Epigenetics in Cardiovascular Disease 0128222581, 9780128222584

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Epigenetics in Cardiovascular Disease

Translational Epigenetics Series Trygve Tollefsbol - Series Editor

Transgenerational Epigenetics Edited by Trygve O. Tollefsbol, 2014 Personalized Epigenetics Edited by Trygve O. Tollefsbol, 2015 Epigenetic Technological Applications Edited by Y. George Zheng, 2015 Epigenetic Cancer Therapy Edited by Steven G. Gray, 2015 DNA Methylation and Complex Human Disease By Michel Neidhart, 2015 Epigenomics in Health and Disease Edited by Mario F. Fraga and Agustin F. F Ferna´ndez, 2015 Epigenetic Gene Expression and Regulation Edited by Suming Huang, Michael Litt and C. Ann Blakey, 2015 Epigenetic Biomarkers and Diagnostics Edited by Jose Luis Garcı´a-Gim
enez, 2015 Drug Discovery in Cancer Epigenetics Edited by Gerda Egger and Paola Barbara Arimondo, 2015

Epigenetics of Aging and Longevity Edited by Alexey Moskalev and Alexander M. Vaiserman, 2017 The Epigenetics of Autoimmunity Edited by Rongxin Zhang, 2018 Epigenetics in Human Disease, Second Edition Edited by Trygve O. Tollefsbol, 2018 Epigenetics of Chronic Pain Edited by Guang Bai and Ke Ren, 2018 Epigenetics of Cancer Prevention Edited by Anupam Bishayee and Deepak Bhatia, 2018 Computational Epigenetics and Diseases Edited by Loo Keat Wei, 2019 Pharmacoepigenetics Edited by Ramo´n Cacabelos, 2019 Epigenetics and Regeneration Edited by Daniela Palacios, 2019 Chromatin Signaling and Neurological Disorders Edited by Olivier Binda, 2019

Medical Epigenetics Edited by Trygve O. Tollefsbol, 2016

Transgenerational Epigenetics, Second Edition Edited by Trygve Tollefsbol, 2019

Chromatin Signaling and Diseases Edited by Olivier Binda and Martin Fernandez-Zapico, 2016

Nutritional Epigenomics Edited by Bradley Ferguson, 2019

Genome Stability Edited by Igor Kovalchuk and Olga Kovalchuk, 2016

Prognostic Epigenetics Edited by Shilpy Sharma, 2019

Chromatin Regulation and Dynamics Edited by Anita G€ ond€or, 2016

Epigenetics of the Immune System Edited by Dieter Kabelitz, 2020

Neuropsychiatric Disorders and Epigenetics Edited by Dag H. Yasui, Jacob Peedicayil and Dennis R. Grayson, 2016

Stem Cell Epigenetics Edited by Eran Meshorer and Giuseppe Testa, 2020

Polycomb Group Proteins Edited by Vincenzo Pirrotta, 2016

Epigenetics Methods Edited by Trygve Tollefsbol, 2020

Epigenetics and Systems Biology Edited by Leonie Ringrose, 2017

Histone Modifications in Therapy Edited by Pedro Castelo-Branco and Carmen Jeronimo, 2020

Cancer and Noncoding RNAs Edited by Jayprokas Chakrabarti and Sanga Mitra, 2017

Environmental Epigenetics in Toxicology and Public Health Edited by Rebecca Fry, 2020

Nuclear Architecture and Dynamics Edited by Christophe Lavelle and Jean-Marc Victor, 2017

Developmental Human Behavioral Epigenetics Edited by Livio Provenzi and Rosario Montirosso, 2020

Epigenetic Mechanisms in Cancer Edited by Sabita Saldanha, 2017

Translational Epigenetics

Epigenetics in Cardiovascular Disease Volume 24

Series Editor

Trygve Tollefsbol Comprehensive Cancer Center, Comprehensive Center for Healthy Aging, University of Alabama at Birmingham, Birmingham, AL, United States

Edited by

Yvan Devaux Head, Cardiovascular Research Unit, Luxembourg Institute of Health, Strassen, Luxembourg

Emma Louise Robinson School of Medicine, Division of Cardiology, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, United States CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-822258-4 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Andre Gerhard Wolff Acquisitions Editor: Peter B. Linsley Editorial Project Manager: Megan Ashdown Production Project Manager: Sreejith Viswanathan Cover Designer: Mark Rogers Typeset by SPi Global, India

Contributors Bence A´gg Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest; Pharmahungary Group, Szeged; Heart and Vascular Center, Semmelweis University, Budapest, Hungary Parisa Aghagolzadeh Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, Lausanne, Switzerland Chukwuemeka George Anene-Nzelu Genome Institute of Singapore; Cardiovascular Disease Translational Research Programme, National University Health System, National University of Singapore Yong Loo Lin School of Medicine, Singapore, Singapore Johannes Backs Institute of Experimental Cardiology, University of Heidelberg; DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Heidelberg, Germany Ferran Barb
e Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRB Lleida, Lleida; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Fay Betsou IBBL (Integrated Biobank of Luxembourg), Dudelange, Luxembourg Stephanie Bezzina Wettinger Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, MSD2080, Malta Andrei Codreanu Hospital Center of Luxembourg, Strassen, Luxembourg Yvan Devaux Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg Christoph Dieterich German Center for Cardiovascular Research (DZHK)—Partner site Heidelberg/Mannheim; Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany Javier Dura´n Institute of Experimental Cardiology, University of Heidelberg; DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Heidelberg, Germany Rosienne Farrugia Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, MSD2080, Malta

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Contributors

Kyriacos Felekkis Department of Life and Health Sciences, School of Sciences and Engineering, University of Nicosia, Nicosia, Cyprus P
eter Ferdinandy Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest; Pharmahungary Group, Szeged, Hungary Roger S-Y Foo Genome Institute of Singapore; Cardiovascular Disease Translational Research Programme, National University Health System, National University of Singapore Yong Loo Lin School of Medicine, Singapore, Singapore Eleftheria Galatou Department of Life and Health Sciences, School of Sciences and Engineering, University of Nicosia, Nicosia, Cyprus David de Gonzalo-Calvo Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRB Lleida, Lleida; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Simona Greco Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Milan, Italy Johannes Grillari TAmiRNA GmbH; Austrian Cluster for Tissue Regeneration, Medical University of Vienna; Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, Vienna, Austria Hakan Gunes Faculty of Medicine, Department of Cardiology, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey Matthias Hackl TAmiRNA GmbH; Austrian Cluster for Tissue Regeneration, Medical University of Vienna, Vienna, Austria Nazha Hamdani Department of Molecular and Experimental Cardiology; Department of Cardiology, St. JosefHospital and Bergmannsheil; Department of Clinical Pharmacology; Institute of Physiology, Ruhr University Bochum, Bochum, Germany Lutz Hein Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine; BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany Carlos Hermenegildo Department of Physiology, Faculty of Medicine and Dentistry, University of Valencia, and INCLIVA Biomedical Research Institute, Valencia, Spain Eduardo Iglesias-Guti
errez Department of Functional Biology, Physiology, University of Oviedo; Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spain

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Benedetta Izzi Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, IS, Italy Kornelia Jaquet Department of Molecular and Experimental Cardiology; Department of Cardiology, St. JosefHospital and Bergmannsheil; Department of Clinical Pharmacology, Ruhr University Bochum, Bochum, Germany Amela Jusic Department of Biology, Faculty of Natural Sciences and Mathematics, University of Tuzla, Tuzla, Bosnia and Herzegovina; Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourg Kanita Karad-uzovi
c-Hadzˇiabdi
c Department of Engineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina Gabriela M. Kuster Department of Biomedicine, University Hospital Basel and University of Basel; Department of Cardiology, University Hospital Basel, Basel, Switzerland Alisia Made` Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Milan, Italy Federica De Majo Department of Molecular Genetics, Faculty of Science and Engineering; CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences; Maastricht University, Maastricht, The Netherlands Fabio Martelli Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Milan, Italy Andreas M€ ugge Department of Molecular and Experimental Cardiology; Department of Cardiology, St. JosefHospital and Bergmannsheil, Ruhr University Bochum, Bochum, Germany Vivien Ngo Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany Susana Novella Department of Physiology, Faculty of Medicine and Dentistry, University of Valencia, and INCLIVA Biomedical Research Institute, Valencia, Spain Ana Bel
en Paes Department of Physiology, Faculty of Medicine and Dentistry, University of Valencia, and INCLIVA Biomedical Research Institute, Valencia, Spain Christos Papaneophytou Department of Life and Health Sciences, School of Sciences and Engineering, University of Nicosia, Nicosia, Cyprus Thierry Pedrazzini Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, Lausanne, Switzerland

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Contributors

Antje Peters €nster, Mu €nster, Department of Genetic Epidemiology, Institute of Human Genetics, University of Mu Germany Lucı´a Pinilla Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRB Lleida, Lleida; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Emma Louise Robinson School of Medicine, Division of Cardiology, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, United States Elisabeth Semmelrock TAmiRNA GmbH, Vienna, Austria Justus Stenzig Department of Experimental Pharmacology and Toxicology, University Medical Centre HamburgEppendorf, Hamburg, Germany Maarten Vanhaverbeke Cardiovascular Medicine, University Hospitals Leuven, Leuven, Belgium Mirko V€ olkers German Center for Cardiovascular Research (DZHK)—Partner site Heidelberg/Mannheim; Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany Leon J. De Windt Department of Molecular Genetics, Faculty of Science and Engineering; CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences; Maastricht University, Maastricht, The Netherlands Johannes Winkler Department of Cardiology, Medical University of Vienna, Vienna, Austria Angela Xuereb Anastasi Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida, MSD2080, Malta Mehmet Birhan Yilmaz Faculty of Medicine, Department of Cardiology, Dokuz Eylul University, Izmir, Turkey

Preface Yvan Devauxa and Emma Louise Robinsonb,c Cardiovascular Research Unit, Luxembourg Institute of Health, Strassen, Luxembourga School of Medicine, Division of Cardiology, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, United Statesb CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlandsc

On behalf of the EU-CardioRNA COST Action CA17129.

The burden of cardiovascular disease Cardiovascular disease (CVD) remains a major cause of disability and death worldwide. According to the 2019 statistics of the World Health Organization (WHO), 17.9 million people die each year from CVD, which represents almost a third of all deaths globally.1 Despite significant improvements in health care, CVD continues to represent a major socioeconomic burden. Risk factors for CVD are either modifiable, such as tobacco use, physical inactivity, excessive food intake, or acquired. Genetic predispositions to CVD have been extensively investigated, yet most large association studies remain at the DNA (genomic) level, searching for associations between nucleotide polymorphisms and the risk of developing a CVD or having a poor clinical outcome after an acute cardiovascular event (e.g., worsening of an acute condition, developing comorbidities affecting other organs, or death). More recently, a better knowledge of the mechanisms regulating gene and protein expression allowed a diversification of these association studies, now focusing also on modifications occurring at the DNA, chromatin, and RNA levels. Those modifications not altering nucleic acid sequences have been grouped under the term “epigenetics,” derived from the Greek “epi” meaning “over, around, on top.” It is now accepted that epigenetic modifications play a major role in regulating cardiovascular homeostasis, CVD development, and progression.

Central dogma of molecular biology Cellular and tissue structure, morphology, biochemistry, and function are determined by the profile of proteins and RNA molecules present. The original central dogma of molecular biology—as defined by Sir Francis Crick in 1957—states that DNA is copied into RNA in a process called transcription, with RNA then being exported from the nucleus for protein synthesis at the ribosomes, in a process called translation (Fig. 1). However, we now know that some RNA molecules are functional as RNA molecules in their own right and are not used to encode proteins, accounting for at least 80% of the transcribed genome.2 This family of RNAs is known as noncoding RNAs (ncRNAs). The expression of protein-coding RNAs (messenger RNAs) and ncRNAs is determined by the rate of transcription. The transcription is under the control of epigenetic mechanisms. Epigenetic mechanisms involve

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FIG. 1 An illustration of the central dogma of molecular biology, the flow of biochemical information from DNA to RNA and protein. Adapted from Genome Research Limited.

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chemical modification either to the DNA or RNA bases themselves or to the proteins (histones) that package DNA into chromatin. Epigenetic mechanisms include: – – – – –

DNA cytosine methylation and its derivatives, Covalent modifications on amino acids of histones, Noncoding RNAs that mediate chromatin structure and regulate other epigenetic modifiers, Higher-order chromatin structure and scaffolding, and Covalent modifications on RNA nucleotides (known specifically as the epitranscriptome).

These epigenetic modifications act in concert with each other to determine the protein and RNA profiles at any given point in time.3

Epigenetic mechanisms Epigenetic mechanisms regulate differentiation, development, homeostasis, aging, and disease. Epigenetic marks are laid down in differentiation and development to program the transcriptome from pluripotency to determine cell fate and define the differentiated cell state. Both in a replication-dependent as well as replication-independent manner, the epigenetic landscape is not static (unlike the genome) but is dynamic throughout replicative and chronological aging. Importantly, the epigenome is responsive to environmental cues such as redox and metabolic or neurohumoral signaling. Wholesale remodeling of DNA methylation, histone modification, noncoding RNA, and RNA modification profiles has been described in the heart and vasculature in CVD.4–7 Epigenetic marks (DNA, RNA, and chromatin modifications) are mediated by epigenetic modifiers. Epigenetic modifiers can be further described according to their function—epigenetic writers, erasers, and readers. These are also dynamically regulated in disease processes. The addition or removal of epigenetic marks, accompanied by binding of epigenetic readers and ncRNAs, in turn regulates gene expression. It does so by altering the chromatin structure at a particular location in the genome; affecting the accessibility of enhancers, promoters, and gene bodies to transcription factors and RNA polymerases; as well as creating platforms for long-distance regulatory genomic interactions. In the case of the epitranscriptome, RNA stability, localization, and function can be modulated. This book addresses the current knowledge and understanding of how epigenetic modifications and modifiers regulate gene expression and different biological processes in the initiation and progression of CVD in the heart and vasculature. It also evaluates the use of epigenetic marks in diagnostic and epigenetic modifiers emerging as therapeutic targets.

Epigenetic mechanisms as biomarkers and treatment targets in CVD Biomarkers are molecules that can be easily and quantitatively measured in biological samples and give information on the presence (diagnostic biomarkers) of or evolution (prognostic biomarkers) of diseases. Traditionally, most biomarkers have been searched for in the bloodstream and belong

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to the family of proteins. As an example, troponins—contractile proteins contained in cardiomyocytes, the cells responsible for heart contractility—are used as diagnostic biomarkers of acute myocardial infarction, since they are released in the blood upon the damage of the cardiac tissue following rupture of blood supply due to obstruction of a coronary artery (also known as heart attack or myocardial infarction). Diversification of biomarker studies led to the discovery that not only proteins (or peptides) can be used as biomarkers, but also changes affecting epigenetic mechanisms that can be quantified in biological fluids or tissue biopsies. Noncoding RNAs for instance, many of which can be found in the bloodstream, are emerging as potential novel biomarkers of CVD. The most widely investigated have been short 21–25 nucleotide ncRNAs, called microRNAs (miRNAs), and their potential to help in personalizing health care has been proposed.8 Other types of epigenetic mechanisms will also be addressed in this book, for both their biomarker value and their therapeutic potential. Indeed, since biomarkers reflect disease progression, they may also have therapeutic potential, as recently discussed.9 The use of epigenetic mechanisms to design novel drugs to treat patients, almost as their use as biomarkers, is only in its infancy, and further work and studies are needed to fully address their potential to be used in precision medicine. Hopes and limitations of the use of epigenetic mechanisms to help in implementing personalized medicine in the cardiovascular field will be discussed in this book.

The EU-CardioRNA COST Action: networking to advance science Networking and sharing of complementary expertise is essential in biomedical research. Only synergistic multicenter investigations have the potential to decipher the mechanisms of complex and multifactorial diseases such as CVD. In this context, researchers from different horizons teamed up in a networking initiative funded by COST. COST (European Cooperation in Science and Technology; https://www.cost.eu) is a H2020-funded organization for research and innovation networks. COST networking tools—known as Actions—are research initiatives across Europe and beyond. They help researchers to grow their ideas in any science and technology field, such as biomedical research. The EU-CardioRNA COST Action CA17129 (https://cardiorna.eu) started in October 2018 for a duration of four years. The main goal of this Action is to catalyze the research on the role of transcriptomics in CVD through networking activities and collaborative exchange of expertise. The Action aims at a better knowledge of how epigenetics and more precisely regulatory RNA molecules affect CVD development.10 Ultimately, this gain of knowledge is expected to allow the development of novel biomarkers and drugs to improve health care of CVD patients. The Action organizes yearly meetings to discuss the most recent scientific signs of progress in the field. Through the funding of Short-Term Scientific Missions (STSMs) between partner laboratories, the Action aims to favor staff mobility, technology transfer, and the setup of synergistic multicenter research projects. The Action provides Inclusiveness Target Country (ITC) grants to help young investigators from less research-intensive countries to attend scientific conferences and discuss their data with their peers. Dissemination activities as well as scientific communication outreach activities are key components of the Action. An overall presentation of EU-CardioRNA’s Action goals and organization has been published.11 This book on Epigenetics in Cardiovascular Disease is a concrete example of such dissemination activities, which has been possible by a joint effort of more than 25 EU-CardioRNA partners from 15 countries.

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Presentation of the book and chapters EU-CardioRNA COST Action members have designed this book tackling the emerging and complex topic of the role of epigenetics in CVD. An introductory section will set the stage, presenting the latest data available on the burden of CVD, a state-of-the-art overview of the different epigenetic mechanisms, as well as an in-depth view of the intracellular molecular pathways regulating epigenetics. A main section has been then assembled to gather the current knowledge of the role of epigenetic mechanisms in CVD. The focus will be on DNA methylation, histone modifications, post-transcriptional RNA modifications, regulatory RNAs, splicing regulation, cardiac transcriptomic remodeling, sex differences, and the role of epigenetics in cardiac development, and induced pluripotent stem cells. The next section will address the potential of epigenetic mechanisms to be used as CVD biomarkers, from the value of peripheral DNA and RNA to be translated to clinical practice, to the promise of long ncRNAs and circular RNAs as heart failure biomarkers. Effect of physical exercise on epigenetic regulation as well as the added value of artificial intelligence in clinical decision making will be presented and discussed. A section on therapeutic potential will present the novel strategies for modulating epigenetic mechanisms in CVD, as well as an overview of emerging epigenetic cardiovascular drugs. A section will present methodological issues in RNA-sequencing and single-cell RNA-seq techniques, will provide recommendations for best experimental practices in RNA research and biomarker development, and will discuss the potential of bioinformatics analysis for target identification. We will finally provide our vision of the potential of epigenetic mechanisms to help in the management of CVD patients, and we will propose directions for future work. The epigenetics field in cardiovascular research is only in its infancy, and we expect that it will lead to the discovery of novel drugs and biomarkers for patient’s benefit.

Acknowledgments This article is based upon the work from EU-CardioRNA COST Action CA17129 (www.cardiorna.eu) supported by COST (European Cooperation in Science and Technology). ELR is supported by a Dutch Heart Foundation (Hartstichting) CVON EARLY-HFPEF-2015 consortium grant (Dutch Heart Foundation) and a CVON RECONNECT Young Talent Programme award. YD is funded by the National Research Fund (grants # C14/BM/8225223 and C17/BM/11613033), the Ministry of Higher Education and Research, and the Fondation Coeur—Daniel Wagner of Luxembourg.

References 1. http://origin.who.int/cardiovascular_diseases/en/. 2. Azodi M, et al. The missing “lnc” between genetics and cardiac disease. Noncoding RNA. 2020;6(1). 3. Unfinished symphony j Nature [Internet]. [cited 2020 March 2019]. Available from: https://www-nature-com. kuleuven.ezproxy.kuleuven.be/articles/441143a. 4. Berulava T, et al. Changes in m6A RNA methylation contribute to heart failure progression by modulating translation. Eur J Heart Fail. 2020;22(1):54–66.

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5. Gilsbach R, et al. Dynamic DNA methylation orchestrates cardiomyocyte development, maturation and disease. Nat Commun. 2014;5:5288. 6. Hermans-Beijnsberger S, van Bilsen M, Schroen B. Long non-coding RNAs in the failing heart and vasculature. Noncoding RNA Res. 2018;3(3):118–130. 7. Kaneda R, et al. Genome-wide histone methylation profile for heart failure. Genes Cells. 2009;14(1):69–77. 8. Goretti E, Wagner DR, Devaux Y. miRNAs as biomarkers of myocardial infarction: a step forward towards personalized medicine? Trends Mol Med. 2014;20(12):716–725. 9. Huang C-K, Kafert-Kasting S, Thum T. Preclinical and clinical development of noncoding RNA therapeutics for cardiovascular disease. Circul Res. 2020;126(5):663–678. 10. Gomes CPC, et al. Regulatory RNAs in heart failure. Circulation. 2020;141(4):313–328. 11. Gomes CP, et al. Catalyzing transcriptomics research in cardiovascular disease: the CardioRNA COST Action CA17129. Noncoding RNA. 2019;5.

CHAPTER

The ever-growing burden of cardiovascular disease

1

Mehmet Birhan Yilmaza and Hakan Gunesb Faculty of Medicine, Department of Cardiology, Dokuz Eylul University, Izmir, Turkeya Faculty of Medicine, Department of Cardiology, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkeyb

1.1 Introduction Cardiovascular diseases, which comprise ischemic cardiac disease, stroke, heart failure, peripheral artery disease, and other vascular diseases, are a leading cause of mortality and morbidity in the world and contribute significantly to impaired quality of life. In 2017, CVD resulted in 17.8 million death and 35.6 million-year disability worldwide. Awareness of prevalence and incidence of CVD and risk factors can provide us with the opportunity for prevention and management of the disease and can lead to improved survival.1

1.2 Financial load of cardiovascular disease Cardiovascular disease (CVD), as a multifaceted disease with multiple stages, is the major contributor of early death all over the world, as about one-third of all deaths are attributable to CVD, and this measure is foreseen to rise further in this decade.2 There are at least three significant domains of CVD: epidemiological burden, which considers frequency in overall population and productive ages; economic burden, which constitutes direct and indirect costs; and disability burden, which accounts for several consequences of these diseases. Cardiovascular health services constitute a significant part of the overall health budget of member countries of the European Society of Cardiology (ESC). The overall burden of health expenditures related to CVD can be brought under two subheadings of fiscal and economic burden. ESC countries diverge significantly when it comes to CVD-related fiscal burden since health systems, structural organizations, and per capita income differ between these countries significantly. According to 2016 reports, health expenditure per person in Kirghizstan was reported to be 240 US Dollars, whereas in Switzerland, this measure reached 7900 US Dollars.2 According to the ESC Atlas report, in 2016, the ratio of current health expenditure to gross domestic product (GDP) ranged from 3.5% to 12.2% in the ESC countries with Germany, France, and Switzerland leading the list and with Romania, Turkey, Egypt, and Kazakhstan at the bottom of the list by reserving 100,000 putative regulatory regions and associated genes during cardiac myocyte development and disease.

2.8 Conclusions Recent studies have unraveled a wide spectrum of molecular epigenetic mechanisms, many of which are of importance for cardiac development and maintenance of physiological gene expression. Future studies will be essential to uncover further details of cardiac epigenetic control. Focusing on cardiac myocytes, the integration of extra- and intracellular signals within the nucleus is expected to result in a better understanding of “pathological gene” expression and its disease-specific control. Single-cell sequencing studies have already started to demonstrate the regional heterogeneity in transcriptional programs of cardiac myocytes and nonmyocytes within the heart. Continuation and further advancement of these techniques will also identify the heterocellular nature and interactions between cardiac cell types, with a particular emphasis on fibroblasts, endothelial cells, immune cells, and other cell types. In addition, understanding and pharmacologically modulating the reprogramming of cells are expected to result in novel strategies for cardiac repair. All of these studies will result in a better understanding of the epigenetic mechanisms of human heart disease and will be essential to devise novel strategies for diagnosis, prognosis, and ultimately also the treatment of heart disease.

Acknowledgments—Sources of funding This work was supported by the DFG—German Research Foundation (CRC 992, project B03, L.H.), the Innovationsfonds des Landes Baden-W€ urttemberg (to L.H), the BIOSS Centre for Biological Signaling Studies, Freiburg, Germany (to L.H.).

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Chapter 2 Epigenetics concepts: An overview

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CHAPTER

From classical signaling pathways to the nucleus

3

Javier Dura´na,b and Johannes Backsa,b Institute of Experimental Cardiology, University of Heidelberg, Heidelberg, Germanya DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Heidelberg, Germanyb

3.1 Introduction Epigenetic signaling can lead to reprogramming of fundamentally different biological processes involved in metabolism, intracellular transport, cellular growth, inflammation, and others. Signaling pathways originating from the second messenger calcium (Ca2+) and 30 -50 -cyclic adenosine monophosphate (cAMP) have recently been studied in the context of epigenetic regulation and cardiac function, allowing a better understanding of the potential antagonistic effects of these two ancient second messengers for the control of cardiac function through epigenetic mechanisms. The history of cAMP and Ca2+ discovery as second messengers is as old as the description of signal transduction cascades itself.1, 2 The involvement of these messengers has been implicated in numerous processes—the detailed description of which is beyond the purpose of this chapter. Briefly, cAMP was initially described by Earl Sutherland, by studying hormone-induced glycogen breakdown in the liver. Its intracellular levels arise upon its conversion from ATP, through the action of an Gα-protein-coupled receptor (Gα-PCR)-activated adenylyl cyclase.2,3 For instance, cAMP stimulates lipolysis in adipocytes, glucose production in striated muscle and hepatocytes or relaxation, and vasodilation in smooth muscle.4 cAMP-response element-binding protein (CREB) and its partners respond to cAMP and are expressed in multiple tissues like neuronal, hepatic, white and brown adipose tissue, and analogs have been found in insects and worms, showing the conservation of the signaling in the animal kingdom.5 As mentioned earlier, cAMP signaling relates to glucose metabolism. cAMP has been suggested to upregulate gluconeogenic genes, such as phosphoenolpyruvate carboxykinase and glucose-6-phosphatase in the liver6 or to promote β-cell survival by stimulating the expression of insulin receptor substrate 2 and the antiapoptotic survival gene B-cell lymphoma 2 in pancreatic β islets.7 Moreover, the action of downstream cAMP signaling is described for more than 4000 genes,8,9 suggesting the versatility of the pathways in gene occupancy and gene transcription control. However, the exact regulatory mechanisms are in many cases not understood. Ca2+ is another important messengers in signal transduction.10 The discovery of Ca2+ as signal came from the inadvertent use of tap water by Sydney Ringer in isolated rat hearts, which contracted, unlike those where he used distilled water.11 Later, Ca2+ was described as a modulator of myosin ATPase activity.12 Ca2+ gradients were unmasked between extra- and intracellular compartments,13 leading to the concept of Ca2+ compartmentalization. Subsequently, Ca2+-binding Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00023-7 Copyright # 2021 Elsevier Inc. All rights reserved.

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Chapter 3 From classical signaling pathways to the nucleus

proteins were identified14 and Ca2+ indicators developed.15 Morgan and Curran16 showed that the expression of the proto-oncogene c-Fos depends on Ca2+, while it was later described that Ca2+dependent calmodulin kinases control the transcription of genes through CREB in neuronal cells, suggesting for first time the relationship between Ca2 + and gene expression.16,17 Typically, extraand intracellular stores contribute to intracellular Ca2+ raises.18 Activation of GqPCR leads the cleavage of membrane-bound phosphatidylinositol 4,5-bisphosphate into inositol (1,4,5) trisphosphate (IP3) and diacylglycerol. IP3 elicits the release of Ca2+ release from the endoplasmic reticulum (ER) through its binding to IP3 receptors in ER membrane.19 The function of Ca2+ as a second messenger allows specific spatiotemporal signaling due to its capacity of compartmentalization and low molecular weight.10 In neurons, for example, the intracellular increasement of Ca2+ is cardinal for the coordinated action in neuronal electrical activity,20 and for fertilization, Ca2+-downstream pathways are required for the resumption of the cell cycle in oocytes.21 In striated muscle, Ca2+ controls contraction, metabolism, development, and gene transcription of many genes involved in adverse cardiac remodeling.22,23 In this chapter, we describe how cAMP and Ca2+ signaling control epigenetic regulation.

3.2 Ca2+-dependent changes in gene expression In the heart, the most described action of Ca2+ is its involvement in electric activity and cardiac contractility, specifically acting as the central player of excitation-contraction coupling (ECC) in cardiomyocytes.24 However, Ca2+ flux is also important to regulate other downstream effectors. For instance, Ca2+ binds to a set of proteins that regulate gene expression, a process often referred to as excitation-transcription coupling (ETC).22 The definite Ca2+ sources in ETC remain incompletely understood, but there is evidence that both Ca2+ generated in the nucleus and the cytoplasm influences transcriptional and epigenetic regulation.19,22 Some studies suggest that nuclear Ca2+ is generated through passive diffusion through nuclear membrane pore, or by the Ca2+ channel IP3 receptor pools in the inner nuclear membrane.19 Oscillations in nuclear Ca2+ could therefore drive gene expression changes.25 Notably, the insulin-like growth factor 1 receptor pool is reported to play a role in perinuclear domains in cardiomyocytes and to serve as an inducer of cytoplasmicindependent nuclear Ca2+, which is required for the reactivation of the transcription factor myocyte enhancer factor 2 (MEF2) in cardiomyocytes26 that seem to play a central role in the pathogenesis of cardiac diseases.27 Intracellular Ca2+ oscillations control Ca2+-dependent signaling by the activation of distinct signaling that binds and/or responds to Ca2+ binding proteins. One of them is the calmodulin, which is a small protein, where each C- and N-terminal possesses two Ca2+-binding EF-hands.28 More than 300 proteins have been shown to bind to calmodulin, showing the high diversity of responses downstream Ca2+ cycling.22 The most described Ca2+-response proteins in ETC are Ca2+/calmodulin-dependent kinase II (CaMKII) and the Ca2+-/calmodulin-dependent serine/threonine phosphatase 3 (calcineurin). Additionally, some isoforms of protein kinase C (PKC), the calmodulin-binding transcription activator 2 (CAMTA2), and the protease calpain participate in transcription in response to intracellular Ca2+ fluctuations, to mention only a few.22 A brief overview of Ca2+-dependent mechanism that leads to gene expression changes is given in Table 3.1 and is shortly discussed later.

3.2 Ca2 +-dependent changes in gene expression

43

Table 3.1 Overview of principal Ca2+-downstream signaling involved in epigenetic regulation. Downstream effector CaMKII

Mechanism

Selected target genes

References

" HDAC4 phosphorylation (Ser632)

" Nr4a1 (direct MEF2 target) " Gfpt2 (indirect) " Hba-a1/a2 " Klf5 " Anp " β-mhc " Klf5

Lehmann et al.29

" Histone 3 (Ser28)

PKC/PKD CAMTA2

" Histone 3 (Ser10) " Phosphorylation HDAC5 (Ser498) " HDAC5 phosphorylation

" Anp " Bnp " β-mhc

Saadatmand et al.30 Awad et al.31 Zhang et al.32; Just et al.33 Song et al.34

3.2.1 Ca2+-/calmodulin-dependent kinase II CaMKII is a Ser/Thr kinase encoded by four separate genes, Camk2a, Camk2b, Camk2g, and Camk2d, each one with a different expression pattern. The Camk2a and Camk2b genes are mainly expressed in the brain35; meanwhile, Camk2g and mainly Camk2d are expressed in the heart and are both upregulated in the diseased heart.36,37 The CaMKII holoenzyme is thought to be formed of 12 subunits from one or a combination of different isoforms, each one consisting of a specific Ser/Thr kinase domain at the N-terminus, a linker domain and an autoregulatory domain, which has the Ca2+/calmodulin (CaM)binding domain. Upon the Ca2+/CaM complex binding, the enzyme autophosphorylates at Thr286/287 (depending on the isoform).38 Besides phosphorylation, CaMKII can be activated by other posttranslational modifications, including oxidation, nitrosylation, and O-GlcNAcylation.39–42 CaMKII exerts its molecular effects by binding and subsequent phosphorylation of target proteins. Regarding the regulation of transcriptional processes, CaMKII has been shown to modify chromatin structure through the phosphorylation of epigenetic regulators and histones.30,31,43 First, CaMKII was identified as an upstream kinase of the chromatin modifying enzyme histone deacetylase 4 (HDAC4).44 HDAC4 belongs to class IIa HDACs, which are important transcriptional regulators in the developing and postnatal heart, and regulates cardiac growth and the maintenance of cardiac function.45,46 Class IIa HDAC members, which also include HDAC5, HDAC7, and HDAC9, regulate the transcription in cardiomyocyte through the activation or repression from several transcription factors of related proteins, including MEF2 but also others.22,47 Activation of MEF2 results in gene expression of fetal, Ca2+ handling, proinflammatory, and metabolic genes.22,29,48 It is puzzling that the deacetylase activity of class II HDACs does not seem to be required for interaction with MEF2 nor is this activity required for full transcriptional repression by MEF2.46,48 Notably, HDAC4 is the only class IIa HDAC that binds directly to CaMKII, since HDAC4 has a unique docking site for CaMKII that is absent in other HDACs.44 CaMKIIδ phosphorylates HDAC4 at Ser 467 and Ser 632, thereby promoting nuclear export of HDAC4. Interestingly, the cytoplasmic CaMKIIδC splice variant phosphorylates HDAC4 at the same sites, thereby blocking nuclear import.44,49 Recently, it was shown that the phosphorylation of HDAC4 at Ser 632 is attenuated by O-GlcNAcylation of HDAC4 at Ser 642, a posttranslational modification that is required for cardioprotective HDAC4 proteolysis.50 HDAC5 is

44

Chapter 3 From classical signaling pathways to the nucleus

also phosphorylated by CaMKII, but this requires hetero-oligomerization with HDAC4, because CaMKII needs to bind HDAC4 to then phosphorylate HDAC5.51 Emerging evidence revealed that CaMKII also phosphorylates histones. Nuclear CaMKIIδB binds to histone 3 and increases its phosphorylation at Ser1031 and Ser28.30 At least for Ser28, it could be shown that this is an activating histone modification, resulting in the expression of globin genes in cardiomyocytes. However, the functional relevance of this surprising finding remains to be investigated, and further studies are needed to gain a full picture of target genes. These data will allow to demonstrate the specific regulation of the chromatin by CaMKII by direct gene signatures. CaMKII exists as 11 splice variants, and apart from both CaMKIIδB and CaMKIIδC as previously mentioned, CaMKIIδA and CaMKIIδ9 might mediate cardiovascular disease.43,52 CaMKIIδA is specifically upregulated in RBM20 cardiomyopathy due to specific and direct RBM20 splicing effects.53 Along this line, mice overexpressing CaMKIIδA present with Ca2+ handling defects, a hypercontractile phenotype, and cardiomyopathy.54 However, its specific function in gene transcription is unknown but it is interesting to note that CaMKIIδA localizes to T-tubules, where important Ca2+ channels are located. CaMKIIδ9 has recently been demonstrated as the highest expressed isoform in the human heart, as well as in other mammals such as mice, rabbits, and rhesus monkeys, it is also upregulated in human cardiomyopathy, and its overexpression in mice generates cardiomyopathy and heart failure.52 CaMKIIδ9 phosphorylates UBE2T (Ser110), a ubiquitin ligase enzyme involved in DNA repair pathways, inducing its downregulation through proteasomal degradation. Interestingly, UBE2T overexpression attenuates the cardiomyopathy and heart failure, locating CaMKIIδ also in DNA repair pathway control.52

3.2.2 Protein kinase C PKC is a homologous group of proteins, which belong to the AGC superfamily of protein kinases, with 10 isoforms described. PKC members are marked by N-terminal regulatory domains, a central hinge region that connecting the C-terminal with the catalytic domain.55 According to cofactor requirement, PKC is divided into 3 different groups: the conventional (c)PKC isoforms that require Ca2+ and diacylglycerol (DAG); novel (n)PKC isoforms that require only DAG; and the atypical (a)PKC isoforms that require neither Ca2+ nor DAG.56 PKCα belongs to cPKC and is the most abundant isoform found in the heart.22 In addition to its regulation by Ca2+, PKCα is cleaved by the Ca2+-response nonlysosomal cysteine protease calpain and generated a C-terminal catalytic fragment.32,57 This catalytic fragment was reported to translocate into the nucleus, to phosphorylate HDAC5, leading to HDAC5 nuclear export and MEF2 activation32 and to mediate agonist-dependent cardiac hypertrophy.58 In addition, PKCμ/protein kinase D (PKD), which acts as a downstream effector kinase of PKC, stimulates the nuclear export of HDAC5.58,59 PKD also participates in the proper heart valve formation in zebra fish targeting Kr€ uppel-like factor 2a and 4a (KLF2a/4a) signaling.33 Whether the latter two PKD effects are related to each other is unclear. It might be therefore interesting to study a potential interaction of MEF2 and KLF2a/4a signaling in response to PKD.

3.2.3 CAMTA2 Another protein that functions as Ca2+-sensitive regulator of gene expression is the calmodulin-binding transcriptional activator 2 (CAMTA2). CAMTA2 is a transcription factor that promotes cardiomyocyte hypertrophy and activates the atrial natriuretic factor (ANP) gene, at least in part, by associating with

3.3 cAMP-dependent epigenetic regulation

45

the cardiac homeodomain protein Nkx2-5. Moreover, CAMTA2 is repressed by HDAC5, mechanism downregulated by the PKD-dependent HDAC5 phosphorylation and nuclear export, leading to CAMTA2 activity increment.34

3.2.4 Ca2+-dependent regulation of alternative splicing In addition to its control of transcription, Ca2+ also regulates alternative splicing. In cultured cardiomyocytes, increased Ca2+ levels connect epigenetic modifications with an increase in the skipping of alternative exons.60 Specifically, elevated intracellular Ca2+ levels induce histone hyperacetylation, which leads to more relaxed chromatin configurations and provides more rapid RNA polymerase II transcription.61 These epigenetic changes were found in the splicing of genes related to development including neurofibromin 1 (Nf1), Kinectin 1 (Ktn1), Ankyrin 2/B (Ank2), Enable homolog 5 (Enah5), and Mef2A. These genes are all related to heart development and cardiac remodeling and are mediated by CaMKIIδ and PKD through the differential dissociation of class II HDACs, as HDAC4.60

3.3 cAMP-dependent epigenetic regulation cAMP is a second messenger regulating cellular functions including Ca2+ handling but also lipid metabolism, cell migration, proliferation, and death.62–64 Its effect on gene transcription in the heart has hardly been studied. Intracellular cAMP levels are increased upon β-adrenergic stimulation through the Gs-PCR-response adenylyl cyclase65. cAMP activates signaling pathways such as cAMP-dependent protein kinase A (PKA), exchange protein directly activated by cAMP (EPAC), and the transcription factor cAMP-response element-binding protein (CREB).66 A brief overview of cAMP-dependent mechanism that leads to gene expression changes is given in Table 3.2 and is shortly discussed later.

3.3.1 Protein kinase A (PKA) Inactive PKA is a tetramer composed of two regulatory (R), which have cAMP-binding domain, and two catalytic (C) subunits with Ser/Thr activity, which have four and three different isoforms, respectively.69 PKA activation occurs when cAMP binds to the four binding sites located on the R subunit dimer, depressing and activating C subunits.70 Multiple genes for R and C subunits have been identified: R subunits are subdivided into RI subclass (RIα and RIβ) and RII subclass (RIIα and RIIβ), whereas C subunits are divided into the three different gene products Cα, Cβ, and Cγ. Moreover, both PKA R and C subunits present splice variants expressed throughout human tissue or in specific tissues (reviewed in Refs. 4,70). Lastly, in contrast to classical activation described previously, evidence indicates that C-active subunits of PKA remain intact bind to holoenzyme upon activation.71 Additionally, some splice variants from Cβ isoforms may bind to R subunits independently of cAMP availability.4 PKA is the major regulator of cardiac β-adrenergic response, and even though the loss or reduction of its activity in cardiomyocytes does not cause adverse remodeling, it is associated with maladaptive remodeling. In the heart, PKA is often reported to control transcription through phosphorylation of the cAMP-response element-binding protein (CREB) at Ser133, which is related to an upregulation in CREB-transcriptional activity. Using transgenic mice, the involvement of CREB in contractile

46

Chapter 3 From classical signaling pathways to the nucleus

Table 3.2 Overview of principal cAMP-downstream signaling involved in epigenetic regulation. Downstream effector PKA

PKA/ABHD5 PKA/mAKAP

Mechanism " Phosphorylation on ABHD5 (Ser237) and release from perilipin " Phosphorylation of PKD (Ser916 and Ser744/748)

# Phosphorylation on HDAC5 (Ser259/498) and HDAC9 (Ser218/448) " Proteolysis of HDAC4 into NT-HDAC4 " PKD-dependent HDAC5 phosphorylation

Selected target genes

References

# Nr4a1 # gfpt2 # Myomaxin # Pdk4 # Bnp # β-MHC

Jebessa et al.63 He et al.67 Backs et al.62

" Mf20 " Myogenin

Lee et al.68

response of heart to adrenergic stimuli has been suggested,72,73 but other studies suggested its contribution to the understanding of PKA/CREB axis with epigenetic mediators.74

3.3.2 PKA and lipid droplet-associated signaling As CaMKII, PKA is activated in response to β-adrenergic signaling.65 However, whereas CaMKII induces cytoplasmic HDAC4 accumulation, PKA releases the lipid droplet-associated protein Ab hydrolase domain containing 5 (ABHD5) from perilipin 5 (PLIN5) and activates a recently identified protease function.63 Notably, HDAC4 is so far the only identified proteolytic substrate of ABHD5. HDAC4 proteolysis does not result in degradation of HDAC4 but in regulated proteolysis, resulting in an N-terminal proteolytic product (HDAC4-NT) .62 We postulated that this process counteracts the aforementioned CaMKII effect on HDAC4 because HDAC4-NT lacks CaMKII-responsive domains but is sufficient to inhibit MEF2 and subsequently nuclear receptor 4A1 (NR4A1).63 Notably, this pathway activated the hexosamine biosynthetic pathway and protein O-GlcNAcylation of a variety of regulators of cardiac function29.50 Thus, HDAC4-NT protects the heart from failure in response to MEF2 activation. These data reveal a unmasked antagonistic roles of Ca2+ and cAMP on the control of gene expression.

3.3.3 Nuclear retention of class IIa HDACs Inhibition of transcription can also be explained by HDAC5 nuclear accumulation upon phosphorylation of HDAC5 at Ser279.75 Despite the initial description that PKA phosphorylates this site directly, recent evidence rather argues for an indirect PKA effect at this site.76 Instead, recent reports suggest that cAMP counteracts MEF2-dependent gene programs by nuclear retention and diminished phosphorylation of HDAC5 (Ser 259/498) in cardiomyocytes.67,77 Mechanistically, HDAC5 hypophosphorylation is due to cAMP/PKA inhibition of PKD. In addition, MEF2 is also a phosphorylation target of PKA at Ser121 and Ser190 and leads to the inhibition of the skeletal muscle differentiation.78 Whether the latter mechanism affects cardiac homeostasis needs to be further investigated.

3.5 Translational perspective

47

3.3.4 A-kinase-anchoring proteins (AKAPs) and cAMP compartmentalization signaling Pioneering work by Zaccolo and Nikolaev indentified distinct cAMP microdomains in cardiomyocytes.79–82 Particularly, it is suggested that spatial organization of PKA is achieved through the interaction of the R-subunits with the A-kinase-anchoring proteins (AKAPs). AKAP family comprises more than 30 proteins that could also been alternative spliced.4 The muscle AKA β (mAKAPβ or AKAP6) is expressed in striated muscle and is differentially localized in the nuclear envelope of cardiomyocytes.83,84 The AKAP family comprises more than 30 proteins that are also alternatively spliced.4 The muscle AKAPβ (mAKAPβ or AKAP6) is expressed in striated muscle and is differentially localized in the nuclear envelope of cardiomyocytes.83,84 In cardiomyocytes, mAKAP signalosome recruits adenylyl cyclase, cAMP effectors as PKA or EPAC, and phosphodiesterase, which downregulates cAMP levels, in perinuclear domains for signaling.85 Notably, mAKAP/PKA was suggested to be crucial for the inhibitory effects of β-adrenergic-induced HDAC5 nuclear export and PKD-dependent HDAC5 phosphorylation.86

3.4 Antagonistic roles of Ca2+ and cAMP signaling As discussed earlier, cAMP/lipid droplet signaling exerts cardioprotective effects, whereas Ca2+dependent signaling is often reported to be associated with cardiac dysfunction. Thus, it is fair to hypothesize that these pathways antagonize each other. In skeletal muscle, β-adrenergic stimulation promotes HDAC4 nuclear retention through the action of cAMP and PKA, as a result of PKA phosphorylation at Ser265/266 of HDAC4.87 Curiously, the activation of EPAC leads to HDAC4 nuclear export, which might be mediated by a CaMKII action downstream of EPAC.87,88 In cardiomyocytes, β-adrenergic stimulation phosphorylates HDAC5 (Ser279) and promotes its nuclear retention, which antagonizes by Gqstimulation of CaMKII and subsequent nuclear export of HDAC5.51,76 PKA and CaMKII differentially regulate sarcoplasmic reticulum Ca2+ leak and uptake, through the phosphorylation of ryanodine receptor type II and phospholamban, respectively.89,90 The differential contribution to Ca2+ handing and HDAC4/5 nuclear location by cAMP and Ca2+ signaling leads to the question how these signaling pathways regulate each other, possibly at a direct and an indirect mechanism, the latter is exemplified by the circumstantial evidence that both PKA and CaMKII phosphorylate H3S28, but this seems to occur not at the same time,30 suggesting independent signaling pathways toward this event. Further investigations are needed to explore whether antagonistic gene programs are controlled in a context-dependent manner.

3.5 Translational perspective Recent discoveries have unraveled that Ca2+-dependent signaling through CaMKII- and cAMPdependent mechanism through lipid droplet-associated signaling control epigenetic factors that are required for cardiac homeostasis in physiological and pathological conditions (Fig. 3.1). For instance, CaMKII inhibition, in particular disrupting the interaction with HDAC4, shows promise in attenuating/suppressing cardiac dysfunction. On the contrary, activating lipid droplet-associated signaling to inhibit epigenetic programs that are linked to MEF2 activation is implied as a new alternative therapeutic approach.

48

Chapter 3 From classical signaling pathways to the nucleus

FIG. 3.1 Simplified summary of effects of Ca2+- and cAMP-associated signaling on cardiac phenotypes. (1) cAMP through lipid droplet-associated signaling induces the proteolysis of HDAC4 into NT-HDAC4 to protect the heart. (2) Ca2+dependent signaling induces nuclear export of HDAC4/5 and derepresses MEF2 to activate disease-causing signaling in cardiomyocytes.

3.6 Future directions Since the discovery of the involvement of class IIa HDACs in cardiac disease,91 several studies on chromatin remodeling with sequencing technology have been described. The use of new sequencing technology at the single-cell level will lead to the identification of specific Ca2+- and cAMP-dependent gene signatures in cardiac cells, potentially further unmasking antagonistic pathways with high therapeutic potential.

Source of funding JB was supported by grants from the Deutsche Forschungsgemeinschaft (BA 2258/9-1; SFB 1118, Project Number 236360313), the DZHK (Deutsches Zentrum f€ur Herz-Kreislauf-Forschung—German Centre for Cardiovascular Research), and the BMBF (German Ministry of Education and Research).

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76. Chang CW, Lee L, Yu D, Dao K, Bossuyt J, Bers DM. Acute beta-adrenergic activation triggers nuclear import of histone deacetylase 5 and delays G(q)-induced transcriptional activation. J Biol Chem. 2013;288:192–204. 77. Sucharov CC, Dockstader K, Nunley K, McKinsey TA, Bristow M. Beta-adrenergic receptor stimulation and activation of protein kinase A protect against alpha1-adrenergic-mediated phosphorylation of protein kinase D and histone deacetylase 5. J Card Fail. 2011;17:592–600. 78. Du M, Perry RL, Nowacki NB, et al. Protein kinase A represses skeletal myogenesis by targeting myocyte enhancer factor 2D. Mol Cell Biol. 2008;28:2952–2970. 79. Burdyga A, Surdo NC, Monterisi S, et al. Phosphatases control PKA-dependent functional microdomains at the outer mitochondrial membrane. Proc Natl Acad Sci U S A. 2018;115:E6497–E6506. 80. Nikolaev VO, Bunemann M, Schmitteckert E, Lohse MJ, Engelhardt S. Cyclic AMP imaging in adult cardiac myocytes reveals far-reaching beta1-adrenergic but locally confined beta2-adrenergic receptor-mediated signaling. Circ Res. 2006;99:1084–1091. 81. Nikolaev VO, Moshkov A, Lyon AR, et al. Beta2-adrenergic receptor redistribution in heart failure changes cAMP compartmentation. Science. 2010;327:1653–1657. 82. Zaccolo M, Pozzan T. Discrete microdomains with high concentration of cAMP in stimulated rat neonatal cardiac myocytes. Science. 2002;295:1711–1715. 83. Kapiloff MS, Schillace RV, Westphal AM, Scott JD. mAKAP: an A-kinase anchoring protein targeted to the nuclear membrane of differentiated myocytes. J Cell Sci. 1999;112(Pt 16):2725–2736. 84. Michel JJ, Townley IK, Dodge-Kafka KL, Zhang F, Kapiloff MS, Scott JD. Spatial restriction of PDK1 activation cascades by anchoring to mAKAPalpha. Mol Cell. 2005;20:661–672. 85. Dodge-Kafka K, Gildart M, Tokarski K, Kapiloff MS. mAKAPbeta signalosomes—a nodal regulator of gene transcription associated with pathological cardiac remodeling. Cell Signal. 2019;63:109357. 86. Dodge-Kafka KL, Gildart M, Li J, Thakur H, Kapiloff MS. Bidirectional regulation of HDAC5 by mAKAPbeta signalosomes in cardiac myocytes. J Mol Cell Cardiol. 2018;118:13–25. 87. Liu Y, Schneider MF. Opposing HDAC4 nuclear fluxes due to phosphorylation by beta-adrenergic activated protein kinase A or by activity or Epac activated CaMKII in skeletal muscle fibres. J Physiol. 2013;591:3605–3623. 88. Pereira L, Metrich M, Fernandez-Velasco M, et al. The cAMP binding protein Epac modulates Ca2 + sparks by a Ca2 +/calmodulin kinase signalling pathway in rat cardiac myocytes. J Physiol. 2007;583:685–694. 89. Fischer TH, Herting J, Tirilomis T, et al. Ca2 +/calmodulin-dependent protein kinase II and protein kinase a differentially regulate sarcoplasmic reticulum Ca2 + leak in human cardiac pathology. Circulation. 2013;128:970–981. 90. Valverde CA, Mazzocchi G, Di Carlo MN, et al. Ablation of phospholamban rescues reperfusion arrhythmias but exacerbates myocardium infarction in hearts with Ca2 +/calmodulin kinase II constitutive phosphorylation of ryanodine receptors. Cardiovasc Res. 2019;115:556–569. 91. Backs J, Olson EN. Control of cardiac growth by histone acetylation/deacetylation. Circ Res. 2006;98:15–24.

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Chukwuemeka George Anene-Nzelua,b, Justus Stenzigc, and Roger S-Y Fooa,b Genome Institute of Singapore, Singapore, Singaporea Cardiovascular Disease Translational Research Programme, National University Health System, National University of Singapore Yong Loo Lin School of Medicine, Singapore, Singaporeb Department of Experimental Pharmacology and Toxicology, University Medical Centre HamburgEppendorf, Hamburg, Germanyc

4.1 DNA methylation in the heart 4.1.1 Preface DNA methylation is one of the earliest forms of DNA modifications and epigenetic control of gene expression to be studied, being discovered several decades ago.1 It entails the addition of a methyl group to the nucleic acid cytosine, in its 5th carbon (C5) position, and in cytosines that are usually observed in a palindromic CG format, often represented as CpG, considering the bridging phosphate group. Methylated cytosines (5mC) are associated with differential gene expression regulation, altering chromatin structure without affecting the DNA sequence. DNA methylation has now been described to pass down both to the next generations in organisms and also to daughter cells in mitosis. To date, other forms of cytosine modifications have also been recognized to exist in the mammalian system (hydroxymethyl-, formyl- and carboxyl-cytosines)2; however, our understanding of the roles of these modifications in the heart is very limited and yet to be fully elucidated. Similarly, the methylated adenosine (6mA) modification has also been described recently in mammalian cells,3 and again this is not yet studied in detail for the heart. In the late 1930s, Conrad Waddington coined the term “epigenetics”4, 5 at a time when underlying mechanisms were not yet understood. The discovery that methyl groups are frequently transferred onto the C5 position of cytosines to form 5-methyl-cytosines (5mC) was made in the early 1950s.6, 7 Soon afterward further research showed that DNA methylation was fundamental for the maintenance of any given cellular differentiation state,8, 9 hence delivering the mechanistic underpinning, at least in part, for the Waddington description of “epigenetics.” At the early times of its discovery, the most frequently observed locations of differential cytosine methylation were CpG-rich stretches of DNA that came to be known as CpG islands.10–12 CpG islands tend to be located in gene-promoter regions in the mammalian genome, and methylation of CpG islands in these loci associated well with corresponding gene suppression.10–12 Subsequently, with genome-wide analyses of cytosine methylation, especially between cancer types and healthy noncancer tissue, it came to be discovered that differential cytosine-methylated loci were more significantly found in CpG-island shores, and even in CpG-island shelves.13 At a molecular level, evidence pointed to cytosine methylation being responsible for Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00016-X Copyright # 2021 Elsevier Inc. All rights reserved.

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maintaining individual differentiated cell states, and DNA methylation also facilitated the addition of other epigenetic marks such as histone modifications, nucleosome positioning, and transcription factor binding.11 The development of iPS cell technology, which enables cellular reprogramming in vitro,14 is also known to be accompanied by rapid changes in DNA methylation and demethylation,15 further supporting the notion that DNA methylation is important for cell identity. This, together with the discovery of enzymes now known to mediate active cytosine demethylation, has shifted the paradigm and reshaped our view on DNA methylation in more recent years.16, 17 DNA methylation is indeed a complex subject. Methylated marks can remain stable for years and DNA methylation is hypothesized to pass down generations. This is proposed to underlie the transgenerational epigenetic phenomena reported in studies such as the “Dutch Hunger Winter” study. In this study, the offspring of pregnant women who were prenatally exposed to starvation during the post-war winter of 1944–1945 were later found to have less DNA methylation of IGF2—a gene associated with diabetes and cardiovascular disease, than their unexposed same-sex siblings.18 Furthermore, DNA CpG methylation is responsible for various classical examples of long-term (stable) gene suppression such as X-chromosome inactivation,19 transposable element silencing20, 21 as well as imprinting.22 There is nonetheless evidence now that active demethylation can indeed proceed as rapid and dynamic responses, responding to external stimuli, and also in heart disease,23 these may indeed be related to the dynamic methylation changes frequently observed in other complex diseases such as cancer24 and neurological disorders.25, 26 Since methyl groups have to be provided as substrates for the cytosine methylation modification, this may also be altered as a result of dietary changes, and the differential abundance of methyl groups in dietary components, or hypothetically even by any external stimulus which may change the availability of these methyl groups, including cellular changes in 1-carbon metabolite intermediates.27 DNA methylation, hence, is a means through which the organism integrates external signals, and cellular memory, in concert with other epigenetic marks, relays signals back to the cytoplasm. The importance of DNA methylation therefore goes beyond merely switching on or off of gene expression during differentiation and development, and it is also involved in the maintenance of tissue homeostasis. Heart disease is characterized by hallmark changes in gene expression.28 Hence, epigenetics, and in particular DNA methylation, can be proposed to act as a store for information, which not only guides transcriptional regulation but is also stable enough to reflect the transcriptional landscape of the cardiac cell.29 These qualities make targeting DNA methylation, an attractive therapeutic option. A number of heart conditions arise due to the continuous exposure of the body to persistent risk factors that may modify DNA methylation marks in the heart. Therefore, a deeper understanding of the nature of the changes in DNA methylation, either beneficial or deleterious, opens up potential new approaches to treat heart disease.

4.1.2 Mechanisms of DNA methylation and demethylation The methylation of the cytosine in DNA is governed by enzymes called DNA methyltransferases.29 The main isoforms of this enzyme in mammals are DNA methyltransferases 1, 3A, and 3B, and these are encoded by DNMT1, DNMT3A, and DNMT3B genes, respectively. Studies have shown that DNMT1 is responsible for the maintenance of DNA methylation, while DNMT 3 (A and B) are believed to be the enzymes in charge of de novo methylation. DNMT2 has been renamed as TRDMT2 as it was discovered to be a tRNA methyltransferase. DNMT3L is related in sequence to the other

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DNMT3 enzymes, it functions as a cofactor that helps with the recruitment and binding of the methyltransferases; however, it does not have methyltransferase activity itself.30 De novo DNA methylation is governed by DNMT3A and 3B. They are the methyltransferases that respond to external stimuli, depositing methyl marks on the genome to regulate gene transcription. These together with the enzymes responsible for active demethylation especially in mitotically quiescent cardiac cells, TET1, TET2, and TET3, are therefore the important factors for cellular DNA methylation plasticity. During cell division, DNMT1 is the enzyme responsible for copying the DNA methylation template from the parent cell to the daughter cell, thereby ensuring that inherited epigenetic marks are passed down to daughter organisms. As methylation of cytosine mostly appears in the context of CpG dinucleotides, DNMT1 copies the methylation mark from a cytosine nucleotide to the complementary cytosine of the neighboring guanine, hence maintaining CpG methylation in a palindromic manner. UHRF1 is an adapter protein that recognizes and binds hemimethylated DNA at replication forks via its YDG domain, recruiting DNMT1 to fill in the methylation gaps, thereby maintaining the DNA methylation and thus preventing passive demethylation as a result of DNA replication.31 For DNA methylation to be maintained over several rounds of DNA replication though, the assistance of both DNMT3A and 3B is generally required.12, 29 S-adenosyl-methionine is used as a substrate for all DNA methyltransferases. The presence of hemimethylation is not required for the activity of DNMT3A and 3B; they can instead also act directly on previously unmethylated DNA.32 Our understanding of DNA methylation and demethylation is far from complete. Previously, it was believed that passive demethylation observed as a result of dilution of the methyl marks during DNA replication and cell division was the only means of DNA demethylation. However, many years after the discovery of DNA methylation, the enzymatic mediators of active DNA demethylation have now been identified. The enzymes involved in DNA demethylation are the ten-eleven translocation enzymes 1 to 3, (TET 1–3) as well as thymine DNA glycosylase (TDG).33, 34 The TET group of enzymes oxidized methyl-cytosines in a number of steps, first to hydroxymethyl-cytosine, followed by formyl-cytosine, and finally converted this to carboxy-cytosine.35, 36 These modifications thence make up the newly discovered aforementioned cytosine modifications in the mammalian system. The enzyme TDG removes the carboxy-cytosine and replaces it with a nonmethylated cytosine through base excision repair (BER).14 The identification of this mechanism has spurred scientific investigation in many biomedical fields. This newfound knowledge of DNA methylation dynamics makes this epigenetic mechanism attractive as a therapeutic target, even in terminally differentiated cells previously deemed unlikely to have methylation alterations in their cellular lifetime since they are nondividing. The classical paradigm is that DNA CpG methylation leads to transcriptional inhibition. This inhibition is brought about by either steric prevention of the binding of transcription factors or by the binding of DNA binding factors that recognize methyl marks,37 thence impinging transcription. These mechanisms, however, only apply to a minor subset of the methylated regions as a number of differentially methylated CpGs play roles that go beyond transcriptional repression.38 Indeed, some activating transcription factors have shown the preference for mCpG bearing sequences.39 Transcriptional repression is often achieved by high methylation levels of CpG-rich regions in CpG islands (,19 Thomson,40) as well as in gene promoter or enhancer regions.38, 41, 42 For this to happen, it is believed that DNA methylation affects both the formation of heterochromatin and the binding of transcription factors that regulate gene expression. These methyl-binding

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proteins (MBPs) include methyl-CpG-binding domain MBD proteins (MBD 1–4 and MeCP2). Other members of the MBP family possess the ability to bind methylated DNA using the zinc finger motifs, while yet another family of MBPs bind methylated DNA using the SET and ring-associated (SRA) domain.43 Furthermore, DNMTs could act in concert with histone methyltransferases and deacetylases to achieve transcriptional repression.44, 45 Further research is ongoing to identify and characterize more chromatin binding factors that are important for DNA methylation and demethylation.46 These transcription factors and reader proteins interact with the DNA methylation and demethylation machinery. A number of loss-of-function studies of some of the DNA-binding proteins such as Mbd1 in mice show that these mice only display neurological phenotypes with no striking cardiovascular symptoms.47 Similarly, Mecp2 knockout mice displayed no overt cardiovascular abnormalities although they recapitulate the neurological phenotype of Rett syndrome.48 DNA methylation often acts together with other epigenetic mechanisms to regulate the transcriptional machinery in the cells. Examples include the association of DNA methylation readers with histone 3 lysine 9 tri-methylation (H3K9me3) and other histone methyltransferases.49 Similarly, the polycomb complex can regulate DNA methylation through TET proteins.21, 50 These observations suggest that DNA methylation could exert opposing functions either by an increase or by a decrease of gene expression. DNA methylation could also act by decreasing activating marks such as the decrease of H3ac, through the recruitment of histone deacetylases (HDAC) by MeCP2 which is a DNA methylation reader.40 Similarly, H3K4me3 marks have been shown to be regulated by cerebellar foliar pattern 1 (CFP1, CXXC1 in humans), a protein which binds to nonmethylated CpGs.20 Further studies into the interplay between DNA methylation and the histone code in the heart are warranted as these are needed to shed more light on how the DNA methylation machinery relates to the transcriptomic differences in heart development and disease.

4.1.3 Mechanisms that regulate DNA methylation and demethylation The regulation of DNA methylation in the heart is not yet fully elucidated. While the enzymes involved in DNA methylation and demethylation are already known, how these processes are regulated in cardiac tissue remains largely unstudied. Most of the studies on DNA methylation and demethylation have been carried out in early development.37 These studies show that a complex interplay of the DNA methylation machinery with transcription factors and other chromatin modifications is required. Although the general principles of these interactions have been elucidated, tissue-specific information for the heart is still lacking. Even to date, this scarcity is largely related to the complexity of cell-type-specific analysis, necessary to detect the potentially subtle dynamic changes in DNA methylation against a large background of stable marks and sample impurity. This problem is discussed in detail later. The mechanisms that regulate the genome-wide DNA methylation marks are quite complex. There is a cooperation between DNA methylation and histone modifications, but it is also tightly connected to transcription factor binding. HP1 (heterochromatin 1), for example, is known to recruit DNMT3A and 3B to sites that bear H3K9me3 marks (HP1,51). It is also known that histone modification and DNA methylation often go hand-in-hand52 and there are many other mechanisms through which DNA methylation may be modulated by histone modifications.49 Histone modification in the heart may be regulated by conventional intracellular signaling, and some of these stress-signaling responses have been identified previously.53, 54 Some of these are now also known to involve the regulation by different

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species of noncoding RNA.55, 56 However, little is known about a possible direct link between noncoding RNA and DNA methylation in adult cardiomyocytes, unlike the case in plants.57 Some evidence has been gathered in developmental biology: IncRNA antisense transcripts such as XIST, Hotair, and Kcnq1ot1 guide the repression complex PRC2 to its target loci on the inactive X-chromosome, thereby establishing the repressive histone methylation mark H3K27me3,58, 59 and can also directly guide DNMT1. DNMT enzymes are often recruited to H3K27me3 marks,60 consequently resulting in DNA methylation of the loci. Furthermore, another group of small noncoding RNA species, called Piwi-interacting RNAs (piRNA), have been shown to guide DNMTs to the loci of interest in mice,61 though this mechanism seems to be largely confined to early developmental stages where transposable elements are silenced in a piRNA-dependent manner.62

4.1.4 The DNA methylation landscape Concerted efforts by different groups have been made to delineate the DNA methylation landscape in different tissues and diseases.38, 41, 42 Large consortiums including the ENCODE Project Consortium,63 Roadmap Epigenomics, and many more have taken advantage of technological advances such as high-throughput sequencing64 to generate and analyze large methylation datasets. Some groups have also increased the resolution to a single base at single-cell levels65, 66 using the technique of wholegenome bisulfite sequencing (BS-seq) for single cells67.68, 69 These new technologies have offered deeper and unprecedented insight into the intricacies of DNA methylation and have challenged some of the widely held views that had seen DNA methylation solely as a means of transcriptional suppression. In differentiated human cells, about 70%-80% of all CpG sites are methylated.38, 70 Cytosine residues in the non-CpG context often have low methylation rates below 5%. However, the percentage increases in embryonic stem (ES) cells and brain tissue.71–73 The importance of cytosine methylation in non-CpG regions is yet to be fully understood,74 but it is believed to also be important for the binding of transcription factors.72, 75, 76 There are limited data on the role of non-CpG methylation in the human heart; hence, the discussion here is focused on CpG methylation. The CpG sites are unevenly spread throughout the genome. When they occur consecutively, spanning up to 1 kb length, this genomic region is called CpG island (CGI). CGI regions occur near transcription start sites (TSSs) of a large number of genes (about 70%); however, more than half of CGIs are also located in intergenic regions.77 Despite the location of CGIs near TSSs, they are often nonmethylated in many cell types, and this may be due to the binding of transcription factors that prevent de novo methylation.38, 41, 42 Strikingly, promoter regions characterized by dense and long CGIs appear to be less sensitive to regulation by DNA methylation than gene promoters that have low or intermediate or CpG density or enhancers that have average CpG density.38, 77–79 The latter group often displays the largest tissue-specific variation. There are, however, some regions that have very high CpG contents that are regulated by DNA methylation in very specific contexts such as in imprinted regions, silencing of transposable elements and X-inactivation.22, 29, 80 In addition to methylation at promoter regions, differential methylation of the gene body (exons and introns) is believed to add an extra layer of transcriptional regulation although the full extent is not yet completely understood. Our current understanding of gene body methylation suggests that it may play a role in the regulation of alternative splicing as well as the inhibition of alternative promoter usage. The finding that exon-intron boundaries are often differentially methylated supports a role for DNA

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methylation in the regulation of alternative splicing.81–83 This mechanism may play a role in the heart, as the DNA-methylation in gene bodies seems to be deposited upon active transcription in an H3K36me3-dependent manner.84 Cotranscriptional splicing may also be influenced by DNA methylation, slowing down the transcriptional machinery, thereby facilitating exon inclusion.82

4.1.5 DNA methylation in the healthy heart Epigenetic signatures are often cell-type-specific, with marks in the heart being distinguishable from those in other organs in the organism.24, 85, 86 Shortly after fertilization, there is a near-complete removal of DNA methylation in both the female and male pronuclei. Afterward, there is a re-establishment of the common general DNA methylation signature of all tissues within the first few days87, 88 so that by implantation, at least 70% of all CpG methylation has been re-introduced.26, 38, 89 While the correct sequence of events is yet to be fully understood,26 it is believed that the majority of DNA methylation map is laid out before organogenesis.88 The remaining small but significant fraction of methylated cytosines both in the heart and in other tissues is established later during organ development and during postnatal growth.90–93 There are clear differences between methylation marks in embryonic stem (ES) cells and differentiated cells. There is a higher than an average number of methylated CpG sites found in ES cells compared to somatic cells, these marks are believed to in part confer pluripotency to the ES cells.38, 71 During embryonic heart development, the DNA methylation marks that are established have been shown to be important for heart-specific regulatory pathways and pathways related to general embryogenesis.90, 91, 93 As cardiac cells mature, there are dynamic changes in DNA methylation, with studies showing between 2500 and 6500 differentially methylated regions between neonatal and adult heart (in mice).91, 93 As cardiomyocytes are terminally differentiated cells with minimal regenerative capacity,94 understanding the changes in DNA methylation in the heart and how this correlates to cell proliferation may present interesting avenues to explore for the field of cardiac regeneration, especially in diseased states. This is particularly interesting as other studies have shown that epigenetic mechanisms play a role in inhibiting proliferation postinjury.95, 96 Cardiomyocyte regeneration as a solution for reversing or preventing the loss of contractility after myocardial injury stands certainly to revolutionize therapeutic options for heart disease. This may be achieved by either stimulating cardiomyocyte proliferation or reprogramming the noncardiac cells in the process of cardiac transdifferentiation.97 As DNA methylation is central to cellular identity, it may therefore be an important factor for the regulation of these processes, such as in iPS cell reprogramming.98 Indeed, the role of DNA methylation in transdifferentiation of cardiomyocytes has been studied to some extent and offers an attractive avenue for further investigation.97, 99 A potential drawback of reprogrammed cells is the fact that some parts of the DNA methylation signatures of the original differentiated cell states are often retained, and this may impair the functional phenotype of the derived cell type. This observation has been made for different cellular transformations100 including cardiac cells.98 Good knowledge of the physiological DNA methylation signature of cardiomyocytes is therefore indispensable for successful cell-based therapy. As terminally differentiated, mature, and functional cardiac cells are needed, future research may routinely have to include the assessment of the DNA methylation profile of the cardiomyocytes as quality control for cells destined to be used in cell replacement therapy.

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4.1.6 DNA methylation in cardiac disease 4.1.6.1 Studies in humans hearts Our current knowledge of DNA methylation in cardiac disease has been formed only by a handful of studies. The first major study on DNA methylation changes in heart disease was published in 201123; thereafter, a number of other studies have also shown methylation changes in response to external stimuli such as mechanical stress91, 101, 102 Although much is yet to be fully understood about the role of DNA methylation in heart disease, these research papers have firmly demonstrated that DNA methylation is important for gene regulation during cardiac disease. Through the early studies of DNA methylation in heart failure,23, 103 the authors hypothesized that hallmark stress-gene responses characterized by the reactivation of fetal genes in cardiac disease (28 would be associated with a corresponding fetal or hypertrophic DNA methylation program. Using microarray technology as well as next-generation sequencing on whole heart tissue, the authors identified a unifying pathological DNA methylation signature. A later study in human dilated cardiomyopathy also detected alterations in DNA methylation in pathways associated with heart disease.104 While these results cannot be compared directly due to different analytical methodologies used and different disease etiologies, both sets of findings were similar. The studies identified differentially regulated genes by their methylation signature, as opposed to the initial study that only looked at differentially expressed genes.103 Although only a very small subset of genomic features is differentially methylated between healthy and diseased hearts, DNA hypomethylation in disease is more common than hypermethylation. In addition, the differentially methylated regions were shown to occur not only on promoters of protein-coding genes but also on gene bodies, as well as putative promoters of noncoding transcripts.23 All three studies sought to identify candidate genes and correlate DNA methylation with transcription. While promoter demethylation is more likely correlated to the upregulation of genes, promoter hypermethylation did not link clearly to reduced expression.23 Further detailed analysis is therefore still needed to elucidate the role of methylation for transcriptomic differences in heart failure. Despite a recent advancement of sample preparation technologies, which have rendered cell-typespecific DNA methylation analysis possible, a few similar studies have followed in subsequent years. Using DNA methylation microarray technology on DNA samples from right and left ventricular tissue, Jo et al. confirmed a unifying DNA methylation signature that differed between health and disease, or at least between left and right ventricles.105 With the same microarray, Pepin et al. investigated DNA methylation using tissue from patients either from ischemic cardiomyopathy (ICM) or nonischemic cardiomyopathy (NICM).106 Here again, cluster analysis revealed common DNA methylation signatures of the disease entities. Moreover, the authors found some members of the KLF family of transcription factors, most prominently KLF15 to be regulated by differential methylation. A cooperative action of DNA methylation and the histone methyltransferase EZH2 on KLF15 and many other epigenetically regulated targets was also reported. In this study, mainly metabolic enzymes were found to be epigenetically regulated. A different approach was taken by Meder et al., who also used the same methylation array on tissue samples from patients with dilated cardiomyopathy, but additionally studied DNA methylation in contemporaneous blood samples of the same patients, and added RNAsequencing data from both heart tissue and blood.107 Combining samples and methods, the authors replicated many of the findings of the aforementioned studies, securing further evidence for the existence of disease-specific methylation signatures. A much smaller set of genes, the expression of which was regulated by DNA methylation, was identified by Glezeva et al., comparing whole tissue DNA

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methylation in heart failure of different etiology.108 Representative profiles of DNA methylomes using blood as a surrogate for disease association studies may gain traction109 but is a topic for discussion elsewhere. While cluster analyses in all these studies may appear unambiguous, some differences between them prevail. In each of them, a DNA methylation pattern specific for heart failure, cardiac hypertrophy, or even different forms of heart failure was identified. Nonetheless, it is noteworthy that the correlation between expression and methylation is low or not present at all in many of the studies. Moreover, whenever differentially methylated sites, candidate genes, or upstream regulators were identified, their number and nature varied greatly among the different studies. Prominent exceptions, however, are the studies of Meder et al., who could reproduce many previous results, and Pepin et al., who observed a robust inverse correlation between DNA methylation and gene expression.106, 107 In general, a much closer relationship between DNA methylation and expression is expected. However, many of the classical cardiac stress-response genes, such as MYH7, NPPB, NPPA, SERCA2A, ACTA1, do not emerge by their DNA methylation signature. There are possible nonmutually exclusive reasons for this observation: i) DNA methylation directly regulates only a subgroup of cardiac-specific genes, ii) DNA methylation regulates expression through modulating noncoding RNA and long-range enhancer elements, or iii) cellular heterogeneity of heart tissue may mask the cell-type-specific role of DNA methylation. Gene regulation is multifactorial; hence, the assumption that DNA methylation only directly regulates a small subset of cardiac genes is plausible. A number of studies have found that only a subset of CpGs is methylated in a cell-type-specific manner, implying that many genes may in fact not be directly regulated by DNA methylation.38, 90 The other reason as mentioned earlier is the fact that distal noncoding regulatory regions such as enhancers may be the ones regulated by CpG methylation in the elements.41 As enhancers often exert their function through 3D chromatin architecture, further studies on the role of DNA methylation at enhancer loci, involving 3D conformation analyses, are therefore needed. Chromosome conformation capture (3C) technologies110, 111 and linkage analysis112 will be required and have been performed to some certain extent.113, 114 In addition, the role of noncoding RNA in cardiac disease is increasingly recognized. So far, several noncoding RNAs with proven relevance for heart disease have been identified. These include Myheart (MHRT)115, 116 and Braveheart in mice (Bvht).117 Given that there is a higher number of long noncoding RNAs (lncRNA) encoded in the human genome than the number of protein-coding genes,118 many more noncoding RNAs important for heart disease will come to be discovered. MicroRNAs are another group of noncoding RNAs that play a role in heart disease and may interact with the DNA methylome or be regulated by DNA methylation. Finally, as most of the studies on DNA methylation have been performed on whole heart tissue, we can easily surmise that the role of DNA methylation in the different cardiac cell types is yet to be clearly explored for both cardiac development and cardiac disease. As cardiomyocytes account for 20%–35% of all cell types in adult heart,91, 119–121 the presence of other cell types such as immune cells, fibroblasts, smooth muscle cells, and endothelial cells will therefore dilute the epigenetic readout if studies do not use purified cell types, even if the DNA methylation signature of these cells in disease is not changed. Indeed, the aforementioned studies have been collectively challenged by a small number of in-depth studies performed on highly purified cardiomyocyte nuclei. Recent advances in sample preparation technology and the identification and sorting of cardiomyocytes or cardiomyocyte nuclei, using markers such as pericentriolar material 1 (PCM1)122 or phospholamban (PLN)123 for

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nuclei, or SIRPA as a cardiomyocyte marker for fluorescent-associated cell sorting (FACS),124 have enabled cell-type-specific analysis. In a comprehensive study, Gilsbach et al. performed DNA methylation analysis by whole-genome bisulfite sequencing and chromatin immunoprecipitation (ChIP) to analyze chromatin occupancy of several modified (activating and repressing) histone marks.123 They complemented their data with RNA-sequencing analysis from the same samples and performed the analysis on purified human cardiomyocyte nuclei from different stages of development and disease: fetal, infant, adult, and failing hearts. This study suggests that many of the previously identified genes and mechanisms might have been false positives, possibly introduced by variable cellular tissue composition between health and disease. Nevertheless, Gilsbach et al. also identified a small set of CpGs to be differentially methylated between health and disease. However, as the authors did not observe a correlation with gene expression, despite in-depth bioinformatics analysis, they conclude that these changes may be more or less a bystander effect and not relevant for disease pathology, in accordance with a previous study in mice from the same group.91 In summary, the study suggests that differential DNA methylation indeed plays an important role in silencing developmental genes and needs to be removed to allow for the expression of cardiac-specific genes during heart development. The reactivation of the fetal gene program, however, at the chromatin level, appears to be mainly driven by histone modifications: H3K27ac and H3K36me3. The same group performed additional analyses on chromatin tertiary structure in a similar setting, albeit in mouse development, using chromosome conformation capture (Hi-C) and whole-genome bisulfite sequencing.114 Here, the authors concluded that DNA methylation is not required for the higher order chromatin organization into topologically associated domains (TADs) and the establishment of active and inactive chromatin compartments. While these studies suggest that if anything, differential DNA methylation in heart failure is likely very subtle, it raises fundamental evolutionary questions: Why should every cardiomyocyte seemingly maintain a complex, energy-consuming machinery that allows for dynamic methylation and demethylation and indeed uses it to some extent—but without any functional role? If this machinery indeed only serves DNA methylation maintenance, does each cardiomyocyte maintain the same precise pattern of DNA methylation, such as throughout the lifetime of an individual, or instead may epimutations be involved in heart failure pathogenesis, a disease of mainly the elderly? While TADs were shown not to be associated with DNA methylation, another type of chromatin domain, called lamina-associated domains (LAD) has been shown to correlate to DNA methylation. LADs are large chromatin domains in contact with the nuclear lamina which have been associated with gene repression. In their study, Cheedipudi et al. performed lamin A (LMNA) ChIP sequencing, reduced representation bisulfite sequencing, and RNA sequencing and observed that LADs were associated with increased CpG methylation and reduced gene expression.113 In summary, to better understand the role of DNA methylation in human heart disease, and the mechanism regulating the methylation machinery, other important questions about chromatin modifications, chromatin architecture, and RNA biology would have to be solved in tandem. An even deeper understanding of the role of individual CpGs—and seemingly also cytosines in other contexts125—will be required to understand why DNA methylation in cardiomyocytes is subtly dynamic. Indeed, how does CpG methylation behave or change throughout the long life span of a nondividing adult cardiomyocyte? For now, studies on human heart tissues and also partially on purified cardiomyocyte nuclei have provided evidence for a specific DNA methylation signature in human heart disease.

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4.1.6.2 DNA methylation in animal models of heart failure In addition to the limited studies using human cardiac tissue to understand the role of DNA methylation on heart failure, there has also been a handful of studies using animal models or in vitro models employed to study this biology. Animal models in which pressure overload-associated cardiac hypertrophy was induced through a transversal aortic constriction (TAC)91, 102, 126 and in vitro hypertrophy models101 have so far been used. The results from in vitro models of hypertrophy are comparable to the animal models of TAC, as the latter is characterized by left ventricular hypertrophy.127 In a study preceding their work on cardiac development and cardiac disease, Gilsbach et al. provided the first evidence in vivo for changes in DNA methylation during cardiac development and disease in mice.91 They employed cardiomyocyte-specific whole-genome bisulfite sequencing (WGBS), to provide detailed analysis of methylation changes. DNA methylation in mouse embryonic stem cells, neonatal, adult, and failing mouse cardiomyocytes was compared; in addition, DNA methylation profiles in cardiomyocyte-specific double knockout of Dnmt3a/3b were computed (using Crerecombinase driven by Mlc2a promoter). These results showed that there was more hypomethylation of CpG regions during development and disease than hypermethylation. The authors found significant associations between DNA methylation at promoter regions and gene regulation, especially during cardiac development. Strikingly, differential DNA methylation was also found at regulatory enhancers harboring binding motifs for cardiac transcription factors such as NKX2.5, MEF2C, and GATA4. The authors also found a close association between DNA methylation and the regulation of genes including Atp2a2, Myh6, Tnni1, Tnnt2, Tnni3, and Myh7 in development. The most prominent differences were found comparing adult heart and embryonic stem cells. Genes related to pluripotency were hypermethylated and switched off in the adult heart, while cardiac genes were hypomethylated and switched on. The authors observed a DNA “demethylation wave” that extended from upstream of the TSS into the first exons correlating with increased gene expression during development. The overall findings indicated that genes that are active in embryonic stem cells but not in cardiomyocytes are increasingly methylated, while genes that are activated in cardiac cells but not in embryonic stem cells are demethylated, with the establishment of corresponding histone marks. Furthermore, they observed that genes that are transiently expressed during heart development (such as Isl1) are not remethylated, and rather a different mechanism such as H3K27me2 deposition is employed for their regulation. Additional experiments were performed by the authors to study the function of DNA methylation in pressure overload-induced heart failure in which a partial reactivation of the fetal DNA methylation signature was observed. This process of reactivation of DNA methylation after the stress is also likely to exist in human hearts, as evidence suggests that DNA methylation is altered in human skeletal muscle already hours after exercise128. Similarly, a different group recently showed that DNA methylation in rat cardiac cells reacts to afterload enhancement already after 7 days in vitro101 and after 4 weeks in vivo.102 In another study which employed the double knockout of the catalytic domains of Dnmt3a and Dnmt3b in a mouse line,129 the authors found that while there were some transcriptomic changes, as well as changes in the methylation level of some promoters, there were no obvious phenotypic differences between DKO and control mice under TAC conditions, implying that de novo methylation may be dispensable in stress-induced gene response in the heart. As such, the data cohere with the same group’s evidence from human cardiomyocytes,123 where their data reflected that active and repressive chromatin structures are established and maintained independently of DNA methylation.114

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In yet another study using a cardiac-specific deletion of Dnmt3b,126 the authors reported that Dnmt3b knockout led to severe systolic insufficiency and myocardial thinning. This was associated with methylation-related aberrant splicing of Myh7. Further analysis of the role of DNA methylation on DNA splicing is warranted to fully understand if the aberrant splicing observed in heart failure correlates to DNA methylation130 and also to unravel to what extent methylation-induced splicing contributes to the pathophysiology of heart failure. To date, different laboratories have established their different models of cardiac-specific deletions of Dnmt3a and Dnmt3b with conflicting results. The discrepancies may be due to a number of reasons. One possible confounder is strain variability. DNA methylation in rodents is known to vary greatly by strain, and studies have shown that strain-dependent DNA methylation variability may be more pronounced than variability due to cell-type differences.112 A study using BALB/cJ and BUB/BnJ mice known to be susceptible and resistant to isoproterenol-induced cardiac dysfunction, respectively, revealed a basal methylation pattern that was strikingly different between both strains, which may predispose either strain to respond differently to external stress131. Some other possible explanations for the conflicting results may include the use of different promoters, the differences in the severity and the duration of the surgical intervention, or the fact that in one study, the catalytic domain of both DNMT isoforms was knocked out, whereas in the other study, the authors performed a complete Dnmt3b knockout. Another study investigating the specific functions of both Dnmt isoforms found that the majority of de novo methylation targets might be shared by both Dnmt3a and Dnmt3b redundantly, with only a subset of differentially methylated cytosines being specifically targeted by either isoform.12 The study therefore implies that deleting a single Dnmt isoform specifically may be of little value as the other isoform may take over the role. These findings make the more pronounced effect of Dnmt3b single knockout tricky to explain. Besides studying global methylation profiles and how they correlate to cardiac development and disease, a number of gene-specific studies have also contributed to our current understanding of DNA methylation and cardiac gene expression. For example, an analysis of the role of DNA methylation on the promoter region of Acta1 was published in the late 1990s.46 In the study, Warnecke and colleagues show that the promoter region of Acta1 is progressively methylated during cardiac development, even though the methylation status did not directly correlate with the expression of the gene— this finding is corroborated by more studies published more recently.91 DNA methylation was also found to play a role in cardiac fibrosis during hypoxia that was associated with an upregulation of Dnmt1 and 3b and a concomitant increase in the expression of collagen-1. Although the exact mechanism is yet to be fully elucidated, the deletion of DNMTs in human fibroblasts resulted in attenuated fibrosis, thus confirming the role of DNA methylation in fibrosis, acting indirectly through the regulation of COL1A1.132 These findings have also been supported by in vivo experiments as nonspecific inhibition of DNMT through treatment by 5-aza-2-deoxycytidine (5-aza) led to reduced fibrosis in spontaneously hypertensive rats.133 Another study, however, yielded conflicting results by showing that downregulation and inhibition of Dnmt1 and 3a were associated with Col1a1 promoter demethylation and increased collagen synthesis134. Further studies are therefore needed to fully understand how DNA methylation regulates fibrosis. Other studies have also shown direct regulation of specific genes through DNA methylation. These include Abcc8 and Abcc9 that are sulfonylurea receptor genes,135 the gene that codes for protein kinase Cε (PKCε),136 and Atp2a2 that codes for the sarcoplasmic reticulum calcium ATPase SERCA2a.101, 137–139

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The promising results and mechanistic insights obtained from these studies open up the possibility of manipulating the methylation signature of specific genes to confer a therapeutic option. Further studies will be needed to explore how to target these genes and how or if they can modulate heart failure.

4.1.7 Targeting DNA methylation for therapy Given that pathological cardiac hypertrophy and failure are characterized by hallmark transcriptomic changes28 and that part of this gene dysregulation is governed by underlying methylation changes, manipulation of DNA methylation is therefore a potential therapeutic strategy. Successful manipulation of the DNA methylome is, however, more challenging than it appears for a number of reasons. Firstly, heart failure is characterized by both hypomethylated regions, as well as hypermethylated regions. The inhibition of either process is potentially impractical. Secondly as the DNA methylation machinery functions in the whole organism, there may be deleterious off-target effects in other tissues. Nevertheless, targeting DNA methylation or demethylation in cardiac cells in vitro or animal models in vivo seeks to show useful proof of principle and to demonstrate the general basis of this biology. Attempts so far have focused on norepinephrine-induced hypertrophy,140 cardiac fibrosis,132, 134 on in vitro and in vivo hypertrophy induced by afterload,101, 102 and on Serca2a expression.139 In one of the studies, treatment with the DNMT inhibitor 5-azacytidine was shown to cause a reversal of norepinephrine-induced hypermethylation, thereby rescuing hypertrophy.140 5-azacytidine treatment also rescued contractility and normalized the expression of a panel of stress genes including Myh7, Nppa, and Nppb. In three other studies, the inhibition of DNMT was shown to be beneficial in reducing the development of fibrosis in vitro.102, 132, 133, 139 The role of DNA methylation in the regulation of Atp2a2 expression, a gene involved in calcium homeostasis and considered very important in heart failure,141 has been studied by different groups. 5aza was employed to prevent promoter methylation of Atp2a2 and was also used to rescue the decrease of Atp2a2 upon treatment with TNF-α in atrial like HL-1 cells.138 In another study, the same group used another DNMT inhibitor, hydralazine to achieve a similar goal.139 These findings have been corroborated independently by two other groups.101, 137 In a model of mechanical-induced hypertrophy using rat engineered heart tissue (EHT), the authors observed genome-wide changes in DNA methylation as early as 7 days and these methylation changes were attenuated by the addition of RG108, which is a nonspecific, non-nucleosidic DNMT inhibitor. Moreover, they replicated their findings in TACinduced hypertrophy in rat in vivo. Here, the RG108-treated group of TAC rats displayed less functional impairment and fibrosis, despite a similar degree of cardiac hypertrophy.102 Therapies aimed at increasing the expression of ATP2A2 or protein activity in heart failure have been explored. These include clinical trials that explored the use of istaroxime, a small molecule that stimulates the activity of SERCA2a142 as well as ATP2A2 gene therapy, a trial which failed at the latephase studies (Celladon press release on Mydicar, 26.04.2015). In light of these studies, blocking the downregulation of ATP2A2 by targeting epigenetic mechanisms may be an attractive alternative strategy. Going forward, we expect to see more DNA methylation-based therapy explored as new technologies and approaches such as siRNA, miRNAs, antibodies, small molecules, and CRISPR-based epigenome editing are developed. DNMT inhibitors that are currently in experimental and clinical use can be classified into two groups: nucleosidic and non-nucleosidic inhibitors, each group has its own advantages and disadvantages. A number of nucleosidic DNMT inhibitors such as azacytidine

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and 5-aza-2-deoxycytidine (5-aza, decitabine) have received both FDA and EMA approval, which makes them attractive as repurposable therapy if they work. However, as nucleosidic compounds need to be integrated into DNA to function,143 their applicability for heart disease is greatly impeded, since cardiac cells are largely nonproliferating.94 For such nondividing cell types, azanucleotides can only be integrated during natural base repair and turnover; hence, their therapeutic utility is limited. Moreover, considering that these molecules may interfere with DNA repair and replication, they could produce cytotoxic side effects since they are more quickly incorporated into rapidly dividing cells.144 Nonnucleosidic inhibitors of DNMTs such as procainamide,145 RG108,146 hydralazine,147 and nanaomycin A148 are inadequately characterized, and their mode of action is not fully understood. Nevertheless, since these molecules may competitively inhibit cofactor binding148 rather than becoming integrated into DNA, they generally display limited cytotoxicity. Although the in vivo pharmacokinetics for RG108 has been published,149 detailed analysis of other effects besides DNMT inhibition is yet to be performed particularly since high micromolar concentrations are needed for DNMT inhibition.148 Despite the ongoing controversy on the exact role of DNA methylation in cardiac disease and the extent to which gene expression may be regulated by dynamic DNA methylation in cardiomyocytes, a new means to probe this or to harness DNA methylation therapeutically, independently of its physiological role, has recently emerged. dCas9-based epigenome editing potentially allows for precise editing of DNA methylation at individual loci in an individual cell type in vivo. It remains to be established if this is feasible therapeutically. Even so, this approach allows for the study of functional effects of DNA methylation restricted to specific and selected individual loci. This approach employs the bacterial immune mechanism clustered regular interspersed short palindromic repeats (CRISPR), based on the nuclease Cas9 (CRISPR-Cas9).150 Originally a mechanism by which bacteria store information about pathogen DNA and use this information to guide a nuclease to invading DNA, leveraging endogenously expressed antisense guide RNA, this system is now extensively used in molecular biology since its discovery. CRISPR-Cas9 has been repurposed for a multitude of applications. Artificially introduced Cas9, together with a guide RNA, cleaves at specific loci in the genome with unprecedented flexibility and to a high degree of precision. Moreover, when individual bases in the cleaving enzyme, typically Cas9, are mutated, the cutting activity can be abolished. The resulting dead-Cas9 (dCas9) enzyme can then be harnessed to deliver other fused proteins, such as transcription factors or epigenetic modifier enzymes, to the DNA site of interest, again using a suitable guide RNA. dCas9 has been used to guide DNMTs and TET enzymes to loci in order to achieve forced DNA methylation or demethylation, respectively.151–153 For epigenetics in heart failure, this approach may be used to deliver a celltype-specific expression manipulation system to cardiomyocytes and to edit DNA methylation at a specific cardiomyocyte gene locus. Cardiomyocyte specificity may be achieved by using an adenoassociated virus (AAV) vector, expressing a dCas9-epignetic modifier fusion construct under the control of a cardiomyocyte-specific promoter together with a set of suitable guide RNAs. While AAV payload represents an obstacle, intein-mediated protein splicing and packaging the system into two AAV entities potentially solves this issue.154

4.1.8 DNA methylation signature as biomarkers for heart failure Although strong evidence for a link between DNA methylation in circulating blood cells and heart failure is lacking, DNA methylation might still be of interest as a biomarker for heart disease. Although plasma concentration of homocysteine, which is the product of the DNA methylation reaction may be

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indicative of disease, it is still heavily debated as a biomarker for cardiac disease.155, 156 The association between the concentration of plasma homocysteine and the DNA methylation in peripheral blood leukocytes (PBL) has been similarly investigated. The authors speculated that low homocysteine reflects low availability of the precursor methionine that may impair physiological DNA methylation.157 However, they observed a correlation between hypermethylation in PBL rather than hypomethylation,158 which argues against a strong predictive role for DNA methylation of circulating blood cells in cardiovascular disease. Other studies have sought to establish correlations between a low degree of DNA methylation at LINE-1 repetitive DNA elements in PBL,159 or elevated mitochondrial DNA methylation160 with ischemic heart disease and stroke. And yet other studies have found associations between DNA methylation in circulating cells and vascular disease.161, 162 As alluded earlier, so far only one study has addressed this issue directly with encouraging results.107 Meder et al. identified several CpGs that may serve as novel biomarkers for heart failure when analyzed in blood cells.

4.1.9 Future outlook DNA methylation in heart failure both for biomarker diagnostics and therapy is an exciting field of research, evolving and sparking hopes of new avenues of management of patients with heart disease in the future. Much progress has been made in recent years and more remains to be studied. A definitive role for DNA methylation in cardiac gene expression regulation, differentiation, renewal, and cardiac disease is now established as these processes each bear unique DNA methylation signatures. Nevertheless, many unanswered questions remain: What are the consequences of DNA methylation changes in cardiac disease pathology? What are the underlying mechanisms responsible for the differential methylation in heart disease? How do we harness this knowledge to develop new therapies? A deep understanding of the DNA methylome in cardiac development and disease is crucial for unlocking the full potential of DNA methylation. At present, while we set about to acquire these fundamental insights using new and advanced tools and technology, the notion of epigenetic therapies for heart failure now presents a real possibility and is indeed exciting.

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78. Bartke T, Vermeulen M, Xhemalce B, Robson SC, Mann M, Kouzarides T. Nucleosome-interacting proteins regulated by DNA and histone methylation. Cell. 2010;143(3):470–484. 79. Brinkman AB, Gu H, Bartels SJ, et al. Sequential ChIP-bisulfite sequencing enables direct genome-scale investigation of chromatin and DNA methylation cross-talk. Genome Res. 2012;22(6):1128–1138. 80. Leung DC, Dong KB, Maksakova IA, et al. Lysine methyltransferase G9a is required for de novo DNA methylation and the establishment, but not the maintenance, of proviral silencing. Proc Natl Acad Sci U S A. 2011;108(14):5718–5723. 81. Chodavarapu RK, Feng S, Bernatavichute YV, et al. Relationship between nucleosome positioning and DNA methylation. Nature. 2010;466(7304):388–392. 82. Maunakea AK, Chepelev I, Cui K, Zhao K. Intragenic DNA methylation modulates alternative splicing by recruiting MeCP2 to promote exon recognition. Cell Res. 2013;23(11):1256–1269. 83. Maunakea AK, Nagarajan RP, Bilenky M, et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature. 2010;466(7303):253–257. 84. Dhayalan A, Rajavelu A, Rathert P, et al. The Dnmt3a PWWP domain reads histone 3 lysine 36 trimethylation and guides DNA methylation. J Biol Chem. 2010;285(34):26114–26120. 85. Kitamura E, Igarashi J, Morohashi A, et al. Analysis of tissue-specific differentially methylated regions (TDMs) in humans. Genomics. 2007;89(3):326–337. 86. Xie L, Weichel B, Ohm JE, Zhang K. An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver. BMC Syst Biol. 2011;5(Suppl 3):S4. 87. Santos F, Hendrich B, Reik W, Dean W. Dynamic reprogramming of DNA methylation in the early mouse embryo. Dev Biol. 2002;241(1):172–182. 88. Zhu P, Guo H, Ren Y, et al. Single-cell DNA methylome sequencing of human preimplantation embryos. Nat Genet. 2018;50(1):12–19. 89. Monk M, Boubelik M, Lehnert S. Temporal and regional changes in DNA methylation in the embryonic, extraembryonic and germ cell lineages during mouse embryo development. Development. 1987;99(3):371–382. 90. Chamberlain AA, Lin M, Lister RL, et al. DNA methylation is developmentally regulated for genes essential for cardiogenesis. J Am Heart Assoc. 2014;3(3):e000976. 91. Gilsbach R, Preissl S, Gruning BA, et al. Dynamic DNA methylation orchestrates cardiomyocyte development, maturation and disease. Nat Commun. 2014;5:5288. 92. Gu Y, Liu GH, Plongthongkum N, et al. Global DNA methylation and transcriptional analyses of human ESCderived cardiomyocytes. Protein Cell. 2013;5. 93. Sim CB, Ziemann M, Kaspi A, et al. Dynamic changes in the cardiac methylome during postnatal development. FASEB J. 2015;29(4):1329–1343. 94. Bergmann O, Bhardwaj RD, Bernard S, et al. Evidence for cardiomyocyte renewal in humans. Science. 2009;324(5923):98–102. 95. Felician G, Collesi C, Lusic M, et al. Epigenetic modification at Notch responsive promoters blunts efficacy of inducing notch pathway reactivation after myocardial infarction. Circ Res. 2014;115(7):636–649. 96. Senyo SE, Steinhauser ML, Pizzimenti CL, et al. Mammalian heart renewal by pre-existing cardiomyocytes. Nature. 2013;493(7432):433–436. 97. Sahara M, Santoro F, Chien KR. Programming and reprogramming a human heart cell. EMBO J. 2015;34 (6):710–738. 98. Sanchez-Freire V, Lee AS, Hu S, et al. Effect of human donor cell source on differentiation and function of cardiac induced pluripotent stem cells. J Am Coll Cardiol. 2014;64(5):436–448. 99. Liu Z, Chen O, Zheng M, et al. Re-patterning of H3K27me3, H3K4me3 and DNA methylation during fibroblast conversion into induced cardiomyocytes. Stem Cell Res. 2016;16(2):507–518. 100. Kim K, Doi A, Wen B, et al. Epigenetic memory in induced pluripotent stem cells. Nature. 2010;467 (7313):285–290.

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101. Stenzig J, Hirt MN, Loser A, et al. DNA methylation in an engineered heart tissue model of cardiac hypertrophy: common signatures and effects of DNA methylation inhibitors. Basic Res Cardiol. 2016;111(1):9. 102. Stenzig J, Schneeberger Y, Loser A, et al. Pharmacological inhibition of DNA methylation attenuates pressure overload-induced cardiac hypertrophy in rats. J Mol Cell Cardiol. 2018;120:53–63. 103. Movassagh M, Choy MK, Goddard M, Bennett MR, Down TA, Foo RS. Differential DNA methylation correlates with differential expression of angiogenic factors in human heart failure. PLoS One. 2010;5(1):e8564. 104. Haas J, Frese KS, Park YJ, et al. Alterations in cardiac DNA methylation in human dilated cardiomyopathy. EMBO Mol Med. 2013;5. 105. Jo B-S, Koh I-U, Bae J-B, et al. Methylome analysis reveals alterations in DNA methylation in the regulatory regions of left ventricle development genes in human dilated cardiomyopathy. Genomics. 2016;108(2): 84–92. 106. Pepin ME, Ha C-M, Crossman DK, et al. Genome-wide DNA methylation encodes cardiac transcriptional reprogramming in human ischemic heart failure. Lab Invest. 2019;99(3):371–386. 107. Meder B, Haas J, Sedaghat-Hamedani F, et al. Epigenome-wide association study identifies cardiac gene patterning and a novel class of biomarkers for heart failure. Circulation. 2017;136(16):1528–1544. 108. Glezeva N, Moran B, Collier P, et al. Targeted DNA methylation profiling of human cardiac tissue reveals novel epigenetic traits and gene deregulation across different heart failure patient subtypes. Circ Heart Fail. 2019;12(3):e005765. 109. Agha G, Mendelson MM, Ward-Caviness CK, et al. Blood leukocyte DNA methylation predicts risk of future myocardial infarction and coronary heart disease. Circulation. 2019;140(8):645–657. 110. Chen T, Ueda Y, Xie S, Li E. A novel Dnmt3a isoform produced from an alternative promoter localizes to euchromatin and its expression correlates with active de novo methylation. J Biol Chem. 2002;277 (41):38746–38754. 111. de Wit E, de Laat W. A decade of 3C technologies: insights into nuclear organization. Genes Dev. 2012;26 (1):11–24. 112. Johnson MD, Mueller M, Adamowicz-Brice M, et al. Genetic analysis of the cardiac methylome at single nucleotide resolution in a model of human cardiovascular disease. PLoS Genet. 2014;10(12):e1004813. 113. Cheedipudi SM, Matkovich SJ, Coarfa C, et al. Genomic reorganization of lamin-associated domains in cardiac myocytes is associated with differential gene expression and DNA methylation in human dilated cardiomyopathy. Circ Res. 2019;124(8):1198–1213. 114. Nothjunge S, Nuhrenberg TG, Gruning BA, et al. DNA methylation signatures follow preformed chromatin compartments in cardiac myocytes. Nat Commun. 2017;8(1):1667. 115. Han P, Li W, Lin CH, et al. A long noncoding RNA protects the heart from pathological hypertrophy. Nature. 2014;514(7520):102–106. 116. Harikrishnan KN, Okabe J, Mathiyalagan P, et al. Sex-based Mhrt methylation chromatinizes MeCP2 in the heart. iScience. 2019;17:288–301. 117. Klattenhoff CA, Scheuermann JC, Surface LE, et al. Braveheart, a long noncoding RNA required for cardiovascular lineage commitment. Cell. 2013;152(3):570–583. 118. Jacob F, Yonis AY, Cuello F, et al. Analysis of tyrosine kinase inhibitor-mediated decline in contractile force in rat engineered heart tissue. PLoS One. 2016;11(2):e0145937. 119. Banerjee I, Fuseler JW, Price RL, Borg TK, Baudino TA. Determination of cell types and numbers during cardiac development in the neonatal and adult rat and mouse. Am J Physiol Heart Circ Physiol. 2007;293(3): H1883–H1891. 120. Nag AC. Study of non-muscle cells of the adult mammalian heart: a fine structural analysis and distribution. Cytobios. 1980;28(109):41–61. 121. Pinto AR, Ilinykh A, Ivey MJ, et al. Revisiting cardiac cellular composition. Circ Res. 2016;118(3):400–409.

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122. Bergmann O, Zdunek S, Alkass K, Druid H, Bernard S, Frisen J. Identification of cardiomyocyte nuclei and assessment of ploidy for the analysis of cell turnover. Exp Cell Res. 2011;317(2):188–194. 123. Gilsbach R, Schwaderer M, Preissl S, et al. Distinct epigenetic programs regulate cardiac myocyte development and disease in the human heart in vivo. Nat Commun. 2018;9(1):391. 124. Dubois NC, Craft AM, Sharma P, et al. SIRPA is a specific cell-surface marker for isolating cardiomyocytes derived from human pluripotent stem cells. Nat Biotechnol. 2011;29(11):1011–1018. 125. Zhang D, Wu B, Wang P, et al. Non-CpG methylation by DNMT3B facilitates REST binding and gene silencing in developing mouse hearts. Nucleic Acids Res. 2017;45(6):3102–3115. 126. Vujic A, Robinson EL, Ito M, et al. Experimental heart failure modelled by the cardiomyocyte-specific loss of an epigenome modifier, DNMT3B. J Mol Cell Cardiol. 2015;82:174–183. 127. Levy D, Larson MG, Vasan RS, Kannel WB, Ho KK. The progression from hypertension to congestive heart failure. JAMA. 1996;275(20):1557–1562. 128. Neuber C, Muller OJ, Hansen FC, et al. Paradoxical effects on force generation after efficient beta1adrenoceptor knockdown in reconstituted heart tissue. J Pharmacol Exp Ther. 2014;349(1):39–46. 129. Nuhrenberg TG, Hammann N, Schnick T, et al. Cardiac Myocyte De Novo DNA Methyltransferases 3a/3b Are Dispensable for Cardiac Function and Remodeling after Chronic Pressure Overload in Mice. PLoS One. 2015;10(6):e0131019. 130. Ames EG, Lawson MJ, Mackey AJ, Holmes JW. Sequencing of mRNA identifies re-expression of fetal splice variants in cardiac hypertrophy. J Mol Cell Cardiol. 2013;62:99–107. 131. Chen H, Orozco LD, Wang J, et al. DNA methylation indicates susceptibility to isoproterenol-induced cardiac pathology and is associated with chromatin states. Circ Res. 2016;118(5):786–797. 132. Watson CJ, Collier P, Tea I, et al. Hypoxia-induced epigenetic modifications are associated with cardiac tissue fibrosis and the development of a myofibroblast-like phenotype. Hum Mol Genet. 2014;23 (8):2176–2188. 133. Watson CJ, Horgan S, Neary R, et al. Epigenetic therapy for the treatment of hypertension-induced cardiac hypertrophy and fibrosis. J Cardiovasc Pharmacol Ther. 2016;21(1):127–137. 134. Pan X, Chen Z, Huang R, Yao Y, Ma G. Transforming growth factor beta1 induces the expression of collagen type I by DNA methylation in cardiac fibroblasts. PLoS One. 2013;8(4):e60335. 135. Fatima N, Schooley Jr JF, Claycomb WC, Flagg TP. Promoter DNA methylation regulates murine SUR1 (Abcc8) and SUR2 (Abcc9) expression in HL-1 cardiomyocytes. PLoS One. 2012;7(7):e41533. 136. Patterson AJ, Xiao D, Xiong F, Dixon B, Zhang L. Hypoxia-derived oxidative stress mediates epigenetic repression of PKCepsilon gene in foetal rat hearts. Cardiovasc Res. 2012;93(2):302–310. 137. Angrisano T, Schiattarella GG, Keller S, et al. Epigenetic switch at atp2a2 and myh7 gene promoters in pressure overload-induced heart failure. PLoS One. 2014;9(9):e106024. 138. Kao YH, Chen YC, Cheng CC, Lee TI, Chen YJ, Chen SA. Tumor necrosis factor-alpha decreases sarcoplasmic reticulum Ca2 +-ATPase expressions via the promoter methylation in cardiomyocytes. Crit Care Med. 2010;38(1):217–222. 139. Kao YH, Cheng CC, Chen YC, et al. Hydralazine-induced promoter demethylation enhances sarcoplasmic reticulum Ca2 +-ATPase and calcium homeostasis in cardiac myocytes. Lab Investig. 2011;91(9): 1291–1297. 140. Xiao D, Dasgupta C, Chen M, et al. Inhibition of DNA methylation reverses norepinephrine-induced cardiac hypertrophy in rats. Cardiovasc Res. 2013;101. 141. Kranias EG, Hajjar RJ. Modulation of cardiac contractility by the phospholamban/SERCA2a regulatome. Circ Res. 2012;110(12):1646–1660. 142. Shah SJ, Blair JE, Filippatos GS, et al. Effects of istaroxime on diastolic stiffness in acute heart failure syndromes: results from the hemodynamic, echocardiographic, and neurohormonal effects of istaroxime, a novel

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intravenous inotropic and lusitropic agent: a randomized controlled trial in patients hospitalized with heart failure (HORIZON-HF) trial. Am Heart J. 2009;157(6):1035–1041. Stresemann C, Lyko F. Modes of action of the DNA methyltransferase inhibitors azacytidine and decitabine. Int J Cancer. 2008;123(1):8–13. Jakovcevski M, Akbarian S. Epigenetic mechanisms in neurological disease. Nat Med. 2012;18(8): 1194–1204. Lee BH, Yegnasubramanian S, Lin X, Nelson WG. Procainamide is a specific inhibitor of DNA methyltransferase 1. J Biol Chem. 2005;280(49):40749–40756. Brueckner B, Garcia Boy R, Siedlecki P, et al. Epigenetic reactivation of tumor suppressor genes by a novel small-molecule inhibitor of human DNA methyltransferases. Cancer Res. 2005;65(14):6305–6311. Greco CM, Kunderfranco P, Rubino M, et al. DNA hydroxymethylation controls cardiomyocyte gene expression in development and hypertrophy. Nat Commun. 2016;7:12418. Kuck D, Singh N, Lyko F, Medina-Franco JL. Novel and selective DNA methyltransferase inhibitors: docking-based virtual screening and experimental evaluation. Bioorg Med Chem. 2010;18(2):822–829. Schneeberger Y, Stenzig J, Hubner F, Schaefer A, Reichenspurner H, Eschenhagen T. Pharmacokinetics of the experimental non-nucleosidic DNA methyl transferase inhibitor N-phthalyl-l-tryptophan (RG 108) in rats. Basic Clin Pharmacol Toxicol. 2016;118(5):327–332. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science (New York, NY). 2012;337(6096):816–821. Liu XS, Wu H, Ji X, et al. Editing DNA methylation in the mammalian genome. Cell. 2016;167(1):233–247. e217. Pulecio J, Verma N, Mejia-Ramirez E, Huangfu D, Raya A. CRISPR/Cas9-based engineering of the epigenome. Cell Stem Cell. 2017;21(4):431–447. Vojta A, Dobrinic P, Tadic V, et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 2016;44(12):5615–5628. Villiger L, Grisch-Chan HM, Lindsay H, et al. Treatment of a metabolic liver disease by in vivo genome base editing in adult mice. Nat Med. 2018;24(10):1519–1525. Dzau VJ. Markers of malign across the cardiovascular continuum: interpretation and application. Circulation. 2004;109(25 Suppl 1):IV1–2. Marti-Carvajal AJ, Sola I, Lathyris D. Homocysteine-lowering interventions for preventing cardiovascular events. Cochrane Database Syst Rev. 2015;1:CD006612. Kim M, Long TI, Arakawa K, Wang R, Yu MC, Laird PW. DNA methylation as a biomarker for cardiovascular disease risk. PLoS One. 2010;5(3):e9692. Castro R, Rivera I, Struys EA, et al. Increased homocysteine and S-adenosylhomocysteine concentrations and DNA hypomethylation in vascular disease. Clin Chem. 2003;49(8):1292–1296. Baccarelli A, Wright R, Bollati V, et al. Ischemic heart disease and stroke in relation to blood DNA methylation. Epidemiology. 2010;21(6):819–828. Byun HM, Benachour N, Zalko D, et al. Epigenetic effects of low perinatal doses of flame retardant BDE-47 on mitochondrial and nuclear genes in rat offspring. Toxicology. 2015;328:152–159. Borghini A, Cervelli T, Galli A, Andreassi MG. DNA modifications in atherosclerosis: from the past to the future. Atherosclerosis. 2013;230(2):202–209. Devalla HD, Schwach V, Ford JW, et al. Atrial-like cardiomyocytes from human pluripotent stem cells are a robust preclinical model for assessing atrial-selective pharmacology. EMBO Mol Med. 2015;7(4):394–410.

CHAPTER

Histone modifications in cardiovascular disease initiation and progression

5

Emma Louise Robinson School of Medicine, Division of Cardiology, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, United States

5.1 Introduction Histone modifications are one of the key dynamic epigenetic modifications underlying gene regulation and chromatin structure in eukaryotic nuclei. Chemical changes to histone proteins alter nucleosome structure and function, working in symphony together to affect local as well as global interactions and gene expressions.1 Aberrant and disease-causing gene expression changes underlie CVD remodeling. In this chapter, we will cover the role of different histone tail modifications in experimental and translational cardiovascular research, divided up according to the family of histone modifiers.

5.1.1 DNA and chromatin structure The dynamic usage of the genome is defined by epigenetic modifications. The modifications on histone tails are one of these key mechanisms. At the core of chromatin is the bead-on-a-string analogous 146 bp of DNA wrapped around each nucleosome through 1.67 left-handed superhelical turns.2 The nucleosome is an octamer composed of two copies each of histone 2A (H2A), histone 2B (H2B), histone 3 (H3), and histone 4 (H4). Between each nucleosome “ball” is up to 90 bp of DNA bound to histone 1 (H1/H5), also known as the “linker” histone, in that it links nucleosomes. The nucleosome core spans a diameter of 2 nm. Further levels of compaction include folding of the nucleosomes into 10-nm fiber, and an additional layer of folding into a 30-nm chromatin secondary structure. These 30-nm chromatin fibers loop, twist, and fold further, including making long-range interactions between different locations in the genome that are not in cis, to form complex tertiary structures which may appear more globular (Fig. 5.1). This complex compaction scheme is not only how nearly 2 m’ worth of double-stranded DNA is reduced to being located within a cell nucleus 6 nm in diameter with a compaction factor of about 10 million times, but serves a critical regulatory function. Dynamic global and local chromatin structures regulate gene expression (transcription). Histones are highly conserved globular cationic (positively charged) proteins of 11-16 kDa. This gives them a high affinity to the negatively charged DNA (PO4-) as well as amenable to reactivity Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00021-3 Copyright # 2021 Elsevier Inc. All rights reserved.

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FIG. 5.1 Basic units of DNA and chromatin structure and organization.

and modification with other chemical groups. It was originally thought, rather simply, that covalent addition of a chemical group, such as an acetyl group (acetylation) or methyl group (methylation), changed the affinity of the histones for DNA, thereby mediating the attraction between the histones and DNA and the accessibility of enhancers, promoters, and gene bodies to transcription factors and RNA polymerase II. We now know that the mechanism for gene regulation is rather more complex than simple chargebased interaction between DNA and histones. A primary mechanism by which histone tail modifications exert their effect on chromatin structure and gene expression is through binding proteins that recognize the modifications. The specificity of binding proteins varies from broad-spectrum to even context-specific. In this way, the histone modifications are “detected,” sometimes beginning a cascade of binding events to form a multicomponent complex. Transcription factors, enhancers, or inhibitors of transcription and other epigenetic modifiers to regulate transcription in a multilayer, complementary, or antagonistic manner.

5.1.2 Histone variants There are primary categories of histone (H1/H5, H2A, H2B, H3, and H4). A number of distinct nonallelic variants of each histone that exists are dispersed throughout the genome, which add a further layer of diversity, chromatin structure, and gene expression regulation.3 Histone variants can differ from a few amino acids to large domains, giving them differential properties in structure and stability,

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sites and affinity for PTMs, and interaction partner binding. A number of carefully curated databases of histone variant homology analysis and their implication in chromatin structure and function.3–5 Inconsistencies exist in the nomenclature of histone variants leading to cross-talk and poor comprehension in the field, which are trying to be unified and standardized.6 There is emerging evidence for a role of histone variant composition in transcriptional regulation, DNA damage repair, chromatin stability, cell cycle regulation apoptosis as well as key developmental processes.7 The mutations or genetic inhibition of certain histone variants are associated with severe developmental defects or cancers in some cases.7–9

5.1.2.1 Histone variants in the heart There is growing evidence for remodeling of histone variant composition and regulation in the heart in aging and disease. Histone H2AX is a variant found in most eukaryotes. H2AX plays a role in maintaining genome stability and protecting against DNA damage and is elevated in abundance as well as phosphorylation status in oxidative and ionizing stress. H2AX is phosphorylated at serine (Ser/S) 139 in response to double-stranded DNA breaks caused by ionizing radiation or DNA-damaging chemotherapeutic agents.10 Both γ-H2AX and phosphoH2AX-S139 are markers of oxidative stress and damage. Levels of H2AX (measured as γ-H2AX positive foci by immunofluorescence) in blood lymphocytes were elevated in heart failure patients 3 months following implantation of a left ventricular assist device (LVAD) and correlated positively with reactive oxygen species (ROS), oxidized low-density lipoprotein levels, and superoxide dismutase (SOD), other proteins involved in DNA damage repair.11 In accordance with its role in oxidative stress, H2A.z is upregulated during cardiac hypertrophy (transverse aortic constriction, TAC) in the neonatal and adult murine hearts. Its knockdown in vivo attenuated the hypertrophic response.12 Single-cell RNA-sequencing identified H2A.Z was enriched in vascular smooth muscle cells (VSMCs) in vascular tissue at key VSMC-specific genes, which was diminished in dedifferentiating VSMCs in diseased vascular tissue. Mechanistically, the presence of H2A.Z promoted the recruitment of Smad3 and Med1 at the VSMC-specific loci, enhancing gene expression, thereby implicating H2A.Z in VSMC differentiation and homeostasis.13 The accumulation of H3.3 occurs in postmitotic cells, such as cardiomyocytes of the heart, through the aging process, with implications for chromatin structure, H3 methylation abundance, and location. This has been demonstrated in the aging mouse heart, with H3.3 levels increased relative to H3.1/2 in 24-month-old C57BL/6 J mice compared with 3-month-old mice accompanied by the changes in H3 modifications including a decrease in H3R17me2 and elevated H3K36me2 and H3K4me1.14 Linker H1 has at least 10 known variants. The profile of H1 variants alters in the adult mouse heart in cardiac hypertrophy and heart failure. H1 variants H1.2 and H1.5 were decreased in abundance in hypertrophied hearts, with H1.0 increased in the transition from hypertrophy into dilation and failure. The knockdown of individual H1 variants was performed in neonatal rat ventricular myocytes (NRVMs), which affected their response to pre-hypertrophic stimuli. H1.3 and H1.4 knockdown exacerbated the hypertrophic gene response (myosin heavy chain (MHC)-α, MHC-β, atrial natriuretic factor (ANF)) as well as increased the cell surface area (hypertrophy measure).15 Conversely, the loss of H1.5 in NRVMs massively decreased ANF expression by 6.2-fold compared with controls, accompanied by decreased global H3K9me3 levels, a repressive heterochromatin mark. These results suggest a role for H1.3 and H1.4 in maintaining cardiomyocyte homeostasis and preventing pathological hypertrophy-associated gene expression in response to stress.15

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The role of other histone variants in cardiovascular homeostasis, disease initiation and progression, and therapeutic targeting remains to be elucidated.

5.1.2.2 Histone turnover in the heart Changes in the relative composition of histone variants can be altered in a replication-dependent or replication-independent manner. In postmitotic cardiomyocytes in the adult heart, significant remodeling of histone variants is unlikely to be through a replication-associated manner. Turnover of H2B was examined in cardiomyocytes through a genetically encoded Dox-responsive GFP-labeled H2B. Surprisingly, in noncycling cardiomyocytes, H2B lifetime was only 2 weeks, in contrast to the turnover rate of cardiomyocytes of 30 days after the index Corus CAD test.11 The gene-expression classifier at baseline tended to relate to this long-term outcome (OR 2.6, P ¼ 0.082). This result to risk-stratify patients for long-term outcome is promising but requires further research. Indeed, besides the patient’s inherent risk of cardiovascular events (that is what we try to probe using RNA-based risk stratification), many factors can result in MACE postintervention (e.g., adherence to antiplatelet therapy and optimal stent implantation). Only one study investigated genes related to the progression of CAD, the holy grail in cardiology.66 They identified a number of genes, but these remained unvalidated. One study reported the expression of a new lncRNA, “CoroMarker” in patients with stable CAD, in both monocytes and plasma.70 This transcript was validated in one medium-sized cohort of 382 patients, and to what extent it can be used in clinical practice remains unknown. The approach, however, was very interesting, since the transcript can also be measured in plasma, which facilitates its translation into clinical practice.

12.6.3 RNAs in heart transplantation Peripheral blood RNA profiling has been extensively studied for the detection of acute rejection in patients who underwent heart transplantation. In the first months after transplantation, patients need to undergo repeated biopsying of the graft (the transplanted heart) to detect acute cellular rejection (ACR) or antibody-mediated rejection (AMR). This encompasses patient discomfort, repeated procedures, and risk of complications. Since peripheral blood consists largely of leukocytes, it is an interesting source to determine the immunological state of a patient. To determine the presence of ACR and avoiding repeated allograft biopsies, the AlloMap® test (CareDx, Brisbane, CA, USA) has been developed and was clinically validated the past years in several international studies. Candidate RNAs from PBMCs were selected based on a pilot study with RNA microarray in patients with and without ACR. In addition, plausible candidate RNAs were selected from the literature. Using linear discriminant analysis, a gene-expression classifier was developed (CARGO study).80 The classifier consists of 11 genes related to T-lymphocyte functioning and steroid responsiveness, 6 housekeeping genes of which the expression did not differ in patients with vs with acute rejection. Three transcripts are included as quality control. Although many transcripts are related to inflammation, bacterial or viral infection does not affect the ability of the classifier to discriminate patients with and without ACR. Only high doses of corticosteroids may impact the AlloMap® score. However, the patients in the clinical trials were enrolled starting 2 months posttransplant. The doses of corticosteroids are typically tapered very fast, down to levels that do not after the classifier anymore when patients are eligible for AlloMap® testing. The interpretation of the test differs from the time frame posttransplantation (2–6 months, >6 months). Generally, the AlloMap® test has a NPV of 98%–100% and PPV of 2%–4%, depending on the selected cutoff. The high NPV but low PPV is attributed to the low incidence of ACR in the patient cohorts. The performance of the test was externally validated in an independent study (CARGO 2).9 As one of the only RNA tests in cardiovascular disease, the clinical effectiveness was demonstrated

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in a clinical study (IMAGE trial).81 Patients (n ¼ 602) at least 6 months posttransplantation were randomized to the standard of care with routine biopsying or the gene-expression group with biopsy in case of an increased risk of rejection based on the RNA test. The RNA group had significantly lower rates of endomyocardial biopsies per year (0.5 vs 3), while they had a similar clinical outcome at 2 years. Costsaving calculations have also been presented. The results of these clinical studies resulted in the implementation of this RNA-based approach in several institutions.82 The RNA approach results in less patient discomfort caused by an endomyocardial biopsy, lower overall costs, and theoretically less risk of sampling error. However, some issues still hinder the implementation in other centers. The AlloMap® test needs to be repeated multiple times during the first year after transplantation, resulting in a significant cost. This may especially be problematic in case the test is not covered by the medical insurance. Second, AMR is not detected by AlloMap®. To tackle the latter issue, a cfDNA assay has been developed to detect donor-specific allograft cfDNA (AlloSure-Heart®). The test is intended to detect both AMR and ACR. Based on a panel of 266 SNPs, cfDNA released by the allograft can be detected without the need to genotype the donor or recipient. First studies show a PPV of ACR of 4.8% and NPV of 98.6%. For AMR, the PPV was 4.2% and NP 98.6%.83 The findings were validated in a second cohort. The clinical utility and possibilities are currently being assessed in a registry (Surveillance HeartCare® Outcomes Registry, SHORE, NCT03695601). The added value of donor-specific cfDNA compared to other cardiac biomarkers, e.g., troponin T, remains unknown.

12.7 Epigenetic biomarkers in cardiovascular disease Current data on DNA methylation marks as biomarkers for prognosis and diagnosis of CVD have mostly emerged through epigenome-wide association studies performed on large cohorts.32, 84 Several studies with a gene-target approach have found an association between DNA methylation and CVD.85, 86 Only, a minority of these studies replicated their findings in independent validation cohorts, and to what extent these findings may improve clinical care remains unknown. Therefore, they are not yet suitable to be used as epigenetic biomarker in clinical practice. In the following paragraphs, we will briefly discuss the clinical potential of epigenome biomarkers. First, we will focus on cardiovascular risk factors, and then, we will cover the prediction of acute cardiovascular events.

12.7.1 DNA methylation and cardiovascular risk factors Many known environmental factors that lead to CVD are mediated by epigenetic changes, including DNA methylation and noncoding RNA regulation of chromatin remodeling. DNA methylation variability has been identified as a marker of obesity, hypertension, low-grade inflammation, serum lipids, type 2 diabetes mellitus, smoking, aging, diet, and physical activity.87–98 Elevated cfDNA levels have been associated with several cardiometabolic risk factors including systemic inflammation, elevated lipids, and higher systolic blood pressure and pulse pressure in both women and men.99 A great contribution to the search for novel CVD epigenetic biomarkers is represented by the identification of methylation changes occurring on genes that regulate blood platelet function and drug response. PEAR1, a tyrosine kinase receptor involved in platelet function modulation and whose

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expression depends upon changes in its promoter methylation level.100 The same locus was found in the Moli family and FLEMENGHO cohorts as linked to inflammation-dependent platelet function variability.101 Glycoprotein VI (GPVI) is another important platelet function receptor with increased expression in patients with coronary heart disease (CHD). GPVI promoter hypomethylation was recently found in CHD patients’ leukocyte DNA, suggesting a possible role for this locus as CHD epigenetic biomarker.102 Another recent study performed on a group of acute MI patients demonstrated that hypermethylation at the glucokinase (GCK) gene is linked to clopidogrel resistance risk.103

12.7.2 DNA methylation and cfDNA in myocardial infarction and heart failure Significant changes in DNA methylation have been observed after acute MI, using pre- and postinfarct whole-blood DNA samples in different patient cohorts.104 The observed changes were consistent and independent from medication use. The identified methylation changes were observed in genes related to cholesterol metabolism and atherosclerosis. Similar results were obtained in a different study comparing patients with MI and healthy controls.105 A recent large-scale study then assessed to what extent methylation changes at baseline can predict future ischemic cardiovascular events in nine independent cohorts.106 Methylation levels at 52 CpG sites were associated with future ischemic events, with high consistency across different cohorts although some heterogeneity was observed, including ethnicityspecific changes. The observed methylation changes were involved in genes related to calcium signaling, atherosclerosis, and kidney function and mapped to regions proximal to lncRNAs. From a clinical point of view, a 5% increase in methylation of one of the identified CpGs related to a 50% increase or decrease of cardiovascular risk, independent of age, sex, and other risk factors. Other studies confirmed that DNA methylation improves the prediction of MI using reclassification calculation on top of conventional cardiovascular risk factors.107 However, to what extent this approach may improve clinical care and outcome of patients with ischemic heart disease or HF remains unknown. Besides DNA methylation in peripheral blood, other approaches have been assessed using mtDNA in platelets as a prediction marker of future CVD events in adults with overweight or obesity.108, 109 Similar observations have been made for heart failure (HF). Genome-wide DNA methylation analysis in blood leukocytes highlighted a differential gene methylation pattern in HF patients compared to normal controls.110, 111 Finally, cfDNA is a promising approach for the discovery of novel epigenetic biomarkers. It was already known that total cfDNA is increased in acute MI and heart failure.112 Very recently, cfDNA epigenetic research has focused on the identification and localization of tissue damage using the cellspecific cfDNA methylation pattern.113 A cardiomyocyte-specific methylation pattern was identified, and this cardiomyocyte-specific cfDNA was higher in patients with acute MI.114 Altogether these findings suggest a possible use of these markers in clinical practice to evaluate the severity of cardiomyocyte injury after MI. To what extent this contributes to improve post-MI outcomes remains unknown.

12.7.3 Common CVD events and risk factor epigenetic biomarkers Some of the genes that have been identified as methylation biomarkers for CVD events or risk prediction are also linked to CVD risk factors. Hypermethylation of the GNAS antisense RNA 1 (NESPAS) was found to be associated with a 3-year risk of MI in elderly women.115 The same region was also found hypermethylated as a consequence of prenatal malnutrition, a condition that predisposes to a

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higher risk of CVD later in life.116, 117 NESPAS is part of the GNAS cluster, a genomic region important for imprinting regulation in humans. Surprisingly, GNAS methylation changes (including NESPAS) have also been described in patients with altered blood platelet function, a known player and risk factor for CVD onset.118–121 More recently, Guarrera and colleagues have shown a link between hypomethylation at the zinc finger and BTB domain-containing protein 12 (ZBTB12) gene and MI risk. The same locus was then also found to be associated, in a different and independent cohort, with shorter TNF-ɑ stimulated whole-blood coagulation time and increased WBC and granulocyte counts, providing a possible functional explanation of this association.122 BRCA1 and CRISP2 differential methylation was identified in patients with atherosclerosis and further validated in an independent cohort with association with subclinical atherosclerosis measures (coronary calcium score and carotid intima-media thickness).123

12.7.4 Drug response prediction for personalized medicine DNA methylation marks of response prediction to a specific treatment in the context of CVD are still very rare. Two studies by Gallego-Fabrega and colleagues have reported that DNA methylation patterns of PPM1A and TRAF3 are, respectively, associated with vascular recurrence in aspirin-treated patients and clopidogrel response and recurrence of ischemic events in patients with stroke.124, 125 These examples suggest that specific DNA methylation patterns can be helpful to predict response to a specific treatment. Additional studies should address to what extent these findings can improve treatment options and patient outcomes.

12.8 Limitations and future perspectives Despite many discovery and validation studies, only a limited number of candidate RNAs and DNAs progressed from the discovery or replication phase to the development of an in vitro diagnostics assay (IVD) with the potential for clinical implementation. For example, there is no miR- or lncRNA-based test or epigenetic test available for use in clinical practice to our knowledge. Inconsistent results are slowing down the implementation of RNA- and DNA-based markers in clinical practice (Fig. 12.1). These inconsistencies are partly caused by the heterogeneous population, in which the discovery of the biomarker has been performed. This emphasizes the need for detailed patient phenotyping, especially in the discovery cohort (e.g., intracoronary imaging, cardiac magnetic resonance imaging). Second, low methodological quality in a technically challenging field is also leading to irreproducible results. Third, since many studies lack external validation, there is a risk of overfitting when creating a model to predict an outcome based on only one study cohort.68 Finally, as with any biomarker, a genome-based test will only be broadly implemented if it has shown an improvement in clinical care or encompasses a cost-reduction compared to the currently available clinical models. Nevertheless, with the rapid improvement in the technological platforms, these approaches will become more interesting. Existing successful RNA-based tests can serve as an example of the feasibility of these novel biomarkers. Furthermore, biomarker-based personalized treatment may become costeffective and can introduce specific targeted therapies into the cardiovascular field.

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CHAPTER

Epigenetics and physical exercise

13

Eduardo Iglesias-Guti
erreza,b, Lucı´a Pinillac,d, Ferran Barb
ec,d, and David de Gonzalo-Calvoc,d Department of Functional Biology, Physiology, University of Oviedo, Spaina Health Research Institute of the Principality of Asturias (ISPA), Oviedo, Spainb Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRB Lleida, Lleida, Spainc CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spaind

13.1 Introduction The term cardiovascular diseases (CVDs) encompasses a group of pathologies of the cardiovascular system, heart and blood vessels: coronary heart disease, acute coronary syndrome, and angina pectoris; cerebrovascular disease; cardiomyopathy and heart failure; rheumatic heart disease; congenital cardiovascular defects; sudden cardiac arrest, ventricular arrhythmias, and inherited channelopathies; valvular disease; disorders of heart rhythm; peripheral arterial disease; deep-vein thrombosis, chronic venous insufficiency; pulmonary embolism; among others.1 According to the 2018 World Health Organization Report on Noncommunicable Diseases,2 CVDs were the major cause of death worldwide, accounting for 17.9 million deaths, which represents 31% of the total number of deaths, with 85% of them due to acute coronary syndrome and cerebrovascular disease. The high prevalence of CVDs is associated with highly distributed preventable behavioral risk factors, such as tobacco use, alcohol abuse, an unhealthy diet, and a sedentary lifestyle. These behaviors result in a number of metabolic and physiological changes: hypertension, hyperlipidemia, overweight/obesity, and/or elevated blood glucose/insulin resistance. These major cardiovascular risk factors and lifestyle behaviors have been used for individual risk stratification. Independent national and international scientific institutions and organizations have published different recommendations for the prevention of CVDs and for the control of the main risk factors.3, 4 CVD risk is also influenced by other markers, modifiable or not, such as psychosocial risk factors, family history of premature CVD, and circulating plasma and urinary biomarkers. Regarding psychosocial risk factors, low socioeconomic status, work-related stress, depression, and anxiety also contribute to an augmented risk of developing CVDs. Finally, a number of promising biomarkers have recently emerged; however, in some cases, their predictive value needs to be confirmed. Furthermore, most of the biomarkers add little to the existing prediction algorithms, particularly for patients who are clearly at high or low risk considering the traditional risk factors.4 Among this group, high-sensitivity C-reactive protein (hsCRP),5 homocysteine,6 lipoprotein-associated phospholipase A2 (Lp-PLA2),7 and apolipoproteins B and A–I8 have received much attention. Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00007-9 Copyright # 2021 Elsevier Inc. All rights reserved.

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There is a close relationship between regular exercise and important cardiovascular health benefits, not only through an improvement in cardiac and vasculature function and structure,9 an increased number of microcirculatory vessels, and an enhanced coronary collateral flow,10, 11 but also through mitigation of the main risk factors for CVDs,12 irrespective of age and sex. In this sense, an exerciseinduced reduction in blood pressure in patients with mild or moderate hypertension has been observed, together with an important reduction in triglycerides and significant increases in HDL cholesterol. More modest, although still relevant, reductions in total cholesterol and LDL cholesterol have also been described. Furthermore, physical exercise significantly contributes to weight management and blood glucose control. These effects have been widely described for aerobic exercise, while the effect of resistance exercise, although positive, is less clear.3 More recently, it has been proposed that regular exercise can also promote cardiovascular health through additional mechanisms apart from its effect on traditional CVD risk factors, such as an increase in vagal tone to the heart, stimulation of myocardial repair, attenuation of sarcopenia, an anti-inflammatory effect of muscle-derived myokines, and an emergent role of the gut microbiota.13 Lifestyle interventions have been described to simultaneously modify several of the risk factors for CVDs, which are highly prevalent in Western societies. Therefore, the promotion of regular exercise should be a key part of public health policies.14

13.2 Cardiovascular adaptations to physical activity As mentioned earlier, important cardiovascular health benefits have been proposed for aerobic exercise training. A number of structural and functional cardiovascular adjustments have been described for aerobic training, occurring mainly at three levels: cardiac, hemodynamic, and vascular adaptations.15 At the cardiac level, the most significant response to long-term aerobic training is an improvement in maximal cardiac output, which allows for a more effective distribution of blood flow. Although the basal heart rate decreases with training, the observed increase in cardiac output is a consequence of a higher systolic volume. This functional response is the result of an increase in heart size, particularly left ventricle wall thickness, and in end-diastolic volume.16 The increase in maximal cardiac output is related to the increase in blood volume, plasma, and erythrocyte mass, which occurs as an adaptation to training.10 Initially, this higher blood volume is a consequence of plasma volume expansion, while erythrocyte mass increase is delayed.17 As a result, arterial diameter increases and arterial wall thickness diminishes, allowing an increase in maximal blood flow.18 Additionally, they also result in an angiogenic stimulus mediated by shear stress, nitric oxide (NO) stimulation, or the release of proangiogenic factors, such as vascular endothelial growth factor (VEGF).15 In contrast, a sedentary lifestyle is one of the major risk factors for CVDs, and it is the fourth main risk factor for mortality worldwide.19 While data regarding sedentary behaviors in many countries are alarming,20 the participation of amateur athletes in highly demanding sporting events, such as marathon runs, is on the rise.21 This increase has been driven, in part, by an augmented public awareness of the benefits of regular exercise.22 Unfortunately, the lower and upper limits of exercise dose, in terms of frequency, intensity, and duration, are not well established.4 Indeed, while numerous studies suggest that prolonged, strenuous exercise, such as running a marathon, has no negative health consequences23 and could even have a beneficial effect,24, 25 several studies call into question the short- and long-term health effects of long-distance running.21, 26 In this sense, the studies that have analyzed how the type,

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duration, and intensity of exercise affect different indicators of heart damage and overload,27, 28 which may help in exploring whether an upper limit for healthy exercise exists, are of particular relevance. It has been shown that strenuous exercise acutely increases the plasma concentration of a number of pathological cardiac biomarkers, even above the range considered to be normal in clinical practice.29, 30 High-sensitivity cardiac troponin T (hs-cTnT), which is useful in the diagnosis and stratification of acute coronary syndrome, and N-terminal probrain natriuretic peptide (NT-proBNP), related to cardiac overload or hemodynamic stress and that is used in the clinical context of congestive heart failure, have been described to increase in healthy individuals after running a marathon.27, 28 In recent years, there has been a growing body of evidence that has proposed that this increase in the plasma concentration of biomarkers of cardiac damage or dysfunction has little clinical relevance.31 The release of these biomarkers would be associated, at least in part, with cardiac adaptations to exercise per se, even in response to extreme exercise such as running a marathon.32 The rapid return to basal levels, together with the absence of symptoms or signs of cardiac disorder during the 72 h following the races, indicates the physiological and transitory nature of the increases in these cardiac biomarkers. However, this situation not only raises doubts about the benefits of prolonged and intense acute exercise for heart health, but also creates a need to clarify the appropriate management of healthy subjects with elevated cardiac biomarkers in relation to exercise.33 Therefore, the investigation of the molecular mechanisms that coordinate the adaptive cardiovascular response to exercise is essential,34 and the information provided by new physiological biomarkers and mediators with the potential capacity to titrate or stratify the cardiac response, such as noncoding RNAs (ncRNAs), could therefore be crucial. Here, we first review the current understanding of the epigenetic mechanisms implicated in exercise-induced cardiovascular adaptations, with a special focus on ncRNAs. Then, we highlight recent advances in the use of ncRNAs as biological makers of exercise-induced responses. Finally, we discuss the main limitations in the field and propose considerations for future studies.

13.3 The noncoding transcriptome and exercise The adaptive response to exercise is largely determined by the regulation of gene expression. However, the exact molecular mechanisms that control gene expression in response to exercise are not completely understood.34 Epigenetic regulation, which includes DNA methylation, histone modification, and ncRNAs, may play a prominent role. In this context, and considering the systemic nature of exercise, the evaluation of potential mediators in intercellular communication, such as ncRNAs, is fundamental. The transcriptome represents all genes expressed in a cell in a given biological state. Although approximately 80% of the genome is transcribed, only 1%–2% of the human genome represents proteincoding genes.35 The majority of the human transcriptome is composed of RNAs that do not encode proteins: ncRNAs. Scientific evidence indicates that ncRNAs are key regulators of gene expression that are responsible for biological complexity. The nematode Caenorhabditis elegans has a similar number of protein-coding genes as humans. However, the proportion of genes that do not encode information for proteins vs protein-coding genes is 17 times higher in humans than in C. elegans.36 Based on molecular size, ncRNAs are classified as small ncRNAs (fewer than 200 nucleotides) and long ncRNAs (more than 200 nucleotides). Among small ncRNAs, and among all ncRNAs in general, microRNAs (miRNAs) are the best known and most extensively studied members. MiRNAs are a group of small ncRNAs (18–25 nucleotides) that act as negative regulators of gene expression by

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degrading or repressing the translation of their target messenger RNAs (mRNAs). MiRNAs are recognized as key mediators of a variety of biological processes. They have a special relevance in the regulation of homeostasis and response to stress.37, 38 MiRNAs may regulate the expression of more than 60% of human protein-coding genes.39 Other ncRNA subclasses that have gained great attention in recent years are long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs). LncRNAs comprise a vast family of ncRNAs that participate in multiple biological processes, including providing scaffolds/guides for epigenetic and transcription factors, RNA splicing, nuclear trafficking and imprinting, among others.40 LncRNAs modulate gene expression at a more complex level than miRNAs.41 Recent advances in sequencing have drawn attention to circRNAs. CircRNAs are single-stranded and covalently closed RNA molecules that lack of free caps or poly(A) tails. Evidence of their possible functions is continuously emerging.42 CircRNAs can act as miRNA sponges in competing endogenous RNA (ceRNA) networks, serve as miRNA reservoirs, regulate RNA transcription, and interact with RNA-binding proteins.43 While there is plenty of evidence of miRNAs concerning exercise-induced cardiovascular adaptations, investigations on lncRNAs and circRNAs are scarce.

13.3.1 Noncoding RNAs as regulators of cardiovascular adaptations to exercise NcRNAs have been recognized as essential mediators in a variety of physiological processes. As such, the deregulation of the ncRNA profile is a feature of a wide array of cardiovascular conditions.44–47 Increasing evidence has shown that ncRNAs, particularly miRNAs, mediate the physiological mechanisms that regulate the cardiac adaptations induced by exercise. A significant body of evidence supports the role of miRNAs in the physiological cardiac hypertrophy prompted by exercise. In a seminal paper, Care` et al.48 demonstrated the downregulation of cardiac miR-1 and miR-133 in rats subjected to treadmill running, a mechanism that was associated with the promotion of physiological cardiac hypertrophy. Fernandes et al.49 evaluated the role of miRNAs in the cardiac renin-angiotensin system in sedentary and trained rats (after moderate-volume swimming training and high-volume swimming training). The authors suggested that training-induced left ventricle (LV) hypertrophy may be partly mediated by miRNAs (miR-27a, miR-27b, and miR-143) that target the gene expression of members of the renin-angiotensin system. Exercise training also induced LV hypertrophy through miRNAs in an independent study.50 In female Wistar rats, swimming exercise (1 h, 5 times/week/8 weeks with 5% body weight overload) altered miRNAs (miR-21, miR-124, miR-144, and miR-145) targeting the PI3K-Akt–mTOR pathway. A training program induced the cardiac expression of different miR-29 family members.51 These changes were associated with the reduction in collagen gene expression and, subsequently, with the reduction in myocardial fibrosis and the improvement in LV compliance. The relationship between exercise training, cardiac miRNAs, and fibrosis has been corroborated in independent investigations.52 A role for cardiac-specific miRNAs in the epigenetic control of training-induced LV hypertrophy has also been proposed. Soci et al.53 reported that a high-volume swimming program induces physiological remodeling by downregulating miR208a and miR-208b. This downregulation was associated with a higher expression of their target genes, which induced the modulation of cardiac metabolic and contractile adaptations. Cardiac miR-222 has been identified as a crucial element in exercise-induced cellular hypertrophy and proliferation in mouse models of exercise by targeting p27, HIPK1/2, and HMBOX1.54 The authors demonstrated that the in vivo inhibition of miR-222 blocks the exercise-induced response. Using two distinct murine exercise

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models, Shi et al.55 reported that miR-17-3p increases in response to exercise and that it induces cardiomyocyte hypertrophy, proliferation, and survival by regulating the expression of genes such as TIMP-3 and PTEN. Different studies performed in other animal models and training programs support the role of miRNAs in exercise-induced cardiac hypertrophy.56, 57 Overall, previous findings are compatible with a model of LV physiological hypertrophy regulated, at least partially, by the changes in the miRNA profile of the heart muscle. The cardiac-induced adaptations mediated by miRNAs include alternative physiological mechanisms. For example, a swimming program in female Wistar rats induced the cardiac expression of miR-126, which served as an angiogenic stimulus through the inhibition of negative regulators of the VEGF pathway.58 MiR-222 has also been implicated in the development of the cardiomyogenic exercise response in normal and adult mouse hearts.59

13.3.2 Noncoding RNAs as regulators of the cardioprotective effects of exercise The response of miRNAs to exercise is not only implicated in physiological adaptations, as ncRNAs can also mediate cardioprotective effects in pathological conditions.60 In apolipoprotein E-null mice, similar to the effect of statin therapy, aerobic exercise induced the expression of the anti-inflammatory miR-146a, which targets components implicated in vascular inflammatory injury in atherosclerosis.61 In humans, the regulation of miRNA expression in circulating monocytes after an acute bout of exercise was associated with the development of anti-inflammatory and anti-atherogenic responses. The authors proposed that exercise-induced alterations in the miRNA profile of monocytes may promote cardiovascular health.62 Complementing the findings discussed earlier, Shi et al. also demonstrated that miR-17-3p protects against adverse cardiac remodeling after ischemia/reperfusion injury and has been proposed as a novel therapeutic target.55 Melo et al.63 evaluated the effect of exercise training on miRNAs implicated in sodium and calcium metabolism (miR-1 and miR-214) after myocardial infarction. The authors demonstrated that a training program in Wistar rats restored the expression levels of miR-1 and miR-214 induced by infarction and prevented the deregulation induced in their molecular targets. This mechanism may be implicated in the protection of ventricular function induced by exercise after myocardial infarction in their animal model. The same authors demonstrated that training after myocardial infarction in rats restores the cardiac expression of a member of the miR-29 family and may contribute to the improvement in ventricular function.64 In a myocardial infarction rat model, miRNAs, in particular miR-29a and miR-101a, mediated the beneficial effect of controlled intermittent aerobic exercise in the healing of cardiac tissues through the regulation of the TGFβ pathway.65 A potential role of miRNAs in cardioprotective mechanisms has also been proposed in the context of heart failure. In an animal model of induced heart failure, exercise training may affect miRNAmRNA regulatory networks implicated in multiple mechanisms, such as cell morphogenesis and death, inflammation, metabolism, and TGF signaling.66 MiR-16, miR-21, and miR-126 have been described as mediators of the peripheral revascularization induced by exercise training in male spontaneously hypertensive rats.67 A role for miR-145 in the exercise-mediated remodeling of arterioles in hypertension and regulation of smooth muscle cell phenotype has also been proposed.68 Concerning lncRNAs, Liu et al.69 proposed that long-term exercise training alleviated the vascular injury induced by insulin resistance via the regulation of a panel of lncRNAs in a high-fat diet-fed animal model. This relationship between exercise, insulin resistance, and lncRNAs has been recently

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corroborated. In a high-fat diet-fed mouse model, exercise reduced insulin resistance, altering the lncRNA MALAT1/microRNA-382-3p/resistin axis.70 Based on previous findings, the cardioprotective effect of ncRNAs in response to exercise emerges as an interesting strategy in the development of novel therapeutic tools,71 not only in the context of CVDs but also for related comorbidities.72

13.4 Circulating noncoding RNAs and exercise The beneficial effect of exercise on health is systemic and not restricted to the tissues and organs that are most actively involved in movement generation. Thus, the analysis of the mechanisms implicated in cell-to-cell communication may be interesting for the complete understanding of the molecular networks of exercise.73 Although initially described as intracellular regulators, ncRNAs have also been detected in body fluids, including blood.74 Extracellular ncRNAs can be secreted in a regulated manner in response to stress. In the extracellular space, ncRNAs seem to participate in intercellular communication, regulating the phenotype of the receptor cells at the paracrine and endocrine levels.75, 76 Intracellular miRNAs can also be passively released by necrotic or damaged cells. Extracellular ncRNAs have been implicated in the regulation of physiological mechanisms, as well as in the onset and development of disease states.77, 78 Since the response to physical activity (acute exercise and training) involves a complex cross-communication mechanism that has profound effects on gene expression, studies focusing on extracellular ncRNAs are fundamental. The first study that evaluated the effect of an acute bout of exercise on the circulating miRNA signature was published in 2011.79 An increasing number of studies have analyzed the effects of acute exercise and training on the circulating miRNA profile during the last decade (previously reviewed in Refs. 80, 81). In general, most of these investigations are focused on heart- and muscle-enriched miRNAs.81–87 Previous studies have demonstrated that acute exercise and training orchestrate the dynamic regulation of the circulating miRNA profile in both health and disease. These changes seem to be dependent on the intensity, duration, and mode of exercise. The mobilization of miRNAs in response to acute exercise is immediate, followed by the restoration of circulating levels after 1–24 h of rest, depending on the duration of exercise.88 The alteration of the circulating miRNA pool is not caused by passive release in the context of exercise-induced tissue damage. The results from independent publications suggested that the release of miRNAs from cells and tissues to the circulation in response to exercise is an active and regulated phenomenon.79, 88

13.4.1 Noncoding RNAs as biomarkers of exercise Due to its high level of complexity, the ncRNA family is expected to contain a wide range of biological indicators. Supporting this hypothesis, a rising number of publications have reported that the transcriptomic signature, particularly the noncoding transcriptomic profile, gives an instantaneous picture of both physiological and pathological status.89–91 Therefore, approaches based on the analysis of the ncRNA signature provide an opportunity for the identification of novel biomarkers, as continually demonstrated by different investigations.92–95

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It is worth noting that extracellular components have been successfully incorporated into clinical practice in the field of oncology. In 2012, the FDA approved the lncRNA prostate cancer antigen 3 (the Progensa® PCA3 assay) as the first ncRNA that is used routinely in clinical practice as a biomarker for the diagnosis of prostate cancer.96 The resulting score is used to guide the decision for an initial biopsy in patients with suspected prostate cancer.97 Similar to their intracellular counterparts, miRNAs compose the subclass of ncRNAs that have gained the most attention in the field of biomarkers. Circulating miRNAs exhibit optimal biochemical properties to become excellent biomarkers. MiRNAs can be obtained using minimally invasive techniques with clinical specimens, are highly stable, and have a long half-life within the sample. miRNAs can be quantified through techniques already available in clinical laboratories, such as quantitative reverse transcription PCR (qPCR). Furthermore, global profiles could be obtained in a single experiment using RT-qPCR or relatively accessible techniques such as next-generation sequencing or microarrays. MiRNA-based tests have been described as a cost-effective alternative for disease monitoring and risk assessment.98 In some cases, miRNAs appear to have a clinical value that is superior to that of markers currently used in clinical practice, e.g., hs-cTnT and NT-pro-BNP.99 Most of these characteristics can be extended to other subclasses of ncRNAs, including lncRNAs and circRNAs.100 The active release of ncRNAs into the extracellular space in response to stress conditions opens the door to their study as biological markers of exercise-related cardiovascular responses101 (Fig. 13.1).

FIG. 13.1 Mobilization of circulating noncoding RNAs in response to exercise at the cardiac level. Exercise-induced cardiac stress leads to the mobilization of noncoding RNAs (ncRNAs). The subsequent release of ncRNAs into the extracellular space and blood circulation may be mediated by active secretion mechanisms. The possibility of passive release due to exercise-induced cardiac damage should not be discarded. Extracellular ncRNAs may be capable of regulating the phenotype and function of target cells at the paracrine level and maybe at endocrine level. NcRNA release in response to exercise opens the door to their study as biomarkers of exercise-related cardiovascular responses.

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Indeed, the presence of transcoronary miRNA gradients suggests a selective release from the heart to the coronary circulation.102 MiRNA expression is more tissue-specific than mRNA expression.103 However, the response induced in noncardiac tissues should also be considered. The detection of miRNAs in the bloodstream may have potential applications in the fields of exercise performance, optimization of exercise recommendations, and evaluation of the maximum limits of healthy exercise. Different authors reported an association between the perturbation of the circulating miRNA profile in response to acute exercise or training and cardiovascular adaptations, suggesting their value as indicators (Table 13.1). One of the topics evaluated in greater detail is the association between circulating miRNAs and cardiovascular fitness. In male endurance athletes, changes in the plasma level of muscle-enriched miRNAs such as miR-1, miR-133a, and miR-206 in response to a marathon race were closely correlated with aerobic performance parameters, such as maximal oxygen consumption (VO2max) and running speed at the individual anaerobic lactate threshold.86 Furthermore, miR-1 and miR-133 were correlated with echocardiographic parameters, including fractional shortening and thickness of the intraventricular septum. Bye et al.104 observed differences in the profile of miR-21, miR-210, and miR222 in the serum of healthy individuals (40–45 years, men and women) with high or low VO2max matched for age, sex, and physical activity levels. The authors suggested these miRNAs as indicators of aerobic fitness. Supporting these findings, Guescini et al.105 demonstrated that the resting levels of miR-1, miR-133b, miR-181a-5p, miR-206, and miR-499 in plasma extracellular vesicles correlated with VO2max in healthy male subjects. MiR-1 and miR-486 have also been correlated with VO2max and resting heart rate.107 A regression analysis demonstrated that miR-1 and miR-486, in combination with age and sex, were independent predictors of VO2max. MiR-486, age, and BMI were also described as independent predictors of heart rate. In another study, the peak exercise levels of miR-146a during acute exhaustive cycling exercise were positively correlated with VO2max in competitive male rowers.79 The changes in the resting levels of miR-20a and changes in VO2max after a 90-day period of rowing training were also directly correlated. Li et al.109 analyzed the changes in miRNAs involved in angiogenesis and inflammation and enriched in muscle and/or cardiac tissue in amateur basketball athletes after a 3-month season. The authors observed different linear correlations of miR-146a, miR208b, and miR-221 with baseline levels of the cardiac marker creatine kinase myocardial band (CK-MB) and VO2 or changes in hsCRP after acute exercise, VO2, and peak workload. A recent study proposed that serum miR-20a decreases significantly after cardiopulmonary exercise testing and serum miR-21 increases after 1 h of acute exercise training in healthy college students.111 Nevertheless, no robust correlation between changes in miRNAs associated with cardiovascular mechanisms and indicators of cardiac function or exercise capacity was reported. The results from other publications suggest the potential application of miRNAs in related fields. For example, Grunig et al.108 evaluated whether a set of circulating miRNAs (miR-21-5p, miR-22-3p, and miR-451) changed following pulmonary hypertension-alleviating supervised exercise training. The results showed that miR-22-3p/miR-451a was decreased in 74.2% of the samples after the intervention. In samples obtained following exercise intervention, a higher composite miRNA value, including the three miRNAs, was decreased in 65% of the samples. The change in miRNA value following was correlated with the 6-min walking distance at baseline. The authors concluded that the specific changes in miRNA following supervised exercise training could indicate different pulmonary hypertension endotypes. Schmitz et al.110 suggested that a controlled 4-week high-intensity interval training intervention alters the levels of plasma miRNAs that regulate cardiac growth and that

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Table 13.1 Studies on circulating microRNAs as biomarkers of exercise-related cardiovascular responses. References

Subjects

Type of exercise

Main findings

Baggish et al. (2011)79

10 male rowers (19.1
0.6 years)

Exercise in cycle ergometer Incremental test until extenuation, before and after a 90-day rowing training period

Banzet et al. (2013)84

9 active males (27– 36 years)

Bye et al. (2013)104

Mooren et al. (2014)86

First cohort: 24 healthy individuals (40–45 years) with high (n ¼ 12) or low (n ¼ 12) VO2max Second cohort: 76 participants from the HUNT Fitness Study with high (n ¼ 12) or low (n ¼ 12) VO2max 14 trained male endurance runners (42.8
6.0 years)

Uphill (high concentric component) and downhill (high eccentric component) walking Fitness test

Correlation between peak exercise levels of miR146a with VO2max Correlation between changes in resting miR20a with changes in VO2max Changes in microRNAs depend on the exercise mode

de Gonzalo-Calvo et al. (2015)88

9 male amateur runners (39.1
2.2 years)

10-km and marathon races

Guescini et al. (2015)105

22 male healthy subjects (26.0
4.8 years)

Fitness test

Wardle et al. (2015)106

Male athletes and controls: 10 strength athletes (22.2
2.1 years), 10 endurance athletes (22.6
3.7 years), and 10 age-matched nonexercising controls (24.0
2.8 years)

Not applicable

Marathon race

Differences in miR-21, miR-210, and miR-222 between patients with high and low VO2max

Correlation between miR1, miR-133a, and miR-206 with aerobic performance parameters such as VO2max and running speed at individual anaerobic lactate threshold Correlation between miR1 and miR-133a with echocardiography parameters The inflammatory microRNA signature differs in response to different doses of aerobic exercise Correlation between the content of miR-1, miR-133b, miR-181a-5p, miR-206, and miR-499 in extracellular vesicles with VO2max The microRNA profile differs between strength and endurance athletes

Continued

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Table 13.1 Studies on circulating microRNAs as biomarkers of exercise-related cardiovascular responses.—cont’d References

Subjects

Type of exercise

Main findings

Clauss et al. (2016)85

30 male marathon runners: 15 amateur (40.1
1.4 years) and 15 elite (40.0
1.7 years)

Marathon race

Denham et al. (2016)107

Long-term exercise: 67 endurance athletes (33.88
10.77 years) and 61 healthy controls (28.69
10.64 years) Acute exercise: 19 healthy men (20.68
2.40 years)

Long-term strenuous aerobic exercise training and a single bout of maximal aerobic exercise

de Gonzalo-Calvo et al. (2018)27

9 male amateur runners (39.1
2.2 years)

10-km and marathon races

Gruning et al. (2018)108

31 pulmonary hypertension patients

Supervised exercise program (3 weeks)

Li et al. (2018)109

10 amateur basketball athletes (25.90
4.95 years)

3-month season

Schmitz et al. (2018)110

63 moderately trained females and males (22.0
1.7 years)

2 workload-matched highintensity interval training protocols (4 weeks) with different recovery periods (30- and 15-s active recovery)

The profile of microRNAs related to atrial remodeling differs in elite and amateur runners Correlation between microRNA levels and echocardiography parameters in elite runners Correlation between miR1 and miR-486 with VO2max and resting heart rate miR-1 and miR-486 are associated with cardiovascular fitness parameters even after adjusting for potential confounding Deregulation in the profile of microRNAs proposed as biomarkers of heart disease A higher composite microRNA value (miR22-3p and miR-21-5p/ miR-451a and spike RNA) was significantly decreased in 65% of the samples miR-22-3p/miR-451a values were significantly decreased in 74.2% of the samples Correlation between miR146a, miR-208b, and miR221 with baseline levels of the cardiac marker CKMB and VO2 or changes in hsCRP after acute exercise, VO2, and peak workload Both high-intensity interval training interventions resulted in elevated resting miR-29c and miR-222 levels

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Table 13.1 Studies on circulating microRNAs as biomarkers of exercise-related cardiovascular responses.—cont’d References

Subjects

Type of exercise

Main findings

Zhou et al. (2020)111

8 male college students (20.75
0.46 years)

Cardiopulmonary exercise testing and 1-h acute exercise training

miR-20a was decreased significantly after cardiopulmonary exercise testing miR-21 was increased after acute exercise training No correlations were identified between the changes of miRNAs associated with cardiovascular mechanisms and cardiac function

CK-MB, creatine kinase myocardial band; VO2max, maximal oxygen consumption.

are protective against pathological cardiac remodeling such as miR-29c and miR-222. In response to a marathon race, Clauss et al.85 described that the plasma profile of miRNAs related to atrial remodeling is essentially different when comparing elite and amateur runners. The results from independent studies point to circulating miRNAs as candidate biomarkers of the type and dose of exercise. Wardle et al.106 observed that the baseline levels of miR-21, miR-146a, miR221, and miR-222 differed between strength and endurance athletes. Banzet et al.84 described a specific profile of miR-1, miR-133a, miR-133b, and miR-208b in response to eccentric and concentric exercise, and we reported that the kinetics and number of miRNAs related to inflammatory mechanisms differed significantly in response to low and high doses of acute exercise in active men.88

13.4.2 Effect of exercise on biomarkers with future application: Circulating miRNAs From an alternative perspective, the analysis of the behavior of circulating miRNAs previously proposed as biomarkers of heart disease in response to exercise, especially long-term exercise, would provide valuable information with future clinical application. We recently addressed this issue.27 We evaluated the levels of a panel of circulating miRNAs previously proposed as biomarkers of acute coronary syndrome and heart failure, among other cardiac conditions, in response to different bouts of acute exercise: 10-km and marathon races. Our data demonstrated that acute aerobic exercise induces an alteration in the profile of circulating miRNAs associated with cardiac pathology in healthy active subjects in the absence of cardiac damage. Therefore, our candidates showed “pseudodisease” signatures in response to acute exercise. The adequate control of exercise as a confounder seems relevant in the future application of ncRNAs in clinical practice. On the contrary, our results also suggest interesting applications of circulating miRNAs in the context of the medical attention of subjects who, after an exercise session, show a clinical scenario suggestive of cardiac pathology (Fig. 13.2). Despite the increase in hs-cTnT levels and other indicators of

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FIG. 13.2 Possible clinical application of circulating microRNAs in the context of exercise. Since participation in sports events and the clinical use of biomarkers has increased in recent years, it is important for clinical practice to take into account the influence of nonpathological conditions, such as acute exercise, on routinely used cardiac biomarkers, such as hs-cTnT and NT-proBNP. Elevations in plasma cardiac biomarkers, even within the ranges considered pathological, are common after resistance exercise in both professional athletes and amateur athletes. In addition, electrocardiographic abnormalities are typical in athletes and in very physically active subjects. This situation complicates clinical decision-making for those subjects who, after an acute bout of exercise, show clinical symptoms suggestive of cardiac pathology. An erroneous diagnostic approach leads, in some cases, to interventional tests with the risk of complications and the need for hospitalization. This implies the poor use of health resources and is psychologically harmful to the athlete. Conversely, not performing hemodynamic monitoring or an interventional test in those patients in whom they are indicated could be a lifethreatening situation. The diagnosis of myocardial injury should be made on the basis of all available information. Therefore, the evaluation of the circulating levels of certain microRNAs (miRNAs) could be an interesting test to ensure that the patient receives adequate attention. For example, several independent laboratories point to the increase in the blood concentrations of cardiac-specific and/or cardiac-enriched miRNAs (miR-1, miR-133a-3p, miR-133b-3p, miR-208a-3p, miR-208b-3p, and miR-499a-5p) as robust indicators of necrosis in patients with myocardial infarction. We have reported an increase in hs-cTnT levels, as well as in other biological markers of myocardial necrosis, in response to 10-km and marathon races, even in the absence of symptoms or signs of cardiac injury. However, the levels of these miRNAs intimately linked to cardiac necrosis showed no variation or even presented levels below the detection limit of our high-sensitivity assay. The miRNAs seem to show greater specificity than hs-cTnT, at least in the context of acute exercise. The determination of these miRNAs in combination with established tests may improve decision-making for those subjects for whom the diagnosis of myocardial lesions raises doubts.

cardiac damage in response to the three races, in some cases above the cutoff for myocardial infarction, the levels of miRNAs previously linked to cardiac necrosis showed no variation or even presented levels below the detection limit, indicating the absence of myocardial necrosis. Therefore, the determination of these miRNAs in combination with established tests will improve decision-making in those subjects for whom the diagnosis of myocardial lesions raises doubts.

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13.5 Limitations and perspectives There is a limited understanding of the molecular mechanisms that mediate the benefits of exercise in contexts of health and disease. The analysis of the noncoding transcriptome provides a new opportunity to identify novel biological markers and to explore the molecular components that promote health. NcRNAs could be useful as indicators of cardiovascular fitness and, consequently, as biomarkers for the development of individualized recommendations for physical activity and training programs. However, to date, a small number of studies have been published on the effect of physical activity, including acute exercise and training, on the profile of circulating miRNAs, with disparities in results.80 This makes it difficult to obtain general and robust conclusions to determine the real role of ncRNAs as biomarkers of exercise-related biological responses or their possible practical implications for health and performance. Key limitations should be addressed before accepting ncRNAs as useful biomarkers of exercise. Similar to other research fields, there is a lack of reproducibility between the results obtained in independent studies. The very large differences in the methodologies used, the experimental designs, and the characteristics of the participants limit the comparison of the findings.80 Furthermore, available studies have used a wide variety of exercise protocols and modalities. Most studies have been performed in young and healthy men, which limits the understanding of this topic in other groups, such as cardiovascular patients. Very few investigations have analyzed the baseline signature of circulating miRNAs in active vs sedentary people or in response to long training periods.79, 82, 104, 106 Whether the circulating ncRNA signature provides useful information to predict the response to an acute bout of exercise or training requires additional analytic approaches with adequate experimental designs, appropriate study populations, and larger sample sizes. The methodological aspects deserve special consideration. The sample type, isolation system, quantification platform, and data analysis, including the normalization methods, have a great impact on the results. Studies including lncRNAs, circRNAs, and other ncRNA subclasses are also needed to clarify the exact role of these subclasses in exerciseinduced cardiovascular adaptations and their roles as biomarkers of exercise responses. Findings from a recent study suggested that exosomal miR-342-5p mediates the cardioprotective effects of long-term exercise on myocardial ischemia/reperfusion injury.112 The authors described miR-342-5p as a novel “exerkine.” However, the biology of ncRNAs as hormone-like factors is still poorly understood.113 The information available about the role of circulating ncRNAs as intercellular communicators in the context of exercise is scarce, and consequently, the potential of the circulating noncoding transcriptome to identify molecular pathways involved in cardiovascular adaptations to training is limited. Furthermore, bioinformatic prediction tools have strong limitations, including biases toward specific molecular mechanisms (e.g., cancer pathways).114 Whether changes in the circulating miRNA profile reflect corresponding changes in the cells or tissues of interest is still unclear. Information about the origin, the mechanism of transport, the destination, and the gene targets of circulating ncRNAs is essential to elucidate the functional roles of these epigenetic mediators in the beneficial effects of exercise for health. Unfortunately, the available data are restricted to descriptive and observational studies, which limits causal inference. These limitations may not be linked to the poor performance of ncRNAs as biomarkers of the exercise response. The use of circulating miRNAs as biomarkers is a real possibility, with useful applications for performance and the optimization of exercise recommendations. Additional efforts are needed.115

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13.6 Conclusions The noncoding transcriptome, miRNAs in particular, constitute an interesting tool to predict and monitor the response to physical exercise. Further investigations should provide relevant information to elucidate whether ncRNAs are useful biomarkers for exercise-related biological responses in both healthy individuals and cardiovascular patients.

Funding CIBERES (CB07/06/2008 to L.P., F.B., and D.d.G.C.) is a project from Carlos III Health Institute. This study was supported by Instituto de Salud Carlos III (ISCIII) through the project “PI19/00907,” cofunded by European Regional Development Fund (ERDF)/“A way to make Europe.”

Conflict of interests F.B. and D.d.G.C. have filed patents on microRNAs as biomarkers.

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77. Thomou T, Mori MA, Dreyfuss JM, et al. Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature. 2017;542(7642):450–455. 78. Ying W, Riopel M, Bandyopadhyay G, et al. Adipose tissue macrophage-derived exosomal miRNAs can modulate in vivo and in vitro insulin sensitivity. Cell. 2017;171(2):372–384.e312. 79. Baggish AL, Hale A, Weiner RB, et al. Dynamic regulation of circulating microRNA during acute exhaustive exercise and sustained aerobic exercise training. J Physiol. 2011;589(Pt 16):3983–3994. 80. Fernandez-Sanjurjo M, de Gonzalo-Calvo D, Fernandez-Garcia B, et al. Circulating microRNA as emerging biomarkers of exercise. Exerc Sport Sci Rev. 2018;46(3):160–171. 81. Gomes CPC, de Gonzalo-Calvo D, Toro R, et al. Non-coding RNAs and exercise: pathophysiological role and clinical application in the cardiovascular system. Clin Sci (Lond). 2018;132(9):925–942. 82. Aoi W, Ichikawa H, Mune K, et al. Muscle-enriched microRNA miR-486 decreases in circulation in response to exercise in young men. Front Physiol. 2013;4:80. 83. Baggish AL, Park J, Min PK, et al. Rapid upregulation and clearance of distinct circulating microRNAs after prolonged aerobic exercise. J Appl Physiol (1985). 2014;116(5):522–531. 84. Banzet S, Chennaoui M, Girard O, et al. Changes in circulating microRNAs levels with exercise modality. J Appl Physiol (1985). 2013;115(9):1237–1244. 85. Clauss S, Wakili R, Hildebrand B, et al. MicroRNAs as biomarkers for acute atrial remodeling in Marathon runners (the miRathon study—a sub-study of the Munich Marathon study). PLoS One. 2016;11(2), e0148599. 86. Mooren FC, Viereck J, Kruger K, Thum T. Circulating microRNAs as potential biomarkers of aerobic exercise capacity. Am J Physiol Heart Circ Physiol. 2014;306(4):H557–H563. 87. Ramos AE, Lo C, Estephan LE, et al. Specific circulating microRNAs display dose-dependent responses to variable intensity and duration of endurance exercise. Am J Physiol Heart Circ Physiol. 2018;315(2): H273–H283. 88. de Gonzalo-Calvo D, Davalos A, Montero A, et al. Circulating inflammatory miRNA signature in response to different doses of aerobic exercise. J Appl Physiol (1985). 2015;119(2):124–134. 89. Du WW, Yang W, Liu E, Yang Z, Dhaliwal P, Yang BB. Foxo3 circular RNA retards cell cycle progression via forming ternary complexes with p21 and CDK2. Nucleic Acids Res. 2016;44(6):2846–2858. 90. Haas J, Mester S, Lai A, et al. Genomic structural variations lead to dysregulation of important coding and noncoding RNA species in dilated cardiomyopathy. EMBO Mol Med. 2018;10(1):107–120. 91. Holdt LM, Stahringer A, Sass K, et al. Circular non-coding RNA ANRIL modulates ribosomal RNA maturation and atherosclerosis in humans. Nat Commun. 2016;7:12429. 92. Ju HQ, Zhao Q, Wang F, et al. A circRNA signature predicts postoperative recurrence in stage II/III colon cancer. EMBO Mol Med. 2019;11(10), e10168. 93. Kolling M, Haddad G, Wegmann U, et al. Circular RNAs in urine of kidney transplant patients with acute T cell-mediated allograft rejection. Clin Chem. 2019;65(10):1287–1294. 94. Sonnenschein K, Wilczek AL, de Gonzalo-Calvo D, et al. Serum circular RNAs act as blood-based biomarkers for hypertrophic obstructive cardiomyopathy. Sci Rep. 2019;9(1):20350. 95. Zuo L, Zhang L, Zu J, et al. Circulating circular RNAs as biomarkers for the diagnosis and prediction of outcomes in acute ischemic stroke. Stroke. 2020;51(1):319–323. 96. de Kok JB, Verhaegh GW, Roelofs RW, et al. DD3(PCA3), a very sensitive and specific marker to detect prostate tumors. Cancer Res. 2002;62(9):2695–2698. 97. Lee GL, Dobi A, Srivastava S. Prostate cancer: diagnostic performance of the PCA3 urine test. Nat Rev Urol. 2011;8(3):123–124. 98. Walter E, Dellago H, Grillari J, Dimai HP, Hackl M. Cost-utility analysis of fracture risk assessment using microRNAs compared with standard tools and no monitoring in the Austrian female population. Bone. 2018;108:44–54. 99. de Gonzalo-Calvo D, Vilades D, Martinez-Camblor P, et al. Circulating microRNAs in suspected stable coronary artery disease: a coronary computed tomography angiography study. J Intern Med. 2019;286 (3):341–355.

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Long noncoding RNAs and circular RNAs as heart failure biomarkers

Amela Jusica,b,* and Yvan Devauxb,* Department of Biology, Faculty of Natural Sciences and Mathematics, University of Tuzla, Tuzla, Bosnia and Herzegovinaa Cardiovascular Research Unit, Luxembourg Institute of Health, Luxembourg, Luxembourgb

14.1 Introduction Heart failure (HF) is one of the most prominent cardiovascular diseases that have a significant burden in terms of morbidity and mortality. Despite the many advances in the biomarker development and therapies based on the renin-angiotensin system or adrenergic pathways, an increased prevalence of HF pinpoints necessity for the identification of more sensitive and reliable biomarkers and specific therapies. Advances in high-throughput sequencing technologies in the last decade have demonstrated that a significant part of the human genome and other complex organisms is transcribed as nonprotein-coding transcripts, i.e., RNA molecules unable to encode proteins. These RNAs have been named noncoding RNAs (ncRNAs).1 NcRNAs are a heterogeneous group of RNA molecules in terms of length, structure, function, localization, and biogenesis. They are typically classified into two major groups according to the length of transcripts, short ncRNAs (200 nt) that represent the most prevalent and functionally versatile class of ncRNAs.2 Based on the lncRNAs’ length distribution, they can be further subdivided into different groups: small lncRNA (200– 950 nt), medium lncRNA (950–4800 nt), and large lncRNA (>4800 nt).2 The human genome contains more small lncRNAs (58%) and large lncRNAs than the mouse genome that contains mostly medium lncRNAs (78%).2 While lncRNAs are linear in the majority, there exists a category of ncRNAs that appear as a circular form, hence named circular RNAs (circRNAs). In the last two decades, ncRNAs have been recognized as functionally and biologically significant molecules in the cardiovascular system. Their regulation in the diseased heart and vessels suggested that they might contribute to the development and progression of cardiovascular disease. Cardiac-enriched ncRNAs have been detected in the circulation of HF patients, suggesting that they may have biomarker properties. In this chapter, we will gather the current knowledge of the value of ncRNAs as biomarkers and therapeutic targets of HF, focusing our attention on lncRNAs and circRNAs. *On behalf of the EU-CardioRNA COST Action (CA17129). Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00009-2 Copyright # 2021 Elsevier Inc. All rights reserved.

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14.2 Long noncoding RNAs 14.2.1 Discovery and biogenesis Tens of thousands of lncRNAs have been identified in a large diversity of species, from viruses, unicellular eukaryotes to humans.2 Unlike messenger RNAs (mRNAs), miRNAs, and snoRNAs, lncRNAs are less evolutionary conserved among different species and they are located mostly in poorly conserved regions of the genome. However, the promoter regions as well as the secondary and tertiary structures of lncRNAs are more conserved as compared to their primary sequences.3 LncRNAs are highly dynamically regulated. Cell-specific lncRNA transcripts are involved in the regulation of gene expression at various levels in both the nucleus and the cytoplasm.4 Although a plethora of lncRNAs have been identified recently, the number of functionally annotated or disease-associated lncRNAs lies in
1% of identified loci.5 There is therefore an obvious gap of knowledge between the number of mapped lncRNAs and the number of experimentally validated lncRNAs. In the absence of a clear understanding of lncRNA sequence-structure-function relationship, lncRNAs can be generally classified according to their features including biogenesis loci, the effect on chromosome architecture and DNA sequence, mechanism of action, targeting mechanism, and subcellular localisation.2, 5, 6 According to their genomic location, lncRNAs are typically classified as (a) intergenic lncRNA, transcribed autonomously from the intergenic regions of protein-coding genes (approximately onethird to one-half of lncRNAs); (b) intronic lncRNA when the entire sequence falls within the intron of a protein-coding gene without overlapping exons; (c) antisense lncRNA, transcribed from the antisense strand to the sense strand overlapping with protein-coding sequence; (d) bidirectional lncRNAs that share the same promoter region as the protein-coding gene, but with opposite transcriptional directions (Fig. 14.1A–D). In general, lncRNA-encoding genes have their own promoters, own transcription factors, and unique DNA motifs. Many lncRNAs are transcribed by the RNA polymerase II and processed similar to mRNAs that can include or exclude 50 -capping (m7G), 30 -polyadenylation, alternative cleavage, alternative polyadenylation, and alternative splicing.3 On the contrary, some lncRNAs including circRNAs and snoRNAs originate from atypical processing of RNA transcripts that are explained in Section 14.3.1. Although premature lncRNAs are mostly modified by a 50 ,7-methylguanosine cap and a 30 poly (A) tail, some functional lncRNAs are not polyadenylated and they are transcribed by the RNA polymerase III. Molecular mechanism of splicing regulation of lncRNAs is not clear yet, but it is known that during the alternative splicing, lncRNAs interact with specific splicing factors and then form RNA-RNA duplexes with pre-mRNA molecules, finally affecting chromatin remodeling and resulting in the completion of the splicing of target genes. Several lncRNAs interact with nuclear RNA binding proteins (a heterogeneous family of nuclear ribonucleoproteins) that are involved in different cellular processes including alternative splicing, mRNA stability, and transcriptional regulation.7 Mass spectrometry experiments have revealed that splicing components including the splicing-related factors Y-box binding protein 1 (YBX1) and poly (RC) binding proteins 1 and 2, as well as the ribosomal machinery recognizes some specific lncRNAs (i.e., HELLP-associated long noncoding RNA (LINC-HELLP)).8

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FIG. 14.1 Biogenesis and classification of long noncoding RNAs. Similar to mRNA, lncRNAs are transcribed by RNA polymerase II, 50 capped, polyA-tailed, and generally alternatively spliced. According to their biogenesis loci, lncRNAs are classified as: (A) intergenic lncRNA, transcribed intergenically from one or both strands; (B) intronic lncRNA, transcribed entirely from introns of protein-coding genes; (C) antisense lncRNA, transcribed from the antisense strand of protein-coding genes, overlapping with protein-coding sequence; and (D) bidirectional lncRNAs, sharing the same promoter region as the protein-coding genes, but with opposite transcriptional directions.

14.2.2 Functional classification As a heterogeneous class of transcripts, lncRNAs have unique patterns of cellular distribution allowing them to exert multiple functions. They act predominantly in the nucleus, where they regulate gene expression at the epigenetic level, but also in the cytoplasm where they regulate translation and posttranslational protein modifications.9 Mature lncRNA molecules are also found in other cellular compartments (i.e., mitochondria) indicating the possibility of retrograde regulation of gene expression between the nucleus and the mitochondria.6 According to the archetypes of molecular functions, lncRNAs can be classified into several functional categories: signal, decoy, guide, scaffold, and enhancer (Fig. 14.2A–E). Signal lncRNAs (Fig. 14.2A) modulate gene expression in a time and space manner and respond to diverse stimuli. For instance, the potassium voltage-gated channel subfamily KQT member 1 opposite strand/antisense transcript 1 (KCNQ1OT1) mediates the transcriptional silencing of multiple genes by interacting with chromatin and recruiting the chromatin-modifying machinery.9, 10

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FIG. 14.2 Mechanisms of action of long noncoding RNAs. (A) Signal lncRNAs modulate gene expression in a time and space manner and respond to diverse stimuli. (B) Decoy lncRNAs bind to transcription factors (TF) or miRNAs to prevent them from binding to their targets regulating transcription or translation. (C) Scaffold lncRNAs act as central platforms for multiple modifying complexes and/or their cofactors to assemble transiently for function. (D) Guide lncRNAs interact with modifying complexes or TF and direct them to specific genes or loci. (E) Enhancer lncRNAs (eRNA) are generated by the enhancers of protein-coding genes and stimulate the expression of target genes through interaction with both the promoter and enhancer regions of a gene.

Decoy lncRNAs (Fig. 14.2B) modulate transcription by titrating regulatory factors such as transcription factors, catalytic proteins, and subunits of larger chromatin-modifying complexes away from chromatin or “sponge” miRNAs leading to broad changes in the cellular transcription. Several well-characterized lncRNAs such as Carl, Chfr, MALAT 1, Mhrt, MIAT, NEAT2, SENCR, and TERRA have shown decoy mode of action. An abundantly expressed lncRNA in mammalian cells, the nuclearenriched abundant transcript 2 (NEAT2), binds and sequesters several splicing factors to nuclear speckles. The inhibition of NEAT2 leads to an altered localization and activity of the splicing factors resulting in an aberrant alternative splicing for a set of pre-mRNAs.8 Scaffold lncRNAs (Fig. 14.2C) act as central molecular platforms accumulating proteins to form ribonucleoprotein complexes and then promoting histone modification. Mechanistic studies postulated that the lncRNA CDKN2B-AS1 (ANRIL) may act as a scaffold for chromatin-modifying polycomb repressive complexes (PRC1 and PRC2) leading to silencing the INK4b-ARF-INK4a locus.9 ANRIL has shown an association with cardiovascular disease susceptibility that can be related to its capability of regulating gene expression in trans resulting in a decreased apoptosis and increased cell proliferation

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and cell adhesion, the most prevalent and essential alterations in atherogenesis.11 Downregulation of ANRIL may play an essential role in the etiology of the coronary artery disease through the vascular senescence. In addition, a study in lymphoblastoid cell lines has demonstrated an association between the coronary artery disease risk variants and CDKN2A/B and ANRIL expression in lymphocytes.12, 13 Guide lncRNAs (Fig. 14.2D) can act as molecular chaperons guiding specific ribonucleoprotein complexes to target chromatin locus. Activity of this type of lncRNAs causes changes in the gene expression in cis or trans.10 Three lncRNAs, HOTAIR, HOTTIP, and FENDRR, form complexes with ribonucleoproteins and conduct their localization to specific target genes. HOTAIR10 and FENDRR9 interact with PRC2 resulting in inhibition of target genes. The lncRNA HOTTIP organizes chromatin domains in order to coordinate long-range gene activation through intermediate transmission of information from higher order chromosomal looping into chromatin modifications.10 Enhancer lncRNAs (Fig. 14.2E) are produced from enhancer elements and stimulate the expression of target genes through interaction with both the promoter and enhancer regions of a gene. They create a chromosomal looping to bring them in proximity.9 For instance, the expression of mouse fetal enhancers mm67, mm85, mm130, and mm132 has been associated with the cardiac-enriched lncRNAs that are critical for their role in cardiac development.14 The downregulation of lncRNAs mm67 and mm85 and their target myocardin and upregulation of mm130 and mm132 have been demonstrated during the 2 weeks postacute myocardial.14 In addition, a new subtype of enhancer lncRNAs called superenhancers (up to 10 kb long) are specialized in the regulation of cell identity particularly in the heart during cardiac regeneration.15 A novel lncRNA, cardiac mesoderm enhancer-associated ncRNA (CARMEN) located in an active superenhancer region, is highly expressed during cardiac precursor cell differentiation and mediates cardiac fate specification.16 Similar to the guide lncRNAs mentioned earlier, CARMEN also acts in trans by directly interacting with PRC2 through SUZ12 and an EZH2, which mediates methylation of lysine 27 on histone 3, a repressive epigenetic mark that promotes cardiac differentiation.17 Notably, most lncRNAs are multifunctional and can use several mechanisms of action leading to the activation or repression of gene expression.9 LncRNAs regulate a wide range of biological functions in the cellular and developmental processes including epigenetic regulation, (post)transcriptional gene regulation, and compartmentalization, control of localization, interaction, and availability of effectors in a specific site to elicit a specific activity. LncRNAs are ubiquitously expressed in many tissues, including the brain and the central nervous system, which have shown the highest diversity of expressed lncRNAs. Although lncRNAs predominantly localize to the nucleus, they can be found in different cellular compartments including the cytoplasm and mitochondria. The export of lncRNAs from the nucleus is prevented by the fact that they harbor cis elements that are associated with nuclear proteins. Also, the presence of a short C-rich sequence derived from Alu elements promotes lncRNA nuclear retention via association with the nuclear matrix protein HNRNPK.4 In the nucleus, lncRNAs are important regulators of nuclear organization and function. LncRNAs have a specific developmental role and regulate chromosome architecture at different levels. For instance, in female mammalians, X-chromosome inactivation occurs to silence one of the two X chromosomes during the early embryonic development to achieve dosage compensation. One of the first-discovered and best-characterized lncRNAs, X-inactive-specific transcript (Xist), is directly involved in the process of X chromosome inactivation, which is initiated by the induction of Xist expression and embryonic development.18 Depletion of Xist in female mice results in loss of X-inactivation and lethality. The Xist expression is regulated by other lncRNAs such as Tsix lncRNA, which is the antisense partner of Xist RNA.19

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LncRNAs directly participate in the transcription regulation in the nucleus by forming R-loop structures or by interfering with Pol II transcription machinery at targeted loci. It has been found that some antisense lncRNAs regulate sense mRNA transcription by forming R-loops that allow lncRNAs to bind in cis and recruit transcription cofactors to corresponding promoter regions. Although not all lncRNAmediated R-loop formations are involved in transcriptional regulation, such mechanism is common for weakly expressed lncRNAs (i.e., Khps1 and antisense transcript, VIM-AS1).4 Most annotated lncRNAs are transcribed by RNA polymerase II (RNA Pol II), and they contain a lower number of exons than mRNAs and lower levels of expression across different tissues. In animal cells, some lncRNAs directly affect RNA Pol II and associated transcriptional machinery by promoting phosphorylation of transcription factors regulating their DNA-binding activity.20 Thus, lncRNAs can regulate the initiation and elongation of transcripts, by the pausing of RNA Pol II. Several lncRNAs are transcribed by RNA polymerase III and can suppress the initiation of transcription. For instance, Human Alu and mouse SINE B2 RNAs inhibit transcription during heat shock.21 Emerging evidence has shown that lncRNAs are involved in the regulation of the integrity and function of nuclear bodies altering gene expression at the posttranscriptional level. The lncRNA NEAT1 regulates the formation of paraspeckles whose number and morphology depend on the expression levels of NEAT1.22 More than 40 proteins are found in paraspeckles indicating their role in the regulation of gene expression together with NEAT1. LncRNAs have multiple functions in the cytoplasm. At the posttranscriptional level, lncRNAs may alter mRNA stability, splicing, protein stability, and subcellular interaction through interactions with diverse RNA binding proteins or through binding with complementary RNA sequences of target genes. For example, the lncRNA MALAT1 is involved in pre-mRNA splicing through interactions with SRSF1 regulating its localization to nuclear speckles, and MALAT1 depletion alters alternative splicing. In addition, MALAT1 could act as miRNA sponge through interaction with SR protein in the nuclear speckles modulating the concentration of splicing competent SR proteins in cells.23 LncRNAs regulate the mRNA stability via associated miRNAs repressing miRNA target genes or by recruiting proteins to degrade mRNA. An abundantly expressed lncRNA in mammalians named NORAD (noncoding RNA activated by DNA damage) acts as a decoy for PUMILIO 1 and PUMILIO 2 (PUM1/2) which stimulate mRNA deadenylation and decapping resulting in decreased translation.24 LncRNAs are involved in the regulation of protein synthesis and posttranslational modification, or they can encode some micropeptides to perform biological functions. Ribosome profiling analysis has revealed that MALAT1 interacts with ribosomes suggesting its involvement in the translation regulation.25 The lncRNA growth arrest-specific 5 (GAS5) interacts with the translation initiation machinery and regulates mRNA translation.26 At the posttranslational level, lncRNA regulates various modifications of proteins including protein phosphorylation, ubiquitination, and acetylation affecting protein degradation or formation. A cytoplasmic lncRNA termed lnc-DC regulates the phosphorylation of STAT3 on Tyr705 by preventing binding of the protein tyrosine phosphatase SHP1 and controls the differentiation of human conventional dendritic cells.27 The lncRNA HOTAIR plays multiple regulatory roles including ubiquitination of proteins, while MALAT1 promotes deacetylation of p53 and inhibits the transcriptional activity of p53, thereby promoting cell proliferation.26 A skeletal muscle-associated lncRNA (LINC00948 in humans and AK009351 in mice) contains a short 138 nucleotide open reading frame (ORF) and has the potential to encode highly conserved amino acid sequence.26 For instance, highly conserved micropeptide in vertebrates named myoregulin (MLN)

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309

is encoded by lncRNA and expressed in all skeletal muscle cells.26 MLN function is similar to phosphoprotein and sarcolipin, acting directly on the sarcoplasmic reticulum Ca2+-ATPase and preventing Ca2+ from entering the sarcoplasmic reticulum.26

14.3 Circular RNAs 14.3.1 Discovery, biogenesis, and classification In addition to lncRNAs, circRNAs are covalently closed, single-stranded RNA molecules with an average length of 500 nucleotides. Although the first circRNA has been discovered more than three decades ago, they are gradually recognized as a novel class of endogenous ncRNAs whose biological function remains to be explored. CircRNAs are abundantly expressed and localized to the cytoplasm and shown tissue-specific and cell-specific expression patterns. The expression levels of circRNAs can be more than 10 times higher than the levels of corresponding linear mRNAs. The covalently closedloop structure without 50 and 30 terminal polarization makes circRNAs resistant to exonucleases and more stable than linear lncRNAs. CircRNAs are highly conserved between mouse and human compared to other lncRNAs. Cellular circularization of RNAs involves spliceosome machinery and occurs in pre-mRNAs transcribed by RNA Pol II. The principal mechanisms of circRNA biogenesis are presented in Fig. 14.3. CircRNAs can arise from exons, introns, intergenic, and untranslational regions by noncanonical splicing named backsplicing which is regulated by coacting elements and transacting factors.28 Based on biogenesis pathways, circRNAs are classified as exonic circRNAs (ecircRNAs), exon-intron circRNAs (eiciRNAs), and intronic circRNAs (ciRNAs). CircRNA biogenesis involves several splicing factors: muscleblind (MBL) that specifically binds with conserved MBL-binding sites in circMbl and its flanking introns, quaking that facilitates circRNA biogenesis by linking the flanking intron sequences and then connecting the adjacent introns, adenosine deaminase that antagonizes circRNA formation by destabilizing RNA pairing, and Asp-Glu-Ala-His (DEAH) box helicase 9 (DHX9) acting as a nuclear RNA resolvase that may unwind RNA pairs flanking circularized exons to inhibit circRNA expression.29 The introns bordering circularized exons usually contain reverse complementary Alu repeats necessary for intron-pairing-driven circularization. During pre-mRNA splicing, Alu repeats bind the two flanking introns, joining them together and allowing the downstream 50 end (the splice donor) to be connected to the upstream 30 end (the splice acceptor).30 Genome-wide studies have reported that complementary flanking Alu elements are important for circRNAs formation but they also could be generated via direct base pairing in the presence of inverted repeat without Alu elements.31 EcircRNAs can be formed by a combination of backsplicing and canonical splicing. EcircRNAs represent the largest class of circRNAs (over 80%) found in animals and plants that are composed of single or several exons joined together with standard 30 -50 covalent carbon link. EcircRNAs are highly stable in the cell with preferential cytoplasmic location and they regulate gene expression and translation through interaction with miRNAs and/or RNA binding proteins (RBPs).32 In some circumstances, introns between the encircled exons escape splicing which results in the formation of retained-intron circRNAs or elciRNAs. ElciRNAs are composed of at least two exons and a retained intron that makes this subclass unique, even though they share some common characteristics with both ecircRNAs and ciRNAs. They are predominantly located in the nucleus in which they regulate transcription of their parental genes in

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FIG. 14.3 Biogenesis and classification of circRNAs. Based on their biogenesis pathways, circRNAs are mainly classified as exonic circRNAs (ecircRNAs), exon-intron circRNAs (eiciRNAs), and intronic circRNAs (ciRNAs). (1) EcircRNAs and eiciRNAs can be generated during pre-mRNA splicing. In this process, RNA-binding proteins or the Alu reverse complementary sequence binds the two flanking introns, joining them together and allowing the downstream 50 end to be connected to the upstream 30 end. (2) By a combination of backsplicing and canonical splicing, ecircRNAs can be produced. Additionally, ecircRNAs and eiciRNAs can be generated by exon skipping. (3) Intronic circular RNAs (ciRNAs) are generated by the canonical splicing machinery from lariat introns that escape from debranching and have 20 -50 head-tail link joint.

cis through interaction with U1 small nuclear ribonucleoprotein (snRNP).33 CircRNAs can also be produced by exon skipping by joining the (30 ) splice acceptor site of an upstream exon and the (50 ) splice donor site of a downstream exon resulting in the generation of skipping exonic and exon-intron circular RNAs. The mature ecircRNAs are produced by trimming the intronic sequences in the lariat. Intronic circRNAs (ciRNAs) are generated by canonical splicing from lariat introns that escape from debranching, and unlike ecircRNAs, ciRNAs have 20 -50 head-tail link joint. By removing the 30 tail downstream from the branch point, stable ciRNAs are produced. They are mostly found in humans and represent a small fraction of circRNAs. CiRNAs are mostly located in the nucleus, and their expression is positively correlated with the expression of their parental mRNA. Although ciRNAs are not abundantly expressed compared to the ecircRNAs, they may play important role in the regulation of gene expression.34 In addition to main circRNA biogenesis pathways, the biogenesis of transfer RNA (tRNA) intronic circRNAs (tricRNAs) has recently been discovered. TricRNAs are formed by tRNA introns during the pre-tRNA splicing catalyzed by a relatively small protein-only complex called tRNA splicing endonuclease (TSEN).35 The pre-tRNA is recognized by the TSEN complex that cleaves an introncontaining precursor into three segments: the 50 exon, an intron, and the 30 exon resulting in a 50 -OH and a 20 ,30 -cyclic phosphate at each site of cleavage.35 The fate of tRNA introns varies between different species. The combination of released tRNA intron terminal ends can result in tricRNA

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formation or phosphorylated tRNA intron on its 50 can be degraded by the 50 to 30 exonuclease Xrn1, creating a supply of nucleotides.35 Differences in circRNA biogenesis might be associated with biological and pathophysiological features.36

14.3.2 Function CircRNAs are involved in many biological processes through regulation of gene expression either by acting as miRNA sponges or through interactions with RPBs.37 CircRNAs contain shared miRNA response elements that allow them to bind competitively to a single or multiple miRNAs resulting in the trapping of miRNA molecules. Thus, the sponged miRNAs would not be able to bind to their target genes that lead to their upregulated expression (since miRNAs act by downregulating the expression of their target genes). CircRNAs can also interact with proteins and inhibit their function. CircMbl inhibits alternative splicing of Mbl pre-mRNA through binding to the muscle blind protein (MBL).38 Circ-FOXO3 represses the progression of the cell cycle through interaction with cell-cycle proteins such as cyclin-dependent kinase 2 (CDK2) and cyclin-dependent kinase inhibitor 1 (p21).39 Although circRNAs are abundantly released into the cytoplasm, few circRNAs remain in the nucleus. The nuclear circRNAs can interact with RNA Pol II in the promoter region of host genes and regulate their transcription. The two eiciRNAs, CircEIF3J and CircPAIP2, have the ability to bind the U1 small nuclear ribonucleoprotein (snRNP) in the nucleus and then interact with RNA Pol II in the gene promoter region, which enhances the transcription of their parental genes.38

14.4 LncRNAs and circRNAs in cardiovascular biology 14.4.1 LncRNAs and circRNAs in cardiovascular development Development of the cardiovascular system including the heart differentiation is a dynamic multistep process depending on the precise control of expression of multiple signal transduction proteins and transcription factors working in a coordinative manner. Disruption of the molecular regulatory network of cardiac morphogenesis may induce congenital heart disease. Although the knowledge regarding the molecular mechanisms that govern cardiogenesis in humans remains elusive, regulatory network analysis revealed dynamic organization of ncRNAs during cardiac differentiation. Numerous lncRNAs and circRNAs have been identified with the stage-specific expression during heart differentiation ranging from early embryonic to cardiomyocytes. Cardiac-enriched lncRNAs with characterized functions in the cardiovascular system are summarized in Table 14.1. Emerging evidence pinpointed lncRNAs as pivotal players in the cardiac development, regulating transcriptionally and posttranscriptionally distinct cardiac signaling pathways.9, 17 Stage-specific analyses identified LINC00261 long noncoding RNA 261, also named Alien, specifically expressed in undifferentiated pluripotent stem cells, cardiovascular progenitors derived from allantoides and lateral plate mesoderm, and differentiated endothelial cells.40 The lncRNA Alien plays diverse roles during cardiovascular development. Alien acts as a pivotal RNA molecule in the regulation of cardiovascular development at an early stage of cardiovascular differentiation, common to both vascular and cardiac progenitors. Loss of Alien results in impaired development of several mesodermal derivatives and defective vascular patterning and cardiac chamber formation.17 Alien participates in the

Table 14.1 Long noncoding RNAs in cardiac development and pathophysiology.

LncRNA

Species

Chromosome location

ALIEN

Human

20

ANRIL

Human

9

Braveheart

Mouse

18

CARMEN

Human

5

Carl

Mouse

Fendrr

Method of detection

Type of sample

Role

Target gene

Reference

hESCs and HUVECs

Cardiac development

FOXA2

40

Plasma and HCAECs

CAD

41, 42

RNA-seq and qPCR ChIP and qPCR

ESCs

Cardiac development

IL-6, IL-8, NF-κB, TNF-α, iNOS, ICAM-1, VCAM-1, and COX-2 Hand1, Hand2, and Mesp1

HFHCs, CPCs, and PBMCs

PRC2, EZH2, and SUZ12

44, 45

2

qPCR

Cardiomyocytes

Phb2

46

Mouse

8

RACEPCR

Foxf1 and Pitx2

47

HOTAIR

Human

12

qPCR

The caudal ends of E9.5 C57BL/ 6 J embryos Cardiac tissue, plasma

Cardiac differentiation,44 essential hypertension, and heart hypertrophy45 Mitochondrial fission and apoptosis in cardiomyocytes Cardiac development

Genes in HOXD locus

48

KCNQ1OT1

Human

11

qPCR

Plasma

49

MTLIPCAR

Human

mtDNA

qPCR

Plasma

sponging miR-2143p Unknown

RNA-seq and qPCR qPCR

Potential biomarker for congenital heart disease Diabetes cardiomyopathy Predict survival of HF patients

43

50

LncRNAuc.167

Human

5

qPCR

P19 cells

MALAT1

Human

11

qPCR

Plasma

MHRT

Human

14

qPCR

Whole blood

NRON

Human

9

qPCR

Whole blood

PANCR

Human

4

qPCR

TERRA

Human

Telomeres

RNA-seq

Left atrial appendage tissues HeLa cells

Upperhand

Mouse

8

ChIP and qPCR

Cardiomyocytes

Cell proliferation, apoptosis, and differentiation of P19; heart development Predict left ventricular dysfunction Predictive biomarker for HF Predictive biomarker for HF Cardiac differentiation Regulation of telomeric DNA damage response Heart development

MEF2c

51, 52

CCNA2, CCNB1/2, CDKN1A, andCDKN1B BRG1

9, 53

NFAT

54

PITX2c

55

Telomeres

9

Hand2

56

54

CAD indicates coronary artery disease; ChIP, chromatin immunoprecipitation assays sequencing; CPCs, cardiac progenitor cells; ESCs, embryonic stem cells; HCAECs, human coronary artery endothelial cells; hESCs, human embryonic stem cells; HF, heart failure; HFHs, human fetal heart cells; HUVEC, human umbilical vein endothelial cells; mtDNA, mitochondrial DNA; PBMCs, peripheral blood mononuclear cells; qPCR, quantitative polymerase chain reaction; RACE PCR, rapid amplification of cDNA ends polymerase chain reaction.

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Chapter 14 Long noncoding RNAs and circular RNAs as heart failure biomarkers

endoderm differentiation regulating positively the transcription of forkhead box A2 (FOXA2) gene, an important regulator of endoderm development.57 Braveheart (Bvht) is a mouse-specific heart-associated lncRNA that plays a pivotal role during cardiac development. Bvht regulates the core cardiovascular gene network and its depletion results in failure of activation of key cardiac factors necessary for correct heart development and cardiomyocyte differentiation.17 It has been found that Bvht is necessary to maintain fetal and neonatal cardiomyocyte homeostasis. Bvht interacts with the PRC2 complex during cardiac lineage commitment and is required for proper cardiac gene expression in mice.43 Bvht also acts upstream of Mesp1 and is required to active Mesp1-driven gene expression program and to promote cardiac cell fate from nascent mesoderm.17 Similarly, Bvht is necessary for embryonic stem cells to acquire cardiac lineage commitment and differentiation into cardiomyocytes. The lncRNA Carmen is a superenhancer-associated and well-conserved lncRNA located upstream of miR-143 and miR-145, two miRNAs involved in cardiovascular development.44 Interestingly, the lncRNA Bvht is located downstream of Carmen whose expression modulates the expression of other cardiac-associated lncRNAs located in the same genomic locus, including Bvht. Carmen is expressed in both fetal and adult hearts and plays an essential role during the earliest steps of cardiac lineage commitment for the regulation of cardiac differentiation from nascent mesoderm by modulating the expression downstream of Mesp1-cardiac gene network.45 The lateral plate mesoderm-specific lncRNA Fendrr is essential for the proper heart and body wall development in the mouse. Depletion of Fendrr results in the upregulation of several transcription factors controlling lateral plate or cardiac mesoderm differentiation, accompanied by a drastic reduction.47 Similar to Bvht, Fendrr interacts with PRC2 as well as with trithorax group/MLL protein (TrxG/MLL) complexes at a specific set of promoters, suggesting that it acts as a modulator of chromatin signatures that define gene activity.47 Although both lncRNAs Bvht and Fendrr interact with PRC2, they may counteract each other to control the extent of PRC2 occupancy because unlike Bvht that decoys PRC2, Fendrr helps to anchor PRC2 on the promoters of Foxf1and Pitx2, two transcription factors essential for early cell fate decision.9 In addition, Fendrr has shown a long-lasting effect. The chromatin signatures of promoters established in the early phase of a differentiation process can persist through several stages of differentiation until the gene is finally activated by a transcription factor.47 Fendrr is located 1250 base pairs upstream of Foxf1, a vital transcription factor for the proper differentiation of lateral mesoderm in splanchnic mesoderm and somatic mesoderm. In silico analyses have revealed that Fendrr interacts with Foxf1 and Pitx2 promoters via the formation of a dsDNA:RNA triplex structure.47, 58 The LncRNA-uc.167 shows a prominent expression in human heart tissue of ventricular septum defect patients.51 LncRNA-uc.167 is well conversed between species. It is located in the antisense strand of Mef2c gene that encodes myocyte enhancer factor 2C and their coexpression follows an inverse pattern throughout cardiac development.51 The overexpression of lncRNA-uc.167 results in the inhibition of Mef2c and the absence of cardiomyocyte maturation in P19 cells that is followed by a higher level of apoptosis and a slower proliferation rate.17 On the contrary, the effects of lncRNA-uc.167 overexpression are partially reduced by Mef2c overexpression, suggesting a functional relationship between them.51 Thus, the coexpression of Mef2c and uc.167 can partially reverse the negative effects of uc.167 on proliferation, apoptosis, and differentiation of P19.51 The human-specific long intergenic ncRNA PANCR is expressed in the adult left atrium and in lower levels in the adult eye. PANCR positively regulates the expression of PITX2c mRNA during

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the differentiation of cardiomyocytes.55 The coexpression mechanisms of PANCR and PITX2c are still unknown and it will be interesting to explore the role of this lincRNA in cardiac development in order to provide more information on left-right asymmetry pathway.55 Hand2-associated lncRNA Upperhand (Uph) is located 150 base pairs upstream of Hand2 gene and shares a bidirectional promoter with this transcription factor. Hand2 is an ancestral regulator of heart development and controls the reprogramming of fibroblasts into cardiomyocytes.56 Uph is cotranscribed bidirectionally with Hand2. Chromatin immunoprecipitation analyses (ChIP) have shown that the termination of Uph transcription results in the loss of Hand2 expression in the heart of Hand2 knock out embryos resulting in right ventricle hypoplasia and embryonic lethality.56 CircRNAs are expressed in cardiovascular tissues and body fluids and are more stable than other ncRNAs due to circularization that protects them from endonuclease activities. Over 10,000 highly expressed circRNAs have been identified in the cardiac tissue from humans and rats as well as in human embryonic stem cells differentiated to cardiomyocytes, consistently with a significant role in the gene regulatory network of cardiac development.59 Functional enrichment studies suggested that circRNAs may play important roles in the pathways that are specifically activated during cardiac differentiation.60 CircRNAs having a known function in cardiovascular system and pathophysiology are summarized in Table 14.2. Several circRNAs expressed in the human heart are derived from the muscle (cardiac and/or skeletal)-expressed genes, including titin (TTN), RYR2, and DMD. The most abundantly expressed circRNAs in the heart tissues are circRNA slc8a1 and circRNA rhobtb3. Slc8a1 regulates a sodium/calcium exchange that is critical for heart development and contraction. Rhobtb3 is a Rab9-regulated ATPase required for endosomes to Golgi transport and is highly expressed in the heart.38 The TTN circRNA isoforms are increased during the progression of the cell differentiation to cardiomyocytes in neonatal and adult rat hearts, indicating that differentially expressed TTN-derived circRNAs may participate in postnatal heart growth.70 A heart-related circRNA (HRCR) is constitutively present in mouse hearts and is repressed in hypertrophic and failing hearts.71 The different expression profiles of two circRNAs, circPCMTD1 and circTUBA1B, have been reported during all cardiac differentiation stages, circPCMTD1 continuously increased, while circTUBA1B continuously decreased.72 A group of 479 circRNAs including circSLC8A1-1, circTTN-275, and circALPK2-1.41 has shown strong positive correlation with the differentiation time course of human embryonic stem cells to cardiomyocytes.73 In addition to heart development, circRNAs play important roles in vascular development. The circRNA cZNF292 expressed in endothelial cells of the human umbilical vein, in microvessels, and in the aorta has a critical role in promoting angiogenesis. In hypoxic condition, the expression of cZNF292 is increased, while in vitro silencing of cZNF292 inhibits the normal function of endothelial cells.71

14.4.2 LncRNA and circRNA landscape in heart failure Heart failure represents one of the major public health problems with an estimated prevalence of 1%– 2% and mortality ranking from 5.6% to 25.7% in the adult population in developed countries.71, 74 High mortality and morbidity of HF is usually linked to the functional and structural damage of ventricular filling or ejection of blood. HF as a terminal stage of most types of cardiovascular diseases results from an altered cardiac homeostasis. Cardiac homeostasis requires the finely tuned balance between chemical and electrical stimuli that are responsible for the complicated mechanism of contraction that provides a continuous circulation of blood throughout the body. However, pathological changes that occur in cardiac muscle under stressful conditions, including change of morphological structures and

Table 14.2 Circular RNAs in cardiovascular system and pathophysiology. CircRNA

Species

Method

Sample

Role

Target

Reference

circ_013216

Mouse

qPCR

Mediator of HF occurrence and development

Mouse

qPCR

ciRS-7 MFACR

Mouse Mouse

qPCR qPCR

miR-181a-3p, miR-486a-5p, and miR-486b-5p miR-124, miR200a, and miR-141 miR-7 miR-652-3p

61

circ_010567

Heart left ventricular tissues Heart left ventricular tissues Heart tissue Cardiomyocytes

62 6, 63

hsa_circ_0124644 MICRA

Human Human

qPCR qPCR

Blood Blood

Unknown Unknown

64 65

HRCR circ-foxo3

Mice Mice

qPCR qPCR

Heart tissue Heart tissue

miR-223 ID-1, E2F1, FAK, and HIF-1α

66 67

circWDR77

Human

qPCR

VSMCs

miR-124

68

CDR1as

Mouse

qPCR

Cardiomyocytes

miR-7a

69

Mediator of HF occurrence and development Inhibits cardiomyocyte apoptosis Upregulates apoptosis and mitochondrial fission Diagnostic biomarker for CAD Predictor of left ventricular dysfunction after acute MI Inhibits cardiac hypertrophy and HF Interacts with ID-1, E2F1, FAK, and HIF-1α and induces cellular senescence in aging hearts Regulates VSMC proliferation and migration via targeting miR-124 and FGF2 Upregulates the expression of PARP and SP1 acting as a miRNA sponge and promotes apoptosis

61

CAD indicates coronary artery disease; HF, heart failure; MI, myocardial infarction; qPCR, quantitative polymerase chain reaction; VSMCs, vascular smooth muscle cells.

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mechanical and chemical activities as well as metabolic patterns, can result in cardiovascular remodeling featuring numerous cardiovascular diseases. Different types of cells such as cardiomyocytes, vascular smooth muscle cells, endothelial cells, and cardiac fibroblasts are involved in cardiac remodeling. Cardiac physiology depends on the precise control of gene expression patterns and disruption of gene transcriptional network may lead to the pathological phenotype. Multiple types of RNA molecules, either encoding proteins (mRNAs) or lacking protein-coding potential (ncRNAs) are largely involved in the cardiac regulation of gene expression.75 NcRNAs are crucial epigenetic regulators of cardiac gene expression and significantly influence cardiac homeostasis and functions.76 Recently, RNA-sequencing-based transcriptome profiling studies have revealed dynamical changes in lncRNA and circRNA expression profiles during pathogenesis in experimental HF models as well as in HF patients. LncRNAs have been found to be altered in the developing or diseased heart and are involved in numerous pathways regulating the pathophysiology of the vascular system associated with HF. Heart-specific myosin heavy-chain-associated RNA (MHRT) lncRNA is expressed at low levels in the fetal heart, increasing in the adult heart.72 Analyses of expression profiles have shown that circulating levels of MHRT as well as of noncoding repressor of NFAT (NRON) lncRNA were significantly higher in plasma samples of HF subjects.54, 77 A study of the lncRNA transcriptome in ischemic HF revealed that lncRNA H19 is significantly upregulated in failing murine hearts, suggesting a role for hypoxia-regulated lncRNA expression in heart failure.78 The functions of H19 are diverse and one of them is to serve as a “sponge” to trap miRNAs and disable their functions.78 For example, H19 as a sponge for miR-675 regulates a wide range of biological processes including cardiac hypertrophy and cardiomyocyte apoptosis through the inhibition of CaMKIIδ and VDAC1 expression.79 Several single nucleotide polymorphisms (SNPs) in lncRNAs including MIAT and antisense noncoding RNA in the INK4 locus (ANRIL) have shown strong correlations with cardiovascular disease. A strong correlation between lncRNAs and HF has been reported for ANRIL, KCNQ1OT1, MIAT, and MALAT1 in a cohort of 414 myocardial infarction patients.80 ANRIL was shown to be highly expressed in atherosclerotic plaques and might be an accurate regulator in the inflammatory nuclear factor kappa-B pathway.42 Given the importance of inflammation in HF progression, ANRIL might respond to inflammatory stimuli in both myocardium and peripheral blood mononuclear cells of HF patients.81 Silencing of ANRIL may have diverse outcomes such as: (1) improved pathological state of myocardial tissue and myocardial remodeling, (2) decreased myocardial collagen deposition area and cardiomyocyte apoptosis, and (3) reduced oxidative level of myocardial tissue in diabetic rats.81, 82 A group of 14 lncRNAs is significantly modulated in nonend-stage HF patients. Among these lncRNAs, CDKN2B-AS1/ANRIL, EGOT, H19, HOTAIR, LOC285194/TUSC7, RMRP, RNY5, SOX2OT, and SRA1 are aberrantly expressed in end-stage failing hearts.81 Transcription of Kcnq1, a gene encoding a potassium channel in the heart, depends on the expression of the un-spliced lncRNA Kcnq1ot1, whose transcription starts at intron 10 of Kcnq1 and in the opposite direction of the host gene. Normal cardiac function depends on the correct potassium channel activity and altered Kcnq1ot1-mediated control of Kcnq1 leads to abnormal heart function.10 Ubiquitously expressed mitochondria-originated lncRNA named long intergenic noncoding RNA predicting cardiac remodeling (LIPCAR) promotes cell proliferation, migration, and phenotypic switch

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of vascular smooth muscle cells.6 LIPCAR is also associated with mitochondrial dysfunction and is regulated in plasma samples from patients developing HF after acute myocardial infarction and predicts survival.50 The knowledge of the expression patterns of circRNAs, their regulation in disease states, and pathophysiological role in HF is still poorly known. Only a few circRNAs have been shown to be involved in cardiac function.71 Random screening of circRNA database has revealed that heart-related circRNA (HRCR) is downregulated in cardiac hypertrophy and HF models.73 RNA-hybrid prediction, biotin-based pull-down assay, Ago2 immunoprecipitation, and fluorescence in situ hybridization proved that HRCR binds miR-223, resulting in the increase of activity-regulated cytoskeleton-associated (ARC) gene expression.71 MiR-223 overexpression is sufficient to induce cardiac hypertrophy, while miR-223 inhibition blocks isoproterenol-induced cardiac hypertrophy in HF mouse model.66 By inhibiting miR-223, HRCR could have a protective effect against cardiac hypertrophy and HF. Furthermore, a circRNA microarray analysis of dysregulated circRNAs in the myocardium of mice with HF caused by myocardial infarction has reported 63 circRNAs differentially expressed 8 weeks after induction of myocardial infarction. 29 circRNAs were upregulated and 34 were downregulated.61 Interestingly, the expression of these circRNAs is not correlated with the expression of their host genes, indicating an independent regulation of circRNAs formation. Quantitative real-time polymerase chain reaction (qPCR) confirmed that two circRNAs, circ_013216 and circ_010567, are upregulated in HF.61 The potential miRNA targets of circ_013216 are miR-181a-3p, miR-486a-5p, and miR-486b-5p, and of circ_010567 are miR-124, miR-200a, and miR-141.61 CircRNA ciRS-7 (circRNA sponge for miR-7) is highly and widely associated with Argonaute (AGO) proteins in a miR-7-dependent manner. The downregulated miR-7 ultimately inhibits cardiomyocyte apoptosis.62 A member of epidermal growth factors receptor family (ERBB2) is targeted by miR-7 in human HF as well as in murine hearts undergoing stress.83 However, the role of circRNA ciRS-7/mir-7/ERBB2 axis in HF is still unclear. The mitochondrial fission and apoptosis-related circRNA (MFACR) mediates cardiomyocyte apoptosis in the heart by directly sequestering and downregulating miR-652-3p in cytoplasm, resulting in suppressed mitochondrial protein MTP18 translation.6 MTP18 is a nuclear-encoded mitochondrial membrane protein contributing to the mitochondrial fission in mammalian cells. Thus, the MFACR/ miR-652-3p/MTP18 axis constitutes a signaling pathway that regulates cardiomyocyte mitochondrial fission and apoptosis which can result in the cardiac remodeling tightly associated with HF.6 Nevertheless, the specific functions of lncRNAs and circRNAs transcripts within the heart or vascular tissue remain poorly explored. Expression profiles of lncRNAs and circRNAs in human HF is a first step toward the exploration of their putative involvement and functional roles in HF.

14.4.3 LncRNAs and circRNAs as biomarkers for heart failure Several studies have shown that ncRNAs can be detected in extracellular body fluids such as plasma or urine and display a dynamic alteration that may reflect the different disease stages.77 Thus, the human noncoding secretome is composed of circulating ncRNAs that could be used as biomarkers for diagnosis and outcome prediction of cardiovascular disease. In comparison with standard protein-based or peptide-based diagnostic assays, the biochemical nature of ncRNAs offers better stability and flexible storage conditions of the samples, and increased sensitivity and specificity.84 Among secreted

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ncRNAs, miRNAs, lncRNAs, and circRNAs have been proposed as biomarkers in different cardiovascular diseases.84 LncRNAs and circRNAs have multiple intracellular regulatory functions and they are able to directly or indirectly alter intercellular communication. They have several structural and functional characteristics that make them interesting disease markers. First, lncRNAs and circRNAs can enter the circulation encapsulated in exosomes and extracellular vesicles or inside of apoptotic bodies released from dying cells that extend their half-lives upon extracellular release. Second, the interaction with RNA-binding proteins extends their stability in the circulation. Third, the circular anatomy of circRNAs makes them resistant to the degradation by exoribonucleases (RNases). Given these characteristics, lncRNAs and circRNAs are more stable and easily detectable in extracellular body fluids. Although the function and regulation of lncRNAs and circRNAs in HF are still ambiguous, several lncRNAs and circRNAs have emerged as promising biomarkers for diagnosis and treatment of HF. Among lncRNAs, mitochondria-originated lncRNA LIPCAR displays plasma levels associated with HF outcomes in elderly patients with a long-term history of arterial hypertension.85 Also, LIPCAR plasma levels are associated with left ventricular remodeling after myocardial infarction and increased risk of developing HF.50 In addition, plasma levels of LIPCAR and H19 were increased in coronary artery disease patients with chronic HF.74, 86 Significantly increased serum levels of the lncRNAs LIPCAR, MIAT, and endothelial cell-enriched migration/differentiation-associated long noncoding RNA (SENCR) were associated with left ventricular remodeling in well-controlled type 2 diabetes patients.87 Tan et al.88 reported significantly increased serum levels of the lncRNA MIAT in patients with coronary heart disease suggesting its diagnostic and prognostic value. Levels of the lncRNA CDKN2B antisense RNA 1 (ANRIL) were increased in serum and peripheral blood mononuclear cells of HF patients compared to control patients indicating its potential role in HF pathogenesis.81Also, the increased plasma levels of ANRIL have shown an association with a higher risk for stent restenosis.89 The circulating lncRNAs NRON and MHRT have been proposed as independent predictive biomarkers for HF.54 Significantly downregulated levels of ANRIL, KCNQ1OT1, MIAT, and MALAT1 have been reported in patients with ST-segment-elevation acute myocardial infarction, predicting the individuals that would develop left ventricular remodeling, particularly in the case of ANRIL and KCNQ1OT1.53 Aberrant expression levels of some circRNAs have been reported in the pathogenesis of cardiovascular disease suggesting that the analysis of the circRNA levels in body fluids may become a new method for CVD diagnosis. For instance, peripheral blood-regulated hsa_circ_0124644 has been proposed as a biomarker for coronary artery disease64 and circular RNA named myocardial infarctionassociated circular RNA (MICRA) was reported as a predictor of left ventricular dysfunction after acute myocardial infarction.65 The predictive value of MICRA has been confirmed in two independent cohorts of acute myocardial infarction patients strengthening the biomarker value of circRNAs.71 Yet, the biomarker potential of lncRNAs and circRNAs for HF remains poorly explored. The possibility of detecting lncRNAs and circRNAs in extracellular body fluids discloses an enormous pool of molecules that may predict early mortality in patients with cardiovascular disease including HF independently of the etiology and may improve clinical decision making for a more personalized treatment of “high-risk” patients.74 In line with that, the standard diagnostic noninvasive cardiac techniques such ultrasound scanning, cardio computer tomography or positron emission tomography and the determination of cardiac electrophysiological parameters need to be supported with protein- or RNA-based biomarkers.84

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14.4.4 LncRNAs and circRNAs as therapeutic targets for heart failure Heart failure therapy has been only slightly changed over the past few years and is based on the combination of renin-angiotensin-aldosterone system blockers and β-blockers. However, the high mortality and morbidity rate of HF pinpoints the necessity for the development of novel diagnostic and therapeutic strategies. The human noncoding secretome may provide an interesting pool of RNA molecules for the treatment of cardiovascular disease including HF. As we outlined in previous sections, lncRNAs and circRNAs display dynamic changes in circulating levels related to the initiation or progression of the disease. Despite a plethora of identified lncRNAs and circRNAs associated with cardiovascular disease, the therapeutic approaches modulating lncRNAs and circRNAs in vivo are still in their infancy. The strategies modulating lncRNAs or circRNAs related to the cardiovascular system should target specific molecular processes, such as inflammation, angiogenesis, fibrosis, or cell growth.9 Manipulating the expression of specific lncRNAs or circRNAs in vivo might be achieved by overexpression or knockdown genetic methods. Currently, gapmeRs (8–50 nucleotides, long single-stranded oligonucleotides) represent the most potent antisense oligonucleotides used for pharmacological silencing of specific nuclear lncRNAs in vivo. GapmeRs consist of a DNA core flanked by two locked nucleic acids (LNAs) that form base pairs with target lncRNA to induce degradation via RNAse H-dependent mechanism.90 The successful therapeutic use of gapmeRs to silence lncRNAs has been demonstrated in animal models. The lncRNAs Chast and Meg3 have been successfully inhibited by gapmeRs in a pressure overload model while the lncRNA Whisper has been targeted in a model of myocardial infarction. In addition, the inhibitory effect of gapmeRs on lncRNAs has been reported for MALAT1, which resulted in a functional reduction in blood flow recovery and capillary density after hind limb ischemia, and for lincRNA-p21 that increased neointimal hyperplasia in an Apoe-knockout mouse model of carotid artery injury.9 On the contrary, therapeutic overexpression of lncRNAs in vivo is more challenging due to the use of viral-mediated gene delivery options, nanoparticles, or RNA mimics. The biochemical nature of circRNAs and a high cell-type specificity and stability raise a question whether circRNAs can be used as therapeutic agents or targets for human disease. The future use of circRNAs as therapeutics could be envisaged as the modulation of native disease-linked circRNAs by therapeutic knockdown or by ectopic expression and/or artificially designed circRNAs with target molecular effects.91 Several in vitro experiments have demonstrated a successful overexpression of protective circRNAs from RNAP II-driven constructs on standard DNA expression vectors and from lentiviral or adenoviral vectors in vivo.91 The circRNAs MFACR, circHIPK3, cZNF609, and circFoxo3 have been inhibited in vivo by using short hairpin (shRNAs) and siRNAs to trigger RNAi.66, 91, 92 In line with these encouraging results, it is conceivable that lncRNAs and circRNAs may serve as therapeutic targets in the near future.

14.5 Translational medicine LncRNAs and circRNAs regulate gene expression epigenetically through various actions at the transcriptional or translational levels rising frequent “off-target” effects and less predictable effects. Although characterization of distinct lncRNAs and circRNAs and their relation to cardiovascular

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pathologies are still emerging, recently they gained attention as interesting diagnostic and therapeutic targets for cardiovascular disease including HF. Several studies demonstrated that lncRNAs and circRNAs are capable to distinguish HF patients with reduced or preserved ejection fraction, different forms of hypertrophic cardiomyopathies, or predict survival in HF and acute myocardial infarction patients.50, 65, 81 The recent concept of targeting lncRNAs and circRNAs to treat cardiovascular condition such as HF is similar to miRNA targeting. In the last decade, miRNAs targeting gained more attention in translational and clinical research due to their therapeutic promise. The most popular approach of modulating miRNA expression is by introducing sequence-specific antisense oligonucleotides (ASO), small-sized single-stranded nucleic acids resistant to cellular degradation. To date, the most widely reported disease processes treated with ASOs are cancer and HIV. The lncRNAs can be targeted by ASO or small interfering RNAs (siRNAs) via the Watson-Crick base pairing principle. For instance, ASO targeting of lncRNA MALAT1 has shown promising results as an effective anticancer drug.93 Several advantages of ASO in comparison with siRNAs include better stability, less off-target effects, free uptake by the cell, and less cytotoxicity. They have shown promising potential to be used in terms of targeting both nuclear or cytoplasmic lncRNAs and circRNAs. Functional studies targeting lncRNAs and circRNAs demonstrated that they are capable to affect cardiac function and outcome in preclinical models. Three lncRNAs, Carl, APF, and NRF have been verified as therapeutic targets for ischemic heart disease, one of the major causes of HF globally.94 CircRNA HRCR acts as an endogenous miR-223 sponge to sequester and inhibit miR-223 activity in cytoplasm resulting in the increased ARC expression in mice.66 Thus, modulation of HRCR, miR-223, and ARC provides an attractive therapeutic pathway for the treatment of cardiac hypertrophy and HF. The development of therapeutics targeting lncRNAs or circRNAs should focus on translating experimental findings to the clinical level and investigating their therapeutic potential for the diagnosis and treatment of HF.

14.6 Challenges and next steps Increasing body of evidence has revealed that lncRNAs and circRNAs may be considered as promising biomarker candidates and/or therapeutic targets for HF. Nevertheless, several challenges need to be addressed. First, future studies need to focus on a deeper understanding of the biological function of lncRNAs and circRNAs in the initiation and progression of cardiovascular disease including HF. In addition, better resources for measuring their interactions between proteins or RNA-RNA interactions need to be provided. Second, lncRNAs tend to be generally expressed at lower levels compared to miRNAs that make their expression profiling by available profiling techniques more challenging. Also, properly sized and properly designed patient cohorts shall be engaged into biomarker discovery studies. Third, lncRNAs are less evolutionarily conserved compared to circRNAs or miRNAs, which limit their clinical application although they may possess biological function. Thus, an in vivo experimental system suitable to study nonconserved human lncRNAs needs to be proposed. Fourth, a few experiments demonstrated a promising therapeutic approach to treat cardiac remodeling by the modulation of lncRNAs or circRNAs. However, before considering any application of targeting lncRNAs and circRNAs in human disease including HF, optimized delivery methods shall be implemented and the side effects and toxicity of modulating gene expression need to be carefully examined. A longstanding problem inherent to the overexpression of lncRNAs or circRNAs is using viral gene delivery methods. Also,

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lncRNAs and circRNAs have shown high tissue specificity and they have to be overexpressed in a celltype-specific manner. LncRNAs and circRNAs may exert different functions with respect to their subcellular localization that is crucial for the targeting strategies of modulation of lncRNAs in subcellular compartments (i.e., nuclear or mitochondrial lncRNAs). Despite the major technical hurdles for using lncRNAs and circRNAs as therapeutic agents and targets, it is not unlikely that in the near future their targeting in cardiovascular pathologies may offer a very powerful tool through which a protein or a signaling pathway could be finely tuned by clinically compatible therapeutics.

14.7 Conclusions As we described in this chapter, lncRNAs and circRNAs are increasingly recognized as important regulatory molecules related to cardiovascular biology and pathophysiology. The investigations summarized here demonstrated that the aberrant expression of lncRNAs and circRNAs strongly correlated with the genesis and progression of heart failure. Integration of current knowledge of lncRNAs and circRNAs with protein-coding and other noncoding genes is essential to deepen our understanding of the signaling pathways and transcriptional events underlying cardiovascular homeostasis and pathological phenotypes. Undoubtedly, as much as we learn about expression patterns of lncRNAs and circRNAs, the higher chance for an improved diagnosis and therapy and better prognosis will be.

Acknowledgments This chapter is based upon the work from EU-CardioRNA COST Action CA17129 (www.cardiorna.eu) supported by European Cooperation in Science and Technology (European Cooperation in Science and Technology). AJ is supported by the University of Tuzla, Bosnia and Herzegovina. YD is funded by the National Research Fund (Grants # C14/BM/8225223 and C17/BM/11613033), the Ministry of Higher Education and Research, and the Fondation Coeur—Daniel Wagner of Luxembourg. AJ is funded by the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skłodowska-Curie Actions individual grant (MITO, No.893435).

Disclosures The authors have no disclosures to declare.

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75. Gomes CPC, Schroen B, Kuster GM, et al. Regulatory RNAs in heart failure. Circulation. 2020;141(4): 313–328. 76. Dangwal S, Schimmel K, Foinguinos A, Xiao K, Thum T. Noncoding RNAs in heart failure. Handb Exp Pharmacol. 2017;243:423–445. 77. Hobuß L, B€ar C, Thum T. Long non-coding RNAs: at the heart of cardiac dysfunction? Front Physiol. 2019;10:30. 78. Choong OK, Chen CY, Zhang J, et al. Hypoxia-induced H19/YB-1 cascade modulates cardiac remodeling after infarction. Theranostics. 2019;9(22):6550–6567. 79. Li X, Wang H, Yao B, Xu W, Chen J, Zhou X. lncRNA H19/miR-675 axis regulates cardiomyocyte apoptosis by targeting VDAC1 in diabetic cardiomyopathy. Sci Rep. 2016;6:36340. 80. Hung J, Miscianinov V, Sluimer JC, Newby DE, Baker AH. Targeting non-coding RNA in vascular biology and disease. Front Physiol. 2018;9:1655. 81. Greco S, Zaccagnini G, Perfetti A, et al. Long noncoding RNA dysregulation in ischemic heart failure. J Transl Med. 2016;14(1):183. 82. Dai W, Lee D. Interfering with long chain noncoding RNA ANRIL expression reduces heart failure in rats with diabetes by inhibiting myocardial oxidative stress. J Cell Biochem. 2019;120(10):18446–18456. 83. Gupta MK, Xi W, Martelli E. Role of miRNA-7 in cardiac function. Circ Res. 2017;121:A103. 84. Stępien E, Costa MC, Kurc S, Drozdz A, Cortez-Dias N, Enguita FJ. The circulating non-coding RNA landscape for biomarker research: lessons and prospects from cardiovascular diseases. Acta Pharmacol Sin. 2018;39(7):1085–1099. 85. Santer L, Lo´pez B, Ravassa S, et al. Circulating Long noncoding RNA LIPCAR predicts heart failure outcomes in patients without chronic kidney disease. Hypertension. 2019;73(4):820–828. 86. Zhang Z, Gao W, Long Q, et al. Increased plasma levels of lncRNA H19 and LIPCAR are associated with increased risk of coronary artery disease in a Chinese population. Sci Rep. 2017;7:7491. 87. de Gonzalo-Calvo D, Kenneweg F, Bang C, et al. Circulating long-non coding RNAs as biomarkers of left ventricular diastolic function and remodelling in patients with well-controlled type 2 diabetes. Sci Rep. 2016;6:37354. 88. Tan J, Liu S, Jiang Q, Yu T, Huang K. LncRNA-MIAT increased in patients with coronary atherosclerotic heart disease. Cardiol Res Pract. 2019;2019:6280194. 89. Wang F, Su X, Liu C, Wu M, Li B. Prognostic value of plasma Long noncoding RNA ANRIL for in-stent restenosis. Med Sci Monit. 2017;23:4733–4739. 90. Lundin KE, Højland T, Hansen BR, et al. Biological activity and biotechnological aspects of locked nucleic acids. Adv Genet. 2013;82:47–107. 91. Holdt LM, Kohlmaier A, Teupser D. Circular RNAs as therapeutic agents and targets. Front Physiol. 2018;9:1262. 92. Zeng Y, Du WW, Wu Y, et al. A circular RNA binds to and activates AKT phosphorylation and nuclear localization reducing apoptosis and enhancing cardiac repair. Theranostics. 2017;7(16):3842. 93. Kumar M, DeVaux RS, Herschkowitz JI. Molecular and cellular changes in breast Cancer and new roles of lncRNAs in breast cancer initiation and progression. Prog Mol Biol Transl Sci. 2016;144:563–586. 94. Han D, Gao Q, Cao F. Long noncoding RNAs (LncRNAs)—the dawning of a new treatment for cardiac hypertrophy and heart failure. Biochim Biophys Acta Mol basis Dis. 2017;1863(8):2078–2084. 



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Artificial intelligence in clinical decision-making for diagnosis of cardiovascular disease using epigenetics mechanisms

15

Kanita Karad-uzovi
c-Hadzˇiabdi
ca and Antje Petersb Department of Engineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovinaa Department of € € Genetic Epidemiology, Institute of Human Genetics, University of Munster, Munster, Germanyb

15.1 Introduction Advancements in genetic research, including technological advances in high-throughput sequencing, have led to a generation of biomedical data at an unprecedented speed and scale. This so-called big data allows for a new paradigm of 21st-century medicine with a potential to save lives of future patients all over the world. Epigenetics is a good example of the importance of combining high-level biomedical laboratory work with customized computer science research. Machine learning methods have been successfully applied to identify essential information from big data needed to diagnose a disease. Machine learning models iteratively learn from the collected structured data and apply the acquired knowledge in disease diagnosis and prediction. They have the potential to assist medical experts and researchers at all levels. In the past decade, there has been a rise in the applications of machine learning methods used in diagnosis of cardiovascular disease. Machine learning is an important subdiscipline of artificial intelligence that aims to train the machine to iteratively learn from data. Two main categories of machine learning are supervised and unsupervised learning. In supervised learning, a model learns during a training phase using a data set labeled by the domain experts. In the test phase, the gained knowledge is used to make predictions of new “unseen” data or to diagnose a disease. Learned parameters in the training phase are the weights, which are computed by minimizing a loss function. The loss function is a measure of the model’s error. In unsupervised learning, labeled data are not available. The machine learning model needs to discover hidden patterns in the given data by generating groups (i.e., clusters) of data, which have specific characteristics in common. One can further identify a semisupervised learning approach, which is a hybrid between supervised and unsupervised methods. Common problems with biomedical data are poor data quality and a lack of labeled data. In such a case, the data need to be annotated by the domain experts before the analysis can be continued. Alternatively, unsupervised learning needs to be performed. Other challenges include the imbalanced class Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00020-1 Copyright # 2021 Elsevier Inc. All rights reserved.

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problem and high data dimensionality, which often occur in combination with low sample size, and integration of heterogeneous data. In the following sections, the machine learning workflow is presented. Subsequently, the most important stages within the workflow in the context of biomedical data and in particular epigenetic data are addressed. Common machine learning challenges are discussed. Finally, practical applications of machine learning methods in biomedicine, cardiology, and epigenetics are reviewed.

15.2 Machine learning 15.2.1 Overview of the machine learning workflow Fig. 15.1 shows a general workflow of the machine learning approaches using epigenetic data in a decision-making process of a cardiovascular disease. The depicted workflow starts with the data collection. The data come from a variety of sources (such as multiomics data, clinical data, environmental data, etc.). This is followed by data integration. The collected data are usually in a raw and unstructured form, which may also need to be annotated by a clinical or domain expert. The next step is a feature engineering process, which includes data preprocessing (data cleaning, formatting, transformation, and handling missing values) and feature selection. Subsequently, an appropriate machine learning model needs to be defined and selected. This is followed by model evaluation, and finally application of the acquired knowledge. An integral part of the mentioned pipeline involves splitting of the data into training, validation, and test sets.

15.2.2 Feature engineering and data set creation After the collection of raw data, a final data set needs to be created. This data set will be used by machine learning methods either in classification or regression tasks (in the case of supervised learning), or to discover hidden patterns in the data (in the case of unsupervised learning). However, any type of model is limited by the quality of information in the data set. Feature engineering is one of the most important steps in machine learning. Essentially, it is the process of identifying and extracting the most relevant features (also known as predictors or attributes) from the available data set that will lead to the desired output (for example, a disease prediction). Remaining features are not informative for achieving the desired output, and should, therefore, be discarded in order to decrease the dimensionality of the data set. Irrelevant features may also result in poor generalization of the test data.

15.2.2.1 Handling missing data Before moving on to feature selection, it is important to handle missing entries in the available data set. Entries may be either “missing at random” (MAR) or “missing not at random” (MNAR). In the MAR case, the reasons for missing data may not be related to the problem. In the MNAR case, the absence of values may provide useful information about the problem, for example, saturation of detectors.1 For large data sets, where the number of samples with missing values is small compared to the size of the data set, the easiest and quickest method to deal with the MAR values is data removal. However, this may not always be possible: for example, data removal may result in a data set where all samples belonging to the control group are removed, giving a whole new meaning to the data. In this case, data imputation needs to be performed. Furthermore, biomedical analysis methods such as some methods

FIG. 15.1 Illustration of the general machine learning workflow.

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for gene expression analysis may result in reduced performance in the presence of missing values. The aim of imputation is to fill in the missing data using statistical methods. The simplest way of imputing missing values is replacing all values with the neutral element with respect to the measurement, for example, 0. An alternative is to compute the overall mean value of a variable and use it to replace the missing value for that variable. This technique is reasonable when dealing with MAR values from a normal distribution. For MNAR values, the obvious disadvantage of replacing the missing value with the mean is that it reduces the variance of the data set. As a result, other means of imputations may be more suitable. Troyanskaya et al.2 perform a comparative analysis of three methods for imputation of missing DNA microarray values: a weighted k-nearest neighbors (KNN) method, a singular value decomposition method, and a row average method. Their results show that KNN-based imputation achieves the best performance in estimating missing microarray values. The method selects k genes that have expression profiles that are similar to the gene that contains the missing value. Afterward, a weighted average of values of expression profiles found in the k selected genes is used to estimate the missing value in the gene. By using predefined metrics for gene similarity, the authors find that the Euclidean distance achieves the best results. If the absence or the presence of values provides useful information about the problem, the best solution is to incorporate this information into the model. This has been performed by Kircher et al.3 In the case of MNAR, simply removing the samples with missing values may introduce a bias into the model, and thus imputation is the better option.

15.2.2.2 Heterogeneous data and data integration In biomedical research, large sets of data with heterogeneous data types have to be processed.4 DNA and RNA sequences, protein structures, phenotype data, and other types of data have to be integrated. One can distinguish several integration approaches5: •

•

•

The model-based integration approach (MBI) performs integration in two steps. First, different models are trained on different data sets independently. Second, an overall model is created based on these models. The overall model features only those variables that describe best the data sets in the first step. One example is the probabilistic causal network framework. It is based on a Bayesian network, where each node is a trait (e.g., a variation in methylation) and the edges are conditional probabilities of the trait to be realized based on the trait in the parent nodes. These networks grow exceedingly with the number of nodes, and thus the best model cannot be found by testing all possible structures. Often, a simulation based on Markov chains is chosen instead. Another example is the classifier network. A classifier predicts the membership of a sample to a certain group or class. The aim is to identify overall classifiers that integrate the individual classifier of the single data sets. The overall classifier is a weighted combination of the single classifiers. Another example for an integration technique is the concatenation-based approach (CBI). Here, all data are combined to a large data matrix. This matrix is used as an input for the model. The different data sets are merged based on common attributes. In many cases, a common attribute of different data samples is the patient ID. The data have to be cleaned, that is, missing values have to be handled and irrelevant features have to be removed. Then, tools such as iCluster6 can be used to identify clusters in the data. A third integration approach is the transformation-based integration approach (TBI). In this technique, each data set is transformed into the same format, for example, a graph, first. The graphs are then merged and used as input for the model of choice.

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15.2.2.3 Feature selection and dimensionality reduction Epigenetic data are very often high-dimensional. High-dimensional data refer to the data containing a very large number of features (variables) greatly exceeding the sample size. Feature selection aims at dimensionality reduction, that is, reducing the initial feature set to most important features that contain information needed to classify a disease. Selection of discriminative features helps to improve the prediction results and to avoid overfitting problems. For the success of a study, it is crucial to involve the domain experts in the feature selection process. They have the required knowledge about which features may be relevant at the problem at hand. Apart from reducing redundant features to obtain the most accurate outcome, the feature selection process may also be used to understand which features contribute most to the accuracy of the model. As an example, one may want to identify the most important epigenetic features or the most important genes with gene expression levels that correspond to a specific disease. Feature selection may be performed by any of the following three methods7,8: Filter methods These methods use the characteristics of the training data to perform feature selection as a preprocessing set prior the actual training taking place. The most popular filter methods are: correlation-based feature selection,9 information gain,10 chi-square,11 and ReliefF.12 Correlation-based feature selection methods aim to select features that are highly correlated to the class and not correlated with each other. Features that are not highly correlated to the class are removed. Redundant features are discarded as they will be highly correlated with some other features. The information gain method produces an ordered ranking of the features. A subset of relevant features is then selected by a defining a threshold. The chi-square method identifies features that represent a class according to the chi-square test, that is, by comparing observed and expected frequency of occurrence. ReliefF is a filter algorithm which is an extension of the Relief13 method. The main idea behind the Relief method is to assess the quality of features based on the ability of the feature to distinguish between nearest neighbor instances. Relevant features on the one hand should differentiate between instances from different classes, and on the other hand have the same value for instances from the same class. Wrapper methods These methods use the prediction performance of each algorithm to evaluate the relevance of the proposed features. In general, any machine learning algorithm can be used for training of the model. The prediction performance is then evaluated using the test data set. Even though wrapper methods usually use an iterative strategy for feature selection, and as a result tend to be computationally expensive, they are efficient in the selection of the best performing features for the specific machine learning method. Embedded methods In embedded methods, feature importance is evaluated by the selected machine learning algorithm during the training phase. Some examples include least absolute shrinkage and selection operator (LASSO) methods,14 Elastic Net,15 decision tree-based algorithms such as CART16 and C4.5.17 Techniques based on LASSO regression furnish certain features with zero weight. This way, features that do not contribute to the final prediction can be identified and thus, removed. The Elastic Net method overcomes limitations of the LASSO method such as the number of selected features being limited by the number of samples by introducing higher orders in the penalty terms. Decision tree-based feature selection algorithms rely on the capability of a node of a decision tree, that is, a feature, to divide a data set into two subsets. The feature is suited better, if the subsets it defines are close to the two subsets the labels of the training set specify. The measure that quantifies this capability is called impurity. The lower the impurity of a node, the higher the importance of the corresponding feature.

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A popular dimensionality reduction technique is principle component analysis (PCA). The main idea behind PCA is that it reduces the dimensionality of the data set while minimizing the loss of information. This is done by finding new uncorrelated variables (features) that are linear functions of variables in the original data set that successively maximize variance.18 An example is reduction of epigenetic variables registered in a large number of dimensions such as the number of genes in a genome-wide association study.19 Johnson et al.20 argue that the possible solution to the “high-dimensional data, small sample number” problem are regularized regression techniques. Regularized regression methods aim to introduce additional constraints in order to decrease the model complexity and ultimately to perform well to test data. Some examples of regularized regression techniques include LASSO regression, ridge regression, and Elastic Net regression.

15.2.2.4 Performance evaluation The performance of machine learning methods is quantified by evaluating the confusion matrix. A confusion matrix is a table, which contains the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) results. From these four measures, most commonly used performance measures for diagnostic predictors are accuracy, sensitivity, specificity, and precision, defined as follows: 1. Accuracy ¼ ((TP + TN)/(TP + FP + FN + TN))  100 2. Sensitivity ¼ (TP/(TP + FN))  100 3. Specificity ¼ (TN/(TN + FP))  100 4. Precision ¼ (TP/(TP+FP))  100 Accuracy measures the overall performance of a model. It measures the proportion of correctly classified instances out of all instances. Sensitivity measures the ability of a model to correctly predict the proportion of positives (e.g., the percentage of diseased patients that are correctly identified as diseased). Specificity measures the model’s ability to correctly predict the proportion of negatives (e.g., the percentage of patients without a disease that are correctly identified without the disease). Precision measures the ability of a model to correctly predict the proportion of correctly classified positives out of all positively classified instances (e.g., the percentage of correctly identified diseased patients that are actually diseased). Overall accuracy is commonly used as a single performance measure. However, if there is an imbalanced class problem, it is not enough to assess the performance using the overall accuracy. Sensitivity and specificity must also be considered for the assessment. In addition, the F-measure may also be used for evaluation. The F-measure is a weighted average of sensitivity and precision. Furthermore, the ROC curve is commonly used to visualize the performance of a binary classifier over all possible thresholds. It is generated by plotting the specificity on the x-axis against the sensitivity on the y-axis over all possible classification thresholds.

15.2.2.5 Imbalanced classes The generated data set may have an imbalanced class size. The samples of a data set can be organized into classes according to their output categories. In a classification problem which is imbalanced, class sizes are considerably different. There is at least one class whose number of instances greatly differs

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from the number of instances of the remaining classes. As a consequence, this may result in a poor performance of classifiers. Even though some classifiers may in general achieve satisfactory overall accuracy results, detailed analysis may reveal poor results of other performance measures such as sensitivity or specificity. Thus, the machine learning method will in general be able to classify a majority class with high accuracy (class with a high number of instances), but the method will poorly perform in classifying the minority class (class with lower number of instances). In practical biomedical applications, it is not uncommon to have an imbalanced biomedical data set. Some examples include diagnosis of rare diseases, where the number of positive observations is much lower than the number of negative observations. As revealed by Lin and Chen,21 other examples include gene expression signatures (used to differentiate primary from rare metastatic adenocarcinomas), identification of different subtypes of cancer, prediction of early intrahepatic recurrence of patients with hepatocellular carcinoma, etc. Imbalanced class size data clearly present a challenge in defining a prediction model for most machine learning methods. Another common problem is class prediction of high-dimensional imbalanced data. Lin and Chen21 identify three main factors that affect the performance of classifiers in minority class prediction: imbalance ratio of class sizes (ratio of minority class size to the majority class size), distribution of minority and majority class data as well as sample size (or the lack of training data). Lin and Chen evaluate a strategy for correction of class imbalanced classification based on (a) the performance of a class imbalanced classifier which depends on the selected machine learning (classification) algorithm, (b) the approach used for class imbalance correction, and (c) the performance metrics used in the evaluation. The most common strategy used for class imbalance correction is the data-based approach where either under-sampling the majority class or over-sampling the minority class is performed. One way to perform over-sampling of the minority class is using the synthetic minority over-sampling (SMOTE) technique.22 Within the SMOTE technique, new synthetic minority class data are generated by performing interpolation between several positive instances that are closely related to each other. Multiple over-sampling and multiple under-sampling ensembles are discussed in Chen et al.,23 where bootstrap samples of equal class size are created in the training set to generate ensemble classifiers. The algorithm-based approach is an alternative strategy used for class imbalance correction. This method modifies the machine learning classification algorithm to account for class imbalance. Some examples include the cost-sensitive learning method. Here, the cost refers to the negative impact of a misclassification with respect to the predictive power of a model. The method modifies the base algorithm by adjusting the decision threshold in order to assign class membership.24 Differential misclassification costs and prior probabilities are taken into account. Another example is the one-class learning approach, where the machine learning method is trained only on a single class to resolve the decision boundary.25

15.2.3 Machine learning algorithms 15.2.3.1 Supervised learning In a supervised learning problem, target labels are known. The problem at hand may be either a classification or a regression problem. In case of a classification problem, a model needs to identify each data sample with a predefined class category. In case of a regression problem, the output is a continuous numerical variable, which needs to be predicted. A data set is randomly split into training and

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test data. Additionally, a validation set may be used to measure the model’s performance by comparing the model’s predictions to actual observations. The training data set is used to fit the model, that is, the model “learns” from the given training data. The validation data set is used for evaluation of the previously fit model. The validation data set also serves for hyperparameter optimization. Hyperparameters are parameters of a model, which have to be set before the learning process starts. In case that the model’s hyperparameters can easily be tuned, the validation data set may be omitted. After the training phase and final model and hyperparameter selection, the selected machine learning method is run on the test data set, which is used for the evaluation of the final model. Test data should ideally be new, unobserved data. Splitting of the data set may either be done using a single train-(validation)-test split or k-fold cross-validation. In a single train-validation-test split, the data are split into a large portion of data allocated for training and the remaining data are further split into validation and test data. In k-fold cross-validation, the data are randomly split into k subsets (or folds) of approximately equally sizes. Training is then performed on k  1 samples (patient inputs), and testing on the remaining 1 sample subset. This is repeated k times, each time using a different subset as test data. The overall accuracy is obtained by computing the average accuracy of k runs. Some common choices for k are 5, 10, or n, where n is the number of samples in the data set. Setting k equal to n is also called leave-one-out cross-validation, and provides an opportunity for each data sample to be used in testing. k-fold cross-validation is usually the choice used for small data sets. The selected model should aim to minimize bias and variance. Bias refers to the difference between the average model prediction and the expected value. Models with high bias tend to be oversimplified. Such models have a very low number of features and result in underfitting. An underfit model fails to learn from the data, and thus results in poor generalization ability to make successful predictions on the unseen test data. Variance provides information of how scattered the predicted values are from the expected values between different realizations of the model. High variance models tend to successfully learn the patterns in the training data set, but perform poorly on the test data set. High variance indicates that the algorithm was also able to model the random noise in the training set. This is commonly also referred to overfitting. The following sections provide a brief summary of most widely used algorithms in the cardiovascular field: support vector machines (SVMs), neural networks, and the random forest algorithm.

Support vector machines SVMs are one of the most popular machine learning algorithms and have been frequently applied to medical problems. This is mostly due to their ability to effectively handle data nonlinearity and they are, therefore, well suited for complex epigenetic data. SVMs apply a kernel (i.e., a mathematical function) to map inputs into a multidimensional space. The model then constructs a hyperplane by maximizing the margin and minimizing the classification error to separate the data set into classes.26 Some commonly used SVM kernels include linear, nonlinear, polynomial, radial basis function, and sigmoid. To get the optimum classification results, it is advisable to test the model using several kernel functions. Another advantage of SVMs is that in general, the algorithm is less sensitive to input errors and is effective in minimizing outliers. They minimize the residuals that are outside a specified range of the estimate and are less susceptible to outliers.27 Due to their underlying structure, they need less memory and tend to be resistant to overfitting.

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Artificial neural networks Artificial neural networks, often referred to simply as neural networks, are another very popular algorithms modeled to mimic the human neural architecture. A neural network consists of artificial nodes (i.e., neurons) that are interconnected to form a network. The general structure of the model is formed by an input layer, one or more hidden layers, and an output layer. Neurons in each layer are connected to the neurons in the subsequent layer through a weighted connection. During the training phase, the model learns from examples provided in the training set. The main goal is to adjust the weights so that the difference between the desired output (labeled data) and the predicted output is minimized. The values of the weights are adjusted during the backpropagation algorithm that uses the gradient decent method26 to minimize the prediction error (i.e., the loss function). After the training is completed (i.e., the error is minimized), the final weights are computed and the knowledge stored in the weights can be used during the testing phase to classify the “unseen” data (e.g., specific patient traits). The number of input and neurons are already defined in the task specifications; however, the number of hidden layers for each task needs to be determined. Just as the SVM, neural networks are powerful in capturing complex nonlinear relationship in the data. Deep learning models are artificial neural networks that contain multiple hidden layers of neurons. In general, they have high accuracy, but are more computationally expensive than other machine learning methods. However, as the computing power of the machines increased overtime, deep learning methods quickly gained popularity. More details on deep learning can be found in a separate section (cf. deep learning). Fig. 15.2 illustrates an example application of neural networks to the classification of cardiovascular disease (CVD) using epigenetic data.

FIG. 15.2 Architecture of an artificial neural network using biomedical data as input.

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Random forest The random forest algorithm is commonly applied in biomedical research. It is a tree-based algorithm that makes a prediction using a group of decision trees, that is, forming a forest (cf. Fig. 15.3). A major drawback of simple decision trees is that in general, they suffer from high variance and therefore tend to overfit the data, especially if the tree is very deep. To overcome the high variance problem, random forests apply bootstrap aggregation, commonly referred to as bagging. In bootstrapping, each decision tree uses a random subset of samples from the training set. The random forest method applies bagging with a slight tweak to the classical bagging approach: instead of using all of the available features, a decision tree is trained by selecting a random subset of features (cf. ensemble methods for more details on bagging). This helps to decrease the correlation between individual decision trees, and hence reduce the error of the random forest. The number of randomly selected subsets of features is a parameter pffiffiffiffithat needs to be set. A general recommendation is to set the number of randomly selected features to N for classification problems and to N/3 for regression problems, where N is the number of predictors (i.e., initial input features) in the classification/regression problem.28 Afterward, the model combines the decision of many different decision trees to produce useful results, where each of the forest’s decision trees is a weaker model than a single decision tree. In classification tasks, the random forest method makes a final decision based on the majority vote of m decision trees, with m  M and where M is the number of all decision trees. In regression, final prediction is done by averaging the decision of m decision trees. Random forests are more powerful and robust than a single decision tree as they reduce overfitting as well as the error due to bias. Each decision tree can be considered as a medical expert, and

FIG. 15.3 Illustration of the random forest algorithm with gene expression and phenotype data as input features. Predicted classification results are “case” or “control.”

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depending on the physician’s expertise, each will apply different patient traits, but their aggregate decision would in general be better than a decision of a single expert.20

15.2.3.2 Unsupervised learning In unsupervised machine learning, the labels of the data are either not available or not yet known. A machine learning model discovers hidden patterns in the data by identifying groups of samples with specific characteristics in common. A measure of the accuracy of the unsupervised learning is the likelihood of a model to generate a certain data set assuming a certain distribution. The aim is either to perform a clustering of the data, that is, to find natural clusters in the data, or to determine an association. Thus, associations among data objects inside large databases are established. One of the most popular unsupervised clustering algorithm is the k-means clustering: k-means clustering aims to put the samples of a data set into k clusters by finding an optimal distribution of samples with a minimal variance within clusters.

15.2.3.3 Semisupervised learning Semisupervised learning utilizes both supervised and unsupervised learning approaches. Semisupervised learning is suitable when there is a large data set but only a portion of the data set is annotated. Briefly, this approach uses the trained model predictions to annotate the unlabeled data. A practical scenario of semisupervised learning is gene-finding systems. In this example, the available data set usually contains an unlabeled set of the entire genome sequence and a smaller set of annotated genes. In the initial step, the model is trained with supervised learning using annotated genes. Afterward, the trained model is used to predict and assign tentative labels to the unlabeled set. The procedure can be iterated to improve the trained model. The process is repeated until the model finds no more predictions, that is, new genes.1

15.2.3.4 Ensemble methods Ensemble methods combine multiple base classifiers (i.e., “weak learners”) to achieve better performance results than a single classifier. In general, ensemble methods attempt to reduce the variance and/ or the bias of the base models. They have been successfully applied in bioinformatics to deal with small sample size, high-dimensional complex data, with applications in classification of gene expression and mass spectrometry-based proteomics data, gene-gene interaction identification, prediction of regulatory elements from DNA and protein sequences, etc.29 Pes et al.30 evaluate the ensemble approach for feature selection in the context of biomarker discovery from genomic data. Recently, AbdollahiArpanahi et al.31 explored deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Random forests are a popular example of ensemble methods, which are effective in dealing with high-dimensional data. Other effective ensemble methods are bagging and boosting, which can deal with small sample size data sets.29 Bagging attempts are used to reduce the high variance problem (over fitting) of individual weak learners. Bagging stands for bootstrap aggregation where each individual model uses the bootstrapped data set (selection of random subsamples of the same size with replacement), and makes predictions based on aggregation of predicted outputs from all models. Boosting and stacking attempts are used to produce a model that is less biased than each individual model (and possibly to reduce variance). Models are trained independently and in parallel. Note: Unlike in random forest, in “classical” bagging, all rather than a random subset of features are selected. Boosting methods apply weighted averaging of individual predictions. Unlike bagging,

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boosting models are trained sequentially where each model depends on the previous one. The basic idea is that each model in the sequence gives more importance (a higher weight) to samples that were incorrectly classified in the previous models (i.e., each model tries to correct the previous model’s mistakes). Popular examples of boosting methods are AdaBoost (adaptive boosting),32 Gradient Boosting,33 and XGBoost.34 Other forms of ensemble methods such as ensemble of SVMs, metaensemble (ensemble of ensembles), and ensemble of heterogeneous classification algorithms have also been utilized in bioinformatics to improve the performance results of single classifiers showing promising results.29,35

15.2.3.5 Deep learning Deep learning algorithms are becoming more and more popular in the field of biomedicine, for example, Zou et al.36 and references therein. With the rise of the computer gaming industry, graphical processing units (GPUs) were developed and improved. These processing units make the training and testing of complex models on large data sets feasible as they are inexpensive and allow for high parallelization. Especially in the field of image processing, deep learning approaches are the most popular machine learning approach. The aim of deep learning approaches is to identify complex patterns in large data sets. The term “deep learning” refers to the usage of large neural networks. Briefly, neural networks are based on artificial neurons. Neural networks attempt to imitate the performance of the human brain. Output data are produced by processing the input data through different layers of processing units and thus different layers of abstraction (cf. neural networks for an introduction). Deep learning algorithms can be applied in supervised, unsupervised, and semisupervised tasks. In the field of biomedicine, prior knowledge of the model underlying the data is not always available for different reasons. Deep learning algorithms are able to gain stable results with the need of less knowledge of the problem at hand compared to machine learning techniques such as random forests or SVMs. In contrast to these methods, the accuracy of deep learning algorithms may be increased by increasing the amount of training data or the size of the network, often without the need of one’s further analysis of the background domain knowledge. Although deep learning algorithms often achieve very good performance results, the decision process is hardly transparent and lacks in interpretability.37 Hence, they are often described as “black boxes.”

15.3 Machine learning applications 15.3.1 Application of machine learning in cardiology Precision medicine is steadily becoming a new field of medicine whose main aim is to provide more personalized, more precise, and data-driven (e.g., genetic data) medical treatments to individual patients. This is especially true due to the availability of a new generation of sequencing technologies. The use of a patient’s genomic information is not only becoming increasingly important and a fundamental step in clinical decision making in cardiovascular medicine, but also it is important in other disciplines such as investigation of rare diseases, cancer diagnosis, or involuntary infertility. Machine learning methods are valuable tools in dealing with large, multidimensional, and rapidly changing data sets. They can provide essential assistance in analysis of risk factors of cardiovascular diseases, including the classification of different types of heart failure. Cardiovascular diseases are mostly heterogeneous with many comorbidities and a long incubation period. Thus, many different factors have to be

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taken into account with very different impacts to the manifesting disease. Machine learning techniques in cardiology are used in many contexts, such as exploring genotypes and phenotypes in diseases, enhancement of patient care, for example, by means of risk management, cutting down readmission and mortality rates, or lowering the cost of treatments.38

15.3.1.1 Image processing Image processing was one of the first applications of machine learning in the biomedical field.7 Different sources of images have been used, such as echocardiographic, X-ray, and magnetic resonance images.39 One such example is the automated discrimination between hypertrophic cardiomyopathy and a physiological hypertrophy of athletes’ hearts based on echocardiographic images.40 A neural network approach was chosen to identify patterns of congestive heart failure based on X-ray images automatically detecting abnormal organ profiles.41 Another example is magnetic resonance imaging (MRI). This technique is challenging in many ways for patients as well as for diagnosticians. Since respiratory motion disrupts the measurement, patients need to hold their breath, so image taking is limited in time. Examinations must be performed by highly experienced operators who determine the optimal imaging planes and volumes. Machine learning techniques can provide automated adjustment of the imaging planes and self-correction during measurement.42 Neural network approaches are used to recover high-quality image with low scan time based on compressed sensing techniques. These approaches make MR images less costly and easier to access. More examples can be found in Ref. 37. The automatic analysis of cardiac data is an interdisciplinary challenge. The citizen science project Kaggle Data Science Bowl illustrates this. In 2016, the competition was all about cardiovascular (CV) diagnosis with deep learning approaches.43 Indeed, the measurement of end-systolic and enddiastolic volumes based on cardiac MRI data was mastered by a team from finance industry. It is not only because of the large amount of data, which a computer could cope with using traditional statistical approaches. Artificial intelligence has the advantage of being able to deliver conclusions from data without the need of too many assumptions of the data.20 It turned out that in case of the automated classification of coronary artery optical coherence tomography images, a combination of a convolutional neuronal network approach and a random forest algorithm gives the most accurate results in the least amount of time.44 This shows that sometimes for feature selection and classification different algorithms are needed. The choice of the machine learning algorithm can be crucial in order to find the most accurate as well as the most efficient solution.

15.3.1.2 Risk factor determination and disease prediction Machine learning can be used to predict CV disease risks and identify biomarkers in population studies. Ambale-Venkatesh et al.45 compare conventional CV risk scores to predictions from different machine learning models. Nine models were developed based on a random forest and a Cox proportional hazard regression model, respectively. All patients were initially free of CV disease and the cardiac outcome was analyzed after 12 years. The identification of the top-20 predictors for different cardiac outcomes using a random survival forest46 is part of the feature engineering that was performed for different machine learning models. Surprisingly, among the top-20 predictors subclinical disease markers as imaging and blood tests were found. Authors point out that standard Cox approaches tend not to converge if all variables and all cases are used at the same time. Random forest approaches can handle this without restriction. Moreover, a comparison to conventional CV risk scores like the Atherosclerotic Cardiovascular Disease (ASCVD) score showed superiority in predictive power of machine

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learning approaches. Often rare events have to be predicted based on a large amount of variables. A random forest approach can also be applied to such cases.47

15.3.2 Application of machine learning using epigenetic data The application of machine learning for diagnosis and treatment of CV disease is not only about data validity but also about correct data interpretation.48 Just as a human expert, a machine learning algorithm has to build up an “intuition” that leads it to the right conclusion. The advantage of a machine learning model over a human brain is that the model will never be distracted by irrationality but will always stick to the law of statistics. Applications of machine learning in the area of epigenetics research combine the intuition of an expert with high-level computational approaches.

15.3.2.1 Prediction of the epigenome Epigenetic processes is a very promising approach to diagnostics.49 In a nutshell, studying epigenetics shows how the environment influences the DNA without changing the DNA sequence. DNA methylation is one of the predominantly studied epigenetic mechanisms. Methylation changes the gene expression in a way that can lead to clinical traits like obesity, cancer, neurodegenerative disorders, infertility, and allergies. The prediction of regions in the DNA that are susceptible for epigenetic changes like DNA methylation in association with a disease is crucial to understand the disease on the one hand and epigenetic processes on the other hand. This can be done using machine learning approaches. For example, in Haque and Holder,50 the influence of environmental toxicants on the epigenome of the germ line of sperms has been investigated with a machine learning tool based on an imbalance class learner. To identify epigenetic changes in the DNA, one has to deal with a large amount of high-dimensional biological data but with only a small amount of cases. Moreover, an epigenetic data set contains many nonmethylated regions and only a very small proportion of methylated regions. The computational approach of choice here is a combination of feature generation, feature selection, and machine learning. Especially deep-learning algorithms are promising to simultaneously select and tune the best features for a classification task based on epigenetics data. One example is the work51 where a standard machine learning algorithm like an SVM approach is compared to a deep-learning approach to predict the CpG methylation state to study the epigenetic features of leukemia. The authors developed a deep learning tool called DeepMethyl and show that the deep learning approach is superior to the SVM approach in certain cases.

15.3.2.2 Histone modifications Epigenetic information such as DNA methylation, nucleosome occupancy, and histone modifications are important features to take into account in order to get an understanding of transcriptional regulation.52 Machine learning helps to understand these mechanisms using, for example, profile of histone modification or transcription factor binding in promoter region.1 Often a machine learning method can only be successful if prior knowledge on the question one wants to answer is taken into account. Prior knowledge on a problem is needed, for example, in the prediction of nucleosome positioning in a DNA sequence using micrococcal nuclease digestion with deep sequencing (MNase-seq). Rather than asking how many nucleosomes a base is covered of as in Ref. 53 one asks whether a base is in a nucleosomefree region or not. One makes the problem a classification problem instead of a regression problem.

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This is only possible if one knows that nucleosome-free regions have a high MNase accessibility and are, therefore, particularly interesting.

15.3.3 Application of machine learning in diagnosis of cardiovascular disease using epigenetic mechanisms Coronary heart disease (CHD) is a complex disease in which genetic as well as environmental factors play a role. Studying the epigenetic features of CHD patients is a promising way to gain better diagnosis approaches and risk factor determination. The diagnosis can be made based on blood samples and, therefore, no invasive and costly procedures like an angiography must be performed. In Refs. 54, 55, studies based on a model for classification of CHD patients and controls based on single nucleotide polymorphisms (SNPs) and DNA methylation sites gained from peripheral blood samples are presented. In Ref. 54, several thousand SNPs and methylation loci were used as features in a random forest approach aiming at the determination of the CHD status. The study comprises 1545 training and 142 test samples. An accuracy, sensitivity, and specificity of more than 70% was gained. Also in the more recent study,55 the classifier is a random forest algorithm. About 1180 training and 524 test samples are used for predicting the 5-year risk of developing a CHD. After feature engineering, 11 SNPs and 6 DNA methylation sites remain as features of the random forest. The approach gives a sensitivity and specificity of more than 70%, respectively. Conventional risk scores as the Framingham risk score or the ASCVD risk score take into account measured or self-reported patient features like cholesterol, diabetes mellitus, or smoking. The study shows that in comparison to the random forest approach, conventional risk scores show a low sensitivity or a bad performance for data samples of females. The random forest gives predictions regardless of the gender. Because of the limited training data which only contains patients and controls of European origin, the approach cannot yet be generalized to different ethnic groups. However, authors aim at a generalization to cases of all ethnics.

15.3.4 Limitations Although artificial intelligence has a huge potential in biomedical research, a number of challenges remain. A machine learning model can overfit a training data set. That is, after a certain number of training and validation iterations, a model may fit the training data very well. This results in almost perfect predictions on training data, yet it may fail to make the correct predictions using the test data. Adding more training data can help prevent overfitting. However, additional training data are often difficult to obtain. Data augmentation, i.e., the use of the same training data in a slightly modified form is one way to increase the data pool. Cross-validation, i.e., randomly splitting the data into training and test samples and mutual use of these samples is another possibility. Other limitations are low data quality and low sample size, which have been addressed in the research community by an improved data sharing infrastructure.27 Low quality data sets are often not sufficient as input for the model of choice. Thus, any machine learning results have to be compared to the results of a random classifier.20 Dealing with imbalanced classes can also be a limitation of the machine learning performance. The correct identification of a data set to be imbalanced as well as the correct handling are crucial. Moreover, some algorithms show a better performance on imbalanced classes than others.56

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15.4 Conclusions In this chapter, we reviewed and discussed most common machine learning techniques and applications used in biomedicine, focusing on cardiovascular disease diagnosis using epigenetic data. The rapid advancements in computer technology have enabled the generation of large amounts of biomedical and clinical data. As a result, the relevance of machine learning applications in biomedical disciplines has steadily gained importance, especially in the last 5 years. Artificial intelligence-based systems are unlikely to replace the traditional doctor-patient relationship. However, they could soon be used by the physicians as an aid in routine medical tasks helping to improve the quality of health care. The future in artificial intelligence and machine learning offers applications to assist human decision makers in diagnosis of cardiovascular disease. Utilization of epigenetic data is also investigated toward this goal. As the research on machine learning applications using epigenetic data is still in its infancy, this aspect has not yet been widely applied. However, as demonstrated in this chapter, it shows promising potential.

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35. Whalen S, Pandey G. A comparative analysis of ensemble classifiers: case studies in genomics. In: The IEEE 13th International Conference on Data Mining (ICDM). IEEE; 2013:807–816. https://doi.org/10.1109/ ICDM.2013.21. 36. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet. 2019;51(1):12–18. 37. Bizopoulos P, Koutsouris D. Deep learning in cardiology. IEEE Rev Biomed Eng. 2019;12:168–193. https:// doi.org/10.1109/rbme.2018.2885714. 38. Krittanawong C, Zhang HJ, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571. 39. Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol. 2019;16(8):601–607. 40. Narula S, Shameer K, Omar AMS, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287–2295. https://doi.org/10.1016/j.jacc.2016.08.062. 41. Seah JCY, Tang JSN, Kitchen A, Gaillard F, Dixon AF. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology. 2019;290(2):514–522. https://doi.org/10.1148/ radiol.2018180887. 42. Leiner T, Rueckert D, Suinesiaputra A, et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson. 2019;61(21). https://doi.org/10.1186/s12968-0190575-y. 43. Kaggle Inc. Second Annual Data Science Bowl. Available at: https://www.kaggle.com/c/second-annual-datascience-bowl/overview (Accessed 14 January 2020). 44. Abdolmanafi A, Duong L, Dahdah N, Cheriet F. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomed Opt Express. 2017;8(2):1203–1220. https://doi. org/10.1364/BOE.8.001203. 45. Ambale-Venkatesh B, Yang X, Wu CO, et al. Cardiovascular event prediction by machine learning. Circ Res. 2017;121(9):1092–1101. https://doi.org/10.1161/CIRCRESAHA.117.311312. 46. Ishwaran H, Kogalur U, Blackstone E, Lauer M. Random survival forests. Ann Appl Stat. 2008;2. https://doi. org/10.1214/08-AOAS169. 47. Pavlou M, Ambler G, Seaman SR, et al. How to develop a more accurate risk prediction model when there are few events. BMJ. 2015;351. https://doi.org/10.1136/bmj.h3868. 48. Diagnostic and Interventional Cardiology. How Machine Learning Is Changing Cardiac Ultrasound. Available at: https://www.dicardiology.com/article/how-machine-learning-changing-cardiac-ultrasound (Accessed 23 February 2020). 49. Holder LB, Haque MM, Skinner MK. Machine learning for epigenetics and future medical applications. Epigenetics. 2017;12(7):505–514. https://doi.org/10.1080/15592294.2017.1329068. 50. Haque M, Holder L, Skinner M. Genome-wide locations of potential epimutations associated with environmentally induced epigenetic transgenerational inheritance of disease using a sequential machine learning prediction approach. PLoS ONE. 2015;10:e0142274. https://doi.org/10.1371/journal.pone.0142274. 51. Wang Y, Liu T, Xu D, et al. Predicting DNA methylation state of CPG dinucleotide using genome topological features and deep networks. Sci Rep. 2016;6(19598). https://doi.org/10.1038/srep19598. 52. Yip K, Cheng C, Bhardwaj N, et al. Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome Biol. 2012;13(R48). https:// doi.org/10.1186/gb-2012-13-9-r48. 53. Segal E, Fondufe-Mittendorf Y, Chen L, et al. A genomic code for nucleosome positioning. Nature. 2006;442:772–778. https://doi.org/10.1038/nature04979.

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CHAPTER

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Therapeutic strategies for modulating epigenetic mechanisms in cardiovascular disease

Johannes Winkler Department of Cardiology, Medical University of Vienna, Vienna, Austria

16.1 RNA as a therapeutic target Widely known as the central dogma of molecular biology, genetic information is processed from DNA via RNA to proteins. Instead of being a mere intermediate carrier, RNA is now recognized as an essential regulating part of gene expression (Preface Figure).1 Coding RNA sequences are transcribed to proteins, and the extent and equilibrium of production of individual protein variants are decisive for biological functions and phenotypes, as well as for pathological processes. Only a fraction of around 1%–2% of the genomic information is transcribed to proteins. Various noncoding RNA species (ncRNAs) are known to have diverging influences on gene regulation, thereby steering central mechanisms of life.2 Ever since discovering these regulatory ncRNAs and their role in health and disease, it has been attempted to target themselves or their functions for medical purposes.3 Nucleic acids are fascinating molecules not only regarding their essential role in molecular biology, but also because of their chemical structure. Sharing a structurally relatively simple backbone framework, specific biological functions are mediated through only four different nucleobases, the sequence of which is decisive for translation into proteins with a large structural variety. At the same time, Watson-Crick base pairing of complementary sequences is not only responsible for replication with enzyme systems, but also for regulation of gene expression on the posttranslational level. These endogenous regulatory mechanisms offer the possibility of pharmacological interventions at the genetic level by blocking or amplifying the biological function of coding or noncoding nucleic acids, including bacterial and viral RNA.4–6 The modular structure offers a facile opportunity for highly specific modulation of a single target, based on its unique gene sequence. For drug applications, synthetic oligonucleotides are predominantly used, while gene therapy (delivery of plasmids expressing functional nucleic acids) is still mainly used for basic research for overexpression or inhibition experiments. Naturally occurring and artificial, chemically derivatized short nucleic acids can be produced by chemical synthesis in a rapid, facile, cost-efficient, and automatic manner.7 In contrast to traditional small molecules, for which optimization in terms of target engagement, pharmacokinetics, and toxicity needs to be done separately for each individual molecule, drug development of oligonucleotides can be largely achieved independently of their individual sequence. In the sense of a platform technology, drug-like properties can be conferred by chemical modification of the scaffold structure, the Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00010-9 Copyright # 2021 Elsevier Inc. All rights reserved.

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phosphate-ribose backbone.8–11 Pharmaceutical technological improvement including drug delivery can be developed for the entire class and is largely independent of the molecular target. When taking the size of the genome into consideration and that stabile counterstrand hybridization can occur despite a few isolated base mismatches, a sequence length of an oligonucleotide of approx. 18–20 bases is sufficient to ensure selective and unique recognition of most coding or ncRNA targets. Functional effects can be induced through a steric block mechanism, i.e., through inhibition of binding of mRNA and the ribosome during translation,12, 13 or by activation of natural enzymatic mechanisms that recognize double-stranded nucleic acids. One major system is the RISC (RNA-induced silencing complex) of the microRNA (miRNA) machinery,14, 15which cleaves duplexes consisting of mRNA and a complementary short RNA. Another relevant nuclease is RNase H, which specifically recognizes duplexes consisting of one DNA and one RNA strand, degrading the RNA component.16 Such a cleavage mechanism confers catalytic activity to oligonucleotides and is thus generally more efficient than a steric block. The catalytic activity is important for pharmaceutical considerations, because these drugs act via an event-driven process as opposed to the binding-mediated process that is the case for most small-molecule drugs.17 Furthermore, while small-molecule drugs are limited in their target space,18 oligonucleotide drug technologies allow the silencing of practically every gene of the genome. In addition, modulation of splice variants and induction of expression through blocking regulatory elements are possible.19 Importantly, therapeutic oligonucleotide technology offers the possibility of personalized medicine “out-of-the-box” by specifically targeting deregulated genes or ncRNAs for each patient. In the current era of affordable and rapid transcriptome analyses, this is no longer science fiction, but a realistic assumption. A personalized oligonucleotide based on the unique patient gene mutation has recently been reported.20 The development and successful application of a splice-correcting oligonucleotide were reported for treating a patient suffering from the rare neurodegenerative Batten disease. Based on the unique gene mutation of the patient, a respective oligonucleotide has been designed to restore a functional form of the disease-causing CLN7 gene.20 The treatment was reported to alleviate symptoms and reduce seizures. Prior to patient treatment under FDA approval, efficacy was evaluated in vitro using patient-derived cells and toxicity was tested on rats. Similar individual oligonucleotide drug development programs have been initiated.21 The possibility of customized drugs for individual patients creates specific ethical and regulatory issues connected with such “N-of-One” studies. Despite their common nature, individual oligonucleotide drugs nevertheless need to pass separate clinical evaluation and approval processes, and regulatory, ethical, and financial issues surrounding production and evaluation of an oligonucleotide for a single patient are matters of discussion. After all, efficacy and safety profiles based on the oligonucleotide sequence, encompassing both on-target and off-target effects, must be elucidated.

16.2 Targeting epigenetics 16.2.1 Small-molecule epigenetic drugs Direct therapeutic modulation of epigenetic enzymes and histones—at the protein level—is possible through small molecules. Prior to the rising interest in epigenetic mechanisms in medicine, a few drugs had been developed that were later revealed to impact epigenetic mechanisms.22 A prime example is

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azacitidine (5-azacytidine), which after being metabolized is incorporated into nucleic acids. The triazine structure of the nucleobase allows the nucleophilic attack of DNA methyltransferase, but prevents the subsequent chemical elimination reaction. Therefore, the enzyme remains covalently bound to the nucleic acid and its function is inhibited.23 Azacitidine and most other epigentically active small-molecule drugs in clinical use are applied in oncology because of their profound impact on nucleic acid replication and function. For a more subtle modulation of the expression of individual genes via epigenetics, isoform-specific targeting of epigenetic writers (methyltransferases) or readers (histone deacetylases, HDACs) is arguably required.22 Several HDAC isoforms are deregulated in cardiomyopathies such as cardiac fibrosis and the diabetic heart24 and in inflammatory processes.25 Some promising preclinical data have been accumulated for counteracting cardiovascular diseases with pan- and isoform-specific HDAC inhibitors,26–29 but clinical development programs are still lacking. A better understanding of HDAC functions in epigenetic contexts as well as stringent translational and clinical evaluation of potential drugs acting on HDACs are required for enabling their medical use. Specifically, the role of individual HDAC isoforms in cardiovascular diseases must be better understood, and eventually specific inhibitors developed. Drug toxicity is also an area of interest, as HDACs are critical regulators of many vital biological processes, raising the potential for adverse effects.30 The epigenetic reader protein bromodomain-containing protein 4 (BRD4), belonging to the BET protein family, has recently been shown to be a promising drug target.31 BET proteins interact with acetylated lysines of histones to epigenetically regulate gene expression. Apabetalone is an inhibitor of BRD4 and is being evaluated for the treatment of atherosclerosis. It increases the expression of Apo A1 in hepatocytes and HDL levels32 and also impacts inflammatory processes.33 Recently presented data on a phase III trial showed that major adverse cardiac events were not statistically significantly reduced, but together with earlier clinical trials and preclinical data novel insights are now available for the development of drugs with higher efficacy and better treatment strategies.34

16.2.2 Oligonucleotides Besides epigenetically active enzymes, the translational level itself is a logical target for therapeutic modulation of epigenetics. One might argue that exogenous oligonucleotides including siRNA and antisense are epigenetic regulators themselves, because they directly impact gene expression regulation in a posttranscriptional mechanism. In a stricter sense, however, only oligonucleotides that target endogenous epigenetic mechanisms, i.e., alterations of DNA structures, can be considered epigenetic drugs.22 The best understood and most widely documented epigenetic mechanism is DNA methylation.35 Addition of a 5-methyl group to cytidine within CpG dinucleotides can alter the activity of a gene segment and thus directly influences gene expression. Since antisense and siRNA oligonucleotides bind to their mRNA counterstrand, and Watson-Crick hybridization fails to discriminate between cytidine and 5-methylcytidine,36 DNA methylation itself is not directly amenable for oligonucleotide therapeutics. However, an excess of gene expression that is triggered by DNA methylation could be countered directly on the expressional level using antisense or siRNA agents. These drugs result in strong silencing of the single target gene and thus appear to be ill-suited for subtle changes across a multitude of genes, which is usually the case in pathological epigenetic processes. Further development of CRISPR/Cas9 gene editing toward clinical use may enable targeted manipulation of methylation patterns.37, 38 Chemically produced nucleic acids including 5-methylated cytidines can be used as inserts in gene editing.

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Likewise, wild-type methylated regions can be exchanged with nonmethylated sequences. In a recent report, the CRISPR/Cas9 system was utilized for the demethylation of the Oct4 promoter region in cell culture, resulting in increased gene expression.39 For addressing concerns of off-target effects and efficacy in vivo, more fundamental research needs to be completed for safe and effective medical application of gene editing.40

16.2.3 MicroRNAs MicroRNAs (miRNAs) are important posttranslational regulators of gene expression that act by incomplete hybridization to the UTR of their target genes. MiRNAs were first described in nematodes in 1993,41 but interest as a biological regulatory mechanism and a potential drug target began to increase only fifteen years ago.42 Complementarity of the seed region, a stretch of eight nucleotides toward the 50 -end of the miRNA is sufficient for target gene regulation.43 One miRNA usually targets several hundreds of distinct genes, and individual genes are often regulated by a few or even dozens of different miRNAs. Unlike siRNAs, miRNAs reduce the expression of, but do not strongly silence their target genes, and often interact with several genes with similar functions. Thus, the miRNA mechanism can be referred to as a concerted and finely tuned regulation of molecular pathways. Due to their regulatory functions altering entire gene sets, miRNAs are attractive molecular targets for pharmaceutical interventions. Specific silencing of single genes through antisense or siRNA is most appropriate for monogenetic diseases and diseases with a distinct and decisive role of the targeted gene. In contrast, therapeutic miRNA modulation generally promises more pronounced beneficial effects in many pathological situations that are characterized by interplay of gene networks. Within the myocardium, miRNAs play an important role in cardiomyogenesis and cell differentiation. The modulation of distinct cardiac miRNAs has been suggested for reprogramming fibroblasts to cardiomyocytes for cardiac repair.44 MiR-1, miR-133, and others have been shown to induce and/or promote reprogramming of cardiac nonmyocyte cells in vitro and in vivo.45, 46 A combination of miR-1, miR-133, miR-208, and miR-499 was shown to be effective in epigenetic reprogramming. In vitro, fibroblasts were transformed into a cardiomyocyte-like phenotype after transfection of the miRNA combination.47 In vivo, lentiviral delivery of the miRNAs improved myocardial function (fractional shortening) and reduced fibrosis.46 Mechanistically, demethylation of histone H3 at lysine 27 (H3K27me3) is involved in the expression of cardiogenic genes by the combination of these four miRNAs.48 These results underline the promise of miRNA-based therapy for cardiac repair. Further preclinical and eventual clinical development including an appropriate and safe drug delivery modality will be required to prove whether miRNA reprogramming can be applied efficaciously and safely in humans. However, precise functional effects of individual miRNAs in regulating cardiovascular physiology and pathology are not yet sufficiently elucidated. The dynamic spatiotemporal regulation of miRNAs as well as variations between sexes and species and dependence on age and comorbidities complicate the definition of viable drug targets. Analyses of different cohorts of HF patients failed to reveal any consistent pattern of miRNA deregulation that was common to all studies.49 In addition, while the expression of only few miRNAs such as miR-1, miR-133a, and miR-208 is restricted to heart or muscle tissue, most other potential myocardial targets are expressed in multiple organs, increasing the potential of side effects and organ toxicity through pharmacological interventions. When correlating biomarkers with therapeutic target validity, it has to be taken into account that the levels of circulating miRNAs do not necessarily reflect tissue expression. As an example, circulating miR-1 levels are higher after a

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myocardial infarction and in acute coronary disease.50 Analysis of human and animal heart tissue has shown a downregulation of miR-1 expression in the infarcted area and an upregulation in the nonischemic myocardium.51 MiR-1 is by far the most abundant miRNA in mammalian hearts52 and affects cardiomyocyte growth.49 Thus, instead of being directly correlated with expression levels in heart cells, circulating levels are obviously largely reflecting cell necrosis rates and the release of intracellular miR-1 into the bloodstream. There is likewise conflicting evidence regarding the role of distinct miRNAs in fibrosis. MiR-29 has been shown to be depleted in fibrotic tissue and to negatively regulate fibrotic mechanisms.53 The miRNA-29 mimic remlarsen (miRagen Therapeutics) has been evaluated in preclinical models of pulmonary and skin fibrosis and has reached early-stage clinical trials. Remlarsen repressed collagen expression and inhibited the development of fibroplasia in incisional skin wounds in healthy volunteers. However, genetic deletion and pharmacologic inhibition of miR-29 improved cardiac function by prevention of hypertrophy and cardiac fibrosis.54 These conflicting results appear to be due to deviating roles in fibroblasts (preventing the expression of fibrotic genes) and cardiomyocytes (derepressing Wnt signaling). Outcomes in preclinical models possibly depend on the extent of overexpression or inhibition. Genetic deletion or constitutive overexpression are in many cases not equivalent to pharmacological manipulation by oligonucleotides. In total, epigenetic regulatory mechanisms by miRNAs are a delicate system for which a thorough understanding is necessary in order to exploit it for therapeutic benefit.

16.3 Therapeutic utility of oligonucleotides Oligonucleotide technologies for epigenetically based applications, i.e., miRNAs and other noncoding RNAs, are still lacking approved therapeutics or investigational drugs in the late-stage clinical trials. Because the drug development process is based primarily on the scaffold structure, and only to a minor extent on the nucleobase sequence, it is useful and essential to consider lessons learned from the development of therapeutic oligonucleotides that target coding RNA sequences, e.g., antisense, siRNA, splice-modulating oligonucleotides, and aptamers.4, 9, 10, 55–58 All of these classes have reached market authorization of at least one compound (Table 16.1). Today, a toolbox of medicinal chemistry and drug delivery technologies for oligonucleotides exists. Prior success stories, but also failures, particularly within the cardiovascular disease field,59–63 need proper consideration for leading the way for manipulation of epigenetic mechanisms in the future. Soon after the first reports of the ability to modulate gene expression in a highly specific manner by antisense oligonucleotides in 1978,12, 13 and by siRNA in 2003,14 a significant hype for developing respective drugs built up in both instances.64 Compared to traditional pharmacologic agents, oligonucleotides have fundamentally different physicochemical characteristics, in an analogous manner to therapeutic antibodies and other recombinant proteins. These parameters include molecular mass and size and polarity, which in turn have a decisive influence on important pharmacokinetic properties.8, 65 In hindsight, it is not surprising that the preclinical and clinical pharmaceutical development process of oligonucleotides experienced minor and major setbacks and ultimately took several decades.10, 58, 66, 67 This was caused in part because initial attempts were based on tools and strategies that had been optimized for small molecules. For inferring drug-like properties, wild-type nucleic acids needed to be improved in terms of stability (i.e., resistance against enzymatic degradation) and for

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Table 16.1 Approved oligonucleotide drugs. Name

Class

Indication

Gene target

Comment

Fomivirsen

Antisense Aptamer

Mipomersen

Antisense

Eteplirsen

Patisiran

Splicemodulating Splicemodulating siRNA

Immediate early region 2 (IE2) of CMV Vascular endothelial growth factor (VEGF) Apolipoprotein B-100 (ApoB-100) Dystrophin (DMD)

Withdrawn

Pegaptanib

Inotersen

Antisense

Volanesorsen

Antisense

Givosiran

siRNA

Cytomegalovirus (CMV) retinitis Age-related macular degeneration (AMD) Familial hypercholesterolemia Duchenne muscular dystrophy (DMD) Spinal muscular atrophy (SMA) Polyneuropathic TTRamyloidosis Polyneuropathic TTRamyloidosis Familial chylomicronemia Acute hepatic porphyria

Golodirsen

Splicemodulating

Duchenne muscular dystrophy (DMD)

Nusinersen

Survival of motor neuron 1 (SMN1) Transthyretin (TTR) Transthyretin (TTR) Apolipoprotein CIII (apoCIII) Aminolevulinic acid synthase 1 (ALAS1) Dystrophin (DMD)

Intraocular injection Concerns of hepatotoxicity Approved in the USA Intrathecal administration Liposomal formulation Restricted access in the USA (REMS) Approved in EU GalNAc conjugate Approved in the USA

enabling cellular uptake into the cytosolic compartment.68 Furthermore, since oligonucleotides with around 20 nucleobases fall within the size limit for renal filtration, optimization included inferring binding to plasma proteins, which effectively prevents rapid elimination.8 Today, adequate solutions exist for avoiding nuclease degradation and for sufficient tissue distribution and cellular uptake, albeit mainly for hepatic targets.69–72 Chemical modification strategies ensure prevention digestion by exoand endonucleases and confer a degree of binding to plasma proteins to raise the molecular size above the renal filtration limit (Fig. 16.1).

16.3.1 Oligonucleotide classes Oligonucleotides can be designed to target mRNA and prevent translation through steric block and RNase H- (antisense, ASO) (Box 16.1) or RISC-mediated degradation (siRNA). For reaching the intracellular site of action, well-functioning and safe delivery systems are already available, namely molecular attachment of a trivalent N-acetyl galactosamine (GalNAc), or packaging into optimized liposomal formulations. However, both are currently restricted to delivering sufficient intracellular drug concentrations only to the liver and few other organs. The first siRNA, patisiran, applied in a liposomal formulation, has been approved by the FDA and EMA in 2018 for the treatment of hereditary ATTR-amyloidosis (Box 16.2).73 There is clinical evidence for a benefit of patisiran in familial amyloidotic polyneuropathy, and the use for treating cardiac amyloidosis is currently under evaluation.71 Liposomal siRNA formulations are infused intravenously,

FIG. 16.1 Schematic view of chemical modifications patterns typical for the various classes of therapeutic oligonucleotides. Distinct functional effectors are activated by either single- or double-stranded oligonucleotides steered by their chemical structure. Double-stranded RNA is recruited by RISC and results in gene silencing when the entire strand is complementary to the mRNA (siRNA), or more subtle modulation of gene expression when only the seed region binds (miRNA). Single-stranded oligonucleotides with at least a stretch of 20 -deoxy nucleotides activate RNase H, which cleaves the mRNA. A single-stranded oligonucleotide without RNase H activation can block the spliceosome. For inhibition of native miRNAs, single-stranded sense strands block mRNA modulation. In all cases, 20 -modifications and phosphorothioate backbones can significantly reduce cleavage by nucleases and prolong pharmacologic activity.

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BOX 16.1 ANTISENSE OLIGONUCLEOTIDES. Antisense oligonucleotides (ASOs) are single-stranded and act via Watson-Crick hybridization to their endogenous target mRNAs. Depending on the chemical modification pattern, the pharmacologic mechanism is a steric translational block or a catalytic degradation of the mRNA by RNase H. This nuclease cleaves the RNA strand of a heteroduplex of RNA and DNA and thus for catalytic activity, ASOs must consist of a minimum of six to eight consecutive DNA nucleotides. Volanesorsen and most ASOs for clinical application consist of five 20 -O-methoxyethyl nucleotides at both ends connected by 10 20 -deoxy nucleotides. This design is known as gapmer and is usually combined with a full PS backbone. In clinically used ASOs, cytidines are usually 5-methylated.

BOX 16.2 SIRNA. Small interfering RNA (siRNA) is a double-stranded oligonucleotide of a length of around 21 nucleotides that induce gene silencing via the RNA-induced silencing complex (RISC). For therapeutic applications, siRNAs are chemically modified to prevent rapid enzymatic degradation. The RISC is sensible to chemical derivatization of siRNA, and thus, the prevailing modification pattern consists of 20 -O-methyl, 20 -fluoro, and a few terminal PS backbone linkages. The first approved siRNA, patisiran, is complexed in lipid nanoparticles and less extensively derivatized. Most investigational siRNAs are bioconjugates with N-acetyl galactosamine (GalNAc) which are endocytosed into hepatocytes via the highly abundant asialoglycoprotein receptor.

while GalNAc-modified siRNAs and ASOs are applied subcutaneously. Covalent attachment of GalNAc, the ligand for the asialoglycoprotein receptor that is highly expressed by hepatocytes, induces sufficient cellular uptake in hepatocytes.74, 75 In November 2019, the FDA approved the first GalNAcsiRNA givosiran, which targets aminolevulinic acid synthase 1 (ALAS1) as a treatment for acute hepatic porphyria.72 A number of further GalNAc-siRNAs and GalNAc antisense agents are currently in clinical trials.10 An additional mechanism of action is interference with splicing, i.e., modulating the translated protein sequence through inclusion (retention) or exclusion (skipping) of exonic sequences by hybridization to specific sites of the pre-mRNA (Box 16.3). A major breakthrough was achieved by the development of nusinersen, which in 2016 became the first approved drug for the treatment of spinal muscular atrophy (SMA).76, 77 SMA is caused by mutations in the SMN1 gene. Nusinersen allows the expression of a functionally analogous protein of the SMN2 gene, which differs from SMN1 by the

BOX 16.3 SPLICE-SWITCHING OLIGONUCLEOTIDES. Oligonucleotides that bind to distinct mRNA sections without inducing its degradation have a splice-modulating effect. Thus, this class of oligonucleotides typically consists of fully 20 -modified ribonucleotides, which are unable to activate RNase H. This mechanism prevents docking of the spliceosome to the respective mRNA site and results in either exon inclusion or exon skipping. Three splice-switching oligonucleotides have reached approval, the systemically applied etelirsen and golodirsen for distinct genetic mutations in Duchenne muscular dystrophy, and the intrathecally injected nusinersen for spinal muscular atrophy.

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exclusion of one exon, and its pharmacological effect is therefore independent of the individual disease-causing mutation. Nusinersen is applied as an intrathecal injection and thus avoids most barriers for reaching its cellular targets.11, 78 The full PS backbone facilitates cellular uptake.79, 80 In a similar manner to mRNA, oligonucleotides can target miRNAs. For therapeutic applications, both promotion of certain miRNAs through overexpression or supplementation (gain-of-function, miRNA mimics) and inhibition through blockage or downregulation (loss-of-function, antagomirs) are feasible. The blocking effect is based on hybridization to the mature miRNA, thereby preventing association with the RISC and expressional regulation of the mRNA targets (Box 16.4). LNA is frequently used for antagomirs and respective agents are successful in counteracting the miRNA function in scientific assays and in preclinical disease models.81 High-affinity binding to miRNA does generally not induce miRNA degradation, but rather acts via a steric and functional block mechanism by sequestration of the targeted miRNA in a heteroduplex.82 Other chemical modifications can result in decreased levels of the targeted mature miRNA, probably by RNase H activation. This aspect needs to be considered for the assessment of successful target engagement.83

BOX 16.4 ANTAGOMIRS. Single-stranded oligonucleotides that inhibit miRNAs are called antagomirs. Binding to the guide strand of a mature miRNA, an antagomir with chemical modification or mispairing at the cleavage site prevents activation of the Ago2 nuclease and consequently modulation of gene expression of the mRNA targets. Compared to longer targets, inhibiting the short miRNA is an additional challenge. Due to the limited target sequence length, optimization of duplex stability by sequence selection is hindered. Thus, chemical alterations that induce higher hybridization stability are particularly beneficial for effective miRNA antagonism. For drug development, chemical modification patterns are similar to ASOs and frequently include LNA nucleotides.

Double- or single-stranded artificial oligonucleotides (miRNA mimics) can functionally substitute for reduced levels of wild-type miRNAs (Box 16.5).6, 84 Only a few clinical phase I/II trials have been undertaken with miRNA mimics. These trials have utilized delivery systems for shielding unmodified miRNA drugs. A trial using a liposomal miR-34 replacement agent was halted because of adverse immune reaction in cancer patients.85 Another phase I trial evaluated the tolerability of a synthetic miR-16 delivered by nonviable bacterial minicells with an anti-EGFR bispecific antibody for treating malignant pleural mesothelioma. Initial results of six patients were reported,86 but no data on further development have been published. The miR-29 mimic remlarsen and a stronger modified miR-29 conjugate for targeted lung delivery are in the early-stage clinical development as antifibrotic drugs.87 Altogether, some promising preclinical and clinical data have been reported, but large-scale randomized clinical trials have not yet been completed and thus the medicinal utility is still not proven.

BOX 16.5 MIRNA MIMICS. Synthetic oligonucleotides with identical sequences as naturally occurring miRNAs are called miRNA mimics. Stabilizing chemical modifications for drug development are equivalent to siRNA, but single guide strands have also been shown to be pharmacologically active. Differing functional outcomes between siRNA and miRNA mimics are caused by the degree of hybridization to the mRNA: While siRNAs are fully complementary to the target sequence and usually targeted at the coding region, miRNAs bind via the seed sequence of seven nucleotides in the 30 -UTR.

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The relatively recently discovered presence of circularized RNAs (circRNAs) in cells and their role, among others, as miRNA sponges,88 has prompted efforts for therapeutic exploitation. Circularization induced higher stability compared to linear nucleic acids.89 The miRNA sponge effect of some circRNA is based on the repetitive inclusion of several seed binding sequences for respective miRNAs. Besides some other applications of circRNAs either as drug targets or structural or functional templates for medical interventions,88 there is a miRNA sponge function of wild-type or engineered circRNAs with an indirect impact on epigenetic functions.90 Because of the longer sequence, current experimental studies make use of circRNA that is transcribed in vitro. The chemical-synthetic production of RNA of several kB lengths followed by circularization is technically possible, and this enables the incorporation of stability-enhancing nonnatural nucleotide derivatives. Compared to short siRNA, ASO, or miRNA oligonucleotides, the longer length and higher molecular size raise synthetic costs and also severely impact intracellular delivery. Thus, therapeutic applications of circRNA face more difficult challenges compared to shorter oligonucleotides.

16.4 Synthetic oligonucleotide chemistry All short therapeutic oligonucleotides are synthesized by a chemical process. Based on pioneering works by Khorana, Letsinger, Carruthers, and others,91–93 oligonucleotides are prepared by the phosphoramidite method in a step-wise, automated, cost-efficient, and rapid manner. The synthetic process allows facile incorporation of chemical modifications such as PS backbone linkages and 20 -O- and nucleobase modifications as well as attachment of fluorophores, dyes, or targeting molecules at the 30 - or 50 -end of the sequence.94 For double-stranded siRNA and miRNA mimics, the individual strands are synthesized separately and subsequently joined by mixing equimolarly. Today, nucleic acids with several kB lengths can be prepared by connecting chemically prepared fragments of around 50 nucleobases through annealing, ligation, and polymerase reactions.7 As a part of defense mechanisms against infections, wild-type nucleic acids are degraded by nucleases in biological fluids within minutes. For avoiding degradation, oligonucleotides must be shielded from nucleases by structural alterations or by encapsulation within carrier systems. A multitude of different derivatizations for oligonucleotides have been developed and tested in preclinical and some also in clinical settings. Only few of those ultimately proved to be suitable for pharmacologic applications. The phosphorothioate (PS) backbone modification is present in almost all clinically active oligonucleotides (Box 16.6).95 Clinically used ASOs and antagomirs usually consist of a full

BOX 16.6 PHOSPHOROTHIOATE. Substitution of a nonbinding oxygen atom for sulfur confers higher nuclease resistance to oligonucleotides. In addition, the phosphorothioate (PS) backbone induces binding to albumin and other plasma proteins, thereby influencing renal filtration, circulation time, and cellular uptake. The PS backbone is associated with toxicity signs, including thrombocytopenia and elevation of liver enzymes. The approved ASO drugs mipomersen, inotersen, and volanesorsen have a full PS backbone. Risk mitigation and evaluation strategies (REMS) ensure safe use of these drugs. Newer generations of investigational ASOs such as tominersen for treating Huntington’s disease have a reduced number of PS linkages for an improved safety profile. GalNAc-siRNAs bioconjugates (givosiran and others in development) possess a maximum of two terminal PS linkages at each oligonucleotide strand and show generally fewer side effects.

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359

PS-backbone. The PS modification confers reduced nuclease cleavage rates and also results in significant binding to albumin and other plasma proteins, which is an important asset for tissue distribution, transcytosis across the endothelium, and cellular uptake. On the downside, phosphorothioates have been linked to off-target effects and toxicity. The first ASO approved for systemic use, mipomersen, has experienced limited commercial success because of toxicity issues.96, 97 The extent of toxicity has been linked to the proportion of PS linkages,98 and therefore, the number of PS modification has been reduced for many newer oligonucleotide drug candidates. Additional 20 -O-modifications (methyl, methoxyethyl, or fluoro) contribute to a stronger resistance against nucleases.99 Optimized siRNA designs allow dosing with several month intervals, because these drugs are highly stable and thus long-acting. The duration of the pharmacological effect is influenced by cell division rates, and therefore, cardiomyocytes are adequate target cells along with hepatocytes, pancreatic beta islet cells, and neuronal cells. Further, clinically relevant structural alterations are locked nucleic acids (LNAs) and bridged nucleic acids (BNAs), both of which structurally constrain the ribose100 and confer more rigid binding to the natural counterstrand than wild-type oligonucleotides (Box 16.7).101 Consequently, LNAs are frequently employed in miRNA-targeted drugs.102

BOX 16.7 LNA. Locked nucleic acid (LNA) and bridged nucleic acid (BNA) are nucleotides with a fixed ribose conformation, induced by covalent bridging of the 20 -and 40 -positions. These modifications increase binding affinity to the counterstrand by reducing the entropic cost for base paring and are thus particularly useful for targeting short sequences. These nucleotide modifications are frequently used for antagomir designs.

16.5 Oligonucleotide drugs in the cardiovascular field 16.5.1 Antisense and siRNA oligonucleotides for treatment of cardiovascular diseases A number of clinical oligonucleotide trials for treating cardiovascular diseases have been completed. In 2018, the ASO volanesorsen was approved for treating the rare lipid disorder familial chylomicronemia syndrome in the European Union, but a similar bid for marketing approval was rejected by the FDA.70 In clinical trials, the PS-ASO that targets the ApoC3 gene was successful in significantly lowering triglyceride blood levels, but this failed to translate into patient-relevant secondary endpoints such as pancreatitis, abdominal pain, and quality of life.103 This is potentially due to the clinical trial design and/or low patient numbers in this very rare genetic disease. Recently, the data of a phase II study of AKCEA-APO(a)-LRx, an ASO targeted at apolipoprotein (a), were reported.104 Lipoprotein(a) levels are genetically determined and are an independent risk factor for cardiovascular disease through a causal link to advancing atherosclerosis and cardiovascular complications.105 AKCEA-APO(a)-LRx reduced lipoprotein(a) levels in a dose-dependent manner up to a reduction of 80% from baseline.104 These results are highly promising for further follow-up phase II and III studies.

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Other advanced oligonucleotide drug development programs focus on lipid metabolism. One noteworthy example is inclisiran, a GalNAc-siRNA that is silencing proprotein convertase subtilisin-kexin type 9 (PCSK9),106 which is critical for binding and intracellular degradation of LDL receptors in hepatocytes, thereby preventing receptor recycling.107 Suppression of PCSK9 at the translational level consequently induces the removal of LDL-C from the bloodstream. Initial data from clinical trials have shown a strong effect of inclisiran on lowering LDL-C levels.59, 60, 108 Inclisiran is evaluated for lowering LDL-C in patients with atherosclerosis or with high cardiovascular risk, for which statin therapy alone was insufficient for achieving the desired LDL-C levels. In a recently presented phase III study, the time-averaged LDL-C lowering effect was more than 50% compared to placebo, with both groups receiving maximally tolerated statin therapy.109 Although a benefit on clinical endpoints is yet to be proven in ongoing longer trials, these results show that siRNA is a promising alternative to other strategies aiming at PCSK9, such as antibodies. A major advantage is the infrequent application by subcutaneous injection that is enabled by the high stability of the chemically modified siRNA molecule.

16.5.2 Antagomirs for cardiovascular pharmacotherapies Despite mounting experience and data in preclinical testing, so far there is only limited experience with antagomirs in clinical evaluations. Miravirsen was tested in clinical trials for the treatment of hepatitis C infections since 2010. Binding human miR-122 protects the viral RNA from enzymatic degradation, and miravirsen inhibits this process by blocking endogenous miR-122.110 In 2016, clinical evaluation was put on hold due to adverse effects.85 Several other miRNA-based agents are in clinical phase I and II trials, but there is still a lack of late-stage clinical trials and robust data on clinical benefit.85 A recent preclinical study has highlighted miRNA expressional changes in distinct HF subtypes. High expression of miR-148a was detected in concentric cardiomyopathy in mice and humans, while low expression was found in dilated cardiomyopathy.111 A miR-148a antagomir promoted eccentric hypertrophic remodeling and dilated cardiomyopathy. It was concluded that a biphasic regulation of miR-148a contributes to the transition from compensatory to decompensated hypertrophy and HF. Different studies have identified various miRNAs that are differentially regulated in HF in animal models, but with distinct miRNA signatures according to animal model and induction of HF. Among these miRNAs, the cluster of miR-212/132, which shares a common intergenic site and the primary transcript, was shown to be upregulated in failing rodent hearts.112, 113 The upregulation was detected in rodent models of different hypertrophic stimuli including Ang II, TAC, or insulin-like growth factor. Overexpression of these two miRNAs induced cardiac hypertrophy in vitro and in vivo. Genetic deletion of miR-212/132 protected mice from TAC-induced hypertrophy.113 Pharmacological inhibition using a PS-LNA antagomir counteracted effects induced by overexpression of miR-212/132 in transgenic mice assessed by ejection fraction and heart weight.114 In a large animal model of AMI-induced HF, the same antagomir successfully improved EF in a dose-dependent manner.114 In these preclinical trials, a good safety profile was reported. While these are encouraging data, translation into clinical success remains to be proven. The antagomir was detected in heart tissue of treated pigs, but relatively high i.v. doses of 5 and 10 mg/kg were necessary for functional improvements. For comparison, the clinically approved siRNA patisiran is applied i.v. at 0.3 mg/kg, and GalNAc-siRNAs such as inclisiran are efficacious at a s.c. dose of approx. 3 mg/kg twice yearly. It is assumed that, in the absence of a heart-specific delivery system, only a fraction of the antagomir dose reaches the myocardium and consequently sequesters its target in cardiomyocytes. Essential roles of miR-132 in neuronal outgrowth

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and sprouting, in angiogenesis and neovascularization, and in inflammatory processes have been reported,115 and potential side effects may possibly be caused by higher drug concentrations in organs such as the liver. Lademirsen is a LNA-PS-oligonucleotide antagomir that inhibits the function of miR-21 and is being tested clinically for the treatment of Alport syndrome, a severe genetic fibrotic kidney disease caused by mutations in type IV collagen. Recently published data demonstrated elevated miR-21 levels in kidneys of Alport patients, localized to damaged tubular epithelial cells and glomeruli.116 In a Col4α3/ mouse model, lademirsen application improved survival, slowed functional decline, and partially reversed abnormal gene expression patterns associated with the disease. A phase II clinical trial for Alport syndrome is currently ongoing. The fibrotic effects of an overabundance of miR-21 were also shown in the myocardium in rodent models. After AMI, miR-21 was upregulated in the ischemic zone,117 upon induction of cardiac fibrosis by isoproterenol118 and in human fibrotic atrial biopsies.119 In mice, an anti-miR-21 oligonucleotide inhibited interstitial cardiac fibrosis and attenuated dysfunction.120 Therefore, miR-21 inhibition may be an option for treating cardiac fibrosis. However, the inherent biodistribution properties of antagomirs are clearly in favor of treating kidney disease vs. heart fibrosis, and since much lower concentrations are reaching the myocardium, side effects in tissues with high accumulation would have to be expected. In an apparently conflicting outcome, a miR-21 mimic reduced cardiac fibrosis and hypertrophy and promoted angiogenesis in a mouse MI model.121 In this study, the artificial miR-21 was delivered in hyaluronan nanoparticles with a size that favors targeting of macrophages, and the effects were attributed to a switch of macrophages from a proinflammatory to a reparative phenotype. These data show the multifaceted roles of miRNAs and that the functional role can differ in different cell types. So far, there is a clear lack of concerted and comprehensive miRNA-based drug development programs. On the contrary, there is an abundance of animal experiments studying the effects of overexpression, genetic deletion, pharmacological substitution, or inhibition of distinct miRNAs.63 Translation to clinical development programs needs to be undertaken carefully. Mirroring the decades-long development of first antisense and then siRNA drugs, there will be a certain learning curve and rules and requirements for successful therapeutic manipulation of miRNAs will need to be refined. However, because of their high molecular similarity, key lessons learned from antisense and siRNA drugs are expected to facilitate and expedite therapeutic miRNA applications. On the contrary, the physiological regulatory roles of miRNAs are significantly more complex compared to a single gene, and the precise functional consequences of therapeutic manipulation of individual miRNAs must be carefully dissected.

16.6 Challenges that need to be addressed Current well-working drug delivery solutions are restricted to hepatic targets, and further optimization is necessary to enable sufficiently effective gene silencing in other organs.58 When using chemically stabilized oligonucleotides without a delivery system, only a small fraction of the dose ends up in cardiac or endothelial tissues.11 Drug accumulation is much stronger in liver and kidney, eventually prone to causing adverse effects in these organs. Furthermore, recent research shows that respective oligonucleotide drugs are largely confined to the interstitial space within the myocardium, failing to be taken up into cells in a sufficient amount.122 Therefore, for many cardiovascular applications, optimized

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tissue distribution and particularly intracellular delivery into endothelium, cardiomyocytes, or cardiac fibroblasts are required. Respective attempts have so far shown generally insufficient success for robust therapeutical results. Despite intense research efforts in this area, the main deficiency for effective and widespread application of oligonucleotide technologies is still their inadequate delivery to intracellular spaces within the target cells. The first issue is an uneven distribution to organs with low concentrations reaching particularly muscle (and brain and other) tissues. Biodistribution can be influenced by alteration of molecule size and lipophilicity. Increasing molecular size is essential to avoid rapid renal elimination.123 The PS backbone induces stronger binding to albumin and other plasma proteins, to which its lipophilicity contributes.124 Adding lipid ligands such as cholesterol and fatty acids to oligonucleotide is a relatively facile method to further increase plasma protein binding.125 Cell membranes are a second barrier, and poor productive uptake into cytosolic compartments—the site of action of the miRNA machinery—is a bottleneck.68 Plasma protein binding is thought to improve cell membrane permeation to a certain extent. A stronger affinity for plasma proteins was recently shown to enhance accumulation in the interstitial space in heart muscle severalfold.122 However, the respective oligonucleotide palmitic acid conjugates were removed by lymphatic fluids in a stronger extent, and thus intracellular uptake and consequently functional effects were increased only approx. twofold.126 The PS backbone modification induces binding to plasma proteins, which in turns facilitates association to cell membranes and receptor-mediated internalization.80, 127 Around 12–14 PS linkages are required for plasma protein binding, and most clinically applied ASOs, but not GalNAc-siRNAs, fulfill this criterion.127 However, after cellular uptake, oligonucleotides are finally localized in endosomal compartments. There is general scientific consensus that only a small fraction of oligonucleotides escape the endosome to reach the cytosol.11 The much higher percentage of the drug remains in endosomal vesicles, which are subsequently transformed via late endosomes to lysosomes, and the internalized molecules are finally degraded. It has been a long-standing challenge to increase the rates of endosomal escape, but so far there is no clearly viable strategy.11, 68, 128 Cationic molecules, including basic peptides and polymers, are able to support cellular uptake and endosomal escape and are widely used in vitro for cell transfection.57, 129–132 For therapeutic applications, however, toxicity and unspecific cell uptake hamper their use. So far, all attempts to use cationic transfection enhancers, including cell-penetrating peptides and endosomolytic polymers, have been unsuccessful in preclinical or clinical evaluation. Further refinement of the respective compounds is necessary for efficient and safe use.69, 133 Cationic peptides and polymers have been shown to destabilize endosomes through the proton sponge effect.134, 135 Cationic carriers, however, can interact with negatively charged serum proteins, leading to aggregation and triggering toxic effects in vivo. The development of more specific endosomolytic agents with reduced toxicity has so far been insufficiently successful. Despite intense research efforts, the full complexity of precise uptake and intracellular trafficking mechanisms is still incompletely understood.68

16.6.1 Nanoparticle delivery An alternative to chemical modification for protection from degrading enzymes, preventing renal elimination, and ensuring cellular uptake is the incorporation of the oligonucleotide agent in liposomes or other nanoparticles. A vast number of distinct nanoparticle delivery systems for improving

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oligonucleotide therapeutics have been developed in academic and industrial research laboratories. Carrier materials include lipids assembled into liposomes, polymers such as polyethylenimine (PEI), poly lactide-coglycolide (PLGA), polyamines, and chitosan, peptides, and many others.57, 58, 136–139 Thousands of iterations of oligonucleotide nanoparticle systems are published every year, differing not only by the carrier material, but also by variations of composite concentrations of multicomponent carriers and cargo, and manufacturing processes, all of which influence final particle size and physicochemical properties. Only a tiny fraction of those systems, however, have reached comprehensive preclinical or clinical evaluations. Intricate multicomponential delivery systems suffer from complicated upscaling and a high regulatory burden, because all individual components, the GMP manufacturing process, and the final product must be comprehensively analyzed and characterized and shown to be reproducible.140 This can make successful pharmaceutical development all but unfeasible. In some cases, there is an apparent disconnect between basic research in academia and pharmaceutical drug development. For successful translational development, close cooperation between experts from both fields is required.141 Packing oligonucleotides in particles avoids degradation by nucleases, can reduce rapid elimination and off-target effects, and facilitate cellular uptake by masking the negative charges of oligonucleotides. Desired properties of nanoparticles are a high encapsulation efficiency, sufficient colloidal stability, facilitation of cell uptake, and release of the cargo after successful delivery to intracellular compartments.142 Among commonly used nanoparticles are liposomes, which for oligonucleotide delivery usually consist of a combination of cationic and neutral lipids.143, 144 Electrostatic interactions between the positive charges of the lipids and the negative charges of the nucleic acid backbone result in effective condensation and in spherical particles. Addition of polyethylene glycol is a frequent strategy to increase half-life of lipid particles.145 Lipid compositions have been refined for efficacy and reduced toxicity through combinatorial chemistry.143 A reduction of the cationic charge improves the safety profile of lipid nanoparticles. Ionisable liposomes have been developed, aiming to reduce electrostatically based adverse effects in extracellular liquids, while inducing endosomal disruption after cellular uptake in the slightly acidic vesicular compartments.146 For nanomedical applications, the physicochemical properties, particularly at the surface of the particles, and particle size are crucial parameters. Ideal particle size is regarded to be within approx. 20 and 150 nm.147 The lower limit is set because of rapid elimination of smaller particles from circulation through the kidneys. Particles larger than around 150 nm are prone to sequestration by the mononuclear phagocyte system (MPS, also known as reticuloendothelial system—RES) with strong uptake in liver and spleen.136 Extravasation, the crossing of the endothelial barrier, shows some dependency on particle size. Nanoparticles are generally accumulating in tissues with leaky blood vessels, found in cancer, and inflamed tissue. In contrast, tight endothelium, such as in healthy hearts, shields organs from passive accumulation of nanoparticles. This effect is known as enhanced retention and permeation effect (EPR).148, 149 In some cases, particle sizes can be modulated for specific cell-type targeting, in particular targeting immune cells by using larger nanoparticles. Delivery of a miR-21 mimic was achieved primarily to cardiac macrophages by using hyaluronan nanoparticles with an average size of 120 nm. In a laser microdissection analysis, delivery to macrophage-enriched zones was confirmed.121 Similarly, researchers utilized a polyketal polymer system to repress the target gene Nox2 via delivery of upstream miRNAs-106b, 148b, and 204 in macrophages. This treatment was effective in improving heart function after MI in mice assessed via fractional shortening, ejection fraction, and infarct size.150

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16.6.2 Targeted delivery via bioconjugation During the last years, the focus of oligonucleotide drug development has shifted from nanoparticle delivery to bioconjugates. Covalent conjugation of a targeting ligand is technically simpler than packaging in nanoparticles and facilitates GMP manufacturing and regulatory aspects. On the contrary, the stoichiometric ratio of ligand to effector molecule is in favor of particles.58 Receptor-mediated uptake is now widely exploited for successful clinical development of antisense and siRNA acting in hepatocytes. This strategy profits not only from the high abundance of the asialoglycoprotein receptor on hepatic cells, but also from its rapid internalization and turnover,74 and the availability of a high-affinity carbohydrate ligand, trivalent GalNAc, that is easily attached to oligonucleotides during chemical synthesis.75, 151 The intense search for similar ligand-receptor combinations for other organs and tissues has so far remained largely arduous.152 One recent promising example is the successful delivery of an ASO-peptide conjugate to pancreatic beta cells via binding and uptake through the glucagon-like peptide-1 receptor (GLP1R).153 This receptor has limited tissue distribution with strong expression on insulin-secreting beta cells and is characterized by rapid internalization and recycling back to the cell surface. Thus, it appears to be well suited for sufficient oligonucleotide uptake upon binding of the peptide ligand, which was covalently attached to the ASO. In a mouse model, in vivo uptake into beta cells was enhanced by the peptide conjugation and increased productive uptake in pancreatic islets respective to the liver.153 For cardiac drug targeting, it is a great challenge to identify and exploit specific receptors, eventually with differential expression within cardiac cell types.154 There are only sparse reports on successful receptor-mediated targeting to cardiac myocytes or fibroblasts. One example is the development of peptides with increased affinity to cardiomyocytes by phage display.155 Attaching these peptides to liposomes afforded a ninefold increase of efficiency in cardiomyocytes of a smallmolecule PARP-1 inhibitor as the liposome cargo compared to unmodified liposomes. Such a system could be modified for oligonucleotide delivery; however, a further optimization of targeting efficiency would be required. In another study, a siRNA was conjugated to a transferrin antibody fragment for improving uptake into skeletal and cardiac muscle cells, achieving efficient gene knockdown in targeted tissues.156 Intracellular trafficking of the transferrin receptor and its ligand is rather complex, and the transferrin ligand is generally recycled out of the cell together with its receptor. For endosomal accumulation and escape, the binding kinetics of the siRNA conjugates to the receptor is essential. Receptor cross-linking is a recently published approach for rationally steering intracellular trafficking of cargos.157

16.6.3 Other delivery approaches Besides molecular targeting of tissues, site-specific drug release might be a viable strategy for specific delivery of oligonucleotide agents to the myocardium. Microbubbles, gas-filled lipid particles with sizes between 10 and 1000 nm, can be disrupted by external cues such as ultrasound, releasing their contents in a site-specific manner.158 In a recent study, a miR-23a antagomir was loaded into microbubbles.159 In a rodent model of phenylephrine-induced ventricular hypertrophy, intravenously injected antagomir-loaded microbubbles were disrupted using an ultrasound imaging system placed at the heart. While phenylephrine treatment increased myocardial miR-23a levels, this increase was

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mitigated by the antagomir, but failed to reach the levels of healthy animals. Consequently, a reduction of LV mass and an improvement of systolic function were detected in antagomir-treated animals compared to the control group. In a different study, a miR-21 oligonucleotide was delivered to pig hearts in a similar manner.160 Increased miRNA levels were detected after ultrasound drug release. An influence of the application modality was reported, with higher efficacy after intracoronary injection compared to intravenous injection, which is likely caused by a higher concentration of microbubble-encapsulated miRNA at the time of ultrasound application. Although the functional improvements in these experiments were limited, the delivery strategy using site-specific oligonucleotide release is attractive. Even though the drug release can be triggered specifically at the site of action, the hurdle of uptake into the intracellular site of action remains. In basic research gain-of-function experiments, miRNA mimics are frequently applied as transgenes using recombinant adeno-associated viral vectors (AAVs), particularly the serotype AAV9, which has demonstrated cardiac tropism.161 Viral delivery results in long-lasting expression up to several months and is easy to produce and apply. However, concerns regarding toxicity and immunogenicity hamper clinical applications. Moreover, transient application of miRNA mimics and antagomirs is often preferred in medical applications over long-term effects. In a recent study, AAV-mediated overexpression of miR-199a in infarcted pig hearts stimulated cardiac repair as shown by reduced scar sizes, improved contractibility, and increased muscle mass.162 The persistent expression subsequently resulted in arrhythmia and death of most treated animals. This study underlines that spatial and particularly time-dependent control of miRNA levels is decisive for optimal clinical benefit. An elegant approach for oligonucleotide delivery is the construction of well-defined threedimensional nanostructures consisting of nucleic acids by taking advantage of the unique base pairing abilities.163 Shape and size can be precisely controlled in a nanometer scale,164 and bioconjugation techniques allow the attachment of targeting moieties. Practical preclinical approaches of miRNA delivery with nucleic acid nanostructures have so far been restricted mainly to cancer.165 Such RNA nanostructures are generally stable against degradation, but concerns of immunogenicity need to be addressed, and further optimization is required.166

16.7 Drug safety and off-target effects Recent progress and current technologies enable robust and durable oligonucleotide drug effects in terms of gene silencing. Class-related toxicity has been a concern particularly for ASOs with PS backbones.167 The new generation of ASOs and siRNAs, of which several are already being marketed, and many more currently in clinical evaluations, show a more favorable toxicity profile.168 For patisiran, givosiran, and inclisiran, only very minor adverse effects have been reported. These GalNAc-siRNAs are thus considered to be safe, effective and specific (in the sense of strong knockdown of the targeted gene in hepatocytes), and long-acting. Nevertheless, potential side effects as well as therapeutic benefits are decisively dependent on the role of the targeted gene in disease. Besides class-related toxicity, benefit, and potential adverse effects can be caused by the pharmacological effect on the selected target gene and in the target tissue. Therefore, selection of a viable and well-characterized gene target is paramount. Results from small animal models gathered by genetic overexpression or knockout of miRNA should generally be interpreted with caution regarding direct translation to a therapeutic application.

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In such models, physiologically and consistently high or low levels of the respective miRNAs are generated, which might not always be predictive for pathologies. Transient pharmacological increase (miRNA mimics) or decrease (antagomirs) often result in significantly different functional effects. Gene regulatory outcomes are dependent on miRNA concentrations, with increased target repression with higher miRNA concentrations. High lentiviral miRNA expression can at least in some cases induce saturation of the miRNA machinery and result in functional inhibition of other miRNAs through competition over RISC.169

16.8 Conclusion Therapeutic targeting of epigenetic mechanisms might be achieved by small molecules that modulate methyltransferases, HDACs, or bromodomain proteins, or by oligonucleotides that mimic or antagonize miRNAs. For cardiovascular diseases, a better understanding of the roles of enzyme isoforms is required followed by rigorous drug development. The oligonucleotide therapeutics field has seen several breakthroughs in the last few years, culminating in recent approval of the first splice-switching oligonucleotide and the first siRNA drug. Today, nine oligonucleotide drugs are in clinical use, and several others are destined for approval during the next few years, including novel treatment options for cardiovascular diseases. A few compounds are already in preclinical and early clinical development for targeting epigenetic mechanisms via modulation of endogenous miRNA levels; however, a better understanding of underlying pathological mechanisms appears to be required for a wide use of such therapeutics. Furthermore, current well-functioning and validated drug delivery options are viable for hepatic targets only. For modulation of miRNAs in other organs, including the heart, optimized drug delivery modalities are required.

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81. Lennox KA, Behlke MA. Chemical modification and design of anti-miRNA oligonucleotides. Gene Ther. 2011;18(12):1111–1120. 82. Davis S, Propp S, Freier SM, et al. Potent inhibition of microRNA in vivo without degradation. Nucleic Acids Res. 2008;37(1):70–77. 83. Thomson DW, Bracken CP, Szubert JM, Goodall GJ. On measuring miRNAs after transient transfection of mimics or antisense inhibitors. PLoS ONE. 2013;8(1), e55214. 84. Matsui M, Prakash TP, Corey DR. Argonaute 2-dependent regulation of gene expression by single-stranded miRNA mimics. Mol Ther. 2016;24(5):946–955. 85. Jones D. Setbacks shadow microRNA therapies in the clinic. Nat Biotechnol. 2018;36(10):909–910. 86. Reid G, Kao SC, Pavlakis N, et al. Clinical development of TargomiRs, a miRNA mimic-based treatment for patients with recurrent thoracic cancer. Epigenomics. 2016;8(8):1079–1085. 87. Montgomery RL, Dalby CM, Van Rooij E, Gallant-Behm C, Inventors; Miragen Therapeutics, Inc., Assignee. Mir-29 mimics and uses thereof. US patent 16/002845. February 5th, 2019. 88. Holdt LM, Kohlmaier A, Teupser D. Circular RNAs as therapeutic agents and targets. Front Physiol. 2018;9:1262. 89. Jeck WR, Sorrentino JA, Wang K, et al. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA. 2013;19(2):141–157. 90. Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495(7441):384–388. 91. Khorana HG. Synthesis and structural analysis of polynucleotides. J Cell Comp Physiol. 1959;54(S1):5–15. 92. Letsinger RL, Mahadevan V. Oligonucleotide synthesis on a polymer support1,2. J Am Chem Soc. 1965; 87(15):3526–3527. 93. McBride LJ, Caruthers MH. An investigation of several deoxynucleoside phosphoramidites useful for synthesizing deoxyoligonucleotides. Tetrahedron Lett. 1983;24(3):245–248. 94. Lorenzer C, Winkler J. Covalent fluorophore labeling of oligonucleotides and generation of other oligonucleotide bioconjugates. In: Ogris M, Sami H, eds. Nanotechnology for Nucleic Acid Delivery: Methods and Protocols. New York, NY: Springer New York; 2019:61–72. 95. Eckstein F. Phosphorothioates, essential components of therapeutic oligonucleotides. Nucleic Acid Ther. 2014;24(6):374–387. 96. Kling J. Safety signal dampens reception for mipomersen antisense. Nat Biotechnol. 2010;28(4): 295–297. 97. Raal FJ, Santos RD, Blom DJ, et al. Mipomersen, an apolipoprotein B synthesis inhibitor, for lowering of LDL cholesterol concentrations in patients with homozygous familial hypercholesterolaemia: a randomised, double-blind, placebo-controlled trial. Lancet. 2010;375(9719):998–1006. 98. Winkler J, Stessl M, Amartey J, Noe C. Off-target effects related to the phosphorothioate modification of nucleic acids. ChemMedChem. 2010;5(8):1344–1352. 99. Ku SH, Jo SD, Lee YK, Kim K, Kim SH. Chemical and structural modifications of RNAi therapeutics. Adv Drug Deliv Rev. 2016;104:16–28. 100. Koshkin AA, Singh SK, Nielsen P, et al. LNA (locked nucleic acids): synthesis of the adenine, cytosine, guanine, 5-methylcytosine, thymine and uracil bicyclonucleoside monomers, oligomerisation, and unprecedented nucleic acid recognition. Tetrahedron. 1998;54(14):3607–3630. 101. Braasch DA, Corey DR. Locked nucleic acid (LNA): fine-tuning the recognition of DNA and RNA. Chem Biol. 2001;8(1):1–7. 102. Elmen J, Lindow M, Schutz S, et al. LNA-mediated microRNA silencing in non-human primates. Nature. 2008;452(7189):896–899. 103. Witztum JL, Gaudet D, Freedman SD, et al. Volanesorsen and triglyceride levels in familial chylomicronemia syndrome. N Engl J Med. 2019;381(6):531–542. 104. Tsimikas S, Karwatowska-Prokopczuk E, Gouni-Berthold I, et al. Lipoprotein(a) reduction in persons with cardiovascular disease. N Engl J Med. 2020;382(3):244–255.

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128. Varkouhi AK, Scholte M, Storm G, Haisma HJ. Endosomal escape pathways for delivery of biologicals. J Control Release. 2011;151(3):220–228. 129. Akinc A, Thomas M, Klibanov AM, Langer R. Exploring polyethylenimine-mediated DNA transfection and the proton sponge hypothesis. J Gene Med. 2005;7(5):657–663. 130. Osborn MF, Alterman JF, Nikan M, et al. Guanabenz (Wytensin™) selectively enhances uptake and efficacy of hydrophobically modified siRNAs. Nucleic Acids Res. 2015;43(18):8664–8672. 131. Andaloussi SE, Lehto T, Mager I, et al. Design of a peptide-based vector, PepFect6, for efficient delivery of siRNA in cell culture and systemically in vivo. Nucleic Acids Res. 2011;39(9):3972–3987. 132. Ezzat K, Andaloussi SE, Zaghloul EM, et al. PepFect 14, a novel cell-penetrating peptide for oligonucleotide delivery in solution and as solid formulation. Nucleic Acids Res. 2011;39(12):5284–5298. 133. Gilleron J, Paramasivam P, Zeigerer A, et al. Identification of siRNA delivery enhancers by a chemical library screen. Nucleic Acids Res. 2015;43(16):7984–8001. 134. Gallo M, Defaus S, Andreu D. 1988–2018: thirty years of drug smuggling at the nano scale. Challenges and opportunities of cell-penetrating peptides in biomedical research. Arch Biochem Biophys. 2019;661:74–86. 135. Allen JK, Brock DJ, Kondow-McConaghy HM, Pellois J-P. Efficient delivery of macromolecules into human cells by improving the Endosomal escape activity of cell-penetrating peptides: lessons learned from dfTAT and its Analogs. Biomolecules. 2018;8(3):50. 136. Wang Y, Miao L, Satterlee A, Huang L. Delivery of oligonucleotides with lipid nanoparticles. Adv Drug Deliv Rev. 2015;87:68–80. 137. H€obel S, Aigner A. Polyethylenimines for siRNA and miRNA delivery in vivo. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2013;5(5):484–501. 138. Jhaveri AM, Torchilin VP. Multifunctional polymeric micelles for delivery of drugs and siRNA. Front Pharmacol. 2014;5:77. 139. Juliano R, Alam MR, Dixit V, Kang H. Mechanisms and strategies for effective delivery of antisense and siRNA oligonucleotides. Nucleic Acids Res. 2008;36(12):4158–4171. 140. Stavis SM, Fagan JA, Stopa M, Liddle JA. Nanoparticle manufacturing—heterogeneity through processes to products. ACS Appl Nano Mater. 2018;1(9):4358–4385. 141. Goldman M, Compton C, Mittleman BB. Public-private partnerships as driving forces in the quest for innovative medicines. Clin Transl Med. 2013;2(1):2. 142. Lee SWL, Paoletti C, Campisi M, et al. MicroRNA delivery through nanoparticles. J Control Release. 2019;313:80–95. 143. Akinc A, Zumbuehl A, Goldberg M, et al. A combinatorial library of lipid-like materials for delivery of RNAi therapeutics. Nat Biotechnol. 2008;26(5):561–569. 144. Jayaraman M, Ansell SM, Mui BL, et al. Maximizing the potency of siRNA lipid nanoparticles for hepatic gene silencing in vivo. Angew Chem Int Ed Engl. 2011;51(34):8529–8533. 145. Suk JS, Xu Q, Kim N, Hanes J, Ensign LM. PEGylation as a strategy for improving nanoparticle-based drug and gene delivery. Adv Drug Deliv Rev. 2016;99(Pt A):28–51. 146. Kanasty R, Dorkin JR, Vegas A, Anderson D. Delivery materials for siRNA therapeutics. Nat Mater. 2013;12 (11):967–977. 147. He C, Hu Y, Yin L, Tang C, Yin C. Effects of particle size and surface charge on cellular uptake and biodistribution of polymeric nanoparticles. Biomaterials. 2010;31(13):3657–3666. 148. Fang J, Nakamura H, Maeda H. The EPR effect: unique features of tumor blood vessels for drug delivery, factors involved, and limitations and augmentation of the effect. Adv Drug Deliv Rev. 2011;63(3):136–151. 149. Maeda H. Toward a full understanding of the EPR effect in primary and metastatic tumors as well as issues related to its heterogeneity. Adv Drug Deliv Rev. 2015;91:3–6. 150. Yang J, Brown ME, Zhang H, et al. High-throughput screening identifies microRNAs that target Nox2 and improve function after acute myocardial infarction. Am J Physiol Heart Circ Physiol. 2017;312(5): H1002–H1012.

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CHAPTER

Single-cell RNA sequencing in cardiovascular science

17

Parisa Aghagolzadeh and Thierry Pedrazzini Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, Lausanne, Switzerland

17.1 Introduction Cardiovascular diseases, defined as genetically determined and acquired disorders affecting the blood vessels and the heart, include hypertension, atherosclerosis, stroke, myocardial infarction, and heart failure and are the leading causes of morbidity and mortality worldwide.1 The underlying mechanisms leading to the development of cardiovascular diseases implicate complex cellular interactions and intricate molecular responses. In particular, dysregulations of coding and noncoding RNA expression in a variety of cell types composing the vessels and the heart represents crucial determinants of cardiovascular pathologies.2 In this context, single-cell RNA sequencing (scRNA-seq) proposes a new approach to simultaneously probe transcriptional heterogeneity in various cell subpopulations associated with pathological conditions. This chapter evaluates therefore the current state of scRNA-seq in cardiovascular research and discusses further developments in this novel technology.

17.2 Basic principles Bulk RNA sequencing is used to analyze RNA samples isolated from tissues containing several million cells from different origins. However, this type of analysis lacks the detailed assessment needed for evaluating the contribution of each cell to the overall phenotype. In contrast, scRNA-seq allows comparing the transcriptomes of thousands of cells at the same time, providing a high-resolution snapshot of the individual contribution. Original attempts to analyze the transcriptome at the single-cell level arose in the 1990s.3, 4 In reality, the current scRNA-seq technology was only described in 2009, taking advantage of the advent of next-generation sequencing.5 Since this initial study, the interest in this approach has been growing exponentially. One of the prime reasons for using scRNA-seq is to evaluate heterogeneity in seemingly homogeneous cell populations. Another reason is to detect small subpopulations that would be missed in bulk population, allowing generating transcriptomic and epigenetic atlases of fetal and adult tissues. Then, the approach enables analyses of cell-state transitions during programming and reprogramming via determining cell trajectories. Therefore, scRNA-seq is being used for identifying cellular intermediates during cellular differentiations or developmental processes. Last but not least, the technique offers a means for evaluating intercellular communications through Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00014-6 Copyright # 2021 Elsevier Inc. All rights reserved.

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paracrine signaling within tissues. More generally speaking, scRNA-seq has been used to investigate the heart and the vasculature in both the mouse and humans to profile the most important cardiovascular cell types such as cardiomyocytes, endothelial cells, smooth muscle cells, epicardial cells, and fibroblasts (Fig. 17.1). Although several different platforms for single-cell RNA library preparation are available, the basic steps remain the same. First, a single-cell suspension, representative of the tissue of interest, needs to be

FIG. 17.1 Basic principles of scRNA-seq and analysis. Dissociation of mouse or human organs, in particular the heart and the blood vessels, to highly viable single-cell suspensions, and capture of individual cells on barcoded beads before proceeding to reverse transcription, library preparation, and sequencing. Three major examples of applications of scRNA-seq in cardiovascular science are shown, specifically production of atlases to investigate cell diversity in organs, assessment of cell population heterogeneity and detection of rare subpopulations, and reconstruction of cell trajectories to recapitulate developmental lineages.

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prepared. This is not completely straightforward since isolating cells from a compact and composite organ, typically the heart, while preserving each cellular fraction, is not an easy process. In addition, time is an issue because the transcriptome will rapidly respond to the new environmental conditions imposed by cell purification methods, potentially introducing biases in the analysis. Like in any scientific approach, some compromises must be accepted. At the end, the production of a good single-cell suspension with high viability (>80%) is essential in single-cell experiments. Then, the next step requires isolating individual cells for library preparation. Different procedures have been utilized in the past, such as limiting dilution, micromanipulation, and fluorescent-activated cell sorting (FACS). However, the current practice takes advantage of technologies that offer automatic single-cell lysis, RNA purification, and cDNA synthesis. Although several different protocols are currently available, the dominant commercial platforms include the SMART-seq2 system, the Cell-seq2 method implemented on a Fluidigm C1 system (CEL-seq2/C1), and the Chromium system from 10
Genomics that is gaining popularity. There are some basic differences between these technologies. For instance, SMART-seq2 uses full transcript sequencing, whereas, in the Cell-seq2/C1 and 10
Genomics approaches, transcripts are identified based on 30 -end sequencing. In addition, the CEL-seq2/C1 system allows analyzing a few hundreds of cells, whereas several thousands of cells can be analyzed simultaneously on a 10
Genomics platform. Some techniques analyze total cellular RNA as compared to the polyA fraction for other approaches. Nevertheless, there are common steps in these different alternatives, which can be summarized as follows. First, cells are individually captured in microplate wells or droplets. Second, RNA is extracted from each cell and labeled with a cell-specific identifier. Third, the RNA is reverse transcribed to create cDNA, which is subsequently amplified for library preparation and then sequenced. Attachment of unique molecular identifiers (UMI), which are molecular tags added to cDNA before amplification, allows identifying unique reads and removing amplification biases to quantify accurately transcript expression.6 Recent improvements include also barcoding cells according to sample identity, allowing library pooling and multiplex sequencing. Oligonucleotidetagged antibodies directed against ubiquitously expressed surface proteins can also be used to assign each cell to its original sample.7 Therefore, one scRNA-seq experiment enables the simultaneous analysis of pooled samples, thereby reducing costs. Despite variations in the methodology, all scRNA-seq approaches make possible measuring transcriptional outputs of individual cells, allowing to evaluate distinct cellular programs under control by genetic and epigenetic cues. However, several points need to be considered. As already alluded to, sample quality is essential. Specimen are inappropriate for scRNA-seq if cell integrity is altered. Indeed, the main source of technical variation is driven by cell preparation.8 Tissue processing for scRNA-seq depends on tissue type and needs to be optimized based on extracellular matrix composition, cellularity, and stiffness. Generally, sample preparation includes enzymatic digestion and mechanical dissociation. For the adult mouse heart, a Langendorff approach is recommended. To avoid aggregations and clumps, cells can be passed through a sample strainer. Removal of debris and dead cells from the single-cell suspension is ultimately critical for the quality of the transcriptomic data. Then, enrichment for the cell type of interest can be achieved using sedimentation, gradient centrifugation, FACS, or magnetic bead selection. Eventually, checking viability of input cells is mandatory before proceeding to library preparation. Briefly, the ideal tissue dissociation protocol will allow producing many viable cells in the shortest possible time while preserving a proper representation of various cellular fractions. Each step influences gene expression and should be carefully optimized to reduce artifacts. Importantly, the requirement for generating single-cell suspensions is a major hurdle

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when assessing preserved clinical samples. In this case, massive parallel single-nucleus RNA sequencing using a microfluidic platform represents a suitable alternative to analyze heterogeneity in, for instance, human postmortem tissues.9 Single-nucleus droplet-based RNA sequencing has proved also useful when analyzing large cells, such as adult rode-shaped cardiomyocytes, which cannot be easily processed by the common microfluidic platforms.10, 11 Then, differentiating true modulation of gene expression from noise is not simple. Gene expression varies in part stochastically. Measuring lowly expressed genes can be difficult in single-cell experiments, which has tendencies to introduce bias for highly expressed genes. In fact, the choice of the scRNA-seq protocol determines the kind of information gathered from such approaches.12 High sensitivity protocols permit detection of weakly expressed genes but measurement of true variation in expression depends on high accuracy.13 Sensitivity relies for a large part on sequencing depth. For instance, the original Fluidigm C1 system allows generating libraries with higher complexity than the other quite popular method implemented on a 10
Genomics platform. In contrast, the 10
Genomics system allows generating data from tens of thousands of cells as compared to a few hundreds for the Fluidigm C1 platform. Therefore, sequencing depth is critical since it represents the most important limiting factor for detecting a large number of genes and to measure true regulation. Still, the identification of small subpopulations requires sequencing RNA from many cells in each sample. In doing so, the number of reads per cell will be inevitably reduced in proportion, making rare transcripts less likely to be detected. Ultimately, costs might turn out to be the main problem when very deep sequencing becomes mandatory to measure the expression of thousands of genes in tens of thousands of cells in several biological samples per experiment.

17.3 Current single-cell RNA-sequencing technologies A single cell contains between 1 and 50 pg of total RNA depending on tissues. Analyzing these minute amounts of RNA at the single-cell level is therefore a challenge.14 Over the last few years, a number of scRNA-seq approaches have been developed.15 Although these different techniques share commonalities with each other, there are also basic differences that make them more or less suitable for meeting the objectives of the proposed experiments. Later, we compare three protocols among the most popular ones, namely the CEL-seq2/C1, the SMART-seq2, and the 10
Genomics methods, which are commonly used in cardiovascular research. The CEL-seq2/C1system is an automated microfluidic platform able to process 96 or 384 individual cells at the same time.16 Single cells are distributed in individual chambers of an integrated fluidic circuit. Each captured cell is lysed, and the released RNA is subsequently reverse transcribed to produce individual sequencing libraries. The CEL-seq2/C1 approach generates partial cDNA transcripts and applies a 30 end-counting method following sequencing on an Illumina sequencer.16 The device can accommodate cells of different sizes by using dedicated cartridges. Accordingly, the cell suspension must be quite homogeneous in shape and size in order to avoid selection bias. Generally, captured cells can be visualized under a microscope before processing, allowing researchers to verify the actual number of captured cells and to eliminate empty wells or those containing doublets.17 Since the CEL-seq2/ C1 system can profile a limited number of cells simultaneously, it makes difficult the identification of

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rare populations and thereby the assessment of cellular heterogeneity. On the contrary, the technology is characterized by a rather high sensitivity, making it suitable for detecting a larger number of genes as compared to the other available systems. In contrast, the Smart-seq2 platform is not completely automated and requires manual pipetting. The method is suitable for the analysis of cells of various sizes and shapes but it is exposed to technical variability. Smart-seq2 is configured to use 96- or 384-well microtiter plates.18 In short, single cells are distributed into prefilled wells containing a lysis buffer. Interestingly, the plates can be stored or processed immediately. Then, the RNA is reverse transcribed into cDNA for highthroughput sequencing. It is important to note that SMART-seq2 profiles full-length cDNAs, allowing users to profit from equal read coverage across the entire transcript. This is particularly useful to detect different isoforms of specific genes or even to evaluate allele-specific expression. However, SMART-seq2 does not integrate UMI or barcodes. Consequently, quantification or multiplexing is not possible.17 Finally, the 10
Genomics Chromium approach is a droplet-based technique that can process thousands of cells in a single session. The system uses gel beads in emulsion acting as individual reaction compartment. Eventually, each gel bead is mixed with a unique sample, i.e., a cell or a nucleus, which is barcoded before proceeding to downstream reactions leading to the preparation of a pooled sequencing library. Posttreatment includes bioinformatic mapping of reads to the original sample, and correction for amplification biases, made possible by the inclusion of UMI. Multiplexing proved its utility in comparative studies aimed at assessing time-dependent expression or treatment effects, as well as in reconstructing developmental trajectories.19 Nevertheless, this technique is subject to some limitations. First, only polyA + transcripts can be analyzed.20 Moreover, the system does not apply a correction for low cell input. Selection bias can occur, which might result in missing rare cell populations if inadequate cell numbers are analyzed.17

17.4 Single-cell RNA-sequencing data analysis As any disruptive method in biology, scRNA-seq dictates new modes of data analysis. Indeed, the established standards applied to analyze bulk RNA-sequencing datasets need to be adapted for single-cell experiments.21 In bulk sequencing, expression of individual genes is analyzed across several distinct subpopulations within the tissue of interest, masking contribution of individual cells. On the contrary, scRNA-seq analysis is intended to detect heterogeneity in samples with high sensitivity.22 However, one possible drawback of such analyses resides in biases introduced by the need for amplification of the small amounts of starting material during library preparation. Essentially, this results in some levels of imprecision, leading to technical noise and inaccuracy. In other words, scRNA-seq generates many zero-expression values.23, 24 Therefore, common normalization methods used in bulk RNA sequencing may not be suitable for scRNA-seq, which requires a series of analyses to remove the individual low-quality data.21 In this case, quality control metrics are calculated for each gene, based on the average expression level across the number of cells expressing this gene, to determine dropout rates.21 Therefore, there are basically two main steps in scRNA-seq data analysis, preprocessing, and downstream analysis.25 The preprocessing phase consists of quality control, quantification of gene

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expression, normalization, batch effect correction, and dimensionality reduction. First, one should make sure to include only viable cells in the analysis. This is normally determined via calculating the number of genes per cell barcode, the number of counts per barcode, and the percentage of reads that map to the mitochondrial genome.26, 27 This process evaluates individual cell integrity and allows removing dying or dead cells and, on the contrary, cell doublets. For example, barcodes associated with few expressed genes and a high amount of mitochondrial RNA are likely derived from cells having lost their cytoplasmic RNA due to membrane rupture. In contrast, doublets are identified as characterized by unexpectedly high read counts assigned to a large number of genes.28 The result of single-cell sequencing is next processed to generate matrices of molecular counts. Processing includes read alignment on the genome, read quality control, and read assignment to their cellular and transcript barcodes. The dimensions of the matrices are given by the number of UMI associated with a given feature and a given barcode. Nevertheless, normalization is mandatory, like in any methods used to quantify gene expression. To adjust for sequencing depth, several approaches have been developed for bulk sequencing but those cannot be used in scRNA-seq analyses because all these methods calculate a single-scale factor that is applied to all genes in the samples. Thus, several models for data normalization have been proposed to specifically address the problem of scRNA-seq datasets. Initially, these methods used also a global-scale factor that made impossible assessing the variation in the relationship between transcript-specific expression and sequencing depth. Specifically, normalization of lowly and highly expressed genes could not be achieved simultaneously. In contrast, SCnorm is a normalization algorithm that groups genes according to the dependence of transcript expression on sequencing depth for every gene and then estimates scale factors within each group.29 Similarly, SC transform performs normalization and variance stabilization based on a UMI-based gene expression matrix, removing technically driven variations while preserving biological heterogeneity. The method can be implemented in R and used in packages such as Seurat, currently the most popular tool for scRNA-seq analysis.30 Finally, unwanted variability might still arise from batch effects. Correction can be applied when cell-type and cell-state compositions are consistent across batches.28 Dimensionality reduction of large datasets facilitates visualization and clustering.31 It represents the next step in scRNA-seq data analysis. Datasets contain expression values for many genes but not all these genes contribute equally to the overall biological variation. In particular, a great number of genes are associated with zero counts.31, 32 Therefore, eliminating uninformative genes, i.e., feature selection, is important for decreasing dimensionality as it significantly reduces technical noise. A principal component analysis (PCA) is a common way to transform the data. However, the t-distributed stochastic neighbor embedding (t-SNE) plot, an unsupervised, nonlinear technique, represents the most valuable alternative.33 It allows mapping multidimensional data to a two-dimensional space on a nearest-neighbor network. It is important to note that t-SNE is not a clustering tool per se since it does not preserve original values. Unsupervised clustering is part of the downstream analysis and takes advantage of different algorithms such as hierarchical clustering, k-means clustering, and graph-based clustering. The downstream analysis includes also the determination of differential gene expression within samples and between samples, identification of cell types based on marker gene expression within clusters, reconstruction of gene regulatory networks, pseudotime and trajectory analyses, and exploration of allele-specific expression.25 All of these analyses can be performed using the wellaccepted Seurat suite21, 22 (https://satijalab.org/seurat/) or the standard monocle pipeline34–36 (http://cole-trapnell-lab.github.io/monocle-release/). However, the field is evolving rapidly and it is likely that improved computational tools will emerged in the future.

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17.5 Single-cell RNA-sequencing strategy to evaluate the noncoding transcriptome The vast majority of scRNA-seq analyses rely on the expression of protein-coding genes (PCGs). However, only 2% of the genome encodes for proteins. The remaining 98% represents the noncoding portion of the genome. Most of the noncoding genome is transcribed into long noncoding RNAs (lncRNAs).37 The total number of lncRNAs in a mammalian genome is estimated to be around 50–100 thousands, many of which are expressed in unique cell types or at narrow developmental time points.38, 39 LncRNAs can operate through diverse modes of action, including molecular guides, scaffold, and decoys for transcription factors (TFs), splicing factors, or microRNAs (miRNAs).40 Many lncRNAs act in both cis (nearby their site of transcription) and trans (at other locations in the genome),41 making lncRNAs ideal regulators of epigenomic remodeling and genome organization.42, 43 These two processes are the key determinants of lineage-specific transcriptional regulation during differentiation and cellular reprogramming.44 Only a couple of single-cell studies integrated noncoding RNAs in their analyses.45, 46 One reason is that lncRNAs are expressed at lower levels than PCGs,37 a characteristic that might preclude their utility in scRNA-seq analysis. However, several points argue in favor of assessing lncRNA expression in single-cell experiments. LncRNAs are multiexonic and polyadenylated transcripts typically transcribed by RNA polymerase II enzyme.47 They are generally found to be more cell-type-specific than PCGs37, 48 and consolidate the molecular differences between cell types that are required to control cell identity. The average low level of expression usually detected using bulk RNA sequencing might therefore reflect functional heterogeneity and biological variability within the samples.46 In addition, some lncRNAs reach levels similar or higher than those of housekeeping genes.49 Therefore, high expression in discrete subpopulations or individual cells could be translated into an apparent low expression at the tissue level. In this context, creating a catalog of the lncRNAs expressed in the tissue of interest to improve annotation greatly enhances the detection of subpopulation-specific lncRNAs.50 Because many lncRNAs are polyadenylated, these molecules are suitable for analyses on available microfluidic platforms such as the 10
Genomics system. In this context, long intergenic noncoding RNAs were identified as key regulators of cardiomyocyte dedifferentiation and proliferation by single-nuclear RNA sequencing.51 Nevertheless, pioneer studies using very deep RNA-seq followed by ab initio computational transcript reconstruction, and integrating chromatin datasets, identified, and functionally characterized large numbers of previously unannotated lncRNAs in the heart, in the vasculature and in relevant cultured cells, including differentiating pluripotent cells.49, 52 The list of lncRNAs with important functions in the cardiovascular system is therefore expending. Future progress in single-cell technologies should integrate this important novel layer of regulatory molecules in the analysis.

17.6 Recent applications of scRNA-seq to characterize the cardiovascular system 17.6.1 The developing heart In recent years, a growing number of studies have taken advantage of scRNA-seq to profile different tissues and cell types in the cardiovascular system. Two early studies evaluated cellular heterogeneity in the developing heart.53, 54 Unbiased classification based on transcriptomic data generated at various

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time points during the development and the postnatal life identified cardiomyocytes, endothelial cells, and cardiac fibroblasts.53 Transcripts that primarily contributed to PCA variation encoded for relevant proteins in each subpopulation, as analyzed in embryos and neonates. This combined approach allowed determining small sets of genes that constituted marker signatures for each cardiac cell types. In particular, this study revealed a previously unidentified Nkx2–5-dependent transcriptional program in cardiomyocytes. On the contrary, the top 10 genes characterizing the identity of the embryonic cardiac fibroblast included collagens, Postn and Sox9. Interestingly, evaluation of the distribution of cells during the development indicates that cardiac fibroblasts arise around day 14 and represent an important proportion of cardiac cells until afterbirth but constitutes only a minor population in the mature heart. Similar data were produced in a second study investigating developmental processes at the single-cell levels during the early phases of cardiogenesis.54 In this case, the authors succeeded in recapitulating the spatial distribution of cardiomyocytes derived from Isl1-positive precursors, known to contribute preferentially to the right ventricle and the outflow tract, between embryonic day E8.5 and E10.5. Along the same vein, using a network-based computational method for single-cell RNA-sequencing analysis, Hand2 was demonstrated to be crucial for the development of the outflow tract.55 Unexpectedly, Hand2 was predicted to be dispensable for right ventricular cell specification, suggesting that congenital heart defects associated with Hand2 deficiency originated from a failure of the outflow tract to develop properly. Then, Lescroart et al. investigated via single-cell experiments the importance of Mesp1 in lineage commitment at the epiblast stage and during mesodermal specification.56 Altogether, this study demonstrated the emergence of various Mesp1-expressing progenitors already at the gastrulation stage, each one of these subpopulations likely contributing to various cardiac and noncardiac lineages. Heterogeneity in Nkx2–5 and Isl1-positive cardiac precursors was also demonstrated using a combined transcriptomic and epigenetic single-cell approach.57 More precisely, multipotent Isl1-positive precursors were found to produce different progenies, whereas significant Nkx2–5 expression in cardiac precursors invariably commits cells to a unique cardiomyocyte fate. In another attempt to decipher cellular orchestration of heart development, Su et al. investigated coronary artery and vein specification.58 Their single-cell approach revealed that sinus venosus-derived endothelial cells become specified to preartery cells during the process of coronary artery formation. Interestingly, the COUP-TFII transcription factor, a master regulator of venous commitment, specifically appeared to block preartery specification, preserving thereby a venous phenotype. Single-cell RNA sequencing has been used also to map the transcriptomic landscape in the developing human heart.59 In this case, the authors captured cells from various regions of the heart from 18 human embryos at different stages of development ranging from 5 to 25 weeks of gestation. Four major cell types were identified, among those cardiomyocytes, endothelial cells, fibroblasts, and epicardial cells. Signaling pathways implicated in heart development, such as the NOTCH, the NRG1, and the BMP pathways, were found to be critically implicated, suggesting synergistic and coordinated interactions between these various cardiac cell populations via paracrine signaling. Interestingly, a comparison of the human and mouse fetal hearts at single-cell resolution demonstrated interspecies differences in gene expression profiles. Specifically, several anatomical features with distinct transcriptional characteristics were unique to the human heart.

17.6.2 The adult heart Single-cell approaches are particularly interesting to identify and characterize rare subpopulations in mature organs in health and disease. For instance, nonmyocyte cell heterogeneity was evaluated in the adult mouse heart.60 The authors identified the expected cell types, including cardiac fibroblasts,

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endothelial cells, smooth muscle cells, and leukocytes. In addition, distinct mesenchymal subsets, likely representing transitions between various cellular phenotypes, were revealed in the course of these experiments. One important finding in this study is the large ligand-receptor network predicted to operate between cardiac cells. Fibroblasts express in particular Vegfa, Igf1, and Fgf2, suggesting communication with endothelial cells and mesenchymal cardiac precursors.61–63 In contrast, adult cardiomyocytes are more difficult to study using single-cell methods due to their large size. Nevertheless, cardiomyocytes were analyzed via scRNA-seq following isolation by FACS.64 In this initial analysis, cardiomyocytes bigger than 130 μm were excluded but lineage-tracing experiments confirmed the inclusion of relevant cardiomyocyte populations. Eventually, cardiomyocytes, endothelial cells, cardiac fibroblasts, smooth muscle cells, and macrophages were identified. Metabolism differed from one cell type to the others as judged from the different expression of mitochondrial genes in each subpopulation. Interestingly, several clusters of cardiac fibroblasts were observed. Eventually, the cytoskeleton-associated protein Ckap4 was found to be specifically increased in activated fibroblasts following ischemia–reperfusion injury. In another study, Nomura et al. isolated cardiomyocytes by Langendorff perfusion and generated single-cell cDNA libraries using a Smart-seq2 protocol. Studying the dynamic transcriptional response to pressure overload allowed the authors to demonstrate the important role of p53 signaling for modulating mitochondrial activity in the late-stage hypertrophy and failure.65 To overcome difficulties in cardiomyocyte assessment, single-nucleus DROP-seq was used to decipher GDF15-mediated cross talk between various cardiac cell types within the maturing heart.10 Combined with high-resolution mass spectrometry, a similar approach was also used to interrogate sinus node function and molecular changes controlling the pacemaker activity during pathological states.11 Then, the response of endothelial cells to myocardial infarction in newly formed cardiac vessels was also investigated in the adult heart. Several clusters were identified among the largely heterogeneous endothelium population. Among the clusters enriched after infarction, three expressed Plvap, a protein whose pattern of expression was known to correlate with cardiac repair.66 Similarly, Farbehi et al. identified 30 different clusters of cardiac nonmyocyte cells in the normal and injured mouse heart. In particular, the authors described a fibroblast lineage trajectory leading to activated fibroblasts expressing and secreting Wif1, a canonical Wnt pathway inhibitor, potentially impacting the fate of various cell types in the heart.67 Finally, heart regeneration is currently the subject of intense investigation. While the adult mammalian heart has limited regenerative capacity, the zebrafish heart demonstrates efficient regeneration following injury. Newly formed cardiomyocytes derive from preexisting cardiomyocytes that reenter the cell cycle and actively proliferate in the damaged heart. Interestingly, single-cell analysis of proliferative cardiomyocytes postinjury identified a unique transcriptional program resembling that of embryonic cardiomyocytes, which were characterized by low mitochondrial activity and increased glycolysis. Metabolic reprogramming was found to be dependent on the NRG1 pathway.68

17.6.3 The vasculature Single-cell approaches have been also instrumental to characterize cellular heterogeneity in the vasculature. Phenotypic modulation of vascular smooth muscle cells occurs in response to various stimuli in diseased states. Dedifferentiation, proliferation, and migration are commonly observed during atherosclerosis. In this context, Dobnikar et al. identified a rare subpopulation of smooth muscle cells expressing the multipotent progenitor marker Sca1 in both normal vessels and in atherosclerotic plaques.69 These cells demonstrated various degrees of dedifferentiation, as shown by a gradual

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downregulation of contractile genes, and a corresponding upregulation of genes known to be activated in smooth muscle cells by inflammation and growth factors. The identified transcriptional signature provided insights into the initial steps leading to the development of vascular disease. Likewise, modulation of vascular smooth muscle cells has been studied in both mouse and human atherosclerotic lesions via scRNA-seq.70 Transcriptomic profiling revealed the crucial importance of TCF21, a basic helix–loop–helix TF expressed by epicardial precursors known to give rise to cardiac fibroblasts and coronary smooth muscle cells during development, in the transformation of vascular smooth muscle cells into fibroblast-like cells named fibromyocytes. Vascular lesions are extremely heterogeneous, characterized by variations in growth rate and different levels of susceptibility to rupture, and demonstrating dissimilarities in cellular composition, especially immune cells.71 Inflammation is therefore a hallmark of atherosclerosis. Hence, scRNA-seq contributed to a better understanding of the role of inflammatory cells in the development of atherosclerotic plaques. In particular, lesions contain different populations of macrophages with distinct functional characteristics. Generally, M1 macrophages are considered proinflammatory cells, whereas M2 macrophages are believed to be beneficial via their anti-inflammatory function.72 Comparing healthy and atherosclerotic aortas, Cochain et al. identified three novel populations of macrophages and one population of monocyte-derived dendritic cells.73 Among the atherosclerosis-associated macrophages observed in advanced-stage lesions, one macrophage subpopulation expressed high levels of the “Triggering Receptor Expressed on Myeloid Cells 2” or TREM2. TREM2 expression was shown previously to inversely correlate with plaque stability.74 Interestingly, TREM-positive macrophages shared also a transcriptional signature resembling that of osteoclasts, indicating possible involvement in plaque calcification. Combining scRNA-seq with lineage tracing of myeloid cells, several activated macrophage populations, exceeding the traditional M1/M2 definition, were also detected in atherosclerotic lesions and found associated with disease progression.73, 75 Interestingly, proliferating stem cell-like monocytes were identified, potentially representing a source of macrophages in both progressing and regressing plaques. Finally, a mouse atlas of the leukocyte populations in aortic atherosclerosis was produced using scRNA-seq and mass spectrometry. Eleven clusters were identified in diseased aortas, each one demonstrating distinct phenotypic characteristics, nevertheless dominated by T cells and myeloid cells.76

17.6.4 Publicly available resources Several key undertakings to generate comprehensive atlases of mouse and human organs at the singlecell level, and to make accessible various databases, have been initiated. The Human BioMolecular Atlas Program (HuBMAP) proposes to map the complete human body at single-cell resolution.77 In the future, HuBMAP will analyze a range of normal tissues, including the vasculature, and possibly at a later time point the heart, and produce spatially resolved maps. Moreover, the Human Cell Atlas consortium envisions building a systematic atlas of healthy and diseased tissues via integrating data generated using a variety of experimental approaches, including scRNA-seq.78 Some important tools are already available online, such as PanglaoDB, which allows exploring published mouse and human scRNA-seq studies79 and the Single Cell Portal accessible at https://singlecell.broadinstitute.org/ single_cell. Han et al. used microwell sequencing to construct a mouse single-cell atlas, analyzing more than 400,000 cells covering all major organs, including the neonatal heart.80 A shotgun approach, based on single-cell combinatorial indexing, was developed to analyze 2 million cells derived from 61

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embryos between 9.5 and 13.5 days of gestation.81 The resulting method, named sci-RNA-seq3, included extraction of single nuclei directly from fresh tissues, and allowed investigating in particular trajectories leading to the development of the cardiac lineage. Finally, the Tabula Muris atlas is a publicly accessible compendium of single-cell data generated from FACS- (SMART-seq2) and droplet-based (10
Genomics) scRNA-seq.82 This database represents a valuable resource for comparing individual transcriptome. Twenty mouse organs are profiled including the heart, allowing to probe cell-to-cell and organ-to-organ relationships. For instance, Zhang et al. used recently the Tabula Muris database to analyze fibroblasts derived from healthy mouse tissues and determined their respective tissue-specific transcriptomic signature.83 Interestingly, cardiac fibroblasts can be distinguished by a higher expression of Tbx20, Gata4, and Hand2. Eventually, the authors used this information to develop a protocol to produce human induced pluripotent stem cell (iPSC)-derived cardiac fibroblasts in vitro.

17.6.5 Programming and reprogramming Understanding the cardiovascular regulatory pathways operating during programming, i.e., differentiation, and reprogramming has been a challenging endeavor. Consequently, attempts to characterize cellular intermediates during specification and differentiation of pluripotent cells toward the cardiovascular fates took advantage of single-cell approaches. In an early study, a Fluidigm C1 system was used to profile cardiac lineage-associated genes in cardiomyocytes, cardiac fibroblasts, smooth muscle cells, and endothelial cells from differentiating mouse embryonic stem cell (ESC)-derived Nkx2.5-positive cardiac precursor cells.84 This study demonstrated in particular that mouse ESCderived cardiomyocytes are remarkably similar to cardiomyocytes isolated from embryos and neonates. In addition, scRNA-seq has proven to be useful in assessing heterogeneity in differentiating human pluripotent cells and in identifying regulatory pathways controlling cardiovascular specification. Via isolating maturing human iPSC-derived cardiomyocytes, specific subpopulations with distinct transcriptional profiles were characterized.85 While all cells expressed the cardiac markers TNNT2 and ACTC1, specific gene expression programs were associated with maturity. For instance, the temporal expression of ISL1, a recognized cardiac precursor marker, was inversely correlated with differentiation. In contrast, the analysis highlighted the importance of NR2F2 and TBX5 for atrial commitment and HEY2 for ventricular specification. Interestingly, NR2F2 regulated genes promoting an atrial phenotype in more immature cells, whereas HEY2 induced ventricular genes in cardiomyocytes emerging at a later time point in differentiation. Cellular heterogeneity and the gene networks controlling cell fate determination in differentiating human iPSCs were also evaluated in a parallel study relying on the analysis of more than 40,000 cells.86 The approach allowed identifying gene regulatory networks implicated in specification into mesendoderm, and subsequently into cardiomyocytes and endothelial cells. In particular, this study provides a comprehensive profiling of the gene regulatory network controlled by HOPX, a central player in cardiomyocytes and endothelial cell differentiation. Human iPSC-derived endothelial cells have also been used to identify regulators of differentiation and maturation, and to model vascular dysfunction in vitro. As for cardiomyocytes, evaluation of heterogeneity during endothelial cell differentiation benefited from single-cell approaches. A first report investigated heterogeneity in human iPSCs induced to differentiate into endothelial cells in the absence or presence of a fragment of laminin 411. Switching matrices during differentiation allowed resolving heterogeneity and produced mature endothelial cells from uniformly committed precursors in vitro and in vivo.87 The efficacy of iPSC differentiation into endothelial cells was investigated in a different

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study, which identified 4 populations characterized by the respective expression of CLDN5, APLNR, GJA5, and ESM1. Each subpopulation defined a metabolically and functionally different cluster.88 Finally, two human ESC lines were evaluated following the induction of mesodermal differentiation.89 A significant transcriptional signature of endothelial cell specification was identified during maturation. Furthermore, pseudotime trajectories also identified the concomitant emergence of mesenchymal cells under these conditions, suggesting the existence of a cellular intermediate representing a binary cell fate decision between the two lineages. Converting differentiated cells into phenotypically different cell types is obtained via forced expression of cell-specific TFs. For instance, the conversion of mouse fibroblasts into induced cardiomyocytes was obtained by overexpressing Gata4, Mef2, and Tbx5.90 This approach holds promise in regenerative therapies for heart disease. However, such strategies suffer from the large cellular heterogeneity and the lack of characterization of the starting fibroblast population, limiting their broad application. In addition, the reprogrammed fibroblasts represent cellular intermediates at various stages of conversion, making difficult a full assessment of reprogramming efficacy by traditional bulk population studies. Therefore, to investigate the early reprogramming events leading to induced cardiomyocyte production, a single-cell transcriptomic analysis was used to identify molecularly distinct cell subpopulations.91 A gradual suppression of the fibroblastic program was apparent during the course of reprogramming. Interestingly, downregulation of factors involved in mRNA processing and splicing characterized fibroblast conversion. Eventually, the silencing of the splicing factor PTBP1 was found to be crucial for the acquisition of the cardiomyocyte gene program. Accordingly, Ptbp1 knockdown enhanced cardiomyocyte lineage gene expression and the overall cardiac TF-induced conversion. In particular, reprogrammed fibroblasts were enriched for genes involved in cardiac contraction and oxidative metabolism. Then, Stone et al. pursued further efforts to decipher the molecular mechanism by which TFs initiate cell fate transition.92 By combining scRNA-seq, chromatin immunoprecipitation followed by sequencing (ChIP-seq) for GATA4, MEF2C, and TBX5, and Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq), the early epigenomic and transcriptional events of reprogramming could be investigated in detail. Each of the three TFs was found capable of promoting chromatin remodeling when expressed alone. However, reprogramming was found to be largely dependent on concomitant MEF2C and TBX5 binding. Eventually, combinatorial expression of the three factors appeared sufficient to restrict conversion to the cardiac fate with no alternative fates being induced during the reprogramming process. Using a machine learning approach, a TF interaction model was developed that predicted the involvement of additional factors during cardiac fate transition, including TCFP2L1 and HIF1a.

17.7 Futures developments Beside scRNA-seq, novel single-cell approaches are under development. This is in particular the case for assessing the epigenomic landscape. For instance, single-cell ATAC-seq allows evaluating DNA accessibility,93, 94 a process that is dependent on chromatin-binding factors that act cooperatively to regulate gene expression, ultimately controlling cell identity and behavior.95 Using single-cell combinatorial indexing (sci)-ATAC-seq has already enabled to produce an atlas of chromatin accessibility in multiple mouse tissues, including the heart.96 The method makes therefore possible a direct comparison with single-cell expression data, and thus, the evaluation of the dynamic cell-specific changes at the

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transcriptomic and epigenomic levels taking place during the responses to various developmental and environmental cues. Along the same vein, the analysis of single-cell DNA methylomes demonstrated the feasibility of profiling in parallel methylation and transcriptional events to investigate associations between regulatory elements and transcription.97 Then, progress is being made in probing cellular heterogeneity using proteomics at single-cell resolution.98 The different single-cell proteomic technologies under evaluation include mass spectrometry, microchip, and staining-based techniques. Nevertheless, the future resides in a combination of technical developments. Untargeted methods to explore spatial gene expression patterns have already been proposed. These include, spatial transcriptomics, which captures RNA from tissue sections on a barcoded bead array, to provide high-definition three-dimensional analysis of RNA expression in organs.99 Similarly, Slide-seq enables transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads.100 In addition, a novel computational approach allows mapping previously identified cell-specific signatures identified in scRNAseq studies onto Slide-seq data to recapitulate the spatial distribution of various cell types within the tissue of interest. Then, Asp et al. extended the concept to cover temporality.101 The approach produced a comprehensive transcriptional analysis of various cell types composing the human embryonic heart at three different developmental stages. Importantly, at each stage, spatial transcriptomics identified unique cellular signatures at distinct anatomical locations, producing at the end a spatiotemporal atlas of the developing human heart. This study identified three different populations of cardiomyocytes, and especially one expressing MYOZ2, present in both atria and ventricles. It is noteworthy that MYOZ2enriched cardiomyocytes were already identified in a previous study assessing heterogeneity in the mouse heart64 and that this gene has been associated with hypertrophic cardiomyopathy in humans.102 Finally, in the future, the resolution will improve and allow determining subcellular RNA localization and translation. In a recent report, Fazal et al. described APEX-seq, a method based on proximity RNA labeling, enabling a comprehensive investigation of the subcellular transcriptome.103 As more techniques are developed, spatiotemporal profiling will continue to provide important information on the mechanisms controlling structural and behavioral cellular organizations within tissues. Our comprehension of the cardiovascular system in health and disease will undoubtedly benefit from these novel approaches.

Acknowledgment Our work is supported in part by grants from the Swiss National Science Foundation, Bern, Switzerland (Grant Nos. CRSII5-173738 and 31003A-182322 to T.P.).

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27. Griffiths JA, Scialdone A, Marioni JC. Using single-cell genomics to understand developmental processes and cell fate decisions. Mol Syst Biol. 2018;14(4), e8046. 28. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6), e8746. 29. Bacher R, Chu LF, Leng N, et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods. 2017;14(6):584–586. 30. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. bioRxiv. 2019;576827. 31. Townes FW, Hicks SC, Aryee MJ, Irizarry RA. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol. 2019;20(1):295. 32. Ding J, Condon A, Shah SP. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat Commun. 2018;9(1):2002. 33. Lvd M, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579–2605. 34. Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14(10):979–982. 35. Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single-cell mRNA quantification and differential analysis with census. Nat Methods. 2017;14(3):309–315. 36. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–386. 37. Derrien T, Johnson R, Bussotti G, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012;22(9):1775–1789. 38. Zhao Y, Li H, Fang S, et al. NONCODE 2016: an informative and valuable data source of long non-coding RNAs. Nucleic Acids Res. 2016;44(D1):D203–D208. 39. Iyer MK, Niknafs YS, Malik R, et al. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet. 2015;47(3):199–208. 40. Ulitsky I, Bartel DP. lincRNAs: genomics, evolution, and mechanisms. Cell. 2013;154(1):26–46. 41. Vance KW, Sansom SN, Lee S, et al. The long non-coding RNA Paupar regulates the expression of both local and distal genes. EMBO J. 2014;33(4):296–311. 42. Mele M, Rinn JL. "Cat’s cradling" the 3D genome by the act of LncRNA transcription. Mol Cell. 2016;62 (5):657–664. 43. Kopp F, Mendell JT. Functional classification and experimental dissection of long noncoding RNAs. Cell. 2018;172(3):393–407. 44. Flynn RA, Chang HY. Long noncoding RNAs in cell-fate programming and reprogramming. Cell Stem Cell. 2014;14(6):752–761. 45. Kim DH, Marinov GK, Pepke S, et al. Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming. Cell Stem Cell. 2015;16(1):88–101. 46. Liu SJ, Nowakowski TJ, Pollen AA, et al. Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biol. 2016;17:67. 47. Rinn JL, Chang HY. Genome regulation by long noncoding RNAs. Annu Rev Biochem. 2012;81:145–166. 48. Cabili MN, Trapnell C, Goff L, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 2011;25(18):1915–1927. 49. Ounzain S, Micheletti R, Beckmann T, et al. Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. Eur Heart J. 2015;36(6):353–368a. 50. Ounzain S, Burdet F, Ibberson M, Pedrazzini T. Discovery and functional characterization of cardiovascular long noncoding RNAs. J Mol Cell Cardiol. 2015;89(Pt A):17–26. 51. Ackers-Johnson M, Tan WLW, Foo RS-Y. Following hearts, one cell at a time: recent applications of singlecell RNA sequencing to the understanding of heart disease. Nat Commun. 2018;9(1):1–4.

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52. Yang K-C, Yamada KA, Patel AY, et al. Deep RNA sequencing reveals dynamic regulation of myocardial noncoding RNAs in failing human heart and remodeling with mechanical circulatory support. Circulation. 2014;129(9):1009–1021. 53. DeLaughter DM, Bick AG, Wakimoto H, et al. Single-cell resolution of temporal gene expression during heart development. Dev Cell. 2016;39(4):480–490. 54. Li G, Xu A, Sim S, et al. Transcriptomic profiling maps anatomically patterned subpopulations among single embryonic cardiac cells. Dev Cell. 2016;39(4):491–507. 55. de Soysa TY, Ranade SS, Okawa S, et al. Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects. Nature. 2019;572(7767):120–124. 56. Lescroart F, Wang X, Lin X, et al. Defining the earliest step of cardiovascular lineage segregation by singlecell RNA-seq. Science (New York, NY). 2018;359(6380):1177–1181. 57. Jia G, Preussner J, Chen X, et al. Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat Commun. 2018;9(1):1–17. 58. Su T, Stanley G, Sinha R, et al. Single-cell analysis of early progenitor cells that build coronary arteries. Nature. 2018;559(7714):356–362. 59. Cui Y, Zheng Y, Liu X, et al. Single-cell transcriptome analysis maps the developmental track of the human heart. Cell Rep. 2019;26(7):1934–1950.e1935. 60. Skelly DA, Squiers GT, McLellan MA, et al. Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 2018;22(3):600–610. 61. Lohela M, Bry M, Tammela T, Alitalo K. VEGFs and receptors involved in angiogenesis versus lymphangiogenesis. Curr Opin Cell Biol. 2009;21(2):154–165. 62. Bach LA. Endothelial cells and the IGF system. J Mol Endocrinol. 2015;54(1):R1–13. 63. Rosenblatt-Velin N, Lepore MG, Cartoni C, Beermann F, Pedrazzini T. FGF-2 controls the differentiation of resident cardiac precursors into functional cardiomyocytes. J Clin Invest. 2005;115(7):1724–1733. 64. Gladka MM, Molenaar B, de Ruiter H, et al. Single-cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation. Circulation. 2018;138 (2):166–180. 65. Nomura S, Satoh M, Fujita T, et al. Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure. Nat Commun. 2018;9(1):1–17. 66. Li Z, Solomonidis EG, Meloni M, et al. Single-cell transcriptome analyses reveal novel targets modulating cardiac neovascularization by resident endothelial cells following myocardial infarction. Eur Heart J. 2019;40(30):2507–2520. 67. Farbehi N, Patrick R, Dorison A, et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. eLife. 2019;8, e43882. 68. Honkoop H, de Bakker DE, Aharonov A, et al. Single-cell analysis uncovers that metabolic reprogramming by ErbB2 signaling is essential for cardiomyocyte proliferation in the regenerating heart. eLife. 2019;8. 69. Dobnikar L, Taylor AL, Chappell J, et al. Disease-relevant transcriptional signatures identified in individual smooth muscle cells from healthy mouse vessels. Nat Commun. 2018;9(1):4567. 70. Wirka RC, Wagh D, Paik DT, et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat Med. 2019;25(8):1280–1289. 71. Otsuka F, Joner M, Prati F, Virmani R, Narula J. Clinical classification of plaque morphology in coronary disease. Nat Rev Cardiol. 2014;11(7):379–389. 72. Chinetti-Gbaguidi G, Baron M, Bouhlel MA, et al. Human atherosclerotic plaque alternative macrophages display low cholesterol handling but high phagocytosis because of distinct activities of the PPARgamma and LXRalpha pathways. Circ Res. 2011;108(8):985–995. 73. Cochain C, Vafadarnejad E, Arampatzi P, et al. Single-cell RNA-Seq reveals the transcriptional landscape and heterogeneity of aortic macrophages in murine atherosclerosis. Circ Res. 2018;122(12):1661–1674.

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74. Rai V, Rao VH, Shao Z, Agrawal DK. Dendritic cells expressing triggering receptor expressed on myeloid cells-1 correlate with plaque stability in symptomatic and asymptomatic patients with carotid stenosis. PLoS One. 2016;11(5). 75. Lin JD, Nishi H, Poles J, et al. Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression. JCI Insight. 2019;4(4). 76. Winkels H, Ehinger E, Vassallo M, et al. Atlas of the immune cell repertoire in mouse atherosclerosis defined by single-cell RNA-sequencing and mass cytometry. Circ Res. 2018;122(12):1675–1688. 77. Consortium H. The human body at cellular resolution: the NIH human BioMolecular Atlas program. Nature. 2019;574(7777):187. 78. Regev A, Teichmann SA, Lander ES, et al. Science forum: the human cell atlas. eLife. 2017;6, e27041. 79. Franz
en O, Gan L-M, Bj€orkegren JL. PanglaoDB: a web server for exploration of mouse and human singlecell RNA sequencing data. Database. 2019;2019. 80. Han X, Wang R, Zhou Y, et al. Mapping the mouse cell atlas by microwell-seq. Cell. 2018;172(5):1091– 1107.e1017. 81. Cao J, Spielmann M, Qiu X, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566(7745):496–502. 82. Muris T. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature. 2018;562 (7727):367–372. 83. Zhang H, Tian L, Shen M, et al. Generation of quiescent cardiac fibroblasts from human induced pluripotent stem cells for in vitro modeling of cardiac fibrosis. Circ Res. 2019;125(5):552–566. 84. Li G, Plonowska K, Kuppusamy R, Sturzu A, Wu SM. Identification of cardiovascular lineage descendants at single-cell resolution. Development (Cambridge, England). 2015;142(5):846–857. 85. Churko JM, Garg P, Treutlein B, et al. Defining human cardiac transcription factor hierarchies using integrated single-cell heterogeneity analysis. Nat Commun. 2018;9(1):4906. 86. Friedman CE, Nguyen Q, Lukowski SW, et al. Analysis of cardiac differentiation at single cell resolution reveals a requirement of hypertrophic signaling for HOPX transcription. bioRxiv. 2018;229294. 87. Ohta R, Niwa A, Taniguchi Y, et al. Laminin-guided highly efficient endothelial commitment from human pluripotent stem cells. Sci Rep. 2016;6:35680. 88. Paik DT, Tian L, Lee J, et al. Large-scale single-cell RNA-seq reveals molecular signatures of heterogeneous populations of human induced pluripotent stem cell-derived endothelial cells. Circ Res. 2018;123 (4):443–450. 89. McCracken IR, Taylor RS, Kok FO, et al. Transcriptional dynamics of pluripotent stem cell-derived endothelial cell differentiation revealed by single-cell RNA sequencing. Eur Heart J. 2019;41(9):1024–1036. 90. Qian L, Huang Y, Spencer CI, et al. In vivo reprogramming of murine cardiac fibroblasts into induced cardiomyocytes. Nature. 2012;485(7400):593–598. 91. Liu Z, Wang L, Welch JD, et al. Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte. Nature. 2017;551(7678):100–104. 92. Stone NR, Gifford CA, Thomas R, et al. Context-specific transcription factor functions regulate epigenomic and transcriptional dynamics during cardiac reprogramming. Cell Stem Cell. 2019;25(1):87–102.e109. 93. Lareau CA, Duarte FM, Chew JG, et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat Biotechnol. 2019;37(8):916–924. 94. Satpathy AT, Granja JM, Yost KE, et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat Biotechnol. 2019;37(8):925–936. 95. Klemm SL, Shipony Z, Greenleaf WJ. Chromatin accessibility and the regulatory epigenome. Nat Rev Genet. 2019;20(4):207–220. 96. Cusanovich DA, Hill AJ, Aghamirzaie D, et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell. 2018;174(5):1309–1324.e1318.

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97. Angermueller C, Clark SJ, Lee HJ, et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods. 2016;13(3):229–232. 98. Yang L, George J, Wang J. Deep profiling of cellular heterogeneity by emerging single-cell proteomic technologies. Proteomics. 2019;1900226. 99. Vickovic S, Eraslan G, Salm
en F, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods. 2019;16(10):987–990. 100. Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science (New York, NY). 2019;363(6434):1463–1467. 101. Asp M, Giacomello S, Larsson L, et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell. 2019;179(7):1647–1660.e1619. 102. Osio A, Tan L, Chen SN, et al. Myozenin 2 is a novel gene for human hypertrophic cardiomyopathy. Circ Res. 2007;100(6):766–768. 103. Fazal FM, Han S, Parker KR, et al. Atlas of subcellular RNA localization revealed by APEX-seq. Cell. 2019;178(2):473–490.e426.

CHAPTER

Good laboratory and experimental practices for microRNA analysis in cardiovascular research

18

Christos Papaneophytou, Eleftheria Galatou, and Kyriacos Felekkis Department of Life and Health Sciences, School of Sciences and Engineering, University of Nicosia, Nicosia, Cyprus

18.1 MicroRNAs as potential biomarkers in cardiovascular diseases Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide.1 The identification of biomarkers to better identify high-risk individuals for the early diagnosis of CVDs and to monitor the progression of this class of diseases are public health priorities.2 However, the use of biomarkers for both the diagnosis and monitoring of the progression of CVDs remains an important area of research.3 Recent studies have revealed the potential of noncoding RNAs (ncRNAs) as prognostic, diagnostic, and therapeutic biomarkers for CVDs.4 Noncoding RNAs (ncRNAs) are abundant RNAs that are not translated into proteins and regulate several cellular processes. Based on their size and function, ncRNAs are classified into (i) small noncoding RNAs,5 including microRNAs (miRNAs), small-interfering RNAs (siRNAs), piwi-interacting RNAs (piRNAs), and others; (ii) long noncoding RNAs (lncRNAs), >200 nucleotides in length that are not translated into protein6; and (iii) circular noncoding RNAs.7 NcRNAs are found in different cell compartments regulating multiple cell functions, but they have also been detected in biological fluids including blood, urine, and saliva. These ncRNAs, known as circulating or cell-free ncRNAs, are released as free molecules (i.e., not bound to any molecule) or they are transported inside vesicles (e.g., exosomes). Moreover, several ncRNAs in circulation are bound to proteins including lipoproteins (low-density lipoprotein—LDL and high-density lipoprotein—HDL) and other proteins, e.g., Argonaut2 (Ago2).8 Detecting ncRNAs in circulation has opened a new field in molecular diagnostics and they have been examined as a potential source of diagnostic and prognostic biomarkers for CVDs and other diseases.9, 10 Among the various types of ncRNAs, the class of miRNAs has exponentially drawn the attention of scientific community because they exhibit the characteristics of an ideal biomarker for CVDs and other diseases as they: (i) can be obtained using noninvasive methods, (ii) exhibit high specificity and sensitivity to the disease, (iii) can be detected at the early stages of the disease, and (iv) have a long half-life within the sample.11 In general, miRNAs are conserved, single-stranded, small (22 nucleotides) ncRNAs that influence numerous biological processes by silencing RNA and regulating the posttranscriptional expression of genes.12 miRNA expression patterns differ almost in all diseases in both tissues and Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00002-X Copyright # 2021 Elsevier Inc. All rights reserved.

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extracellularly, contributing to disease pathogenesis. In detail, changes in both intracellular and extracellular13 miRNA levels have been implicated in a variety of diseases including CVD,14 cancer,15 and neurodegenerative diseases.16 Moreover, miRNAs are essential regulators of normal cardiovascular function and play pivotal roles in several, if not all, aspects of cardiovascular biology.17 The biogenesis and release mechanisms of miRNAs in circulation have been previously reviewed.18 The role of miRNAs in the cardiovascular function and the pathogenesis of CVDs as well as their potential as prognostic/diagnostic/therapeutic biomarkers have also been extensively reviewed elsewhere19 and, therefore, will not be discussed here. Even though miRNAs exhibit high potential as diagnostic and therapeutic biomarkers, the accurate determination of their levels in circulation remains a challenge mainly due to their small size, relatively low concentrations, sequence homology with precursor forms, and the absence of reference miRNAs.20 Thus, the determination of circulating levels of miRNAs to monitor the progression of various diseases including CVDs is in its infancy.21 Quantification of miRNAs in blood samples is a process that involves several steps including (i) blood collection and processing, (ii) miRNA isolation, (iii) miRNA amplification, and (iv) data interpretation, normalization of the results, and quality control. Despite the recent improvements in the determination of miRNA levels in blood samples including plasma and serum (circulating miRNAs), there are several inconsistent publications on the alterations (increases or decreases) of cardiac miRNA levels in circulation22 which complicates the direct comparison of studies. The development of different preanalytical protocols by several research groups, the use of different miRNA extraction methods or the use of various commercially available miRNA isolation kits, the employment of different detection methods (quantitative RT-PCR (qRT-PCR), microarrays, next-generation sequencing (NGS), etc.), and the lack of a universal normalization strategy have contributed to these discrepancies.22 The inconsistency among studies highlights the need to standardize the analysis of miRNAs in circulation while several issues must be addressed for the translation of miRNAs from basic research into a clinical care scenario.23 Even though high-quality miRNAs can be easily extracted from several types of cells and tissues including cell lines, fresh tissues, and formalin-fixed paraffin-embedded tissues,11, 24 the extraction and detection of miRNAs from biofluids, including whole blood, serum, plasma, urine, etc., are not well established. Additionally, their detection and quantification outside the cells are subject to several preanalytical and analytical challenges.25, 26 Furthermore, various methodological aspects affect the quantification of miRNAs in plasma/serum including (i) the limited sample volumes, (ii) the low recovery of miRNA, (iii) the interference of anticoagulants in PCR-based detection methods, and (iv) the lack of universal endogenous and exogenous controls.27 In the past years, preanalytical considerations and modifications to the qRT-PCR protocol (the most widely used method for the determination of miRNAs levels), specific to the amplification of circulating miRNA have been developed28, 29 and suggestions to improve data handling have been proposed.30, 31 However, the lack of standard protocols and guidelines in studying miRNAs as biomarkers for CVDs and other diseases has also contributed to the heterogeneity of results among different studies. Thus, uniform protocols and specific guidelines to reduce heterogeneity among studies regarding circulating miRNA are needed. In this chapter, we propose some recommendations for future research in miRNAs and we summarize the known good laboratory and experimental practices that should be followed in miRNA studies for CVDs. Moreover, we focus on the detection and quantification of circulating levels of miRNAs using qPCR-based methods.

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18.2 Good laboratory practices when studying circulating miRNAs for cardiovascular diseases Several critical steps must be followed to ensure the quality of the results when studying circulating miRNAs as biomarkers for CVDs. In general, the accuracy of miRNA quantification in blood samples including plasma and serum is related to several factors that, directly or indirectly, impact the quality of the results.22 Therefore, it is essential to have a quality control system for monitoring the entire process of miRNA quantification from blood collection, storage, and handling to the normalization of the results.23 Thus, the introduction of both good laboratory practices (GLP) and good experimental practices (GEP) in miRNA studies will help to minimize the discrepancies among various studies. GLP is defined as “a quality system concerned with the organizational process and the conditions under which laboratory experiments are planned, performed, monitored, recorded, archived, and reported.”32 Several platforms for miRNA quantification including qRT-PCR, microarray, and NGS are available; however, it is still quite challenging to accurately determine the levels of miRNAs, especially the levels of miRNAs in biofluids. The qPCR platform is now being routinely used to measure specific miRNAs in circulation due to the ease of use and its high sensitivity.33 However, the determination of miRNA levels as biomarkers for CVDs is associated with specific technical challenges including those related to preanalytic variation, sample storage, and handling.22, 28 Regardless of the experimental question addressed, each PCR-based experiment consists of several common steps: experimental design, quality control, read alignment, assigning reads to miRNAs, and estimating miRNA levels. Careful considerations should be given to laboratory design/setup and operation within laboratories in which PCR-based reactions (including qRT-PCR) are carried out because the extreme sensitivity of such techniques can easily result in contamination.34 In the following paragraphs, we discuss the GLP that should be employed during the determination of miRNAs in blood samples.

18.2.1 The workflow of determination of circulating miRNA levels using qRT-PCR As mentioned earlier, the most popular and straightforward technique for validating and accurately quantifying miRNAs in the clinical laboratory context is qRT-PCR because it is more specific and sensitive compared to the other assays35 allowing the high-throughput miRNA quantification over a wide dynamic range.36 qPCR is also relatively inexpensive (compared to other techniques) and flexible making it the method of choice for studying circulating miRNAs.37, 38 Importantly, the appropriate instruments are already available in most laboratories because other qRT-PCR-based tests are already routinely used for in vitro diagnostic applications.39 The first step in a qRT-PCR experiment is to convert miRNA into cDNA that subsequently can be analyzed using the same method as a conventional qPCR experiment. Amplification begins by using a miRNA-specific primer and a stem-loop/poly(A) primer while either the TaqMan® probe or SYBR® Green is used to monitor the synthesis of the amplified product. The workflow of determination of miRNA levels in blood samples (serum/plasma) using the qRT-PCR technique is illustrated in Fig. 18.1 and is composed of five key steps: (1) blood collection and storage; (2) plasma or serum recovery; (3) extraction of miRNAs from samples and assessment of their concentration and quality; (4) polyadenylation and synthesis of cDNA from extracted total miRNA through reverse transcription and determination of the target miRNA levels using quantitative PCR; and (5) data analysis using appropriate normalization method(s).

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FIG. 18.1 A schematic diagram of workflow for miRNAs quantification in serum/plasma samples showing five key steps indicating the point(s) of consideration during each step. Blood samples must be obtained from CVD patients or healthy individuals using techniques that minimize hemolysis (Step 1). Subsequently, serum or plasma should be recovered within 4–6 h after blood collection using standard centrifugation conditions (applied force, time, temperature, etc.) to avoid hemolysis and introduction of miRNAs from cellular components of the blood (Step 2). miRNAs should be isolated from 1 mL of serum/plasma samples stored at 80°C in small aliquots while special attention should be given when comparing fresh samples with archival samples (Step 3). Quantification of miRNAs in plasma/serum samples is routinely carried out using qRT-PCR under specific conditions to eliminate the risk of contamination while when it is possible the experiments before and after qRT-PCR are carried out in different rooms or areas (Step 4). Finally, the data are stored properly, and the miRNAs expression levels are normalized using the appropriate control(s) (Step 5). All steps are discussed in detail within the text.

18.2.2 Standard operating procedures An essential component of running a laboratory for the determination of miRNA levels in circulation for research or diagnostic purposes is the establishment of standard operating procedures (SOPs). The main objective of SOPs is to ensure consistency, accuracy, and quality of data by providing step-bystep instructions on how to perform procedures correctly.40 Moreover, SOPs harmonize laboratory practices, reduce human errors while they help ensure compliance with the study protocol, regulations, and international standards. In general, SOPs warrant that all process and assay methods during the determination of miRNA levels are standardized, thus contributing to reproducibility. In general, each SOP should describe one task clearly and accurately while also informing the experimenter of everything that needs to know and how to do it. The development of SOPs allows for the analysis of samples collected by different groups because they followed the same processes for sample (including blood samples) acquisition, handling, storage, and analysis, and all steps in all processes are documented in detail. Small alterations in SOPs between groups (or within groups) collecting blood samples may result in discrepancies among results from different groups due to variations at several steps in the collection and handling process.41 All SOPs in a miRNA laboratory must be reviewed, updated regularly, and approved by the laboratory manager to ensure that all procedures used in the laboratory are up to date and accurate.

18.2.2.1 Good practices for blood collection and handling The collection of blood samples and their subsequent processing are among the most important steps when analyzing miRNAs. The different setup of experiments and approaches that are employed from different groups to analyze miRNAs in blood samples lead to bias in the final results22 and therefore, a

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universal SOP for the isolation, and processing of blood samples in uniform conditions must be developed. Recent studies highlight the impact of hemolysis occurring during blood collection and processing on the miRNAs profile and/or levels in plasma and serum samples.42 Even though it is impossible to avoid in vivo hemolysis, in vitro hemolysis may be minimized by following the general guidelines of blood collection, storage, and handling that are recommended by the Early Detection Research Network (ERDN; https://edrn.nci.nih.gov/).41 Moreover, small-diameter needles (23 gauge) must not be used for blood collection in miRNA studies because they induce hemolysis and miRNAs are released from red blood cells (RBCs) affecting the levels of cell-free miRNAs.43 It has also been reported that the storage temperature and time also have a significant impact on the miRNA levels in plasma/serum samples and the concentration of various miRNAs (including miR-16 that is widely being used as an internal control) is altered after storage for 24 to 72 h at 4°C or 20°C.29 Although miRNAs in blood samples are stable up to 24 h at room temperature,44 it is highly recommended to centrifuge blood samples within 2–6 h after the collection to recover plasma/serum27 because miRNAs may be released from the cellular components of blood (especially from RBCs that are rich in miRNAs) into the serum of plasma during the storage time and/or during the subsequent processing. Human blood plasma is a challenging specimen type because it contains high levels of endogenous RNase activity and additional sources of variation. Despite endogenous miRNAs are protected from RNases in their native state, they are degraded within seconds if extracted and spiked back into plasma, suggesting that a suitable RNA extraction method should completely and rapidly inactivate RNases.44, 45 Other “preanalytical” variables including storage temperature and time, centrifugation conditions, etc., alter the concentration of cellular components in plasma/serum and therefore the miRNA levels and profile in such samples (discussed further in this chapter), indicating that these variables need to be considered and standardized when plasma and serum are used as a source of miRNAs.46, 47 Furthermore, miRNAs exist in different physical states in blood plasma, i.e., within vesicles/exosomes, associated with small Ago2-containing protein complexes or bound on LDL/HDL, suggesting that specimenprocessing conditions that alter vesicle content will influence miRNA profiles.48 Studies aiming to identify and/or validate miRNAs as biomarkers for CVDs or other diseases usually include both freshly collected serum/plasma and retrospective archived samples. Subsequently, an SOP must be developed and general guidelines must be followed for the recovery of serum/plasma and their subsequent storage and handling in uniform conditions.21

18.2.2.2 Good practices for serum/plasma recovery Isolation of plasma or serum by centrifugation is probably one of the most essential preanalytical steps, because the centrifugation conditions that are applied to recover serum or plasma may affect the miRNA composition and/or levels in such samples.49 In general, the centrifugation conditions including the type of rotor, applying force, acceleration, time, and duration are important factors that need to be standardized because cell debris, residual platelets, etc., may contribute miRNAs in serum/ plasma.42, 46 However, recovery of plasma or serum is generally carried out using different centrifugation conditions50 and their alteration may lead to the recovery of platelet-poor or platelet-rich serum or plasma.51 Overall, prolonged centrifugations at a high speed may induce hemolysis and release of miRNAs from RBCs, whereas brief centrifugations at a low speed usually result in the poor separation of RBCs and other cellular components from plasma/serum.52 In detail, according to the ERDN guidelines for blood collection and processing41 for serum collection the blood samples must be centrifuged after the clotting period (30–60 min at room temperature). For plasma recovery, after blood collection, the tubes should be inverted carefully 8–10 times to mix blood and anticoagulant and subsequently

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must be centrifuged. For both plasma and serum recovery, blood samples should be centrifuged in a horizontal rotor (swing-out head) at 1100–1300 g, for 10–20 min at room temperature (or at 4 °C). If the blood is not centrifuged immediately at the end of the clotting time (for serum), after mixing the anticoagulant with blood (for plasma), the samples should be stored upright at 4 °C for no longer than 4 h. Interestingly, some groups suggested that plasma/serum should be recentrifuged (e.g., at 2500 g for 15 min at 4 °C) before miRNA isolation to remove residual cellular component that may release miRNAs in plasma/serum.29, 53 Importantly, particular attention needs to be paid when serum is selected as a source of miRNAs because the time between blood collection and centrifugation may also have an impact on miRNA profile and/or levels. In detail, samples that are allowed to sit less than 30 min will probably contain cellular components and other contaminants contributing miRNAs. On the contrary, a time interval between blood collection and centrifugation longer than 60 min may promote hemolysis, and therefore, the introduction of miRNAs not normally found in serum samples.41

18.2.2.3 Long-term storage of biological samples It has been previously reported that miRNAs are stable in archival serum/plasma samples stored at 80°C for a long time.54–56 However, repetitive freeze/thaw cycles may have an impact on miRNA levels and/or profiles in serum samples.56, 57 On the contrary, repetitive freeze/thaw cycles (up to eight cycles) do not appear to have an impact on miRNA levels and profiles in plasma.44 Therefore, general guidelines on how to handle and store serum and plasma samples for miRNA isolation are needed and recently different groups proposed the following practices21: I. Blood samples including serum and plasma must be stored into 1-mL aliquots while unused aliquots must be stored at 80°C at the time of miRNA isolation. II. miRNAs should be isolated from at least 1 mL of serum/plasma and subsequently stored in small aliquots at 80°C for future analysis to eliminate repeated freeze/thaw cycles. III. For prospective studies, it is highly recommended to store the freshly recovered serum or plasma at 80°C (at least overnight) to allow direct comparison to archival samples (discussed further in the following).

18.2.2.4 Good recovery of archival sample practices Studies aiming to identify or validate miRNAs as biomarkers for CVDs and other diseases involve the use of retrospective archived serum/plasma stored at 80°C. Several studies highlight the need to develop a general protocol for handling the archival samples because the thawing temperature and speed alter the miRNA profile in serum and plasma samples21, 58, 59 leading to discrepancies among different studies. For example, significantly higher levels of various miRNAs (e.g., miR-451a and miR-93-5p) were detected when samples were thawed quickly at 37°C compared to those obtained when samples were thawed slowly at 4°C.21 This discrepancy was partly explained by the fact that miRNAs enclosed in large particles either precipitated during the first centrifugation step to remove cryo-precipitates when the samples are thawed at 4°C or are destroyed at 37°C, releasing miRNAs. Thus, based on the SOP regarding blood sample storage and handling proposed by EDRN,41 archived serum/plasma samples stored at 80°C must be frozen/thawed at least one time before miRNA determination.

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18.2.2.5 Good qRT-PCR practices to minimize contamination Despite qPCR is considered as a straightforward and generally trouble-free method, some drawbacks complicate the reaction producing false-positive or false-negative results60 and large experiments can become quite labor-intensive to carry out. Furthermore, for the detection of miRNAs only one flanking primer is specific to the miRNA, and therefore, special attention should be given to ensure that only the target miRNA is being amplified, especially when using SYBR® Green. Importantly, the short length of miRNAs is a significant disadvantage, especially when trying to differentiate miRNAs that may only differ by few bases because melting temperatures can be too low and very similar.61 Even though with qPCR, the risk of contamination decreases because amplification of the miRNAs takes place in a closed system, several factors may introduce contamination in the reaction mixture. Therefore, the correct workflow must be followed when studying miRNAs to reduce contamination and ensure that the GLPs are followed. In general, to minimize contamination, different areas in the laboratory should be physically separated and there should be two major separations between the work done before amplification (pre-PCR) and that performed after amplification (post-PCR). According to WHO GLP principles, “facilities and equipment must be sufficient, adequate, and spacious enough to avoid problems such as overcrowding, cross-contamination, or confusion between projects.”62 In general, contamination prevention begins with the separation of work areas. Where possible, PCR facilities should be organized into three discrete areas/rooms (Fig. 18.2) as follows63: I. a sample-processing area (dirty), where the miRNA extraction is carried out, II. a reagent preparation area (clean) to prepare PCR reagents and prepare the reaction mixtures, III. an amplification area (semiclean), where the qPCR machine would perform amplification and detection/quantification of target miRNAs. The workflow between these rooms/areas must be unidirectional, i.e., from clean areas to the contaminated areas, but not vice versa and if possible, the relative air pressure and direction should differ. Importantly, consumables, laboratory coast, and equipment should be dedicated to each area. Both laboratory coats and gloves must be changed between areas as well as other personal protective equipment, and hands must be washed. If possible, it is helpful to color-code pipettes and racks, as well as laboratory coats in different areas to be able to easily monitor movement between different areas.

FIG. 18.2 A diagram showing the unidirectional workflow in a PCR laboratory for the determination of miRNA levels in plasma/serum samples.

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No working materials should be brought back to earlier stages, including pens, notebooks, and hard disks, or memory sticks. Moreover, the powder on powdered-gloves interferences with PCR-based assays thus the use of powder-free gloves is highly recommended.64, 65 The extraction of miRNAs from blood samples (or other biofluids and tissue samples) must be performed in areas where PCR products have not been handled. A second clean area is required for sample processing, i.e., to set up the reverse transcriptase step of qRT-PCR. The amplification room/area, where the PCR machines are housed, should contain a clear booking system to keep a record of the experiments, especially when PCR machines are shared. Changing gloves and laboratory coats is also essential to prevent contamination when performing qRT-PCR. In addition to performing different qPCR processes in different rooms, methods to eliminate amplification product carryover contamination have been suggested.34, 66 The following practices and methods have been proposed to prevent carryover contamination67: I. When setting up a qPCR experiment, it is essential to wear powder-free gloves to avoid contaminating the reagents and/or reaction mixture. II. False-positive results may occur because of carryover from another PCR reaction, and therefore, the appropriate negative and positive controls (when possible) should be used. III. The quality of the results is usually dependent on the use of optimal concentration of key reagents including dNTPs, MgCl2, primers, cDNA, or DNA polymerase. However, the inappropriate concentration of these reagents may lead to inconsistent results. When troubleshooting PCR, only one reagent should be examined at a time. IV. To eliminate carryover contamination in qRT-PCR experiments, it is essential to minimize the number of pipetting and handling steps. V. All PCR workbenches must be decontaminated using a 10% bleach solution and 70% alcohol. Moreover, all the pipettes/pipette tips, tubes, racks, and gloves must be UV-irradiated overnight. VI. A different pipette tip should be used for the pipetting of each of the PCR components. VII. Aliquoting of reagents and primers is highly recommended to minimize contamination and reduce assay downtime. VIII. Each area and/or room should have dedicated reagents and supplies. IX. Equipment, supplies, and reagents should never be transferred from one area to another; three sets of pipettes must be used, one for each area/room. X. Appropriate gloves and laboratory coats should be used at each worksite; when moving to a new area, experimenters must put on new gloves and laboratory coats.

18.2.3 Quality assessment of circulating miRNA analysis Quality assessment is important both before and after the determination of miRNA levels. The maintenance of a quality management system is vital to a laboratory for providing accurate results every time. The term “quality” in laboratories refers to (i) quality control (QC) and (ii) quality assurance (QA). In general, QC is a system of routine and consistent controls, which helps to ensure data integrity, completeness accuracy while it facilitates, identify and correct any errors or omissions that have occurred during experimental procedures as well as recording and archiving processing results and quality control data. The QC system should contain all procedures used in each assay to assure that a test run is valid and results are reliable. QA includes a planned scheme of review procedures, usually performed by an independent “third party laboratory,” which monitors QC procedures and confirms that quality

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objectives have been (and are being) met. In general, QA is an important element of a quality management system and is composed of the documentation of the results, SOPs, quality control of samples, and an external quality assessment scheme. miRNAs are members of the nucleic acids’ family, and therefore, they are analyzed with the same methods as long RNAs. However, problems begin with the quantification of miRNA concentration in plasma/serum samples because their concentrations are below the detection limits of the classical spectrophotometric techniques for quantifying nucleic acids. As we discussed previously, several methods for the determination of miRNAs expression levels have been developed, although the three most common being microarrays, NGS, and qRT-PCR while the latter has become the gold standard method. Regarding quantification of mRNAs, factors affecting qRT-PCR such as RNA quality and inhibitors have been extensively studied and the effect of RNA integrity on both the performance of qRT-PCR and quantification of the results has been reported.68 It has also been suggested that when studying miRNAs, the evaluation of RNA integrity should be carried out as a routine step in the pre-PCR phase levels because RNA integrity may have an impact on the expression profile and/or levels of miRNAs in circulation.68 Quality criteria for both old and new PCR-based assays are under continuous development and they modified when is needed. Recently, a two-tailed qRT-PCR panel for quality control of circulating miRNA studies has been reported.69 This approach employs two hemiprobes complementary to two different parts of the targeted miRNA, is highly sensitive, and enables discrimination of highly homologous miRNAs.69, 70 Moreover, during the past two decades, several PCR-based assay protocols have been developed, validated, optimized, and implemented, for use in various clinical applications.36 However, quality criteria to comply with both GLP and international guidelines for the quantification/validation of miRNAs as biomarkers a variety of diseases including CVDs have not yet been reported. Nevertheless, the following practices are recommended: • • •

•

Records of the results must be kept, to help to identify errors at an early stage. All assays should be validated before the introduction into routine use. The validation of an in-house assay for the quantification of miRNAs in circulation refers to the validation of the total process. Any change(s) at any step of the whole process, including the miRNA extraction method, reagents, qPCR cycling parameters, the introduction of internal or external control(s) and/or inhibitor(s), will need a detailed reassessment of the whole process. The performance of new batches of reagents including primers, PCR mix, etc., must be evaluated using well-established characterized control(s) and recorded as an auditable record.

18.3 Good experimental practices when studying circulating miRNAs for cardiovascular diseases As we mentioned earlier, despite the recent advancements in the quantification of miRNAs in blood and other biological fluids, there are many contradictory published studies on the alterations of cardiac miRNAs expression levels and/or composition.22 Surprisingly, a comparison of miRNA profiles reported by different groups for the same disease, including CVDs, revealed variations on the expression levels of specific miRNAs highlighting the need to standardize the quantification and validation of miRNAs.21 To this end, GEP should be established to ensure that high-quality experiments are carried

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out. GEP is concerned with the conditions under which experiments be planned, conducted, assessed, recorded, and interpreted so that their results should be comparable and reliable. As shown in Fig. 18.3, and we discussed previously (Fig. 18.1), the first step to quantify miRNAs in blood samples is to select the suitable miRNA source, i.e., blood fraction (whole blood, serum, or plasma) and subsequently, miRNAs are extracted using either a traditional phenol-based nucleotide extraction method (e.g., phenol-chloroform) or a commercially available column-based miRNA extraction kit. The levels of miRNAs can be determined using different techniques, although qRT-PCR, NGS, and microarrays are the most widely used methods. When qRT-PCR is employed for the determination of miRNA level data, it is essential to normalize the data using endogenous and/or exogenous controls.71 When studying miRNAs for CVDs, the experimenter should take into account the unique properties of biofluid that can make the determination and/or validation of miRNA as biomarkers a challenge. In the center of growing interest in using miRNAs for CVDs, technical standardization is essential because many methodologies have been used to isolate and determine the levels of miRNAs from blood samples and other biofluids.22 Following blood collection and plasma/serum recovery, a variety of techniques are employed to extract, analyze, and normalize miRNA levels. The effect of different techniques on the results of downstream miRNA determination and quantification remains unclear, highlighting the need to provide universal “best experimental practices” and standardization. Importantly, it has been reported that the sample type and the blood collection tube, the miRNA extraction method, as well as the detection method and normalization strategy, have a significant impact on miRNA profile and/or levels.23, 72 It is well known that enzymes that are routinely being used for the amplification and analysis of miRNA can be affected by different blood components that may copurified with miRNAs. Importantly, the extraction, quantification, and normalization strategy are often modified by different groups, resulting in discrepancies between different studies. Consequently, these methods must be standardized, validated, and optimized.73 The factors affecting the quality of the results are summarized in Table 18.1 and subsequently are discussed further in the following paragraphs.

FIG. 18.3 Flowchart summarizing the main steps during the determination of miRNA levels.

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Table 18.1 Different steps and general considerations in circulating miRNA detection. Step

General considerations

Selection of blood fraction Whole blood Serum Plasma

– – – – – – –

Cellular components may contribute RNAs Repetitive freeze/thaw affects miRNA profile miRNAs may be released from cellular components during coagulation Preferred over serum Repetitive freeze/thaw does not affect miRNA profile Cellular components may contribute miRNA Anticoagulants may interfere with downstream assays

Selection of anticoagulant for plasma recovery EDTA Heparin Citrate

– – – – – –

Recommended by most studies High miRNA recovery Inhibits qRT-PCR Treatment with heparinase is recommended May increase miRNA detection May inhibit qRT-PCR

– – – –

Recovery of low-quality miRNAs Organic solvents impact downstream assays A variety of commercially available kits are available Each kit provides a different miRNA yield and/or quality

– – – – – – – – – – – –

Low cost High speed Low throughput Detection only of known miRNAs Instrumentation is available in almost all laboratories User friendly High throughput A low amount of starting material is required Novel miRNAs can be detected High cost of equipment A high amount of starting material is required A preamplification step is required

miRNA extraction method Phenol/chloroform-based Column-based Detection method qRT-PCR

NGS

Microarrays Normalization method Endogenous controls (e.g., hsa-miR-16) Exogenous controls (e.g., celmiR-150) Ratio-based normalization

– Cellular components may contribute miRNAs that are used as endogenous controls – A reliable “housekeeping” miRNA has not been identified – Unstable in crude plasma/serum – The optimal timing of miRNA addition should be determined – All miRNAs are used as housekeeping – The use of identical initial volumes and is highly recommended

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18.3.1 Always use the same blood fraction and collection tube for miRNA analysis The choice of blood fraction (serum, plasma, or whole blood) and processing conditions significantly affects the miRNAs profile and/or levels, and therefore, several factors at this early stage have an impact on the quality of the results. However, usually, these factors are overlooked. The use of whole blood as a source of miRNAs should be avoided because several cellular components may also release miRNAs. Furthermore, it has been demonstrated that the expression levels of specific miRNAs in serum differ from that in plasma even in samples obtained from the same individuals at the same time. It has been reported that the elevated miRNA levels in serum compared to plasma may be due to additional miRNA released by blood cells and platelets during the blood coagulation process.53 In detail, the coagulation process is a stressful condition for blood cells promoting the release of specific miRNAs/RNAs74 while during coagulation high amounts of miRNAs that are found in platelets75 might also be released into the serum.76 It has also been suggested that the miRNA levels in plasma differ from that in serum due to the RNA/miRNA “trafficking” between the cellular components of the blood and the extracellular environment.53 For example, it has been demonstrated that the concentrations of three miRNAs (miR-15b, miR-16, and miR-24) were higher in plasma than serum,29 whereas another study reported no difference in the concentrations of these miRNAs in plasma and serum.44 These discrepancies in miRNA expression profiles in serum and plasma between studies could be due to cell lysis or variation in sample-processing protocols. It is important to mention that during lysis of erythrocytes and other blood components, hemoglobin and lactoferrin may be released, interfering with subsequent qRT-PCR.77 Thus, it is vital to compare circulating miRNA changes within the same sample type since the disease-associated cell-free miRNAs identified may be different between plasma and serum. Additional attention should be given in the case of archived samples, which are mostly stored as serum.78 When plasma is selected as a source of miRNAs for biomarker studies, particular attention should be given to the choice of anticoagulant because some anticoagulants may impact the downstream assays. The effect of the anticoagulants on the miRNA profile in blood samples has been extensively reviewed elsewhere.22 In the clinical practice, the most widely used anticoagulants for plasma collection are EDTA,79 sodium citrate,80 and heparin.81 A key point is to avoid the use of anticoagulants that affect enzymes necessary in miRNA profiling. For example, the enzymes reverse transcriptase and polymerase used in qRT-PCR and NGS are inhibited by heparin.77 In this case, heparinase treatment is required before analysis to increase miRNA detection.81 In addition to heparin, sodium citrate could also interfere with qPCR assessment,82, 83 whereas it has been reported that miRNA expression levels were higher in EDTA-anticoagulated blood compared with blood collected in sodium citrate.84 Overall anticoagulants may have one or more modes of action, such as calcium chelation, inhibition of platelet activation, and protease inhibition. Because several anticoagulants are available, it is probably easiest to make a negative recommendation: do not use heparin-based anticoagulants.78 As we discussed earlier, hemolysis occurring during blood collection and/or processing may affect miRNA levels and/or profile in plasma/serum.42 Thus, it has been suggested to determine the levels of hemolysis of plasma/serum samples before extraction on miRNAs. Even though the degree of hemolysis can be easily determined by measuring the absorbance of blood samples at 414 nm (the maximum absorption wavelength of free hemoglobin),85 this method has several limitations. Therefore, when studying miRNA as biomarkers, the levels of specific miRNAs (e.g., miR-15b, miR-16, or

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miR-451) could be used as an indicator to evaluate the degree of hemolysis and to decide whether a blood sample is appropriate for the quantification of miRNAs related to CVDs.71, 85 In conclusion, miRNA expression levels and/or profiles obtained from (i) different blood fractions or (ii) when plasma is used as a source of miRNAs from tubes treated with different anticoagulants, must not be included in the same study when analyzing circulating miRNAs as biomarkers for CVDs, to avoid conflicting results.

18.3.2 Always use the same miRNA extraction method/kit in a study Many different commercial kits are available for the extraction of total RNA including miRNA from different tissues, cells, and biofluids. RNA/miRNA extraction methods are mostly based on phenol/ chloroform extraction and are categorized into three groups: (i) the phenol-based methods that used organic solvents, phase separation while RNA is recovered by precipitation, (ii) combined phenol and column-based methods, and (iii) the phenol-free techniques that use a lysis reagent to release RNA in the solution and a column for RNA recovery.27, 51 To ensure the removal of protein content from the sample (plasma or serum), the ratio of lysis buffer to sample must be increased several folds. This step is the most variable step in the extraction protocols to recover miRNAs from blood samples. In most cases, the choice of extraction method is based on the experimenter’s experience, the available initial volume of sample, and the subsequent use of the extracted miRNA samples. For example, less experienced experiments often avoid the use of a TRIzol-based protocol to extract RNA/miRNAs for qPCR analyses because it is more challenging to minimize phenol contamination. Thought, TRIzol is a recommended method for safe long-term storage of samples, as it denatures proteins including RNases.86 On the contrary, when miRNA of a high quality is required for a specific application, e.g., sequencing, then a spin column-based RNA extraction kit should be used, such as miRVana PARIS kit and RNeasy Plus Universal Mini Kit. A major advantage of miRNA extraction columnbased methods is that they employ a straightforward on-column genomic DNA-elimination step, which excludes genomic DNA that could interfere with qPCR especially when the primers are not specific to cDNA. Recent studies compared the yield and quality of miRNAs obtained by the commercially available miRNA column-based isolation kits (RNAdvance, MagMAX, miRCURY Biofluids, Quick-RNA, Direct-zol, miRNeasy, mirVana, and NucleoSpin) according to manufacturer recommendations as well as protocol modifications to obtain high-quality RNA from small sample volumes.87, 88 The general observations were that the yield of plasma miRNAs from some kits was extremely low, whereas miRNeasy showed better efficiency in terms of purity and recovery. Taken together, even though there is no conclusion regarding which method or miRNA extraction kit is the best, there is a general agreement that the several existing kits for miRNA extraction provide high variability in yield, quality, purity, and composition of miRNAs. Thus, for the validity of the results, it is essential to use the same miRNA isolation kit in the same study.

18.3.3 Detection methods and normalization strategies Nowadays, there is a range of techniques such as qRT-PCR, miRNA microarrays, and NGS that are widely being used to detect miRNA and quantify miRNA expression profiles in plasma and serum samples with the former being the preferred method for detection of specific sets of miRNAs of interest and

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the later mostly applied for large-scale profile.89, 90 The characteristics, advantages, and limitations of these methods have been extensively reviewed elsewhere25, 91 and are beyond the aim of this chapter. The qRT-PCR-based methods exhibit many advantages compared to other techniques in detecting very small quantities of miRNAs. Moreover, qRT-PCR-based methods are very specific and sensitive and can also be applied to precursor-miRNA (pre-miRNA) and primary-miRNA (pri-miRNA) profiling.92 However, the main disadvantage of these methods is their low throughputs, because they can only analyze a small number of samples in a single reaction, as compared to microarrays.93 Interestingly high reproducibility with qPCR-array was demonstrated by comparing replicate results from the same RNA sample.94 On the contrary, microarray-based methods that have been employed to analyze a comprehensive miRNA expression profiling need higher amounts of starting material (i.e., miRNA) for analysis, and this is a drawback for the routine analysis of miRNA especially when the yield of extracted miRNA is low. However, these hybridization methods allow a larger number of parallel measurements.95 Moreover, an important difference from mRNA microarray is that the short length of miRNAs restricts the flexibility of the probe(s) to be designed at the unique DNA sequence site on the gene to avoid the cross-hybridization.96 Thus, microarray-based methods are affected by similar sequences among families that have high sequence homology and clusters of mature miRNAs. Moreover, microarrays require a preamplification step that is likely to introduce nonlinearity into the measurement process and may alter the real concentration of miRNAs.94 NGS is a powerful approach for discovering new miRNAs and profiling their expression status in biological samples. This method needs lower amounts of starting material and due to higher throughputs and lower cost has been considered as the most suitable method for miRNA analysis. However, NGS can introduce errors during library preparation, computational infrastructure is required for data analysis and interpretation and also cannot be used to determine absolute quantification as it is still difficult to quantify the levels of miRNAs.25 Currently, qRT-PCR is the gold standard method for the quantification miRNA biomarkers in serum and plasma because they contain very low amounts of miRNAs and the detection miRNAs in such samples require a highly sensitive and accurate method.97 To determine accurately the levels of analyzed miRNAs and to remove sample-to-sample variations, normalization methods are required to be performed; however, it is yet unresolved how the data should be normalized.98 Quantification of miRNA requires normalization of data using endogenous and exogenous reference miRNAs for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference miRNAs remains the main issue in miRNA studies and can have a serious impact on the miRNA levels and, therefore, on the biological interpretation of data.30 In general, two main approaches are used to normalize the data obtained from qRT-PCR: (i) the absolute quantification method that determines the input copy number, by comparing the PCR signal to a standard curve and (ii) the relative quantification approach that relates the PCR-derived cycle threshold (Cq) of target miRNAs to that of a reference endogenous miRNA, which is stably expressed, from the same sample.99 However, there are no universally accepted reference “housekeeping” genes or transcripts for miRNA data normalization and this lack has led to the introduction of several normalization strategies that usually lead to different results. Thereby, it is essential to choose the most valid miRNA data normalization approach. Considering recent publication, numerous endogenous reference genes have been used for miRNA quantification100–103 and have been recently reviewed.104 Small nucleolar RNA U6 (RNU6),

18.4 Conclusions

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hsa-miR-16-5p, and total RNA concentration are frequently used RNAs as endogenous reference transcripts in several studies.27 Although small nucleolar RNAs display stable expression in single studies, their expression levels can change under different experimental or disease conditions.100 A recent study evaluated the stability of five reference genes (small nucleolar U6, SNORD48, SNORD44, miR-16, and 5S ribosomal RNA) in atrial tissue samples from patients with atrial fibrillation.105 The researchers demonstrated that the two nucleolar RNAs, SNORD44 and SNORD48, displayed the best performance of all candidate genes, whereas U6 displayed the highest variability compared to the other reference transcripts. It has also been demonstrated that miR-16-5p that is involved in erythropoiesis and is highly expressed in red blood cells, exerts increased expression in hemolyzed plasma.42 Moreover, it is noteworthy to mention that higher amounts of miRNAs are released in the circulation disease in CVD patients compared to healthy individuals. Therefore, when studying miRNA as biomarkers for CVDs, it is highly recommended to use identical volumes of starting material (e.g., plasma or serum) for miRNA extraction instead of using the same amount of total RNA.106 Another approach that is widely used for the normalization of qRT-PCR data is the relative normalization to exogenous reference genes method, which employs synthetic nonhuman spike-in miRNAs that do not have homologous sequences in human miRNAs.44 The Caenorhabditis elegans miRNA cel-miR-39, cel-miR-54, and cel-miR-238 are the most used spike-in miRNAs and is required to be added to the samples at the early steps of RNA extraction before reverse transcription of RNA to avoid their degradation by plasma RNases.98 Normalization is the most important step when quantifying miRNA expression levels. Even though several reference endogenous and exogenous control miRNAs have been identified, further studies are needed to establish a universal normalization method.107 Regarding the identification of miRNAs as biomarkers for CVDs, it is vital to identify and validate the best endogenous control for each type of CVD, because the expression profiles and/or expression levels of specific miRNA may vary in different types of CVDs. Thus, it has been suggested that for normalization of miRNA levels, multiple endogenous and exogenous spiked-in miRNAs should be used as controls for data normalization. Importantly, it has been proposed that to have accurate results, all blood samples must be simultaneously handled using equal volumes of starting material (serum or plasma)71, 108 instead of using the same amount of total RNA because as we mentioned earlier, in CVD patients, higher amounts of miRNAs can be released in circulation compared to healthy individuals.106

18.4 Conclusions In this chapter, we highlight the need to develop SOPs regarding the choice of starting material (blood fraction), sample (plasma or serum) isolation protocol, detection, and normalization methods for the determination of miRNAs. Even though miRNAs exhibit great potential as biomarkers for CVDs, before their use in clinical practice, all miRNA findings must be standardized and validated. Moreover, all preanalytical and analytical processes must be standardized to avoid potential technical biases. To allow translating miRNA findings into clinical application, the development and application of SOPs on how to analyze miRNAs from blood samples (including plasma and serum) are needed. Importantly, general guidelines must be developed at all levels from blood collection, storage, and handling (to minimize hemolysis) to the centrifugation condition to recover plasma/serum and to RNA extraction, quantification, and normalization to minimize the discrepancies among different studies.

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47. Pritchard CC, Kroh E, Wood B, et al. Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies. Cancer Prev Res (Philadelphia, PA). 2012;5(3):492–497. 48. Arroyo JD, Chevillet JR, Kroh EM, et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci U S A. 2011;108(12):5003–5008. 49. Cheng HH, Yi HS, Kim Y, et al. Plasma processing conditions substantially influence circulating microRNA biomarker levels. PLoS One. 2013;8(6), e64795. 50. Cadamuro J, Mrazek C, Leichtle AB, et al. Influence of centrifugation conditions on the results of 77 routine clinical chemistry analytes using standard vacuum blood collection tubes and the new BD-Barricor tubes. Biochem Med. 2018;28(1), 010704. 51. Tiberio P, Callari M, Angeloni V, Daidone MG, Appierto V. Challenges in using circulating miRNAs as cancer biomarkers. Biomed Res Int. 2015;2015:731479. 52. Lesche D, Geyer R, Lienhard D, et al. Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome. Metabolomics. 2016;12(10):159. 53. Wang K, Yuan Y, Cho J-H, McClarty S, Baxter D, Galas DJ. Comparing the microRNA spectrum between serum and plasma. PLoS One. 2012;7(7), e41561. 54. Bryant RJ, Pawlowski T, Catto JW, et al. Changes in circulating microRNA levels associated with prostate cancer. Br J Cancer. 2012;106(4):768–774. 55. Turchinovich A, Weiz L, Langheinz A, Burwinkel B. Characterization of extracellular circulating microRNA. Nucleic Acids Res. 2011;39(16):7223–7233. 56. Koberle V, Pleli T, Schmithals C, et al. Differential stability of cell-free circulating microRNAs: implications for their utilization as biomarkers. PLoS One. 2013;8(9), e75184. 57. Leidner RS, Li L, Thompson CL. Dampening enthusiasm for circulating microRNA in breast cancer. PLoS One. 2013;8(3), e57841. 58. Glinge C, Clauss S, Boddum K, et al. Stability of circulating blood-based microRNAs—pre-analytic methodological considerations. PLoS One. 2017;12(2), e0167969. 59. Zhao H, Shen J, Hu Q, et al. Effects of preanalytic variables on circulating microRNAs in whole blood. Cancer Epidemiol Biomarkers Prev. 2014;23(12):2643–2648. 60. Lorenz TC. Polymerase chain reaction: basic protocol plus troubleshooting and optimization strategies. J Vis Exp. 2012;63, e3998. 61. Vester B, Wengel J. LNA (locked nucleic acid): high-affinity targeting of complementary RNA and DNA. Biochemistry. 2004;43(42):13233–13241. 62. World Health Organization. Good Laboratory Practice (GLP): Quality Practices for Regulated Non-Clinical Research and Development. 2nd ed. Switzerland: WHO; 2009:1–328. 63. Wallace S. Creating a molecular atmosphere: phase 2. Lab Med. 2014;45(2):e56–e58. 64. de Lomas JG, Sunzeri FJ, Busch MP. False-negative results by polymerase chain reaction due to contamination by glove powder. Transfusion. 1992;32(1):83–85. 65. Demeke T, Jenkins GR. Influence of DNA extraction methods, PCR inhibitors and quantification methods on real-time PCR assay of biotechnology-derived traits. Anal Bioanal Chem. 2010;396(6):1977–1990. 66. Aslanzadeh J. Preventing PCR amplification carryover contamination in a clinical laboratory. Ann Clin Lab Sci. 2004;34(4):389–396. 67. Nolan T, Huggett J, Sanchez E, Sanders R, Redshaw N, Wilkes T. Good Practice Guide for the Application of Quantitative PCR (qPCR). Teddington: LGC; 2013. 68. Becker C, Hammerle-Fickinger A, Riedmaier I, Pfaffl MW. mRNA and microRNA quality control for RT-qPCR analysis. Methods. 2010;50(4):237–243. 69. Androvic P, Valihrach L, Elling J, Sjoback R, Kubista M. Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification. Nucleic Acids Res. 2017;45(15):e144. 70. Androvic P, Romanyuk N, Urdzikova-Machova L, Rohlova E, Kubista M, Valihrach L. Two-tailed RT-qPCR panel for quality control of circulating microRNA studies. Sci Rep. 2019;9(1):4255.

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71. Cheng G. Circulating miRNAs: roles in cancer diagnosis, prognosis and therapy. Adv Drug Deliv Rev. 2015;81:75–93. 72. Brown RAM, Epis MR, Horsham JL, Kabir TD, Richardson KL, Leedman PJ. Total RNA extraction from tissues for microRNA and target gene expression analysis: not all kits are created equal. BMC Biotechnol. 2018;18(1):16. 73. El-Khoury V, Pierson S, Kaoma T, Bernardin F, Berchem G. Assessing cellular and circulating miRNA recovery: the impact of the RNA isolation method and the quantity of input material. Sci Rep. 2016;6:19529. 74. Wang K, Zhang S, Weber J, Baxter D, Galas DJ. Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Res. 2010;38(20):7248–7259. 75. Osman A, Falker K. Characterization of human platelet microRNA by quantitative PCR coupled with an annotation network for predicted target genes. Platelets. 2011;22(6):433–441. 76. Hsieh SY, Chen RK, Pan YH, Lee HL. Systematical evaluation of the effects of sample collection procedures on low-molecular-weight serum/plasma proteome profiling. Proteomics. 2006;6(10):3189–3198. 77. Al-Soud WA, Radstrom P. Purification and characterization of PCR-inhibitory components in blood cells. J Clin Microbiol. 2001;39(2):485–493. 78. Witwer KW, Buza´s EI, Bemis LT, et al. Standardization of sample collection, isolation and analysis methods in extracellular vesicle research. J Extracell Vesicles. 2013;2. https://doi.org/10.3402/jev.v3402i3400.20360. 79. Hunter MP, Ismail N, Zhang X, et al. Detection of microRNA expression in human peripheral blood microvesicles. PLoS One. 2008;3(11), e3694. 80. Ai J, Zhang R, Li Y, et al. Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. Biochem Biophys Res Commun. 2010;391(1):73–77. 81. Heegaard NH, Schetter AJ, Welsh JA, Yoneda M, Bowman ED, Harris CC. Circulating micro-RNA expression profiles in early stage nonsmall cell lung cancer. Int J Cancer. 2012;130(6):1378–1386. 82. Garcia ME, Blanco JL, Caballero J, Gargallo-Viola D. Anticoagulants interfere with PCR used to diagnose invasive aspergillosis. J Clin Microbiol. 2002;40(4):1567–1568. 83. Moldovan L, Batte K, Wang Y, Wisler J, Piper M. Analyzing the circulating microRNAs in exosomes/extracellular vesicles from serum or plasma by qRT-PCR. Method Mol Biol (Clifton, NJ). 2013;1024:129–145. 84. Fichtlscherer S, De Rosa S, Fox H, et al. Circulating microRNAs in patients with coronary artery disease. Circ Res. 2010;107(5):677–684. 85. Kirschner MB, Kao SC, Edelman JJ, et al. Haemolysis during sample preparation alters microRNA content of plasma. PLoS One. 2011;6(9), e24145. 86. Ma W, Wang M, Wang Z-Q, et al. Effect of long-term storage in TRIzol on microarray-based gene expression profiling. Cancer Epidemiol Biomarkers Prev. 2010;19(10):2445–2452. 87. Li X, Mauro M, Williams Z. Comparison of plasma extracellular RNA isolation kits reveals kit-dependent biases. Biotechniques. 2015;59(1):13–17. 88. Vigneron N, Meryet-Figuiere M, Guttin A, et al. Towards a new standardized method for circulating miRNAs profiling in clinical studies: interest of the exogenous normalization to improve miRNA signature accuracy. Mol Oncol. 2016;10(7):981–992. 89. Koshiol J, Wang E, Zhao Y, Marincola F, Landi MT. Strengths and limitations of laboratory procedures for microRNA detection. Cancer Epidemiol Biomarkers Prev. 2010;19(4):907–911. 90. Redshaw N, Wilkes T, Whale A, Cowen S, Huggett J, Foy CA. A comparison of miRNA isolation and RTqPCR technologies and their effects on quantification accuracy and repeatability. Biotechniques. 2013;54 (3):155–164. 91. Kong W, Zhao JJ, He L, Cheng JQ. Strategies for profiling microRNA expression. J Cell Physiol. 2009;218 (1):22–25. 92. O’Hara AJ, Vahrson W, Dittmer DP. Gene alteration and precursor and mature microRNA transcription changes contribute to the miRNA signature of primary effusion lymphoma. Blood. 2008;111(4):2347–2353. 93. Benes V, Castoldi M. Expression profiling of microRNA using real-time quantitative PCR, how to use it and what is available. Methods. 2010;50(4):244–249.

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94. Chen Y, Gelfond JAL, McManus LM, Shireman PK. Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis. BMC Genomics. 2009;10(1):407. 95. Yoon HR, Lee JM, Jung J, Lee CS, Chung BH, Jung Y. Highly improved specificity for hybridization-based microRNA detection by controlled surface dissociation. Analyst. 2014;139(1):259–265. 96. Motameny S, Wolters S, N€urnberg P, Schumacher B. Next generation sequencing of miRNAs—strategies, resources and methods. Genes (Basel). 2010;1(1):70–84. 97. Kang K, Peng X, Luo J, Gou D. Identification of circulating miRNA biomarkers based on global quantitative real-time PCR profiling. J Anim Sci Biotechnol. 2012;3(1):4. 98. Marabita F, de Candia P, Torri A, Tegner J, Abrignani S, Rossi RL. Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR. Brief Bioinform. 2016;17(2):204–212. 99. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2( Delta Delta C(T)) method. Methods. 2001;25(4):402–408. 100. Mahdipour M, van Tol HTA, Stout TAE, Roelen BAJ. Validating reference microRNAs for normalizing qRT-PCR data in bovine oocytes and preimplantation embryos. BMC Dev Biol. 2015;15:25. 101. Song J, Bai Z, Han W, et al. Identification of suitable reference genes for qPCR analysis of serum microRNA in gastric cancer patients. Dig Dis Sci. 2012;57(4):897–904. 102. Serafin A, Foco L, Blankenburg H, et al. Identification of a set of endogenous reference genes for miRNA expression studies in Parkinson’s disease blood samples. BMC Res Notes. 2014;7:715. 103. Roth C, Rack B, Muller V, Janni W, Pantel K, Schwarzenbach H. Circulating microRNAs as blood-based markers for patients with primary and metastatic breast cancer. Breast Cancer Res. 2010;12(6):R90. 104. Donati S, Ciuffi S, Brandi ML. Human circulating miRNAs real-time qRT-PCR-based analysis: an overview of endogenous reference genes used for data normalization. Int J Mol Sci. 2019;20(18):4353. 105. Mase` M, Grasso M, Avogaro L, et al. Selection of reference genes is critical for miRNA expression analysis in human cardiac tissue. A focus on atrial fibrillation. Sci Rep. 2017;7(1):41127. 106. Sohel MH. Extracellular/circulating microRNAs: release mechanisms, functions, and challenges. Achiev Life Sci. 2016;10(2):175–186. 107. Tijsen AJ, Pinto YM, Creemers EE. Circulating microRNAs as diagnostic biomarkers for cardiovascular diseases. Am J Physiol Heart Circ Physiol. 2012;303(9):H1085–H1095. 108. Faraldi M, Gomarasca M, Sansoni V, Perego S, Banfi G, Lombardi G. Normalization strategies differently affect circulating miRNA profile associated with the training status. Sci Rep. 2019;9(1):1584.

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Analytical challenges in microRNA biomarker development: Best practices for analyzing microRNAs in cell-free biofluids

19

Matthias Hackla,b, Elisabeth Semmelrocka, and Johannes Grillaria,b,c TAmiRNA GmbH, Vienna, Austriaa Austrian Cluster for Tissue Regeneration, Medical University of Vienna, Vienna, Austriab Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, Vienna, Austriac

19.1 The promises and challenges of cell-free microRNA biomarkers MicroRNAs (miRNAs) in liquid biopsies show great potential as minimally invasive biomarkers based on several key attributes: as short, noncoding regulatory RNAs, miRNAs fulfill important biological functions, are limited in number1 (currently 2654 human mature miRNAs), are highly conserved across species, and are present in clinical samples that can be collected minimal-invasively such as serum, plasma, urine, saliva, or synovial fluid.2 In addition, several miRNA genes were found to be transcribed only in specific cell types, such as miR-1 in muscle, miR-122 in liver, miR-216 in pancreas, miR-223 in hematopoietic cells, or miR-124 in brain tissue.3 The analysis of these cell-type-enriched/cell-typespecific miRNAs in biofluids allows to link cell-free miRNA profiles to specific tissues or origin, which is especially useful in the context of acute events resulting in increasing cell damage such as trauma, organ injury, or ischemic events. Thus, there is a continued high interest in utilizing extracellular miRNAs as biomarkers to drive decision making in drug development,4 disease diagnosis5 or prognosis, and treatment monitoring6 in many disease areas including cardiovascular disease. However, following the initial excitement about the promise of extracellular miRNA biomarkers, several challenges specifically associated with miRNA biomarker development in biofluids were observed, which could have limited the reproducibility of published miRNA biomarker results: (1) The presence in biofluids usually comes at much lower concentrations compared to cells or tissues. Therefore, assays must be optimized and carefully validated for their fitness to detect and quantify extracellular miRNAs. (2) The RNA composition in biofluids can be influenced by the sample collection procedure and is therefore much more sensitive toward preanalytical variability. (3) Although the stability of miRNAs in biofluids is presumably high, sample quality such as the presence of enzyme inhibitors, or contaminating cells, can easily confound circulating miRNA data. Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00004-3 Copyright # 2021 Elsevier Inc. All rights reserved.

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Consequently, the quality of circulating miRNA data varies strongly, and many studies have initially missed to use or to provide sufficient information on required quality controls. In order to ensure success in using common assays such as reverse transcription-quantitative polymerase chain reaction (RT-qPCR) or next-generation sequencing (NGS) for miRNA profiling in cell-free blood samples, this chapter aims to (1) describe common sources of preanalytical variability and how to mitigate them, (2) key analytical challenges for cell-free miRNA analysis when using RT-qPCR or NGS, and (3) summarize best practices for sample and data quality control to ensure successful cell-free miRNA experiments.

19.2 Common sources of preanalytical variability during miRNA analysis in cell-free biofluids The majority of research and clinical studies that have attempted to investigate cell-free miRNAs have relied on blood-based fluids. Hence, most is known about the preanalytical variability for this sample type, and Table 19.1 gives a summary of common sources of preanalytical variability associated with cell-free blood samples.

Table 19.1 Types, sources, and mitigation of preanalytical variability associated with cell-free miRNA experiments using serum or plasma. Source of variability Sample type

Sample quality

Description of variability

Mitigation

Serum is a collection of fluid portion after blood coagulation. Blood coagulation releases miRNA from platelets Plasma is a collection of fluid portion without blood coagulation, thus preventing the release of miRNAs from platelets Hemolysis means the lysis of red blood cells during phlebotomy, resulting in the release of red blood cell-enriched miRNAs such as miR451a into the cell-free fluid. Negatively affects both serum and plasma

Specify and maintain the same cell-free blood sample type throughout a biomarker development project, or perform systematic testing of the impact of changing sample type

Avoid • drawing blood from a hematoma • probing a traumatic venipuncture • drawing the plunger back too forcefully, if using a needle and syringe • small needlesVenipuncture site needs to be dry Monitor hemolysis in all samples using endogenous controls or OD414 measurements and exclude hemolytic samples from the analysis Ideally, the selectivity of biomarker candidates against hemolysis is tested

19.2 Common sources of preanalytical variability during miRNA analysis

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Table 19.1 Types, sources, and mitigation of preanalytical variability associated with cell-free miRNA experiments using serum or plasma—cont’d Source of variability

Description of variability

Mitigation

Sample collection tube

Type of anticoagulant: In case of plasma, different types of anticoagulants are common: EDTA, citrate, CTAD, or lithium heparin. The type of anticoagulant can impact the results due to variable efficiency for platelet inhibition

Processing condition

Centrifugation parameters: Centrifugation is intended to separate cells from fluid. Lower speeds do not remove platelets, i.e., will yield platelet-rich plasma (PRP), which is not cell-free

CTAD (citrate theophylline adenosine dipyridamole) and citrate plasma show least variability when samples are incubated several hours before centrifugation Heparin can inhibit RT-qPCR. Heparinase treatment of extracted total RNA can be performed to reduce/avoid inhibition Have a defined sample collection protocol (SOP) in place, which defines • the choice of serum vs plasma and the rationale behind this choice • the type of collection tube and anticoagulant (plasma)processing conditions such as centrifugation speed and time, temperature, incubation periods

Sample storage

Double-centrifugation has been shown to result in complete removal of platelets and is therefore considered the ideal choice for cell-free miRNA analysis in blood Prolonged incubation times prior to sample processing and storage can result in miRNA release due to platelet activation or reduction due to degradation Effects of incubation on miRNAs cannot be generalized, since it depends on the origin of miRNAs

Standardize and document sample incubation prior to processing using SOPs

19.2.1 Sample type Processing of blood into serum means that platelets become activated during sample collection, before they and other blood cells are separated from the serum by centrifugation. During their activation, platelets release their intracellular miRNA content. Although the miRNA repertoire in platelets is not encompassing all human miRNAs, there contain several highly abundant miRNAs, which are also found in other cell types, such as miR-21-5p. Thus, miRNA analysis in serum is biased toward the platelet miRNA transcriptome.7 In contrast, processing of plasma means that whole blood is collected in the presence of an anticoagulant such as EDTA, sodium citrate, CTAD, or lithium heparin. Thereby, platelet activation is inhibited, resulting in a lower presence of platelet-derived miRNAs in plasma.

19.2.2 Sample quality Hemolysis can add bias to the analysis of serum/plasma miRNA levels due to contamination with cellular RNA.8 Three studies have confirmed miR-16 and miR-451a as being highly abundant in red blood cells (RBCs) and found levels of these miRNAs in serum and plasma to be most affected by

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hemolysis.8–10 However, since RBCs are a rich source of miRNAs despite being anucleate, many other miRNAs will be affected by this bias: for example, it was found that RBC-associated miRNAs can be increased up 20- to 30-fold in hemolyzed plasma.9 Among these miRNAs was, for example, miR-92a, a proposed colon cancer biomarker.11 This is important, because cancer patients have an increased predisposition for hemolytic disorders potentially causing systematic errors in the analysis. Several studies have systematically assessed the impact of hemolysis by spiking nonhemolyzed serum samples with lysates from the corresponding RBCs.10 They showed that miRNAs with high abundance in RBCs, such as miR-15b, miR-16, and miR-24, were increased in a dose-dependent fashion. Only celltype-specific miRNAs that are absent or low-abundant in RBCs are not affected by hemolysis, such as miR-122, a predominantly liver-specific miRNA.12 Therefore, any study aiming to detect circulating miRNA levels in serum or plasma should assess the presence of hemolysis in all samples. This can be done visually; however, hemolysis can cause bias even if not visible to the eye. Thus, more sensitive methods to determine the degree of hemolysis should be used: for this purpose, Blondal et al. developed the hemolysis index, which relates the level of a miRNA highly expressed in red blood cells (miR-451a), with a miRNA that is largely unaffected by hemolysis (miR-23a-3p).13 The authors found that delta Cq (miR-23a-3p-miR-451a) is a good measure of the degree of hemolysis, where values of more than 5 are indicative of possible erythrocyte miRNA contamination, and a delta Cq higher than 7 is indicative of a high risk of hemolysis, potentially affecting the acquired data. It should be mentioned that the dCq value of miR-23a-miR-451a is different in mouse and rat biofluid samples and might differ based on the RT-qPCR platform used. Therefore, an alternative for determining hemolysis is the measurement of free hemoglobin using a spectrophotometer such as NanoDrop™. Human serum or plasma samples are classified as being hemolyzed if the absorption at 414 nm is exceeding 0.2. However, the presence of small amounts of cellular contamination in serum or plasma samples is not readily detectable by visual or spectrophotometric means. Tips on how to avoid hemolysis14: • • • • • • • • •

Use good and consistent sample collection devices throughout a study (e.g., BD Vacutainer), Follow manufacturer’s instructions, Avoid drawing blood from a hematoma, Avoid foaming of the sample, Make sure the venipuncture site is dry, Avoid a probing, traumatic venipuncture, Avoid prolonged tourniquet application or fist clenching, Use correct size needle (
22 gauge), Fill vacuum tubes completely.

19.2.3 Sample collection tubes Mussbacher et al. have shown that EDTA compared to citrate or CTAD results in higher residual activation of platelets by analyzing platelet-stored proteins such as PF-4 and TSP-1.15 Recently, the authors have repeated this study using platelet-enriched and cardiovascular-disease-associated miRNAs as readouts, to find that CTAD plasma shows the best performance compared to EDTA and citrate.16

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19.2.4 Sample processing conditions plasma collection occurs in the presence of an anticoagulant, which inhibits the activation of platelets and hence clotting of blood. Therefore, platelets remain in suspension, and centrifugation of plasma intends to reduce/remove platelet content before sample storage. Cheng et al. have shown that depending on the centrifugation protocol that is used, the depletion of platelets varies significantly.17 To fully deplete platelets, a double-centrifugation protocol is required. Such platelet-poor plasma (PPP) represents the “best” matrix for cell-free miRNA analysis as the data generated will suffer least bias derived from platelet RNA.

19.2.5 Sample stability RNA stability is another crucial factor in the preanalytical phase, since RNA integrity impacts downstream analyses.18 Mitchell et al.19 were the first to demonstrate that plasma storage at room temperature for up to 24 h had minimal effect on selected miRNAs as measured by RT-qPCR. However, this observation was limited to a small set of miRNAs, and the effect of sample incubation prior to centrifugation (i.e., separation of cells from cell-free component) was not tested. In addition, no information on analytical variability was provided; therefore, the minimum detectable effect sizes were not known. Mussbacher et al. (unpublished data) tested incubation for up to 24 h at room temperature and 4°C prior to centrifugation and reported changes in miRNA concentration over time: plateletenriched miRNAs were observed to increase after 24 h, presumably due to platelet activation, while the levels of liver-enriched miR-122 were observed to decrease over time, presumably due to RNA degradation. Recently, it was shown that miRNA levels obtained from exosomes isolated from fresh plasma were significantly lower compared to exosome isolations from plasma that had been frozen and thawed.20 These findings indicate the importance of the freezing cycle and the time when experiments are performed.

19.3 Sources of analytical variability: RT-qPCR and NGS The majority of targeted analytical assays for miRNA quantification consist of two steps: first, RNA extraction from the matrix of interest, and second, the hybridization to miRNA probes or primers that generate a target-specific signal that can be quantified. Besides classical methods such as RT-qPCR, other techniques have emerged with other readout platforms such as flow cytometers, or taking advantage of immunochemistry.21 For untargeted analysis of miRNAs, NGS is the platform of choice, as it enables unbiased genome-wide characterization of miRNAs in biological matrices. Since the vast majority of miRNA studies have used RT-qPCR or NGS, this chapter addresses sources of analytical variability for both platforms and RNA extraction, which precedes these analyses.

19.3.1 RNA Extraction miRNAs can be present in various RNA carriers, specifically protein complexes and extracellular vesicles (EVs). Arroyo et al. have shown that the majority of miRNAs in plasma are present in protein complexes, while other (fewer) miRNAs seem to be exclusively or predominately released from cells

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via EVs.22 The same has recently been observed for EV miRNA and total miRNAs in conditioned media obtained from the adipose tissue stromal vascular fraction.23 This means that total RNA extracts from at least these biofluids will largely represent the miRNA fraction in protein complexes and that the purification of EVs can unravel changes in miRNAs that would have otherwise remained unseen. To exemplify this, Priglinger et al. have generated miRNA profiles from total RNA as well as the EV population obtained from cells that were harvested from fat tissue of lipedema patients. They found that extracellular miRNAs in EVs were more able to distinguish healthy from diseased individuals than total miRNA pattern.23 Therefore, it is an important decision whether or not to purify specific miRNA carriers prior to RNA extraction or focus on the entire extracellular miRNA content in a biofluid. For methods and best practices for EV isolation, the reader is referred to specific literature on this topic,24 which is beyond the scope of this chapter. The second important aspect of RNA isolation is that there is usually only a small number of miRNAs present in biofluids. Our observation is that amounts seem to be highest in serum, plasma, milk, or synovial fluid, and lower in urine, cerebrospinal fluid (CSF), brain microdialysates, or stool. Thus, RNA isolation methods have to be optimized to enhance the recovery of short RNAs, for instance, by adding carriers such as glycogen- or phage-derived RNA to improve RNA precipitation and recovery. Upscaling of input volumes can be done, but is often limited by sample availability, especially in case of retrospective studies using stored samples from biobanks. Besides contaminations with cellular RNA described in Section 19.2, RNA isolated from bodily fluids may contain inhibitors for many downstream applications.25 Most often, heparin has been cited in this context; however, high levels of hemoglobin, lactoferrin, and IgG in blood, or urea in urine, may inhibit, for example, PCR enzymes. In addition, our observation is that certain matrices such as stool contain high concentrations of inhibitors, which requires predilution of total RNA prior to analysis. Several studies have attempted to compare many of the available commercial RNA isolation kits to home-brew protocols (manual RNA isolation) for quantification of extracellular miRNAs, using either RT-qPCR or NGS as a readout.26–29 The general observation is that the method of RNA isolation can have a large impact on the results30 and should therefore be tested and optimized before being used on a large number of samples. Interestingly, the miRNeasy kit from Qiagen was found to perform well across a number of studies, but results in a higher percentage of unmapped reads in NGS compared to other kits.28 Our own observation is that precipitation-based methods (with or without column purification) have higher yield compared to bead-based protocols (1–2 Cq-values). However, depending on the abundance of the targeted miRNA, this loss of sensitivity might not be critical, and since bead-based methods can be fully automated, they might be useful when processing large number of samples.

19.3.2 Spike-in controls due to the lack of reference RNAs insufficient sensitivity and specificity of spectrophotometric / fluorescence-based RNA detection for miRNA quantification, the so-called spike-ins have gained broad acceptance for monitoring RNA extraction efficiency from biofluids and analytical variability in general. Spike-ins are synthetic RNA or DNA oligonucleotides that resemble the features of miRNAs (e.g., length and chemical modification) and are added to each sample at equimolar amounts prior to RNA extraction, e.g., within the sample lysis buffer. Most commonly, nonhuman/nonmammalian miRNAs such as cel-miR-39-3p, cel-miR-54, or ath-miR-159 are used.

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Thus, the quantification of spike-in controls gives valuable insight into the quality/variability of the assay such as RT-qPCR or NGS. In case of RT-qPCR, spike-ins should be added at multiple steps during the workflow to be able to determine the variability and performance of each step and to identify failed samples. •

•

•

RNA spike-ins: Synthetic RNA spike-ins are added to the lysis buffer during RNA extraction. These controls give information about the overall variability in the RT-qPCR workflow and can detect samples where RNA extraction has failed. RT spike-ins: Synthetic RT spike-ins such as nonmammalian “cel-miR-39” are added to the reverse transcription master mix. It provides information about the variance arising during reverse transcription and PCR amplification and helps to identify samples with low RT efficiency due to the presence of enzyme inhibitors. Reverse transcription efficiency is known to introduce the highest technical variance to RT-qPCR data. PCR spike-ins: mixtures of synthetic DNA oligonucleotides and primers are used as a reference PCR reaction, which allows to monitor PCR efficiency detection of PCR inhibitors.

Careful evaluation of spike-in results can help to identify potential outliers and to exclude samples with low data quality from statistical analysis. Spike-ins can further be used for the normalization of Cqvalues of endogenous miRNAs to reduce technical variability. A value outside the acceptable range indicates that the analysis has failed and that the sample is not valid. The acceptable range is based on assay validation runs using different reagent lots and operators. Fig. 19.1 provides examples of our own results for spike-in controls when used for RT-qPCR (Fig. 19.1A) or NGS workflows (Fig. 19.1B). In both cases, uniform Cq-values obtained for spikein controls demonstrate successful and homogenous RNA isolation as well as RT-PCR or NGS analysis across all samples, respectively. However, it is important to mention that synthetic spike-ins do not reveal the RNA content and quality in the biological sample.

19.3.3 RT-qPCR analysis of cell-free miRNAs Different chemistries have been developed and are commercially available for RT-qPCR analysis of miRNAs, and it is beyond the scope of this chapter to discuss them all. The miRQC study has attempted to compare different RT-qPCR protocols (also NGS and microarray) for miRNA quantification in general, not specifically for biofluids.31 The group reported in their study that RT-qPCR assays have higher sensitivity compared to microarray-based technologies where signal amplification is not as efficient. However, RT-qPCR protocols with a preamplification step showed higher variability compared to protocols without preamplification. It should be mentioned that RT-qPCR chemistries might have been improved since this study was performed in 2014. In general, the challenge with miRNA RT-qPCR is the short length and heterogeneous GC content, resulting in highly variable hybridization behaviors. Therefore, it is important to notice that in order to achieve specific and sensitive quantification of miRNAs, the protocol should use specific solutions such as primer/probes or chemical modifications of primer nucleic acids that enhance hybridization performance. This is especially important if parallel or multiplex quantification of miRNAs is envisaged, where equal PCR annealing temperatures are used to amplify potentially diverse miRNA sequences.

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FIG. 19.1 Typical results for spike-in controls in RT-qPCR and NGS experiments. (A) Raw RT-qPCR data (Cq-values) are shown for three types of spike-ins that are commonly used in the RT-qPCR workflow: PCR spike-in (orange), which is added only prior to the PCR amplification; cDNA spike-in (blue), which is added to total RNA prior to RT-qPCR; RNA spike-in (gray), which is added to the lysis buffer prior to RNA isolation. (B) For sRNA NGS analysis, RNA spike-ins can be used. Shown here are the results (reads per million, RPMs) for three different spike-ins that were added at 1:100 ratio prior to the RNA extraction.

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Another layer of complexity in this respect are miRNA isoforms (isomiRs), which means that for a specific mature miRNA, multiple sequence isoforms can be present in the matrix. It is known that miRNA isoform diversity is not random but can be subject to systematic changes depending on the cellular state. For example, it has been shown that cancer types can be discriminated based on their isomiR profile.32 Depending on the type of RT-qPCR chemistry, multiple or only one isoform might be detected and quantified. Ideally, analytical validation of RT-qPCR protocols includes the assessment of assay specificity against isoforms. Data normalization is an issue applicable to all RT-qPCR protocols, and it is a topic of high debate. In principle, the goal of every normalization is to minimize variation that can mask or exaggerate true biological changes, thereby increasing the precision and accuracy of expression measurements. The choice of a normalization strategy is, however, anything but trivial. The purpose of many miRNA RT-qPCR expression experiments is to identify differences between two or more groups of samples, such as a control and a diseased specimen. Thus, the aim of normalization is to remove as much variation as possible between groups except for that difference that is a consequence of the disease state itself. When working with cells or tissues, the use of the so-called “reference” or “housekeeping” genes is common practice. The assumption is that these genes are expressed at very constant rate and independent of changes in the physiologic state. Hence, they allow to account for differences in total RNA input, which might arise due to the imprecision of determining RNA concentrations, or variable RNA integrity. When working with cell-free biofluids for miRNA analysis, accurate quantification of extracted RNA, and specifically miRNA concentrations, is not always possible and reliable, due to the low RNA concentration and missing selectivity against contaminations such as phenol (see Section 19.3.2). However, compared to experiments with cells or tissues, the input amounts—i.e., the volume of biofluid, total RNA and cDNA—can be standardized to a specific volume. Since the RT-qPCR workflow is applied to multiple samples in parallel (Fig. 19.2), and under the assumption that standardizing the procedures of RNA extraction, reverse transcription and PCR amplification will result in equal efficiencies across all samples, no normalization would be required before analysis. This assumption about “equal assay efficiency across all samples” can be monitored by adding spike-in controls at every step of the workflow (see discussed earlier and Fig. 19.1). This enables capturing the workflow variability and eventually performs normalization to reduce analytical variability. For this purpose, we use the Cq-values obtained from RNA spike-in controls, because they reflect the overall variability introduced by RNA extraction, reverse transcription, and PCR amplification. In our experience, most variability is introduced during reverse transcription.

19.3.4 RT-qPCR assay validation for analysis of cell-free miRNAs Prior to using RT-qPCR in biomarker performance evaluation studies or clinical validation studies, analytical assay performance needs to be validated. For cell-free miRNA biomarkers, four areas must be considered for the validation of assays: (1) Definition of preanalytical conditions: this means that defined protocols for sample collection, processing, and storage must be available.

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FIG. 19.2 RT-qPCR Workflow for circulating miRNA analysis. Single-plex RT-qPCR workflows that intend to quantify multiple miRNAs/spike-ins in parallel consist of the steps of (1) RNA extraction, (2) reverse transcription of RNA into cDNA using a “universal chemistry,” (3) preparation of a cDNA/PCR Mix, and (4) performance of real-time PCR analysis in 96- or 384-well plates for multiple miRNA targets. (5) The last step is data analysis, where analytical variability needs to be removed by normalization.

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(2) Definition of analytical performance requirements and validation acceptance criteria: building on available data for effect sizes, biomarker abundance, etc., the performance requirements in order for the assay to be valid must be defined. This includes, for example, the definition of the required assay precision, accuracy, analytical measurement range, etc. (3) Characterization and documentation of assay performance: it means the completion of a validation work plan and validation report. Furthermore, during the development of the assay validation work plan, several key analytical parameters must also be considered: (1) Accuracy: determined using spike-ins or sample titration, (2) Analytical Measurement Range: based on a dilution series to determine the lower limit of quantification and detection, (3) Precision: assay variability for intra- and interday use, (4) Selectivity: the sensitivity of the assay toward preanalytical bias such as hemolysis or platelet contamination/activation, (5) Specificity: the specificity of the targeted miRNA assay against related miRNA families or miRNA isoforms. Fauth et al. have recently reported the validation of an RT-qPCR assay for the detection of miR-146a5p, miR-155-5p, miR-382-5p, and miR-451a in EDTA plasma considering the optimization of preanalytical conditions and evaluation of sensitivity (analytical measurement range), precision, accuracy, matrix effects (dilution of matrix), and stability for 4 months.33 Thereby, the authors showed that it was possible to develop and validate a good laboratory practice (GLP)-compliant RT-qPCR assay for extracellular miRNA quantification in plasma.

19.3.5 NGS analysis of cell-free miRNAs Small RNA (sRNA) NGS has become an appealing alternative to RT-qPCR for circulating miRNA analysis, especially for biomarker discovery or exploratory translational studies. However, sRNA NGS protocols have initially required higher RNA input amounts (i.e., >100 ng), which were difficult to achieve for biofluids. However, newly available chemistries and modifications of the conventional adapter-ligation protocols allow for lower RNA input (20) to obtain sufficient DNA amounts for sequencing and the unequal amplification properties of miRNAs might disturb miRNA quantification due to the formation of PCR duplicates. The introduction of unique molecular identifiers in the adapter sequences can mitigate the bias resulting from PCR duplicates.36

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(3) Adapter ligation bias: Ligation efficiency for short RNAs between the sequencing adapters can be highly variable. When there is little RNA (miRNA) present, this “ligation bias” will adversely affect the coverage and quantification of low abundant miRNAs even further. Thus, several groups have tried to develop protocols that lower adapter ligation bias. For example, the introduction of randomized ends (e.g., “4 N”) into the adapters were shown to equalize coverage of synthetic (equimolar) miRNA samples.37 Alternatively, protocols that only require single-adapter ligation due to circularization have been shown to reduce ligation bias.34 (4) Low miRNA content in some biofluids requires optimization of sequencing depth: several studies have reported that the ratio of miRNA reads to total reads can be low (20%) for serum or plasma.38, 39 We and others have observed that sequencing depth needs to be optimized (increased) for such biofluids in order to obtain meaningful quantitative data as well as miRNA complexity.26, 28, 40 (5) NGS data normalization: Most commonly, sRNA NGS data are normalized by accounting for the difference in library size, i.e., by calculating reads or counts per million (RPMs/CPMs). However, Lutzmayer et al. have shown that certain biological states or treatments might result in an overall shift (increase or decrease) of miRNA levels. In such cases, RPM normalization will mask effects, which absolute quantification would have revealed.41 To overcome this challenge, they have developed a robust design for a spike-in calibrator, which is a mix of spike-ins in a defined attomole range. Adding this calibrator to a fixed amount of total RNA (mass or volume) enables conversion of read counts to molecule numbers and hence absolute quantification by NGS. These challenges need to address by implementing mitigation strategies in the NGS protocol. Subsequently, the optimized protocol should undergo the so-called fit-for-purpose validation to determine the precision, accuracy, and the analytical measurement range of the assay, especially when it will be used in a clinical study. Once this has been completed, small RNA NGS can produce reliable quantitative miRNA data on a genome-wide level and is a cost-efficient assay for any biomarker discovery study.

19.4 Sources of biological variance Another very important variable, which undoubtedly is very difficult to standardize, is the individual itself. Diet, exercise, age, and ethnicity are only a few factors that are known to influence interindividual as well as intraindividual variability of miRNA levels. Thus, developing a good understanding of the factors contributing to biological variation in miRNAs is essential for epidemiological and clinical research, particularly with respect to investigating miRNAs for disease risk assessment. There are only few studies evaluating the intraindividual variability of circulating miRNAs over time.42–44 Vogt et al. have reported interindividual variability of circulating miRNAs in the serum of 40 normal healthy volunteers who each donated 6 samples to the study (total n ¼ 240).45 Large differences in interindividual variability were observed for the tested miRNAs from 2.1-fold (miR-17-5p) to 116.9-fold change (miR-122-5p) in relative expression. In case of miR-122-5p, a putative biomarker for prognosis of drug-induced liver injury, this variability was observed to be influenced by donor ethnicity, since variability within Caucasians was observed to be threefold lower.

References

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Heegaard et al.46 identified circulating miRNAs that undergo clear rhythmic fluctuations in abundance during a 24-h period in a group of 24 healthy young male individuals. Moreover, it was shown that miRNAs are uniquely and dynamically regulated in response to acute exhaustive exercise and sustained aerobic exercise training.47 The bottom line is that when publishing results from miRNA studies, it is pivotal to provide a comprehensive description not only of the preanalytical and analytical factors used, but also of demographic and clinical characteristics of the tested population in order to allow others to perform the exact same experiments in validation studies. It is furthermore important to notice that similar data for other biofluids that are frequently used to study extracellular miRNAs such as urine are currently missing. We recommend to evaluate whether the following inclusion/exclusion criteria can be included when designing circulating miRNA experiments: • • • • •

•

Fasted blood draws between 8 and 10 a.m. in the morning to prevent diurnal variations. Any type of medical treatment—in elderly populations, specifically treatment with heparin in the last 24 h—should be captured with a questionnaire. Renal function should not be impaired. We recommend an estimated glomerular filtration rate cutoff of 30 mL/min. Record lifestyle parameters such as regular exercise, smoking, and diet. Hormones such as estradiol have downstream effects on miRNA transcription. Thus, age and gender have an impact on circulating miRNA levels. This needs to be considered when planning case/control studies. Record donor ethnicity and try to keep numbers balanced between different groups or restrict to a single group.

19.5 Conclusion The analysis of circulating miRNAs in biofluids offers the potential for the discovery of novel biomarkers and new biological insights. As outlined herein, important considerations regarding the preanalytical, analytical, and biological factors that cause variability in miRNA data must be addressed to ensure reliable results.

References 1. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47(D1):D155–D162. https://doi.org/10.1093/nar/gky1141. 2. Li Y-H, Tavallaee G, Tokar T, et al. Identification of synovial fluid microRNA signature in knee osteoarthritis: differentiating early- and late-stage knee osteoarthritis. Osteoarthr Cartil. 2016;24(9):1577–1586. https://doi. org/10.1016/j.joca.2016.04.019. 3. Bushel PR, Caiment F, Wu H, et al. RATEmiRs: the rat atlas of tissue-specific and enriched miRNAs database. BMC Genomics. 2018;19(1). https://doi.org/10.1186/s12864-018-5220-x. 4. Schraml E, Hackl M, Grillari J. MicroRNAs and toxicology : a love marriage. Toxicol Rep. 2017;4 (April):634–636. https://doi.org/10.1016/j.toxrep.2017.11.001.

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5. Starlinger P, Hackl H, Pereyra D, et al. Predicting postoperative liver dysfunction based on blood derived MicroRNA signatures. Hepatology. 2019;19. https://doi.org/10.1002/hep.30572. 6. Krammer TL, Mayr M, Hackl M. microRNAs as promising biomarkers of platelet activity in antiplatelet therapy monitoring. Int J Mol Sci. 2020;21. 7. Sunderland N, Skroblin P, Barwari T, et al. MicroRNA biomarkers and platelet reactivity. Circ Res. 2017;1– 19. https://doi.org/10.1161/CIRCRESAHA.116.309303. 8. Kirschner MB, Kao SC, Edelman JJ, et al. Haemolysis during sample preparation alters microRNA content of plasma. PLoS One. 2011;6(9). https://doi.org/10.1371/journal.pone.0024145. 9. Pritchard CC, Cheng HH, Tewari M, et al. MicroRNA profiling: approaches and considerations Colin. Nat Rev Genet. 2015;13(5):358–369. https://doi.org/10.1038/nrg3198.MicroRNA. 10. McDonald JS, Milosevic D, Reddi HV, Grebe SK, Algeciras-Schimnich A. Analysis of circulating microRNA: preanalytical and analytical challenges. Clin Chem. 2011;57(6):833–840. https://doi.org/10.1373/ clinchem.2010.157198. 11. Huang Z, Huang D, Ni S, Peng Z, Sheng W, Du X. Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. Int J Cancer. 2010;127(1):118–126. https://doi.org/10.1002/ijc.25007. 12. Petriv OI, Kuchenbauer F, Delaney AD, et al. Comprehensive microRNA expression profiling of the hematopoietic hierarchy. Proc Natl Acad Sci. 2010;107(35):15443–15448. https://doi.org/10.1073/ pnas.1009320107. 13. Blondal T, Jensby Nielsen S, Baker A, et al. Assessing sample and miRNA profile quality in serum and plasma or other biofluids. Methods. 2013;59(1):164–169. https://doi.org/10.1016/j.ymeth.2012.09.015. 14. Heyer NJ, Derzon JH, Winges L, et al. Effectiveness of practices to reduce blood sample hemolysis in EDs: a laboratory medicine best practices systematic review and meta-analysis. Clin Biochem. 2012;45(13– 14):1012–1032. https://doi.org/10.1016/j.clinbiochem.2012.08.002. 15. Mussbacher M, Schrottmaier WC, Salzmann M, et al. Optimized plasma preparation is essential to monitor platelet-stored molecules in humans. PLoS One. 2017;12(12):e0188921. https://doi.org/10.1371/journal. pone.0188921. 16. Mussbacher M, Krammer TL, Heber S, et al. Impact of anticoagulation and sample processing on the quantification of human blood-derived microRNA signatures. Cells. 2020;9(8):1915. https://doi.org/10.3390/ cells9081915. 17. Cheng HH, Yi HS, Kim Y, et al. Plasma processing conditions substantially influence circulating microRNA biomarker levels. PLoS One. 2013;8(6):e64795. https://doi.org/10.1371/journal.pone.0064795. 18. Schroeder A, Mueller O, Stocker S, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;7:1–14. https://doi.org/10.1186/1471-2199-7-3. 19. Mitchell PS, Parkin RK, Kroh EM, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci. 2008;105(30):10513–10518. https://doi.org/10.1073/pnas.0804549105. 20. Sanz-Rubio D, Martin-Burriel I, Gil A, et al. Stability of circulating exosomal miRNAs in healthy subjects. Sci Rep. 2018;8(1):10306. https://doi.org/10.1038/s41598-018-28748-5. 21. Rissin DM, Lo´pez-Longarela B, Pernagallo S, et al. Polymerase-free measurement of microRNA-122 with single base specificity using single molecule arrays: detection of drug-induced liver injury. PLoS One. 2017;12(7):e0179669. https://doi.org/10.1371/journal.pone.0179669. 22. Arroyo JD, Chevillet JR, Kroh EM, et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci. 2011;108(12):5003–5008. https://doi.org/ 10.1073/pnas.1019055108. 23. Priglinger E, Strohmeier K, Weigl M, et al. SVF-derived extracellular vesicles carry characteristic miRNAs in lipedema. Sci Rep. 2020;10(1). https://doi.org/10.1038/s41598-020-64215-w. 24. Hill AF, Pegtel DM, Lambertz U, et al. ISEV position paper: extracellular vesicle RNA analysis and bioinformatics. J Extracell Vesicles. 2013;2(1):22859. https://doi.org/10.3402/jev.v2i0.22859.

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25. Schrader C, Schielke A, Ellerbroek L, Johne R. PCR inhibitors - occurrence, properties and removal. J Appl Microbiol. 2012;113(5):1014–1026. https://doi.org/10.1111/j.1365-2672.2012.05384.x. 26. Srinivasan S, Yeri A, Cheah PS, et al. Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation. Cell. 2019;177(2):446–462.e16. https://doi.org/10.1016/j.cell.2019.03.024. 27. El-Khoury V, Pierson S, Kaoma T, Bernardin F, Berchem G. Assessing cellular and circulating miRNA recovery: the impact of the RNA isolation method and the quantity of input material. Sci Rep. 2016;6(1). https:// doi.org/10.1038/srep19529. 28. Wong RKY, MacMahon M, Woodside JV, Simpson DA. A comparison of RNA extraction and sequencing protocols for detection of small RNAs in plasma. BMC Genomics. 2019;20(1). https://doi.org/10.1186/ s12864-019-5826-7. 29. Wright K, de Silva K, Purdie AC, Plain KM. Comparison of methods for miRNA isolation and quantification from ovine plasma. Sci Rep. 2020;10(1). https://doi.org/10.1038/s41598-020-57659-7. 30. Kloten V, Neumann MHD, Di Pasquale F, et al. Multicenter evaluation of circulating plasma MicroRNA extraction Technologies for the Development of clinically feasible reverse transcription quantitative PCR and next-generation sequencing analytical work flows. Clin Chem. 2019;65(9):1132–1140. https://doi.org/ 10.1373/clinchem.2019.303271. 31. Mestdagh P, Hartmann N, Baeriswyl L, et al. Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods. 2014;11(8):809–815. https://doi.org/10.1038/ nmeth.3014. 32. Telonis AG, Magee R, Loher P, Chervoneva I, Londin E, Rigoutsos I. Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types. Nucleic Acids Res. 2017;45(6):2973–2985. https://doi.org/10.1093/nar/gkx082. 33. Fauth M, Hegewald AB, Schmitz L, Krone DJ, Saul MJ. Validation of extracellular miRNA quantification in blood samples using RT-qPCR. FASEB BioAdv. 2019;1(8):481–492. https://doi.org/10.1096/fba.201900018. 34. Barbera´n-Soler S, Vo JM, Hogans RE, Dallas A, Johnston BH, Kazakov SA. Decreasing miRNA sequencing bias using a single adapter and circularization approach. Genome Biol. 2018;19(1). https://doi.org/10.1186/ s13059-018-1488-z. 35. Shore S, Henderson JM, Lebedev A, et al. Small RNA library preparation method for next-generation sequencing using chemical modifications to prevent adapter dimer formation. PLoS One. 2016;11(11):e0167009. https://doi.org/10.1371/journal.pone.0167009. 36. Fu Y, Wu P-H, Beane T, Zamore PD, Weng Z. Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. BMC Genomics. 2018;19(1). https://doi.org/10.1186/s12864-018-4933-1. 37. Giraldez MD, Spengler RM, Etheridge A, et al. Comprehensive multi-center assessment of small RNA-seq methods for quantitative miRNA profiling. Nat Biotechnol. 2018;36(8):746–757. https://doi.org/10.1038/ nbt.4183. 38. Godoy PM, Bhakta NR, Barczak AJ, et al. Large differences in small RNA composition between human biofluids. Cell Rep. 2018;25(5):1346–1358. https://doi.org/10.1016/j.celrep.2018.10.014. 39. Etheridge A, Wang K, Baxter D, Galas D. Preparation of small RNA NGS libraries from biofluids. In: Patel T, ed. Extracellular RNA. New York: Springer; 2018:163–175. Methods in Molecular Biology; vol. 1740. https:// doi.org/10.1007/978-1-4939-7652-2_13. 40. Chu CP, Nabity MB. Comparison of RNA isolation and library preparation methods for small RNA sequencing of canine biofluids. Vet Clin Pathol. 2019;48(2):310–319. https://doi.org/10.1111/vcp.12743. 41. Lutzmayer S, Enugutti B, Nodine MD. Novel small RNA spike-in oligonucleotides enable absolute normalization of small RNA-Seq data. Sci Rep. 2017;7(1). https://doi.org/10.1038/s41598-017-06174-3. 42. Wu J, Cai H, Xiang Y-B, et al. Intra-individual variation of miRNA expression levels in human plasma samples. Biomarkers. 2018;0(0):1–8. https://doi.org/10.1080/1354750X.2018.1427794.

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43. Bertoia ML, Bertrand KA, Sawyer SJ, Rimm EB, Mukamal KJ, Jeyaseelan K. Reproducibility of circulating MicroRNAs in stored plasma samples. PLoS One. 2015;10(8):1–15. https://doi.org/10.1371/journal. pone.0136665. 44. Binderup HG, Madsen JS, Henrik N, et al. Quantification of microRNA levels in plasma—impact of preanalytical and analytical conditions. PLoS One. 2018;1–13. 45. Vogt J, Sheinson D, Katavolos P, et al. Variance component analysis of circulating miR-122 in serum from healthy human volunteers. PLoS One. 2019;14(7):e0220406. https://doi.org/10.1371/journal.pone.0220406. 46. Heegaard NHH, Carlsen AL, Lilje B, et al. Diurnal variations of human circulating cell-free micro-RNA. PLoS One. 2016;11(8):e0160577. https://doi.org/10.1371/journal.pone.0160577. 47. Baggish AL, Hale A, Weiner RB, et al. Dynamic regulation of circulating microRNA during acute exhaustive exercise and sustained aerobic exercise training: circulating microRNA in exercise. J Physiol. 2011;589 (16):3983–3994. https://doi.org/10.1113/jphysiol.2011.213363.

CHAPTER

Concept of biological reference materials for RNA analysis in cardiovascular disease

20

Fay Betsoua,* and Andrei Codreanub a

IBBL (Integrated Biobank of Luxembourg), Dudelange, Luxembourg Hospital Center of Luxembourg, Strassen, Luxembourgb

20.1 Introduction Before any new diagnostic assay is CE-marked and can be deployed in health care, thorough validation must take place.1 The scientific validity and reproducibility of research and development, identifying new biomarkers, benefit when researchers have access to biospecimens certified for their clinical provenance. The specificity of diagnostic biomarkers can better be validated when true-positive samples are available as reference materials (RMs), certified for their clinical provenance. The clinical validity of prognostic or predictive biomarkers can better be demonstrated when baseline samples are available as RM, certified for their clinical provenance from patients who have had a positive or a negative outcome. More specifically, after the initial identification of a biomarker, its formal validation, in view of a regulatory submission, includes preanalytical and analytical validation, where, among other parameters, the robustness of the candidate biomarker(s) must be assessed against common preanalytical and analytical variables. The robustness of a biomarker may differ according to the clinical-pathological background, comorbidities, and medications; therefore, it is important for the diagnostic industry and reference laboratories to have access to well-characterized specimens. Validation also includes clinical validation, where the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the biomarker must be assessed. The correct assessment of these values again requires access to well-characterized biospecimens. Biospecimens certified for their clinical provenance (biospecimens from true-positive cases) would ensure accurate evaluation of analytical and clinical performance characteristics of new biomarkers and thus accelerate translation into clinical practice. When RMs are provided to end users, the context of their use should be specified. The context of use of an RM can differ according to the type of assigned value. An RM with quantitative assigned values may be used for the calibration of equipment, or for the validation of the accuracy of a quantitative analytical method. An RM with nominal assigned values may be used, not only for the validation of the trueness of a qualitative method, but also for the validation of biomarkers, as described *Current affiliation: Laboratoire National de Sante, Dudelange, Luxembourg Epigenetics in Cardiovascular Disease. https://doi.org/10.1016/B978-0-12-822258-4.00005-5 Copyright # 2021 Elsevier Inc. All rights reserved.

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previously. The context of use of an RM should also be described in terms of the types of analytical methods in which this RM can be used. For example, a DNA RM, with an assigned quantitative value for its concentration, may be suitable for use in spectrophotometric, but not spectrofluorimetric methods. Similarly, a DNA RM, with an assigned nominal value for its sequence, may be suitable for use in PCR but not in methylation analyses. The concept of biospecimens certified for nominal clinical properties has been highlighted with use cases from the areas of infectious, oncological, and neurodegenerative diseases,2 but not cardiovascular diseases. The poor availability of tissue myocardial biospecimens and the presence of confounding factors related to comorbidities and natural aging of the cardiovascular system are major specific difficulties for the certification of the nominal properties of clinical biospecimens in heart disease.

20.2 Clinical and biological context Although mortality after ST-segment elevation myocardial infarction (MI) has considerably declined to a one-year rate of around 7%, the number of patients developing heart failure as a result of MI is on the rise. Apart from early reperfusion by primary percutaneous coronary angioplasty, there is currently no established therapy for reducing MI size. The process of reperfusion itself can paradoxically induce further myocardial damage and cardiomyocyte death, a phenomenon called ischemia-reperfusion injury (IRI).3 Thus, new cardioprotective therapies are required to improve clinical outcomes after ST-segment elevation MI. Important pathways that are activated during MI include inflammatory pathways, monocyte/macrophage activation, the renin-angiotensin-aldosterone system, endothelin, adrenergic and cytokine, and growth factor pathways.4–6 Critical signaling pathways involved in the pathogenesis must be identified and understood. RNA is a critical biomolecule for both the identification of therapeutic targets and the identification of diagnostic or prognostic biomarkers.4–6 No-reflow is the phenomenon in which, after the timely opening of an occluded artery, microvascular obstruction persists and leads to the lack of perfusion to portions of the myocardium. It is considered to be part of the IRI and associated oxidative stress. No-reflow is unlikely to contribute directly to additional myocyte cell death, with ultrastructural evidence of the microvasculature injury lagging behind ultrastructural evidence of cardiomyocyte cell death. However, no-reflow inhibits the healing phase of MI, given that blood-borne trophic factors and cells crucial to the removal of necrotic debris and scar can neither enter nor exit the zone of no-reflow.7 Up to 40%–50% of the patients presenting with ST-segment elevation MI have a no-reflow. Risk factors for no-reflow include older age, bigger thrombus burden, pain-to-balloon time 4 h, low initial thrombolysis in myocardial infarction flow (1), long target lesion, and high SYNTAX score II result.8–10 Microvascular injury can negatively influence the therapeutic outcome of reperfusion therapies, independently from infarct size.11, 12 Diagnosis is currently based on noninvasive imaging techniques, such as echocardiography contrast imaging, nuclear imaging with 201Tl-labeled or 99mTc-labeled albumin microspheres, and cardiac magnetic resonance (CMR) with contrast. CMR perfusion deficits correlate closely by location and shape with perfusion defects observed using thioflavin S staining in preclinical studies.13 The development of circulating predictive biomarkers that are quick and easy to measure would contribute to the identification of patients at highest risk of no-reflow and patient stratification for novel treatments aimed at activating cardioprotective pathways in different cardiac or circulating cell types.14

20.3 Production of RMs

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Such pathways may be related to mitochondria-targeted antioxidants15, 16 or pericyte-relaxing molecules.17 The identification of high-risk patients for no-reflow would also increase the efficiency of related clinical trials. Postinfarction left ventricular remodeling (REM) accompanies the tissue repair and scar generation and is the result of the acute loss of myocardium and the abrupt increase in loading conditions. It involves the left ventricle globally with dilation, distortion of ventricular shape, mural hypertrophy, and proliferation of the extracellular matrix, resulting in various levels of ventricular dysfunction and ultimately heart failure.18 Molecular changes, occurring in the cardiomyocytes and leading to REM, involve the activation of the fetal gene expression program and metabolic changes leading to energy depletion.19 Considerable efforts have been made in experimental reparative therapy development, using mesenchymal stromal cells (MSCs), hematopoietic and endothelial progenitor cells, or mononuclear cells that are derived from bone marrow and other sources.20, 21 The contribution of cardiac stem cells to muscle self-repair is still a matter of debate, as well as the regeneration potential of implanted cells.22 The cardioprotective effects of cell therapies appear to be more related to their paracrine effect, as the applied cells are known to influence fibrosis, angiogenesis, and inflammation, and by these means, to affect cardiac healing responses and scar properties.23 The development of circulating diagnostic biomarkers, including markers for myocardial stress, extracellular matrix REM, or miRNAs,24 would improve patient follow-up with more intensive treatment by existing infarct size-reducing or neurohormonal blockade drugs. Biomarkers related to phenotyping or gene expression profiles of peripheral monocytes and their potential interaction with the healing process of the heart could further improve therapeutic management.

20.3 Production of RMs Two types of reference biospecimens for no-reflow and REM are described here: (i) citrate plasma and (ii) RNA extracted from isolated monocytes from CPT tubes, with the following corresponding context of use: No-reflow REM Citrate plasma Monocyte RNA

Extracellular vesicle analyses Circulating cell-free RNA analyses Cellular RNA analyses

Plasma is the matrix that is most often used for the identification of circulating biomarkers, among which extracellular vesicles and cell-free-circulating RNA species represent an important component. In parallel, peripheral blood mononuclear cells (PBMCs) are receiving attention since their gene expression profiles show similarities to those of the cardiac tissue25 and can be representative of cardiac inflammatory conditions.26 Recent data suggest that PBMC gene expression levels of specific cytokine coding genes may be indicative of heart failure.27 For all these reasons, plasma and PBMCs are both intensively used as input materials in biomarker research and such research would benefit from the existence of relevant reference materials.

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20.3.1 Biobanking Centralized and standardized processing and storage of biospecimens is increasingly being performed in biobank infrastructures. Biobanks follow best practices28 and are able to seek accreditation to a new ISO standard.29 Many biobanks are embedded in the healthcare system or operate in close collaboration with hospitals and, hence, are called clinical biobanks. Biobank laboratories, processing clinical biospecimens and producing fluid, cellular, or molecular derivatives thereof, can validate their processing methods for fitness for purpose, reproducibility, robustness, homogeneity, and stability of the output material, according to ISO FDIS 21899:2020 Biotechnology—General Requirements for the validation and verification of processing methods for biological materials in biobanks. As we will see in the following sections, these are essentially the same performance characteristics that have to be assessed during RM production. Furthermore, many biobanks perform more or less extensive quality control and characterization assays on the specimens they produce. Quality control may include the measurements of molecular concentration, molecular integrity, molecular or cellular purity, cellular viability or functionality, and cell enumeration. Characterization assays may include cell composition of whole blood, histological composition of tissue samples, hemolytic, lipemic, and icteric indices of serum or plasma, and urinalysis strip results of urine. This means that clinical accredited biobanks, with a portfolio of validated processing methods, are well positioned to produce clinical RMs.

20.3.2 Processing: Required documentation Biospecimens for no-reflow should be collected as close as possible to the onset of the ST-segment elevation MI. Based on the available data, we suggest that biospecimens for REM be collected at two time points: at presentation (before primary angioplasty and during MI), and at 48 h following presentation30 and that the RM includes paired samples. For both plasma and monocyte RNA specimens, and for both no-reflow and REM conditions, the following preanalytical variables should be documented: type of collection tube, precentrifugation delay and temperature, centrifugation conditions (speed, time, temperature, and brake), postcentrifugation delay and temperature, storage container, and temperature.31 Freeze-thaw cycles should also be documented. For monocyte RNA, the details concerning the PBMC isolation protocol, the monocyte isolation protocol, and the RNA extraction protocol should be reported. If RNA is extracted postcryopreservation, the details of the cryopreservation protocol and the duration of the cryopreservation should be reported.32

20.3.3 Purity assessment The absence of blood and lipid contamination in plasma is assessed by the HLI indices. This characterization is important because hemolytic or lipemic indices higher than the minimum index make circulating miRNA measurements uninterpretable. Lipid contamination can be estimated by triglyceride concentration not higher than 300 mg/dL and blood contamination can be estimated by hemoglobin concentration not higher than 0.3 g/L. The purity of the isolated monocytes should be determined by flow cytometry with anti-CD14 and anti-CD45 antibodies and should be higher than 90%. High purity is important for the accuracy of gene expression measurements.

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20.3.4 Homogeneity assessment Homogeneity is assessed through a stratified random sampling plan across the RM production, consisting of at least eight different aliquots of plasma or eight different aliquots of monocyte RNA. Homogeneity is important in RM production because all end users should be using exactly the same material. Homogeneity of plasma samples can be assessed by microparticle counting. Total microparticle counting can be performed by an impedance-based method. Plasma homogeneity testing, relative to at least one of the critical no-reflow and REM-related biomarkers mentioned earlier, such as C-reactive protein (CRP) or endothelin-1, measured by a validated assay, must also be performed. Homogeneity of monocyte RNA samples can be assessed by spectrophotometry. In practical terms, all the aliquots of the plasma or RNA material should be homogeneous based on the concentration of the measurements discussed previously.

20.3.5 Stability assessment Short- and long-term stability studies of plasma, relative to specific miRNA targets, measured by RT-qPCR assays, or better by droplet digital PCR assays, which is a reference method, should be performed. Stability studies of plasma, relative to extracellular vesicles, should also be performed, after extracellular vesicle isolation by a validated method, extracellular vesicle enumeration by a method such as qNano, and measurement of specific extracellular vesicle surface epitopes by ELISA or a flow cytometry-validated method. Short- and long-term stability studies of monocyte RNA, relative to specific downstream applications (e.g., RNA sequencing) should be performed. In practical terms, different aliquots of the plasma or the isolated RNA are put at their long-term storage conditions and are periodically analyzed for the measurements discussed earlier. Stability is important because the end user of an RM should know until when the RM can be used with confidence.

20.3.6 Nominal value assignment and characterization Cardiac magnetic resonance (CMR) has emerged as the most reliable imaging modality for assessing the consequences of a reperfused MI and the cardioprotective efficacy of therapies aimed at reducing the adverse remodeling. It provides therefore the main dataset for value assignment for both no-reflow and REM. Associated clinical and angiographic data are also needed. No-reflow assessment by CMR is based on microvascular obstruction and intramyocardial hemorrhage quantification. Microvascular obstruction can be identified as the areas of dark core within the area of MI, whereas hemorrhage quantification relies mostly on T2*-weighted imaging.12 Specifically for REM, CMR examination should be done at two time points: as close as possible to the onset of the MI and 3 to 6 months later, in order to assess and document the functional consequences of REM on left ventricular size and function. (1) The early CMR will lead to the documentation of the left ventricular size and global function, MI size, the area of no-reflow, area at risk, and myocardial salvage. (2) The late CMR will lead to the quantification and documentation of the size and transmural extent of the scar, left ventricular size, and global function.

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CMR findings are dependent on the timing of the CMR scan and to some extent on the CMR protocol used. Therefore, there is a need for standardization, in order to strengthen the robustness of the technique, facilitate prospective or retrospective collaborative research,33 and also enable the production of RM. The specificities of the CMR (timing, equipment reference, and protocol settings) should be documented and reported. Angiographic variables of interest are the following: thrombolysis in myocardial infarction flow grade after reperfusion, myocardial blush grade, the presence of significant stenoses on nonculprit coronary arteries (estimated angiographically, by invasive functional assessment using fractional flow reserve (FFR) or instant wave-free ratio (iFR), or using a deferred noninvasive functional test for ischemia).34

20.3.7 Complementary characterization Different biomarkers for no-reflow have been published and their determination and reporting would add value to the RM. These include high neutrophil-to-lymphocyte ratio, increased mean platelet volume, hyperglycemia, high neutrophil count, high monocyte count, low hemoglobin concentration, increased platelet-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio, high platelet reactivity level, high-sensitivity CRP and endothelin-1 levels, upregulation of adhesion molecules CD18, and upregulation of monocyte and neutrophil activator molecules.35 As mentioned earlier, the value assignment for REM requires data collected at two time points: one early after infarction and one at 6-month follow-up. Value assignment is based on criteria, such as the clinical definition of a 20% increase of the left ventricular end-diastolic volume, scar size, ejection fraction, and end-systolic volume. The area under the curve for markers of myocardial necrosis (i.e., cardiac-specific troponin I) provides an accurate measure of the amount of myocardium lost. Transthoracic echocardiography is widely used in patients with MI, with the number of segments showing wall motion abnormality providing an estimate of infarct size. Cardiac magnetic resonance imaging (MRI) with the use of delayed contrast agent gadolinium enhancement (also able to quantify the noreflow area, if present) provides a more accurate measurement and is considered the gold standard.36 Other important data to be reported include scar size and transmural extent, left ventricular size, ejection fraction and mass, peri-infarction perfusion quantification, and remote myocardial interstitial fibrosis as a mark of global left ventricular remodeling. Finally, data related to the donor of the samples: age, sex, comorbidities, such as diabetes, treatments (including contraceptives, statins, and angiotensin-converting enzyme (ACE) inhibitors), and smoking status, should be reported.

20.3.8 Confidence in the nominal values The user of the reference material should have confidence in the assigned nominal values: “no-reflow plasma,” “no-reflow monocyte RNA,” “REM plasma,” or “REM monocyte RNA.” Therefore, according to a draft ISO REMCO technical standard, which is in preparation as of January 2021, the following information items should be provided to the end user of the RM: – The basis for the assigned value: description of the CMR results, clinical and angiographic data, and the timing of their acquisition;

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– A description of the origin of the material: description of the clinical case of provenance; – A description of procedures for minimizing contamination: reference to the procedures of the biobank related to traceability and chain of custody that reduce the risk of specimen mix-up; – Information on the nominal test methods used in value assignment: technical specificities of the CMR method, of the angiographic explorations, and of the laboratory methods used to measure the parameters of the complementary characterization of the RM; – Information on reference data used to support value assignment: reference clinical textbooks or official diagnostic guideline documents including definitions and diagnostic algorithms; – A qualitative or quantitative indication of confidence in the assigned value: for example, “the test results provide very strong evidence for the assigned value of REM.” A quantitative statement of confidence in assigned nominal values is, however, not required. In other words, although each laboratory method, used to measure every particular analyte, has an analytical uncertainty that can be expressed in terms of coefficient of variation (CV%), it is not necessary to provide a statement of confidence of the type “there is x% probability that this plasma material is a true ‘no-reflow’ plasma material.”

20.3.9 Fitness for purpose Citrate plasma specimens, when prepared appropriately, with gentle inversion of the blood tube, keeping the tube in an upright position, 30-min precentrifugation time at room temperature, centrifugation without brake, storage at 80°C, and no freeze-thaw cycles, are fit for purpose of circulating miRNAs, and for extracellular vesicle analyses. Extracellular vesicles may lead to (i) validation of cardiacspecific miRNAs contained in circulating cardiac extracellular vesicles, as diagnostic or predictive biomarkers, and (ii) validation of exosomes as cardioprotective agents.37,38 The preanalytical quality of the citrate plasma can be shown by measuring IL8.39 The concentration of IL8 should be lower than 22 pg/mL. Using this cutoff provides a sensitivity of 86% in eliminating citrate plasma samples produced from the blood that has been exposed to room temperature for over 48 h. The suitability of plasma for downstream miRNA analyses can be demonstrated by the absence of hemoglobin contamination (ELISA or spectrophotometry). The suitability of plasma for downstream extracellular vesicle analyses can be confirmed by the characterization of the isolated extracellular vesicles, based on extracellular vesicle-ubiquitous surface markers, such as CD9 and CD63, and plateletderived extracellular vesicle markers, such as CD61 and CD42.40,41 The suitability of monocyte RNA for downstream sequencing or gene expression microarray studies can be demonstrated by (i) measurement of the RNA integrity number (RIN > 6, by microfluidic electrophoresis); (ii) measurement of the PBMC preanalytical score (PBMC preanalytical score