim DY, Hao J, Liu R, Turner G, Shi FD, Rho JM (2012) Inflammation-mediated memory dysfunction and effects of a ketogenic diet in a murine model of multiple sclerosis. PLoS One 7(5):e35476. https://doi.org/10.1371/jour nal.pone.0035476 62. Ahn Y, Narous M, Tobias R, Rho JM, Mychasiuk R (2014) The ketogenic diet modifies social and metabolic alterations identified in the prenatal valproic acid model of autism spectrum disorder. Dev Neurosci 36 (5):371–380 63. Choi IY, Piccio L, Childress P, Bollman B, Ghosh A, Brandhorst S et al (2016) A diet mimicking fasting promotes regeneration and reduces autoimmunity and multiple sclerosis symptoms. Cell Rep 15(10):2136–2146 64. Smith J, Rho JM, Teskey GC (2016) Ketogenic diet restores aberrant cortical motor maps and excitation-to-inhibition imbalance in the BTBR mouse model of autism spectrum disorder. Behav Brain Res 304:67–70 65. Newell C, Bomhof MR, Reimer RA, Hittel DS, Rho JM, Shearer J (2016) Ketogenic diet modifies the gut microbiota in a murine model of autism spectrum disorder. Mol Autism 7(1):37. https://doi.org/10.1186/ s13229-016-0099-3 66. Mychasiuk R, Rho JM (2017) Genetic modifications associated with ketogenic diet treatment in the BTBR(T+Tf/J) mouse model of autism spectrum disorder. Autism Res 10 (3):456–471 67. Ruskin DN, Murphy MI, Slade SL, Masino SA (2017) Ketogenic diet improves behaviors in a maternal immune activation model of autism spectrum disorder. PLoS One 12(2): e0171643. https://doi.org/10.1371/journal. pone.0171643 68. Cheng B, Yang X, An L, Gao B, Liu X, Liu S (2009) Ketogenic diet protects dopaminergic neurons against 6-OHDA neurotoxicity via up-regulating glutathione in a rat model of Parkinson’s disease. Brain Res 1286:25–31 69. Murphy P, Likhodii SS, Hatamian M, McIntyre Burnham W (2005) Effect of the ketogenic diet on the activity level of Wistar rats. Pediatr Res 57(3):353–357

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72. Zhao Z, Lange DJ, Voustianiouk A, MacGrogan D, Ho L, Suh J et al (2006) A ketogenic diet as a potential novel therapeutic intervention in amyotrophic lateral sclerosis. BMC Neurosci 7:29. https://doi.org/10. 1186/1471-2202-7-29 73. Verpeut JL, DiCicco-Bloom E, Bello NT (2016) Ketogenic diet exposure during the juvenile period increases social behaviors and forebrain neural activation in adult engrailed 2 null mice. Physiol Behav 161:90–98

Part II Protocols

Chapter 5 An In Vivo/Ex Vivo Study Design to Investigate Effects of Chronic Conditions and Therapeutic Compounds on Adipose Stem Cells in Animal Models Hane´l Sadie-Van Gijsen, Liske Kotze´-Ho¨rstmann, and Barbara Huisamen Abstract With the dramatic rise in the global prevalence of obesity and lack of success at addressing this public health issue, there is an urgency to develop new tools with which to study obesity and putative weight-loss products. Pre-adipocyte cell lines have been widely used as a model for adipocyte biology and obesity over the past four decades, but the applicability of results from these cell lines is limited. This chapter will describe an in vivo/ex vivo study design that can be employed to examine the effects of diets and other chronic physiological or pathophysiological conditions on the biology of adipose stem cells (ASCs), as a model for the progression and management of obesity. This type of study design is superior to short-term in vitro experiments in pre-adipocyte cell lines or ASCs, as chronic in vivo conditions cannot be recapitulated in cell culture. Rather, this in vivo/ex vivo study design provides researchers the opportunity to assess the progressive effects of long-term insults or interventions on the reprogramming of ASC behavior. In addition, this model allows us to study the metabolic effects of chronic conditions and therapeutic compounds at a systemic level as well as at the level of adipose tissue and ASCs, in order to provide a whole-body context for the findings. Key words Adipose stem cells, Cell culture, Obesogenic diets, Botanical extracts, Animal models, In vivo/ex vivo

1

Introduction With the global rise in obesity and our collective lack of success to date in addressing this pandemic, it has become imperative that we devise new tools with which to study both obesity and the products that may potentially be used to manage or even reverse this condition [1]. On a cellular level, pre-adipocyte cell lines have limited application in this regard, as we cannot reproduce the complex systemic milieu that governs the onset and progression of obesity and the effects on adipocytes and their precursors. On the other hand, results from clinical studies are difficult to interpret due to

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_5, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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the immense scope of confounding factors in human obesity including age, gender, genetic factors, glucose and lipid metabolism, previous and present sex hormone status, diet, and socioeconomic factors. Animal models in which obesity has been induced in a controlled manner afford us the opportunity to examine the mechanisms underlying the impact of obesity on adipose tissue function and dysfunction. In particular, such animal models allow us to study the effects of obesity and metabolic disturbances on the biology of adipose stem cells (ASCs), the tissue-resident precursors of mature adipocytes. It is now recognized that ASCs are not only affected by obesity but are transformed along the way into active participants in the pathophysiology of this condition [1]. Furthermore, ASCs maintained in primary culture retain a memory of their in vivo milieu before isolation [1], and therefore differences in the long-term reprogramming of ASCs during obesity can be studied ex vivo. This chapter will outline our methodology for studying the effects of diet-induced obesity on ASCs, and for evaluating the potential therapeutic effects of botanical extracts, by using an in vivo/ex vivo study design. In this approach, the obesity and treatment are established in wild-type animals in vivo, and changes in the biology of the ASCs are subsequently studied ex vivo in primary cell culture. However, beyond the specific examples of diet-induced obesity and therapeutic botanicals referred to in this chapter, the principles of this type of study design may be more broadly applied to any chronic physiological and pathophysiological condition, such as aging, diabetes, malnutrition, exercise, and pregnancy, and almost any type of therapeutic intervention. In addition, while the protocols described below apply to rats, these protocols can be readily modified for application in mice and rabbits. A typical study design for examining diet-induced obesity in rats and for evaluating the potential of a botanical extract to reverse the effects of obesogenic diets on ASCs is shown in Fig. 1. The number of animals in the study can be determined by a statistical power calculation, but this will be influenced by the nature of the outcomes chosen to be measured. In the figure, two different obesogenic diets, OB1 and OB2, are compared with each other and with a control diet of laboratory rodent chow. The composition of both the lean and the obesogenic diets has to be known. When using laboratory-formulated diets, this information should be made available by the manufacturers. Alternatively, “home-made” diets will need to be analyzed by a food science laboratory. During the first phase of the study, obesity is established by starting young weanling animals of the same age and weight on an obesogenic diet. After 10 weeks on the diet, the test compound is introduced while the animals remain in their respective diet groups. Within each diet group, half of the animals are treated with the compound, while the

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Fig. 1 Typical in vivo/ex vivo study design to examine diet-induced obesity and the potential of a botanical extract to counteract the effects of the obesogenic diets on ASCs. Ctrl control laboratory chow diet, OB1 obesogenic diet 1, OB2 obesogenic diet 2, Rb Rooibos (Aspalathus linearis), OGTT oral glucose tolerance test, ASC adipose stem cells

others remain untreated. Several of our studies examine the metabolic effects of a concentrated extract of Rooibos (Rb: Aspalathus linearis) that is endemic to South Africa, but almost any other drug or compound can be evaluated by following this study design. Near the end of the study, or at various time points relevant to the study design, an overnight fast and oral glucose tolerance test (OGTT) can be performed to measure the metabolic status of the animals. For experiments in which ASCs are to be harvested from animals and maintained in culture, it is important to remember that the subculturing and adipogenic differentiation of each isolate requires approximately 4 weeks. This needs to be taken into consideration when planning how many samples can be handled per week, which will also be influenced by incubator space and number of researchers or technicians available for daily maintenance of these cultures. To illustrate, for four animals per week and studying ASCs from two adipose depots per animal, eight ASC isolates are generated. As each ASC isolate requires 4 weeks in culture to differentiate into mature adipocytes, at any given time there will be 32 ASC isolates at varying stages of differentiation. The more advanced cultures will be at passage 2 (P2) and plated into the requisite culture vessels, depending on the experimental design, and will therefore occupy a considerable amount of incubator space. As a result, more than one incubator may be required. Alternatively, if only one incubator is available, fewer animals per week may be used but this will extend the duration of the study. It is crucial to perform this logistical planning before the advent of the study, as

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this will determine the timing (“staggering”) of the introduction of the animals into the study. This will ensure that the animals are all the same age and weight at the start of the experiment and have all spent the same amount of time on the diet and/or the drug treatment, unless these are desired variables in the study design. In order to track diet-induced obesity and the accompanying changes in metabolic status, the animals need to be weighed weekly, and food and water intake recorded daily. In addition, changes in glucose and lipid metabolism also have to be determined. Two of the primary measures of glucose metabolism are the oral glucose tolerance test (OGTT) after an overnight fast, and blood biochemistry analysis for the determination of fasting blood glucose and serum insulin levels to calculate the HOMA-IR, an index of insulin resistance [2]. Additional considerations with regard to glucose tolerance testing in rodents have been discussed elsewhere [3]. We have noted that the process of fasting and subsequent glucose challenge associated with the OGTT results in disturbances of systemic glucose metabolism that requires several days of normal feeding to resolve (unpublished observations). For this reason, we perform the overnight fast and OGTT 1 week before euthanization, to allow sufficient time for the glucose metabolism of the animals to recover. One limitation of performing the OGTT at some point prior to the end of the study is that only a small volume (400–500 μL) of fasted serum can be collected per animal, but larger volumes of non-fasted serum (2–3 mL) can be collected at the point of euthanization. This will impact the number and type of blood analyses that may be performed and needs to be carefully considered.

2

Materials

2.1 Drug-Containing Gelatin Blocks (See Note 1)

1. Drug of choice in powdered form. 2. Gelatin and strawberry-flavored jelly. 3. Microwave oven. 4. Candy mold (See Note 2).

2.2

OGTT

1. Rat model of obesity (See Note 3). 2. Sodium pentobarbital (Eutha-naze). 3. Sterile lancet. 4. Glucometer and sampling strips. 5. 50% glucose solution.

2.3 Isolation and Culturing of Primary ASCs

1. Large dissection scissors. 2. Forceps and sterile scalpel blades.

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3. Sterile 75% ethanol. 4. Start-up medium (SM): Dulbecco’s Modified Eagle Medium (DMEM) containing 1% penicillin–streptomycin (P-S) and 20% fetal bovine serum (FBS) (See Note 4). 5. Growth media (GM): 65% DMEM containing 1% P-S, 25% DMEM supplemented with 1% UltraGlutamine™ and 1% P-S, and 10% FBS. 6. Vehicle control medium: GM containing 0.1% ethanol and 0.1% DMSO. 7. Adipogenic medium (AM): GM containing 0.5 mM isobutylmethylxanthine (IBMX) in DMSO, 56 μM indomethacin in ethanol, 10 μM insulin, and 1 μM dexamethasone in ethanol. 8. Sterile 10
collagenase solution in Hanks’ buffered saline solution (HBSS) with magnesium and calcium (with or without phenol red) stored as single-use 1 mL aliquots at 20  C (See Note 5). 9. Sterile 10% fraction V bovine serum albumin (BSA) stock solution in phosphate-buffered saline (PBS) stored in singleuse 1.5 mL aliquots at 20  C. 10. One sterile 50 mL centrifuge tube with 15 mL DMEM (supplemented with 1% P-S) and one sterile 50 mL centrifuge tube with 15 mL 75% ethanol for each tissue sample that will be collected(See Note 6). 11. Sterile plastic pasteur pipettes. 12. Sterile PBS. 13. Trypsin. 14. 1% Oil Red O (ORO) stock in anhydrous isopropanol (stirred overnight to dissolve). 15. 0.01% (w/v) Crystal Violet solution in water (diluted from 1% w/v stock solution). 16. Light microscope connected to a camera. 17. ImageJ image analysis software.

3

Methods

3.1 Administration of Extracts or Drugs Using Jelly Blocks (See Note 7)

1. Calculate the amount of drug or extract to be received per animal, based on the daily dose and the body weight of the animal (mg/kg). 2. Weigh out 1 g gelatin powder and 1.92 g flavored jelly powder into a laboratory beaker and add 12 mL cold water (See Note 8).

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3. Boil the suspension by heating in a microwave oven for 10 s bursts until the powder is dissolved. 4. Stir briefly to allow the solution to cool (See Note 9). 5. Add the drug or extract and stir thoroughly until dissolved. 6. Immediately pour into the molds and refrigerate. 7. Prepare and store the jelly blocks in a dark room if the compound or extract is light-sensitive. 3.2 Procedure for OGTT

1. Once the fasted state has been reached, inject the animal with a small dose (10 mg for animals weighing 300–450 g) of sodium pentobarbital to induce a mild state of anesthesia without suppressing the gag reflex (See Note 10). 2. Determine the fasting blood glucose level by pricking the tip of the tail, where the tail vein is located, with a lancet and collecting a drop of blood for testing with a standard glucometer. 3. Collect blood for metabolic screening, including fasting serum insulin levels in order to calculate HOMA-IR (See Note 11). 4. Once the animal has recovered from the anesthesia, initiate the OGTT by administering a bolus of glucose (1 mg/g body weight) by means of oral gavage. 5. Record blood glucose levels at 3, 5, 10, 15, 20, 25, 30, 45, 60, 90, and 120 min with the glucometer.

3.3 Harvesting of Adipose Tissue for Isolation of Primary ASCs (See Note 12)

1. Inject a lethal dose of sodium pentobarbital (80 mg for an animal of 350–450 g) into the peritoneum or use another established method of euthanasia. 2. Monitor the pedal (foot) and tail-pinch pain reflexes, as well as the palpebral (blinking) reflex in the eye. 3. Once these reflexes are lost, deep anesthesia is established and the abdomen can be opened using large dissection scissors. 4. If organs other than adipose tissue are to be collected from the animal, remove these at this point (See Note 13). 5. When excising the inguinal subcutaneous (SQ) fat, do not cut through the abdominal wall, but separate the skin from the abdominal wall and ensure that no epididymal fat is collected. 6. Do not harvest more than 3 cm3 adipose tissue, as this will exceed the digestion capacity of the subsequent collagenase reaction. 7. Collect the contralateral inguinal fat pad for parallel studies such as histology or protein extraction. 8. Rinse the excised tissue in sterile 75% ethanol, and then transfer the tissue into the tube with culture medium so that it is completely submerged.

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9. For the harvesting of perirenal visceral (PV) fat, cut through the abdominal wall to expose the kidneys and gastrointestinal tract (GIT). 10. If the GIT has not been removed, gently push it to one side with closed forceps, to enable better visualization of the kidneys. 11. Pull the kidney toward the midline with forceps and cut along the division between the PV and retroperitoneal (RP) fat pads (this can be seen as a faint line in the adipose tissue and can be visualized more clearly by gently pulling the kidney away from the RP fat). 12. Dissect out the whole kidney together with the PV fat, and then trim off the PV fat from around the kidney, with the adrenal gland embedded in the fat, taking care to remove the entire depot. 13. If one PV fat pad is sufficient starting material (>500 mg) for the ASC isolation protocol, collect the contralateral PV fat pad for parallel studies. 14. Rinse in 75% ethanol and transfer into culture media, as above. 15. Weigh the PV fat (this includes the adrenal gland embedded in the fat and the small amount of brown fat surrounding the renal blood vessels, which form a small fraction of the overall weight of the excised tissue) (See Note 6). 3.4 Isolation of ASCs from Excised Fat by Collagenase Digestion

1. In a sterile biosafety cabinet, pour out the fat tissue and DMEM into a sterile 100 mm cell culture dish. 2. Remove the adrenal and any attached brown fat at this point, using a sterile scalpel blade and forceps. 3. Use a sterile scalpel blade and forceps to shred the tissue (See Note 14). 4. Transfer each tissue sample into a sterile 50 mL tube containing 7.5 mL of HBSS. 5. Add 1.5 mL of 10% BSA solution and 1 mL of 0.75% collagenase solution. 6. Incubate for 30 min in a 37  C water bath, shaking the tubes every few minutes. 7. After 30 min, centrifuge the tubes for 5 min at 1500
g, generating a top fraction containing buoyant adipocytes, a middle liquid fraction (HBSS), and a stromal vascular fraction (SVF) cell pellet at the bottom (See Note 15). 8. Use a sterile plastic pasteur pipette to push aside the top fraction and aspirate the pellet (See Note 16). 9. Transfer the pellet into a clean 15 mL tube.

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10. Add PBS to the cell pellet to a final volume of 12 mL and centrifuge 5 min at 1500
g. 11. Gently remove the supernatant by pouring it out or aspirating with a pipette (See Note 17). 12. Resuspend the pellet in 1 mL SM by gently pipetting up and down (some pellets, especially from SQ fat, may contain fibrous material that will not resuspend completely). 13. Seed the entire suspension, including any fibrous material, in a 100 mm cell culture dish containing 9 mL SM. 14. Place the plate in a CO2 cell culture incubator at 37  C for 24 h (See Note 18). 15. After 24 h, pour off the start-up media and any unattached debris or remove the media with a pipette (do not perform this step with vacuum suction). 16. Add 10 mL GM into the dish without washing the cells. 17. Incubate the plate as above for 48 h. 18. After 48 h, remove the GM and replace with fresh GM without washing the cells. 19. Incubate as above for 72 h (See Note 19). 3.5 Passaging of ASC Cultures (Fig. 2) (See Note 20)

1. Remove the GM and wash once with PBS. 2. Remove the PBS and add 1 mL of trypsin onto the cells. 3. Swirl the dish to distribute the trypsin evenly over the cells and incubate for 2 min in the cell culture incubator, or until the cells have detached from the surface (no longer than 5 min). 4. Repeatedly pipette the trypsin solution over the cells to dislodge any remaining attached cells. 5. Add 8–9 mL GM to the culture dish and divide the cell suspension equally between three to four culture dishes (do not pellet the cells by centrifugation).

SM GM

GM

d0

d3

d1

tr (P1) GM d6

tr (P2) GM d9

GM

AM

AM

AM

AM

AM

d12

d15

d18

d21

d24

d27

Fig. 2 Ideal time line of ASC cultures. Cells are isolated on d0 and seeded in start-up media (SM). After 24 h (d1), the media is changed to growth media (GM) and is replaced again on d3. Cultures are trypsinized on d6 (P0–P1) and on d9 (P1–P2) for plating. Media is changed on d12 (GM), and adipogenesis is induced on d15 with adipogenic induction media (AM), provided that the culture is post-confluent. The cells are treated with AM for 12 days (in parallel with vehicle control media), with media changes every 3 days. Cells are only washed before trypsinization. Color code: black ¼ P0; white ¼ P1; striped ¼ P2, pre-confluent in GM; checked ¼ P2, post-confluent in AM

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6. Add GM to each dish to a final volume of 10 mL and incubate as above for 72 h. 7. If the cells reach 70–90% confluency during this time, seed the cells directly as appropriate for the experimental plan using the trypsinization protocol above (See Note 21). 8. If the cells only reach 60–70% confluency, replace the medium and culture an additional 24 h, taking care not to let the cultures exceed 90% confluency, as the cells will become resistant to trypsinization. 3.6 Adipogenic Differentiation (See Note 22)

1. Once the cultures have reached post-confluence, aspirate the GM from all wells and dishes (do not wash). 2. Add either control medium or AM to the appropriate wells and dishes (See Note 23 and Subheading 2.3, item 7). 3. Perform medium changes every 3 days without washing the cells. 4. Terminate the differentiation experiments on day 12 after induction, which allows for a robust adipogenic response but without reaching a response plateau (See Note 24).

3.7 ORO Staining (See Note 25)

1. Dilute the ORO stock solution with water to 0.7% and filter twice through Whatman paper to remove any precipitate immediately before use (See Note 26). 2. Aspirate the culture media and add ORO working solution directly into the wells (do not wash the cells), so that each well contains 400–500 μL solution per well (for a 12-well plate). 3. Incubate for 30 min, aspirate the excess stain, and rinse the wells 3–5 times with deionized water by pipetting against the side of the well to avoid disruption (See Note 27). 4. Aspirate all the water from the wells and visualize the stained wells at 10
magnification by using a light microscope connected to a camera (the stained lipid droplets will show as red, and the unstained background should show as white or pale gray). 5. Capture four random images for each well (one in each quadrant of the well) (See Note 28). 6. Convert each image to a red/green/blue stack using the ImageJ software and quantify the percentage stained area using the green channel, which will show the stained areas as black. 7. Set a fixed threshold to be used for all images, to exclude background staining. 8. Calculate the average of the percentage area stained for all 12 images.

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9. As an alternative quantification procedure, aspirate the water from the wells and extract the ORO stain by adding anhydrous isopropanol to cover the bottom of the culture plate or well (500 μL per well on a 12-well plate). 10. Incubate for 5 min at room temperature and then pipette each sample into a micro-centrifuge tube. 11. Wash each well with 75% ethanol and remove completely. 12. Counterstain the wells with Crystal Violet solution for 5 min (500 μL per well on a 12-well plate). 13. Aspirate the excess stain and rinse the wells three times with PBS. 14. Extract the Crystal Violet stain with 75% ethanol for 5 min at room temperature (500 μL per well on a 12-well plate) and pipette each sample into a micro-centrifuge tube. 15. Quantify the intensity of the extracted ORO stain at 510 nm, and that of the extracted Crystal Violet solution at 570 nm. 16. For each well, normalize the ORO value to the Crystal Violet value to control for differences in culture density, especially as AM treatment reduces the proliferation of ASC cultures in order to facilitate adipogenic differentiation [4] (See Note 29).

4

Notes 1. Botanical extracts are often water-insoluble, bitter, or otherwise unpalatable, and therefore difficult to administer to animals in controlled dosages. In addition, antioxidants in particular are also often light-sensitive. For these reasons, it is not always possible to administer these compounds through drinking water or food. Daily oral gavage allows for stringent dosage control, but is time-consuming, especially for a large animal study, and may impose unnecessary stress on the animals. Animals should also not receive daily doses of anesthetic, as described for the OGTT protocol above, as this will have a considerable impact on their metabolism and feeding behavior. In order to circumvent these obstacles, we have devised a method of oral administration of botanical extracts by means of jelly blocks [5]. (Jelly is a sweetened form of gelatin and is commonly referred to as Jello in the United States.) Any tea, extract, or drug, regardless of water solubility, can be administered this way, and the sweetness of the jelly masks the taste of the extract. In our experience, red (strawberry-flavored) jelly, but not green jelly, is preferred by rats, specifically albino Wistar rats, which may be a specific feature of these animals due to their impaired vision and smell, compared to other rodents [6].

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2. We prepare the jelly blocks in silicone candy molds, as the jelly blocks can then be removed with greater ease than would be the case with fixed plastic molds such as ice-cube trays. In our experience, a jelly block with a volume of 1.5 mL is easily consumed by rats. Jelly blocks prepared in molds with a larger volume can be divided into equal portions for animals of the same weight. 3. The total number of animals in the study will be determined by the number of groups and the number of animals per group. This should be determined by a statistical power calculation. It is essential to have enough animals per group to allow for robust statistical analysis; it is unethical to reduce the number of animals to such a point that the data generated are powerless, as this would render the entire study invalid and the research animals wasted. Importantly, all studies involving animal experimentation should be approved by the relevant institutional and national agencies. Animals should be housed in small groups, under a 12 h/12 h day–night cycle at a room temperature between 20 and 26  C, with ad libitum access to food and water, unless the study design requires specific changes to these conditions. Such changes would likely have to be approved by an animal research ethics committee. The principle that governs laboratory animal research is commonly referred to as “the three R’s,” namely, “replace, re-use, and refine” [7, 8]. This principle guides researchers to use as few animals as possible, while at the same time ensuring that the numbers of animals per group are sufficient to ensure robust statistical power of the results, to avoid meaningless studies in which animals were used in vain. One way of applying this principle is to share animals between research projects, by harvesting as many organs and tissues as possible from each animal to simultaneously provide material for multiple projects. This study design has two main advantages. First, the collective number of research animals used at any particular research institution is vastly reduced, thereby saving on costs and animal handling. Second, and more importantly from a research perspective, a thorough analysis of multiple organs from the same animal provides a more systemic context of the results obtained for each organ system and promotes our understanding of the impact of chronic conditions such as diet and disease on the whole body. 4. ASC proliferation and differentiation are sensitive to the type of FBS used. In our experience, HyClone FBS yields satisfactory results. If FBS from a different source is to be used, it is recommended that this should be tested and optimized first. 5. Although many lyophilized collagenase preparations are available globally, in our experience, Worthington type I

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collagenase is superior to other products. For the isolation of ASCs from rat adipose tissue, we routinely use a final concentration of 0.075% collagenase in our tissue digestion reactions, but this concentration can be adjusted as needed. 6. If the mass of the collected adipose tissue needs to be determined, weigh the tube with DMEM and note the mass. Once the tissue is in the tube, weigh it again and subtract the two values to determine the tissue mass. 7. A training period of 7 days with control jelly blocks (not containing any extract or drug) greatly improves the chance of successfully habituating the animals to consume the whole jelly block once drug/extract administration starts. As a control for the additional nutritional value of the jelly block, animals not receiving the drug or extract should receive control jelly blocks. In our experience, some animals consume the jelly blocks with much more enthusiasm than others and will also consume the partially eaten jelly blocks of their cage mates, resulting in uncontrolled dosage. For this reason, the animals can be temporarily transferred into small individual cages and left to consume their jelly block, before being returned to their communal cages. Addition of a small amount of peanut butter may make the jelly block more appealing to animals that are otherwise reluctant to consume the jelly, but this may constitute an additional metabolic variable and should be avoided if at all possible. 8. These amounts and volumes can be scaled up as needed. The addition of extra gelatin prevents the jelly blocks from being “floppy” and assists the rats in eating the jelly block, as they usually hold the jelly block with their front paws. 9. This solution is highly saturated and starts setting almost immediately. 10. This will minimize the stress of the blood collection and oral gavage on the animal. While anesthesia can interfere with the glucose response in many different ways, the administration of a small dose of anesthesia may reduce the animal’s stress responses during the handling associated with OGTT. Specifically, pentobarbital has been found not to affect blood glucose or insulin release [3]. 11. For our work, 1 mL of blood is drawn from the carotid artery under deep anesthesia induced by inhaled isoflurane, and the animal is subsequently left for a few min to recover from the anesthesia. This step must be performed by an experienced laboratory animal technician or staff veterinarian. 12. Detailed descriptions and schematics of the various adipose tissue depots in rats can be found in references [9, 10]. In rats, the inguinal fat is a major subcutaneous (SQ) fat depot

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and is located below the skin, in the fold between the hind-leg and the lower abdomen. The visceral fat in humans and rodents comprises several subdepots which are not interchangeable [1]. Therefore, the choice of visceral fat depot should be carefully considered and described in the study methodology. In humans, the omental and mesenteric fat depots are considered to be “true” visceral depots, because the circulation from these depots drains into the hepatic portal vein [1, 9, 10]. However, omental fat is virtually absent in rats, especially in lean animals, and can be confused with the pancreas [9, 10]. The mesenteric fat is situated alongside the gastrointestinal tract but contains considerable quantities of blood vessels and lymph nodes which cannot be surgically isolated [9, 10]. In male rats, the epididymal fat pad is the largest and most accessible [9] and is therefore commonly used [1], but it does not have a human or a female equivalent, and therefore the broader applicability of results from this depot may be limited. The retroperitoneal (RP) fat, dorsal to the kidneys, is intra-abdominal but is not strictly speaking considered to function as visceral fat [9, 10]. Although this also applies to the perirenal visceral (PV) depot, we prefer to use PV fat as a representative visceral fat depot, as it has been shown in humans to reflect changes in overall visceral fat [11–13]. In our experience, the size of the PV fat depot differs with diet and can be a valid marker of visceral adiposity. Also, from a practical point of view, the PV depot, even in lean animals, provides sufficient starting material to yield a high number of ASCs, particularly in older adult animals at the end of a feeding study (>350 g body weight). Lean Wistar rats with a body weight of 350–450 g typically have approximately 500 mg of PV fat on one side, which is sufficient for our ASC isolation protocol, but with diet-induced obesity this often increases to 800–1100 mg. However, if the PV depot is small (80% confluence in passage 0 following isolation. (c) There is a significant correlation between the weight (g) of the animals and the ex vivo growth rate of MSCs post isolation. Wild-type control (C57BL6/J) mice weigh between 17 and 30 g whereas obese prediabetic (B.6Cg-Lepob/J) mice weigh 35–60 g

3. Collect the RNA lysates from each well and transfer to 1.5 mL RNase-free microfuge tubes and store at 80
C. 4. Proceed with RNA isolation using an RNA isolation mini kit according to the manufacturer’s instructions (see Note 16). 3.4 Harvesting of Protein from Isolated Cells

1. After the cells reach 80–100% confluence in passage 0 (from Subheading 3.2, step 8) aspirate the media from each well. 2. Wash the cells once with 1 mL prewarmed PBS per well and remove all PBS from the wells. 3. Add 100 μL ice-cold protein isolation buffer to each well. 4. Scrape the adherent cells from the plate using a cell scraper and transfer the lysates to microcentrifuge tubes. 5. Store at 80
C until further analysis (see Note 17).

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Fig. 3 Flow cytometry characterization of isolated cell populations indicates that >80% of the isolated cell populations express the stem cell marker, Sca-1 with a smaller proportion of cells (30–50%) also expressing the stem cell factor receptor, CD117 (c-kit). None of the cells expressed the hematopoietic marker, CD45 3.5

Flow Cytometry

1. Centrifuge cell suspensions (from Subheading 3.2, step 12) at 400  g for 5 min, discard supernatants and resuspend the cell pellets in flow cytometry staining solution at a concentration of 1  106 cells per 100 μL. 2. Co-label the cells in fluorescence-activated cell sorting (FACS) tubes with a panel of fluorescent antibodies targeted at surface markers for 30 min at room temperature in the dark (Fig. 3) (see Note 18). 3. Determine optimum concentrations of antibodies through single stain titration experiments (see Note 19). 4. Take care to select a panel of antibodies that have minimal fluorescent overlap (excitation and emmision ranges) and compatible with the instrument to be used (see Note 20). 5. Use isotope controls and unstained negative controls for gating purposes.

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1. Seed the isolated cells (from Subheading 3.2, step 12) into three separate 96-well plates at a density of 4.5  104 cells/well in isolation media (100 μL/well) and incubate the plates for 48 h at 37
C in 90% humidified air with 5% CO2 until the cells reach 70% confluence (see Note 21). 2. After 48 h, aspirate the media and replace with 150 μL SGM and/or treatments of choice and incubate each plate as above for either 24, 48 or 96 h. 3. After each incubation, aspirate the media and wash the cells once with 150 μL prewarmed PBS per well. 4. Fix the cells for approximately 30 s with 150 μL ice-cold methanol per well. 5. Remove the methanol and wash cells once with 150 μL PBS per well. 6. Add 150 μL CV working solution to each well and incubate 5 min at room temperature. 7. Remove the CV solution and wash twice with 200 μL PBS per well (see Note 22). 8. Remove the PBS and take representative images of the stained cells at 10 magnification using a light microscope (Fig. 4) (see Note 23). 9. Add 100 μL 70% EtOH to each well and leave on a shaking platform for approximately 5 min to dissolve the stain. 10. Read the absorbance values using a spectrophotometer or colorimeter plate reader at 570 nm.

3.7 Assessing Proliferation Rate

1. Seed isolated cells (from Subheading 3.2, step 12) into three separate 96-well plates at a density of 4.5  104 cells/well in isolation media (100 μL/well) and incubate the plates 24 h at 37
C in 90% humidified air with 5% CO2 to allow cellular adherence. 2. After 24 h, remove the isolation media and add 150 μL SGM and/or treatment of choice to each well and incubate each plates as above for either 24, 48, or 96 h. 3. Four hours before the end of each incubation add 5 μL BrdU labeling solution to each well as appropriate and continue to incubate as above for the full period (see Note 24). 4. Remove media from each well and analyze using the BrdU ELISA kit as per the manufacturer’s instructions.

3.8 Assessing Differentiation Capacity

1. Seed the isolated cells (from Subheading 3.2, step 12) into two separate 96-well plates at a density of 4.5  104 cells/well in isolation media (100 μL/well) and incubate the plates as above for 48 h to allow for cellular adherence.

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Fig. 4 Cell viability (Crystal Violet staining) is maintained in both healthy control (a–c) and impaired diabetic MSCs (d–f) in passage 1 over a period of at least 96 h after adherence in standard growth media

2. After 48 h replace the isolation media with SGM and incubate the cells further until they reach 100% confluence, with media exchange every 3 days (see Note 25). 3. Upon reaching 100% confluence, aspirate the SGM and add 150 μL of either AM or OM per well in the respective plates and incubate as above. 4. For adipogenesis, change the AM three times per week for a maximum of 14 days (see Note 26).

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Fig. 5 Adipogenesis (Oil Red O staining).Representative images of lipid accumulation present after (a) 7 days differentiation in healthy control MSCs and (b) 14 days differentiation in impaired diabetic MSCs

5. After 14 days of AM treatment, remove the media from the wells, add 200 μL ORO working solution to each well and incubate at room temperature for 30 min. 6. After 30 min, aspirate the ORO solution and wash each well three times with 200 μL dH2O per well (see Note 27). 7. Take 4–8 representative images of each well at 20 magnification using the light microscope (Fig. 5) (see Note 28). 8. Quantify the percentage of lipid accumulation (% surface area) using the Image J software. 9. For osteogenesis, change the OM twice per week for a maximum of 14 days (see Note 29). 10. After 14 days of OM treatment remove all of the media from the wells and wash the cells once with 200 μL PBS per well. 11. Fix the cells by adding 200 μL 70% EtOH per well and leave at room temperature for 5 min. 12. After fixing, wash the cells once with 200 μL dH2O per well. 13. Add 200 μL Alizarin Red S solution to each well and incubate overnight at room temperature with gentle agitation on a shaking platform. 14. The next day, aspirate the stain and wash each well three times using 200 μL dH2O per well. 15. After washing, add 200 μL PBS to each well and take 4–8 representative images per well at 10 magnification using the light microscope (Fig. 6) (see Note 30). 16. Quantify the percentage mineralization (% surface area) using the Image J software.

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Fig. 6 Osteogenesis (Alizarin Red S staining). Representative images of mineralization present after (a) 7 days differentiation in healthy control MSCs and (b) 14 days of differentiation in impaired diabetic MSCs

4

Notes 1. The protease inhibitor cocktail consists of 104 mM AEBSF, 80 μM Aprotinin, 4 mM Bestatin, 1.14 mM E-64, 2 mM Leupeptin, and 1.5 mM Pepstatin A. Each of these components has specific inhibitory properties to prevent degradation of proteins during the storing and processing of samples. 2. To completely dissolve the ORO, place the solution on a magnetic stirrer for approximately 30 min at room temperature. Sediment formation is known to occur, and it is therefore recommended that the solution is filtered three times using Whatman’s filter paper before use. 3. To completely dissolve the Alizarin Red S, place the solution on a magnetic stirrer for approximately 30 min at room temperature. Adjust the pH to 4.2 and filter the solution using Whatman’s filter paper to remove all visible sediment prior to use. 4. All experimentation involving animals must be cleared with the correct institutional ethics committee and conform to national and regional legislation. The cervical dislocation of animals should be performed by well-trained registered para-veterinary professional as per the AmericanVeterinary Medical Association (AVMA) Guidelines on Euthanasia (June 2007) [27]. All biohazardous waste should be disposed of appropriately. 5. The femurs of mice are small and can easily break if handled with force. Care should thus be taken during the dissection process to not fracture the bones. 6. It is recommended that a separate tube containing 70% EtOH is used for the rinsing of femurs from each animal to avoid cross-contamination.

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7. Both femurs from the same animal can be placed in the same sterile tube containing transport media. It is recommended that the tubes are clearly labeled with either pencil or a marker that will not dissipate when it comes in contact with 70% EtOH. 8. The empty 100 mm2 petri dish provides a sterile work surface to clean the bone prior to aspirating the marrow and is not used for culturing of cells. 9. The bone should look “clean” with no other tissue (muscle, tendons, fat) attached to it when the bone marrow is flushed. This will prevent contamination with other cell types. If preferred, a sterile gauze (wound dressing) can also be used to clean the bones using a rotating motion. 10. Cutting of the proximal and distal edges of the bones can easily shatter the femur shaft. Care should be taken to not damage the femur and to ensure that no bone fragments are flushed with the marrow into the cell culture plate. 11. It is recommended that the isolation media is prewarmed to 37
C in a waterbath. The wells in the culture plate should already contain 1 mL isolation media prior to flushing of the bone marrow. The final volume of isolation media in each well will thus be 3 mL. 12. When viewing the isolated cells under the microscope during this first 96 h period, a substantial amount of debris and red blood cells will be visible with very few or no MSCs clearly evident. In the case of obese prediabetic animals, the bone marrow contains a large amount of lipids that will also be present during this stage. For direct comparison between healthy control and obese diabetic isolations it is recommended that MSCs are allowed to adhere and expand for a period of 96 h prior to washing away the debris. 13. There is a direct correlation between the age/weight of the animals and the growth rate of MSCs post isolation. For healthy control MSCs, the isolated cells should reach 80% confluence in passage 0 within 6–8 days post isolation, whereas MSCs derived from obese prediabetic animals can take up to 18 days to reach 80% confluence. However, it is not recommended to extent the culture period for passage 0 beyond 18 days as cells become senecent during prolonged culture. If a larger number of cells are required it is recommended that the bone marrow from a number of animals are pooled together to increase the cell yield. 14. The FBS contained within the SGM is known to quench enzymatic activity. It is therefore essential that all residual FBS is washed away from the adherent cells prior to dissociation through enzymatic digestion.

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15. The adherent cells can be difficult to liberate. Gentle tapping of the plate will assist the process. It is furthermore recommended that the cells are incubated at 37
C during enzymatic digestion for a maximum of 5 min. If all the cells are not liberated after 5 min, the process can be repeated as necessary. 16. A single well of a 6-well plate should give an RNA yield of between 150 and 550 ng/μL. If a higher yield is required, the cells isolated from both femurs can be pooled by using the same 350 μL RNA lysis solution for both wells. RNA can be used for numerous downstream analysis techniques such as micro-array profiling, PCR and/or qPCR to assess differences in the mRNA or miRNA expression profiles between healthy and impaired MSCs. 17. A single well of a 6-well plate should give between 10 and 30 μg/μL protein. The harvested protein can be used for numerous downstream analysis techniques such as Western blotting and/or ELISAs. 18. MSCs will express the stem cell markers (Sca-1, CD73, CD90, CD105), whereas hematopoietic stem cells will express CD45. No differences in the expression of these markers are evident between healthy control and impaired MSCs. 19. Refer to manufacturer datasheets for detailed information regarding recommended antibody concentrations. Titrations are recommended to ensure that the optimum concentration is used that will give the least amount of nonspecific background staining. 20. Since multicolor analysis is required, fluorescent compensation settings should be established through a compensation experiment and regions of positive and negative staining determined using a fluorochrome minus one (FMO) experiment. 21. For direct comparisons this should be performed in parallel for cells isolated from healthy control and obese diabetic animals. 22. Care should be taken to ensure that all unbound crystal violet is washed away from each well to avoid non-specific false-positive results. 23. It is recommended that images are taken of the wells without the presence of PBS (dry wells). Prolonged PBS exposure can cause the crystal violet stain to fade. 24. BrdU incorporates into the DNA of proliferating cells during DNA synthesis. The extent of BrdU incorporation is measured using spectrophotometry and is thus directly proportional to the number of proliferating cells. 25. For successful differentiation of MSCs, it is essential that the cells are allowed to reach 100% confluence prior to induction of differentiation.

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26. For adipogenesis, it is recommended that the adipogenic differentiation media is changed on day 1, 3, and 5 (Monday, Wednesday, Friday) each week for consistency between experiments. Lipid accumulation will be evident in healthy control MSCs within 7 days of differentiation, whereas the adipogenic capacity of impaired MSCs will show only few lipid droplets even after 14 days of differentiation. 27. Plates can be stored dry overnight at 4
C prior to taking representative images. 28. For osteogenesis, it is recommended that the osteogenic differentiation media is changed on day 1 and 4 (Monday and Thursday) each week for consistency between experiments. Extensive mineralization will occur in healthy control MSCs within 7 days of differentiation, whereas a similar extent of mineralization will be evident only after 14 days of differentiation in impaired MSCs. 29. During washing, care should be taken to gently pipette and aspirate the dH2O to avoid the rupture and/or detachment of lipid droplets and cells. 30. Plates can be stored dry overnight at 4
C prior to taking representative images. The areas of mineralization should be stained bright red. Care should be taken to avoid the detachment and lifting of mineralized areas from the plate during staining.

Acknowledgements This research was supported by the National Research Foundation (NRF) of South Africa. References 1. Bianco P, Riminucci M, Gronthos S, Robey PG (2001) Bone marrow stromal stem cells: nature, biology, and potential applications. Stem Cells 19(3):180–192 2. Morrison SJ, Scadden DT (2014) The bone marrow niche for haematopoietic stem cells. Nature 505(7843):327–334 3. Schatteman GC (2004) Adult bone marrowderived hemangioblasts, endothelial cell progenitors, and EPCs. Curr Top Dev Biol 64:141–180 4. Polymeri A, Giannobile WV, Kaigler D (2016) Bone marrow stromal stem cells in tissue engineering and regenerative medicine. Horm Metab Res 48(11):700–713

5. Fijany A, Sayadi LR, Khoshab N, Banyard DA, Shaterian A, Alexander M et al (2019) Mesenchymal stem cell dysfunction in diabetes. Mol Biol Rep 46(1):1459–1475 6. Kornicka K, Houston J, Marycz K (2018) Dysfunction of mesenchymal stem cells isolated from metabolic syndrome and type 2 diabetic patients as result of oxidative stress and autophagy may limit their potential therapeutic use. Stem Cell Rev 14(3):337–345 7. van de Vyver M (2017) Intrinsic mesenchymal stem cell dysfunction in diabetes mellitus: implications for autologous cell therapy. Stem Cells Dev 26(14):1042–1053 8. Mangialardi G, Madeddu P (2016) Bone marrow-derived stem cells: a mixed blessing in

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the multifaceted world of diabetic complications. Curr Diab Rep 16(5):43. https://doi. org/10.1007/s11892-016-0730-x 9. van de Vyver M, Niesler C, Myburgh KH, Ferris WF (2016) Delayed wound healing and dysregulation of IL6/STAT3 signalling in MSCs derived from pre-diabetic obese mice. Mol Cell Endocrinol 426:1–10 10. Mehrbani Azar Y, Green R, Niesler CU, van de Vyver M (2018) Antioxidant preconditioning improves the paracrine responsiveness of bone marrow mesenchymal stem cells to diabetic wound fluid. Stem Cells Dev. https://doi. org/10.1089/scd.2018.0145 11. Tan J, Zhou L, Zhou Y et al (2017) The influence of diabetes mellitus on proliferation and osteoblastic differentiation of MSCs. Curr Stem Cell Res Ther 12(5):388–400 12. Bakopoulou A, Apatzidou D, Aggelidou E, Gousopoulou E, Leyhausen G, Volk J et al (2017) Isolation and prolonged expansion of oral mesenchymal stem cells under clinicalgrade, GMP-compliant conditions differentially affects “stemness” properties. Stem Cell Res Ther 8(1):247. https://doi.org/10. 1186/s13287-017-0705-0 13. Briquet A, Dubois S, Bekaert S, Dolhet M, Beguin Y, Gothot A (2010) Prolonged ex vivo culture of human bone marrow mesenchymal stem cells influences their supportive activity toward NOD/SCID-repopulating cells and committed progenitor cells of B lymphoid and myeloid lineages. Haematologica 95 (1):47–56 14. Lu L, Song H-F, Zhang W-G, Liu XQ, Zhu Q, Cheng XL et al (2012) Potential role of 20S proteasome in maintaining stem cell integrity of human bone marrow stromal cells in prolonged culture expansion. Biochem Biophys Res Commun 422(1):121–127 15. Vacanti V, Kong E, Suzuki G, Sato K, Canty JM, Lee T (2005) Phenotypic changes of adult porcine mesenchymal stem cells induced by prolonged passaging in culture. J Cell Physiol 205(2):194–201 16. Jackson Laboratories Mouse strain 000632 17. Batt RA, Everard DM, Gillies G, Wilkinson M, Wilson CA, Yeo TA (1982) Investigation into

the hypogonadism of the obese mouse (genotype Ob/Ob). J Reprod Fertil 64(2):363–371 18. Batt RA, Hambi M (1982) Development of the hypothermia in obese mice (genotype Ob/Ob). Int J Obes 6(4):391–397 19. Rath EA, Thenen SW (1980) Influence of age and genetic background on in vivo fatty acid synthesis in obese (Ob/Ob) mice. Biochim Biophys Acta 618:18–27 20. Dubuc PU (1976) The development of obesity, hyperinsulinemia, and hyperglycemia in Ob/Ob mice. Metab Clin Exp 25 (1):1567–1574 21. Boozer CN, Mayer J (1976) Effects of longterm restricted insulin production in obesehyperglycemic (genotype Ob/Ob) mice. Diabetologia 12(2):181–187 22. Ewart-Toland A, Mounzih K, Qiu J, Chehab FF (1999) Effect of the genetic background on the reproduction of leptin-deficient obese mice. Endocrinology 140(2):732–738 23. Seitz O, Schu¨rmann C, Hermes N, Mu¨ller E, Pfeilschifter J, Frank S et al (2010) Wound healing in mice with high-fat diet- or Ob gene-induced diabetes-obesity syndromes: a comparative study. Exp Diabetes Res 2010:476969. https://doi.org/10.1155/ 2010/476969 24. Yu WH, Kimura M, Walczewska A et al (1997) Role of leptin in hypothalamic-pituitary function. Proc Natl Acad Sci U S A 94 (3):1023–1028 25. Cameron CM, Kostyo JL, Adamafio NA, Dunbar JC (1987) Metabolic basis for the diabetogenic action of growth hormone in the obese (Ob/Ob) mouse. Endocrinology 120 (4):1568–1575 26. Schultz MB, Sinclair DA (2016) When stem cells grow old: phenotypes and mechanisms of stem cell aging. Development 143(1):3–14 27. Sivula CP, Suckow MA (2018) Euthanasia. In: Weichbrod RH, Thompson GA, Norton JN (eds) Management of animal care and use programs in research, education, and testing, 2nd edn. CRC Press/Taylor & Francis, Boca Raton, FL. ISBN-10: 9781498748445

Chapter 7 Investigating Alcohol Behavior and Physiology Using Drosophila melanogaster Aliza K. De Nobrega, Kristine V. Luz, Katherine N. Lyons, and Lisa C. Lyons Abstract Drosophila melanogaster, the fruit fly, is one of the most versatile models for biomedical studies due to the economical husbandry, rapid generation time, and the array of tools for spatial and temporal gene manipulation. The relatively short lifespan of Drosophila (60–80 days) and the high degree of molecular conservation across species make Drosophila ideal to study the complexities of aging. Alcohol is the most abused drug worldwide and alcohol use disorders represent a significant public health problem and economic burden to individuals and society. Stereotypical alcohol-induced behaviors and the underlying molecular mechanisms are conserved from flies to humans making Drosophila a practical model for investigating the development of alcohol-induced behaviors and alcohol pathologies. Here, we outline how to assemble an efficient and controlled alcohol vapor delivery system, the FlyBar, and review paradigms and protocols for the assessment of alcohol-induced behaviors and physiology in Drosophila including the loss-of-righting reflex, sedation, tolerance, alcohol metabolism, and gut permeability. Key words Alcohol use disorder, Drosophila, Alcohol abuse, Aging

1

Introduction Alcohol use disorders and binge alcohol consumption are global economic and health issues resulting in more than three million deaths annually [1]. Middle-aged and older adults are specifically vulnerable to issues associated with alcohol misuse as 75% of the alcohol poisoning deaths and 90% of the alcohol-liver cirrhosis deaths occur in these age groups [2–6]. Although binge drinking is most commonly associated with young adults, the percentage of older adults engaging in binge drinking episodes is increasing with between 10% and 15% of adults over 50 binge drinking [7, 8]. The development of new approaches to investigate the cellular and molecular mechanisms underlying alcohol use disorder and alcohol pathologies is crucial for effectively preventing and treating alcohol-induced tissue injury and thereby extending health span.

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_7, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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Investigating the mechanisms underlying the bidirectional relationship between alcohol and health span in aging animals using rodent models can pose experimental challenges. In the past decade, model organisms including Caenorhabditis elegans, Drosophila melanogaster and rodents have been leveraged to provide insight into genetic mechanisms implicated in human diseases and disorders. The cost-effectiveness and wide availability of tools to manipulate gene expression with spatial and temporal accuracy make Drosophila perfectly suited for studies of aging, health span, and alcohol neurobiology [9–24]. The biological responses at the molecular, physiological, and behavioral levels following alcohol exposure are largely conserved between humans and Drosophila [17, 18, 24, 25]. Upon initial exposure to low concentrations of alcohol, flies exhibit hyperactivity followed by loss-of-motor control as alcohol exposure progresses [26–29]. Prolonged exposure to alcohol results in sedation and eventually death [26–29]. Flies develop functional tolerance dependent upon neural changes similar to humans in which drug tolerance has been implicated as a key step leading to increased alcohol consumption and addictive behaviors. Flies also exhibit sex differences in alcohol sensitivity, similar to mammals, with females more sensitive to the acute effects of alcohol at the behavioral level but males more susceptible to alcohol-induced mortality [30, 31]. The circadian clock modulates the behavioral sensitivity to alcohol with increased alcohol sensitivity and mortality observed at night following acute and repetitive alcohol exposures [30, 32]. Ablating circadian clock function either genetically or environmentally increases alcohol sensitivity and mortality [30, 32]. We have also found that older flies exhibit increased alcohol sensitivity and mortality in response to acute and repeated alcohol exposures [33]. Here, we outline how to set up an economical and efficient alcohol-delivery system, the FlyBar, similar to the one used in our lab. We detail straightforward assays for the assessment of alcoholinduced behaviors and physiological responses in flies. These techniques are suitable for behavioral assays across genotypes, ages, or conditions, and multiple FlyBars may be used for larger screens. We also highlight technical issues in these assays that may frustrate researchers and pitfalls that may confound experimental results. Experimental results are presented for many of the described protocols illustrating the effects of alcohol on behavior and physiology across age groups or in flies with non-functional circadian clocks. Given the evolutionary conservation at the behavioral, physiological, and molecular levels, experiments in Drosophila such as the ones described here can provide a foundation of knowledge in alcohol neurobiology that can provide insight for future development of therapeutics and treatments for alcohol use disorder and alcoholinduced pathologies.

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Fig. 1 The FlyBar. The FlyBar delivers a predetermined percentage of alcohol vapor by mixing proportional fractions of air bubbled through deionized water with a proportional fraction of air bubbled through 95% alcohol

The FlyBar (Fig. 1) is an inexpensive, easily assembled apparatus designed to deliver controlled concentrations of alcohol vapor to flies in four observation vials by mixing streams of air bubbled through either deionized water or 95% ethanol. Advantages of the FlyBar compared to other methods of ethanol delivery include the ability to control the mixing of the air streams to obtain consistent concentrations of ethanol vapor and the ability to easily change the desired concentration of ethanol vapor between experiments as needed for comparisons of different sexes, ages, or genotypes. More detailed instructions with a video guide can be found in [34]. Proper culturing and handling of Drosophila are necessary to minimize stress experienced by the flies during development or aging, thereby reducing variability in the data. For this reason, certain basic tools are necessary including a stereo-microscope with good optical quality and magnification of 4–6
for handling live flies (and 40
for dissections), incubators, or climatecontrolled rooms and a fly pad connected to a CO2 tank for anesthetizing and sorting the flies (Fig. 2). Some of the tools can be made with a little imagination including the fly aspirator used for picking up individual flies, fly morgue, and traps to dispose of unwanted flies and trap escapees (Fig. 2).

2 2.1

Materials Building a FlyBar

1. Aquarium aerator pump (sized for 60 gal aquarium, Tetra Whisper). 2. Silicone tubing 1/800 and 1/400 (inner diameter).

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Fig. 2 Basic tools in every Drosophila researcher’s tool kit. (a) Bottles containing molasses media to culture flies. (b) Holding vials with fly media for collecting flies for experiments. (c) Kim wipes. (d) Fly pad and needle for administering CO2. (e) Stereomicroscope. (f) Light source. (g) Fly-trap filled with apple cider vinegar. (h) Funnel. (i) Vial holder to hold vials while dispensing flies. (j) Fly aspirator for sorting flies. (k) Mouse pad or soft surface for tapping vials

3. 120 y-connectors. 4. Straight length of glass pipette approximately 5.500 long. 5. Glass pipette with a 90 bend (500 ). 6. Quick disconnects. 7. Plastic tube clamps. 8. Miniature air regulator. 9. Miniature air regulator mounting bracket. 10. Gilmont size 12 flow meter. 11. Tool clips. 12. Narrow Drosophila vials (23
75 mm). 13. Rubber stoppers with two holes. 14. 5 mm diameter Pyrex glass tubes. 15. Teflon tape. 16. 1 mL disposable glass pipets. 17. Fine nylon netting.

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1. Fly bottles. 2. Fly media. 3. Fly aspirator (also known as poofter [35–37]).

2.3 Alcohol Exposure Testing

1. FlyBar. 2. Timer. 3. 190 proof absolute ethanol. 4. Mini flashlights with red filters. 5. Cotton plugs or other fly closures for vials. 6. NAD-ADH Reagent Multiple Test Vial (Sigma-Aldrich). 7. Plastic pestle for microtubes. 8. 1.5 mL Eppendorf tubes. 9. Refrigerated centrifuge. 10. 50 mM cold Tris–HCl buffer (pH 7.5). 11. Bradford protein assay dye reagents.

2.4 Gut and Brain Permeability Assay

1. FD&C Blue Dye #1. 2. Ingredients to make fly media. 3. Stereo microscope with digital camera.

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3.1 Assembly of the FlyBar (See Note 1)

1. Generate an air stream using an aquarium aerator connected to a length of flexible silicone tubing to provide a consistent airflow. 2. Split the airflow using a y-connector by connecting the first branch to the airflow regulator that controls the total amount of air through the system (typically 1000 mL/min for four observation vials (Fig. 1)). 3. Connect the second branch to a quick connector to allow interruption of the air stream at the start and end of the experiment without affecting the calibrated airflow (the addition of a y-connector to each branch of the tubing allows the branches to be connected to the airflow regulator). 4. Connect the airflow regulator to the airflow meters. 5. To set up the alcohol and water bottles, fill one bottle with 100 mL deionized or MilliQ water and one bottle with 100 mL 95% ethanol. 6. Insert the straight section of glass pipette through one hole in the rubber stopper extending into the liquid (either deionized

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water or 95% ethanol) until it is 1 cm from the bottom of the bottle to provide an air inlet (see Note 2). 7. Insert the elbow section of glass pipette into the remaining hole in the rubber stopper until the end of the glass pipette just enters the bottle (not touching the liquid). 8. Connect the glass pipettes to flexible tubing attached to a y-connector. 9. Place the bottles filled with ethanol and water into a water bath to keep both bottles at a constant temperature 2  C higher than ambient air temperature (see Note 3). 10. Reunite the airstreams using a y-connector and use another length of silicone tubing to direct the airflow through an empty mixing flask or bottle with bent glass pipette sections inserted through a two-holed rubber stopper. 11. Use another length of silicone tubing for the outlet mixed airstream. 12. Split the outlet airstream emerging from the mixing flask two times using the y-connectors and lengths of silicone tubing to obtain four smaller streams of air, one for each observation vial. 13. To facilitate larger experiments, use two identical setups (see Note 4). 14. Set up the observation vials using empty fly vials sealed with a rubber stopper containing two holes through which the 5 mm glass tubes provide an inlet and an outlet for the alcohol vapor (see Note 5). 15. Cover the end of the first glass tube with netting and keep the netting in place using a small piece of flexible plastic tubing. 16. Insert this tube through the first hole until it extends to approximately half the length of the vial. 17. If needed, use Teflon tape to obtain a snug fit. 18. Insert the second glass tube also with the end covered by netting until it is flush with the inside edge of the rubber stopper. 19. Place the vials horizontally on a white piece of paper to maximize contrast with the flies under dim red light conditions. 20. Test the system by mixing appropriate fractions of the airstream bubbled through alcohol and the airstream bubbled through water. 21. Monitor the air pressure continuously and make adjustments as needed to maintain desired mixing of the air streams (see Note 6).

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1. To control for the effect of age and circadian factors, match all flies for age across all experiments and time points to minimize variability as is standard for other behavioral analyses (see Note 8). Example timelines for fly husbandry for age-matched experiments are shown in Fig. 3. 2. Rear flies in incubators or climate-controlled fly rooms at 25  C under 12 h light/12 h dark cycles (LD) with relative humidity maintained between 60 and 70% and humidified (preferably 70%) conditions. 3. Ensure that the food on which flies are grown is nutritionally consistent across experiments to reduce variability. 4. As large numbers of flies are needed for alcohol behavioral assays, make new populations of flies by transferring young 8to 10-day-old flies to bottles containing Drosophila media. 5. Use only 60–80 flies per bottle as overcrowding can induce stress in the flies.

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Fig. 3 Examples of experimental timelines used for behavioral assays. (a) Experiment timeline used to assess circadian regulation of alcohol sensitivity using LoRR or sedation as a marker. (b) Timeline to assess the effect of circadian disruption associated with aging on alcohol-induced mortality using the repeat-binge-like paradigm. (c) Timeline to assess the effect of aging on the development of rapid alcohol tolerance. (d) Timeline to assess the effect of aging on the development of long-term or chronic alcohol tolerance

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6. Allow 5–7 days for parents to mate and females to lay eggs, after which remove the parents from the bottles (flies in bottles eclose 12–14 days after the bottles were made, although some genetic mutations can alter the number of days it takes for offspring to eclose) (see Note 9). 7. Clear any existing flies from the bottles 1 day prior to collecting flies for experiments unless virgin flies are needed in which case, do not clear bottles more than 8 h before collection. 8. Collect batches of approximately 25–35 flies using an aspirator toward the end of the light period and transfer to fresh food vials (see Note 10). 9. To do this, empty one or two bottles into a holding vial and use a strong light source to direct flies to the far end of the vial (see Note 11). 10. Label each vial with appropriate information (date, genotype, age, condition). 11. On the day of the experiment, place all flies housed in different conditions or incubators in the experimental behavior room for at least 1 h prior to the experiment (see Note 12). 3.3 Loss-of-Righting Reflex and Sedation (See Notes 13 and 14)

1. Set up the work station during the light prior to the acclimation of the flies. 2. Otherwise, set up the work station in the dark using only dim red lights (remember to check the water level and temperature in the water bath). 3. Place vials of flies in the dark experimental behavior room to habituate for at least 1 h prior to exposure to alcohol as acclimation of the flies to the conditions in the behavior room reduces variability between experiments (see Note 15). 4. Prior to exposure of the flies to alcohol, connect all vials and check the pressure levels for the alcohol and water vapor. 5. Run the system with air bubbled through the water and alcohol bottles for at least 10 min to calibrate and stabilize prior to starting the experiment. 6. Depending on the percentage of alcohol being administered, adjust until ratio of pressures for alcohol and water vapor is at the desired percentage (i.e., 40% ethanol vapor, adjust the water and alcohol pressure to 580 and 420 mL, respectively). 7. Use the quick release to stop the airflow. 8. Transfer the flies to the clean empty labeled vials 2 min prior to the start of the experiment (see Note 16). 9. To start the experiment, reconnect the airflow to the observation vials.

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10. Check all pressure levels at the beginning of the experiment as well as periodically throughout the duration of alcohol exposure to maintain consistency of the alcohol exposure. 11. Make small adjustments as necessary to maintain desired alcohol concentration. 12. Start a timer at the beginning of the experiment. 13. Every 5 min, gently tap the vial and count the number of flies that cannot right themselves (stand-up) within 4 s after receiving the tap. 14. Use hand-held small flashlights with a red filter to visualize the flies. 15. Do not shine the red light directly on the vials or use for an extended period of time (see Note 17). 16. Record data on a behavior sheet. Count flies every 5 min for a 1-h alcohol exposure. 17. For sensitive genotype groups, test the Loss of Righting Refles (LoRR) every 2.5 min or lower the alcohol vapor concentration. 18. Make notes about the setup and experimental conditions on the behavior sheet (see Note 18). 19. To measure alcohol sedation, use the same procedures but assess the behavioral endpoint differently. 20. Score flies as sedated if they are lying immobile with no coordinated leg movement, particularly for the two front legs (while sedated, spontaneous twitching may be observed in the middle and hind legs of the fly [38]). 21. At the end of the experiment, count the total number of flies in each observation vial. 22. If the flies are not to be used for any additional procedures, place the vials of flies in the freezer or on ice to facilitate easier counting. 23. If flies are going to be used for further experiments or additional alcohol exposures, transfer them to food vials but ensure that these remain horizontal until the flies have recovered to prevent them from sticking to the food. 24. Treat control flies as above except the vials should be exposed to water vapor only. 25. To analyze the results of the behavioral experiments, plot the time course with the percentage of flies with the measure behavior at each time point. 26. Using linear extrapolation from the linear portion of the sigmoid curve, determine the 50% Loss of Righting Refles (LoRR), 50% sedation, or 50% recovery time as shown in Fig. 4 with measurements across different age groups.

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Fig. 4 Aging increases the alcohol sensitivity as measured using the Loss of Righting Reflex (LoRR). LoRR was measured during a 30% alcohol vapor exposure in age-matched populations of male and female wildtype Canton-S flies (n ¼ 8 vials of flies per group). (a) The time necessary for 50% of young flies (10 days) to lose their righting reflex during alcohol exposure is significantly longer than that of middle-aged (20 days) and older (30 days) flies (ANOVA F2, 21 ¼ 28.25, p < 0.001). (b) Complete time course of alcohol exposure showing the percent of flies exhibiting LoRR for 10 days, 20 days, and 30 days measured every 5 min during a 1-h alcohol exposure is shown

27. When all experiments for the day have been finished, disconnect the tubing in the FlyBar, dry the stoppers in the alcohol and water bottles, and leave them to dry overnight (moisture accumulating in the tubes can cause the pressure to fluctuate within the system). 28. Ensure that fresh bottles of alcohol and distilled water are used daily. 3.4 Recovery from Alcohol-Induced Sedation (See Note 19)

1. To determine the time to recovery, perform the alcohol exposure protocol above with exposure continuing until 100% of the flies are sedated as it is necessary to use a consistent starting point for all experimental groups (see Note 20). 2. Once 100% of the flies are sedated, transfer the flies to clean holding vials that are positioned horizontally on a white sheet of paper. 3. To assess recovery of postural control, tap the vials every 5 min and count the number of flies standing upright or walking. 4. Determine the time at which 50% of the flies have recovered from sedation (RT50) using linear extrapolation.

3.5 Repeat Exposures to Alcohol (Fig. 3b) (See Note 21)

1. Upon eclosion, collect and house flies in the appropriate light cycle until the desired age. 2. For circadian experiments, transfer flies to constant dark conditions 2 days prior to the initial alcohol exposure. 3. Set up the workstation as described above and remember to acclimate flies to the behavior room and the dim red light for 1 h prior to alcohol exposure.

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4. Expose flies to 1 h alcohol vapor at the same time (exposures separated by 24 h) on 3 consecutive days (see Note 22). 5. Following alcohol exposure, transfer the flies to clean vials containing fly media positioned horizontally to prevent the flies sticking to the food while sedated and allow the flies to recover before storing the vials upright. 6. Once flies have recovered, transfer the vials back to the incubator. 7. Assess alcohol-induced mortality 24 and 48 h following a single alcohol exposure. 8. For the repeat alcohol exposure paradigm, assess mortality daily for 7 days (or as long as needed) following the last alcohol exposure (see Note 23). 9. Remove deceased flies using an aspirator if possible. 3.6 Alcohol Tolerance (Fig. 3c, d) (See Note 24)

1. Upon eclosion, collect flies and house in the appropriate conditions until the desired age. 2. Sort flies into two groups: “naı¨ve” (flies that will not receive a pre-exposure of alcohol) and “pre-exposed” (flies that will receive a short alcohol exposure prior to the alcohol tolerance test) (see Note 25). 3. Set up the workstation and acclimate the flies as described above. 4. For rapid tolerance, expose the “pre-exposed” flies to 50% alcohol vapor for 30 min, which should be done 4.5 h prior to the alcohol tolerance test (see Note 26). 5. As a control group, expose the naı¨ve flies to water vapor during the pre-exposure period. 6. Following alcohol exposure, transfer the flies to clean vials containing fly media positioned horizontally to prevent the flies sticking to the food while sedated and allow the flies to recover. 7. Test the alcohol sensitivity of the flies 4 or 28 h later to assess the development of rapid and long-term tolerance, respectively. 8. Assess chronic tolerance using multiple alcohol exposures as an alternative (see Note 27).

3.7 Alcohol Absorbance and Alcohol Clearance (See Note 28)

1. Separate the flies into at least six groups of 20 in vials with media. 2. Choose typical time points for this assay such as a baseline control group and groups immediately after alcohol exposure, and 30 min, 1 h, 2 h, and 4 h post alcohol exposure. 3. Label the vials “B” for baseline: “0 Alc,” “0.5 Alc,” “1 Alc,” “2 Alc,” and “4 Alc.”

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4. Expose the flies to 40% alcohol vapor for 20 min (seeNote 29). 5. Immediately after exposure, put the flies labeled “0 Alc” into Eppendorf tubes on dry ice. Leave the tubes on dry ice or transfer samples to 80  C freezer until you have collected all the samples. 6. Repeat for the samples labeled “0.5 Alc,” “1 Alc,” “2 Alc,” and “4 Alc” at 30 min, 1 h, 2 h, and 4 h following the alcohol exposure. 7. Perform the alcohol assay using the NAD-ADH Reagent Multiple Test Vial according to the manufacturer by first preparing the reagents and warming these to room temperature. 8. Homogenize the 20 flies in 200 μL of 50 mM Tris–HCl (pH 7.5) working on ice to reduce alcohol metabolism by the alcohol dehydrogenase from the flies. 9. Centrifuge at 15,000
g for 20 min at 4  C. 10. While the samples are centrifuging, label a set of Eppendorf tubes (1 tube/sample) and keep them on ice. 11. Label a second set of Eppendorf tubes (1 tube/sample + tubes for the alcohol standards and a blank with buffer) and make the alcohol standards for 0%, 0.0125%, 0.025%, 0.05%, 0.08% and 0.10% alcohol. 12. When the tubes have been centrifuged, transfer 100 μL of the samples into the cold tubes (be sure there are no fly parts). 13. Dilute the sample as needed depending upon the alcohol exposure and duration. 14. Add 2.5 μL of the sample or standard onto a 96-well plate and add 250 μL of the alcohol reagent to each well. 15. Insert the plate into a microplate reader and shake the plate for 5 s. 16. Incubate for no more than 10 min at room temperature. 17. Measure the absorbance at 340 nm and complete the readings within 20 min. 18. Calculate the alcohol concentration using the manufacturer’s instructions (Fig. 5) (see Note 30). 19. Normalize alcohol absorbance to total protein using the Bradford assay to eliminate the effect of body size variation between groups of flies. 3.8 Gut and Brain Permeability Assays (See Note 31)

1. Prepare normal fly food withholding a small portion of the water (several hundred milliliters) and maintain over heat at liquid stage. 2. Use FD&C blue dye #1 at 2.5% weight/volume based on the total volume of the food prepared.

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3. Dissolve blue dye powder into water using magnetic bar to stir. 4. Remove the food from the heat and stir well. 5. Add in the dissolved blue dye in water and stir thoroughly. 6. Dispense into narrow Drosophila vials, approximately 3 mL per vial. 7. After raising flies to desired age, expose flies to alcohol using the procedures given above. 8. After alcohol exposure and recovery, transfer the flies to the blue food vials. 9. If repeat alcohol exposures are being performed, house flies on blue food vials in between exposures (see Note 32). 10. Using a dissecting microscope, check the flies for any blue color outside of the gut 4–5 days after transfer to the dyed media (see Notes 33 and 34). 11. Photograph flies using a microscope with an attached digital camera (Fig. 6) (see Notes 35–37).

4

Notes 1. There are alternative alcohol delivery systems that can be used with similar effectiveness including the FlyGram and the Inebriometer [28, 39].

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Fig. 6 Aging and circadian perturbation increase gut permeability. Flies were exposed to alcohol using the three binge-like exposure paradigms during either the late day at circadian time (CT) 9 or during the late night at CT 21 and then transferred to FD&C glue dye #1 containing food and assessed for the Smurf phenotype on days 4 and 5 following the last alcohol exposure. (a) Representative photographs of flies exposed to either water or alcohol. (b) Young flies exposed to alcohol during the late night exhibited increased gut permeability compared to flies exposed to alcohol during the late night reflecting circadian modulation of alcohol-induced gut permeability. Aging significantly increases gut permeability following alcohol exposure. (c) Circadian dysfunction either through genetic mutation in the core clock gene period (per01) or through environmental disruption using constant light significantly increases gut permeability and the percentage of flies expressing the Smurf phenotype

2. One milliliter borosilicate glass pipettes can be cut to form glass tubing sections of the correct size to fit the rubber stoppers. The 90 bend in the glass can be created by heating a glass pipette section using a Bunsen burner.

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3. In our experiments, the environmental room is maintained at 25  C, while the water bath is at 27  C. 4. It is not recommended that more than 8 observation vials are used as it can become difficult to perform measurements during the experiment within the desired time windows. 5. Glass tubes such as those used in the Trikinetics system fit the holes in the rubber stopper. 6. The continuous running of several FlyBar assays in parallel or even a single assay in a small room can lead to a noticeable accumulation of alcohol vapor if the room is not wellventilated. To avoid continual release of alcohol vapor that potentially can affect the researcher in a closed room, an appropriate system needs to be put in place that adequately removes alcohol vapor generated during the experiment. To remove alcohol vapors, connect a 6–1200 piece of tubing onto the second glass tube protruding from each vial, bundle them, and direct to a funnel-vacuum system. Researchers should also ensure that the experimental testing room is adequately ventilated. 7. For the alcohol behavioral assays, we avoid the use of anesthesia, but, if necessary, it is recommended that the flies are sorted with ample time for recovery from the anesthesia (at least 2 days). 8. The procedures describe the preparation of experimental animals for assays that can be performed under many conditions (including light-dark cycles, constant dark for circadian experiments, or constant light for circadian disruption experiments). Due to the effects of acute light on fly behavior, most behavioral assays should be conducted in the dark using dim red light even for those time points testing behavior during the light portion of the LD cycle or under constant light conditions. Flies should be acclimated to the dark for at least 1 h prior to the experiment to minimize variability between experiments. Be consistent in acclimation procedures and lighting conditions. If the circadian clock is the variable tested, experiments must be performed in the circadian condition of constant darkness. 9. Fly larvae in bottles can cause the food to become too liquefied and increase the humidity of the bottles. When this occurs, place a KimWipe in the bottles after clearing them to soak up the extra moisture so that the newly eclosed flies with delicate wings do not become stuck to the media. If flies are raised in vials, it is essential to change the flies to new vials every few days to maintain consistency of the media. Do not overcrowd flies in vials or bottles as this will cause developmental stress on the flies and alter responses to alcohol during adulthood.

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10. It is advisable to keep a strict collection schedule in order to control the conditions of the fly populations in the bottles. Overcrowding can increase the humidity in the bottles and stress the flies. Collect flies at the end of the day-light period on day 1 and store them for 24 h under LD conditions in holding vials containing a small amount of high agar concentration food to minimize food stickiness. To ensure healthy, normally developed flies are collected, only use flies collected within the first 3–4 days after eclosion starts in a culture bottle. If separating sexes for an experiment, use the CO2 station and stereomicroscope to separate the sexes. Remove the flies from the CO2 platform as quickly as possible to minimize the effects of the anesthesia on behavior. 11. The exact number of flies in each vial is not critical as behavioral observations are reported in percentages with total number of flies counted at the end of each experiment. However, overcrowding or too few flies can lead to skewed results. 12. House flies in climate-controlled incubators. 13. Loss of motor control and the onset of sedation represent alcohol-induced behaviors that are measured across species from invertebrates to humans. Drosophila has a natural righting reflex that occurs rapidly using coordination of alternating legs to push against a surface. Increasing alcohol exposure affects the coordination of leg movements impairing the flies’ ability to regain control of motor reflexes after being knocked over. The loss-of-righting reflex (LoRR) and alcohol-induced sedation are easily assessed. In this assay, loss of motor control in the flies is similar to the loss-of-righting reflex assay in mice [40]. Longer periods of alcohol exposure or higher concentrations of alcohol vapor result in sedation, a sleep-like state characterized by the lack of coordinated leg movement [30, 33, 34]. The FlyBar allows refinement of the alcohol concentration to quantify the differences in these two behavioral responses to alcohol as well as increasing the efficiency of screening large collections of genotypes. The experimental timeline in Fig. 3a is an example of a typical LoRR or sedation experiment in our lab. Multiple groups of flies should be tested within a single set of behavioral experiments to increase the robustness of the experimental design and minimize variability specific to a single alcohol exposure. 14. If testing for circadian effects, maintain flies under constant darkness (DD) conditions at 25  C for 2 days. Independent experiments should be performed using a minimum of six time points (circadian time (CT) 1, 5, 9, 13, 17, and 21) to test for circadian modulation of behavior. For example, observations of CT 1 and CT 13 can be obtained simultaneously if two

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incubators with opposite light-dark schedules are used for entrainment. Similarly, experiments testing age effects should compare different age groups of flies with alcohol exposures at the same time. Control groups of flies should be exposed to water vapor alone for comparison. 15. Temperature and humidity strongly affect fly behavior. Elevated temperature and humidity cause flies to further open their spiracles increasing heart and respiration rate resulting in increased metabolic activity, nutrient metabolism, and stress [41, 42]. These variations can influence the outcomes when measuring alcohol behaviors. 16. Throughout this procedure, it is necessary to use clean empty vials. Although this may seem wasteful, flies can release pheromones and other chemicals into the vials that act as signaling molecules [43] and affect the behavioral or stress responses of subsequent batches of flies placed in the vials. To reuse vials, wipe thoroughly between experiments with distilled water. 17. It is recommended that the red light be at least 1200 from the observation vial. 18. These protocols depend heavily on behavioral observations making it necessary to use a standardized protocol to avoid small changes between observers or with time. Control experiments with known results should be performed periodically to verify the function of the experimental apparatus and that behavioral observations remain consistent and within the same range over months and years. To prevent potential bias, vials of experimental flies should be coded so that the individual making the behavioral observations is blind to the genotype, age, or time point of the groups of flies being tested. Individuals performing behavioral experiments should avoid wearing perfumes or colognes or using scented soap in the lab to avoid potential confounds with behavioral responses. 19. The duration of recovery following alcohol-induced sedation can influence subsequent alcohol intake and consumption patterns, in particular binge drinking. The duration of recovery may be influenced by different factors than those modulating alcohol sensitivity or alcohol metabolism. In Drosophila and mice, alcohol metabolism, sedation, and recovery can be separated [44, 45]. Thus, it is important to independently measure the time needed to recover from the sedating effects of alcohol. As it can be difficult to define what constitutes recovery, it is necessary to use a discrete behavior point to measure recovery across experiments. The recovery of motor and postural control is a defined behavior that can be measured, although it may not represent complete recovery from alcohol sedation.

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Experimentally, the restoration of the righting reflex can be used to assess when the flies regain motor control [30]. 20. It is important to note that the sedation end point will depend on age, genotype, sex, and other factors. Across species, alcohol sensitivity and alcohol preferences depend upon genetic background [32, 34]. Consequently, some commonly used markers for transgenic fly lines affect alcohol sensitivity and the length of time needed for sedation. Thus, it may be necessary to perform recovery experiments separately for different genotypes or age groups as the time to total sedation needs to be similar between groups within one experiment or alcohol exposure. For example, the yellow mutation (y) frequently makes fly lines more susceptible to the sedating effects of alcohol while the commonly used loss of function white mutation w1118 that results in white eyes and reduced biogenic amine levels [46] makes flies more resistant to alcohol exposure [34, 47]. 21. Binge drinking represents the most common form of alcohol abuse contributing to acute alcohol mortality with approximately 15% of adults in the US binge drinking at least 4 times per month [48, 49]. Given the increasing incidence of binge drinking among older adults [7], using behavioral paradigms of binge drinking with animal models of aging is critical for understanding the effects of alcohol on lifespan and health. Commonly, we use a repeat binge-like alcohol exposure paradigm to assess alcohol-induced mortality across age groups and in flies deficient in circadian function [33]. An example of an experimental timeline for a repeat alcohol exposure paradigm is shown in Fig. 3b. To determine the correct alcohol concentration to use, it may be necessary to do single alcohol exposure mortality counts first prior to using a repeat binge-like alcohol paradigm. For long-term chronic alcohol exposures, flies may be housed on varying concentrations of ethanol incorporated into the food [33]. 22. The circadian clock modulates alcohol sensitivity in flies, mice, and humans [30, 32, 50]. For flies, the time of least sensitivity to alcohol is the mid-to-late day while greatest alcohol sensitivity occurs during the mid-to-late night [32]. Given the time of day variation in alcohol sensitivity, it is important to always have the alcohol exposures occur at the same circadian time within and between experiments. 23. Although females are more sensitive to alcohol as shown with LoRR and sedation, male flies exhibit increased acute alcohol mortality [31]. Consequently, it may be important in experiments to track the sex of the flies in daily mortality counts. Seven days is usually sufficient for tracking alcohol-induced

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mortality with no significant difference observed between alcohol- and water vapor-exposed flies from 7 days post-exposure onward. 24. The development of tolerance in response to a drug facilitates increased intake of the drug and is a key step in physical dependence and addiction. Tolerance can be either metabolic which includes the machinery that facilitates removal of the alcohol from the body or functional which comprises changes in neural plasticity [51]. The development of functional alcohol tolerance appears to be necessary for addiction. There are at least two types of functional alcohol tolerance, rapid and chronic tolerance, based upon the time frame that tolerance is observed and likely dependent upon different molecular mechanisms, similar to the neural plasticity observed with short- and long-term memory. Rapid tolerance develops following a single acute exposure to alcohol and can be observed after the initial alcohol has been cleared from the system, whereas chronic tolerance develops in response to repeated and prolonged exposures to alcohol [51]. The FlyBar can be used to assess both forms of tolerance; only the timeline of exposure needs to be altered (Fig. 3c, d). 25. Figure 3c, d are examples of experimental timelines that can be used to assess rapid and chronic tolerance, respectively. 26. Care should be taken in choosing the alcohol vapor concentration, as those that are too low will allow the development of tolerance during a 1-h period. Although we typically use 50% alcohol vapor for tolerance pre-exposures and tolerance tests as this works across age groups, some labs using young flies use much higher alcohol exposures [26, 52]. Four hours is sufficient for alcohol to be metabolized and cleared in Drosophila [53]. 27. It is unclear mechanistically as to the differences between chronic and long-term alcohol tolerance in flies. LoRR or sedation can be measured during the test. Tolerance in these tests is defined as an increase in the mean time of the population to reach 50% LoRR or 50% sedation with comparisons made between naı¨ve and pre-exposed flies used for quantification. 28. Many factors influence the absorption and clearance of alcohol from the body including age, sex, presence of other drugs in the system, time of day, and health state of the animal. Measuring alcohol absorbance in groups of flies at various time points after alcohol exposure allows one to determine alcohol absorption and alcohol clearance at the population level [32, 53]. Our lab has used alcohol absorbance assays to investigate circadian modulation of alcohol sensitivity and to probe the mechanisms

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underlying increased alcohol sensitivity with age. As this experiment involves multiple groups for one experiment, it is necessary to collect a large number of age-matched flies for the experimental condition to be tested. 29. For this assay, one does not want the flies to become sedated as involuntary opening of the spiracles upon sedation will increase the rate of alcohol entry and absorbance [32]. Shorter alcohol exposure durations may also be used with higher alcohol concentrations. All vials of flies should be exposed to alcohol simultaneously with the exception of the baseline control group which should be placed in an empty vial for 30 min and then frozen. 30. The data in Fig. 5 were collected using this assay and demonstrate the effect of aging and circadian disruption on alcohol absorbance and the rate of alcohol clearance from flies. The amount of alcohol absorbed is not statistically different between 10-d and 20-d wildtype Canton-S flies (Fig. 5a). However, older flies (30 days) absorb more alcohol during a short exposure to a low concentration of alcohol. Genetic disruption of the core circadian gene period (per01) has effect neither on the amount of alcohol absorbed nor on the rate of alcohol clearance (Fig. 5b), although per01 flies exhibit increased alcohol-induced mortality [33]. 31. Excessive or chronic alcohol consumption is one of the primary causes of liver disease causing almost half of liver disease-related deaths [54] However, the factors which influence the development of alcohol steatosis and cirrhosis remain unclear as only about 30% of heavy drinkers develop alcoholic liver disease [55, 56]. Alcohol-induced changes in intestinal and gut permeability resulting in gut leakiness appear to be a major factor in the development of alcoholic liver disease [57–59]. Although recent research has started to identify factors modulating and the underlying molecular mechanisms of alcohol-induced permeability [60, 61], much remains to be determined. Drosophila, with its powerful genetic tools and thousands of mutant lines, provides an excellent model system to investigate molecular mechanisms contributing to gut permeability or the integrity of the blood brain barrier. The “Smurf assay” in which blue dye is incorporated into the Drosophila food provides a fast and easy method for screening of gut permeability and intestinal dysfunction [62]. Previously, the Smurf assay has been used to assess the physiology of aging on gut integrity as well as the effects of traumatic brain injury on gut integrity [62–64]. 32. Higher alcohol exposures can also result in breakdown of the blood brain barrier resulting in a Smurf phenotype in the head.

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33. The presence of the blue dye outside of the gut indicates a breakdown of the intestinal barrier. Flies may be checked for “smurfness” as early as 24 h after exposure to the blue dye and daily thereafter. However, it can be difficult to detect the early signs of gut leakiness due to the paleness of the blue color in the fly initially. Alcohol toxicity can take several days to induce gut leakiness. Determination of the Smurf phenotype may have to be performed twice per day (early morning and evening) for several days as we have found that with alcohol toxicity the flies die within 10 h after expressing a Smurf phenotype [65]. As our lab and others have found, flies raised and maintained on dye-containing food for extended durations survived and reproduced similar to flies raised on food without dye indicating that there is no toxicity due to the dye [65, 66]. 34. Be careful when measuring and incorporating the blue powder into the fly food. Wear gloves and use disposable items if possible as the blue dye powder can easily stain materials it contacts. 35. Although for the vast majority of flies, flies can be classified as non-Smurf or Smurf, the phenotype can be continuous and some flies have an intermediate phenotype making it difficult to classify [66] unless one has a photographic record. Common data analysis, in our lab and others [62, 66], utilizes a binary system for the Smurf phenotype often using the proportion of Smurf flies over the total number of flies. We investigated the effect of alcohol on gut hyperpermeability with aging or circadian clock dysfunction using the Smurf assay as shown in Fig. 6. The percent flies that express a Smurf phenotype are calculated for each experiment. 36. Blue dye on the feet and legs or the proboscis of the fly is normal due to contact with the dye-containing media. In some cases, for final determination of the Smurf phenotype it may be necessary to transfer the fly to a vial of non-dye containing food for 15 min allowing the fly to clean any dye from its exterior surface before determining whether the fly expresses a Smurf phenotype. The intermediate Smurf phenotype can be observed with aging as aging alone contributes to gut hyperpermeability [62]. Eventually if flies are left on Smurf food long enough, all flies in old age will start to develop a Smurf phenotype. 37. We also have used the Smurf assay with flies raised on different concentrations of a high-fat diet to assess the effects of a high fat diet on gut integrity (K. Lyons data not shown).

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13. Sun Y, Yolitz J, Wang C, Spangler E, Zhan M, Zou S (2013) Aging studies in Drosophila melanogaster. Methods Mol Biol 1048:77–93 14. Beramendi A, Peron S, Casanova G, Reggiani C, Cantera R (2007) Neuromuscular junction in abdominal muscles of Drosophila melanogaster during adulthood and aging. J Comp Neurol 501(4):498–508 15. Cornelissen G, Otsuka K (2016) Chronobiology of aging: a mini-review. Gerontology 63 (2):118–128 16. Robertson M, Keene AC (2013) Molecular mechanisms of age-related sleep loss in the fruit fly – a mini-review. Gerontology 59 (4):334–339 17. Devineni AV, Heberlein U (2013) The evolution of Drosophila melanogaster as a model for alcohol research. Annu Rev Neurosci 36:121–138 18. Narayanan AS, Rothenfluh A (2016) I believe I can Fly!: use of Drosophila as a model organism in neuropsychopharmacology research. Neuropsychopharmacology 41(6):1439–1446 19. Egenriether SM, Chow ES, Krauth N, Giebultowicz JM (2015) Accelerated food source location in aging Drosophila. Aging Cell 14 (5):916–918 20. Eleftherianos I, Castillo JC (2012) Molecular mechanisms of aging and immune system regulation in Drosophila. Int J Mol Sci 13 (8):9826–9844 21. Rakshit K, Krishnan N, Guzik EM, Pyza E, Giebultowicz JM (2012) Effects of aging on the molecular circadian oscillations in Drosophila. Chronobiol Int 29(1):5–14 22. Saitoe M, Horiuchi J, Tamura T, Ito N (2005) Drosophila as a novel animal model for studying the genetics of age-related memory impairment. Rev Neurosci 16(2):137–149 23. Ratliff EP, Mauntz RE, Kotzebue RW, Gonzalez A, Achal M, Barekat A et al (2015) Aging and autophagic function influences the progressive decline of adult Drosophila behaviors. PLoS One 10(7):e0132768. https:// doi.org/10.1371/journal.pone.0132768 24. Park A, Ghezzi A, Wijesekera TP, Atkinson NS (2017) Genetics and genomics of alcohol responses in Drosophila. Neuropharmacology 122:22–35. https://doi.org/10.1016/j. neuropharm.2017.01.032 25. Landayan D, Wolf FW (2015) Shared neurocircuitry underlying feeding and drugs of abuse in Drosophila. Biom J 38(6):496–509 26. Devineni AV, Heberlein U (2009) Preferential ethanol consumption in Drosophila models

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Chapter 8 Using Genome-Editing Tools to Develop a Novel In Situ Coincidence Reporter Assay for Screening ATAD3A Transcriptional Inhibitors Liwei Lang and Yong Teng Abstract Transgene-based reporter gene assays have been used for discovery of inhibitors targeting vital gene transcription. In traditional assays, the reporter gene is commonly fused with a cloned promoter and integrated into a random genomic location. This has been widely applied but significantly dampened by disadvantages, including incomplete cis-acting elements, the influence of foreign epigenetic environments, and generation of false hits that disrupt the luciferase reporter activity. Therefore, there is a need to develop novel strategies for developing in situ reporter assays closely mimicking endogenous gene expression without disrupting its function. By employing the CRISPR-Cas9 system, we developed an effective in situ coincidence reporter system with a selection marker in the endogenous locus of ATAD3A, which provides a means of screening for transcription-targeted lead compounds with high confidence. Key words ATAD3A, CRISPR-Cas9, Coincidence reporter, In situ, Transcriptional inhibition

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Introduction Transgene-based reporter gene assays have been widely used to identify transcriptional inhibitors targeting vital gene transcription on a large scale. In traditional transgene-based reporter gene assays, reporter genes, such as firefly and Renilla luciferases, are commonly fused with a cloned promoter and integrated into a random genomic location. Success has been largely hampered by the lack of complete cis-acting elements, the influence of foreign epigenetic environments, and the generation of false hits that disrupt the luciferase enzyme activity [1, 2]. To circumvent these disadvantages, several genomic editing tools including the adeno-associated virus (AAV) system [2], zinc finger nucleases (ZFNs) [3], transcription activator-like effector-based nucleases (TALENs) [4], and clustered regularly interspaced short palindromic repeats (CRISPR-Cas9) system [2, 5] have been developed to mimic the

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_8, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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endogenous regulation of the target gene transcription more precisely. Compared with other genome-editing systems, the ability of CRISPR-Cas9 to precisely and efficiently alter endogenous gene expression through targeted genome editing makes possible the wide use of this system as in situ reporter gene assays [6]. The insertion of a reporter gene into genomic deoxyribonucleic acid (DNA) can be achieved through homology-directed repair (HDR) in the CRISPR-Cas9 system. Given the fact that single reporter gene assay is frequently influenced by false hits in high throughput screening (HTS) [7], several strategies have been developed to circumvent this, including generating dual reporter genes with recombinase-mediated cassette exchange (RMCE) [2], and recruiting a coincidence reporter gene system [8]. pCI9.4, a promoterless version of FLuc-P2A-NLuc coincidence reporter plasmid containing a puromycin resistance gene [puromycin N-acetyltransferase, (pac)], has been constructed recently as an ideal template vector to develop in situ coincidence reporter gene assays [9]. The expression of firefly luciferase (FLuc) and nanoluciferase (NLuc) is simultaneously driven by the same target transcription regulatory elements, such as the promoter and enhancer regions. Moreover, insertion of coincidence reporter genes in the target gene locus through HDR can be efficiently enriched by puromycin selection. ATPase family AAA domain containing 3A (ATAD3A) is a gene encoding a ubiquitously expressed mitochondrial membrane protein that contributes to mitochondrial dynamics, nucleoid organization, cell growth, and cholesterol metabolism [10, 11]. Interestingly, elevated ATAD3A gene expression is strongly associated with low survival of patients with breast, lung, and other types of cancer [10–12]. Knockdown of ATAD3A by lentivirusmediated small harpin ribonucleic acid (shRNAs) significantly induces repression of tumor growth and metastasis [10], indicating that ATAD3A may be an attractive target for cancer treatment. Currently, directly targeting ATAD3A using small molecule inhibitors is challenging and not feasible. Here, we offer an effective way to search for ATAD3A-targeted transcriptional inhibitors by developing a novel in situ coincidence reporter gene assay. Our novel reporter assays are powerful in identifying transcription-targeted lead compounds with high confidence, which should interest a range of cancer scientists and clinicians who seek to assess the feasibility of manipulating currently undruggable targets for therapeutic purposes.

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Materials 1. Vectors: pCI9.4 and pSpCas9(BB)-2A-Puro (PX459) V2.0 (Addgene; Watertown, MA, USA) (see Note 1).

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2. 1 μg/μL sgATAD3A oligonucleotides. 3. FastDigest BpiI restriction endonuclease (ThermoFisher Scientific, Waltham, MA, USA) (see Note 2). 4. KpnI, HindIII, SalI, and PciI restriction endonucleases (New England Biolabs; Ipswich, MA, USA) (see Note 2). 5. QIAquick polymerase chain reaction (PCR) Purification Kit (Qiagen, Hilden, Germany) (see Note 3). 6. Q5 High-Fidelity DNA polymerase (New England Biolabs, Ipswich, MA, USA). 7. PureLink™ Quick Gel Extraction Kit (Invitrogen, Carlsbad, CA, USA) (see Note 3). 8. Quick Ligation Kit (New England Biolabs, Ipswich, MA, USA) (see Note 3). 9. Super optimal broth with catabolite repression (SOC) medium, lysogeny broth (LB) agar plates containing 100 μg/ mL ampicillin. 10. QIAprep Spin Miniprep Kit (Qiagen, Hilden, Germany) (see Note 3). 11. NEB® Stable Competent Escherichia coli (New England Biolabs, Ipswich, MA, USA) (see Note 4). 12. Lipofectamine™ 3000 transfection reagent (Invitrogen, Carlsbad, CA, USA). 13. HN12 cells (see Note 5). 14. Dulbecco’s Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS), 100 IU/mL penicillin and 100 μg/ mL streptomycin. 15. Puromycin (Sigma-Aldrich, St. Louis, MO, USA). 16. GeneJET Genomic DNA Purification Kit (ThermoFisher Scientific, Waltham, MA, USA ) (see Note 3). 17. 96-well solid white tissue culture–treated assay plates (Corning, Corning, NY, USA). 18. Nano-Glo® Dual-Luciferase® Reporter Assay System (Promega, Madison, WI, USA) (see Note 3). 19. GloMax® 20/20 Luminometer (Promega, Madison, WI, USA) (see Note 6). 20. SpectraMax® L Microplate Reader (Molecular Devices; San Jose, CA, USA) (see Note 6). 21. Actinomycin D (Sigma-Aldrich, St. Louis, MO, USA).

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Methods

3.1 Generation of CRISPR-Cas9 Plasmid Targeting ATAD3A

1. Design target sequence of sgRNA for ATAD3A which locates in the upstream and next to transcriptional start site (TSS) of ATAD3A using online tool (https://zlab.bio/guide-designresources) (see Note 7). 2. Add 1 μL each oligonucleotide to 16 μL double distilled water (DDW). 3. Heat at 95
C 5 min on a heating block. 4. Add 2 μL 1 M NaCl, replace on heating block and decrease temperature gradually to room temperature. 5. Dilute the annealed oligonucleotides 1:10 by adding 1 μL each oligonucleotide to 9 μL of DDW. 6. Digest pX459V2 plasmid with BpiI. 7. Run agarose gel and purify the linearized plasmid DNA using PureLink™ Quick Gel Extraction Kit. 8. Ligate the 10 ng annealed sgATAD3A oligos and 50 ng linearized pX459V2 using the Quick Ligation kit. 9. Transform the plasmid into a competent E. coli strain. 10. Pick three colonies into 5 mL LB with100 μg/mL ampicillin, incubate the culture and isolate the plasmid DNA from cultures by using the QIAprep spin miniprep kit. 11. Verify the sequence of each colony using the U6-forward primer. 12. Designate correct insertion of sgATAD3A into pX459V2 as pX459V2-sgATAD3A.

3.2 Preparation of Coincidence Reporter Gene Construct with Homology Sequences Targeting ATAD3A (Fig. 1)

1. Design primers for amplify the homology left arm and right arm of ATAD3A for the HDR template. 2. Amplify the homology left arm of ATAD3A (961 bp) with HN12 genomic DNA using the Q5 High-Fidelity DNA polymerase. 3. Run the products on a 1% agarose gel and purify the ATAT3Aleft arm using PureLink™ Quick Gel Extraction Kit. 4. Simultaneously digest purified 1 μg ATAD3A-left arm products and 1 μg pCI9.4 in separate microcentrifuge tubes using KpnI and HindIII DNA restriction enzymes. 5. Purify linearized pCI9.4 and ATAD3A-left arm using the QIAquick PCR Purification Kit. 6. Ligate the purified ATAD3A-left arm fragment and linearized pCI9.4 using Quick Ligation Kit using a 3:1 (fragment;plasmid) ratio (see Note 8).

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Fig. 1 The in situ coincidence reporter gene assay in searching for the transcriptional inhibitors of ATAD3A. (a) Schematic of pCI9.4-ATAD3A. The coincidence reporter genes and pac gene are adjacent to homology arms of ATAD3A. LA homology left arm, RA homology right arm, pac puromycin resistance gene, FLuc firefly luciferase, NLuc nanoluciferase. (b) Schematic figure showing insertion of a cassette into in the native ATAD3A locus on Chromosome 1. The cassette contains coincidence reporter genes (FLuc-P2A-NLuc) and the pac gene. TSS transcriptional start site

7. Transform 2 μL ligation reaction into NEB® Stable Competent E. coli according to manufacturer’s protocol. 8. Pick five single colonies and grow each overnight in 5 mL LB containing 100 μg/mL ampicillin. 9. Isolate DNA from 4 mL bacterial culture using QIAprep Spin Miniprep kKit. 10. Digest plasmids with KpnI and HindIII DNA restriction enzymes as above. 11. Run on an agarose gel to identify colonies that produce plasmids with the ATAD3A-left arm insertion (see Note 9). 12. Amplify the homologous right arm of ATAD3A (960 bp) from HN12 genomic DNA using the Q5 High-Fidelity DNA polymerase. 13. Run 1% agarose gel and purify ATAT3A-right arm using PureLink™ Quick Gel Extraction Kit. 14. Simultaneously digest 1 μg ATAD3A-right arm product and 1 μg pCI9.4-left arm ATAD3A in separate 1.5 mL microcentrifuge tubes using SalI and PciI DNA restriction enzymes. 15. Repeat steps 3–11, using SalI and PciI to identify colonies that produce plasmids with the ATAD3A-right arm insertion. 16. Designate as pCI9.4-ATAD3A (see Note 10).

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3.3 Generate HN12-ATAD3A-FLucNLuc Cells (SeeNote 11)

1. Seed the HN12 cells in a 6-well tissue culture plate overnight at 37
C under 5% CO2. 2. Transfect sequence-verified pX459V2-sgATAD3A pCI9.4-ATAD3A at a 1:1 ratio into HN12 cells.

and

3. After 24 h following transfection, seed cells into three 100 mm dishes and three 96-well plates using limiting dilution to obtain monoclones. 4. Incubate cells with 1.0 μg/mL puromycin and select puromycin-resistant cells over 2–3 weeks. 5. Select single drug resistant clones and subculture these for expansion. 6. Determine the FLuc and NLuc expression in each clone using Nano-Glo® Dual-Luciferase® Reporter Assay System. 7. Verify correct insertion in the ATAD3A gene by PCR using standard procedures. 8. Prepare genomic DNA using GeneJET Genomic DNA Purification Kit and verify insertion of coincidence report genes with puromycin resistance gene into the locus of ATAD3A with the left and right arm-verified primers by PCR (see Note 12). 9. Confirm transcriptional repression of the coincidence reporter genes induced by potential ATAD3A transcriptional inhibitors (see Note 13).

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Notes 1. pCI 9.4 is a version of the FLuc-P2A-NLuc coincidence reporter plasmid but does not contain a promoter (instead it contains a 50 multiple cloning site for insertion of a promoter or response element of interest (https://www.addgene.org/ 74230/). pSpCas9(BB)-2A-Puro (PX459) V2.0 was a gift from Feng Zhang [6] and pCI9.4 was a gift from James Inglese [9]. 2. This protocol has been optimized for use with these restriction endonucleases. The same enzymes from other suppliers will work but optimization may be required. 3. This protocol has been optimized for use with these kits. Other kits may be used although optimization for specific use may be required. 4. This protocol has been optimized for use with these E. coli cells. Other E. coli cells may be used although optimization for specific use will be required. 5. HN12 cells are part of the OPC-22 oral and pharyngeal cancer cell line panel. These cells are available from multiple suppliers

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such as ThermoFisher Scientific, Promocell, and Accegen Biotechnology. 6. This protocol has been optimized for use with this equipment. Other similar devices may be used, although optimization for specific use will be required. 7. CRISPR/Cas tools include software and bioinformatics to aid in the design of guide RNAs (gRNAs) for use with the CRISPR/Cas system. 8. Using a higher molar ratio of the intended insert increases the chances of a successful ligation. This should be determined by optimization studies using different insert: plasmid ratios. 9. This should be verified by DNA sequencing. 10. This provides the template for HDR. 11. Ensure that all procedures for use of human cell lines have been approved by the appropriate agency. 12. The PCR product for the correct insertion of left side is 1346 bp and that for the right side is 1401 bp. 13. Plate 1  104 cells into a 96-well white solid-bottom plate overnight and treat cells with the potential ATAD3A transcriptional inhibitors (e.g. 1 μM actinomycin D), followed by detection of the luminescent signal for each luminescent reporter within 16 h.

Acknowledgments This research was supported by NIH grant R03DE028387 and R01DE028351 (to Y.T.). References 1. Chau N-M, Rogers P, Aherne W, Carroll V, Collins I, McDonald E et al (2005) Identification of novel small molecule inhibitors of hypoxia-inducible factor-1 that differentially block hypoxia-inducible factor-1 activity and hypoxia-inducible factor-1α induction in response to hypoxic stress and growth factors. Cancer Res 65(11):4918–4928 2. Lang L, Ding H-F, Chen X, Sun S-Y, Liu G, Yan C (2015) Internal ribosome entry sitebased bicistronic in situ reporter assays for discovery of transcription-targeted lead compounds. Chem Biol 22(7):957–964 3. Dansithong W, Paul S, Scoles DR, Pulst SM, Huynh DP (2015) Generation of SNCA cell models using zinc finger nuclease (ZFN) technology for efficient high-throughput drug

screening. PLoS One 10(8):e0136930. https://doi.org/10.1371/journal.pone. 0136930 4. Hasson SA, Fogel AI, Wang C, MacArthur R, Guha R, Heman-Ackah S et al (2015) Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter. ACS Chem Biol 10 (5):1188–1197 5. Yang W, Zhang S, Zhang Y, Huang X (2017) A novel strategy to dissect endogenous gene transcriptional regulation in live cells. Biochem Biophys Res Commun 487(3):573–579 6. Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F (2013) Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8 (11):2281–2308

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7. Auld DS, Southall NT, Jadhav A, Johnson RL, Diller DJ, Simeonov A et al (2008) Characterization of chemical libraries for luciferase inhibitory activity. J Med Chem 51(8):2372–2386 8. Cheng KC, Inglese J (2012) A coincidence reporter-gene system for high-throughput screening. Nat Methods 9(10):937. https:// doi.org/10.1038/nmeth.2170 9. Schuck BW, MacArthur R, Inglese J (2017) Quantitative high-throughput screening using a coincidence reporter biocircuit. Curr Protoc Neurosci 79:5.32.1–5.32.27. https://doi. org/10.1002/cpns.27

10. Teng Y, Ren X, Li H, Shull A, Kim J, Cowell JK (2016) Mitochondrial ATAD3A combines with GRP78 to regulate the WASF3 metastasis-promoting protein. Oncogene 35 (3):333–343 11. Dickerson T, Jauregui CE, Teng Y (2017) Friend or foe? Mitochondria as a pharmacological target in cancer treatment. Future Med Chem 9(18):2197–2210 12. Fang H-Y, Chang C-L, Hsu S-H, Huang C-Y, Chiang S-F, Chiou S-H et al (2010) ATPase family AAA domain-containing 3A is a novel anti-apoptotic factor in lung adenocarcinoma cells. J Cell Sci 123(Pt 7):1171–1180

Chapter 9 Visualizing and Evaluating Cancer Cell Growth and Invasion by a Novel 3D Culture System Chloe Shay, Liwei Lang, and Yong Teng Abstract With the development of new materials and technologies, it is possible to access gene function and drug metabolism using a three-dimensional (3D) cell culture system, which is more suitable for mimicking the in vivo microenvironment of cultured tumor cells ex vivo. SeedEZ is a novel and versatile tool that allows culturing of different types of cells with user convenience and in a desired sequence. This system provides a bridge between traditional 2D culture and animal experiments. Here, we provide two examples demonstrating how to evaluate cancer cell growth by the SeedEZ 3D scaffold and cancer cell invasion by the SeedEZ 3D ring, in order to promote understanding of the necessity of this novel cell culture system. Key words 3D cell culture, SeedEZ, Growth and invasion, Cancer research

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Introduction Two-dimensional (2D) cell culture has been wildly used to elucidate cancer biology mechanisms and evaluate the efficiency of anticancer treatments in vitro. However, 2D culture has inherent drawbacks such as limited surface area and the absence of spatial contact, which limits the potential to predict cancer cell responses in vivo [1]. Recently, 3D cell culture systems that exhibit a higher potential of mimicking the in vivo microenvironment of cultured tumor cells ex vivo have been developed and applied [2, 3]. Developed 3D cultures, ranging from the simple cell spheroid model to complex tissues, offer a useful platform for better understanding the biological characteristics of cancer cells and for cancer stem cell enrichment and drug discovery. 3D cell culture systems can be classified into two categories: (1) scaffold techniques and (2) scaffold-free techniques [4]. The SeedEZ scaffold is a complete 3D cell culture system that provides a convenient framework for organ-like tissue models and 3D cellbased assays. Due to the inherent differences in complexity and

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_9, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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functionality, the choice of model is usually dependent on the application. In order to promote understanding of the necessity of SeedEZ 3D cell culture system, we evaluated the effects of ATPase family AAA domain containing 3A (ATAD3A) on cancer cell growth and the effects of NCK-associated protein 1 (NCKAP1) on cancer cell invasion using the SeedEZ 3D scaffold and 3D ring approaches, respectively. Targeted deletion of the gene encoding ATAD3A led to hyperactivated mitophagy in mouse hematopoietic cells and mice showed reduced survival, decreased bone-marrow cellularity, erythroid anemia, and B cell lymphopenia [5]. NCKAP1 is highly expressed in primary non-small-cell lung cancer compared to normal lung tissues, and expression of this protein is associated with histological tumor grade, metastasis, and poor survival rate of patients [6]. The protocols for both 3D culture procedures using the ATAD3A knockout (KO) and NCKAP1 knockdown (KD) cancer cells are presented in this chapter.

2

Materials 1. SC-CO48 SeedEZ scaffold discs for 48 well plates (Lena Biosciences, Atlanta, GA, USA) (see Note 1). 2. SC-CO24 SeedEZ 3D rings (Lena Biosciences, Atlanta, GA, USA) (see Note 2). 3. Poly-D-lysine (PDL) hydrobromide (Sigma-Aldrich, St. Louis, MO, USA). 4. Bovine serum albumin (BSA) (Sigma-Aldrich, St. Louis, MO, USA). 5. HN12 head and neck cancer cell line (see Note 3). 6. H661 non-small cell lung cancer cell line (see Note 4). 7. Culture medium: Dulbecco’s Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS), 100 IU/ mL penicillin and 100 μg/mL streptomycin. 8. Freshly prepared 4% formaldehyde in phosphate buffered saline (PBS). 9. Triton X-100 (Sigma-Aldrich, St. Louis, MO, USA). 10. 1 μg/mL 40 ,6-diamidino-2-phenylindole (DAPI) (SigmaAldrich, St. Louis, MO, USA). 11. Phosphate-buffered saline (PBS). 12. PBS containing 0.1% Tween-20 (PBST). 13. Texas Red-X phalloidin: 1:200 diluted in 1% BSA (Invitrogen; Waltham, MA, USA).

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14. Aqueous mounting liquid for fluorescence (Immu-Mount; ThermoScientific, Waltham, MA, USA). 15. BD Matrigel™ Matrix (BD Biosciences; Oxford, UK) (see Note 5). 16. 48- and 24-well culture plates. 17. Cell culture incubator. 18. Fluorescence Switzerland).

3

microscope

(Carl

Zeiss

AG;

Feldbach,

Methods

3.1 Visualizing and Evaluating Cancer Cell Growth Using a 3D System

1. Coat 48-well SeedEZ scaffold discs with 250 μL 100 μg/mL PDL hydrobromide in a 48-well plate. 2. Incubate for at least 6 h (see Note 6). 3. Aspirate poly-D-lysine hydrobromide solution and wash the scaffold with sterile de-ionized water three times and dry. 4. Prepare ATAD3A knockout (KO) and the parental HN12 cell suspensions in DMEM medium with a concentration of 1  105 cells/mL (see Note 7). 5. Seed 40 μL cells into each scaffold by dispensing near the center of the scaffold. 6. Place up to 48 discs for testing different conditions and using multiple replicates in a 48-well culture plate. 7. Cover the plate, transfer to an incubator and incubate at 37  C under 5% CO2 for at least 30 min (see Note 8). 8. Add 250 μL culture medium per well, and culture cells for 2 weeks at 37  C under 5% CO2 (see Note 9). 9. At the endpoint of culture, remove the medium, wash the scaffold with PBS three times. 10. Fix the cells with 4% formaldehyde for 10 min. 11. Wash the scaffolds with PBS three times (2 min/wash). 12. Add 0.1% Triton-X100 and leave for 15 min. 13. Wash the scaffold with PBS three times (2 min/wash). 14. Stain cells with Texas Red-X phalloidin (1:200 diluted in 1% BSA) for 30 min (see Note 10). 15. Wash the scaffolds with PBST three times (5 min/wash). 16. Stain cells with 1 μg/mL DAPI for 10 min (see Note 11). 17. Wash the scaffolds with PBST three times (5 min/wash). 18. Transfer the scaffolds on to glass slides, add one drop of aqueous mounting liquid and add the cover glass.

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Fig. 1 Effect of ATAD3A gene loss on HN12 cell growth after 2-week culture in the SeedEZ 3D scaffold. Red indicates cells stained with Texas Red-X phalloidin, and blue indicates cells stained with DAPI

19. Take images using a fluorescence microscope (Fig. 1). 20. Measure and quantify the fluorescence intensity by Image J (see Note 12). 3.2 Visualizing and Evaluating Cancer Cell Invasion Using a 3D System

1. Freeze 24-well SeedEZ 3D scaffold rings at ‑20  C overnight. 2. Dip the rings into the ice-cold coating mixture (see Note 13). 3. Solidify the coating mixture by at 37  C under 5% CO2 for 4 h. 4. Remove any solid gel in the middle part the rings. Cool the SeedEZ rings to 4  C by placing into a refrigerator. 5. Prepare NCKAP1 knockdown (KD) and control H661 cell suspensions in the coating mixture at a concentration of 1  105 cells/mL (see Note 14). 6. Add 10 μL cell suspension into the center of SeedEZ ring. 7. Solidify the cell suspension in the coating mixture by incubation at 37  C under 5% CO2 for 4 h. 8. Add 500 μL culture medium per well, and culture cells for 2 weeks at 37  C under 5% CO2 (see Note 9). 9. After 2 weeks, remove the medium and wash the scaffold with PBS three times. 10. Fix cells with 4% formaldehyde for 10 min and wash with PBS three times (2 min/wash). 11. Add 0.1% Triton-X100 and leave for 15 min and wash with PBS three times (2 min/wash).

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Bright field

171

Merge

Control

Edge of ring

NCKAP1 KD

Edge of ring

Fig. 2 Effect of NCKAP1 gene loss on H661 cell invasion after 2-week culture in the SeedEZ 3D ring. Red indicates cells stained with Texas Red-X phalloidin

12. Stain cells with diluted Texas Red-X phalloidin for 30 min as above and wash with PBST three times (5 min/wash) (see Note 15). 13. Transfer on to glass slides, add one drop of aqueous mounting liquid and add the cover glass. 14. Take images using a fluorescence microscope (Fig. 2). 15. Measure the distance of cell invasion from the ring to the outside scaffold.

4

Notes 1. This is an inert, hydrophilic, 3D scaffold disc (9.5 mm diameter) optimized for long-term cell growth. It is suitable for cell culture and drug testing, amenable to extracellular matrices and compatible with all cell types, cell culture reagents, stains, coatings, extracellular matrices and hydrogels used in cell culture applications. This format is suitable for in-well assays. 2. This is an inert, hydrophilic, 3D scaffold disc (14.3 mm diameter) optimized for long-term cell growth, accepts extracellular matrices and suitable for cell culture and drug testing, as above via in-well assays.

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3. HN12 cells are part of the OPC-22 oral and pharyngeal cancer cell line panel [7]. 4. H661 cells are derived from metastatic site (lymph node) of non-small cell lung cancer [8]. 5. The BD Matrigel basement membrane matrix is used for attachment and differentiation of both normal and transformed cells requiring anchorage. Such cells include neurons, vascular endothelial cells, hepatocytes and various tumor cells. It is also useful for tumor cell invasion assays and supports propagation of these cells. 6. Overnight incubation is recommended to minimize waiting time on the first day. 7. ATAD3A is a mitochondrial protein contributing to mitochondrial dynamics and metabolism. Our previous study shows that loss of ATAD3A expression inhibited cell proliferation and tumor growth [9, 10]. In this study, ATAD3A KO HN12 cells were generated using the CRISPR/Cas 9 system. 8. This step allows cells to start adhering. HN12 cells are easier to attach to the scaffold than H661 cells. 9. Cells seeded into the SeedEZ may be of various origins and sources, and the required incubation time may vary for the different cell types and different research purposes. During the period of long-term culture, the cells must be fed every 2–3 days by exchanging half of the medium. 10. Texas Red-X phalloidin is a high-affinity F-actin probe conjugated to a red fluorescent Texas Red-X dye with excitation and emission optima of 591 nm and 608 nm, respectively. 11. DAPI is a blue-fluorescent deoxyribonucleic acid (DNA) stain for the cellular nucleus. Excitation/Emission: 358/461 nm. 12. Image J can be freely downloaded from https://imagej.nih. gov/ij/download.html. 13. The coating can also be achieved by dispensing coating mixture with pipette. Pipet 100 μL of coating mixture (the ratio of DMEM medium and Matrigel is 1:1) into the outer portion of the ring, move the pipette around the scaffold to ensure the coating mixture is even and saturated. 14. NCKAP1 is a protein that associates with the Src homology 3 (SH3) domain of NCK protein both in vitro and in intact cells. Our previous study shows that NCKAP1 localizes along the lamellipodia and mediate contact-dependent cell migration and invasion [6, 11], with no effects on cancer cell growth. In this study, NCKAP1 KD H661 cells were achieved by lentiviral-mediated gene interference. 15. Cells can be optionally stained with 1 μg/mL DAPI for 10 min and wash with PBST three times (5 min/wash).

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Acknowledgments This research was supported by NIH grant R03DE028387 and R01DE028351 (to Y.T.). References 1. Duval K, Grover H, Han L-H, Mou Y, Pegoraro AF, Fredberg J et al (2017) Modeling physiological events in 2D vs. 3D cell culture. Physiology 32:266–777 2. Yamada KM, Cukierman E (2007) Modeling tissue morphogenesis and cancer in 3D. Cell 130:601–610 3. Lv D, Hu Z, Lu L, Lu H, Xu X (2017) Threedimensional cell culture: a powerful tool in tumor research and drug discovery. Oncol Lett 14:6999–7010 4. Fang Y, Eglen RM (2017) Three-dimensional cell cultures in drug discovery and development. SLAS Discov 22(5):456–472 5. Jin G, Xu C, Zhang X, Long J, Rezaeian AH, Liu C et al (2018) Atad3a suppresses Pink1dependent mitophagy to maintain homeostasis of hematopoietic progenitor cells. Nat Immunol 19(1):29–40 6. Xiong Y, He L, Shay C, Lang L, Loveless J, Yu J et al (2019) Nck-associated protein 1 associates with HSP90 to drive metastasis in human nonsmall-cell lung cancer. J Exp Clin Cancer Res

38(1):122. https://doi.org/10.1186/ s13046-019-1124-0 7. Lee BKB, Gan CP, Chang JK, Tan JL, Fadlullah MZ, Abdul Rahman ZA et al (2018) GENIPAC: a genomic information portal for head and neck cancer cell systems. J Dent Res 97 (8):909–916 8. Carney DN, Gazdar AF, Nau M, Minna JD (1985) Biological heterogeneity of small cell lung cancer. Semin Oncol 12(3):289–303 9. Teng Y, Ren X, Li H, Shull A, Kim J, Cowell JK (2016) Mitochondrial ATAD3A combines with GRP78 to regulate the WASF3 metastasis-promoting protein. Oncogene 35 (3):333–343 10. Dickerson T, Jauregui CE, Teng Y (2017) Friend or foe? Mitochondria as a pharmacological target in cancer treatment. Future Med Chem 9(18):2197–2210 11. Teng Y, Qin H, Bahassan A, Bendzunas NG, Kennedy EJ, Cowell JK (2016) The WASF3NCKAP1-CYFIP1 complex is essential for breast cancer metastasis. Cancer Res 76 (17):5133–5142

Chapter 10 Exploiting Plug-and-Play Electrochemical Biosensors to Determine the Role of FGF19 in Sorafenib-Mediated Superoxide and Nitric Oxide Production in Hepatocellular Carcinoma Cells Lixia Gao and Yong Teng Abstract Electrochemical biosensors provide rapid, selective, and sensitive diagnostic platforms for detecting and monitoring biochemical processes in living systems in vivo and in vitro, and have been widely applied in various fields of biology and medicine. Sorafenib is a multi-kinase inhibitor used as a standard therapy for advanced hepatocellular carcinoma (HCC). However, the molecular basis for sorafenib resistance in HCC remains elusive. Recently, we developed new protocols for an electrochemical biosensor and applied these to monitor the levels of superoxide and nitric oxide produced in HCC cells, in the presence or absence of sorafenib. We also employed electrochemical biosensor to determine the release of profiles of superoxide and nitric oxide in sorafenib-treated HCC cells under the influence of fibroblast growth factor 19 expression levels. Here we present protocols to highlight the utility of electrochemical strategies in drug and gene studies. Key words Electrochemical biosensors, Sorafenib, FGF19, Superoxide, Nitric oxide

1

Introduction Biosensor-related research has experienced explosive growth over the last two decades and several methods have been successfully used to immobilize biological recognition molecules onto sensing surfaces with full functionality in biosensor-binding assays, such as enzyme-based electrochemical biosensors [1]. By converting a biological response into an electrical signal, electrochemical biosensors provide an attractive means to determine the content of biological samples or drugs through detecting and monitoring biochemical processes in living systems upon physiological or pathological changes [1]. Details of electrochemical detection can be found in our review paper published in 2016 [1]. These electroanalytical methods generally require a three-electrode setup (working,

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_10, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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Fig. 1 Schematic diagram of classic three-electrode electrochemical biosensor detection system

counter, reference) with a potentiostat (Fig. 1). Electrochemical biosensors may be useful for drug-based and genetic studies through advances in quantification of biological or biochemical processes in a variety of biomedical and biotechnological applications in a cost- and time-effective manner. Superoxide (O2l
) and nitric oxide (NO) are the major free radicals and important signaling molecules that contribute to the pathogenesis of many diseases, including Alzheimer’s disease, myocardial infarction, and cancer. It is noteworthy that O2l
and NO can react with each other to produce significant amounts of a more oxidatively-active molecule called the peroxynitrite anion (ONOO
). This molecule causes deoxyribonucleic acid (DNA) fragmentation and lipid oxidation. Thus, there is a need to develop a method to monitor release of O2l
and NO in cells in order to evaluate oxidative stress. Fibroblast growth factor 19 (FGF19) is a metabolic regulator gene that belongs to the hormone-like FGF family of signal molecules, and has activity as an ileum-derived postprandial hormone [2–4]. FGF19 regulates hepatic bile acid levels through modulation of bile acid synthesis when it binds to the specific receptor FGFR4 [5]. FGF19 has been identified as an oncogenic driver in hepatocellular carcinoma (HCC) cells, and our previous studies have demonstrated that the FGF19/FGFR4 signaling contributes to the resistance of these cells to sorafenib [6, 7]. By employing electrochemical biosensors, we revealed that over-expression of FGF19 abrogated the sorafenib-induced increase in the intracellular levels of O2l
and NO in HCC cells, leading to apoptosis resistance. These results provide a critical rationale for targeting the FGF19 signaling axis in patients with sorafenib-resistant HCC. Here, we present new protocols for detecting the intracellular levels of O2l
and NO using electrochemical biosensors.

O2l– and NO Determination in HCC Cells

2

177

Materials (See Note 1)

2.1 Synthesis of O2l
Electrochemical Sensor

1. Deoxyribonucleic acid (DNA), low molecular weight from salmon sperm (see Note 2). 2. 0.1 M MnSO4. 3. 0.1 M K3PO4. 4. Superoxide dismutase (SOD). 5. 0.5 mg/mL multi-walled carbon nanotube (CNT) (SigmaAldrich, St. Louis, MO, USA) (see Note 2). 6. 5% Nafion® 117 solution (Sigma-Aldrich, St. Louis, MO, USA). 7. 0.01 M phosphate-buffered saline (PBS). 8. 15 mg/mL graphene oxide. 9. 0.1 M KO2.

2.2 Synthesis of NO Electrochemical Sensor

1. 0.4 g poly-vinylpyrrolidone (PVP). 2. 0.434 g Ce(NO3)3l6H2O. 3. 3 mM NaOH. 4. 1 mM hemoglobin.

2.3 Polishing of Glassy Carbon Electrode

1. 0.3 and 0.05 mm alumina powder (ChenHua Instruments, Shanghai, China). 2. 0.1 M KCL. 3. 5 mM K3Fe(CN)6. 4. 5 mM K4[Fe(CN)6]l3H2O.

2.4

Regents

1. HCC cells MHCC97L (ATCC, Rockville, MD, USA). 2. Sorafenib tosylate (Selleckchem; Houston, TX, USA). 3. Complete growth medium: 90% Gibco® Dulbecco’s modified Eagle’s medium (DMEM), containing 10% fetal bovine serum (FBS). 4. 0.25% trypsin-ethylenediaminetetraacetic EDTA).

acid

(Trypsin-

5. 0.01 M 3-(N-morpholino) propane sulfonic acid (MOPS) buffer (pH 7.2). 6. Dimethyl sulfoxide (DMSO). 2.5 Equipment (See Note 3)

1. Cyclic voltammetry (CV) potentiostat. 2. CHI760E electrochemical detector, glassy carbon electrode (d ¼ 3 mm) (ChenHua Instruments).

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3. Hg/HgCl2/KCl reference electrode (ChenHua Instruments, Shanghai, China). 4. A platinum wire counter electrode (ChenHua Instruments, Shanghai, China). 5. Teflon-lined autoclave.

3 3.1

Methods Cell Culture

1. Remove the cryovial containing the frozen MHCC97L cells from liquid nitrogen storage and immediately place it into a 37  C water bath. 2. Quickly thaw MHCC97L cells ( 78, 80

25

2

127 > 84

25

NA

124 > 78

25

NMN

335 > 123

20

2

H4-NAM

339 > 127

20

+

NAD

664 > 428, 524

18

2

H4-NAD+

668 > 428, 524

18

NAAD

665 > 136, 428

35

NADH

666 > 514

20

2

671 > 654

20

NaMN

336 > 124

25

NADP

744 > 136, 508

30

NADPH

746 > 729

20

cADPR

560 > 136

28

ADPR

542 > 232, 428

28

H4-NMN

H5-NADH

1. Process spectra and peak integrate peak areas integrated using the Xcalibur software (Table 1). 2. Set the data for automated processing using the same software (see Note 16). 3. Determine the concentrations of the NADome components in the biological samples using the respective standard curves.

4

Notes 1. It is well known that the type of anticoagulant used affects the NADome. For example, Li  Heparin and EDTA increase the ionization efficiency of many metabolites. In addition, plastic from the blood collection tubes produces chemical noise that varies from one manufacturer to the other. 2. Other salt solutions (e.g., Hank’s) or cell culture media (e.g., RPMI 1640) can also be used. 3. Ficoll-Paque density gradient media should be warmed to 18–20  C prior to use.

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4. Platelets should be stored in polypropylene, polyethylene or polycarbonate lab ware. 5. Column selection is important when quantifying the NADome. The LUNA column is suitable for measuring NAD + and its metabolites in terms of accuracy, reliability, and reproducibility. 6. Prolonged exposure to room temperature is an important factor that affects the NADome since the sample will contain billions of metabolic active cells; therefore, it is important to keep the samples on ice and rapid separation of plasma is needed. 7. Ficoll-Paque media bottle should be inverted several times to ensure thorough mixing. Ficoll-Paque media can be either withdrawn by syringe or by using a pipette: Aseptic techniques should be used to withdraw the required volume of FicollPaque media. 8. The upper plasma layer may be used for other studies. 9. To prevent contamination, transfer the two thirds of plateletrich plasma from blood fractionation without disturbing the buffy coat layer. 10. Prostaglandin E1 (PGE1, 1 μM final concentration) may also be added to prevent platelet activation. 11. Avoid resuspending the platelet pellet to prevent unnecessary platelet activation) by gently adding wash buffer and withdrawing it slowly using a pipette. 12. Salts are less soluble in ACN (0.003 g NaCl/kg solvent at 25  C), compared with methanol (14 g NaCl/kg solvent at 25  C) and therefore using ACN for extraction will help remove excess salts found in CSF samples that can cause signal suppression during mass spectrometry analysis. 13. β-Nicotinamide adenine dinucleotide hydrate (Sigma, N1511) should be used to make NAD+ standard solution. It may be difficult to dissolve some of the NAD+ metabolites in water, and they may be vortexed rigorously until dissolved. Aliquots of NAD+ and its metabolites should be stored in –80  C freezer. Avoid multiple freeze/thaw cycles with stock solutions. Frozen stock solutions of NAD+ metabolites are stable at 80  C for at least 1 year. 14. It is crucial that the pH of the mobile phase is adjusted to 9.5. At lower pH levels of 9 or lower there will be an increased retention of all metabolites and a drop in the quality of the peaks that can be obtained for the phosphorylated metabolites NADP and NADPH, wider peaks will result in the inability to accurately quantify these metabolites at low levels.

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215

15. Once retention times for metabolites are established, time segments can be set up in the method file so that only a limited number of metabolites are scanned during pre-set timeframes. This will give increased sensitivity and allow lower levels of metabolites to be detected in samples where NAD+ levels are low. If this approach is taken, it is important to note that the gradient and/or time segments may need to be adjusted as the column ages and retention of metabolites will be affected and adjustments will ensure no metabolites are missed in the pre-set time windows. 16. Using the processing setup feature each metabolite can be added with corresponding retention time and internal standard. Levels for the standard curve can also be included. Then in sequence view, the pre-saved processing method can be added to the sequence followed by a batch reprocess. Then the updated sequence can be opened in the quan browser feature of the software to check automated integration of peaks have been performed correctly and any peaks missed can be manually integrated. The data set can then by exported as an Excel report to complete further statistical analysis of samples. Process sequence of samples using the processing method, check integrations of peaks are correct, and then export data for completion of further statistical analysis.

Acknowledgments N.B. is the recipient of the Australian Research Council Discovery Early Career Research Award at the University of New South Wales, Sydney, Australia. References 1. Massudi H, Grant R, Guillemin GJ, Braidy N (2012) NAD+ metabolism and oxidative stress: the golden nucleotide on a crown of thorns. Redox Rep 17(1):28–46 2. Braidy N, Guillemin GJ, Mansour H, ChanLing T, Poljak A, Grant R (2011) Age related changes in NAD+ metabolism oxidative stress and Sirt1 activity in wistar rats. PLoS One 6(4): e19194. https://doi.org/10.1371/journal. pone.0019194 3. Braidy N, Poljak A, Grant R, Jayasena T, Mansour H, Chan-Ling T et al (2014) Mapping NAD(+) metabolism in the brain of ageing Wistar rats: potential targets for influencing brain senescence. Biogerontology 15 (2):177–198

4. Braidy N, Poljak A, Grant R, Jayasena T, Mansour H, Chan-Ling T et al (2015) Differential expression of sirtuins in the aging rat brain. Front Cell Neurosci 9:167. https://doi. org/10.3389/fncel.2015.00167 5. Braidy N, Berg J, Clement J, Khorshidi F, Poljak A, Jayasena T et al (2018) Role of nicotinamide adenine dinucleotide and related precursors as therapeutic targets for age-related degenerative diseases: rationale, biochemistry, pharmacokinetics, and outcomes. Antioxid Redox Signal 30(2):251–294 6. Trammell SA, Brenner C (2013) Targeted, LCMS-based metabolomics for quantitative measurement of NAD(+) metabolites. Comput Struct Biotechnol J 4:e201301012. https:// doi.org/10.5936/csbj.201301012

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7. Clement J, Wong M, Poljak A, Sachdev P, Braidy N (2018) The plasma NAD(+) metabolome is dysregulated in “normal” aging. Rejuvenation Res 22(2):121–130 8. Seyedsadjadi N, Berg J, Bilgin AA, Braidy N, Salonikas C, Grant R (2018) High protein intake is associated with low plasma NAD+ levels in a healthy human cohort. PLoS One 13(8):e0201968. https://doi.org/10.1371/ journal.pone.0201968 9. Long AN, Owens K, Schlappal AE, Kristian T, Fishman PS, Schuh RA (2015) Effect of nicotinamide mononucleotide on brain mitochondrial respiratory deficits in an Alzheimer’s disease-relevant murine model. BMC Neurol 15:19. https://doi.org/10.1186/s12883015-0272-x

10. Trammell SA, Schmidt MS, Weidemann BJ, Redpath P, Jaksch F, Dellinger RW et al (2016) Nicotinamide riboside is uniquely and orally bioavailable in mice and humans. Nat Commun 7:12948. https://doi.org/10. 1038/ncomms12948 11. Casabona G, Sturiale L, L’Episcopo MR, Raciti G, Fazzio A, Sarpietro MG et al (1995) HPLC analysis of cyclic adenosine diphosphate ribose and adenosine diphosphate ribose: determination of NAD+ metabolites in hippocampal membranes. Ital J Biochem 44 (5):258–268 12. Bernofsky C, Swan M (1973) An improved cycling assay for nicotinamide adenine dinucleotide. Anal Biochem 53(2):452–458

Chapter 14 Two-Dimensional Gel Electrophoresis Combined with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Analysis of Eye Lens to Identify Biomarkers of Age-Related Cataract Paul C. Guest Abstract This chapter describes the application of two-dimensional gel electrophoresis (2DGE) combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) in the analysis of rat eye lens proteins. The main purpose was to identify proteins that may serve as potential biomarkers in age-related cataract formation. This includes the family of proteins known as the crystallins. Structural proteins and enzymes involved antioxidant activities. In addition, we also analyzed lenses from other species to illustrate the potential of using this technique in clinical and preclinical biomarker studies. Key words Aging, Diabetes, Eye lens, Proteomics, 2D gel electrophoresis, Crystallin dimer, Cytoskeletal protein, Metabolic enzyme, Redox enzyme

1

Introduction The lens is the main structure for focusing light in the eye onto the retina [1]. A healthy lens has a high refractive index and transparency, and appropriate flexibility such that the attached ciliary muscles can easily modulate its curvature when needed for changes in focal length. Light striking the retina leads to activation of photoreceptors called rods and cones that convert the incident light via signal transduction into nerve impulses. These impulses are sent via the optic nerve to the visual centers of the brain which translate them into the perception of images. The lens is composed of fiber cells arranged in layers like an onion around a nucleus region [2] (Fig. 1). The outer edge consists of a single layer of epithelial cells, which constitute the only part of the lens that maintains its metabolic activity (Dahm et al. 2011). The high transparency and refractive index of the lens fiber layers

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_14, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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A) Pupil Lens

Retina

Light Optic nerve Aqueous humor Vitreous humor Corneal

B)

Lens fibre

Cross section showing hexagonal structure

Fig. 1 (a) Diagram of the eye with major components labeled. (b) Cross-section of human eye lens showing layers of fiber cells resembling the structure of an onion. The cross-section of this shows the hexagonal shape of the fiber cells

are achieved through expression of crystallin proteins at high concentration [3] and by removal of large cellular structures that can cause light scatter [4, 5]. The crystallins are comprised of three basic forms called alpha, beta, and gamma, which comprise approximately 80% of the lens protein content (Fig. 2) [6]. The crystallins have a high refractive index and absorptivity of UV radiation due to the fact that they have a high content of aromatic and sulfurcontaining amino acids [7–9]. The crystallins and other lens proteins are stable with little or no turnover since the cells that produce them lose their nuclei during differentiation. For this reason, any event or stressor that alters the structure or the levels of these proteins could disrupt lens architecture, altering the optical density of the lens, leading to an increase in light scattering. This normally results in cataract formation, which is the most widely occurring visual impairment in humans [10].

Two-Dimensional Gel Electrophoresis of Rat Lens Proteins

219

Alpha-A crystallin

Alpha-B crystallin

Beta-crystallin

Gamma-crystallin

Fig. 2 Molecular models of the three major forms of crystallin proteins

Other proteins in the lens are primarily involved in maintaining homeostasis of the lens fibers and defense of the crystallins against irreversible damage [11]. For example, cytoskeletal proteins confer the critical structure and shape of the lens fiber cells and redox enzymes help to maintain a reduced state, which protects the lens against oxidative stress and damage from UV light. In addition, glycolysis and the pentose phosphate pathway are mostly responsible for the energy needs of the cells. Advanced age increases the risk of cataract as does the occurrence of age-related diseases such as type 2 diabetes mellitus [12, 13]. A Western blot analysis found decreased levels of the filamentous protein vimentin in two cataractous lenses obtained from humans [14]. A proteomic study analyzed the effects of mutations associated with hereditary human cataract formation and found increased cross-linking and degradation of alpha-A crystallin; increased interaction of alpha-A crystallin with filensin, actin, or creatine kinase B; acidification of beta-B1 crystallin; and an association between alpha-A crystallin and beta-A3/A1 crystallin in heterozygous lenses [15]. In homozygous lenses, the analysis found increased associations between alpha-A crystallin and beta-B3, beta-A2, and beta-A4 crystallins, and decreased levels of beta-B1 crystallin, gelsolin, and calpain 3. Another proteomic study that used 8-plex isobaric tagging for relative and absolute quantitation mass spectrometry (iTRAQ-MS) identified nine proteins that were present at different levels in aging human lenses [16]. They found that the levels of fatty acid-binding protein and pterin-4-alphacarbinolamine dehydratase were increased, and the levels of alpha-

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crystallin B chain (CRYAB), glutathione synthetase, phakinin, gamma-crystallin C, phosphoglycerate kinase 1, betainehomocysteine S-methyltransferase 1, and spectrin beta chain were decreased. The above studies were capable of detecting proteins associated with cataract formation that were present at different levels. We describe here a protocol for a two-dimensional gel electrophoresis (2DGE) analysis combined with matrix-assisted laser desorption/ ionization time-of-flight (MALDI-TOF) MS [17], which is capable of detecting post-translational modifications of proteins such as phosphorylation [18], carbonylation [19], proteolysis [20], and dimerization [21]. We previously demonstrated proteolysis of cytoskeletal proteins, dimerization of crystallins, and alterated levels of metabolic and redox enzymes in a comparison of male and female rat lenses [22]. This chapter presents a basic protocol for 2DGE MALDI-TOF MS analysis of rat lens using mass fingerprinting for protein identification (Fig. 3). This involves: (1) extraction of lens

2DGE rat lens proteins

Measure peptide ions m/z by TOF Pick protein spot from gel Ion Flight Path

Digest

Apply peptides to target

Sample Target

E-Field

Mass Detector

Grid

Ion spectrum - MS fingerprint

UniProtKB Protein ID Experimental mass fingerprint used to search protein database (database proteins digested in silico)

Abundance

1753.8024

1016.5341

2211.1214

αA crystallin m/z

Fig. 3 Flow diagram showing the 2DGE MALDI-TOF mass fingerprinting process. First, the protein spot is isolated from the 2DGE gel. Next, the sample is digested with an enzyme like trypsin to produce peptides. These digests are spotted onto the MALDI target plate. Next, the peptides are ionized with a laser and accelerated toward a target in the mass spectrometer. The time of flight is inversely proportional to the mass/ charge (m/z). This produces a peptide fingerprint that can be used to search a database for identification of the parent protein

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proteins; (2) separation of proteins by 2DGE; (3) isolation of the protein spots of interest from the gel; (4) digestion with an enzyme like trypsin to produce peptides; (5) spotting of peptides onto a MALDI target plate; (6) insertion of the plate into the mass spectrometer; (7) ionization of the peptides by a laser; (8) TOF to peptide ions to a detector; (9) production of a peptide fingerprint; and (10) searching a protein database such as UniProt to identify the parent protein. We also performed a 2DGE comparison of rat, dog, and human lens tissue since all could be used for disease-based cataract research as well as in drug development safety studies.

2 2.1

Materials Samples

1. Lenses from 20-week-old rats, 2-year-old dogs, and 60-yearold humans (see Note 1). 2. Extraction buffer: 30 mM Tris (pH 8.0), containing Complete Protease Inhibitors (Roche Diagnostics). 3. Sonicator with microprobe. 4. Microcentrifuge capable of 13,000
g with adaptors for 0.5 and 1.5 mL-capacity microcentrifuge tubes.

2.2

2DGE

1. 24 cm long immobilized pH gradient (IPG) strips with pH range 3–10 (see Note 2). 2. Strip hydration buffer: 30 mM Tris (pH 8.0), 7 M urea, 2 M thiourea, 4% CHAPS, 2% dithiothreitol (DTT), and 2% pH 3–10 IPG buffer (see Note 3). 3. Reducing equilibration buffer: 50 mM Tris (pH 6.8), 6 M urea, 30% glycerol, 2% sodium docyl sulfate (SDS), 1% DTT, and 0.01% bromophenol blue (BPB [see Note 4]). 4. Alkylating equilibration buffer: 50 mM Tris–HCl (pH 6.8), 6 M urea, 30% (v/v) glycerol, 2% SDS, 2% iodoacetamide (IAA), and 0.01% BPB (see Note 5). 5. 24 cm IPGphor ceramic IPG strip holders (coffins [GE Healthcare]) or similar. 6. First-dimension isoelectric focusing (IEF) system: Ettan™ IPGphor™ 3 Isoelectric Focusing System (GE Healthcare) or similar. 7. Second-dimension gel buffer: 12.5% acrylamide, 0.32 bisacrylamide, 0.375 M Tris–HCl (pH 8.8), 0.1% SDS, 0.1% ammonium persulfate (APS), and 0.025–0.09% tetramethylethylenediamine (TEMED; see Note 6). 8. Running buffer: 25 mM Tris–HCl, 192 mM glycine, and 0.1% SDS (pH 8.3). 9. Sealing buffer: 0.5% agarose in running buffer.

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10. Second-dimension system: Ettan DALT6 Electrophoresis System (GE Healthcare) or similar. 11. Power supply. 12. Tupperware tub with the bottom slightly larger in area than that of the 2D gel. 13. SYPRO® Ruby Protein Gel Stain (Thermo Fisher Scientific). 14. Fix solution: 50% methanol/7% acetic acid. 15. Wash solution: 10% methanol/7% acetic acid. 16. Ultrapure water. 17. Orbital shaker. 18. Typhoon 9410 Variable Mode Imager (GE Healthcare) or other UV imaging system (optimal with UV transillumination, 473–488 and 532 nm laser sources). 2.3

In-Gel Digestion

1. 70% isopropanol. 2. 25 mM NH4HCO3. 3. Dehydration solution: 25 mM NH4HCO3/50% acetonitrile (ACN). 4. 50% ACN/5% formic acid. 5. Trypsin solution: freshly prepared 12.5 ng/μL porcine trypsin/25 mM NH4HCO3 (see Note 7). 6. Reduction solution: freshly prepared 10 mM DTT/25 mM NH4HCO3. 7. Alkylation solution: freshly prepared 55 mM IAA/25 mM NH4HCO3. 8. 0.5 and 1.5 mL-capacity LoBind microcentrifuge tubes (see Note 8). 9. Formic acid. 10. Ziptips (Millipore). 11. Ziptip buffer A: 2% ACN/0.1% formic acid. 12. Ziptip buffer B: 80% ACN/0.1% formic acid. 13. MALDI-TOF matrix: (CHCA; see Note 9).

2.4

MALDI-TOF MS

α-Cyano-4-hydroxycinnamic

acid

1. MALDI-TOF target plates (PE Biosystems). 2. Voyager™ Biospectrometry Workstation (PE Biosystems) or similar. 3. Protein Prospector MS-Fit (University of California, San Francisco, CA, USA). 4. UniProt protein database [23].

Two-Dimensional Gel Electrophoresis of Rat Lens Proteins

3

223

Methods

3.1 Lens Homogenization

1. Suspend lenses in extraction buffer at a 1:10 tissue:buffer ratio in 1.5 mL microcentrifuge tubes and leave on ice. 2. Wearing ear protection, introduce the Sonicator microprobe into the tube such that it is just touching the top of the liquid. 3. Turn on the Sonicator and gradually increase power to approximately one quarter for a total time of 30 s, attempting to minimize frothing. 4. Centrifuge the sample at 13,000
g for 30 min at 4  C in the microcentrifuge. 5. Collect the supernatants containing mainly soluble proteins and proceed immediately to the 2DGE stage.

3.2

2DGE Analysis

1. Add 10 μL lens soluble protein extract to a 1.5 mL microcentrifuge tube containing 440 μL rehydration buffer. 2. Place the IPG strip in a coffin and add sample along the full length gradually. 3. Leave the strip to hydrate inside the coffin for 12 h at room temperature (see Note 10). 4. Carry out IEF by attaching the coffin to the IPGphor system and electrophorese at 200 V for 1 h, 500 V for 1 h, 1000 V for 1 h, and 8000 V for 8 h at room temperature (see Note 11). 5. Prepare the resolving gel the next morning when the IEF is nearing completion (see Note 12). 6. Add the APS and TEMED reagents to the gel solution and pour the mixture immediately between assembled low fluorescence glass plates leaving a space of approximately 3 cm from the top of the plates (see Note 13). 7. Layer butanol on top of the gel to achieve a flat surface when it polymerizes. 8. After completion of the focusing stage, immerse the IPG strip for 10 min in 100 mL reducing equilibration buffer. 9. Remove this buffer and immerse the strip for 10 min in 100 mL alkylating equilibration buffer. 10. After polymerization of the second-dimension gel, rinse the butanol of the gel surface with water and apply the equilibrated IPG strip so that it rests immediately on top of the gel. 11. Seal the strip in pace with 0.5% agarose in second-dimension electrophoresis buffer on top (see Note 14). 12. Carry out electrophoresis for 1 h at 60 V and then set the power supply to 30 μA/gel for the remainder of the run.

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13. When the BPB dye front has reached the bottom of the gel, switch the power off. 14. Disassemble gel plates and carefully place gel in a suitably sized tub so that it can spread completely flat in the bottom of the tub. 15. Add 100 mL of fix solution and leave on an orbital shaker for 30 min and then repeat with fresh fix solution. 16. Remove the fix solution. 17. Add 60 mL SYPRO Ruby and leave overnight in the covered container on the orbital shaker (see Note 15). 18. Transfer the gel to a clean container and add 100 mL of wash solution for 30 min on the shaker. 19. Rinse the gel in ultrapure water two times for 5 min and acquire images with the UV imaging system (Figs. 4 and 5). 20. Excise spots of interest using a sterile pipette tip or a spot picking robot if available (see Note 16). 3.3 In-Gel Digestion (See Note 17)

1. Wipe down all work surfaces with 70% isopropanol (see Note 18). 2. Excise spots of interest from the SYPRO Ruby-stained gel using a sterile pipette tip or a spot picking robot and place into 0.5 mL microcentrifuge tubes. 3. Add 30 μL (or enough to cover) 25 mM NH4HCO3/50% ACN and vortex 10 min. 4. Remove the supernatant and discard. 5. Repeat steps 3 and 4 and place in a Speed Vac for approximately 20 min to completely dry the gel plugs. 6. Add enough reduction solution to cover the dried gels (approximately 25 μL). 7. Vortex, centrifuge briefly to bring contents to the bottom of the tube, and allow reduction to proceed for 1 h at 56  C. 8. Remove supernatant and add 25 μL alkylation solution to the gel pieces. 9. Vortex, centrifuge briefly and allow reduction to occur for 45 min in the dark at room temperature. 10. Remove the supernatant, add 100 μL 25 mM NH4HCO3, and vortex 10 min. 11. Centrifuge briefly, remove the supernatant, and add 25 μL of dehydration solution to the gel pieces (or enough to cover it). 12. Vortex 5 min, centrifuge, and repeat the dehydration one time. 13. Dry the gel pieces in the Speed Vac as above.

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225

A) Over exposed image of high molecular weight region of the rat lens 2G gel GELS TKT

HSPA8

PKM2 ENO1 CRYBB1 VIM

CRYBB1

CRYAA ALDOC AKR1B1

B)

GAPDH

Normal exposure to visualize low molecular weight crystallins

α, β, γ crystallins

Fig. 4 (a) Overexposed image of the high molecular weight region of the 2DGE analysis of rat lens proteins (see Table 1 for a list of proteins identified by MALDITOF mass fingerprinting). (b) Normal exposure to aid visualization of the low molecular weight crystallins

14. Add trypsin solution to cover the gel plugs (approximately 5 μL). 15. Add about three times the above volume of trypsin solution (approximately 20 μL total). 16. Rehydrate the gel pieces on ice or at 4  C for 10 min. 17. Centrifuge and add 25 mM NH4HCO3 if needed to just cover the rehydrated gel pieces.

Paul C. Guest

Rat

98

MW (kDa)

66 43 34 21

α, β, γ crystallins

98

Dog

MW (kDa)

66 43

34 α, β , γ crystallins

21

98

Human

66 MW (kDa)

226

43 34

α, β , γ crystallins

21 3

pI

10

Fig. 5 Comparative analysis of (a) rat, (b) dog, and (c) human lens proteins, revealing approximately the same pattern of abundant crystallin proteins

18. Centrifuge briefly and incubate at 37  C for 4–15 h (see Note 19). 19. Centrifuge briefly and transfer the digest solution (aqueous extraction) into a clean 0.5 mL microcentrifuge tube to recover the aqueous peptides. 20. Add formic acid to each sample so that the final formic acid concentration is 5.0% to stop the digestion (see Note 20).

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227

Table 1 Mass fingerprinting search parameters Enzyme

Trypsin

Sequences

Human, Rodent

Mass window

10,000–200,000 Da

Modifications

Carboxyamidomethylation of cysteine Oxidation of methionine Modification of glutamine to pyroglutamic acid

Matching peptides

Six or more

21. If desired, hydrophobic peptides can be recovered by adding 30 μL (enough to cover) of 50% acetonitrile/5% formic acid to the gel plugs, vortexing 20 min, sonicating 5 min, and centrifuging. 22. Reduce the volumes of both extracts to dryness in the Speed Vac and resuspend in 10 mL 0.1 formic acid. 23. Equilibrate Ziptips by aspirating 10 μL buffer B carefully into each tip, taking care not to introduce any air, dispense into waste, and repeat. 24. Aspirate 10 μL buffer A into each tip, dispense into waste, and repeat. 25. Aspirate 10 μL sample, dispense, repeat ten times, and collect. 3.4 MALDI-TOF Mass Fingerprinting Analysis

1. Combine 1 μL peptides with 0.5 μL 10 μg/μL CHCA on a MALDI-TOF target plate and allow to dry 20 min. 2. Determine peptide masses using the Voyager-DE STR Biospectrometry Workstation or similar as described [22] (see Note 21). 3. Identify proteins by searching the UniProt databases using MS-Fit. 4. Carry out searches using the parameters listed in Table 1. 5. Accept positive identifications based on at least six matching peptide masses or greater than 30% peptide coverage of the theoretical sequences (Table 2; see Note 22).

4

Notes 1. Ensure that all procedures and approvals regarding the study of animals are in order and in compliance with the Home Office Guidance on the Operation of the UK Animal Scientific Procedures Act of 1986, or similar. For work with human tissues,

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Table 2 List of high molecular weight proteins identified in the soluble lens fraction Acc

Spot

Protein

Peptides

P13120

GELS

Gelsolin

6

P06109

HSPA8

Heat shock cognate 71 kDa protein

22

P40142

TKT

Transketolase

11

P11981

PKM2

Pyruvate kinase M2 isozyme

14

P31000

VIM

Vimentin

14

P04764

ENO1

Alpha enolase

10

P02523

CRYBB1

Beta-B1 crystallin (high molecular weight)

8–14

P02490

CRYAA

Alpha-A crystallin (high molecular weight)

6–7

P09117

ALDOC

Fructose-bisphosphate aldolase C

13

P07943

AKR1B1

Aldose reductase

9

P04797

GAPD

Glyceraldehyde 3-P dehydrogenase

6

The table gives the UniProt accession code (Acc), the protein code, the protein name, and the number of unique tryptic peptides used in the identification

ensure that the proper institutional approvals and facilities to dispose of waste are in place. 2. The pH range of IPG strips should be chosen for maximum resolution of the targeted proteins. We have chosen a pH range of 3–10 in attempt to cover most cellular proteins. Also, we used the largest strips (24 cm) to increase loading capacity to improve isolation of low-abundance proteins. 3. Ensure that the IPG buffer used matches the IPG strips for pH range for the best separation of proteins according to isoelectric point. 4. The DTT in this solution reduces disulfide bonds in proteins to yield free sulfhydryl groups. The dye is added to visualize the migration of the dye front upon second-dimension electrophoresis. However, this can be added at a later stage prior to second-dimension electrophoresis. 5. The IAA in this solution alkylates the free sulfhydryl groups to avoid reformation of disulfide bonds. 6. The APS and TEMED should be added only before pouring the second-dimension gel, as these initiate the polymerization process. 7. This is made fresh to minimize autoproteolysis of the trypsin. 8. These tubes are used to minimize protein loss.

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9. The matrix is used to absorb photon energy from the laser beam in the mass spectrometer to induce peptide ionization and it acts as a solvent to ensure maximum ionization. 10. This step can be done overnight to eliminate waiting time. 11. Ensure that a maximum current setting of 50 mA per strip is applied. We and others have found that a step gradient such as the one applied here improves the focusing and minimizes horizontal streaking of proteins. 12. Again, this is to minimize waiting time. The acrylamide and N, N0 -methylenebisacrylamide concentrations for the resolving gel are selected based on the molecular weight range separation required. Lower percentages of acrylamide (5–7.5%) are better for resolving high molecular weight proteins and higher percentages (15–20%) are better for separating lower molecular weight proteins. The 12.5% gel was chosen here as this gives good separation of a wide range of proteins from 10 to 200 kDa. Remember to add the APS and TEMED when you are ready to make the gel as these reagents will initiate the polymerization of the gel. 13. This allows space at the top of the second-dimension gel to apply the focused and equilibrated IPG strips. 14. The user should make sure that this is done without creating air bubbles underneath the IPG strips as this will impede the downward migration of the protein spots during electrophoresis, which may lead to a distorted or streaky appearance. 15. Fluorescent stains should be protected from light exposure. 16. In this study, we used the ProPic spot picking robot from PerkinElmer Life Sciences. 17. It is also possible to use a digestion robot at this stage, such as the ProGest robot from PerkinElmer Life Sciences. 18. This is to help minimize potential contamination of the area with unwanted proteins. 19. This could be done overnight to minimize waiting time. 20. This procedure normally leads to the aqueous peptides leaching out of the gel and these are easily analyzed in the mass spectrometer. 21. Protein identification can be achieved using many kinds of protein-based mass spectrometry platforms. Here we have used peptide mass fingerprinting, and internal mass calibration was achieved using porcine trypsin autolysis digestion products. 22. The identification of crystallin molecules at high molecular weights indicates that these are likely to be oligomeric forms such as dimers and trimers. At the same time, the identification

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of structural proteins at lower molecular weights than predicted could indicate that these are proteolytic fragments. We also detected the presence of several metabolic and redox enzymes. All of these can be altered in cataractous lenses and may therefore be early biomarkers of cataract formation. Thus, these could be used in preclinical studies when screening for drugs that have cataract safety considerations. References 1. Ray NJ (2015) Biophysical chemistry of the ageing eye lens. Biophys Rev 7(4):353–368 2. Moreau KL, King JA (2012) Protein misfolding and aggregation in cataract disease and prospects for prevention. Trends Mol Med 18 (5):273–282 3. Aarts HJ, Lubsen NH, Schoenmakers JG (1989) Crystallin gene expression during rat lens development. Eur J Biochem 183 (1):31–36 4. Dahm R, van Marle J, Quinlan RA, Prescott AR, Vrensen GFJM (2011) Homeostasis in the vertebrate lens: mechanisms of solute exchange. Philos Trans R Soc Lond B 366 (1568):1265–1277 5. Mochizuki T, Masai I (2014) The lens equator: a platform for molecular machinery that regulates the switch from cell proliferation to differentiation in the vertebrate lens. Develop Growth Differ 56(5):387–401 6. Wistow G (2012) The human crystallin gene families. Hum Genomics 6:26. https://doi. org/10.1186/1479-7364-6-26 7. Chen J, Callis PR, King J (2009) Mechanism of the very efficient quenching of tryptophan fluorescence in human γD- and γS-crystallins: the γ-crystallin fold may have evolved to protect tryptophan residues from ultraviolet photodamage. Biochemistry 48 (17):3708–3716 8. Zhao H, Brown PH, Magone MT, Schuck P (2011) The molecular refractive function of lens γ-crystallins. J Mol Biol 411(3):680–699 9. Mahendiran K, Elie C, Nebel J-C, Ryan A, Pierscionek BK (2014) Primary sequence contribution to the optical function of the eye lens. Sci Rep 4:5195. https://doi.org/10.1038/ srep05195 10. Flaxman SR, Bourne RRA, Resnikoff S, Ackland P, Braithwaite T, Cicinelli MV et al (2017) Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health 5(12):e1221–e1234

11. Hejtmancik JF, Riazuddin SA, McGreal R, Liu W, Cvekl A, Shiels A (2015) Lens biology and biochemistry. Prog Mol Biol Transl Sci 134:169–201 12. Leasher JL, Bourne RR, Flaxman SR, Jonas JB, Keeffe J, Vision Loss Expert Group of the Global Burden of Disease Study et al (2016) Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990 to 2010. Diabetes Care 39(9):1643–1649 13. Periyasamy P, Shinohara T (2017) Age-related cataracts: Role of unfolded protein response, Ca2+ mobilization, epigenetic DNA modifications, and loss of Nrf2/Keap1 dependent cytoprotection. Prog Retin Eye Res 60:1–19 14. Zhou J, Hui Y, Li Y (2001) Expression of vimentin in lens epithelial cells of age-related cataract. Zhonghua Yan Ke Za Zhi 37 (5):342–345 15. Andley UP, Malone JP, Townsend RR (2014) In vivo substrates of the lens molecular chaperones αA-crystallin and αB-crystallin. PLoS One 9(4):e95507. https://doi.org/10.1371/jour nal.pone.0095507 16. Zhou HY, Yan H, Wang LL, Yan WJ, Shui YB, Beebe DC (2015) Quantitative proteomics analysis by iTRAQ in human nuclear cataracts of different ages and normal lens nuclei. Proteomics Clin Appl 9(7-8):776–786 17. Matsui NM, Smith DM, Clauser KR, Fichmann J, Andrews LE, Sullivan CM (1997) Immobilized pH gradient two-dimensional gel electrophoresis and mass spectrometric identification of cytokineregulated proteins in ME-180 cervical carcinoma cells. Electrophoresis 18(3–4):409–417 18. Mu¨ller DR, Schindler P, Coulot M, Voshol H, van Oostrum J (1999) Mass spectrometric characterization of stathmin isoforms separated by 2D PAGE. J Mass Spectrom 34(4):336–345 19. England K, Cotter T (2004) Identification of carbonylated proteins by MALDI-TOF mass spectroscopy reveals susceptibility of

Two-Dimensional Gel Electrophoresis of Rat Lens Proteins ER. Biochem Biophys Res Commun 320 (1):123–130 20. Person MD, Shen J, Traner A, Hensley SC, Lo HH, Abbruzzese JL et al (2006) Protein fragment domains identified using 2D gel electrophoresis/MALDI-TOF. J Biomol Tech 17(2):145–456 21. Wo¨hlbrand L, Ruppersberg HS, Feenders C, Blasius B, Braun HP, Rabus R (2016) Analysis of membrane-protein complexes of the marine

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sulfate reducer Desulfobacula toluolica Tol2 by 1D blue native-PAGE complexome profiling and 2D blue native-/SDS-PAGE. Proteomics 16(6):973–988 22. Guest PC, Skynner HA, Salim K, Tattersall FD, Knowles MR, Atack JR (2006) Detection of gender differences in rat lens proteins using 2-D-DIGE. Proteomics 6(2):667–676 23. https://www.uniprot.org/

Chapter 15 Testing the Effects of Dietary Seafood Consumption on Depressive Symptoms Maximus Berger, G. Paul Amminger, Robyn McDermott, Paul C. Guest, and Zolta´n Sarnyai Abstract This chapter presents a protocol for assessing the effects dietary seafood consumption on depressive symptoms. We designed a cross-sectional study of 206 participants recruited in two Torres Strait Island communities. Depressive symptoms were assessed using the adapted Patient Health Questionnaire-9 (aPHQ-9), diet was analyzed with a structured questionnaire, omega-3 and omega-6 fatty acid concentrations were measured via a capillary dried blood spot system, and plasma levels of triglycerides and cholesterol were measured by gas-phase chromatography. Finally, we tested the relationship between seafood consumption, blood lipid concentrations, and depression scores using independent samples t-tests and a logistic and quantile regression model. Key words Depression, Patient Health Questionnaire-9, Screening, Omega-3 fatty acids, Omega-6 fatty acids, Eicosapentaenoic acid, Docosahexaenoic acid

1

Introduction The dietary consumption of fish and other seafood has been linked with lower incidence of a number of diseases [1–4]. For example, a lower incidence of cardiovascular disease has been observed in the Greenland Inuit population and this has been attributed to their consumption of seafood rich in long chain omega-3 polyunsaturated fatty acids [5, 6]. A potential therapeutic benefit of omega-3 fatty acids has also been observed in the case of psychiatric disorders such as depression [7–9]. Fish and seafood are the most important sources of several essential omega-3 fatty acids, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (Fig. 1). Low levels of EPA and DHA have been reported in patients with major depression [10–13] and a study published in The Lancet in 1998 showed that the intake of omega-3 fatty acids through fish consumption

Paul C. Guest (ed.), Clinical and Preclinical Models for Maximizing Healthspan: Methods and Protocols, Methods in Molecular Biology, vol. 2138, https://doi.org/10.1007/978-1-0716-0471-7_15, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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Maximus Berger et al.

Omega-3

ω

3 Eicosapentaenoic acid (EPA)

Omega-3

ω

3 Docosahexaenoic acid (DHA)

Omega-6

ω

6 Linolenic acid

Fig. 1 Structure of example omega-3 and omega 6 fatty acids

may be protective against developing this psychiatric disorder [14]. However, Western diets are characterized by an increased content of omega-6 fatty acids (Fig. 1) with lower levels of the omega-3 variety [15]. This pattern of fatty acid intake is thought to be correlated with increased prevalence of metabolic, cardiovascular, and cognitive disorders, including depression [16, 17]. Depression is one of the leading causes of disability worldwide and was estimated to affect 4.4% of the world population in 2015 [18]. Depression affects people of all ages with prevalence peaking in older adulthood (55–74 years) [18]. There is also a greater risk of depression for certain social or ethnic groups such as the Aboriginal people in the Southwest Pacific region. Reports indicate that the Aboriginal and Torres Strait Islander people of Australia are affected by psychological distress and poor mental health, compared with the general population [19], although this may vary between regions and communities [20]. A recent study investigated potential associations of fish consumption and omega-3 and omega-6 fatty acid levels with depressive symptoms in two Torres Strait Islander communities [21]. This revealed an association of higher omega-3 fatty acid levels from natural sources with lower rates of depression. Here, we present a protocol that includes patient recruitment, demographic assessment, dietary behavior, and blood lipid analysis to determine links between dietary seafood consumption and depressive symptoms in the community. The participants were recruited as part of a community health screening program in the Mer and Waiben Torres Strait Island communities (Fig. 2). These islands are situated between the Northern tip of Queensland, Australia, and the Western Province of Papua New Guinea with greater access to seafood, compared to populations in intercity and rural mainland communities. The depression scale used in the assessment was an adapted form of the Patient Health

Testing Antidepressant Effects of Seafood

235

Fig. 2 Satellite map showing the location of the two Torres Strait Islander communities investigated here. Waiben Island is also known as Thursday Island and Mer Island is also known as Murray Island

Questionnaire-9 (aPHQ-9) that was created for specific use across multiple Indigenous Australian communities (Table 1) [16].

2

Materials 1. Participants: volunteers from the Waiben Island (n ¼ 106) and Mer Island (n ¼ 100) communities (see Note 1). 2. Depression assessment tool—adapted Patient Health Questionnaire-9 (aPHQ-9) (see Note 2). 3. Questionnaire for assessment of takeout and seafood (clams, crab, crayfish, fish, octopus, oysters and prawns) consumption (see Note 3). 4. Sterile finger prick device for drawing blood drops. 5. Blood collection paper (see Note 4).

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Table 1 The adapted Patient Health Questionnaire-9 (aPHQ-9) In the last 2 weeks, how often have you been feeling the following: Days

A little Most of None bit the time

All of the time

1. Have you been feeling slack, not wanted to do anything?

0

1

2

3

2. Have you been feeling unhappy, depressed, really no good, that your spirit was sad?

0

1

2

3

3. Have you found it hard to sleep at night, or had other problems with sleeping?

0

1

2

3

4. Have you felt tired or weak, that you have no energy?

0

1

2

3

5. Have you been eating too much food?

0

1

2

3

6. Have you been feeling bad about yourself, that you are useless, no good, that you have let your family down?

0

1

2

3

7. Have you felt like you can’t think straight or clearly, its hard to learn new things or concentrate?

0

1

2

3

8. Have you been talking slowly or moving around really slow?

0

1

2

3

9. Have you been thinking about hurting yourself or killing yourself?

0

1

2

3

Total score (0–27) This is an adapted for use across Australian aboriginal communities [16]

6. 1% (v/v) H2SO4. 7. Anhydrous methanol. 8. 5 mL sealed vials (Wheaton, Millville, NJ, USA). 9. Sterile needle/syringe device for drawing 8 mL blood. 10. 9-mL-capacity EDTA plasma tubes. 11. Bench top centrifuge for plasma preparation. 12. Hewlett-Packard 6890 Gas Chromatography System (Palo Alto, CA, USA) equipped with a 50 m  0.32 mm BPX70 capillary column (film thickness 0.25 mm) (Trajan Scientific Australia Pty Ltd., Melbourne, Victoria, Australia), programmed temperature vaporization injector, and a flame ionization detector (FID) (see Note 5). 13. Lipid standards (Nu-Chek Prep Inc., Elysian, MN, USA) and Hewlett-Packard Chemstation data system. 14. Statistical software package (see Note 6).

Testing Antidepressant Effects of Seafood

3

237

Methods 1. Carry out demographic assessment of all participants for numbers of males and females, age, body mass index (BMI), number of seafood and takeout meals per week. 2. Determine aPHQ-9 depression scores of all subjects as part of the standard community healthcare program (see Note 7) (Table 2). 3. Draw blood using a sterile needle device for a finger pricking to produce a drop of blood. 4. Dab blood drop onto filter paper. 5. Dry at room temperature for at least 5 h. 6. Add 2 mL of 1% (v/v) H2SO4 in anhydrous methanol in a 5 mL sealed vial. 7. Heat at 70  C for 3 h. 8. Separate the fatty acid methyl esters on the Hewlett-Packard 6890 Gas Chromatography System with the injector set at 250  C and the FID at 300  C [22]. 9. Set the programmed temperature ramp to 140–240  C. 10. Set the helium carrier gas at a flow rate of 35 cm/s in the column and the inlet split ratio to 20:1. 11. Identify and quantify fatty acids by comparing retention times and peak areas to those of lipid standards using the HewlettPackard Chemstation data system. 12. Determine EPA and DHA levels (see Notes 5 and 8). 13. Determine total omega-6 and omega-3 fatty acids and calculate ratios for omega-6/omega-3 (see Note 9).

Table 2 Demographics of the Waiben and Mer Islanders attending the health assessment P-value

Waiben

Mer

Number

106

100

Male/Female

60/46

55/45

0.817

Age (years)

38.7  15.1

42.2  18.6

0.139

BMI (kg/m )

31.3  7.3

31.9  7.2

0.600

Takeout meals/week

1.1  1.4

0.5  1.0