MicroRNA Profiling: Methods and Protocols (Methods in Molecular Biology, 2595) 1071628224, 9781071628225

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Methods in Molecular Biology 2595

Sweta Rani Editor

MicroRNA Profiling Methods and Protocols Second Edition

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.

MicroRNA Profiling Methods and Protocols Second Edition

Edited by

Sweta Rani Department of Science, South East Technological University, Waterford, Ireland

Editor Sweta Rani Department of Science South East Technological University Waterford, Ireland

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-2822-5 ISBN 978-1-0716-2823-2 (eBook) https://doi.org/10.1007/978-1-0716-2823-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023, Corrected Publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface microRNAs (miRNAs) are small non-coding RNAs. miRNAs play a central role in regulating the expression of genes at the post-translation level. One miRNA can bind to multiple mRNAs and have multiple binding sites regulating multiple targets and pathways. Dysregulation of miRNAs is associated with several diseases and is a key regulator of several biological processes. Expression of miRNAs can be detected in tissue specimens, biological fluids, as well as cell lines and have a potential as biomarkers for therapeutical and medicinal interventions. This is the second edition of microRNA Profiling from the Methods in Molecular Biology series. This book collates chapters contributed by experts in their respective fields. It includes both classical techniques and newer approaches to isolate and profile miRNAs. The chapters describe detailed step-by-step protocol and troubleshooting tips ensuring successful experiment. This volume includes chapters that comprehensively describe miRNA biogenesis and its function in regulating progression of several diseases. The first few chapters describe optimised protocol of isolating RNA from various samples including exosomes, serum specimens and cell lines and determine the quality and quantity of RNA. 3D cell culture allows the cells to interact with their surroundings in all the three dimensions. Two of the chapters in this book describe the 3D techniques used to culture cells and ways to transfect miRNAs. Exosomes or extracellular vesicles (EVs) are secreted by almost all cell types and are one of the mechanisms of intercellular communication. EVs are known to contain not only RNAs, proteins, lipids and metabolites but also miRNAs. miRNAs encapsulated in these EVs are protected from degradation by plasma ribonucleases. Circulating miRNAs hold immense potential as diagnostic, prognostic and/or predictive biomarkers. Some of the chapters compare and contrast various methods to profile exosomal miRNA. The last two chapters include easy-to-follow protocol for analysing miRNA profiling data, reducing technical difficulties. Galaxy software is used to analyse genomic data including miRNA and miRDeep2 for identification and quantification of miRNA from sequencing data. miRDeep-P2 can also be used for miRNA annotation in plants using deep sequencing data. Waterford, Ireland

Sweta Rani

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

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1 miRNA Biogenesis and Regulation of Diseases: An Updated Overview . . . . . . . . Anchal Vishnoi and Sweta Rani 2 Exosomal MicroRNA Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neda Rahimian, Javid Sadri Nahand, Michael R. Hamblin, and Hamed Mirzaei 3 MicroRNA Expression Profiling Using Agilent One-Color Microarray . . . . . . . . Carmela Dell’Aversana, Giulia Sgueglia, Nunzio Del Gaudio, and Lucia Altucci 4 A Simple Method for Profiling and Analyzing MicroRNAs from Small Volume Samples Using a qPCR-Based Platform . . . . . . . . . . . . . . . . . . Aisling Leavy and Eva M. Jimenez-Mateos 5 Exosomal MicroRNAs: Comprehensive Methods from Exosome Isolation to miRNA Extraction and Purity Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . Erika D’Agostino, Annamaria Muro, Giulia Sgueglia, Crescenzo Massaro, Carmela Dell’Aversana, and Lucia Altucci 6 Detection of MicroRNAs in Brain Slices Using In Situ Hybridization . . . . . . . . . Sean Quinlan, Christine Henke, Gary P. Brennan, David C. Henshall, and Eva M. Jimenez-Mateos 7 MicroRNA Profiling of Cell Lines and Xenografts by Quantitative PCR: MicroRNA Expression Level Determination by qPCR . . . . . . . . . . . . . . . . . Ariadna Boloix and Miguel F. Segura 8 Assessment of Basic Biological Functions Exerted by miRNAs . . . . . . . . . . . . . . . . Ellen King, John Nolan, and Olga Piskareva 9 Serum MicroRNAs Profiling in Age-Related Macular Degeneration . . . . . . . . . . . Hanan Elshelmani, David Keegan, and Sweta Rani 10 Exosomal MicroRNA Discovery in Age-Related Macular Degeneration . . . . . . . Hanan Elshelmani and Sweta Rani 11 MicroRNA Profiling Using a PCR-Based Method . . . . . . . . . . . . . . . . . . . . . . . . . . Giuliana A. de Ferronato, Marcela B. Cerezetti, Alessandra Bridi, Cibele M. Prado, Gislaine dos Santos, Nata´lia M. Bastos, Paola M. S. da Rosa, Juliana G. Ferst, and Juliano C. da Silveira 12 Guidelines on Designing MicroRNA Sponges: From Construction to Stable Cell Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manoela Marques Ortega and Hakim Bouamar 13 Evaluating Single-Nucleotide Variants in MicroRNA Targeting Sites and Mature MicroRNA In Vitro Cell Culture by Luciferase Reporter Gene Assays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Je´ssica Silva dos Santos, Gabriel Alves Bonafe´, Gustavo Jacob Lourenc¸o, and Manoela Marques Ortega

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Assessment of Cell Cytotoxicity in 3D Biomaterial Scaffolds Following miRNA Transfection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elizabeth Sainsbury, Lara Costard, Fergal J. O’Brien, and Caroline M. Curtin 15 Evaluation of miRNA Expression in 3D In Vitro Scaffold-Based Cancer Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Catherine Murphy, Ciara Gallagher, and Olga Piskareva 16 Bioinformatics Analysis of miRNA Sequencing Data . . . . . . . . . . . . . . . . . . . . . . . . Hrishikesh A. Lokhande 17 Plant MicroRNA Identification and Annotation Using Deep Sequencing Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Kuang, Yongxin Zhao, and Xiaozeng Yang Correction to: Exosomal MicroRNAs: Comprehensive Methods from Exosome Isolation to miRNA Extraction and Purity Analysis . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors LUCIA ALTUCCI • Institute Experimental Endocrinology and Oncology ‘Gaetano Salvatore’ (IEOS)-National Research Council (CNR), Naples, Italy; Department of Precision ` degli studi della Campania “Luigi Vanvitelli”, Naples, Italy; Medicine, Universita BIOGEM, Ariano Irpino, Italy NATA´LIA M. BASTOS • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil ARIADNA BOLOIX • Group of Childhood Cancer and Blood Disorders, Hospital Universitari Vall d’Hebron, Vall d’Hebron Institut de Recerca, VHIR, Universitat Auto`noma de Barcelona, Barcelona, Spain GABRIEL ALVES BONAFE´ • Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, Sa˜o Francisco University, Braganc¸a Paulista, Sa˜o Paulo, Brazil HAKIM BOUAMAR • Department of Cellular and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA GARY P. BRENNAN • Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland ALESSANDRA BRIDI • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil MARCELA B. CEREZETTI • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil LARA COSTARD • Department of Anatomy & Regenerative Medicine, Tissue Engineering Research Group (TERG), Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland CAROLINE M. CURTIN • Department of Anatomy & Regenerative Medicine, Tissue Engineering Research Group (TERG), Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland; Trinity Centre for Biomedical Engineering (TCBE), Trinity College Dublin (TCD), Dublin, Ireland; Advanced Materials and Bioengineering Research Centre (AMBER), RCSI and TCD, Dublin, Ireland ` degli studi della ERIKA D’AGOSTINO • Department of Precision Medicine, Universita Campania “Luigi Vanvitelli”, Naples, Italy PAOLA M. S. DA ROSA • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil JULIANO C. DA SILVEIRA • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil GIULIANA A. DE FERRONATO • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil ` degli studi della NUNZIO DEL GAUDIO • Department of Precision Medicine, Universita Campania “Luigi Vanvitelli”, Naples, Italy ` degli studi della CARMELA DELL’AVERSANA • Department of Precision Medicine, Universita Campania “Luigi Vanvitelli”, Naples, Italy; Institute Experimental Endocrinology and Oncology ‘Gaetano Salvatore’ (IEOS)-National Research Council (CNR), Naples, Italy GISLAINE DOS SANTOS • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil JE´SSICA SILVA DOS SANTOS • Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, Sa˜o Francisco University, Braganc¸a Paulista, Sa˜o Paulo, Brazil

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HANAN ELSHELMANI • Mater Vision Institute, The Mater Misericordiae University Hospital, Dublin, Ireland; Zoology Department, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland JULIANA G. FERST • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil CIARA GALLAGHER • Department of Anatomy and Regenerative Medicine, Cancer Bioengineering Group, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Department of Anatomy and Regenerative Medicine, Tissue Engineering Research Group, RCSI University of Medicine and Health Sciences, Dublin, Ireland MICHAEL R. HAMBLIN • Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein, South Africa CHRISTINE HENKE • Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland DAVID C. HENSHALL • Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland EVA M. JIMENEZ-MATEOS • Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Green College, Dublin, Ireland; Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland DAVID KEEGAN • Mater Vision Institute, The Mater Misericordiae University Hospital, Dublin, Ireland ELLEN KING • Department of Anatomy and Regenerative Medicine, Cancer BioEngineering Group, Royal College of Surgeons in Ireland, Dublin, Ireland; School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland ZHENG KUANG • Beijing Agro-biotechnology Research Center, Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, P.R. China AISLING LEAVY • Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Green College, Dublin, Ireland HRISHIKESH A. LOKHANDE • Brigham and Women’s Hospital, Boston, MA, USA GUSTAVO JACOB LOURENC¸O • Laboratory of Cancer Genetics, School of Medical Sciences, University of Campinas, Campinas, Sa˜o Paulo, Brazil ` degli studi della CRESCENZO MASSARO • Department of Precision Medicine, Universita Campania “Luigi Vanvitelli”, Naples, Italy HAMED MIRZAEI • Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran; Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran ` degli studi della ANNAMARIA MURO • Department of Precision Medicine, Universita ` degli studi di Napoli “Federico Campania “Luigi Vanvitelli”, Naples, Italy; Universita II”, Naples, Italy CATHERINE MURPHY • Department of Anatomy and Regenerative Medicine, Cancer Bioengineering Group, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Department of Anatomy and Regenerative Medicine, Tissue Engineering Research Group, RCSI University of Medicine and Health Sciences, Dublin, Ireland

Contributors

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JAVID SADRI NAHAND • Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran JOHN NOLAN • National Children’s Research Centre, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland FERGAL J. O’BRIEN • Department of Anatomy & Regenerative Medicine, Tissue Engineering Research Group (TERG), Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland; Trinity Centre for Biomedical Engineering (TCBE), Trinity College Dublin (TCD), Dublin, Ireland; Advanced Materials and Bioengineering Research Centre (AMBER), RCSI and TCD, Dublin, Ireland MANOELA MARQUES ORTEGA • Laboratory of Cell and Molecular Tumor Biology and Bioactive Compounds, Sa˜o Francisco University, Braganc¸a Paulista, Sa˜o Paulo, Brazil OLGA PISKAREVA • Department of Anatomy and Regenerative Medicine, Cancer Bioengineering Group, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Department of Anatomy and Regenerative Medicine, Tissue Engineering Research Group, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Advanced Materials and Bioengineering Research Centre (AMBER), RCSI and TCD, Dublin, Ireland; National Children’s Research Centre, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland CIBELE M. PRADO • Department of Veterinary Medicine, College of Animal Sciences and Food Engineering, University of Sa˜o Paulo, Pirassununga, SP, Brazil SEAN QUINLAN • Discipline of Physiology, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland NEDA RAHIMIAN • Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran; Department of Internal Medicine, School of Medicine, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran SWETA RANI • Department of Science, South East Technological University, Waterford, Ireland ELIZABETH SAINSBURY • Department of Anatomy & Regenerative Medicine, Tissue Engineering Research Group (TERG), Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland MIGUEL F. SEGURA • Group of Childhood Cancer and Blood Disorders, Hospital Universitari Vall d’Hebron, Vall d’Hebron Institut de Recerca, VHIR, Universitat Auto`noma de Barcelona, Barcelona, Spain ` degli studi della GIULIA SGUEGLIA • Department of Precision Medicine, Universita Campania “Luigi Vanvitelli”, Naples, Italy ANCHAL VISHNOI • Department of Biophysics, University of Delhi, New Delhi, India XIAOZENG YANG • Beijing Agro-biotechnology Research Center, Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, P.R. China YONGXIN ZHAO • Beijing Agro-biotechnology Research Center, Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, P.R. China

Chapter 1 miRNA Biogenesis and Regulation of Diseases: An Updated Overview Anchal Vishnoi and Sweta Rani Abstract MicroRNAs (miRNAs) are small RNA molecules, with their role in gene silencing and translational repression by binding to the target mRNAs. Since it was discovered in 1993, miRNA is found in all eukaryotic cells conserved across the species. miRNA-size molecules are also known to be found in prokaryotes. Regulation of miRNAs is extensively studied for their role in biological processes as well as in development and progression of various human diseases including neurodegenerative diseases, cardiovascular disease, and cancer. miRNA-based therapy has a promising application, and with a good delivery system, miRNA therapeutics can potentially be a success. miRNAs and EVs have potential therapeutic and prognostic application in a range of disease models. This chapter summarizes miRNA biogenesis and explores their potential roles in a variety of diseases. miRNAs hold huge potential for diagnostic and prognostic biomarkers and as predictors of drug response. Key words miRNA, Neurodegenerative diseases, Cardiovascular disease, Cancer, Extracellular vesicle

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Introduction

1.1 MicroRNAs (miRNAs)

MicroRNA (miRNA) was first observed in Caenorhabditis elegans [1] and is the most abundant small RNA [2]. It has now been detected in nearly all animal model systems, and their numbers largely correlate with the complexity of the organism [3]. Humans have approximately 2000 annotated miRNA genes, and the total number of microRNA loci annotated is 24, 521 loci in 206 species [4]. The human genome consists of large number of miRNA genes accounting for 1–5% of all predicted human genes [5], and mammalian miRNAs are known to regulate approximately 30% of all protein-coding genes [6]. As multiple miRNAs target the same mRNA, there is no linear correlation between miRNA and mRNA expression [7]. Ambros and Ruvkun were the first to discover miRNA in 1993. Lin-4 was the first miRNA to be discovered in C. elegans [1, 8]. In 2000 a second miRNA was discovered called

Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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let-7 and found to be conserved across the species [9]. This finding boosted the miRNA discovery studies, and soon many more miRNAs were found in C. elegans, Drosophila melanogaster, and human genomes [10–12]. miRNAs are small non-coding RNAs, single-stranded RNA molecules of approximately 21–23 nucleotides in length. miRNA has a uridine at their 5′-end and partially complementary to the 3′-end untranslated regions of the messenger RNA (mRNA). miRNA recruits Argonaute (AGO) protein complex to a complementary target mRNA, which results in translation repression or degradation or deadenylation of the mRNA [13].

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miRNA Biogenesis

2.1 Transcription of miRNA

The miRNA biogenesis in humans follows a two-step process with nuclear and cytoplasmic cleavage event. In the nucleus, the miRNAs are transcribed as a long transcript called pri-miRNA, either by their own promoters or by sharing promoters of their host gene [14]. Transcription factors bind to the transcription start site, and enhancers facilitate the binding of RNA polymerase [15]. For the majority of miRNA, among the two RNA polymerase RNA pol II and RNA pol III, RNA pol II is thought to be responsible for the pri-miRNA transcription (Fig. 1a). The preference for RNA pol II is evident by the length of pri-miRNA, which is more than 1 kb longer than the pol III transcript. Also, pri-miRNA contains sequences of uridine residues, which terminates pol III transcription. These all support the preference of pol II for transcription of pri-miRNA though there is exception, transcription of miR-142 by RNA pol III [16]. In addition to the abovementioned features, the transcriptional start sites are located far away from the genes, and promoters contain features typical of RNA pol II [17]. The transcriptional regulation of miRNA sometimes follow feedback loop where positive or negative regulation of miRNA downregulates or amplifies their own expression [18]. miRNAs which reside in introns are known as mirtrons. Mirtrons’ presence is widespread in Drosophila, C. elegans, vertebrates, and plants [19]. The transcription, primarily for mirtrons, takes place independent of the host gene (Fig. 1a).

2.2 Formation of PremiRNA

The nuclear cleavage of the pri-miRNA is carried out by Drosha RNase III endonuclease (Fig. 1a (2)). Drosha RNase III endonuclease cleaves both strands of the stem at sites near the base of the primary stem loop releasing 60–70 nt stem loop intermediate, called miRNA precursor, or the pre-miRNA. Only that, pri-miRNA matures into functional miRNA that has a flexible terminal loop (≥10 bp) and 5′ phosphate and ~2 nt 3′ singlestranded RNA overhangs. It is mediated via RNase III

miRNA Biogenesis and Regulation of Diseases: An Updated Overview Animal miRNA

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Fig. 1 miRNA biogenesis in (a) animal and (b) plant. (1) RNA pol II responsible for the pri-miRNA transcription. (2) Drosha RNase III endonuclease cleaves pri-miRNA near the base of the primary stem loop. In plants, DCL1 has a similar function as Drosha. (3). The pre-miRNA transported into the cytoplasm (4). Pre-miRNA maturation in the cytoplasm is carried out by RNase III endonuclease Dicer (5). The helicase domain recognizes the loop region and cleaves both the strands (6). Short RNA fragment called miRNA (7). miRNA binds to their target mRNA and negatively regulates its expression

endonuclease Drosha and the double-stranded RNA-binding protein DiGeorge syndrome critical region gene 8 (DGCR8) also known as pasha [14]. The pre- miRNA has a staggered cut with 5′ phosphate and 3′ 2 nucleotide overhang [20]. The mirtrons are exception and bypass the Drosha pathway releasing the precursor splicing [21] (Fig. 1a). This pre-miRNA is transported into the cytoplasm through the interaction of exportin-5 and Ran-GTP [22] (Fig. 1a (3)). 2.3 Maturation of Pre-miRNA in Cytoplasm

The pre-miRNA maturation in the cytoplasm is further carried out by RNase III endonuclease Dicer (Fig. 1a (4)). It recognizes the 5′ phosphate and 3′ overhang approximately at the two helical turn away from the base and cut the double strand. The cleavage separates the loop structure, and the imperfect double strand is known as miRNA:miRNA* complex. miRNA is a mature miRNA, whereas miRNA* is the opposing arm of the miRNA. This complex is shortlived as revealed by cloning of the miRNA [23, 24]. The Dicer is characterized by the presence of helicase, a PAZ domain, double-stranded RNA binding domain, and a RNAIII domain. The PAZ domain recognizes the 3′ overhang [25]. The helicase domain recognized the loop region, and the two RNAIII

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domain cleaves both the strands; the whole procedure occurs only when the miRNA complex is loaded in the Dicer [25, 26]. In plants, Dicer-like 1 (DCL1) protein has similar function as Drosha and perhaps functions as Dicer (which is not clear yet) in processing the miRNA-miRNA* complex in the nucleus [27]. This complex is then transported into the nucleus by HASTY, the Arabidopsis homolog of exportin 5 [28] (Fig. 1b).

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Regulation of Dicer Different pathways regulate Dicer, which in turn regulates the amount of miRNA in the cell. In humans sometimes, the Dicer is regulated by its miRNA through the binding sites, as present in the let-7 miRNA [29]. In addition to miRNA regulation of Dicer activity, the helicase domain has an auto-inhibitory effect on Dicer [30]. Many protein interactions also affect the efficiency of the Dicer. The HIV-1 TAR RNA-binding protein (TRBP) and protein activator of PKR (PACT), a dsRNA-binding protein, increase the cleavage efficiency of Dicer through helicase binding [30, 31]. On the other hand, the monocyte chemoattractant protein 1-induced protein 1 (MCPIP1), also known as Zinc-finger CCCH-type containing 12A (Zc3h12a), degrades the miRNA precursor. The MCPIP1 is a nuclease, which works opposite to Dicer cleaving the loop region of miRNA precursor resulting in its rapid degradation [32].

3.1 Maturation of miRNA in RISC

Both in plants and animals, the miRNA from the miRNA-miRNA* complex is loaded into RNA-induced silencing complex (RISC). Further, maturation of miRNAs is carried out by the RISC loading complex (RLC) [33]. The Argonaute protein, a catalytic component of RISC, helps in uptake of miRNA and freeing of Dicer. In some case, as in mammalian cells, Ago2, a Argonaute protein, have endonuclease activity and cleave the 3′ arm of the miRNA before being processed by Dicer. This in turn may help in determining the mature miRNA strand [33, 34]. The structure of the miRNA is likely to determine the specificity of Ago2 [34, 35]. There are three categories of Argonaute, (1) Ago, which works in miRNA and siRNA pathways; (2) piwi, which regulates the piRNAs; and (3) worm-specific subfamilies [36]. It then directs RISC to downregulate the target gene. The miRNA either cleaves the target mRNA if it has the sufficient complementarity to the miRNA or represses the translation of mRNA [37]. The target recognition of mRNA by miRNA is done by conserved region of miRNA, as in the case of invertebrate miRNA where 2–8 residues are perfectly complementary to 3′ UTR motifs which is shown to mediate posttranscriptional repression [38].

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The Argonaute proteins in addition to targeting the mRNA also regulate the stability of the miRNAs. In C. elegans by unknown mechanism, the ALG-1 (Argonautes) play a role in miRNA stability and precursor processing [39]. Association of miRNA with the Argonaute protein protects them from exonuclease (XRN-1 and XRN-2) degradation [39]. The base pairing of miRNA with its target also determines miRNA stability. In Drosophila, a strong base pairing between the target and miRNA leads to addition of nucleotide at 3′ end, which further trims the miRNA [40]. Similarly, in humans, a uridine is added to miR 223 when there is perfect match with the target, which leads to its degradation [41]. The addition of nucleotide at 3′ end of miRNA does not always lead to its degradation. In mice, poly(A) polymerase GLD-2 determines the addition of single adenosine at the 3′ end of the miR-122, which in turn protects it from the exonuclease activity and thus increasing its life [42]. In another example, RNA-binding protein Quaking (QKI) stabilizes the structure of miR-20a in human cells [43]. The QKI is a tumor suppressor, regulating glioblastoma multiforme (GBM) pathogenesis, by its effect on miR-20a. The level of miR-20a levels decrease in absence of QKI, which, in turn, increases the level of TGF β ~ R2 involved in oncogenesis [43]. The miRNA biogenesis pathway is also regulated by other miRNAs. For example, in mouse, the miR-709 binds to the complimentary sites of primary miR-15a/16-1 and represses it’s Drosha processing [44]. Therefore, it can be said that there are different pathways, which degrade or stabilize the structure of miRNA altering its level in the cell.

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Extracellular Vesicles (EVs) Extracellular vesicles (EVs) are nano-sized (40–100 nm in diameter) vesicles of endosomal origin [45]. Pan and Johnstone were the first to isolate small vesicles secreted by sheep reticulocytes in 1980 [46]. The cargo is driven into the EVs from the cytoplasm of the host cell. EV cargo includes proteins, microRNA (miRNA), mRNA, and lipids [47, 48]. EVs are subsequently internalized by other cells via direct membrane fusion, endocytosis, or cell-typespecific phagocytosis [49–51]. EVs are known to transfer signaling molecules to the nearby cells or circulated via circulatory and lymphatic system [52].

4.1 Role of EVs and miRNA in Diseases

With the discovery of miRNAs, it has garnered huge interest in determining their role in various diseases. Dysregulation of miRNA expression plays an important role in disease progression, and their expression profiling is likely to become important diagnostic and prognostic tools. miRNA therapeutics is not only challenging but also promising for several diseases.

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4.2 Neurodegenerative Diseases

The cause of central nervous system diseases could be attributed to the altered expression of miRNAs. The study of miRNA expression could lead to novel molecular information and therapeutic option including enhancing or inhibiting specific miRNAs to improve the disease treatment. Earlier in 2012, Xin et al. demonstrated that mesenchymal stem cells (MSCs) communicate via exosomes with brain parenchymal cells and regulate neurite outgrowth by horizontal transfer of miR-133b to neural cells in vitro [53]. MSCs are known to contribute to neurological recovery after stroke, and with the discovery of exosomes as a carrier of miR-133b to astrocytes and neurons after cerebral ischemia, a theory has emerged that these exosomes shuttle miR-133b stimulating neurite outgrowth and thereby improving recovery after stroke [53]. miRNA is associated with post-transcriptional gene suppression regulating the pathogenesis of neurodegenerative diseases including age-related macular degeneration (AMD). miR-486-5p and miR-626 were found to be upregulated, and miR-885-5p was downregulated in AMD patients compared to control groups and is found to be associated with apoptosis and neovascularization. Both apoptosis and neovascularization pathways regulate the pathogenesis of AMD [54, 55]. A study carried out by ElShelmani et al. showed that AMD-derived exosomes significantly induced both angiogenesis and vasculogenesis in vitro when compared to control groups, suggesting presence of angiomiRs (miRNAs associated with angiogenesis) [55].

4.3 Cardiovascular Disease

miRNAs play an important role in the development of cardiac tissue at all stages [56]. Various miRNAs, in particular, miR-143, regulate myocardial cell morphology and are also essential for the functioning and formation of cardiac chamber. Vascular smooth muscle cell (VSMC) differentiation is also regulated via miR-143 and miR-145 [57]. In recent years, it has been reported that deregulation of miRNA is associated with several cardiovascular diseases. Decreased expression of miR-143/145 was reported by acute and chronic vascular stresses [57]. miRNAs associated with MSC-EVs also play an important role in cardioprotection and were found that miR-22-loaded EVs targeted methyl CpG-binding protein 2 (Mecp2) promoting cardiac remodelling following myocardial infarction [58]. Similarly, miR-221 level was found to be significantly higher in MSC-EVs compared to their parent MSCs, enhancing cardioprotection by reducing the expression of p53 upregulated modulator of apoptosis (PUMA) [59].

miRNA Biogenesis and Regulation of Diseases: An Updated Overview

4.4

5

Cancer

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miRNAs are known as critical regulators of gene expression, and in cancer, miRNAs play a role in oncogenesis, metastasis, and resistance to various therapies. miRNAs can be classified as oncogenes (oncomirs), tumor-suppressor genes, pro-metastatic (“metastamiRs”), and metastasis suppressor [60]. A number of studies suggest that miRNAs exist in sera that are associated with non-smallcell lung cancer. A group of six miRNAs (miR- 30c- 1*, miR-616*, miR-146b-3p, miR-566, miR-550, and miR-939) was found to exist at substantially higher levels in the ADC compared to control sera [61]. Loss of miR-486 expression in stage 1 NSCLC tumors, compared to adjacent non-cancerous lung tissues, suggests that its downregulation may be important in lung cancer development. miR-486 is reported to be a potent tumor suppressor of lung cancer, regulating components of insulin growth factor (IGF) signaling, including insulin-like growth factor 1 (IGF1), IGF1 receptor (IGF1R), and phosphoinositide-3-kinase, regulatory subunit 1 (alpha) (PIK3R1 or p85a) both in vitro and in vivo [62]. miRNAs have the potential as breast cancer biomarkers. A study analyzing 54 luminal A-like breast cancer blood samples and 56 normal blood samples has reported that the expression of 3 miRNAs (miR-29a, miR-181a, and miR-652) has the potential to facilitate accurate subtype-specific breast tumor diagnosis in combination with mammography [63]. miR-10b overexpression not only initiates invasion and metastasis in breast cancer models, but its expression in primary breast carcinomas also correlates with clinical progression [64]. Circulating EVs isolated from liquid biopsy including blood, saliva, and urine has garnered a lot of attention in recent years. Tumor-derived miRNAs associated with EVs (exosomal miRNA) are known to change the physiological state of extracellular matrix (ECM), creating a niche for metastatic cancer cells. Exosomal miR-155 was found to be highly expressed in melanoma, which upregulated vascular endothelial growth factor A (VEGFA) and fibroblast growth factor 2 (FGF2), promoting blood vessel formation in the tumor [65]. M2 macrophage polarization was promoted by MSC-secreted exosomal miR-let-7c, enhancing angiogenesis affecting multiple myeloma microenvironment, and regulating tumor progression [66]. miR-1249-5p, miR-6737-5p, and miR-6819-5p downregulate TP53 expression in fibroblast that in turn promote stroma-mediated tumor growth [67].

Extracellular Vesicles (EVs) as Delivery Vehicle for miRNAs EVs are very similar to the synthetic nanoparticles in size and function. As EVs are naturally produced, they are not immunogenic, have low toxicity, and are easily taken up by the recipient cells. EVs are known to deliver small RNAs and drug across the biological barriers including the blood-brain barrier. The targeting

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abilities of EVs can also be increased to increase the specificity of drug delivery. Therapeutic effects of EVs are mediated via its ability to transfer biological information in the form of proteins, mRNA, and miRNA from one cell to another. EVs also hold immense potential for precision medicine where EVs can be used as targeted drug (or RNAi) delivery system for cell-specific disease treatment. 5.1 Engineering EVs with miRNAs

EVs can be isolated from any cell line of choice, and mesenchymal stem cells (MSCs) are more frequently used [68–70]. MSCs can be obtained from various sources including the bone marrow and umbilical cord. miRNAs can be passively loaded in EVs by transfecting miRNA of choice into MSCs [71]. EVs can also be loaded with exogenous cargoes by transfecting using electroporation [72].

5.2 Delivery of miRNA Using EVs

EVs can be used for targeted delivery of miRNA. Liang et al. modified EVs by fusing chondrocyte-affinity peptide (CAP) with the lysosome-associated membrane glycoprotein 2b protein present on the surface of EVs and named them CAP-exosomes. CAP-exosomes were able to specifically deliver encapsulated miR-140 into chondrocytes in vitro and into deep cartilage region in vivo [73]. Small EVs with a diameter of 40–120 nm was found to deliver miRNAs that target gene regulating inflammation. Small EVs isolated from MSCs primed with IFN-ɣ significantly upregulated expression of anti-inflammatory markers including Cd206, Cx3cr1, etc. compared to small EVs isolated from unprimed MSCs [74]. Yu et al. transfected bone marrow mesenchymal stem cells with miRNA-29b and miR NC before isolating exosomes. Accelerated motor function was observed when miRNA-29b exosomes were intravenously injected into spinal cord injury rat model compared to physiological saline administered rat model [71].

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Chapter 2 Exosomal MicroRNA Profiling Neda Rahimian, Javid Sadri Nahand, Michael R. Hamblin, and Hamed Mirzaei Abstract Exosomes are extracellular vesicles, which have the ability to convey various types of cargo between cells. Lately, a great amount of interest has been paid to exosomal microRNAs (miRNAs), since much evidence has suggested that the sorting of miRNAs into exosomes is not an accidental process. It has been shown that exosomal miRNAs (exo-miRNAs) are implicated in a variety of cellular processes including (but not limited to) cell migration, apoptosis, proliferation, and autophagy. Exosomes can play a role in cardiovascular diseases and can be used as diagnostic biomarkers for several diseases, especially cancer. Tremendous advances in technology have led to the development of various platforms for miRNA profiling. Each platform has its own limitations and strengths that need to be understood in order to use them properly. In the current review, we summarize some exo-miRNAs that are relevant to exo-miRNA profiling studies and describe new methods used for the measurement of miRNA profiles in different human bodily fluids. Key words MicroRNA, Exosome, Isolation

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Introduction Exosomes belong to the family of extracellular vesicles, with a size ranging from 40 to 100 nm, and are present in virtually, for example, from living cells and organisms. Almost all cells can secrete exosomes into the extracellular space, which occurs following fusion of intracellular organelles with the plasma membrane. Exosomes are mostly comprised of proteins and lipids [1] but also contain different nucleic acids such as mRNAs, various non-coding RNAs (ncRNAs), and microRNAs (miRNAs) [2, 3]. During the circulation of exosomes, they may be taken up by nearby or remote cells, and they can then modify the function of recipient cells. Nowadays, increasing attention has been given to exosomes since their effects in recipient cells were discovered [4]. MicroRNAs are a member of the family of noncoding RNAs, which regulate gene expression by inhibiting mRNA translation and inducing mRNA degradation [5]. It has been demonstrated

Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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that miRNAs play important roles in many biological processes such as cell differentiation, proliferation, migration, as well as cancer initiation and development [6–8]. There is much evidence in support of the fact that miRNAs are capable of prolonged existence in different human body fluids, such as urine, blood, saliva, and breast milk [9, 10]. MiRNAs can be stored inside microvesicles or exosomes, high-density lipoproteins (HDL) [11, 12], or transported by Argonaute2 (AGO2), a crucial component of the RNA-induced signaling complex (RISC) [13]. These properties allow miRNAs to be safe from degradation and maintain their stability. The role of miRNAs in exosomes has been the focus of many recent studies, since these vesicles can transmit biological information. The transfer of information through circulating vesicles is considered the third most important type of cell-to-cell communication and signaling [14, 15]. Enzymes and ATP are necessary for exosome formation and secretion, and the miRNA and mRNA profiles of exosomes differ somewhat from their parental cells [16]. As a result, cells probably have an active screening mechanism for exosomes and their cargos. Additionally, much effort has been made to understand the effects of transferred exosomes on recipient cells. A number of studies have shown that exosomal cargos have the potential to be used as diagnostic or prognostic biomarkers for various malignancies, including brain, ovarian, lung, breast, and prostate cancer [2, 17–20]. The use of a suitable and reliable isolation method is a key in the factor of success of these studies. Measurement of exosomal miRNAs (exo-miRNAs) expression can also be useful for systems-level study of gene regulation, especially when exo-miRNA measurement is combined with mRNA profiling and other genome-scale datasets. Moreover, emerging evidence has demonstrated that exo-miRNAs can be preserved in a variety of sample types such as urine, serum, and formalin-fixed tissue blocks and can be detected with higher sensitivity compared with proteins. Therefore, much interest has been directed toward the development of exo-miRNAs as diagnostic markers for various diseases, including cancer, autoimmune, and cardiovascular diseases [21–24]. Nowadays, different methods have been applied for identifying exosomal miRNAs, and the most common methods are nextgeneration sequencing technology (NGS), reverse transcriptionpolymerase chain reaction (RT-PCR), and digital PCR. Although these procedures have good analytical capability, there are some problems in using them in clinical settings [25]. The diagnostic ability of RT-PCR for exo-miRNAs detection has been shown; however, this method has low sensitivity and is expensive for routine use [26]. Digital PCR is able to detect relatively low quantity of miRNAs; however, this method is highly complicated and expensive [26, 27]. NGS often needs several complex pretreatment

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processes [28, 29]. Recent studies have suggested that the construction of a biosensor is more rapid and sensitive than these three methods, and biosensors are also easy to run [25]. In the present review, we describe some exo-miRNAs that are relevant to exomiRNA-profiling studies and summarize some new methods used for the measurement of miRNA profiles in different human body fluids.

2

Exosome Isolation The methods using for isolation of exosomes include ultracentrifugation, density-gradient centrifugation, ultrafiltration, precipitation, and immunoisolation. Several methods for exosomes characterization have been introduced including nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and flow cytometry. Also, several nano-based methods for exosome isolation have been introduced including surface plasmon resonance (SPR)-based nanosensors, resistive pulse sensing (RPS), and nano-DLD. Tables 1 and 2 compare existing exosome isolation methods, which are further explained in the following sections.

2.1 Ultracentrifugation

Exosomes are hard to isolate because they are so small. Despite these challenges, some laboratories have managed to separate exosomes through ultracentrifugation, ultrafiltration, chromatography, polymer-based precipitation, and affinity capture with magnetic beads attached to antibodies [30]. On the other hand, the optimum technique to isolate and scale up exosomes depends on the kind of samples employed. Ultracentrifugation is a common process for pelleting lipoproteins, extravesicular protein complexes, aggregates, and some pollutants; however, it is unsuitable for isolating exosomes from clinical samples since it is time-consuming and labor-intensive, needs expensive apparatus, and involves repeated overnight centrifugation steps [31]. Gurunathan et al. [32] isolated low-density and high-density vesicles from yeast using ultracentrifugation and density gradient ultracentrifugation, respectively. Compared to ultracentrifugation and precipitation-based approaches, density gradient centrifugation produces the purest exosome population [33]. Centrifugation can be used to separate and isolate cells, EV subpopulations, and proteins from mixtures because of their different sizes. Differential ultracentrifugation and density gradient ultracentrifugation are the two types of preparative ultracentrifugation. Surprisingly, the density of the payload contained within the vesicles affects the exosome separation process, which might be problematic. Ultracentrifugation is appropriate for larger sample volumes, but not however useful for clinical samples in small volumes. In the ultracentrifugation process, high

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Table 1 Comparison of different exosome separation technologies

Method

Purity/ yield

Advantage

Disadvantage

Note

Ultrafiltration

High/low Size uniformity, faster Low yield; possible Use filter membrane than pore blockage in the with defined ultracentrifugation, membrane; clogging molecular weight needs no special can trap vesicles or size exclusion equipment reducing the yield limits

Differential centrifugation

Low/ Lower cost and time, medium high sample yields

Can distinguish Low-medium purity, different sizes and cost of large number densities between of initial samples, cells, dead cells, mechanical damage cell debris, and induced by highexosomes speed centrifugation

Density gradient centrifugation

Low/ High practicability medium

Time-consuming

Polymer-based techniques

Low/high Fast procedure, convenient operation

Unstable quality of kits, Commercial kits high expense

Precipitation

Changing the Low/high Short time, high cost, The co-precipitation solubility or good efficiency and non-exosome dispersibility of contaminants exosomes with involved in products synthetic polymers of precipitation or PEG

Uses a discontinuous sucrose or iodixanol gradient

High/low High purity, specific Capture-based separation techniques (magnetic beads and immunoaffinity)

Separate exosomes with Making use of the targeted proteins interaction only, high reagent between specific cost, and only proteins on the working with cellsurface of free samples exosomes such as CD9 and CD63 and the antibody fixed on the magnetic beads or microfluid CHIP

Microfluidicsbased techniques

Complicated equipment Difficult to operate

High

Fast separation Continuous process Higher purity

Size-based microfluidics Immunoaffinitybased microfluidic separation Dynamic microfluidics

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Table 2 Different exosome characterization techniques

Method

Minimum resolution

Advantage

Disadvantage

Note TEM can resolve the characteristic feature of exosomes, a cup-shaped morphology which is due to exosome collapsing

Transmission electron microscopy (TEM)

C (rs2910164) in the miR-146a, related to suppressing of BRCA1/2 DNA repair protein, with the risk and survival of colorectal cancer (CRC) patients, as well as miR-146a and BRCA1/2 levels and miR binding efficiency. Moreover, pri-miRs of miR-146a containing G (wild-type) and C (variant) alleles were cloned into pcDNA3.3 vector and co-transfected in HT-29 colorectal cancer cell line. Luciferase reporter assay was performed to assess miR-146a binding to BRCA2 3′-UTR region in HT-29 [12]. miR-146a has been also described as one of the key regulatory molecules that negatively regulate NF-κB pathway by targeting and repressing the tumor necrosis factor receptor–associated factor 6 (TRAF6) [13]. Thus, a second study has analyzed the effect of mature miR-146a on the NF-κB pathway in glioblastoma (GBM) cell lines, U87MG and T98G, through pcDNA3.3-miR-146a cloned vector and NF-κB promoter/luciferase reporter plasmid transfections [14] Thus, DNA fragments containing pri-miRs of miR-146a were inserted into a pcDNA3.3 mammalian expression vector, previously constructed [14].

2

Materials BRCA1/2 are crucial proteins involved in homologous recombination, which is the most effective method of double-strand break repair [15]. The BRCA1/2 expression levels are known as lower in CRC patients [16], indicating repair deficiency favoring damage accumulation in the disease. The SNV rs2910164 in miR-146a has been previously associated with suppression of BRCA1/2 DNA repair protein, with the risk and survival of CRC patients, as well as miR-146a and BRCA1/2 levels and miR binding efficiency [10]. Thus, the binding and activity of miR-146a on 3′-UTR of BRCA2 target sequences were based on the expression of luciferase as a reporter gene fused to the UTR sequence in the presence of plasmids containing the pre-miR-146a to further be tested in in vitro cell culture assays. The pri-miRs of miR-146a containing G and C alleles for the rs2910164 were cloned into pcDNA3.3 vector (miR-146a + pcDNA3.3 clone). Next, the BRCA2 3′-UTR sequence was cloned into pMIR-report™ luciferase vector. Both clones were co-transfected in HT-29 [12]. miR-146a is also known as a modulator of NF-κB by targeting IRAK1 and TRAF6 [14]. Our previous study showed that dipotassium glycyrrhizinate (DPG), a dipotassium salt of glycyrrhetic acid, which is a compound isolated from licorice (Glycyrrhiza gla-

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bra), inhibited cell growth and induced apoptosis of GBM cells in vitro by overexpressing miR-146a that positively inhibited NF-κB through suppressing TRAF6 after DPG action [14]. Thus, the miR-146a + pcDNA3.3 clone containing the G allele was co-transfected in U87MG and T98G GBM cell lines plus NF-κB promoter/luciferase reporter vector [14]. 2.1 Samples: DNA Isolation

1. DNA samples were obtained from peripheral blood leukocytes (healthy blood donors). 2. Protein digestion buffer w/ lithium dodecyl sulfate (LDS) (see Note 1). 3. Proteinase K is stored at -20 °C. 4. Lithium chloride (LiCl). 5. Ethanol. 6. Nuclease-free water.

2.2 Sample Preparation: Conventional PCR

1. Buffer (10×) (Thermo Fisher – catalog number: B38). 7. Dimethyl sulfoxide (DMSO) (Merck – catalog number: 276855). 2. Magnesium chloride MgCl2 (50 mM) (Thermo Fisher – catalog number: y02016b). 3. Phosphated Deoxyribonucleotides dNTPs (10 mM) (Thermo Fisher – catalog number: 7710). 4. Taq DNA Polymerase (5 U/μL) (Thermo Fisher – catalog number: 11615–010). 5. Oligonucleotides to obtain the miR-146a sequence containing the wild-type (GG) and variant (CC) genotypes for the rs2910164 and the 3′-UTR region of the BRCA2 gene (Table 1; Figs. 2a and 3a) (see Note 2).

Table 1 Oligonucleotides used to obtain the regions of interest for the study Primers for conventional PCR pcDNA_miR-146MUT forward (KpnI)

5′ gcGGTACCGTTTATAACTCATGAGTGCC 3′

pcDNA_miR146MUT reverse (XhoI)

5′ atCTCGAGCTTATACCTTCAGAGCCTG 3′

pMIRvector_BRCA2 forward (SpeI)

5′ gcACTAGTAACATACCATTTTCTTTTAG 3′

pMIRvector_BRCA2 reverse (MluI)

5′ atACGCGTGGAATTAGAGTTACACTGAG 3′

The final concentration used for the PCR reaction was 10 pmol/ μL for each pair

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6. First, resuspend forward and reverse oligonucleotides to get a final concentration of 200 μM each (stock primers). Second, mix 5 μL of 200 μM forward plus 40 μL nuclease-free water to get 20 μM (primers for use). Repeat the same using the reverse oligonucleotide. Keep stock and use primers at -80 °C and 20 °C, respectively. 7. Agarose (Kasvi – catalog number: K9-9100). 8. Buffer (TEB) Tris, EDTA, and boric acid (10×) (see Note 3). 9. DNA ladder 100 bp (Thermo Fisher – catalog number: 76712). 10. Loading buffer (6×) (Thermo Fisher – catalog number: 76715). 11. Ethidium bromide solution (Thermo Fisher – catalog number: 15585-011). 12. NanoDrop™ 2000/2000c Spectrophotometers (Thermo Fisher – catalog number: ND2000CLAPTOP). 13. Molecular Imager Gel Doc XR (Bio-Rad – catalog number: 1708195). 2.3

Vectors

1. The pcDNA3.3 and the pMIR-REPORT™ Luciferase empty vectors (Clontech, Palo Alto, USA) were used for the insertion of miR-146a (Fig. 1a) and the BRCA2 3′-UTR sequences (Fig. 1b), respectively. 1. Purification: Wizard® SV Gel and PCR Clean-Up System kits (Promega – catalog number: A9281).

Fig. 1 Representative map of the pcDNA3.3 and pmiR-REPORT Luciferase empty vectors used for cloning the genomic region of microRNA (miR)-146a and part of the sequence of the 3′-untranslated region (UTR) of the BRCA2 gene, respectively

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2. Tango buffer (10×) with bovine serum albumin (BSA) (Thermo Fisher Scientific – catalog number: BY5).

2.4 Clones’ Constructions: miR146a + pcDNA3.3 and 3′-UTRBRCA2 + pMIRREPORT Luciferase

3. Buffer 2.1 (10×) (New England Biolabs – catalog number: B7202S).

2.4.1 Inserts (miR-146aSequence and 3′-UTRBRCA2) and Empty Vectors (pcDNA3.3 and pMIRREPORT™ Luciferase Vectors): Purification and Digestion

5. BSA 10% (20×).

2.4.2 pcDNA3.3 + miR146a And pMIR-REPORT +3′-UTR BRCA2: Ligation

4. Restriction enzymes as KpnI (10 U/μL) (New England Biolabs, catalog number R3142), XhoI (10 U/μL) (New England Biolabs, catalog number R0146S), SpeI (10 U/μL) (New England Biolabs, catalog number R0133S), and MluI (10 U/ μL) (New England Biolabs, catalog number R0198S). 6. Nuclease-free water.

1. Buffer T4 (5×) (Thermo Fisher Scientific, catalog number: 1262296). 2. T4 ligase (5×) (Thermo Fisher Scientific, catalog number: 15224-017). 3. Nuclease-free water.

2.4.3 Bacteria Transformation

1. Luria-Bertani (LB) culture medium (Difico ™ – catalog number: 244520). 2. LB agar culture medium (Difico ™ – catalog number: 244520). 3. Non-competent Escherichia coli DH10B (Thermo Fisher Scientific – catalog number: EC0113). 4. Ampicillin antibiotic (Sigma – catalog number: A9393). 5. Petri dish (Dispopetri – catalog number: P0034). 6. PureLink™ HiPure Plasmid Midiprep Kit (Thermo Fisher Scientific – catalog number: K210004).

2.5 Co-transfections into Cell Lines

1. Human colon adenocarcinoma cells (HT-29) (ATCC®, HTB-38™). 2. Human U-87MG and T98G cell lines from human brain (glioblastoma astrocytoma) (ATCC®, HTB-14™, and CRL-1690™). 3. Dulbecco’s modified Eagle’s medium – high glucose (DMEM). 4. Fetal bovine serum (FBS) (Vitrocel – catalog number: S0013). 5. Ampicillin antibiotic (Vitrocel – catalog number: P0223). 6. Renilla Luciferase Control Reporter Vectors (pRL-TK). 7. Lipofectamine® 2000 DNA Transfection Reagent (Thermo Fisher Scientific – catalog number: 11668030). 8. Dual-Luciferase® Reporter Assay System (Promega – catalog number: E1910).

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Methods

3.1 Samples: Isolation of DNA

1. Isolate DNA samples from peripheral blood leukocytes. Cell lysis should be performed using 400 μL of protein digestion buffer with LDS and 20 μL of proteinase K, incubated for approximately 1 h at 55 °C. 2. For DNA precipitation, add 200 μL of 7.5 M LiCl, and incubate for 1 h in a freezer at -20 °C. Then centrifuge at relative centrifugal force (RCF) 35,000 × g for 10 min. 3. Transfer the supernatant to a new 1.5 mL tube, add 1000 μL of absolute alcohol, and centrifuge again at 35,000 × g for 10 min. 4. Then wash the samples with 70% alcohol, and after complete alcohol evaporation, add 40 μL of RNase-free water. The concentrations and quality of the DNA samples must be measured in the NanoDrop™ 2000/2000c spectrophotometer equipment.

3.2 Sample Preparation: Conventional PCR

1. A total of seven DNA samples obtained as in Subheading 3.1 were used for the amplification of the region of interest (miR-146a-sequence) by polymerase chain reaction (PCR), four samples being from individuals carrying the (GG) genotype for the variant rs2910164 and three samples from individuals carrying the (CC) genotype for the same variant. In addition, five of the seven samples above were used for the amplification of the 3′-UTR region of the BRCA2 gene. 2. Set up the PCR reaction according to Table 2, and set the thermocycler as in Table 3. 3. Next, the PCR product was analyzed using gel electrophoresis using 1.5% agarose gel (1.5 g of agarose in 100 mL of TEB buffer plus 1 μL of ethidium bromide). Dissolve agarose powder in TEB buffer by boiling for 2 min in microwave, avoiding bubbles. 4. After agarose solidification, samples were loaded as follows: only 1 μL of each of the PCR products plus 2 μL of loading buffer (6×) loaded to the gel and run in TEB buffer. 5. Apply an electrical current of 120 V and 50 mA for approximately 1 h. Visualize samples on an ultraviolet (UV) light. 6. The regions of interest amplified are shown in Figs. 2b and 3b.

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Table 2 Polymerase chain reaction (PCR) reaction mix Volume (μL) for 1 reaction

a

Volume (μL) for 8 reactions

a

Volume (μL) for 6 reactions

Reagents

miR146a

3′-UTR-BRCA2 miR-146a

3′-UTR BRCA2

RNAse-free water

15.75

18.25

126.0

109.5

Buffer (10×)

2.5

2.5

20.0

15.0

MgCl2 – 50 mM

2.0

2.0

16.0

12.0

dNTP – 10 mM

0.5

0.5

4.0

3.0

Primer – 20 μmol

0.5

0.5

4.0

3.0

Taq DNA polimerase – 5 U/μL

0.25

0.25

2.0

1.5

DMSOb

2.5

–

20.0

–

Total volume

24

24

192/8 = 24

144/6 = 24

a

The volume of each reagent was calculated considering a negative control reaction for the experiment Dimethyl sulfoxide DMSO: It was used only for the amplification of miR-146a

b

Table 3 Thermocycler settings

a

Stage

Temperature (°C)

Time (minutes)

Hold

95

5

Cycle (35 cycles)

95 60 72

1 1 1

Hold

75

10

Holda

4

1

This step is to keep the samples preserved before being removed from the thermocycler

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Fig. 2 (a) Sequence of the genomic region of microRNA (miR)-146a (red), including the binding sequence of the miR to the predicted target gene, called the seed sequence (green), and the region of the polymorphism 60C > G (C, blue) were included in the design. Sense and antisense sequence (black) for amplification by polymerase chain reaction (PCR) and subsequent insertion into pcDNA3.3 plasmid. The gene sequence of mature miRNA-146a was extracted from http://www.ensembl.org/. (b) Genomic region of miR-146a PCR amplified. Well 1 represents 100 base pairs (bp) marker; columns 2–5 represent the miR-146a amplification containing the dominant homozygous genotype (GG); well 6–8 miR-146a amplification containing the homozygous variant genotype (CC); well 9 negative control of the reaction

Fig. 3 (a) Part of the sequence of the 3′-untranslated region (UTR) of the BRCA2 gene (lilac), including the binding region of microRNA (miR)-146a called seed sequence (green), and sense and antisense (black) primers were included in the design for reaction amplification polymerase chain (PCR) and subsequent insertion into plasmid pmiR-REPORT Luciferase. The mRNA sequence of the BRCA2 gene was extracted from the website http://www.ensembl.org/. (b) 292-base pair (bp) fragments obtained after amplification by conventional PCR of the 3′-UTR sequence of the BRCA2 gene (wells 2–6). The DNA size marker, ladder 100 bp, is shown in well 1; well 7 represents the negative control of the reaction

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3.3 Clone Constructions: miR146a + pcDNA3.3 and 3′-UTRBRCA2 + pMIRREPORT Luciferase

1. After amplifying the regions of interest by conventional PCR as above, purify PCR products using the Wizard® SV PCR CleanUp System kit (see Note 4).

3.3.1 Inserts (miR-146aSequence and 3′-UTRBRCA2) and Empty Vectors (pcDNA3.3 and pMIRREPORT™ Luciferase Vectors): Purification and Digestion

3. Further, enzymatically digest inserts (miR-146a-sequence and 3′-UTR-BRCA2) and empty vectors (pcDNA3.3 and pMIRREPORT™ Luciferase vectors) using KpnI and XhoI enzymes, which recognize the GGTACC (miR-146a) and CTCGAG sequences (pcDNA3.3 vector), as well as SpeI and MluI enzymes, which recognize the ACTAGT (3′-UTR-BRCA2) and ACGCGT sequences (pMIR-REPORT Luciferase vector).

2. Determine the concentration of DNA obtained from the PCR product after the purification process by NanoDrop™ 2000/ 2000c spectrophotometer (Table 4).

4. Enzymatic digestion must be carried out at a constant temperature of 37 °C for 2 h in a thermocycler or in a water bath. The volumes and concentrations of each enzymatic reaction are specified in Table 5. 5. Analyze the digestion reactions using 1.2% agarose gel, and subject to electrophoresis with an electric current of 150 V at 40 mA for approximately 1 h. 6. Visualize the gel under UV light to carefully cut the specific fragment containing the properly digested DNA (inserts and vectors) using a scalpel. Purify the digested DNA (inserts and vectors) using the Wizard® SV (see Note 4) Gel and PCR Clean-Up System kit (see Note 5). 7. The samples’ DNA concentrations obtained after purification are shown in Table 6. 3.3.2 pcDNA3.3 + miR146a And pMIR-REPORT +3′-UTR-BRCA2: Ligation

1. The ligation reactions must be planned based on (1) vector size (base pairs); (2) vector concentrations [ng/μL]; and (3) insert size (base pairs). Furthermore, different proportions of the vectors and insert amounts should be planned for greater chances of successful ligation (1:3 or 1:6 proportions). Use the website http://www.insilico.uni-duesseldorf.de/Lig_ Input.html to calculate the required insert DNA. 2. Set up the ligation reactions as shown in Table 7. The ligation reaction is carried out at constant temperature of 14 °C for 24 h in a thermocycler.

3.3.3 Bacteria Transformation

1. Insert the constructs (pcDNA3.3 + miR-146a and pMIRREPORT +3′-UTR-BRCA2) into E. coli DH10B competent bacteria by heat shock (see Note 6). 2. Add 5 μL of each construct in 15 μL of DH10B bacteria, and incubate for 30 min on ice. Afterward, place this mix in a water bath at 42 °C for 30 s and then quickly on ice for 10 min.

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Table 4 DNA concentration of inserts after purification PCR products

[ng/μL]

a

a

miR-146a

96.8

1.87

1.22

BRCA2

72.6

1.90

1.23

260/280

260/230

a

260/280 and 260/230 wavelength ratios above 1.80 are indicative of the presence of proteins and organic compounds, respectively

Table 5 Components used in the enzymatic digestion reaction of miR-146a and 3′-UTR-BRCA2 inserts and pcDNA3.3 and pMIR-REPORT Luciferase vectors Inserts 3′-UTR BRCA2

miR-146a Reagents

Volume (μL)

Reagents

Volume (μL)

Tango buffer with BSA (10×)

1.5

Buffer 2.1 (10×)

1.5

KpnI (10 U/μL)

0.5

SpeI (10 U/μL)

0.25

XhoI (10 U/μL)

0.5

MluI (10 U/μL)

0.5

miR-146aa

10.0

3′-UTR-BRCA2a

10.0

RNAse-free water

2.5

BSA (10%)

1.5

RNAse-free water

1.25

Vectors pcDNA3.3

a

pMIR-REPORT luciferase

Tango buffer with BSA (10×)

1.5

Buffer 2.1 (10×)

1.5

KpnI (10 U/μL)

0.5

SpeI (10 U/μL)

0.25

XhoI (10 U/μL)

0.5

MluI (10 U/μL)

0.5

pcDNA3.3b

0.8

pMIR-REPORT Luciferaseb

1.0

RNAse-free water

11.7

BSA (10%)

1.5

RNAse-free water

10.25

Volumes used for the miR-146a-sequence and 3′-UTR-BRCA2 inserts were based on their concentrations as presented in Table 4 b Volumes used for pcDNA3.3 and pMIR-REPORT Luciferase vectors were based on their initial concentration [11061.5 ng/ μL and 877.8 ng/ μL, respectively

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Table 6 Inserts’ and vectors’ DNA concentrations after purification Samples

[ng/μL]

a

a

miR-146a-sequence

11.2

1.57

0.26

3′-UTR-BRCA2

17.5

1.75

0.44

pcDNA3.3

14.2

1.72

0.31

pMIR-REPORT luciferase

14.8

1.81

0.26

260/280

260/230

a

260/280 and 260/230 wavelength ratios above 1.80 are indicative of the presence of contaminants such as proteins and organic compounds, respectively

Table 7 Ligation reactions 1:3–3′-UTR-BRCA2 + pMIR-REPORT Luciferase

1:3 – miR-146a + pcDNA3.3

Reagents

Volume (μL)

Reagents

Volume (μL)

Buffer T4 (5×)

4.0

Buffer T4 (5×)

4.0

0.4

a

1.0

3.6

b

3.5

T4 ligase (1 U/μL)

1.0

T4 ligase (1 U/μL)

1.0

RNAse-free water

9.4

RNAse-free water

10.5

a

3′UTR-BRCA2

b

pMIR-REPORT luciferase

1:6–3′UTR-BRCA2 + pMIR-REPORT luciferase Buffer T4 (5×)

miR-146a-sequence pcDNA3.3

1:6 miR-146a + pcDNA3.3

4.0

Buffer T4 (5×)

4

1.0

a

1.5

3.6

b

3.5

T4 ligase (1 U/μL)

1.0

T4 ligase (1 U/μL)

1.0

RNAse-free water

9.4

RNAse-free water

10.0

a

3′-UTR-BRCA2

b

pMIR-REPORT luciferase

miR-146a-sequence pcDNA3.3

a

Volumes used for the 3’-UTR-BRCA2 and miR-146a-sequence inserts. bpMIR-REPORT Luciferase and pcDNA3.3 vectors were based on concentrations in Table 6

3. Add 250 μL of antibiotic-free liquid LB medium followed by incubation at 37 °C for 1 h in a shaking incubator at 170 rpm. Then, spread the mix on Petri dishes with LB agar medium containing 100 ng/mL of antibiotic ampicillin. Incubate at 37 °C overnight. 4. The next day, isolate 5–10 ampicillin-resistant colonies, place them in 5 mL of liquid LB medium containing 100 ng/mL of antibiotic ampicillin, and incubate them at 37 °C for 16 h in a shaking incubator at 170 rpm.

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Fig. 4 (a) Fragments of 383 base pairs (bp) obtained after amplification by conventional polymerase chain reaction (PCR) of the genomic region of mature miR-146a extracted from different selected colonies after cloning the abovementioned region in pcDNA3.3 vector. Wells 2 and 3 represent GG genotype for the singlenucleotide variant (SNV) 60C > G; wells 8–11 represent CC genotype for SNV 60C > G; well 12 represents the negative control of the reaction. The DNA size marker, ladder 100 bp, is shown in well 1. (b) 292 bp fragments obtained after amplification by conventional PCR of the 3′-UTR sequence of the BRCA2 gene from different selected colonies after cloning the abovementioned region in pmiR-REPORT Luciferase vector (wells 2 and 3). Wells 4 and 5 represent negative colonies, and column 6 corresponds to the negative control. The DNA size marker, ladder 100 bp, is shown in well 1. (c) 1% agarose gel electrophoresis showing a digested DNA using the restriction enzymes SpeI and MluI. Well 1 shows the DNA size marker, ladder 100 bp. Lane 2 represents the empty pmiR-REPORT Luciferase vector digested using SpeI and MluI. Well 3 represents a successfully inserted 292 bp fragment regarding to the sequence of the mRNA of the BRCA2 gene

5. Afterward, use 500 μL of each cultivated colony for DNA extraction using the PureLink™ HiPure Plasmid Midiprep Kit. Freeze 4.5 mL of each colony at -80 °C by adding 100% glycerol. 6. To determine if the inserts are successfully cloned, perform PCR, as shown in Fig. 4a, b. 7. Figure 4c shows an insert successfully cloned using SpeI and MluI enzymes (cloning enzymes). Sequencing can also be performed to determine successful cloning (data not shown). 3.4

Luciferase

1. Luciferase assay will assess whether miR-146a carrying the rs2910164 variants (wild-type GG or homozygous variant CC) is capable of binding to the 3′-UTR of BRCA2 mRNA in HT-29 cells. 2. In addition, luciferase assay will allow to assess whether mature miR-146a will modulate NF-κB pathway in U87MG and T98G cells by transfecting miR-146a + pcDNA3.3 clone containing the rs2910164 G allele construction plus NF-κB promoter/luciferase reporter vector. 3. Renilla luciferase vector can be used as a transfection control in both the experiments.

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Table 8 Volume and concentration of DNA mix for transfection in the HT-29, U-87MG, and T98G cell lines Luciferase A) pMIR-REPORT luciferase + 3′-UTR-BRCA2 and Renilla and pcDNA3.3 + miR-146a (GG) pMIR-REPORT luciferase +3′-UTR-BRCA2 (10 ng/μL)

3.5 μL

pcDNA3.3 + miR-146a (GG) (10 ng/μL)

3.5 μL

pRL-TK (5 ng/μL)

3.5 μL

DMEM without serum and antibiotic

165.0 μL

B) pMIR-REPORT luciferase + 3′-UTR-BRCA2 and Renilla and pcDNA3.3 + miR-146a (CC) pMIR-REPORT luciferase +3′-UTR-BRCA2 (10 ng/μL)

3.5 μL

pcDNA3.3 + miR-146a (CC) (10 ng/μL)

3.5 μL

pRL-TK (5 ng/μL)

3.5 μL

DMEM without serum and antibiotic

165.0 μL

C) pMIR-REPORT luciferase + 3′-UTR-BRCA2 and Renilla and pcDNA3.3 (empty vector) pMIR-REPORT luciferase +3′-UTR-BRCA2 (10 ng/μL)

3.5 μL

pcDNA3.3 empty vector (10 ng/μL)

3.5 μL

pRL-TK (5 ng/μL)

3.5 μL

DMEM without serum and antibiotic

165.0 μL

D) pcDNA3.3 + miR-146a (GG) + NF-κB promoter/luciferase reporter NF-κB promoter/luciferase reporter (10 ng/μL)

3.5 μL

pcDNA3.3 + miR-146a (GG) (10 ng/μL)

3.5 μL

pRL-TK (5 ng/μL)

3.5 μL

DMEM without serum and antibiotic

165.0 μL

E) pcDNA3.3 (empty vector) + NF-κB promoter/luciferase reporter NF-κB promoter/luciferase reporter (10 ng/μL)

3.5 μL

pcDNA3.3 empty vector (10 ng/μL)

3.5 μL

pRL-TK (5 ng/μL)

3.5 μL

DMEM without serum and antibiotic

165.0 μL

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4. Constructs are transfected into HT-29, U-87MG, and T98G cell lines using Lipofectamine 2000 reagent according to manufacturer’s guidelines, as described below (step 5–9), in triplicate. 5. For transfection, seed 1 × 106 cells/well in a 24-well plate in DMEM high-glucose culture medium supplemented with 10% fetal bovine serum and 1% antibiotic ampicillin/streptomycin. Transfect the cells when 70% confluent. Each transfection should be performed in triplicate. 6. For DNA transfections, prepare the DNA mix in 1.5 mL tubes as shown in Table 8 (A–E mixes). 7. In five different 1.5 mL tubes, mix 825 μL of serum-free highglucose DMEM medium plus 50 μL of Lipofectamine. 8. Add 175 μL of the above mix (serum-free high-glucose DMEM medium plus 50 μL of Lipofectamine) in each DNA mix tubes (A–D mixes). Incubate for 5 min at room temperature. 9. Add 100 μL of the above preparation in each well of the 12-well plate, and incubate cells plus DNA mixes for 48 h at 37 °C. 10. After 48 h, harvest cells to perform the luciferase assay using the Dual-Luciferase Reporter Assay kit, following manufacturer’s instructions (see Note 7). The luminescence reading may be measured in the luminometer equipment GloMax® Multi Detection System. 3.5 Luciferase Results

1. In the first study, the pcDNA3.3-miR-146a-G presented increased binding capacity to the 3′-UTR region of BRCA2 compared to pcDNA3.3-miR-146a-C. In addition, the G allele altered the binding affinity between miR-146a and its BRCA2 3′-UTR region target, thus enhancing suppression of BRCA2 expression. Our results suggest that single-nucleotide variant rs2910164 does not influence CRC risk in Brazilian patients; however, the GG genotype could act as a factor of worse prognosis in patients with advanced disease due to suppression of BRCA1/2 modulated by miR-146a [12]. 2. The second study has shown that the promoter activity of NF-κB in U87MG- and T98G-pcDNA3.3-miR-146a presented higher compared to pcDNA3.3-empty vector cells. In contrast, NF-κB activity was significantly reduced when U87MG-pcDNA3.3-miR-146a and T98G-pcDNA3.3-miR146a cells were treated with DPG [14].

MicroRNA Activity, 3′-UTR, Luciferase Assay, In Vitro Cell Culture

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Notes 1. For the protein digestion buffer w/ LDS solution, use 100 μL Tris-HCL 2 M, 400 μL of 0.5 M EDTA, 40 μL 5 M NaCl, and 500 μL SDS 20%, and make up to 20 mL with distilled water. 2. Resuspend forward and reverse oligonucleotides to get a final concentration of 200 μM each (stock primers). Second, mix 5 μL of 200 μM forward plus 40 μL RNase-free water to get 20 μM (diluted primers used). Repeat the same using the reverse oligonucleotide. Keep stock and diluted primers at 80 °C and -20 °C, respectively. 3. For the 10× TEB buffer solution, mix 107.81 g of Tris, 5.8 g of EDTA, and 55.0 g of boric acid in 1 l of distilled water. 4. Add an equal volume of membrane binding solution to the PCR amplification. Insert SV Mini column into collection tube. Transfer dissolved gel mixture or prepared PCR product to the mini column assembly. Incubate at room temperature for 1 min. Centrifuge at 16,000 × g for 1 min. Discard flowthrough, and reinsert mini column into collection tube. Add 700 μL membrane wash solution (ethanol added). Centrifuge at 16,000 × g for 1 min. Discard flow-through, and reinsert mini column into collection tube. Repeat step 4 with 500 μL membrane wash solution. Centrifuge at 16,000 × g for 5 min. Empty the collection tube, and re-centrifuge the column assembly for 1 min with the microcentrifuge lid open (or off) to allow evaporation of any residual ethanol. Carefully transfer mini column to a clean 1.5 mL microcentrifuge tube. Add 50 μL of nuclease-free water to the mini column. Incubate at room temperature for 1 min. Centrifuge at 16,000 × g for 1 min. Discard mini column, and store DNA at 4 °C or 20 °C. 5. Following electrophoresis, excise DNA band from gel, and place gel slice in a 1.5 mL microcentrifuge tube. Add 10 μL membrane binding solution per 10 mg of gel slice. Vortex and incubate at 50–65 °C until gel slice is completely dissolved. 6. For non-competent bacteria, it is necessary that before transformation, they are incubated in a 0.1 M calcium chloride solution for 30 min on ice. 7. The Dual-Luciferase® Reporter (DLR™) Assay System provides an efficient means of performing dual-reporter assays. In the DLR™ Assay, the activities of firefly (Photinus pyralis) and Renilla (Renilla reniformis) luciferases are measured sequentially from a single sample. Thus, pre-dispense 100 μL of Luciferase Assay Reagent II (LAR II) into the appropriate number of luminometer tubes to complete the desired number of DLR™ Assays. Program the luminometer to perform a 2-s

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premeasurement delay, followed by a 10-s measurement period for each reporter assay. Carefully transfer up to 20 μL of cell lysate into the luminometer tube containing LAR II. Mix by pipetting 2 or 3 times. Do not vortex. Place the tube in the luminometer, and initiate reading. If available, use a reagent injector to dispense 100 μL of Stop & Glo® Reagent. If using a manual luminometer, remove the sample tube from the luminometer, add 100 μL of Stop & Glo® Reagent, and vortex briefly to mix. Replace the sample in the luminometer, and initiate reading. If the luminometer is not connected to a printer or computer, record the firefly luciferase and Renilla luciferase activity measurements.

Acknowledgments The pcDNA3.3, pMIR-reporter-Luciferase, NF-κB promoter/ luciferase reporter, and pRL-TK empty vectors were donated kindly by Professor Ricardo Aguiar, University of Texas Health Science Center, San Antonio, USA. References 1. Eulalio A, Huntzinger E, Izaurralde E (2008) Getting to the root of miRNA-mediated gene silencing. Cell 132:9–14 2. Georges M, Coppieters W, Charlier C (2007) Polymorphic miRNA-mediated gene regulation: contribution to phenotypic variation and disease. Curr Opin Genet Dev 17:166–176 3. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297 4. miRBase. Available at: https://mirbase.org/ search.shtml. Accessed 9 Nov 2021 5. Kawahara Y (2014) Human diseases caused by germline and somatic abnormalities in microRNA and microRNA-related genes. Congenit Anom 54:12–21. https://doi.org/10.1111/ cga.12043 6. Ryan BM, Robles AI, Harris CC (2010) Genetic variation in microRNA networks: the implications for cancer research. Nat Rev Cancer 10, 389–402. https://doi.org/10.1038/ nrc2867. Erratum in: Nat Rev Cancer (2010) 10:523 7. Slaby O, Bienertova-Vasku J, Svoboda M, Vyzula R (2012) Genetic polymorphisms and microRNAs: new direction in molecular epidemiology of solid cancer. J Cell Mol Med 16:8– 21. https://doi.org/10.1111/j.1582-4934. 2011.01359.x

8. Georges M, Coppieters W, Charlier C (2007) Polymorphic miRNA-mediated gene regulation: contribution to phenotypic variation and disease. Curr Opin Genet Dev 17:166–176. https://doi.org/10.1016/j.gde.2007.04.005 9. Zorc M, Skok DJ, Godnic I, Calin GA, Horvat S, Jiang Z, Dovc P, Kunej T (2012) Catalog of microRNA seed polymorphisms in vertebrates. PLoS 7:e30737. https://doi.org/ 10.1371/journal.pone.0030737 10. Shen J, Ambrosone CB, DiCioccio RA, Odunsi K, Lele SB, Zhao H (2008) A functional polymorphism in the miR-146a gene and age of familial breast/ovarian cancer diagnosis. Carcinogenesis 29:1963–1966. https:// doi.org/10.1093/carcin/bgn172 11. Garcia AI, Buisson M, Bertrand P, Rimokh R, Rouleau E, Lopez BS, Lidereau R, Mikae´lian I, Mazoyer S (2011) Down-regulation of BRCA1 expression by miR-146a and miR-146b-5p in triple negative sporadic breast cancers. EMBO Mol Med 3:279–290. https:// doi.org/10.1002/emmm.201100136 12. Santos JSD, Zunta GL, Negrini AB, Ribeiro MSG, Martinez CAR, Ribeiro ML, Lourenc¸o GJ, Ortega MM (2020) The association of a single-nucleotide variant in the microRNA146a with advanced colorectal cancer prognosis. Tumour Biol 42:1010428320923856

MicroRNA Activity, 3′-UTR, Luciferase Assay, In Vitro Cell Culture 13. Zhou C, Zhao L, Wang K, Qi Q, Wang M, Yang L, Sun P, Mu H (2019) MicroRNA146a inhibits NF-κB activation and pro-inflammatory cytokine production by regulating IRAK1 expression in THP-1 cells. Exp Ther Med 18:3078–3084. https://doi.org/ 10.3892/etm.2019.7881 14. Bonafe´ GA, Dos Santos JS, Ziegler JV, Umezawa K, Ribeiro ML, Rocha T, Ortega MM (2019) Growth inhibitory effects of dipotassium glycyrrhizinate in glioblastoma cell lines by targeting microRNAs through the

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NF-κB signaling pathway. Front Cell Neurosci 13:216 15. Moynahan ME, Chiu JW, Koller BH, Jasin M (1999) BRCA1 controls homology-directed DNA repair. Mol Cell 4:511–518. https:// doi.org/10.1016/s1097-2765(00)80202-6 16. Sopik V, Phelan C, Cybulski C, Narod SA (2015) BRCA1 and BRCA2 mutations and the risk for colorectal cancer. Clin Genet 87: 411–418. https://doi.org/10.1111/cge. 12497

Chapter 14 Assessment of Cell Cytotoxicity in 3D Biomaterial Scaffolds Following miRNA Transfection Elizabeth Sainsbury, Lara Costard, Fergal J. O’Brien, and Caroline M. Curtin Abstract Assessment of cell cytotoxicity following transfection of cells with microRNA (miRNA) is an essential step in the evaluation of basic miRNA functional effects within cells in both 2D and 3D microenvironments. The lactate dehydrogenase (LDH) assay is a colorimetric assay that provides a basic, dependable method for determining cellular cytotoxicity through assessment of the level of plasma membrane damage in a cell population. Here, we describe the overexpression of miRNA in breast cancer cells when cultured in 3D collagen-based biomaterial scaffolds, achieved by Lipofectamine transfection, with subsequent examination of cell cytotoxicity using the LDH assay. Key words miRNA, Breast cancer, 3D biomaterial scaffolds, Transfection, Cell cytotoxicity

1

Introduction MicroRNAs (miRNAs) are short RNAs, consisting of 19–25 nucleotides that regulate protein expression post-transcriptionally by binding to messenger RNA (mRNA), and are generated mostly from non-coding genes or introns [1]. They regulate about 30–50% of the human genome and are therefore involved in multiple physiological and pathological processes [2]. One miRNA can potentially bind to multiple binding sites and therefore regulate multiple targets and pathways. Altered miRNA profiles have been detected in several physiological and pathological processes, and as a result, regulation of miRNA expression may be a potential tool to treat several conditions. The efficacy of miRNA therapeutics relies on the delivery of a significant amount of miRNAs to the target site, which can then elicit a powerful response, protected from nuclease degradation found in bodily fluids and without causing unwanted side effects [3]. To address these points, nonviral vectors can be used to

Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_14, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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successfully deliver genes including miRNA mimics and inhibitors to a diseased site and ultimately result in a therapeutic response [4–9]. Nonviral vectors are used by our group as they can overcome safety concerns associated with viral vectors such as insertional mutagenesis and adverse immune responses [10–13]. These vectors bind with potential therapeutic miRNAs through electrostatic interactions, and the overall net charge of the resulting complexes dictates the uptake of the vector-miRNA complex into the cell [8]. However, as miRNA delivery is limited to local and parental injection depending on the disease, the amount of miRNA reaching a target organ is limited. Chemical modification of the miRNA can prevent degradation upon administration but does not improve cellular uptake or the separation of the miRNAs from their delivery system [3]. This highlights the need for improved delivery methods for the transport of miRNAs to their target organ or tissue. As a result, many research groups have shown that 3D biomaterials can be loaded with miRNAs and implanted in vivo at target sites, ensuring efficient localized delivery of miRNAs [4, 5, 14]. Lipofectamine is a nonviral vector and is one of the most commonly used nonviral vectors for gene therapy applications. Lipofectamine utilizes lipofection, a method of lipid-mediated transfection which is influenced by cationic lipid molecules, to enable transfection of cells. However, the high transfection efficiency is compromised with high toxicity, which varies among different cell types [15]. It is therefore necessary to always titrate the miRNA concentration and assay time within a particular cell type and within a specific biomaterial scaffold to obtain the desired gene silencing. Following transfection with miRNA in 2D and 3D biomaterial scaffolds, it is imperative to assess its influence on cell behavior such as cell cytotoxicity. Lactate dehydrogenase (LDH) is a cytosolic enzyme and a reliable indicator of cytotoxicity. Upon damage to the cell membrane, LDH is released from the cytoplasm into the surrounding cell culture media. The amount of LDH released into the cell culture media can be quantified by an enzyme coupled reaction whereby LDH catalyzes the conversion of lactate to pyruvate via NAD+ reduction to NADH. Diaphorase oxidizes NADH which leads to the reduction of iodo-nitro-tetrazolium salt (INT) to a red formazan product that can be measured spectrophotometrically at 490 nm. The amount of formazan detected is directly proportional to the amount of LDH released into the cell culture media (Fig. 1) [Adapted from 16]. Therefore, cell viability after transfection with Lipofectamine in 3D culture can be assessed by measuring the amount of LDH within the cell culture media.

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Fig. 1 Coupled enzymatic reaction of lactate dehydrogenase to formazan

2 2.1

Materials Cell Line

2.2 Scaffold Fabrication

The adherent triple-negative breast cancer cell line MDA-MB-231 used in this study was obtained from the American Type Culture Collection (ATCC). 1. Collagen type I, stored at 4
C. 2. Hyaluronic acid sodium salt derived from Streptococcus equi, stored at 20
C. 3. Chondroitin-6-sulfate, isolated from shark cartilage, stored at 4
C. 4. Acetic acid (0.5 M). 5. VirTis Genesis 25 EL freeze drier. 6. Vacuum oven. 7. 1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDAC) 6 mM, stored at 20
C. 8. N-hydroxysuccinimide (NHS) 2 mM, stored at room temperature.

2.3

Growth Media

1. Dulbecco’s modified Eagle medium high glucose, stored at 4
C. 2. Fetal bovine serum, stored at 20
C. 3. L-glutamine, stored at 20
C. 4. Primacine, stored at 20
C.

2.4 Transfection Reagents

1. Opti-MEM, stored at 4
C. 2. Lipofectamine RNAiMAX, stored at 4
C. 3. miRIDIAN microRNA Hairpin Inhibitor Red Transfection Control, stored at 20
C (see Note 1).

2.5

Cell Viability

1. CyQuant LDH Cytotoxicity Assay kit, stored at 20
C.

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Methods

3.1 Scaffold Fabrication

1. Prepare the collagen-based scaffolds as per previously published protocols [17, 18]. Briefly, homogenize collagen with hyaluronic acid (HyA) in 0.5 M acetic acid solution to make a collagen-HyA slurry and collagen with chondroitin sulfate in an acetic acid solution to make a collagen-CS slurry. Degas each slurry to remove air bubbles, and freeze-dry the slurry at a constant cooling rate of 1
C min1 to a final freezing temperature of 40
C in a freeze drier for 24 h. 2. Sterilize the freeze-dried scaffolds by dehydrothermal crosslinking at 105
C for 24 h under vacuum at 0.05 Bar. 3. Scaffolds are cut using a biopsy punch to the desired size (e.g., typically within the range of 6–10 mm diameter). 4. Perform chemical crosslinking with 6 mM EDAC and 2 mM NHS for 2 h at room temperature. 5. Remove excess unreacted EDAC-NHS mix by rinsing 3 times with fresh PBS (10 min/wash) before storing at 4
C for up to 1 week before use.

3.2 Seeding Collagen-Based Scaffolds

1. Place one scaffold per well in a 24-well suspension plate. 2. Seed 1.5  105 MDA-MB-231 breast cancer cells dropwise onto each collagen-based scaffold in a volume of ~20 μL. 3. Incubate cells seeded on collagen-based scaffolds for 30 min at 37
C and 5% CO2. 4. After incubation, add 1 mL of growth media to each well, and return to the incubator. 5. For comparison to cells grown in 2D, seed 1.5  105 MDA-MB-231 breast cancer cells into 3 wells of a 6-well adherent plate. 6. Bring up to 1 mL of growth media in each well, and return to the incubator.

3.3 Gene Activation of Collagen-Based Scaffolds

1. After incubation of the cell seeded scaffolds for 24 h, the cells will have attached to the scaffolds and are ready for transfection. 2. Remove the growth media from each well, replace with 850 μL of Opti-MEM, and return to the incubator for at least 1 h (see Note 2). 3. For transfection of a sample size of n ¼ 3, mix 6.6 μL of Lipofectamine with 240 μL of Opti-MEM in one Eppendorf (final volume ¼ 246.6 μL) and 3.3 μL of miRIDIAN microRNA Hairpin Inhibitor Red Transfection Control with 243 μL

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Opti-MEM in the second Eppendorf (final volume ¼ 246.6 μL). Mix the contents of both Eppendorfs together, and incubate at room temperate for 5 min (see Note 3). 4. Remove the Opti-MEM from each well, and add 150 μL of the final mix dropwise to each scaffold. Leave to incubate for a minimum of 15 min. 5. Slowly add 850 μL of Opti-MEM to each well, and incubate at 37
C and 5% CO2. 6. After 4 h of incubation, remove the Opti-MEM from the wells, and replace it with 1 mL of growth media. 7. For 2D transfection, perform using volumes as described in steps 2–6 for 3D transfection. 3.4 Cytotoxicity Assay

1. After transfection (24 h), transfer 50 μL of growth media from each 2D and 3D collagen-based scaffold well into a clear 96-well flat plate (repeat, for example, on day 7 and 14 of culture or at desired time points). 2. Aliquot 50 μL of the reaction mixture from the LDH cytotoxicity kit (reaction mixture consists of 600 μL of assay buffer stock solution with 11.4 mL of substrate stock solution made by gentle mixing and protected from light until use) into each well, and mix by gentle tapping (see Note 4). 3. Incubate the plate at room temperature for 30 min protected from light. 4. After incubation, add 50 μL of Stop Solution from the LDH cytotoxicity kit to each well, and mix by gentle tapping. 5. Measure the absorbance at 490 nm and 680 nm (see Note 5). 6. To determine viability % relative to the control (e.g. non-transfected cells), subtract the 680 nm absorbance value from the 490 nm absorbance value, and use the absorbance values in the formula below (Fig. 2) (see Note 6): Viability% ¼ non‐transfected cells=transfected cells  100

4

Notes 1. miRIDIAN microRNA Hairpin Inhibitor Red Transfection Control (Horizon) is a positive control used to quantify cell transfection efficiency. This miRNA does not code for any particular gene and therefore does not alter gene expression or cellular signalling. The miRNA is tagged with the fluorescent label CY3 and can be detected after transfection by flow cytometry and fluorescence microscopy.

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Fig. 2 Viability of MDA-MB-231 cells after transfection with Lipofectamine 2000. Lipofectamine showed low levels of cytotoxicity with 80% of MDA-MB-231 cells viable 24 h after transfection

2. Serum in the growth media negatively affects the transfection efficiency of Lipofectamine. Therefore, the scaffolds must be soaked in Opti-MEM for at least an hour before the transfection is carried out, to allow for the diffusion of Opti-MEM throughout the collagen-based scaffold. 3. Before complexing Lipofectamine with the miRNA of choice, calculate N/P ratios (i.e., the ratio of amines in Lipofectamine to phosphates in RNA). Effective N/P ratios differ among cell types, and therefore optimization is required to each cell type before delivery of therapeutic miRNA. 4. One vial of the CyQUANT Reaction Mixture is adequate for testing two 96-well plates. Though it states in the manufacturer’s instructions that unused reaction mixture can be stored at 20
C protected from light for 3–4 weeks with tolerance for three freeze/thaw cycles without affecting the activity, we found that this was not the case and noted a color change after 1 week. Therefore, it is recommended to make up the amount of reaction mixture required for each experiment on the day. 5. Burst any bubbles present in the wells of the clear 96-well plate before reading absorbance as they may affect absorbance readings. 6. Absorbance values at 680 nm are subtracted from 490 nm readings as it is considered a background reading. 7. CyQUANT LDH Cytotoxicity kit comes with a lysis buffer that can be used to lyse non-transfected cells as a positive control in 2D. Lysis buffer is used at 10% of the volume of media in the well and incubated for 45 min at 37
C. To ensure sufficient diffusion into the scaffolds, it is necessary to crush the scaffolds using a forceps and vortex for 30 s  3).

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8. If there is serum in the cell culture media, include a control of cell culture media with no cells to determine background LDH activity in the presence of serum. 9. The protocol was optimized for the use of the breast cancer cell line MDA-MB-231; however, it is widely applicable to other cell lines. Please refer to Lipofectamine RNAiMAX guidelines for recommended initial concentrations to be used with different cell lines.

Acknowledgments This work was supported by the Health Research Board (HRB) in Ireland under grant number ILP-POR-2019-023. References 1. Kim S, Hwang DW, Lee DS (2009) A study of microRNAs in silico and in vivo: bioimaging of microRNA biogenesis and regulation. FEBS J 276(8):2165–2174. https://doi.org/10. 1111/j.1742-4658.2009.06935.x 2. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1): 15–20. https://doi.org/10.1016/j.cell.2004. 12.035 3. Zhang Y, Wang Z, Gemeinhart RA (2013) Progress in microRNA delivery. J Control Release 172(3):962–974. https://doi.org/10. 1016/j.jconrel.2013.09.015 4. Mencia Castano I, Curtin CM, Shaw G, Murphy JM, Duffy GP, O’Brien FJ (2015) A novel collagen-nanohydroxyapatite microRNA-activated scaffold for tissue engineering applications capable of efficient delivery of both miR-mimics and antagomiRs to human mesenchymal stem cells. J Control Release 200:42– 51. https://doi.org/10.1016/j.jconrel.2014. 12.034 5. Castano IM, Raftery RM, Chen G, Cavanagh B, Quinn B, Duffy GP, O’Brien FJ, Curtin CM (2020) Rapid bone repair with the recruitment of CD206(+)M2-like macrophages using non-viral scaffold-mediated miR-133a inhibition of host cells. Acta Biomater 109:267–279. https://doi.org/10.1016/j. actbio.2020.03.042 6. Mencia Castano I, Curtin CM, Duffy GP, O’Brien FJ (2019) Harnessing an inhibitory role of miR-16 in osteogenesis by human mesenchymal stem cells for advanced scaffoldbased bone tissue engineering. Tissue Eng

Part A 25(1-2):24–33. https://doi.org/10. 1089/ten.TEA.2017.0460 7. Costard LS, Kelly DC, Power RN, Hobbs C, Jaskaniec S, Nicolosi V, Cavanagh BL, Curtin CM, O’Brien FJ (2020) Layered double hydroxide as a potent non-viral vector for nucleic acid delivery using gene-activated scaffolds for tissue regeneration applications. Pharmaceutics 12(12). https://doi.org/10.3390/ pharmaceutics12121219 8. Curtin CM, Castano IM, O’Brien FJ (2018) Scaffold-based microRNA therapies in regenerative medicine and cancer. Adv Healthc Mater 7(1). https://doi.org/10.1002/adhm. 201700695 9. Mencia Castano I, Curtin CM, Duffy GP, O’Brien FJ (2016) Next generation bone tissue engineering: non-viral miR-133a inhibition using collagen-nanohydroxyapatite scaffolds rapidly enhances osteogenesis. Sci Rep 6:27941. https://doi.org/10.1038/ srep27941 10. Curtin CM, Cunniffe GM, Lyons FG, Bessho K, Dickson GR, Duffy GP, O’Brien FJ (2012) Innovative collagen nanohydroxyapatite scaffolds offer a highly efficient non-viral gene delivery platform for stem cellmediated bone formation. Adv Mater 24(6): 749–754. https://doi.org/10.1002/adma. 201103828 11. Curtin CM, Tierney EG, McSorley K, Cryan SA, Duffy GP, O’Brien FJ (2015) Combinatorial gene therapy accelerates bone regeneration: non-viral dual delivery of VEGF and BMP2 in a collagen-nanohydroxyapatite scaffold. Adv Healthc Mater 4(2):223–227. https://doi. org/10.1002/adhm.201400397

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12. Power RN, Cavanagh BL, Dixon JE, Curtin CM, O’Brien FJ (2022) Development of a gene-activated scaffold incorporating multifunctional cell-penetrating peptides for pSDF1alpha delivery for enhanced angiogenesis in tissue engineering applications. Int J Mol Sci 2 3 ( 3 ) . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / ijms23031460 13. Raftery RM, Tierney EG, Curtin CM, Cryan SA, O’Brien FJ (2015) Development of a geneactivated scaffold platform for tissue engineering applications using chitosan-pDNA nanoparticles on collagen-based scaffolds. J Control Release 210:84–94. https://doi.org/ 10.1016/j.jconrel.2015.05.005 14. Li Y, Fan L, Liu S, Liu W, Zhang H, Zhou T, Wu D, Yang P, Shen L, Chen J, Jin Y (2013) The promotion of bone regeneration through positive regulation of angiogenic-osteogenic coupling using microRNA-26a. Biomaterials 34(21):5048–5058. https://doi.org/10. 1016/j.biomaterials.2013.03.052

15. Wang T, Larcher LM, Ma L, Veedu RN (2018) Systematic screening of commonly used commercial transfection reagents towards efficient transfection of single-stranded oligonucleotides. Molecules 23(10). https://doi.org/10. 3390/molecules23102564 16. CyQUANT™ LDH Cytotoxicity Assay Kit. Invitrogen 17. O’Brien FJ, Harley BA, Yannas IV, Gibson L (2004) Influence of freezing rate on pore structure in freeze-dried collagen-GAG scaffolds. Biomaterials 25(6):1077–1086. https://doi. org/10.1016/s0142-9612(03)00630-6 18. Matsiko A, Levingstone TJ, O’Brien FJ, Gleeson JP (2012) Addition of hyaluronic acid improves cellular infiltration and promotes early-stage chondrogenesis in a collagen-based scaffold for cartilage tissue engineering. J Mech Behav Biomed Mater 11:41–52. https://doi. org/10.1016/j.jmbbm.2011.11.012

Chapter 15 Evaluation of miRNA Expression in 3D In Vitro Scaffold-Based Cancer Models Catherine Murphy, Ciara Gallagher, and Olga Piskareva Abstract Accumulating experimental evidence suggests that 3D in vitro cancer models strengthen our understanding of vital processes in the tumor microenvironment (TME) and accelerate the drug discovery pipeline. Previous studies examining the effects of specific miRNAs on cancer cells in vitro have involved ectopic expression of miRNA mimics in 2D in vitro culture. Assessment of cell viability and gene expression ensures that upregulation of the chosen miRNA and repression of its target genes have been achieved. However, this 2D culture is overly simplified and lacks the complex cell to extracellular matrix (ECM) interactions observed in the native TME, yielding results often not reproduced when progressed to in vivo studies. Hence, this chapter describes a novel method of overexpressing the miRNA mimic in cells cultured on 3D collagen-based scaffolds adapted from tissue engineering techniques. Cell growth on scaffolds is sequentially monitored via a DNA quantification assay, and overexpression of the miRNA mimic and repression of its target gene is assessed via reverse transcription quantitative PCR (RT-qPCR). Key words miRNA, Transfection, 3D Models, Microtissue, Scaffolds, Gene expression, Proliferation, Neuroblastoma

1

Introduction Since the revolutionary discovery of microRNAs (miRNAs), it is well recognized that these small non-coding nucleic acids play a crucial role in regulating gene expression at a post-transcriptional level [1]. miRNAs are short sequences of non-coding RNA with an average length of 22 nucleotides [2]. More than 1000 miRNAs are encoded in the human genome, with some still being discovered [3]. They regulate many cellular pathways, including cell development, differentiation, proliferation, death, and metabolism [4], and they have been shown to play a role in many pathological conditions, including cancer, cardiovascular disease, autoimmunity, and psychiatric disorders [4–8]. miRNAs, along with small-interfering RNAs (siRNAs), regulate gene expression through RNA silencing mechanisms. A miRNA targeting multiple genetic pathways

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involved in cancer cell proliferation, apoptosis, or differentiation would be most desirable for use as a potential therapeutic, as mutation of multiple target sites would be required for cancer cells to become resistant to treatment. Although miRNA-mediated cancer therapeutics have been the subject of intensive cancer research, the successful application of miRNAs as a cancer therapy in vivo is minimal [9]. In order to understand the role of the primary biological function of a miRNA, we must first confirm its ability to target a gene of interest and then assess whether its up- or downregulation affects cell viability in vitro. This is traditionally done via ectopic expression of a miRNA of interest in cells grown in conventional 2D cell culture through lipofection, one of the many methods of transfection employing cationic lipid molecules [10]. To account for any background toxic effects associated with transfection, a scrambled oligonucleotide is used as a negative control, which does not target any genes. Uptake efficiency varies with lipofection, so a positive control is required. We recommend using the siRNA targeting KIFF11, a molecular motor protein involved in spindle dynamics which, when downregulated, induces mitotic arrest and “rounding up” of transfected cells leading to death [11]. The impact of the miRNA of interest is therefore assessed relative to these two controls [12]. Successful transfection of cells with a given miRNA can be confirmed via reverse transcription quantitative PCR (RT-qPCR), and cell viability and proliferation can be assessed throughout the transfection process using various methods, including metabolic assays and DNA quantification. Recent advances in cancer research highlight the limitations of conventional 2D culture that uses flat surfaces for cell growth. This assay format limits both cell-cell and cell-matrix interactions, as well as metabolic gradients and cell polarity [13]. Instead, the focus is shifting towards 3D in vitro microtissues that can resemble the complex 3D architecture of the native tumor microenvironment (TME). These models were originally designed for use in tissue engineering and regenerative medicine. However, cancer studies using these 3D models have demonstrated better prediction of in vivo cellular response to chemotherapeutics than 2D toxicity assays [14–16]. One of the many 3D techniques involves the use of collagen-based scaffolds, which serve as a biomimetic microenvironment that facilitates cell-matrix interactions as well as cell migration and invasion [15]. Collagen is a popular choice for manufacturing these scaffolds due to its biocompatibility and natural origin. Collagen is a principal component of the in vivo TME and plays a role in many normal biological processes, including tissue repair, angiogenesis, tissue morphogenesis, cell adhesion, and migration [17]. To increase the complexity of these collagenbased scaffolds and better mimic niche microenvironments of different tissues, other common extracellular matrix (ECM)

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components can also be incorporated. For example, nanohydroxyapatite (nHA) is the main inorganic constituent of the mineral composition of the human bone [18] and is, therefore, an attractive addition when modelling cancers with primary tumors or metastases in the bone. The fabrication process of collagen scaffolds supplemented with nHA (Coll-I-nHA) has been well documented in research [16, 19–26]. A recently published protocol from our group details the assembly of neuroblastoma cells on Coll-I-nHA scaffolds, subsequent maintenance, takedown, and analysis [15]. To fabricate these scaffolds, bovine type I fibrillar collagen is blended in 0.05 M acetic acid (0.5 wt%). nHA particles [24] are then added to the collagen slurry at a 2:1 ratio to the weight of the collagen, and the slurry is blended for a total of 3–4 h. The slurry is degassed and freeze-dried in stainless steel molds through a
40  C cycle. Scaffold sheets are sterilized and physically cross-linked using dehydrothermal treatment at 105  C. Biopsy punches are used to generate cylindrical scaffolds 6 mm in diameter which are hydrated in phosphate-buffered saline (PBS). These hydrated scaffolds are then chemically cross-linked in a solution of 3 mM N-(3-dimethylaminopropyl)-N0 -ethylcarbodiimide hydrochloride and 5.5 mM N-hydroxysuccinimide (EDAC/NHS) in distilled water, which enhances the constructs mechanical properties and reproducibility [26]. Cells can then be added to these scaffolds by simply pipetting a cell suspension onto the center of the scaffold in 24-well tissue culture plates, as described in our protocol [15]. We have described several downstream analyses which can be performed on cells grown on these scaffolds, including cell viability and proliferation assays, histological staining, and gene expression analysis [15]. Previously published work from our group also demonstrated that neuroblastoma cells grown on Coll-nHA displayed a physiological similarity to in vivo models, superior to cells grown in 2D models. This study included a model of chemotherapeutic response which confirmed that cells grown in scaffolds are more clinically relevant for therapeutic testing than cells grown in conventional 2D culture [16]. This protocol details the use of the 3D in vitro scaffold model described above for overexpression of miRNAs in cancer cells. Altered expression of the miRNA and its target genes is assessed by RNA extraction and RT-qPCR, and cell growth on scaffolds both pre-and post-transfection is assessed by DNA extraction and quantification (Fig. 1). We highlight important nuances of 3D transfection techniques that factor in the larger volumes often required to fully cover or submerge models and the impact these increased volumes have on substance concentrations and incubation periods.

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Fig. 1 Key experimental aims and timepoints for transfection of cancer cells grown on 3D collagen-based scaffolds with miRNA mimics. Cells are assembled on scaffolds on day 0, maintained until day 7 when transfections are performed, followed by further maintenance for the desired timeframe. Gene expression analysis and DNA quantification can be performed at multiple time points throughout the experiment, depending on the aims. Illustration created with BioRender

2 2.1

Materials Cell Culture

1. The adherent neuroblastoma cell line Kelly was obtained from the American Type Culture Collection (ATCC). 2. RPMI 1640 media, stored at 4  C. 3. 10% fetal bovine serum, stored at 4  C. 4. 1% Pen/Strep, stored at 4  C. 5. Phosphate-buffered saline (PBS), stored at 4  C.

2.2 Transfection Reagents

1. Plain RPMI 1640 media, stored at 4  C. 2. Opti-MEM® Reduced Serum Medium (Gibco), stored at 4  C. 3. Lipofectamine™ RNAiMAX (Invitrogen), stored at 4  C. 4. Negative Control #1 (Ambion), stored at
20  C. 5. mirVana™ miR-324 Mimic (Ambion), stored at
20  C.

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1. QIAzol Lysis Reagent (QIAGEN), stored in a safety cabinet at room temperature. 2. miRNeasy kit (QIAGEN), stored at room temperature. 3. High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), stored at
20  C. 4. TaqMan® Universal PCR Master Mix (Applied Biosystems), stored at 4  C. 5. TaqMan® Gene Expression Master Mix (Applied Biosystems), stored at 4  C. 6. TaqMan™ MicroRNA Assay (Applied Biosystems) for miR324-5p (Assay ID 000539) and RNU44 (Assay ID 001094), stored at
20  C. 7. TaqMan® Gene Expression Assays (Applied Biosystems) for VDAC1 (Assay ID Hs01631624_gH) and RPLP0 (Assay ID Hs99999902_m1), stored at
20  C.

2.4 PicoGreen Reagents

1. Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen), stored at 4  C. 2. 1% Triton X-100 in 0.1 M Sodium bicarbonate, stored at room temperature.

2.5

Equipment

1. Class II down-flow recirculating laminar flow cabinet. 2. Forma™ Steri-Cycle™ CO2 Incubator, 5% CO2 at 37  C. 3. Perkin Elmer Victor™X3 plate reader. 4. Tissue Lyser LT (Qiagen). 5. 24-well non-adherent tissue culture plates (CELLSTAR®). 6. Opaque black 96-well microplates (Costar). 7. Pipettes: 0.001–1 mL single-channel and 0.01–0.3 mL multichannel. 8. Veriti™ 96-well thermal cycler (Applied Biosystems). 9. 7500 Real-Time System (Applied Biosystems).

3

Methods

3.1 Assembly of Cells on Scaffolds

1. 6 mm diameter Coll-I-nHA scaffolds are fully hydrated in PBS for a minimum of 12 h at 4  C (see Note 1). 2. Using a tweezers, scaffolds are transferred from PBS container to the wells of a non-adherent 24-well plate, one scaffold per well skin-side-down. Scaffolds should be lifted gently by the corner and pressed against the side of the sterile PBS container to remove excess liquid [15] (see Note 2).

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3. Kelly cells grown in T175 flasks are harvested, counted, and resuspended in an appropriate volume of media to seed 200,000 cells per scaffold in a volume of 20 μL (see Note 3). 4. Using a P20 pipette, 20 μL of cell suspension is added to the center of each scaffold in the non-adherent 24-well plate [15], and plates are incubated at 37  C with 5% CO2 for 3 h to allow attachment (see Note 4). 5. Using a P1000 pipette, 1 mL of full growth medium is slowly added to each well before returning plates to the incubator for 24 h. 6. After 24 h, scaffolds are transferred to fresh non-adherent 24-well plates, and 2 mL complete growth medium is added (see Note 5). 7. Scaffold medium consumption is monitored every 2–3 days and replenished as required by removing spent medium and slowly adding 2 mL fresh medium (see Notes 6 and 7). 3.2 miRNA Forward Transfection

1. MiRNA forward transfections are carried out 7 days after assembly of cells on Coll-I-nHA scaffolds. 2. On the morning of transfections, full growth media is removed from wells and replaced with 1 mL pre-warmed Opti-MEM® Reduced serum medium or RPMI 1640 without serum. Cells are incubated in this medium for 1–2 h. 3. For each well to be transfected, prepare miRNA mimic duplexLipofectamine™ RNAiMAX complexes (steps 4–6) (see Notes 8 and 9). 4. Dilute 3.3 μL of miRNA mimic (50 μM) in 100 μL OptiMEM® Reduced serum medium, and mix gently. 5. Mix Lipofectamine™ RNAiMAX gently, and dilute 4 μL in 100 μL Opti-MEM® Reduced serum medium. Mix gently. Leave for 5 min. 6. Mix the diluted Lipofectamine™ RNAiMAX with the diluted miRNA mimic, and incubate at room temperature for 20–30 min to allow complexes to form (see Note 10). 7. Add the miRNA mimic-Lipofectamine™ RNAiMAX dropwise to appropriate wells on top of the scaffold giving a final volume of 200 μL per scaffold/well and a final miRNA concentration of 825 nM. Allow complexes to diffuse within the scaffold (see Note 11). 8. Incubate plates for 40 min at 37  C with 5% CO2 (see Note 12). 9. Add 300 μL Opti-MEM® Reduced serum medium per well to ensure scaffolds are fully submerged and incubate for 5–14 h at 37  C with 5% CO2.

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10. Remove transfection medium, and replace with 2 mL complete growth medium. 11. Cells in scaffolds are then maintained at 37  C with 5% CO2 for 48 h until taken down for miRNA and gene expression analysis and growth assessment via the PicoGreen dsDNA assay (see Note 13). 3.3

RNA Extraction

1. For each selected time point: remove scaffold replicates from wells using sterile tweezers, and place each scaffold into a 2 mL round-bottom centrifuge tube. 2. In the fume hood, add 700 μL of QIAzol phenol/guanidinebased cell lysis reagent to each tube to lyse the cells in scaffolds, and allow for recovery of high-quality RNA. Store at
80  C (see Note 14). 3. Perform RNA extraction from cells in scaffolds using the miRNeasy kit as per manufacturer’s guidelines. 4. Using the NanoDrop, quantify the concentration of extracted RNA, and assess the 260/230 nm and 260/280 nm purity ratios (see Note 15).

3.4 Gene Expression Analysis

1. To generate complementary DNA (cDNA) from extracted RNA, prepare samples using the High-Capacity cDNA Reverse Transcription Kit as per the kit’s protocol (see Note 16). 2. Synthesize cDNA on a Veriti 96-well thermal cycler under the following conditions: (a) For miRNA expression analysis: 16  C (30 min), 42  C (30 min), 85  C (5 min), and 4  C (1). (b) For target gene expression analysis: 25  C (10 min), 37  C (120 min), 85  C (5 min), and 4  C (1). 3. To assess the expression of miR-324-5p, target gene VDAC1, and endogenous control RPLP0, carry out a quantitative PCR (qPCR) reaction using the Applied Biosystems TaqMan® user guides for Small RNA assays and Gene Expression assays (see Note 17). 4. Carry out qPCR on the 7500 Real-Time System to obtain cycle threshold (Ct) values under the following conditions: 50  C (2 min), 95  C (10 min), [95  C (15 s), 60  C (1 min)]  40 cycles. 5. Use the comparative cycle threshold method (2-ΔΔCt) to normalize the Ct values for each gene target (VDAC1) against the Ct values for endogenous control RPLP0 and for each miRNA against the Ct values for endogenous controls RNU44 or RNU48 (steps 6–8). 6. For each sample, subtract the average Ct value of endogenous control from the average Ct value of the target to get the ΔCt.

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7. Next, subtract the ΔCt of negative control samples from the ΔCt of each treated sample to get the ΔΔCt. 8. Finally, raise 2 to the power of minus ΔΔCt. of each sample to get the relative quantification (RQ) value. 9. Carry out unpaired t-tests to assess significant differences in expression of miR-324-5p and VDAC1 in samples transfected with miR-324-5p vs negative control. 3.5 PicoGreen dsDNA Assay

1. Prepare a DNA extraction buffer of 1% Triton X-100 in 0.1 M NaHCO3 solution. 2. For each selected time point: remove scaffold replicates from wells using sterile tweezers, and place each scaffold into a 2 mL round-bottom centrifuge tube containing 1 mL of DNA extraction buffer. Store at
80  C (see Note 18). 3. To lyse cells in the scaffolds, carry out three freeze-thaw cycles (steps 4–6). 4. Remove scaffolds in DNA extraction buffer from
80  C, and leave to thaw fully at room temperature. 5. Vortex samples for 10–20 s, and return to
80  C overnight or until completely frozen. 6. Repeat this process for a total of three cycles. 7. To maximize DNA yield, use a tissue lyser to disrupt cells in the scaffolds further (steps 8 and 9). 8. Place a metal bead in the 2 mL centrifuge tube containing a scaffold in DNA extraction buffer. 9. Place the tube within the adapter of the tissue lyser, and shake the sample at 50 oscillations/second for 2–3 min. 10. To quantify DNA extracted from cells in scaffolds, the PicoGreen dsDNA kit is used as per the manufacturer’s guidelines [15](see Notes 19–21).

3.6

Example Results

1. Relative expression of miR-324-5p in transfected cells. RNA was extracted from Kelly cells grown on Coll-I-nHA 48 h posttransfection. cDNA was synthesized by reverse transcription (RT), and expression of miR-324-5p was determined by quantitative PCR (qPCR). The comparative cycle threshold (Ct) method was used to determine the relative expression of miR-324-5p in cells transfected with a miR-324-5p mimic and with a scrambled miRNA negative control (Fig. 2). Expression of miR-324-5p was roughly 500-fold higher in cells transfected with the mimic. 2. Relative expression of miR-324-5p target gene VDAC1 in transfected cells. RNA was extracted from Kelly cells grown on CollI-nHA at 48 h post-transfection. cDNA was synthesized by reverse transcription (RT), and expression of the target gene

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Fig. 2 Relative expression of miR-324-5p in Kelly cells grown on Coll-I-nHA 48 h post-transfection with miR-324-5p vs a scrambled negative control. Expression was calculated using the comparative cycle threshold (Ct) method with normalization against the housekeeping gene RNU44. miR-324-5p expression was on average 500-fold higher in cells transfected with the miRNA compared to those transfected with a scrambled miRNA control

VDAC1, as well as endogenous control RPLP0, was determined by quantitative PCR (qPCR). The comparative cycle threshold (Ct) method was used to determine the expression of VDAC1 in cells transfected with a miR-324-5p mimic and with a scrambled miRNA negative control relative to RNU44. A two-tailed unpaired t-test was performed to assess whether VDAC1 expression was significantly dysregulated in response to miR-324-5p transfection (Fig. 3). This gene was significantly downregulated in miR-324-5p transfected cells ( p ¼ 0.012). 3. Cell numbers at day 14 for transfected cells on Coll-I-nHA. DNA was extracted and quantified as described above for Kelly cells grown on Coll-I-nHA, which were untreated, transfected with a scrambled miRNA negative control, and transfected with miR-324-5p. DNA concentrations were converted to cells per scaffold, and an ordinary one-way ANOVA with Tukey’s multiple comparison test was used to assess significant differences between the growth of transfected cells (Fig. 4). No significant differences in cell growth were detected by this analysis ( p > 0.05).

4

Notes 1. The protocol was optimized for the use of the neuroblastoma cell line Kelly grown on Coll-I-nHA. However, it is widely applicable to other cell lines and scaffold compositions. Please refer to Lipofectamine™ RNAiMAX guidelines for recommended initial concentrations to be used with different cell

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Fig. 3 Relative expression of VDAC1 in Kelly cells grown on Coll-I-nHA 48 h posttransfection with miR-324-5p vs a scrambled negative control. Expression was calculated using the comparative cycle threshold (Ct) method with normalization against the housekeeping gene RPLP0. A two-tailed unpaired T-test demonstrated significant downregulation of VDAC1 in Kelly cells transfected with miR-324-5p compared to cells transfected with a scrambled miRNA control ( p ¼ 0.012)

Fig. 4 Growth of treated vs untreated Kelly cells grown on Coll-I-nHA at day 14. The number of cells per scaffold was indirectly quantified using the PicoGreen dsDNA assay. Error bars represent the standard deviation between biological triplicate values. An ordinary one-way ANOVA statistically assessed differences in growth with Tukey’s multiple comparison test. No significant differences in cell numbers were detected when comparing cells transfected with miR-324-5p, cells transfected with a scrambled negative control, and untreated cells (ns p > 0.05)

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lines. Values should remain the same for different scaffold compositions with the same dimensions (6 mm  4 mm), and provided cells are seeded at the same density (200,000 cells/scaffold). 2. Full hydration of scaffolds in PBS will typically take ~12 h. Scaffolds in PBS can be stored long term at 4  C but should be brought to room temperature before adding cells. 3. Cell seeding density on scaffolds may vary dependent on the cell line. A recent paper demonstrated that the optimum seeding density for two neuroblastoma cell lines, Kelly and IMR-32, is 200,000 cells per scaffold (12). 4. Cells should be plated in at least biological triplicate for each time point, condition, and analysis to allow for inherent variation in cell plating, attachment, and growth on scaffolds. For example, if it is desired to analyze untreated, transfected, and a scrambled control (3 conditions), at day 1, 7, and 14 (3 time points) via DNA quantification and gene expression (2 analyses) in biological triplicate, a total of 54 scaffolds should be plated and seeded (3  3  2  3 ¼ 54). 5. Transfer of scaffolds to fresh 24-well plates on day 1 removes cells that have adhered to the bottom of the plastic 24-well plates rather than attaching to the scaffolds. It is useful to visually monitor the bottom of the well under the microscope at regular intervals to assess the level of cell adherence to the plastic. If adherence is high, it is recommended that scaffolds are transferred to fresh 24-well plates weekly for the duration of the experiment. 6. When removing media and PBS from scaffolds in wells, use the slow setting on the pipette gun to ensure scaffolds are not sucked up with the media. 7. When adding media or washing wells with PBS, dispense slowly down the side of the well, and gently rock by hand. Dispensing directly onto scaffolds may dislodge weakly adherent cells. 8. This protocol was optimized to use Lipofectamine™ RNAiMAX reagent. However, it can be adapted for use with other common transfection reagents. Please refer to the manufacturer’s guidelines for prior optimization and recommendations. 9. Transfections should be performed in at least biological triplicate for each condition and time point to allow inherent variation between scaffolds. Each scaffold is considered to be one biological repeat. 10. Diluted miRNA mimic should be added to diluted Lipofectamine™ RNAiMAX to avoid damage of the Lipofectamine particles.

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11. Avoid rocking plate to ensure the complexes diffuse throughout the scaffold reaching cells. Rocking plate will cause the transfection reagent to settle at the wells’ side rather than penetrating the scaffold. 12. A final transfection volume of 500 μL is sufficient when working with 6  4 mm scaffolds in 24-well plates. When working with different plates or size scaffolds, volumes may need to be scaled up or down. However, immediate addition of this volume of media will dilute miRNA complexes within the well, so it is recommended to incubate scaffolds in a 200 μL transfection reagent for 40 min before adding 300 μL medium to allow complexes to enter cells. 13. Maintenance of cells in scaffolds for 48 h post-transfection is sufficient to capture miRNA uptake by cells and used in this protocol for demonstrating purposes. However, the period can be longer or shorter depending on experimental conditions, cell line doubling time, and metabolic activity. It is recommended that in initial experiments, scaffolds are taken down at 24, 48, and 72 h post-transfection to determine the optimum duration. 14. 700 μL of QIAzol is suggested as step one of the QIAGEN miRNeasy extraction protocol to disrupt cell samples with this volume of QIAzol. Use of QIAzol as opposed to alternative cell lysis reagents is strongly advised due to its strong ability to penetrate into scaffolds. 15. If a poor 260/230 purity ratio < 2.0 is obtained for extracted RNA, ethanol precipitation can be used to remove contaminants. 16. The reverse transcription reaction setup varies depending on whether the aim is to assess gene expression or miRNA expression. For gene expression, 10 RT random primers are used. For miRNA expression, 10 RT random primers are replaced with specific 5 RT miRNA primers instead. Please refer to the Applied Biosystems TaqMan® Small RNA Assays User Guide for this reaction. 17. Analyses should be performed in at least triplicate for each treatment to allow for inherent variation in cell plating. 18. For use in the TissueLyser, place scaffolds in DNA extraction buffer into round-bottom centrifuge tubes as the metal bead may become lodged in a tapered-bottom tube. 19. DNA quantification values may reside at the lower end of the PicoGreen standard curve. The standards may therefore need to be adjusted to represent the sample readings better.

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20. If the average concentration of DNA per cell is known for the cell line being used, DNA quantification results can be converted to cells per scaffold: Sample DNA concentration ðng per mLÞ Average concentration of DNA per cell ðngÞ ¼ number of cells in a sample 21. Plotting a growth curve from sequential time points allows for identifying optimum time points for assessment of cell viability as cell growth will plateau at a certain point due to reaching max capacity and exhaustion of media. References 1. O’Brien J, Hayder H, Zayed Y, Peng C (2018) Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol (Lausanne) 9:402. https://doi.org/10. 3389/FENDO.2018.00402 2. Ha M, Kim VN (2014) Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol 158(15):509–524. https://doi.org/10.1038/ nrm3838 3. de Rie D, Abugessaisa I, Alam T et al (2017) An integrated expression atlas of miRNAs and their promoters in human and mouse. Nat Biotechnol 359(35):872–878. https://doi. org/10.1038/nbt.3947 4. Urbich C, Kuehbacher A, Dimmeler S (2008) Role of microRNAs in vascular diseases, inflammation, and angiogenesis. Cardiovasc Res 79: 581–588. https://doi.org/10.1093/CVR/ CVN156 5. Lin S, Gregory RI (2015) MicroRNA biogenesis pathways in cancer. Nat Rev Cancer 156(15):321–333. https://doi.org/10.1038/ nrc3932 6. Romaine S, Tomaszewski M, Condorelli G, Samani N (2015) MicroRNAs in cardiovascular disease: an introduction for clinicians. Heart 101:921–928. https://doi.org/10.1136/ HEARTJNL-2013-305402 7. Pua H, Ansel K (2015) MicroRNA regulation of allergic inflammation and asthma. Curr Opin Immunol 36:101–108. https://doi. org/10.1016/J.COI.2015.07.006 8. Issler O, Chen A (2015) Determining the role of microRNAs in psychiatric disorders. Nat Rev Neurosci 16:201–212. https://doi.org/10. 1038/NRN3879 9. Rupaimoole R, Slack FJ (2017) MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev

Drug Discov 163(16):203–222. https://doi. org/10.1038/nrd.2016.246 10. Kim TK, Eberwine JH (2010) Mammalian cell transfection: the present and the future. Anal Bioanal Chem 397:3173. https://doi.org/10. 1007/S00216-010-3821-6 11. Wan X, Zhang Y, Lan M et al (2018) Meiotic arrest and spindle defects are associated with altered KIF11 expression in porcine oocytes. Environ Mol Mutagen 59:805–812. https:// doi.org/10.1002/EM.22213 12. Nolan J, Stallings RL, Piskereva O (2017) Assessment of basic biological functions exerted by miRNAs. Methods Mol Biol 1509: 11–16. https://doi.org/10.1007/978-14939-6524-3_2 13. Nolan JC, Frawley T, Tighe J et al (2020) Preclinical models for neuroblastoma: advances and challenges. Cancer Lett 474:53–62. https://doi.org/10.1016/J.CANLET.2020. 01.015 14. Costard LS, Hosn RR, Ramanayake H et al (2021) Influences of the 3D microenvironment on cancer cell behaviour and treatment responsiveness: a recent update on lung, breast and prostate cancer models. Acta Biomater. https://doi.org/10.1016/j.actbio.2021. 01.023 15. Gallagher C, Murphy C, O’Brien FJ, Piskareva O (2021) Three-dimensional in vitro biomimetic model of Neuroblastoma using collagen-based scaffolds. J Vis Exp:e62627. https://doi.org/10.3791/62627 16. Curtin C, Nolan JC, Conlon R et al (2018) A physiologically relevant 3D collagen-based scaffold–neuroblastoma cell system exhibits chemosensitivity similar to orthotopic xenograft models. Acta Biomater 70:84–97. https://doi.org/10.1016/j.actbio.2018. 02.004

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17. Ouellette JN, Drifka CR, Pointer KB et al (2021) Navigating the collagen jungle: the biomedical potential of fiber organization in cancer. Bioengineering 8:1–19 18. Lowe B, Hardy JG, Walsh LJ (2020) Optimizing Nanohydroxyapatite nanocomposites for bone tissue engineering. ACS Omega 5:1–9 19. Curtin CM, Cunniffe GM, Lyons FG et al (2012) Innovative collagen nanohydroxyapatite scaffolds offer a highly efficient non-viral gene delivery platform for stem cellmediated bone formation. Adv Mater 24:749– 754. https://doi.org/10.1002/adma. 201103828 20. O’Brien FJ, Harley BA, Yannas IV, Gibson L (2004) Influence of freezing rate on pore structure in freeze-dried collagen-GAG scaffolds. Biomaterials 25:1077–1086. https://doi.org/ 10.1016/S0142-9612(03)00630-6 21. Haugh MG, Jaasma MJ, O’Brien FJ (2009) The effect of dehydrothermal treatment on the mechanical and structural properties of collagen-GAG scaffolds. J Biomed Mater Res A 89:363–369. https://doi.org/10.1002/ jbm.a.31955 22. O’Brien FJ, Harley BA, Yannas IV, Gibson LJ (2005) The effect of pore size on cell adhesion in collagen-GAG scaffolds. Biomaterials 26:

433–441. https://doi.org/10.1016/j. biomaterials.2004.02.052 23. Murphy CM, Haugh MG, O’Brien FJ (2010) The effect of mean pore size on cell attachment, proliferation and migration in collagenglycosaminoglycan scaffolds for bone tissue engineering. Biomaterials 31:461–466. https://doi.org/10.1016/j.biomaterials. 2009.09.063 24. Cunniffe GM, Dickson GR, Partap S et al (2010) Development and characterisation of a collagen nano-hydroxyapatite composite scaffold for bone tissue engineering. J Mater Sci Mater Med 21:2293–2298. https://doi.org/ 10.1007/s10856-009-3964-1 25. Haugh MG, Murphy CM, McKiernan RC et al (2011) Crosslinking and mechanical properties significantly influence cell attachment, proliferation, and migration within collagen glycosaminoglycan scaffolds. Tissue Eng Part A 17: 1201–1208. https://doi.org/10.1089/ten. tea.2010.0590 26. Tierney CM, Haugh MG, Liedl J et al (2009) The effects of collagen concentration and crosslink density on the biological, structural and mechanical properties of collagen-GAG scaffolds for bone tissue engineering. J Mech Behav Biomed Mater 2:202–209. https://doi. org/10.1016/j.jmbbm.2008.08.007

Chapter 16 Bioinformatics Analysis of miRNA Sequencing Data Hrishikesh A. Lokhande Abstract The bioinformatics analysis of miRNA is a complicated task with multiple operations and steps involved from processing of raw sequence data to finally identifying accurate microRNAs associated with the phenotypes of interest. A complete analysis process demands a high level of technical expertise in programming, statistics, and data management. The goal of this chapter is to reduce the burden of technical expertise and provide readers the opportunity to understand crucial steps involved in the analysis of miRNA sequencing data. In this chapter, we describe methods and tools employed in processing of miRNA reads, quality control, alignment, quantification, and differential expression analysis. Key words Bioinformatics, Galaxy, FASTQC, mirDeep2, DESeq2, Enrichment analysis

1

Introduction The advent of high-throughput sequencing such as nextgeneration sequencing has made accurate quantification of molecules such as miRNA within reach. Profiling of microRNA is highly employed in areas such as clinical research to identify the miRNA as potential biomarkers for therapeutics and medical interventions [1]. The reduced cost and improvement in the sequencing depth have resulted in ever-increasing data [2]. Scientists have been uniquely challenged to develop algorithms and advanced processes to analyze data and provide meaningful insights. To account for the computational challenges, several tools and methods are developed to address basic-to-complex bioinformatics processes such as alignment and quantification to functional prediction of miRNA. Bioinformatics pipelines such as miRge2.0 [3], miARma-seq [4], DIANA-map [5], etc. offer an end-to-end solution for miRNArelated analysis, but they require installations along with many software prerequisites and working knowledge of Linux-like operating systems. This is complicated and extremely overwhelming to individuals without an information technology background.

Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_16, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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In this chapter, we have reduced the technical difficulties associated with bioinformatics analysis. We have used Galaxy to run all the operations explained in this chapter. The goal of this chapter is to provide users the opportunity to learn and understand crucial steps involved in investigating miRNA reads to identifying miRNA that can explain important phenotypes. This chapter prepares users to use their differential expression data for enrichment analysis.

2

Materials 1. Computer. Any computer with Internet connection and access to web browser. 2. Data download. To download the data, go to https://github.com/ advancedmirna/BookChapter, and click on Download zip. The folder BookChapter-main includes sub-folders such as DESeq2, FAST_raw, counts, etc. The data in each folder will be used for several steps explained in this chapter. 3. Galaxy. Galaxy [6] is an open-source project web-based platform that provides access to various analysis workflows, bioinformatics tools, and other data-related operations. You will need to register on the European Galaxy website (https://usegalaxy. eu/) and confirm the registration through the Galaxy Account Activation email that will be sent to your email inbox. Note: Without registration and confirmation, you might not be able to run the operations provided with this chapter. 4. R (optional). This is only applicable to individuals who would like to run R scripts provided with this chapter for combining reads counts and for differential expression analysis. Download and installation guide is available on https:// www.r-project.org [7].

3 3.1

Methods FASTQ

The bioinformatics analysis of miRNA starts with the investigation of the FASTQ files. A FASTQ file is a text file produced by converting base-calls (BCL) obtained at the end of the sequencing run [8]. If the samples are pooled for sequencing, a separate process called demultiplexing is run to ensure that individual FASTQ files are produced for every sample. A single FASTQ file labelled R1 if produced for a single read run and a paired end run produces two files R1 and R2.

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Fig. 1 An example for a FASTQ file. Each biological sequence is represented as four lines

Each read in a FASTQ is represented by four lines: (a) Sequence identifier: This line starts with an “@”, followed by information about the instrument (ID), run number, flow cell ID, lane, etc. (b) The nucleotide sequence. (c) A separator, denoted with a plus (+) sign. (d) The base call quality scores. These are indicated by Phred +33 encoding and ASCII characters. FASTQ files are the starting point for all bioinformatics next-generation sequencing analysis, and various tools are designed to view, edit, and process these files. Figure 1 shows a few lines of a FASTQ file representing a single raw sequence. For this chapter, we will be using publicly available miRNA FASTQ files from the sequence read archive (SRA) repository [9]. The full dataset is available on SRA with the accession number PRJNA778353. This chapter will focus on identifying differentially expressed genes between adenoma cancer and adjacent normal tissues. To reduce the computational time and resources, we will only use the three samples from each group, and data for each sample was reduced to 50,000 read sequences by a random selection process. We have selected adenoma samples SRR16832121, SRR16832122, and SRR16832123 and normal samples SRR16832125, SRR16832126, and SRR16382127 [10]. 3.2 Quality Control of the FASTQ Files

FASTQC [11] is a graphical tool that provides quality control metrics for next-generation sequencing data. For every FASTQ file, FASTQC produces an HTML output that can be visualized with any web browser. The tool has multiple functions (modules) that provide different information on sequence dataset. For each function, there are flags to indicate “Pass,” “Warn,” and “Fail” for assisting users to investigate issues associated with the samples. We will be using the Galaxy [6] platform to perform FASTQ sequence quality assessment using the FASTQC workflow. For this section, we would be using the raw FASTQ files from the FASTQ_raw folder.

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Please follow the steps listed below: 1. Log in to your galaxy account at https://usegalaxy.eu/. 2. On the search tool box, type FASTQC, and select (by clicking) the workflow named “FastQC Read and Quality reports.” 3. Click the upload button under the “Raw read data from your current history” tab, then choose local files, and upload file SRR16832121_1.fastq.gz from the FASTQ_raw folder of the downloaded folder. Press start and initiate the upload. Click the cancel button to go back to the main page. 4. On the right side of the screen, you will see the file upload status, once the uploading process is finished, the tab will turn green. (This applies to all operations.) 5. FASTQC offers multiple functions to customize the QC process; for this section, we will keep everything to default, and press “Execute.” (Execute should be at the end of the page.) 6. You should notice an additional two tabs on the right side named “FASTQC on data*: RAW Data” and “FASTQC on data*: Web page.” 7. Clicking on “FASTQC on data*: Web page” will give you option to view, edit, and delete data. Press view to see the output produced by the FASTQC program. 8. FASTQC produces important metrics such as Basic Statistics, Per base sequence quality, Per sequence quality, etc. 9. You can download the output by pressing the download button. We have performed FASTQC on all our samples and added the results under the FASTQC folder of the downloaded folder. FASTQC is an important tool to check the overall data quality of the sequences. Figures 2a, b show both base quality graph and distribution of read length across all samples. Further reading on FASTQ is recommended to understand different analysis modules. The Babraham Bioinformatics page at https://www.bioinformatics. babraham.ac.uk/projects/fastqc/ explains in depth every module with examples of good and bad FASTQ files. Note: You might notice “Warn” and “Fail” flags on certain modules especially narrow distribution of GC content and abundance of overrepresented sequences. These are common for miRNA sequencing data. 3.3 Trimming Reads to Remove Adapters

Read sequences generated through sequencing usually contain adapter molecules added at the 30 or 50 end of the molecule. Before proceeding to important steps such as alignment and quantification, it is essential to remove the adapter molecules. Removal of adapters has been shown to improve the overall mapping rate. Publicly available dataset often does not carry the accurate adapter

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Fig. 2 (a) The plot represents the overall sequence quality distribution. The read sequences show 51 bases, and the overall quality of the sequence is very good. (b) The sequence length distribution is the same across all the reads in the FASTQ file

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sequence information. To overcome this issue, adapter prediction tools such as DNApi [12] are extensively used. DNApi is a python utility and requires installation on a Linux-like system and other software prerequisites. DNApi uses two user-defined input length of K-mer (k) and filtering ratio (r) for less common k-mers. For the purposes of this chapter, we have analyzed our read data using DNApi with default parameters. Our analysis has identified “AGATCGGAAGAG” as a 30 adapter. We encourage users to read more on DNApi to get a sense of the adapter prediction process. Once the adapter was identified, we further performed adapter removal by using the Cutadapt [13] utility. Like DNApi, Cutadapt was also run on default setting and by providing the predicted adapter sequence input. The download folder has a subfolder named “Trimmed_with_cutadapt” and has trimmed FASTQ files. 3.4 miRNA Alignment and Quantification

An important step in the analysis of miRNA data is the identification and quantification of miRNA reads. The quantified set of miRNA data serves as the input for operations such as differential expression analysis. There are many different tools designed for the identification and quantification such as miRge [3], miARma-seq [14], etc. For the purposes of this step, we will use the miRDeep2 [15] workflow on galaxy. The process presented below follows a recently submitted article on bioRxiv related to the miRDeep2 galaxy workflow [23]. 1. Log in to your galaxy account at https://usegalaxy.eu/. 2. On the search tool box, type mirdeep2, and select (by clicking) the workflow named “MirDeep2 Mapper process and map reads to reference genome.” 3. Click the upload button under the “Deep sequencing reads” area, upload file, and then choose local files and “SRR16832121_trimmed.fastq.gz” from the “Trimmed_with_cutadapt” folder. 4. Once the upload status is complete, the tab on the left will turn green. 5. Turn “Remove reads with non-standard nucleotides” to yes. 6. Choose “Human (Homo sapiens): hg19” under select a reference genome. 7. Click on execute, and wait for the mapping process to complete. 8. You will see two new processes named “Mapping output on MiRDeep Mapper on data * in ARF format” and “Collapsed reads of MiRDeep2 Mapper on data *” on the right. We have added the pre-generated ARF and the collapsed read file for all samples under the “count/ARF” and “count/collapsed_fasta” folders.

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9. For the next step of quantification, click on “MiRDeep2 Quantifier fast quantification of reads mapping to known miRbase precursors.” 10. The “collapsed deep sequencing reads” section should show the collapsed read file name under it by default. If it is not shown, use the drop-down menu to select the file. 11. For precursor and mature miRNA sequences, please upload files named “stem-loop_seq.fasta” and “mature_miRNA. fasta,” respectively, from “precursor_mature_fasta” folder. Once uploaded, select the proper file from the dropdown menu. 12. Under “Search in species,” select human, and click execute. 13. Two new processes with names “MiRDeep2 Quantification on data* (html report)” and “output of MiRDeep2” should appear on the right side of the screen. 14. Once the process is completed, both files will contain read counts for each of the detected miRNAs. 15. By clicking the save button under “output of MiRDeep2 Quantifier on data*”. 16. The file is usually downloaded with a “.tabular” extension. Please change the extension to .tsv for a text editor to open. The second column is the raw read_count which will be used for differential expression analysis. We have also provided the quantified information under the counts/Quantified folder. 17. Repeat steps 1–16 for each of the remaining five trimmed FASTQ files. 3.5

miRDeep2

miRDeep2 is a famous software utility designed for identification and quantification of novel and known miRNA from sequencing data. The miRDeep2 galaxy workflow provides an uncomplicated access to the miRDeep2 package. The actual miRDeep2 Perl package includes several individual programs and consecutive steps and requires multiple user-defined options. Within galaxy, the processing of miRDeep2 is twofold: in the first process, trimmed reads are mapped to the reference genome hg19 [16] by the bowtie (version 1) [17] using an existing indexed file within galaxy. This process produces an ARF file which includes mapping information to the reference genome and a collapsed reads file with all unique sequences. Figure 3a, b shows a galaxy output of both the collapsed and the ARF file. The second quantification process requires two additional files, a predefined miRNA precursor sequence file and a mature miRNA sequence file. Both can be downloaded from miRbase [18]. We have provided both these sequence files stem-loop_seq.fasta and mature_miRNA.fasta in the precursor_mature_fasta folder. In the quantification step, the collapsed reads are mapped

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Fig. 3 (a) Galaxy output shows the unique collapsed reads generated from the miRDeep2 process. The biological sequence can be seen at the last column. (b) Galaxy output displaying the ARF files generated from the miRDeep2 process. The second column “read_count” represents number of reads aligned to a particular reference miRNA

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against both the miRNA precursor sequences and mature sequences, to create counts that are further used for downstream analysis. Note: ARF is a propriety format for miRDeep2. It contains about 13 columns with mapping formation. More on ARF can be found at https://www.mdc-berlin.de/content/mirdeep2documentation. 3.6 Generating Precursor Expression Matrix for All Samples

Before performing differential expression analysis, we will have to combine the quantified results for each of the samples used in the analysis. We will be using the miRNA precursor counts for differential expression analysis. We calculated total precursor counts by adding counts of individual mature read counts associated with unique miRbase precursor ID. These counts can also be found with the galaxy “MirDeep2 Quantifier HTML report.” To avoid adding programming steps for combining the data, we are providing a total expression file named ExpressionMatrix.csv along with the R-program DifferentialExpression.R that was used to generate this file. Please make sure that the “.tsv” count files for all individual samples and the R-program are present in the same folder. The program also removes non-expressed miRNA from the dataset. The R-program for above programming operations is under the DESeq2 folder. Table 1 represents the expression matrix used for differential expression analysis.

Table 1 Combined precursor miRNA expression matrix. The expression matrix presented above shows miRNA precursor counts for each individual sample. The custom program combineReads. R was used to generate this. The expression matrix was filtered to remove unexpressed miRNAs SRR16832121 SRR16832122 SRR16832123 SRR16832125 SRR16832126 SRR16832127 hsa-let- 139 7a-1

193

306

394

86

110

hsa-let- 137 7a-2

190

303

390

85

108

hsa-let- 138 7a-3

192

306

396

86

110

hsa-let- 131 7b

123

121

155

45

70

hsa-let- 8 7c

14

14

97

15

13

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3.7 Differential Expression Analysis

Differential expression analysis is the core for many miRNA research study. There are many bioinformatics tools such as edgeR [19], limma+voom [20], etc. which are designed to assist with differential expression analysis. For this chapter, we will use DESeq2 [21] to perform differential expression analysis between the adenoma and the normal tissue samples. DESeq2 by Love et al. is a popular bioconductor package that performs differential expression analysis using a negative binomial model. The tool provides multiple functions to perform normalization, visualization, data transformation, etc. Since DESeq2 is outside the scope of this chapter, we will only focus on a few functions, but readers are encouraged to read the DESeq2 manuscript and the vignette. The output of differential expression as shown in Table 2 includes the following: 1. Base mean (baseMean): This is the mean of the normalized counts values accounting for library size. 2. Log2 fold change (log2FoldChange): This represents the effect size. This explains the difference between expression of cancer and normal samples. This measure is reported on a logarithmic scale to base2. 3. Standard Error (lfcSE): Standard of the log2 fold change. 4. Wald Statistic (stat). 5. Wald test p-value (pvalue). 6. BH adjusted p-value (padj).

Table 2 Results of differential expression analysis. The result from the differential expression analysis of adenoma vs normal samples using DESeq2 is presented below. The function DESeqDataSetFromMatrix was used to produce the DESeq2 object, and the function DESeq was used to perform differential expression analysis baseMean

log2FoldChange lfcSE

stat

pvalue

padj

hsa-mir100

115.277645

2.794004338

0.487555981 5.730632883 1.00E08

1.47E-06

hsa-mir99a

289.0820891 4.797809454

0.843731335 5.686418476 1.30E08

1.47E-06

hsa-mir30a

140.3092439 2.467805052

0.484229123 5.096358178 3.46E07

2.62E-05

4.201520598

0.841501433 4.992885851 5.95E07

3.38E-05

330.9229162 3.497280931

0.821614224 4.256597355 2.08E05

0.00094233

hsa-mir143 hsa-mir145

11172.45543

Fig. 4 (a) The top four differentially expressed miRNAs were used to make boxplots to represent the overall normalized count distribution across the different phenotypes. The plots shows that the miRNAs are downregulated in all the adenoma samples. (b) A volcano plot displaying the log10 P-value vs the log2 foldchange for all the miRNA used in the differential expression analysis

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Figure 4a represents a boxplot for the top four differentially expressed miRNAs. From the figure, it is easy to notice that the expression of the adenoma samples is downregulated for all miRNA. We used normalized counts obtained from DESeq2. These were further log2 transformed to produce these plots. Figure 4b represents a volcano plot showing statistical significance versus fold change for all the miRNA in the analysis. The volcano plot was created using the “EnhancedVolcano” bioconductor library [22]. The default P-value cutoff (P < 0.05) was changed to include more miRNA.

References 1. Hanna J, Hossain GS, Kocerha J (2019) The potential for microRNA therapeutics and clinical research. Front Genet 10:478 2. Green ED, Gunter C, Biesecker LG, Di Francesco V, Easter CL, Feingold EA et al (2002) Strategic vision for improving human health at the forefront of genomics. Nature 586:683–692 3. Lu Y, Baras AS, Halushka MK (2018) miRge 2.0 for comprehensive analysis of microRNA sequencing data. BMC Bioinform 19:275 ˜ ez-Torres R, Rojas A 4. Andre´s-Leo´n E, Nu´n (2016) miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Rep 6:25749 5. Alexiou A, Zisis D, Kavakiotis I, Miliotis M, Koussounadis A, Karagkouni D, Hatzigeorgiou AG (2021) DIANA-mAP: Analyzing miRNA from raw NGS data to quantification. Genes 12:46 6. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Cech M, Chilton J et al (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46:W537–W544 7. https://www.r-project.org/ 8. https://support.illumina.com/bulletins/201 6/04/fastq-files-explained.html 9. Leinonen R, Sugawara H, Shumway M (2011) The sequence read archive. Nucleic Acids Res 39:D19–D21 10. Zhu M, Dang Y, Yang Z, Liu Y, Zhang L, Xu Y, Zhou W, Ji G (2020) Comprehensive RNA sequencing in adenoma-cancer transition identified predictive biomarkers and therapeutic targets of human CRC. Mol Ther Nucleic Acids 20:25–33

11. Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics. babraham.ac.uk/projects/fastqc 12. Tsuji J, Weng Z (2016) DNApi: a de novo adapter prediction algorithm for small RNA sequencing data. PLoS One 11:e0164228 13. Marti M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 1:10–12 14. Andre´s-Leo´n E, Rojas AM (2018) miARmaSeq, a comprehensive pipeline for the simultaneous study and integration of miRNA and mRNA expression data. Methods 152:31–40 15. Friedla¨nder MR, Mackowiak SD, Li N, Chen W, Rajewsky N (2021) miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40:37–52 16. https://www.ncbi.nlm.nih.gov/assembly/ GCF_000001405.13/ 17. Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25 18. Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Res 47:D155–D162 19. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140 20. Law CW, Chen Y, Shi W et al (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29

Bioinformatics Analysis of miRNA Sequencing Data 21. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550 22. Blighe K, Rana S, Lewis M (2021) EnhancedVolcano: publication-ready volcano plots with

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enhanced colouring and labeling. Available at: h t t p s : // g i t h u b . c o m / k e v i n b l i g h e / EnhancedVolcano 2 3 . h t t p s : // w w w . b i o r x i v . o r g / c o n tent/10.1101/2021.10.19.464446v1. full.pdf

Chapter 17 Plant MicroRNA Identification and Annotation Using Deep Sequencing Data Zheng Kuang, Yongxin Zhao, and Xiaozeng Yang Abstract MicroRNAs (miRNAs) are endogenous non-coding small RNAs, which regulate gene expression at the post-transcriptional level. A large number of studies have revealed that they play key roles in diverse life activities, such as growth and development. In the last decade, deep sequencing technology has generated substantial small RNA sequencing (sRNA-Seq) data. Meanwhile, numerous tools have been developed to identify miRNAs from these sRNA-Seq data, resulting in a surge of miRNA annotations. Among these tools, the series of miRDeep-P and miRDeep-P2 have been widely used in plant miRNA annotation. Here, we employed miRDeep-P2 to demonstrate the plant miRNA annotation processes step by step using the deep sequencing data. Key words Plant miRNA, miRNA prediction, miRNA annotation, Deep sequencing, miRDeep-P2

1

Introduction MicroRNAs (miRNAs) comprise 20–24 nucleotide (nt) endogenous small RNAs (sRNAs) that function as posttranscriptional gene regulators in animals and plants [1, 2]. Many studies have focused on identifying miRNAs and their functions in various organisms in the last two decades, especially after the emergence of deep sequencing or next-generation sequencing (NGS) technologies [3, 4]. However, accurate and effective annotation of plant miRNAs is limited by their uniqueness, such as more prevalent large paralogous families and more variable precursors [5]. In addition, the application of NGS data is hampered by false positives caused by other sRNAs and the high computational resources and time required [6].

Zheng Kuang and Yongxin Zhao contributed equally with all other contributors. Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_17, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Along with deep sequencing technology, two major types of tools have been developed to identify and annotate miRNAs from NGS datasets [5]. The tools in the first category capture sequence similarity and the features extracted from the accumulated knowledge on miRNA biogenesis. These include signature reads along precursors, the specific secondary structure of precursors, etc. The second group of tools discovers the miRNA features through various machine learning methods to distinguish miRNA candidates from other genes and sRNAs. A few tools are also employed in both annotation strategies. Although these tools have greatly accelerated the research on miRNAs and identified a large number of miRNAs, they have some limitations [5]. The first category has successfully annotated many new and species-specific miRNAs. However, a lot of false positives have been introduced. The machine learning tools have achieved greater success in the annotation of animal miRNAs, while complex features of plant miRNAs have limited their use in plant miRNA annotation [6–8]. miRDeep-P2 (miRDP2) [6], as a widely used tool in the first category, is a fast and accurate tool for plant miRNA annotation. miRDP2 saves computing resources and time by adopting new filtering strategies and optimizing scoring algorithms. These include preselecting conserved miRNAs, setting thresholds for the number of reads corresponding to mature miRNAs, and dividing large genomes into small fragments, thus addressing the challenges caused by NGS data. miRDP2 also uses the latest updated plant miRNA annotation criteria, especially when filtering out siRNAs from miRNAs, to improve the accuracy and sensitivity of miRNA annotation [6]. This chapter includes the workflow of predicting and annotating plant miRNAs with miRDP2. Known and novel miRNAs can be predicted in any species of interests using publicly available genome reference and sRNA-Seq data. Moreover, a set of auxiliary scripts can further annotate the miRNAs families, the expression profile, and the genome cluster information. Therefore, the pipeline can be adopted for miRNA analysis for any genome of interest, providing insights into the function and evolution of the miRNAome.

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2.1 sRNA-Seq Datasets

Deep sequencing datasets are used as inputs to predict miRNA accurately. The sRNA fraction is enriched from extracted total RNA via electrophoresis. Single-end libraries are then prepared, and the reads acquired are used for the subsequent analysis. Multiple tissues/spatial samples should be used to detect tissue- and developmental stage-specific miRNAs. In our test environment,

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we use Perl v5.22.2, bowtie v1.2.2, bowtie2 v2.4.2, and Vienna RNA package v2.1.9 to run the pipeline. So, the programs in the same or later version should be sufficient. Two publicly available sRNA-Seq datasets, SRR11521540 and SRR12228127, in Arabidopsis thaliana are used to demonstrate the pipeline of miRNA annotation using miRDP2. 2.2 Installation and Computational Resources

miRDP2 consists of a set of Perl and Bash scripts, and thus Perl environment (https://www.perl.org) is required on the system. Besides, some additional tools needed in the pipeline should be installed before use. 1. The bowtie/bowtie2 (http://bowtie-bio.sourceforge.net/ index.shtml) [9, 10] is used to map sRNA-Seq reads in the miRDP2 pipeline. The bowtie/bowtie2 index file is prepared before use, either manually built or downloaded from the bowtie website. 2. The Vienna RNA package (https://www.tbi.univie.ac.at/ RNA) [11] is used to predict RNA secondary structure. Together, Perl, bowtie/bowtie2 aligner, and ViennaRNA package are prerequisites to run the pipeline. The pipeline does not require much hardware resources and has been tested on personal computers and cluster servers. The random access memory (RAM) availability, which is dependent on the size of the genome file and sRNA-Seq libraries, is critical for the program. Tens of Gigabytes of RAM may be needed for species such as wheat (Triticum aestivum) with a very large genome.

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3.1 Overview of miRNA Prediction and Annotation

The general workflow for predicting and annotating miRNAs from sRNA-Seq datasets using miRDP2 is shown in Fig. 1. 1. The input reads are cleaned and trimmed to remove adapter sequences and low-quality reads using FastQC [12] and Cutadapt [13] or other adapter-trimming programs. 2. The clean reads are then passed to the miRDP2 pipeline and processed by the pre-processing script to filter low abundance reads and non-miRNA reads derived from other types of non-coding RNAs (ncRNAs). 3. The processed reads are mapped to the indexed genome sequences using bowtie/bowtie2 with no mismatches by default.

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Genome sequence

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Scoring & filtering Predicted miRNAs Annotation miRNA basic information, expression, cluster

Fig. 1 General workflow of miRNA prediction and annotation using miRDP2

4. Candidate precursors are then extracted from the flanking regions of mapped reads based on the mapping results. RNAfold from the ViennaRNA package is used to determine the secondary structure of candidate precursors. 5. All clean reads are mapped to the candidate precursors using bowtie/bowtie2 to generate a reads distribution profile, known as reads signature. 6. The secondary structure prediction and reads distribution profile are included in the core scoring script to distinguish miRNA candidates from other sequences. 7. The miRNA candidates are then scanned using a filtering script to classify miRNAs based on the plant-specific criteria. The final predictions are assigned to different miRNA families mainly based on their mature sequences. 3.2 miRNA Prediction 3.2.1 Installation

1. Download the package from SourceForge (https:// sourceforge.net/projects-/mirdp2/files/latest_version/), and decompress all the files to install miRDP2: 1. tar -xvzf miRDP2-v*.tar.gz. The folder contains all scripts of miRDP2 and a bash script, miRDP2-pipeline-v*.bash, that initializes the prediction.

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2. A non-miRNA ncRNA index is necessary for the pipeline, and the sequence file is also available in miRDP2 SourceForge site. Its index could be built using the following commands once the sequence file has been downloaded: 1. tar -xvzf ncRNA_rfam.tar.gz. 2. bowtie-build -f ./ncRNA_rfam.fa / miRDP2_v*/script/index/rfam_index. 3. #Use bowtie2-build if you prefer to use bowtie2 in the later analysis. 3.2.2

Reads Cleaning

The raw sRNA-Seq data should be cleaned and trimmed before miRNA prediction. The general profiles of the quality and sequence overrepresentation of the sRNA-Seq datasets could be achieved by FastQC, while the low-quality part and/or attached adapter sequences can be removed by Cutadapt and Trim_galore. Notably, these tools are not included in the miRDP2 pipeline and should be installed in advance. 1. The FastQC program provides a comprehensive report of the quality of the sRNA-seq dataset. Use the following command to run the program: 1. fastqc . . . . The program then works automatically and generates an HTML report file for each library. The report presents the overall quality of the reads, the nucleotide preference in each position, overrepresented sequences, and many other aspects of the library to help qualify the sequencing result. 2. Cutadapt can efficiently remove the 30 adapters and low-quality reads with the given adapter sequences using the following command: 1. cutadapt -a --max-n 0 --discarduntrimmed -q 20 -o . The --max-n 0 option removes reads with “N”s in the sequences. The --discard-untrimmed option removes reads without adapters. The -q option trims the low-quality 30 ends from the reads.

3.2.3

Genome Indexing

The bowtie/bowtie2 indexes of the corresponding genome files are required before running the miRDP2 pipeline. The bowtie/bowtie2 website has several pre-built indexes of the most used model species. For other species, the genome indexes should be locally built from the reference sequences using the bowtie-build (or bowtie2-build) command: 1. bowtie-build -f .

/

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3.2.4

Several files are passed to the bash script to run the miRDP2 pipeline:

miRDP2 Prediction

1. bash /miRDP2_v*/miRDP2-vx.x.x.bash -g -x -q -i -o . The is the genome sequence in FASTA format. The is the pre-built bowtie/bowtie2 index in Subheading 3.2.3. The is the cleaned sRNA-Seq reads file in FASTQ format. The is manually named. The miRDP2 output files are established to a new folder under this output folder. The *_filter_P_prediction file is the final output file. It contains positions and sequences of miRNAs predicted from the given sRNA-seq library (Fig. 2a).

A

on r iti so os ur n ep r rec sitio u t P a po M

e tiv me so nta o e s d m e ro ran pr ds ID Re rea Ch St Chr4 + Chr5 + Chr5 Chr5 Chr5 Chr1 Chr5 Chr5 Chr5 + Chr5 + … …

reads4305008_x28756 reads4407332_x546 reads4373480_x872 reads4151874_x460 reads4305008_x28756 reads4367606_x797 reads853164_x2 reads4138760_x1486 reads4238969_x103 reads4305008_x28756 …

Chr4_83 Chr5_0 Chr5_198 Chr5_209 Chr5_292 Chr1_238 Chr5_298 Chr5_302 Chr5_34 Chr5_44 …

15074946..15074966 287587..287607 19009156..19009176 18358871..18358891 3456710..3456730 23345384..23345405 2641600..2641619 2634935..2634954 5169993..5170013 9136127..9136147 …

e

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ID000 MIR156 ID000 MIR156 ID000 MIR156 ID000 MIR156 ID000 MIR156 ID000 MIR156 ID000 MIR156 ID000 MIR156 ID000 MIR157 ID000 MIR157 ID000 MIR157 …

Ath-CL1 Ath-CL2 Ath-CL3 Ath-CL4 Ath-CL5 Ath-CL6

ID

Arab Arab Arab Arab Arab Arab Arab Arab Arab Arab Arab

Chr2 Chr4 Chr4 Chr5 Chr5 Chr5 Chr2 Chr5 Chr1 Chr1 Chr3

m re-

P

P 10676450 15074925 15415406 3456625 3867193 9136106 8412504 22597000 24913184 24921084 6244507

10676573 15075046 15415530 3456752 3867330 9136237 8412628 22597127 24913316 24921216 6244713 …

CAAGA ............. AGGAA ......(((.(( TAAGA ............. GTTGA (((.((((((( TGTGA ............. GAGTG .......((((. GGGAA ((((..((((. CGAGA ............. ATTGA ............. TTTGG .......((((. TTTGA ....((((.((

10676468 15074946 15415424 3456643 3867214 9136127 8412522 22597018 24913202 24921105 6244525

ies ec Sp

Arabidopsis_thaliana Arabidopsis_thaliana Arabidopsis_thaliana Arabidopsis_thaliana Arabidopsis_thaliana Arabidopsis_thaliana

10676551 15075027 15415508 3456730 3867311 9136218 8412606 22597105 24913294 24921197 6244691 …

TGACA (((((((((( TGACA (((((((((( TGACA (((((((((( TGACA (((((((((( TGACA (((((((((( TGACA ((((.(((((( CGACA .(((((((((( TTGAC .((.((((((( TTGAC .((((((((( TTGAC .((((((((( TTGAC (.(((((((((

Ath-miR156a Ath-miR156b Ath-miR156c Ath-miR156d Ath-miR156e Ath-miR156f Ath-miR156g Ath-miR156h Ath-miR157a Ath-miR157b Ath-miR157c

10676531 15074946 15415488 3456710 3867214 9136127 8412586 22597084 24913202 24921176 6244672 …

10676551 15074966 15415508 3456730 3867234 9136147 8412606 22597105 24913223 24921197 6244691

TGACAGAAGAGAGTGAGC TGGAGAAGCAGGGCACGT TGCCTGGCTCCCTGTATGC TCGCTTGGTGCAGGTCGG TGACAGAAGAGAGTGAGC AGGGCGCCTCTCCATTGG ACCACCGCTTCTGCTACGA GAGGCAGCGGTTCATCGA TCTGGGATGAATTTGGATC TGACAGAAGAGAGTGAGC …

EN ion iR ess Pm acc

A RN mi star

A RN ure mi mat

iR

iR

m ri-

+ + + + -

TGACAGAAGAGAGTGAGCACA TGGAGAAGCAGGGCACGTGCA TGCCTGGCTCCCTGTATGCCA TCGCTTGGTGCAGGTCGGGAA TGACAGAAGAGAGTGAGCACA TGCCAAAGGAGAGTTGCCCTGA ACCACCGCTTCTGCTACGAA TCGATAAACCTCTGCATCCA TCTGGGATGAATTTGGATCTA TGACAGAAGAGAGTGAGCACA …

NA

NA

m d an oso ID rom h c Ath-MIR156a Ath-MIR156b Ath-MIR156c Ath-MIR156d Ath-MIR156e Ath-MIR156f Ath-MIR156g Ath-MIR156h Ath-MIR157a Ath-MIR157b Ath-MIR157c

15074946..15075027 287587..287737 19009094..19009176 18358801..18358891 3456643..3456730 23345384..23345498 2641475..2641619 2634935..2635015 5169993..5170132 9136127..9136218 …

r so ur e ec nc Pr que se

re e atu nc M que e s

TGACAAth-miR156a* TGACAAth-miR156b* TGACAAth-miR156c* TGACAAth-miR156d* TGACAAth-miR156e* TGACAAth-miR156f* CGACAAth-miR156g* TTGACAth-miR156h* GCTCTAth-miR157a* GCTCTAth-miR157b* TTGACAth-miR157c*

ter er us mb Cl me Ath-MIR395d=MIR_ID000000065,Ath-MIR395e=MIR_ID000000064 Ath-MIR447a=MIR_ID000000080,Ath-MIR447b=MIR_ID000000079 Ath-MIR5026=MIR_ID000000081,Ath-MIRN7=MIR_ID000000115 Ath-MIR162a=MIR_ID000000023,Ath-MIR834=MIR_ID000000097 Ath-MIR166c=MIR_ID000000031,Ath-MIR166d=MIR_ID000000032 Ath-MIR398b=MIR_ID000000071,Ath-MIR398c=MIR_ID000000072

10676470 15075006 15415426 3456645 3867290 9136197 8412524 22597020 24913276 24921104 6244527 …

10676490 15075026 15415446 3456665 3867310 9136217 8412544 22597040 24913296 24921124 6244547

CTCAC CTCAC CTCAC GCTCA GCTTA GCTCA GCTTA CTCTC TTGAC TTGAC GCTCT

http://ww http://ww http://ww http://ww http://ww http://ww http://ww http://ww http://ww http://ww http://ww …

ter on us iti Cl pos Chr1 - 26269969 26272878 Chr4 - 1528127 1535666 Chr4 + 7844532 7846869 Chr5 - 2634916 2641641 Chr5 + 2838632 2840748 Chr5 + 4691014 4694818

D

A RN mi

Ath-MIR156a Ath-MIR156b Ath-MIR156c Ath-MIR156d Ath-MIR156e Ath-MIR156f Ath-MIR156g Ath-MIR156h Ath-MIR157a Ath-MIR157b Ath-MIR157c Ath-MIR157d Ath-MIR158a Ath-MIR158b Ath-MIR159a Ath-MIR159b Ath-MIR160a Ath-MIR160b Ath-MIR160c Ath-MIR161 Ath-MIR162a Ath-MIR162b Ath-MIR164a Ath-MIR164b Ath-MIR164c Ath-MIR165a Ath-MIR165b Ath-MIR166a Ath-MIR166b Ath-MIR166c Ath-MIR167a Ath-MIR167b Ath-MIR167c

n

sio

ID

MIR_ID000000004 MIR_ID000000008 MIR_ID000000007 MIR_ID000000011 MIR_ID000000010 MIR_ID000000012 MIR_ID000000005 MIR_ID000000009 MIR_ID000000003 MIR_ID000000002 MIR_ID000000006 MIR_ID000000001 MIR_ID000000014 MIR_ID000000013 MIR_ID000000016 MIR_ID000000015 MIR_ID000000019 MIR_ID000000018 MIR_ID000000020 MIR_ID000000021 MIR_ID000000023 MIR_ID000000022 MIR_ID000000025 MIR_ID000000026 MIR_ID000000024 MIR_ID000000027 MIR_ID000000030 MIR_ID000000028 MIR_ID000000029 MIR_ID000000031 MIR_ID000000036 MIR_ID000000037 MIR_ID000000038

res

p Ex 296.344 296.344 296.344 311.22 295.862 295.862 1.44735 0.12061 118.441 118.441 45.5914 1.36694 1034.93 134.241 2528.23 2179.1 43.9833 14.7951 43.9833 3733.15 1569.04 1569.04 17.3682 26.9367 11.7798 793.669 794.714 1779.83 1684.39 1686.16 70.2365 70.2365 0.28143

1434.65 1434.75 1434.65 1436.15 1436.6 1436.6 6.42851 0.39867 4364.26 4364.26 110.979 0.19933 19676.7 336.625 1028.86 0.64783 72.3581 53.6207 72.3581 3.78734 75.4478 75.4478 54.9164 27.209 10.6145 0.24917 0.1495 0.1495 0.09967 0.09967 34.2854 11.661 1.24583

Fig. 2 Examples of result tables from miRNA prediction and annotation scripts. A. miRDP2 prediction result. B. Basic information table of annotated miRNAs. C. Cluster information table of annotated miRNAs. D. Expression table of annotated miRNAs.

Fig. 2 Examples of result tables from miRNA prediction and annotation scripts. (a) miRDP2 prediction result. (b) Basic information table of annotated miRNAs. (c) Cluster information table of annotated miRNAs. (d) Expression table of annotated miRNAs

Plant miRNA Annotation by miRDeep-P2 3.2.5 Detailed Processes of miRDP2

245

1. Reads pre-processing: The clean reads should be filtered to reduce the false-positive rate and computing complexity. Those reads that are too short (24 nt) and matching Rfam ncRNA sequences (annotated plant ncRNAs except for miRNAs, mainly consisting of rRNAs, tRNAs, snRNAs, and snoRNAs) are excluded. Reads with Reads Per Million (RPM) value not less than 10 are considered as potentially derived from bona fide miRNA hairpins (see Notes 1 and 2), as they are likely to originate from highly expressed miRNA loci. The script also retrieves reads correlated to known miRNA mature sequences to obtain known miRNAs with low expression levels. These core reads are then used to locate putative miRNA precursors. 2. Precursor candidate extraction: The core reads extracted from clean reads are mapped to genome references by bowtie/bowtie2, with no mismatches allowed by default (see Notes 1 and 2). Reads mapping to more than 15 locations are discarded, as they are possibly derived from repeat elements (see Notes 1 and 2). Adjacent reads are clustered and merged as a candidate region. Each region theoretically represents a potential miRNA locus. Flanking sequence (300 nt) covering both upstream and downstream of the cluster is then extracted to form a potential precursor sequence for retrieving the corresponding precursor (see Notes 1 and 2). The flanking sequence is extracted twice, one with a longer 50 part and shorter 30 part and the other with a shorter 50 part and longer 30 part. These mimics the features of miRNA hairpin since the miRNA mature sequence is located in one end of the precursor. 3. Precursor candidate scoring: RNAfold predicts the secondary structures of extracted precursors at default setting. The results are presented in plain text, with paired brackets representing base-pairing nucleotides. The dots represent mismatched or bulged nucleotides. After mapping the clean reads to the precursor candidates, the results are formatted to generate the reads signature file. The scoring algorithm processes these structures and signature files. Sequences with unappreciated stem-loop structures and reads distribution patterns are not satisfying Dicerlike (DCL) processing characteristics and thus are filtered by the algorithm. Candidates with low complementarity in miRNA mature/star duplex region (complemented nucleotides 20% fuzzy reads lay over the boundary of mature/star region, non-overlapping mature and star regions in secondary structure, and absence of 30 overhangs in star sequences, are all considered to be unsatisfied with miRNA prediction criteria.

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The other candidates are passed to a probability model to quantitatively determine the extent the structure and reads distribution signature matches the theoretical pattern [7]. Such candidates have well-complemented stem-loop and reads count peaking in mature (and star) and a few sparsely distributed reads in other parts of the stem-loop. The candidates with scores above the threshold are considered the true miRNAs and kept for further assessment. 4. Plant-specific criteria filtering: The candidates are further filtered using plant-specific criteria [14]. More than five mismatches or more than 2 nt asymmetric nucleotides in the mature/star duplex region are not allowed. Also, reads in the mature/star region should consist of no less than 75% of the total reads in the precursor sequence. The miRNAs with 23/24 nt mature sequence should be distinguished from siRNAs as follows: 1. There should be at least one mismatch between mature and star sequences. 2. The reads in the mature region should not be less than 20 RPM. 3. At least one read should correspond to the star sequence in sRNA-Seq libraries to confirm the prediction. Candidates that pass all the filtration are printed into one sheet as the final prediction result (Fig. 2a) (see Note 3 for more discussion about prediction result). 3.3 miRNA Annotation

An additional set of scripts is provided in the miRDP2 pipeline to process the predicted miRNAs for assigning miRNAs into different families. The scripts compare predicted mature miRNA candidate sequences with the set of mature miRNA sequences from Plant miRNA Encyclopedia (PmiREN) [15] database to determine their families. A table containing position, sequences, and structures of pri- and pre-miRNAs and mature/star sequences has been provided for further analysis (Fig. 2b). The cluster information is ascertained based on their genomic location. The expression level of mature sequences is calculated based on sRNA-Seq data.

3.3.1

The following command should be used to run the annotation script:

miRDP2 Annotation

1. bash /miRDP2_v*/script/annotation/annotate.bash -g -m -q

-s

-o . The is the genome file in FASTA format. The is the location containing miRDP2 created result folder. The is the cleaned

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reads file in FASTQ format. The is an underscoredelimited species name (e.g., Arabidopsis_thaliana) used for miRNA naming. The program outputs three files: a basic information file containing sequence, structure, and genomic position, a mature expression file containing miRNA expression (in RPM), and a miRNA cluster file. The temporary files produced during the process are also kept for troubleshooting or other uses. 3.3.2 Detailed Processes in the Pipeline

1. miRNA family identification: The predicted miRNAs are first merged based on their genomic locations to remove redundancy in results from different sRNA-Seq libraries to determine their family. Mature sequences are then compared with known miRNA mature sequences in PmiREN [15]. The mature sequences are extended by 1 nt in both ends to allow detection of DCL splicing variants. The predicted miRNA mature sequences are mapped to the known mature sequences using bowtie, allowing only two mismatches. Each miRNA is then assigned to its family based on the mapping results (see Note 4 for more discussion). 2. Expression and cluster profile: After miRNA annotation, some additional data are also calculated by the scripts. The cluster information (Fig. 2c) and expression level (Fig. 2d) are computed based on the sRNA-Seq libraries and miRNA position. The expression profile across different libraries is determined by counting reads in the mature regions. Given that miRNA dicing could be wobbling, the reads that fall into mature 1 nt region can also be generated by DCL slicing and thus should be taken into account. Some miRNAs, such as miR395, are tandemly located in the genome and form gene clusters. miRNA clusters are found under a certain threshold of gene interval length. In general, miRNAs with less than 10 kb spacing are considered to be in the same cluster.

4

Notes 1. Parameter selection: The performance of the prediction pipeline can be improved by changing the default parameters under certain circumstances. The parameters include: -L/--locate option limits the number of positions that one reads can map to; default is 15. -N/--length option represents the length of the extracted flanking sequence; the default is 300 (nt). -M/--mismatch option is the allowed mismatches of reads mapping; default is 0.

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-R/--rpm option restricts the RPM value of reads potentially related to miRNA mature sequences during pre-processing; the default is 10 (RPM). Therefore, smaller -R and larger -L and -N can be used to obtain more results (at the cost of lower accuracy). The -N option reflects the length of miRNA precursors, and our tested result suggests that, in general, at 250–350 nts, most miRNA candidates could be retrieved in plants. Adopting an optimized threshold for a specific species can enhance accuracy and reduce false-positive results. 2. Species-specific criteria: A set of default parameters are preset to achieve good performance in all species. However, the parameters could be further revised for specific species/tissues/ purposes to fit the characteristics of miRNAs in the species for optimized performance. Higher -N option allows the detection of unique miRNAs with extremely long precursors. Lower -R option increases the sensitivity on miRNAs with low expression level. Higher -L option may be necessary to identify miRNAs from polyploid genomes or with too many copies. 3. IsomiRs and strand selections: Sometimes, the predicted miRNAs may have different mature sequences compared with known relatives, either with slightly different 50 and 30 ends or switch between canonical mature and star sequences. The imprecise slicing of DCL could cause the wobbling end, generating various forms of sRNA sequences with 1 or 2 nt sliding in both ends [16]. Besides, many other proteins such as nucleotidyltransferase and exoribonuclease can also cause the lengthening or shortening of miRNA 30 end [17]. The canonical miRNA* sequences in model species may also be predicted as mature in specific species or tissues. This could indicate a potential function of miRNA* of well-studied miRNAs. miRNA* sequences are intrinsically possessing the potential to regulate the target genes [18]. Moreover, the relative expression level of mature/star sequences can be temporally and spatially dynamic [19]. Under certain circumstances, miRNA* sequences may become the major payload of AGO proteins and thus could be detected by miRDP2. 4. Family assignment: The mature sequences of miRNAs should be aligned and compared to assign different family IDs to each miRNA locus. Some miRNA families, such as miR319 and miR159, are closely related and share quite similar mature sequences [20]. The miRNAs can be manually distinguished since miR319 and miR159 have different nucleotide characteristics in certain positions [20].

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Moreover, the novel miRNA family members that cannot map to known miRNAs should be assigned into novel families. Comparison of mature sequences can help identify whether those miRNAs belong to the same families. Proper assignment of novel miRNA families could help analyze the presence and conservation of novel miRNAs among different species. 5. Online resources: There are some online resources to help with using miRDP2. The two test datasets and the output of miRDP2 pipeline used in this chapter are available in the SourceForge website (https://sourceforge.net/projects/mirdp2-/ files/latest_version/). Users can access them freely to check the installation of the pipeline or to inspect the running process in detail. A manual file with more information about miRDP2 pipeline is also provided in this SourceForge website. It introduces the development, the installation, and the usage of miRDP2 pipeline. It also discusses the function of each script and some issues using miRDP2. A video protocol published online is available as well [21]. It has exhibited the operation of how to install and run miRDP2 pipeline on a personal computer.

Acknowledgement This work was supported by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS) (KJCX201907-2 and KJCX20200204 to X.Y, and QNJJ202019 to Y.Z.) and the National Natural Science Foundation of China (NSFC) (32070248 to X.Y.). References 1. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2): 215–233 2. Voinnet O (2009) Origin, biogenesis, and activity of plant microRNAs. Cell 136(4): 669–687 3. Yang XZ, Fishilevich E, German MA et al (2021) Elucidation of the microRNA transcriptome in Western corn rootworm reveals its dynamic and evolutionary complexity. Genom Proteom Bioinf. https://doi.org/10. 1016/j.gpb.2019.03.008 4. Shi Y, Xia H, Cheng X et al (2021) Genomewide miRNA analysis and integrated network for flavonoid biosynthesis in Osmanthus fragrans. BMC Genomics 22(1):141

5. Zhao YX, Kuang Z, Wang Y et al (2021) MicroRNA annotation in plants: current status and challenges. Brief Bioinform. https://doi. org/10.1093/bib/bbab075 6. Kuang Z, Wang Y, Li L et al (2019) miRDeepP2: accurate and fast analysis of the microRNA transcriptome in plants. Bioinformatics 35(14):2521–2522 7. Yang XZ, Li L (2011) miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 27(18): 2614–2615 8. Taylor RS, Tarver JE, Hiscock SJ et al (2014) Evolutionary history of plant microRNAs. Trends Plant Sci 19(3):175–182

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9. Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25 10. Langmead B, Salzberg SL (2012) Fast gappedread alignment with bowtie 2. Nat Methods 9(4):357–359 11. Hofacker IL (2003) Vienna RNA secondary structure server. Nucleic Acids Res 31(13): 3429–3431 12. Brown J, Pirrung M, McCue LA (2017) FQC dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool. Bioinformatics 33(19): 3137–3139 13. Kechin A, Boyarskikh U, Kel A et al (2017) cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing. J Comput Biol 24(11): 1138–1143 14. Axtell MJ, Meyers BC (2018) Revisiting criteria for plant MicroRNA annotation in the era of big data. Plant Cell 30(2):272–284 15. Guo ZL, Kuang Z, Wang Y et al (2020) PmiREN: a comprehensive encyclopedia of plant

miRNAs. Nucleic Acids Res 48(D1):D1114– D1121 16. Kim VN (2005) MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol 6(5):376–385 17. Neilsen CT, Goodall GJ, Bracken CP (2012) IsomiRs–the overlooked repertoire in the dynamic microRNAome. Trends Genet 28(11):544–549 18. Zhang XM, Zhao HW, Gao S et al (2011) Arabidopsis Argonaute 2 regulates innate immunity via miRNA393*-mediated silencing of a Golgi-localized SNARE gene, MEMB12. Mol Cell 42(3):356–366 19. Liu WW, Meng J, Cui J et al (2017) Characterization and function of MicroRNA*s in plants. Front Plant Sci 8:2200 20. Palatnik JF, Allen E, Wu XL et al (2003) Control of leaf morphogenesis by microRNAs. Nature 425(6955):257–263 21. Wang Y, Kuang Z, Li L et al (2020) A bioinformatics pipeline to accurately and efficiently analyze the MicroRNA transcriptomes in plants. J Vis Exp. https://doi.org/10.3791/ 59864

Correction to: Exosomal MicroRNAs: Comprehensive Methods from Exosome Isolation to miRNA Extraction and Purity Analysis Erika D’Agostino, Annamaria Muro, Giulia Sgueglia, Crescenzo Massaro, Carmela Dell’Aversana, and Lucia Altucci

Correction to: Chapter 5 in: Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_5 In the original version of this book, the first and the last names of the authors of Chapter 5 were in a flipped format (D’Agostino Erika, Muro Annamaria, Sgueglia Giulia, Massaro Crescenzo, Dell’Aversana Carmela, and Altucci Lucia). This has been rectified in the updated version of this book. The correct format is: Erika D’Agostino, Annamaria Muro, Giulia Sgueglia, Crescenzo Massaro, Carmela Dell’Aversana, and Lucia Altucci

The updated original version of this chapter can be found at https://doi.org/10.1007/978-1-0716-2823-2_5 Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2_18, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

C1

INDEX A

F

Age-related macular degeneration (AMD)..................... 6, 123, 124, 137–140, 148 Atrophic AMD ..................................................... 139, 155

FastQC.................................................................. 227, 228

B

Galaxy .................................................. 226–228, 230–233 Gene expression ........................................... 7, 13, 21, 49, 52, 56–58, 63, 75, 104, 110, 115, 116, 138, 159, 207, 211, 213–215, 217, 221, 222

Bioinformatics ............................................ 146, 160, 161, 163, 168, 169, 225–228, 234 Brain.............................................. 6, 14, 21, 39, 129, 189 Breast cancer........................................................ 7, 21, 27, 34, 35, 39, 186, 205, 206, 209

C Cancer.....................................................7, 14, 22, 24, 25, 27, 29, 33–35, 38–40, 49, 75, 76, 101, 102, 115, 124, 139, 160, 171, 186, 211–214, 227, 234 Cell cytotoxicity ............................................................ 204 Cell viability ................................................ 116, 119–121, 204, 212, 213, 223 CNS ...........................................................................93, 94

D Deep sequencing .................................230, 231, 239, 240 DESeq2................................................226, 233, 234, 236 Detection accuracy (DA) ................................................ 29 Differential ultracentrifugation (dUC) ............. 15, 77, 87 Dry AMD ............................................................. 123, 124

E Enrichment analysis ...................................................... 226 Exosomal miRNAs (exo-miRNAs) ..................14, 15, 21, 22, 24–30, 32–35, 37, 40, 75, 76, 78, 81, 85 Exosome ..........................................................6, 8, 13–25, 27, 29, 32–38, 40, 75–78, 81–85, 87–89, 130, 138, 139, 141, 142, 147–149, 151, 155, 157 characterization ...................................................17, 77 RNA extraction ................................................ 37, 148 RNA quality control ...........................................79, 86 Expression profile........................................ 101, 240, 247

G

I In situ hybridization .................................................93–99 In vitro cell culture............................................... 185–200 Isolation ...................................................... 14, 15, 17, 18, 20, 21, 27, 29, 34, 37, 40, 52, 67, 76, 77, 83, 103, 127, 134, 147, 157, 163, 190

L Luciferase assay............................................ 194, 198, 199

M Machine learning (ML) ................................................ 240 Microarray ................................................ 33, 49–63, 102, 138, 145, 160, 161 MicroRNA (miRNA) ...................................... 1–8, 13–40, 49–63, 65–72, 75–89, 93–99, 101–113, 115–121, 123–135, 137–157, 159–169, 171–181, 185–200, 203–209, 211–223, 225–236, 239–249 activity................................... 4, 22, 49, 138, 140, 172 annotation ............................................. 240, 241, 247 prediction........................................................ 243, 245 sponges ........................................................... 171–182 Microtissue .................................................................... 212 miRDeep2 .........................................................v, 230–233 miRDeep-P2.................................................................. 240 miRNA-based therapies ....................................5, 76, 115, 124, 139, 203, 204, 208, 212 Molecular biology ......................................................... 160

Sweta Rani (ed.), MicroRNA Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 2595, https://doi.org/10.1007/978-1-0716-2823-2, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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MICRORNA PROFILING: METHODS AND PROTOCOLS

252 Index N

Neovascular AMD...............................124, 138, 148, 149 Neuroblastoma.................................. 103, 104, 112, 117, 213, 214, 219, 221 Neurodegenerative disorders.......................................... 94

P PCR-based.................................................................22, 66 Plant miRNA ............................................... 239, 240, 246 Platform ................................................27, 29, 33, 50, 51, 65, 77, 102, 140, 144, 226, 227 Post-transcriptional modulation .................................. 159 Profile....................................................14, 15, 23, 30, 66, 67, 78, 151, 152, 160, 163, 203, 242, 243, 247 Proliferation............................................14, 49, 105, 116, 120, 160, 211–213

Serum.......................................................... 14, 18, 20, 24, 25, 30, 31, 33, 35, 36, 38–40, 77, 79, 103, 117, 118, 124–127, 129, 130, 134, 140, 142, 143, 146, 147, 156, 181, 197, 198, 205, 208, 209, 214, 216 Slide ....................................................... 50–52, 57–60, 63 Staining ...................................................... 57, 78, 89, 213

T 3D biomaterial scaffolds ............................................... 204 3D models ..................................................................... 212 Transfection...................................................34, 103–105, 110–112, 116–121, 186, 194, 197, 198, 204–208, 212–214, 216, 217, 219, 221, 222

U 3’-UTR ................................................186, 192, 196, 197

R Real-time quantitative PCR (qPCR) ................. 101–113, 135, 140, 144, 145, 154, 156, 167, 217–219 Retinal pigment epithelium (RPE) .................... 106, 123, 125–127, 135, 137–139, 148 RT-qPCR protocol............................................... 160, 169

S Scaffolds............................................... 204–208, 212–223

V Vectors construction ..................................................... 172

W Wet AMD ............................................................. 124, 138