Epigenetic biomarkers and diagnostics

EPIGENETIC BIOMARKERS AND DIAGNOSTICS Edited by José Luis García-Giménez Center for Biomedical Network Research on Rare Diseases (CIBERER), Madrid, Spain; Medicine and Dentistry School; Biomedical Research Institute INCLIVA, University of Valencia, Spain AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, UK 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Copyright © 2016 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 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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 ISBN: 978-0-12-801899-6 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress For information on all Academic Press publications visit our website at http://store.elsevier.com/ Publisher: Mica Haley Acquisition Editor: Catherine Van Der Laan Editorial Project Manager: Lisa Eppich Production Project Manager: Chris Wortley Designer: Mark Rogers Typeset by TNQ Books and Journals www.tnq.co.in Printed and bound in the United States of America List of Contributors Abdelhalim Boukaba  Drug Discovery Pipeline, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Science, Guangzhou, People’s Republic of China Carolina Abril-Tormo  IBSP-CV Biobank, FISABIO, Valencia, Spain Sahar Al-Mahdawi  Department of Life Sciences, College of Health & Life Sciences, Brunel University London, Uxbridge, UK; Synthetic Biology Theme, Institute of Environment, Health & Societies, Brunel University London, Uxbridge, UK Eoin Brennan  Diabetic Complications Division, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia Enrique J Busó  Unidad Central de Investigación, University of Valencia, Valencia, Spain Diogo Almeida-Rios Cancer Biology and Epigenetics Group – Research Center, Portuguese Oncology Institute, Porto, Portugal; Department of Pathology, Portuguese Oncology Institute, Porto, Portugal F Javier Carmona Sanz  Human Oncology & Pathogenesis Program (HOPP), Memorial SloanKettering Cancer Center (MSKCC), New York, NY, USA; Cancer Epigenetics and Biology Program (PEBC), Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, Spain Sara Anjomani Virmouni  Department of Life Sciences, College of Health & Life Sciences, Brunel University London, Uxbridge, UK; Synthetic Biology Theme, Institute of Environment, Health & Societies, Brunel University London, Uxbridge, UK Raimundo Cervera  Hematology and Oncology Unit, Biomedical Research Institute INCLIVA, Valencia, Spain Juan Ausio  Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada Alfredo Ciccodicola  Institute of Genetics and Biophysics “Adriano Buzzati-Traverso”, CNR, Naples, Italy; Department of Science and Technology, University Parthenope of Naples, Italy Bharati Bapat Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pathology, University Health Network, Toronto, ON, Canada Joan Climent  Hematology and Oncology Unit, Biomedical Research Institute INCLIVA, Valencia, Spain Valerio Costa  Institute of Genetics and Biophysics “Adriano Buzzati-Traverso”, CNR, Naples, Italy Charlotte L Bevan  Department of Surgery & Cancer, Imperial Centre for Translational & Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK Jenefer M Blackwell  Telethon Kids Institute, The University of Western Australia, Subiaco, WA, Australia Ana B Crujeiras  Laboratory of Molecular and Cellular Endocrinology, Instituto de Investigación Sanitaria (IDIS), Complejo Hospitalario Universitario de Santiago (CHUS) and Santiago de Compostela University (USC), Santiago de Compostela, Spain; CIBER Fisiopatología de la Obesidad y la Nutrición (CIBERobn), Madrid, Spain Tiziana Bonaldi  Department of Experimental Oncology, European Institute of Oncology, Milano, Italy Alessandro Cuomo  Department of Experimental Oncology, European Institute of Oncology, Milano, Italy xi xii List of Contributors Avery DeVries  Department of Cellular and Molecular Medicine, Arizona Respiratory Center and Arizona Center for the Biology of Complex Diseases, University of Arizona, Tucson, AZ, USA Angel Diaz-Lagares  Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain Roberta Esposito  Institute of Genetics and Biophysics “Adriano Buzzati-Traverso”, CNR, Naples, Italy Alessandro Fatica  Department of Biology and Biotechnology Charles Darwin, Sapienza University of Rome, Rome, Italy Alfredo Ferro  Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy Claire E Fletcher  Department of Surgery & Cancer, Imperial Centre for Translational & Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK Ana-Maria Florea  Department of Neuropathology, Heinrich-Heine-University Düsseldorf, Dusseldorf, Germany Ernest Fraenkel  Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Yu Fujita  Division of Molecular and Cellular Medicine, National Cancer Center Research Institute, Tokyo, Japan Tomohiro Fujiwara  Department of Orthopaedic Surgery, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan; Center for Innovative Clinical Medicine, Okayama University Hospital, Okayama, Japan; Division of Molecular and Cellular Medicine, National Cancer Center Research Institute, Tokyo, Japan Miriam Gagliardi  Institute of Genetics and Biophysics “Adriano Buzzati-Traverso”, CNR, Naples, Italy José Luis García-Giménez  Center for Biomedical Network Research on Rare Diseases (CIBERER), National Institute of Health Carlos IIII, Spain; Department of Physiology, Medicine and Dentistry School, University of Valencia, Valencia, Spain; Biomedical Research Institute INCLIVA, University of Valencia, Valencia, Spain Rosalba Giugno  Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy Catherine Godson Conway Institute, Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland Inờs Graỗa Cancer Biology and Epigenetics Group – Research Center, Portuguese Oncology Institute, Porto, Portugal; School of Allied Health Sciences (ESTSP), Polytechnic of Porto, Porto, Portugal Kirsten Grønbæk  Department of Hematology, Rigshospitalet, Copenhagen, Denmark Shinji Hagiwara  Diabetic Complications Division, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia Rui Henrique  Cancer Biology & Epigenetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute, Porto, Portugal; Department of Pathology, Portuguese Oncology Institute, Porto, Portugal; Department of Pathology and Molecular Immunology, Institute of Biomedical Sciences Abel Salazar – University of Porto (ICBAS-UP), Porto, Portugal José Santiago Ibañez Cabellos  Center for Biomedical Network Research on Rare Diseases, Medicine and Dentistry School, University of Valencia, Valencia, Spain; Biomedical Research Institute INCLIVA, Valencia, Spain Marisa Iborra  Gastroenterology Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain Toyotaka Ishibashi  Division of Life Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong, HKSAR; Department of Biomedical Engineer, Hong Kong University of Science and Technology, Kowloon, Hong Kong, HKSAR Sarra E Jamieson  Telethon Kids Institute, The University of Western Australia, Subiaco, WA, Australia Carmen Jerónimo  Cancer Biology & Epigenetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute, Porto, Portugal; Department of Pathology and Molecular Immunology, Institute of Biomedical Sciences Abel Salazar – University of Porto (ICBAS-UP), Porto, Portugal Sadhana Joshi  Department of Nutritional Medicine, Interactive Research School for Health Affairs, Bharati Vidyapeeth University, Pune, Maharashtra, India List of Contributors xiii Phillip Kantharidis  Diabetic Complications Division, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia Takahiro Ochiya  Division of Molecular and Cellular Medicine, National Cancer Center Research Institute, Tokyo, Japan Akira Kawai  Department of Musculoskeletal Oncology, National Cancer Center Hospital, Tokyo, Japan Toshifumi Ozaki  Department of Orthopaedic Surgery, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan Vinita Khot  Department of Nutritional Medicine, Interactive Research School for Health Affairs, Bharati Vidyapeeth University, Pune, Maharashtra, India Lasse Sommer Kristensen  Department of Hematology, Rigshospitalet, Copenhagen, Denmark Ana Lluch  Hematology and Oncology Unit, Biomedical Research Institute INCLIVA, Valencia, Spain Federico V Pallardó  Center for Biomedical Network Research on Rare Diseases, Medicine and Dentistry School, University of Valencia, Valencia, Spain; Biomedical Research Institute INCLIVA, Valencia, Spain Lorena Peiró-Chova  INCLIVA Biobank, INCLIVA Biomedical Research Institute, Valencia, Spain José Antonio López-Guerrero  Laboratory of Molecular Biology and Biobank, Fundacion Instituto Valenciano de Oncologia, Valencia, Spain Marco Pellegrini  Laboratory of Integrative Systems Medicine (LISM), Institute of Informatics and Telematics (IIT) and Institute of Clinical Physiology (IFC), National Research Council (CNR), Pisa, Italy Paula Lopez-Serra  Epigenetic and Cancer Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain Tandy L.D Petrov  Department of Biology, The University of Alabama at Birmingham, Birmingham, AL, USA Annita Louloupi  Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands Olga Bahamonde Ponce  INCLIVA Biobank, INCLIVA Biomedical Research Institute, Valencia, Spain Luca Magnani  Department of Surgery and Cancer Imperial Centre for Translational and Experimental Medicine, Imperial College Hammersmith, London, UK Mark A Pook  Department of Life Sciences, College of Health & Life Sciences, Brunel University London, Uxbridge, UK; Synthetic Biology Theme, Institute of Environment, Health & Societies, Brunel University London, Uxbridge, UK Jacobo Martínez-Santamaría IBSP-CV Biobank, FISABIO, Valencia, Spain; Valencian Biobank Network, FISABIO, Valencia, Spain Alfredo Pulvirenti  Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy Maria R Matarazzo  Institute of Genetics and Biophysics “Adriano Buzzati-Traverso”, CNR, Naples, Italy João Ramalho-Carvalho  Cancer Biology & Epigenetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute, Porto, Portugal Aaron McClelland  Diabetic Complications Division, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia Pamela Milani  Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA Yutaka Nezu  Division of Molecular and Cellular Medicine, National Cancer Center Research Institute, Tokyo, Japan Roberta Noberini  Center of Genomic Science, Istituto Italiano di Tecnologia, Milano, Italy Alberto Ramos  Hematology and Oncology Unit, Biomedical Research Institute INCLIVA, Valencia, Spain George Rasti  Chromatin Biology Laboratory, Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain Nicole C Riddle  Department of Biology, The University of Alabama at Birmingham, Birmingham, AL, USA xiv List of Contributors Peter H.J Riegman  Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands Carlos Romá Mateo  Center for Biomedical Network Research on Rare Diseases, Medicine and Dentistry School, University of Valencia, Valencia, Spain; Biomedical Research Institute INCLIVA, Valencia, Spain Francesco Russo  Laboratory of Integrative Systems Medicine (LISM), Institute of Informatics and Telematics (IIT) and Institute of Clinical Physiology (IFC), National Research Council (CNR), Pisa, Italy; Department of Computer Science, University of Pisa, Pisa, Italy Fabian Sanchis-Gomar  Department of Physiology, Medicine and Dentistry School, University of Valencia, Valencia, Spain; Biomedical Research Institute INCLIVA, University of Valencia, Valencia, Spain Juan Sandoval  Epigenetic and Cancer Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain Flavia Scoyni  Department of Biology and Biotechnology Charles Darwin, Sapienza University of Rome, Rome, Italy Marta Seco Cervera  Center for Biomedical Network Research on Rare Diseases, Medicine and Dentistry School, University of Valencia, Valencia, Spain; Biomedical Research Institute INCLIVA, Valencia, Spain Akifumi Shibakawa  Department of Surgery & Cancer, Imperial Centre for Translational & Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK Nicolas G Simonet  Chromatin Biology Laboratory, Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain Ailsa Sita-Lumsden  Department of Surgery & Cancer, Imperial Centre for Translational & Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, London, UK Olafur Andri Stefansson  Cancer Research Laboratory, Faculty of Medicine, University of Iceland, Reykjavik, Iceland Deepali Sundrani  Department of Nutritional Medicine, Interactive Research School for Health Affairs, Bharati Vidyapeeth University, Pune, Maharashtra, India Genevieve Syn  Telethon Kids Institute, The University of Western Australia, Subiaco, WA, Australia Trygve O Tollefsbol  Comprehensive Cancer Center, Center for Aging, Comprehensive Diabetes Center, Nutrition Obesity Research Center, Cell Senescence Culture Facility, University of Alabama at Birmingham, Birmingham, AL, USA Eneda Toska  Human Oncology & Pathogenesis Program (HOPP), Memorial Sloan-Kettering Cancer Center (MSKCC), New York, NY, USA Marianne B Treppendahl  Department of Hematology, Rigshospitalet, Copenhagen, Denmark Toshikazu Ushijima  Chief of Division of Epigenomics, National Cancer Center Research Institute, Tokyo, Japan Alejandro Vaquero  Chromatin Biology Laboratory, Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain Donata Vercelli  Department of Cellular and Molecular Medicine, Arizona Respiratory Center and Arizona Center for the Biology of Complex Diseases, University of Arizona, Tucson, AZ, USA Filipa Quintela Vieira  Cancer Biology and Epigenetics Group – Research Center, Portuguese Oncology Institute, Porto, Portugal; School of Allied Health Sciences (ESTSP), Polytechnic of Porto, Porto, Portugal Yinan Zhang  Division of Life Science, Hong Kong University of Science and Technology, Kowloon, Hong Kong, HKSAR Fang Zhao  Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada Wilbert Zwart  Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands Preface Epigenetics is an emerging frontier of biology, and its definition is continuously being adapted based on new scientific findings In fact, the NIH Roadmap Epigenomics Project has recently defined epigenetics as “the heritable changes in gene activity and expression (in the progeny of cells or of individuals) and also stable, long-term alterations in the transcriptional potential of a cell that are not necessarily heritable.” In this regard, epigenetics includes DNA methylation, noncoding RNAs, and histone posttranslational modifications This integrative definition of epigenetics reflects the potential of the discipline to expand beyond the control of a particular gene expression program for each cell type, defining the cellular and developmental identity and function of cells, and, finally, translating this potential to health and disease conditions in human beings Due to the rapid progress in the field of epigenetics, new and promising m ­ ethodologies to advance biomedical research are being developed Epigenetic research and epigenetic pharmaceutical drug development are now considered areas of great interest and promise in the biomedical scene The advantage of human epigenetics compared with human genetics and genomics is that it provides vital information about gene function in individual cell types, while incorporating information from the environment and lifestyle, and unlike most genetic defects causative of human disease, epigenetic alterations are modulable and reversible The volume Epigenetic Biomarkers and Diagnostics is intended to describe both epigenetic biomarkers that can be adopted into clinical routine as well as advanced technologies and tools for their analysis In this regard, epigenetic ­biomarkers provide clinicians valuable information about the presence or absence of a disease (­diagnostic value), the patient prognosis (prognostic value), the response to a specific treatment (predictive value), the effects of ongoing treatment (­ therapy-monitoring biomarkers), and the future risk of disease development (risk prediction) Furthermore, several advantages may arise from the use of epigenetic ­biomarkers versus gene expression in clinical practice, such as higher stability, for example, in biofluids They can also fill clinical gaps by bridging genetic information, mRNA transcription, and protein translation In consequence, the associations between epigenome alterations and diseases become clearer, ­ providing a way to act directly on gene expression by ­developing specific drugs or even by adopting healthy lifestyles Many methodologies, including classical methods and next-generation-sequencing-based technologies, are available to clinicians and researchers to identify new epigenetic biomarkers and analyze them from several biological sources In this context, genome-wide methylation analysis, chromatin immunoprecipitation coupled with high-throughput platforms, and noncoding RNA sequencing are described in this volume Furthermore, some techniques for DNA methylation analysis are more likely to be rapidly adopted in clinical laboratories, such as EpiTYPER MassARRAY, methyl specific PCR (MSP), and pyrosequencing On the other hand, immunoassays are well established in xv xvi Preface clinical laboratories for the analysis of histones (i.e., inflammatory and autoimmune diseases) However, it is expected that the incorporation of mass spectrometry technologies into laboratories for clinical diagnostics (replacing routine immunoassays) will be the tendency in the coming years, as will be the analysis of histone posttranslational modifications associated with pathological states Although it is not possible to cover all epigenetic markers, this volume includes chapters describing the most promising biomarkers for cancer (i.e., breast, lung, colon, etc.), metabolic disorders (i.e., diabetes and obesity), autoimmune diseases, infertility, allergy, infectious diseases, and neurological disorders; and, where possible, we will focus our attention on those which are feasible to be adopted for clinical use This book was written in a ­comprehensive manner by outstanding experts in their corresponding fields for a broad target audience such as advanced students, basic scientists, biomedical and biotechnological companies, as well as clinical researchers, clinicians (i.e., pathologists, immunologists, oncologists, endocrinologists, etc.) and analysts from clinical laboratories who can adopt these potential biomarkers into clinical practice In the coming years, epigenetics will continue to provide an exciting future in biomedicine and clinical practice The chapters covered in Epigenetic Biomarkers and Diagnostics highlight the unprecedented impact of epigenetics in clinical diagnostics and will contribute to the discovery and development of new epigenetic biomarkers in the future José Luis García-Giménez Valencia, Spain C H A P T E R Epigenetic Biomarkers: New Findings, Perspectives, and Future Directions in Diagnostics José Luis García-Giménez1, Toshikazu Ushijima2, Trygve O Tollefsbol3 1Department Physiology, Center for Biomedical Network Research on Rare Diseases, National Institute of Health Carlos IIII, Institute of Health Research INCLIVA, Medicine and Dentistry School, University of Valencia, Valencia, Spain; 2Chief of Division of Epigenomics, National Cancer Center Research Institute, Tokyo, Japan; 3Comprehensive Cancer Center, Center for Aging, Comprehensive Diabetes Center, Nutrition Obesity Research Center, Cell Senescence Culture Facility, University of Alabama at Birmingham, Birmingham, AL, USA O U T L I N E Introduction 2 Epigenetic Mechanisms 2.1 DNA Methylation 2.2 Histone PTMs and Histone Variants 2.3 Noncoding RNAs 3 11 Epigenetics: Perspective of Implantation in Clinical Laboratories 12 Perspectives of Epigenetics in Diagnostics 14 Epigenetic Biomarkers and In Vitro Diagnostics8 List of Abbreviations 15 References15 Epigenetic Biomarkers and the Clinical Laboratory10 Epigenetic Biomarkers and Diagnostics http://dx.doi.org/10.1016/B978-0-12-801899-6.00001-2 Epigenetic Biomarkers: An Overview of Recent Advances Copyright © 2016 Elsevier Inc All rights reserved 1.  EPIGENETIC BIOMARKERS AND DIAGNOSTICS 1. INTRODUCTION The literal meaning of the term epigenetic is “above or on top of genetics,” although it has had many different definitions over the years Conrad Hal Waddington was the first to define epigenetics, in 1942, as “the branch of biology which studies the causal interaction between genes and their products, which bring the phenotype into being” [1] In 1990, Holiday defined epigenetics as “the study of the mechanisms of temporal and spatial control of gene function during the development of organisms” [2] A few years later, epigenetics was defined in a narrower manner, as the different epigenetic modifications or mechanisms that produce heritable changes affecting gene expression without affecting the DNA sequence In 2001, Jenuwein and Allis proposed the histone code as an epigenetic mechanism which is a critical feature of a genome-wide mechanism of information storage and retrieval and that considerably expands the information potential of the genetic code Therefore, they pointed out that epigenetics imparts a fundamental regulatory system beyond the sequence information of our genetic code [3] By 2007, the definition of epigenetics had changed yet again, when Bird defined epigenetics as “the structural adaptation of chromosomal regions so as to register, signal or perpetuate altered activity states” [4] The same year, Goldberg, Allis, and Bernstein defined epigenetics as “the study of any potentially stable and, ideally, heritable change in gene expression or cellular phenotype that occurs without changes in Watson-Crick base pairing of DNA” [5] It is evident that both definitions proposed by Bird and Goldberg et al were inclusive of transient chemical modifications of DNA and histones; modifications that have not always been universally accepted and that are still a subject of debate Therefore, in 2008, a consensus definition was made at a Cold Spring Harbor meeting, giving a more integrative definition for epigenetics as a “stably heritable phenotype resulting from changes in a chromosome without alterations in the DNA sequence” [6] It is evident that epigenetics is an emerging frontier of science, so its definition is continuously being adapted based on new scientific findings In fact, the NIH Roadmap Epigenomics Project defines epigenetics as “the heritable changes in gene activity and expression (in the progeny of cells or of individuals) and also stable, long-term alterations in the transcriptional potential of a cell that are not necessarily heritable” [7] (www.roadmapepigenomics.org) In this regard, epigenetics includes DNA methylation, noncoding RNAs (ncRNAs), and histone posttranslational modifications (PTMs) This last integrative definition of epigenetics reflects the potential of epigenetics to go beyond the control of a particular gene expression to produce a unique gene expression program of each cell type, defining the cellular and developmental identity [8], as well as potential health and disease outcomes [9] Our epigenome is characterized by its ability to dynamically respond to intra- and extracellular stimuli, so that epigenetic changes are reversible and in consequence are potential contributors to health and disease Recent progress in the field of epigenetics opens promising ways to advance biomedical research Epigenetic research and epigenetic pharmaceutical drug development are now considered a bright spot in the biomedical research field This research will contribute to biomarker discovery, new therapy development, computational and bioinformatics training, and the development of new next-generation sequencing (NGS) technologies and applications The advantage of epigenetics compared to genetics is that it provides vital information about gene function in individual cell types and incorporates information from the environment Furthermore, several advantages may arise from the use of epigenetic biomarkers versus gene expression (by measuring mRNAs) Epigenetic biomarkers have shown higher stability in fluids and formalin-fixed paraffin-embedded (FFPE) biospecimens compared References clinical strategy when diagnosing, monitoring, and treating micro- and macrovascular complications associated with diabetes LIST OF ABBREVIATIONS 3′-UTR 3′-Untranslated region β-cell  Pancreatic islet beta cell Anti-miR  MicroRNA inhibitor ATP  Adenosine triphosphate DPP4  Dipeptidyl peptidase-4 FPG test  Fasting plasma glucose test GDM  Gestational diabetes mellitus GLP-1  Glucagon-like polypeptide HbA1c test  Hemoglobin A1c test HDL  High-density lipoprotein LNA  Locked nucleic acid miRNA  MicroRNA MODY  Maturity-onset diabetes of the young mRNA  Messenger RNA mTOR pathway  Mammalian target of rapamycin pathway NOD mice  Nonobese diabetic mice OGTT  Oral glucose tolerance test SREBP  Sterol regulatory element-binding proteins Type diabetes  Insulin-dependent diabetes mellitus Type diabetes  Noninsulin-dependent diabetes mellitus TZDs  Thiazolidinediones/glitazones References [1] Patterson CC, Dahlquist GG, Gyurus E, Green A, Soltesz G, Group ES Incidence trends for childhood type diabetes in Europe during 1989–2003 and predicted new cases 2005–20: a multicentre prospective registration study Lancet June 13, 2009;373(9680):2027–33 [2] Gardner SG, Bingley PJ, Sawtell PA, Weeks S, Gale EA Rising incidence of insulin dependent diabetes in children aged under 5 years in the Oxford region: time trend analysis The Bart’s-Oxford Study Group BMJ September 20, 1997;315(7110):713–7 [3] Karvonen M, 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modification, 430 ACSL3, 335t–341t ACTB, 164 Activating protein gamma (AP-2g), 469 Acute lymphoblastic leukemia (ALL) lncRNAs, 227 miRNAs, 225–227, 226t Acute myeloid leukemia (AML), 92–94, 98t aberrant myeloid progenitors, 102, 103t BCL-2 family proteins, 101–102, 103t BCL2L10 methylation, 103t, 104 CD25 antigen expression, 102 CDKN2B methylation, 105t–106t chromosomal abnormalities, 98 DNMT3A mutation, 100t Fas expression, 102, 103t French Prognostic Scoring System (FPSS), 97t global methylation, 105, 105t–106t histone acetylation, 434 IPSS-R, 97t methylation signatures, 104 miR-29b, 101, 103t MLL5 expression, 101, 103t ncRNAs, 231 platelet doubling time, 97, 97t point mutation, 99 programmed death-1 (PD-1), 104, 105t–106t TET2 mutation, 100t TP53 mutations, 99–100, 100t ADARB1, 335t–341t ADCY1, 321–322, 322t ADCY5, 266t ADCYAP1R1, 342t–345t Additional sex combs like transcriptional regulator (ASXL1), 99 Adenocarcinoma (ADC), and miRNA, 543, 546–549 Adenomatous polyposis coli (APC), 104, 157, 164, 165t–166t, 168, 185t–186t, 266t, 282, 286–288, 305t–306t Adenosine A2A receptor (A2AR), 525 Adenosine triphosphate (ATP), 44–45, 176, 319 miRNA, 646 Adherens junctions-associated protein (AJAP1), 184 Adiponectin, 366t–367t ADIPOQ, 48 ADORA2A, 408–409 ADP-ribosylation, histones, 426 ADRB2, 342t–345t Affinity purification, 139 AGO2 immunoprecipitation (AGO2-IP), 218 AIR, 80, 217, 222–223 AK5, 305t–306t AK081227, 226t ALDH1A2, 388–389 ALDH3B2, 335t–341t Allele-specific oligo (ASO), 123–124 Allergic diseases, 331 and epigenetics, 333–347 phenotypes and genes, 332–333 ALOX5AP, 335t–341t ALOX12, 335t–341t Alpha-1D adrenergic receptor (a1D adrenoreceptor), (ADRA1D), 184 a-Ketoglutarate (a-KG), 44–45 a-KG dehydrogenase (a-KGDH), 44–45 663 Alpha-linolenic acid (ALA), 359 ALU-C4 (ALU), 158, 163–164 Alveolar rhabdomyosarcoma (ARMS), miRNA in, 625t–627t, 629 ALX1, 266t ALX4, 164, 165t–166t Alzheimer’s disease (AD), 45–47, 49–50, 58, 431 DNA methylation, 404–406, 405t DNA methylation studies by sequenom, 149–150 epigenetic interventions, 431 PTMs of histones, 431 AMACR, 286 Amplification of intermethylated sites (AIMS), 121–123 Amplified fragment length polymorphism (AFLP), 71 Amyloid precursor protein (APP), 404–406, 411, 431 Amyotrophic lateral sclerosis (ALS), 526–528 DNA methylation, 406–408 epigenetic features modulation for therapy, 530–531 epigenetic modifications, 528–530 Androgen receptor (AR), 276, 279–281, 451, 458–459, 564–565 Androgen receptor transcript splice variant (AR-V7), 564–565, 566t–567t, 573t–578t Androgen-deprivation therapy (ADT), 456t–457t for prostate cancer, 276, 280–281, 285 Angelman syndrome (AS), 157, 159t–160t, 162 MPS technique, 161 MS-HRM, 168 MS-MLPA, 167 Angiotensin-II type-1 receptor gene (AGTR1), 57 ANRIL, 226t, 227 664 Anterior gradient (AGR2), 566t–567t, 573t–578t prostate cancer, 564, 566t–567t, 573t–578t AOX1, 287 Appropriate for gestational age (AGA), 360 AQPEP, 321–322, 322t Arachidonic acid (AA), 359 ARG1, 342t–345t ARG2, 342t–345t Aryl-hydrocarbon receptor repressor (AHRR), 183 Aspirin-intolerant asthma (AIA), 335t–341t Asthma and allergy, 331–350 Ataxia telangiectasia mutated (ATM), 599–600 miRNA, 593t–595t, 597–598 Atopic dermatitis (AD), 332–333, 335t–341t ATP13A2, 406 ATP-binding cassette transporter A1 (ABCA1), 181–182 ATXN7, 410 AURKA, 335t–341t 5-Aza-2’-deoxycytidine (5-aza-dC), 156, 169–170, 190 obesity, 317, 323 Azacytidine, cellular uptake and intracellular processing of, 94f B B3GALT1, 335t–341t Bacteria DNA methylation, 390 histone modifications, 392–393 miRNAs, 387 BamHI R fragment leftward open reading frame (BRLF1), 379t–384t, 390 BamHI Z fragment leftward open reading frame (BZLF1), 379t–384t, 390 BCL2, 101–102, 103t, 282, 305t–306t BCL2L10 methylation, 104, 105t–106t BCSG1, 305t–306t bdnf, 523–525 Beckwith–Wiedemann syndrome (BWS) miRNAs, 226t ncRNAs, 230 Benign prostatic hyperplasia (BPH), 558, 560, 561t, 565–569, 566t–567t miRNAs, 243–244 INDEX β3 adrenergic receptor (ADRB3), 321–322, 322t β-cell pancreatic, 644–646, 646f, 652 diabetes, 644, 646 miR-7, 648 miR-375, 647–648 Betaine–homocysteine methyltransferase (BHMT), 356–357 Bicalutamide, 456t–457t Bile duct cancer, prosequencing, 189 Biobanks anonymized data, 23 anonymous data, 23 in biomedical research, 24–27 as centers for enhancing security and quality of samples and data, 26–27 coded data, 23 contribution to epigenetic and epigenomic studies, 30–31 definition, 20–21 epigenetic biomarkers, 27–30 epigenetics and, 27–31 ethical issues, 22–24 general or archival collections, 20–21 general processes, 21–22 identified data, 23 open consent, 23–25 process approach, 21f project-driven collection, 20–21 as research infrastructure, 24–25 associated data, 25 informed consent and governance, 25 sustainability, 25 types, 20–21 Biological resources centers (BRCs), 20 Biomedical research, biobanks in, 24–27 Biorepository, 20 Biospecimen Reporting for Improved Study Quality (BRISQ), 22 Bisulfite sequencing (BS-seq), 72–73 Bisulfite-converted DNA, primer design for, 108f, 109t Bladder cancer methylation biomarkers, 165t–166t prosequencing, 188–189 Body fluids, nuclear components circulating in, 498–499 Body mass index (BMI), 314 DNA methylation, 321 Bone morphogenic protein (BMP3), 73–74 Bone morphogenic protein (BMP6), prostate cancer, 563–564, 566t–567t, 573t–578t Bone sarcoma, 614–615 circulating miRNAs, 632–633 osteosarcoma, 632–633 miRNAs, 615–624 chondrosarcoma, 624 Ewing sarcoma, 622–624 osteosarcoma, 615–622 Brain-derived neurotrophic factor (BDNF), 229, 521 Braveheart (Bvht), 81 Breast cancer, 297–298 DNA methylation biomarkers, 304–306 BRCA1 methylation, 302–303 hormone receptor methylation, 302 milestones, 298–300, 299f molecular subtype, 303 patterns, 301–306 as therapeutic target, 306–307 epigenetic complexes as mutational targets, 471 epigenetic factors in mammary gland development, 469–471 epigenetic modifications as biomarkers, 471–473 epigenetic modifiers as potential drug targets, 473–474 epigenetic regulation, 468–469 hormonal dependence, 468–469 methylated genes, 305t–306t methylation biomarkers, 165t–166t miRNA acquired genomic and epigenomic changes affecting, 596–597 biological significance, 590–592 DNA repair genes as targets of, 598–600 early breast cancer development screening, 604 genetic variants and binding sites, 592–596 as prognostic biomarkers, 600–604 as regulators of the DNA damage response, 597–598 subtypes, 468–469 INDEX Breast cancer 1, early onset (BRCA1), 305t–306t methylation, breast cancer, 302–303 miRNA, 590–591, 593t–595t, 597–600, 602–605 Breast cancer 2, early onset (BRCA2), 298, 305t–306t miRNA, 590–592, 593t–595t, 597–600, 604–605 Bromodomain 2/3/4/testis-specific (BRDT), 41 Bromodomain and extraterminal (BET), 459 prostate cancer, 459 BS genome-wide, 82t BS-PCR assays, 82t C C1orf114, 287 C5Orf4, 286 C9orf50, 169 C11orf47, 335t–341t CACNA1B, 335t–341t CAGE, 283 cAMP response element-binding protein (CREB), 419 Cancer DNA methylation, 263 DNA methylation studies by sequenom, 146–148 histone acetylation, 434 histone methylation, 434–437 histone phosphorylation, 437 histone ubiquitination, 437–438 lncRNAs, 227–229 miRNAs, 225–227 presequencing, 183–189 bile duct cancer, 189 bladder cancer, 188–189 colorectal cancer, 188 glioblastoma, 184–187 leukemia, 189 liver cancer, 188 lung cancer, 189 pancreatic cancer, 188 prostate cancer, 187–188 Cancer stem cell (CSC), and miRNA, 616t–619t, 623 Cancers of unknown primary origin, 267 Cancer/testis antigens (CTA), 93–94 CAPG, 165t–166t 5-Carboxylcytosine (5caC), 39, 69, 277, 401–403 Alzheimer’s disease, 404–406, 405t and CNS, 403–404 FXTAS, 409 CASP8, 165t–166t Castration resistance, 276 Castration-resistant prostate cancer, 276, 280–281, 455–459, 456t–457t, 561t, 562, 564, 566t–567t, 567–568, 573t–578t CAV1, 282, 305t–306t CCL5, 335t–341t CCND2, 185t–186t, 188, 225, 281, 287–288, 305t–306t CCTC-binding factor (CTCF), 401–403 SCA7, 410 CD14, 342t–345t CD25 antigen expression, 102, 103t CD44, 287–288 CDH1, 47, 104, 184, 189, 276, 282, 304, 305t–306t, 335t–341t CDH3, 305t–306t CDH11, 366t–367t CDH13, 47, 104, 168, 266t, 305t–306t CDK6, 225, 227 CDKL5 (Xp22), 51t–52t CDKN1B, 276 CDKN1C, 305t–306t CDKN2A, 157, 281, 305t–306t CDKN2A/p14, 165t–166t CDKN2A/p16, 165t–166t, 183–184 CDKN2b, 227 CDKN2B methylation, 103–104, 105t–106t CDKN2B-AS1, 227 CEL, 335t–341t Cell-free DNAs, 566t–567t as biomarkers for prostate cancer, 565–569 hypermethylation, 565–568 microsatellite instability in serum/plasma, 568–569 mtDNA in serum/plasma, 569 Central nervous system (CNS) DNA methylation, 403–404 and regulated gene expression, 520 Centromeric protein A (CENP-A), 484 Centromeric region instability, 51t–52t Cervical intraepithelial neoplasia (CIN), DNA methylation, 389–390 Cervical intraepithelial neoplasia (CIN3+), 184 665 CFLAR, 335t–341t CHFR, 335t–341t ChIP–chip, 128–129 ChIP-IT® FFPE Chromatin Preparation Kit, 29 Cholangiocarcinoma (CCA) DNA methylation, 390–391 histone marker, 393 Cholinergic receptor M1 (CHRM1), 523 Chondrosarcoma, miRNAs in, 624 Chromatin, Chromatin confirmation capture (3C methods), 78–79 Chromatin immunoprecipitation (ChIP), 10, 75–77, 80–81, 82t, 120–121, 127–128, 228 analysis of HIV, 391–392 parasites, 393 Chromatin immunoprecipitation sequencing (ChIP-seq), 120–121, 122t, 127–129 biomarkers and therapeutic targets identification, 130–131 data analysis, 129–130 Huntington’s disease (HD), 524–525 lincRNAs, 217–218 workflow, 128f Chromatin interaction analysis using paired end tag sequencing (ChIA-PET), 79 Chromatin structure, 77–79 3D chromatin packaging, 78–79 accessibility assays, 77–78 applications, 79 in Huntington’s disease (HD), 521–524 Chromosomal abnormalities, 98, 98t Chromosome open reading frame 72 (C9ORF72), 406–408, 411 Chronic kidney disease (CKD), 47 Chronic myelomonocytic leukemia (CMML), 92–93, 97, 97t–98t, 99–101, 100t, 103t, 105t–106t clinical predictors, 97 Circadian clock genes, 320 Circular RNAs, 249–250 ExoCarta, 251 HMDD, 252 miRandola, 250–251 Vesilepedia, 251–252 Circular RNAs (circRNAs), 242 Circulating cell-free DNA, 573t–578t 666 Circulating miRNAs as biomarkers for prostate cancer, 559–563 diagnostic biomarkers, 559–560 hormones, 562–563 predictive biomarkers, 562 prognostic biomarkers, 560–562 in bone sarcoma, 632–633 as clinical biomarker for diabetes, 654–656 effects of drug on, 245t in soft tissue sarcomas, 632–633 Circulating mRNAs, as biomarkers for prostate cancer, 563–565 Circulating ncRNAs, classes, 242t Circulating nucleic acids, 242 Circulating-free deoxyribonucleic acid (cfDNA), breast cancer, 304 CJUN and CMYB expression, 100–101, 103t CLC, 335t–341t Clinical Laboratory Improvement Amendments (CLIA), 14 CLK2, 335t–341t Clostridial toxin-like ADPribosyltransferase (ARTC), 426 CMML patients, 97, 97t c-myc, 227, 366t–367t COL5A1, 366t–367t COL8A1, 366t–367t COL9A3, 366t–367t COL15A1, 335t–341t COL19A1, 366t–367t Collagen alpha-2(VI) chain (COL6A2), 184 Collision-induced dissociation (CID), 199–200 Colorectal cancer (CRC), 159t–160t CIMP positive, 157 digital MethyLight, 169 methylation biomarkers, 165t–166t Multiplex MethyLight, 164 prosequencing, 188 Septin (SEPT9) methylation, 157 Combined bisulfite restriction analysis (COBRA), 71–72 Community advisory boards (CABs), 25–27 Compensatory anti-inflammatory response syndrome (CARS), 500–502, 501f, 507, 510 INDEX Competing endogenous RNA (ceRNA), 248–250 miRNAs, 226t ncRNAs, 230 Complementary deoxyribonucleic acid (cDNA), breast cancer, 298–300 Complex diseases Alzheimer’s disease, 49–50 MetS and diabetes, 47–49 Complex I of mtDNA, 566t–567t Composite measures, of DNA methylation, 70 Comprehensive high-throughput array for relative methylation (CHARM), 122t ConfirmMDx, 571t Copy number variation (CNV), 50 Corticotropin-releasing hormone (CRH), 363, 366t–367t Corticotropin-releasing hormonebinding protein (CRHBP), 363, 366t–367t COX2, 458 CpG island methylator phenotype (CIMP), 157 CpG islands, 260, 262, 300–301, 315–316, 315f and 5mC, 260 hypermethylation of, 263 CpG shores, 3–4 CpG sites, 3–4, 106–107, 106f, 108f, 109–110, 139, 144, 151, 156–157, 159t–160t, 162–163, 168, 315–316, 315f, 319–321, 353–354, 360–362 DNA hydroxymethylation, 169 DNA methylation, 138, 140, 144, 148, 150, 161 MethyLight, 163 unmethylated, 158 methylated, 139, 146 CREB-binding protein (CBP), 522–523, 522f CREB-binding protein mutations, 51t–52t CREB-binding protein mutations (CREBBP), 51t–52t, 55 CREBBP (16p13.3), 51t–52t CRHBP, 366t–367t CRIP1, 283, 335t–341t CST6, 305t–306t CTLA4, 335t–341t Curcumin, 456t–457t Cutaneous T cell lymphoma (CTCL), 434 CXADR, 321–322, 322t CXorf40A, 335t–341t CYBRD1, 335t–341t 2,3-Cyclic nucleotide phosphodiesterase (CNP), 150 Cyclin-dependent kinase inhibitor (CDKN1A), 46–47 Cyclin-dependent kinase inhibitor 2A gene (p16INK4A/CDKN2A), 304, 305t–306t Cyclin-dependent kinase like (CDKL5), 51t–52t, 54 CYP1B1, 283 CYP11A1, 366t–367t CYP19, 366t–367t CYP26A1, 335t–341t CYP27B1, 366t–367t Cystathionine β-synthase (CBS), 42–44, 43f, 356–357, 358f Cystic fibrosis transmembrane conductance regulator (CFTR), 185t–186t, 188 Cytidine deaminase (CDA), 96, 96t Cytidine kinase (CK), 94f, 95 Cytosine, 150–151 methylated, 139 nonmethylated, 139 unmethylated, 139–140 Cytosine–guanine See CpG sites D Damage-associated molecular pattern (DAMP), 393–394 DCC, 305t–306t DCR2 (TNFRSF10D), 184 Death-associated protein (DAXX), 452t, 454 Death-associated protein kinase (DAPK), 47, 165t–166t, 185t–186t, 189, 305t–306t Decitabine, cellular uptake and intracellular processing of, 94f Dedifferentiated liposarcoma (DDLS) definition, 628 miRNA, 625t–627t, 628 DEFA1, 335t–341t Dendritic cells (DC), and SIRS, 502 Deoxycytidine kinase (DCK), 93f–94f, 95–96, 96t Deoxyribonuclease (DNase), 77–78 INDEX Dermatophagoides farina (Df), 335t–341t Dermatophagoides pteronyssinus (Dp), 335t–341t Deubiquitinating enzyme (DUB), 425, 438 Developmental Origins of Health and Disease (DOHaD), 352, 356–360 3-Dezaneplanocin-A (DZNeP), 458 DGKZ, 286, 321–322, 322t Diabetes mellitus, 644–645 adipose tissue, miRNA, 650–651 circulating miRNAs, 654–656 liver, miRNA in, 648–649 MetS and, 47–49 miR-1, 652 miR-7, 648 miR-29a/b/c, 651 miR-33a/33b, 650 miR-103, 651 miR-122, 649–650 miR-133, 652 miR-143, 651 miR-365, 651 miR-375, 647–648 presequencing, 180–181 skeletal muscle, miRNA in, 651–652 Dichlorodiphenyltrichloroethane (DDT), 182 Dichlorodiphenyldichloroethylene (DDE), 182 Differential methylation hybridization (DMH), 121–123 Differentially methylated positions (DMPs), 360 Differentially methylated regions (DMRs), 364, 369f Digital MethyLight, 159t–160t, 168–169 for cancers, 165t–166t Dihydropyrimidinase-like (DPYSL2), 150 Diisocyanate-induced occupational asthma (DA), 335t–341t Dimethylation on lysine of histone (H3K4me2), 452t, 470f Dinucleotide triphosphate (dNTP), 142–143 Dipeptidyl peptidase-4 (DPP4), let-7, 652–653, 655t Diphtheria toxinlike ADPribosyltransferase (ARTD), 426 Disseminated intravascular coagulation (DIC), 499–500, 505, 506t, 509–510 DLC-1, 386–387 D-loop region, 566t–567t DNA conversion, using bisulfite, 139–140 DNA damage response (DDR), 530, 591–592, 597–599, 604–605 DNA demethylation, 261 DNA hypomethylation, 51t–52t DNA methylation, 3–5, 4f, 39–40, 69–74, 103–104, 120–121, 138, 260–261, 300–306, 353 affinity purification, 139 Alzheimer’s disease, 404–406 amyotrophic lateral sclerosis, 406–408 as an epigenetic marker, 156 applications, 73–74 arrays, 123–125 bacteria, 390 BCL2L10 methylation, 104 breast cancer, 298–300 biomarkers, 304–306 patterns, 301–306 therapeutic target, 306–307 in cancer, 263 CDKN2B methylation, 103–104 in clinical application, future of, 169–170 CNS, 403–404 composite measures of, 70 conversion of DNA using bisulfite, 139–140 in diagnostics, 156–157 DNA methyltransferases, 300–301 during embryonic development, 355–356, 355f in enhancers and super-enhancers, 262–263 environmental and lifestyle factors, 316–317 fragile X-associated tremor/ataxia syndrome, 409 Friedreich ataxia, 409–410 genome-wide assays, 72–73 global methylation, 105 monitoring in clinical samples, 110–111 Huntington’s disease, 408–409, 524–525 667 intergenic hypomethylated regions (HMRS), 263 LCPUFA and, 359–360 in lung cancer, 263–269, 264f biomarkers for drug response prediction, 268 biomarkers with diagnostic values, 265–267 biomarkers with prognostic values, 267–268 epigenetic therapy, 268–269 macronutrients and, 357 methods, 138–145 methylation signatures, 104 methylation-sensitive restriction enzyme digestion, 139 micronutrients and, 359 and miRNAs, 264–265 nutritional regulation of, 356–357 and obesity, 317–318 biomarkers, 319–322 overview, 314–316 parasites, 390–391 Parkinson’s disease, 406 programmed death-1 (PD-1), 104 in promoters, 262 restriction enzyme digestion for analyzing, 158 restriction enzyme-based assays, 70–71 Sequenom EpiTYPER system, 140–145 Sequenom studies Alzheimer’s disease, 149–150 cancer, 146–148 obesity, 148–149 spinocerebellar ataxia 7, 410 traditional bisulfite conversionbased assays, 71–72 viruses, 388–390 DNA methylation biomarkers, single-locus, monitoring, 106–110 clinical sample analysis, challenges of, 108 MIP-based methods, 110 MPS-based methods, 109–110 PCR primer design strategies, 108–109 PCR-based methods development of, 107 and sodium bisulfite treatment, 107 668 DNA methylation canyon (DMC), 278 DNA methylation valley (DMV), 278 DNA methylome, reading, 126–131 DNA methyltransferase (DNMT), 92–94, 260–261, 277–278, 300–301, 353, 355, 358f, 363, 378f, 379t–384t, 388, 401–403, 402f, 411–412 AD, 404–406, 405t ALS, 524, 530–531, 532t breast cancer, 298, 300–302, 307 and CNS, 403–404 Huntington’s disease (HD), 524 miRNAs, 222, 223f miRNAs and, 265 PD, 405t, 406 DNA (cytosine-5-)-methyltransferase (DNMT1), 39, 277–278, 403–404 DNA (cytosine-5-)-methyltransferase (DNMT2), 277–278 DNA (cytosine-5-)-methyltransferase alpha/beta (DNMT3A and 3B), 39, 48, 51t–52t, 227, 229–230, 277–278, 403–404 DNA methyltransferase 3B, 51t–52t DNA methyltransferase enzymes (DNMTs), obesity, 316, 319 DNA methyltransferase family, control de novo and maintenance DNA methylation (DNMT), 177, 182–183 DNA methyltransferase inhibitor (DNMTi), 92, 99 cellular pathways affected by, 95f clinical predictors, 96–97, 96t in CMML patients, 97, 97t French Prognostic Scoring System for MDS patients, 96–97, 97t platelet doubling time, 97, 97t in hematological malignancies treatment, 92–93 mechanism of action, 93–94, 93f molecular predictors, 97t, 98–105 chromosomal abnormalities, 98, 98t DNA methylation, 103–104, 103t gene expression, 100–102, 103t point mutation, 99–100, 100t overall survival, 96 resistance, and pharmacological factors, 95–96 INDEX DNA methyltransferases and 3b (DNMT1/3b), 121–123 DNA(cytosine-5-)methyltransferases (DNMTs), 3, 4f DNMT3L, 277–278 Docetaxel, 456t–457t Docosahexaenoic acid (DHA), 359–360 Dot blot assay, methylation-specific, 159t–160t Ductal carcinoma in situ (DCIS), 600–601 E E1A-binding protein mutations, 51t–52t E2F3, 227 Eastern Cooperative Oncology Group (ECOG), 96–97 E-cadherin See CDH1 EDNRB, 281 EFEMP, 458 EFEMP1, 286 EFNA3, 335t–341t Electron capture dissociation (ECD), 199–200 Electron transfer dissociation (ETD), 199–200 Electronic health record (EHR), 25–26 Electrospray ionization (ESI), 199–200 Embryonal rhabdomyosarcoma (ERMS), miRNA, 629–630 Embryonic development, DNA methylation during, 355–356 EMD, 335t–341t Encyclopedia of DNA Elements (ENCODE), 128–130 Endothelin (EDN3), 184 Enhancer of zeste homolog (EZH2), 51t–52t, 451, 452t, 453, 458, 468–471, 472t, 473–474 Enhancers and super-enhancers, DNA methylation in, 262–263 Environmental and toxicological biomarkers, presequencing, 182–183 Environmental specimen biobanks, 30–31 Enzyme-linked immunosorbent assay (ELISA), 70, 75–76, 82t, 452t, 453, 528–529 prostate cancer, 568, 573t–578t EOMES, 164, 165t–166t EP300 (22q13.2), 51t–52t EPB49, 335t–341t Epidermal growth factor receptor (EGFR), 269 Epigenetic biomarkers and biobanks, 27–30 and clinical laboratory, 10–11, 57 recent advances, overview, 11–12 and in vitro diagnostics, 8–10 Epigenetic drugs, for metabolic diseases, 323 Epigenetic mechanisms, 353–354 complex diseases Alzheimer’s disease, 49–50 MetS and diabetes, 47–49 DNA methylation, 3–5, 353 in gene regulation, overview, 38–39 DNA methylation, 39–40 histone modification, 40–41 microRNAs, 41–42 noncoding RNAs, 41–42 histone modifications, 353 histone PTMs and histone variants, 5–7 ncRNAs, 353–354 noncoding RNAs, 7–8 regulation of gene expression by, 354 Epigenetic modifications, in ALS, 528–530 Epigenetic pharmaceutical drug development, 2–3 Epigenetic-associated regulatory elements, 261–263 Epigenetic-based biomarkers, knowledge about, 13 Epigenetics, 352–353 and biobanks, 27–31 definitions, implementation in clinical laboratories, 12–14 metabolism, 38 and metabolism intersection lifestyle, nutrition, and physical activity, 45–47 methionine cycle and transsulfuration pathway, 42–44 tricarboxylic acid cycle, 44–45 perspectives in diagnosis, 14–15 in physiological conditions, 260 in rare diseases Friedreich’s ataxia, 56–57 ICF syndrome, 50–53 Rett syndrome, 53–54 669 INDEX Rubinstein–Taybi syndrome, 54–55 Weaver Syndrome 2, 55–56 Epigenome-wide association studies (EWAS), 11 obesity, 315f, 321–322, 322t Epigenomic studies, and biobanks, 30–31 Epithelial–mesenchymal transition (EMT), 304–306, 544–545, 591–592, 593t–595t, 601–604 Epithelioid sarcoma, miRNAs in, 622 Epstein–Barr virus (EBV), DNA methylation, 390 ERG-associated protein with SET domain (ESET), 522f, 523 ERP27, 335t–341t Erythropoietin (EPO), 184 Estrogen receptor (ER), 130–131, 305t–306t breast cancer, 302–303, 305t–306t Estrogen receptor (ESR1), 46–47, 280–281 breast cancer, 302, 306–307 Estrogen receptor (ESR2), 281 Estrogen receptor a (ERa), 104 breast cancer, 468–469, 470f, 471–472, 473f Ethical issues, 14 in biobanks, 22–24 European Medicines Agency (EMA), 3, 8–9, 23, 92–93 EVX1, 266t Ewing sarcoma, miRNAs in, 622–624 Excretory-secretory products, 393 ExoCarta, 251 Expanded polyQ-Huntingtin (ExpHtt), 432 Exposomes, 30–31 EZH2, 227, 452t mutations in, 51t–52t EZH2 (7q36.1), 51t–52t F Facial anomalies syndrome (ICF1), 51t–52t FAM19A4, 335t–341t FAM112A, 335t–341t FAM181A, 335t–341t Familial ALS (FALS), 526–528, 530 Fanconi anemia (FA), 597 Fas expression, 102, 103t FBLIM1, 321–322, 322t FCER1G, 342t–345t FCER2, 335t–341t Fetal growth restricted (FGR), 360 Fetal-occult blood test (FOBT), 157 FFPE tissue, MS analysis of hPTMs, 207 Fibroblast growth factor (FGF2), 46–47 miR panel, 573t–578t 5hmC immunoprecipitation with high-throughput sequencing (hMeDIP-seq), 125 5mC immunoprecipitation (MeDIP), 125–126 with DNA microarrays, 125–126 5mC immunoprecipitation with high-throughput sequencing (MeDIP-seq), 122t, 125 5mC immunoprecipitation with hybridization with array (MeDIP-chip), 125–126 5mC oxidative derivatives, quantification of, 125 Flavin adenine dinucleotide (FAD), 319, 424 Flaviviridae, 386–387 Flow cytometric scoring system (FCSS), 102 Fluorescence in situ hybridization (FISH), 162 Fluorescence recovery after photobleaching (FRAP) assays, 487–488 Fms-like tyrosine kinase-1 (FLT-1), 362–363, 366t–367t FNDC4, 321–322, 322t Forced expiratory volume in s (FEV1), 335t–341t Forkhead box P3 (FOXP3), 102, 342t–345t Forkhead box protein A1 (FOXA1), 469 Formaldehyde-assisted isolation of regulatory elements sequencing (FAIRE-seq), 78 Formalin-fixed paraffin-embedded tissues (FFPE), 2–3, 9, 28–29, 108, 110–111, 158, 164, 167, 386–387, 389 chromatin from, 29 miRNAs from, 28–29 protein extraction from, 29 PTM-directed analysis in, 29 5-Formylcytosine (5fC), 39, 69, 72–73, 277, 401–403 Alzheimer’s disease, 404–406, 405t and CNS, 403–404 FXTAS, 409 FOX2A, 305t–306t Fractional exhaled nitric oxide (FeNO), 335t–341t Fragile X mental retardation protein (FMRP), 161 Fragile X mental retardation-1 (FMR1), 161, 401–403, 409 Fragile X syndrome (FXS), 401–403, 409 MSP technique, 161 Fragile X-associated tremor/ataxia syndrome (FXTAS), DNA methylation, 409 Frataxin gene (FXN), 51t–52t, 56–57, 409–411, 433 Frataxin protein, 51t–52t Free fatty acid receptor (FFAR), 180–181 French Prognostic Scoring System (FPSS), 96–97, 97t Friedreich’s ataxia (FRDA), 50, 51t–52t, 56–57, 433 DNA methylation, 409–410 epigenetic interventions, 433 Frontotemporal dementia (FTD), 528–530 Frozen tissue, MS analysis of hPTMs, 205–206 Fumarate hydrolase (FH), 44–45 Functional foods, for metabolic diseases, 323 Fused in sarcoma (FUS), 406–408 G GABRE, 288 GAS6, 287 GATA1, 335t–341t GATA3, 342t–345t GATA4, 388–389 Gene activation, miRNA in, 542 Gene expression, 103t aberrant myeloid progenitors, 102 BCL-2 family proteins, 101–102 CD25 antigen expression, 102 CJUN and CMYB expression, 100–101 Fas expression, 102 microRNA-29b (miR-29b), 101 MLL5 expression, 101 phosphoinositide-phospholipase C ß1, 101 regulatory T cells (Tregs), 102 Gene methylation panel, 573t–578t Gene of interest (GOI), 161, 163 Gene silencing, miRNA in, 542 670 Genome sequencing, 126–131 ChIP-seq experiments, 127–129 biomarkers and therapeutic targets identification, 130–131 data analysis, 129–130 third-generation sequencing, 127 Genome-wide analysis (GWA), infertility, 491–492 Genome-wide association studies (GWAS), 47, 49 allergic diseases, 333, 346–347 ncRNAs, 228–229 Genome-wide DNA methylation assays, 72–73 Gestational diabetes, 365 Gestational diabetes mellitus (GDM), 644–645 Glioblastoma methylation biomarkers, 165t–166t prosequencing, 184–187 Global methylation, 105, 105t–106t monitoring in clinical samples, 110–111 Glucagon-like polypeptide (GLP-1), 652 Glucocorticoid receptor (GR), 364–365, 366t–367t macronutrients, 357 GLUT4, 321–322, 322t Glutathione S-transferase P1 (GSTP1), 157, 162, 165t–166t, 185t–186t, 188, 282, 285–288 prostate cancer, 565–567, 566t–567t, 570–580, 573t–578t Glutathione synthesis (GSH) pathway, 43f GNAS, 366t–367t Government regulations, 14 GPC3, 305t–306t GPR55, 335t–341t GRFRA1, 266t GRIA3, 388–389 Growth arrest and DNA-damageinducible protein 45 (Gadd45), 261 GSK3B, 366t–367t GSTM5, 321–322, 322t GSTP1 promoter hypermethylation (GPH), 565–568, 566t–567t, 573t–578t Guanine, 138 Guanine nucleotide-binding protein alpha subunit (GNAS), 365, 366t–367t Guanine–cytosine (GC), 300 INDEX H H1foo, 481t H1t, 481t H1t phosphorylation, 481t H2A.Bbd, 481t, 487–488 H2A.X, 481–484, 481t H2A.X phosphorylation, 481t H2BFW, 481t, 487 H3, 506t H3.3, 481t, 484 H3Ac, 452t H3K9me2, 452t H3K18Ac, 452t H3K27me3, 51t–52t, 452t H3t, 481t H4, 506t H4 acetylation, 481t H4K20me1, 452t H4K20me2, 452t H4K20me3, 452t H19, 47, 80, 157, 217 HAPLN3, 287 HBEGF, 282 HDAC4, 366t–367t HDAC6, 335t–341t HDACi 4b, 527t Helicobacter pylori, 377, 387 Hemoglobin A1c test (HbA1c test), 654, 655t Hepadnaviridae, 386 Heparan sulfate glucosamine 3-O-sulfotransferase (HS3ST2), 184 Hepatitis B virus (HBV), 379t–385t miRNAs, 386 Hepatitis C virus (HCV), 379t–385t miRNAs, 386–387 Hepatocellular carcinoma, 379t–385t miRNAs, 386–387 presequencing, 188 Hepatocellular carcinoma cells (HCC), 225 miRNAs, 226t HER2 (ERBB2), breast cancer, 590–591, 593t–595t, 596, 601 Herpes simplex virus, 332–333 Herpes simplex virus type (HSV-1), 379t–384t histone marks, 391–392 HES5, 286 Heterochromatin protein (HP1), 223, 230, 426 cancer, 437 Friedreich’s ataxia, 433 Heterogeneous methylation, 106f Heterogeneous nuclear ribonucleoproteins (hnRNPs), 530 HIF3A, 321, 322t High resolution melting (HRM), 110–111, 111t High resolution (HR) MS, 199–201 High-density lipoprotein (HDL), 562 miRNA, 650 miRNAs, 245 High-performance liquid chromatography (HPLC), 70, 82t, 110–111, 197–199, 201–202 High-resolution melting analysis, 168 for cancers, 165t–166t High-temperature requirement A serine peptidase (HtrA1), 306 HILS, 481t HIST1H4F, 266t Histone 2A lysine 119 monoubiquitylation (H2AK119ubi), 524 Histone 2B lysine 120 monoubiquitylation (H2BK120ubi), 524 histone lysine trimethylation (H3K4me3), 452t, 522f, 523–524 Histone lysine (H3K9), 522f, 523–526, 529–530 Histone lysine trimethylation (H3K9me3), 522f, 523 Histone lysine 27 (H3K27), 529–530 Histone lysines and 14 (H3K9/ K14), 530 Histone arginine (H4R3), 530 Histone acetyltransferase (HAT), 44f, 48, 55, 222, 418–419, 420t–421t, 448–449, 521–523 cancer, 434 Histone acetyltransferases inhibitor (HATi), 458 Histone deacetylase (HDAC), 44f, 45–47, 49–50, 54, 418–419, 428–429, 448–450, 473–474 AD, 431 cancer, 434 Friedreich’s ataxia, 433 groups, 419–421, 420t–421t PD, 432 psoriasis, 430 systematic sclerosis, 429 Histone deacetylase inhibitor (HDACi), 92, 99, 101, 104, 307, 454–458 ALS, 531 Huntington’s disease (HD), 523, 525–526, 527t 671 INDEX Histone demethylase (HDM), 222, 448–450, 521–524 Histone demethylases inhibitor (HDMi), 458–459 Histone lysine methyltransferase (HKMT), 101 Histone marks, 468 Histone methyltransferase (HMT), 42, 43f, 50, 223f–224f, 448–450, 521–524 Histone methyltransferases inhibitor (HMTi), 458–459 Histone modifications, 40–41, 74–77, 353, 418, 448–450 applications, 76–77 assays that provide location information, 75–76 autoimmune diseases, 428–430 HIV, 430 psoriasis, 430 rheumatoid arthritis, 428–429 systemic lupus erythematosus, 429 systemic sclerosis, 429 Type I diabetes, 429–430 bacteria, 392–393 cancer, 433–438 and functional significance, 418 global levels of, 74–75 Huntington’s disease (HD), 521–524 neurodegenerative diseases AD, 431 Friedreich’s ataxia, 433 Huntington’s disease (HD), 432–433 PD, 431–432 parasites, 393–394 in prostate cancer, 450–451 viruses, 391–392 Histone PTMs, 197–203 effects on speratogenesis, 489–490 and histone variants, 5–7, 6f mass spectrometry analysis clinical sample, 205–208 data analysis, 201 epigenetic biomarker discovery, 203–205 histone isolation and sample preparation, 197–199, 206f histone variants characterization, 203 hPTMs quantitation, 201–203 methods, 199–201 workflow, 198f Histone variants, as biomarkers for infertility, 491–492 Histones, 418 acetylation, 418–421, 449–450 and cancer, 434 ADP-ribosylation, 426 methylation, 421–424, 450 and cancer, 434–437 phosphorylation, 425 and cancer, 437 SUMOylation, 426 ubiquitination, 425 and cancer, 437–438 HIV, histone modifications, 430 HLA-DMA, 335t–341t HLADPA1, 165t–166t HMDD, 252 Homocysteine (HCys), 42–44 Homologous recombination (HR), 597–600 HormomiRs, 562–563 Hormone receptor methylation, breast cancer, 302 Hospital-integrated biobanks (HIB), 20, 28–29 HOTAIR, 217, 222–223, 226t, 228 HOXA5, 305t–306t HOXA9, 164, 165t–166t, 266t HOXA9_1, 165t–166t HOXA9_2, 165t–166t HOXC6, 286 HOXD3, 164, 165t–166t, 286, 288 HOXD11, 305t–306t HPRT, 315–316 HPSE, 283 HSD3B1, 366t–367t HTT, 408–409, 520 Human epidermal growth factor receptor (HER2/Her-2 (ErbB-2)), 298, 303 Human nucleoside transporters (hNTs), 95 Human papillomavirus (HPV), 379t–384t DNA methylation, 388–389 Human somatic and gametogenesis histone variants, 480f Huntingtin (Htt), 432 Huntington’s disease (HD), 432–433, 520–521 chromatin remodeling, 521–524 DNA methylation, 408–409 DNA methylation, 524–525 epigenetic features modulation for therapy, 525–526 epigenetic interventions, 433 histone modifications, 521–524 PTMs of histones, 432–433 Hydrophilic interaction liquid chromatography (HILIC), 197, 199 Hydrophobic interaction chromatography (HiC), 78–79 5-Hydroxymethylation, 169 5-Hydroxymethylcytosine (5hmC), 39–40, 49, 56, 69, 72–73, 107, 110, 156, 277, 401–403, 402f, 411 ALS, 406–408 Alzheimer’s disease, 404–406, 405t and CNS, 403–404 FRDA, 409–410 FXTAS, 409 Huntington’s disease (HD), 408–409, 522f, 525 obesity, 316 Parkinson’s disease, 406 5-Hydroxytryptamine receptor 2A (HTR2A), 321–322, 322t Hypermethylated in cancer (HIC1), 184, 305t–306t Hypermethylation, in prostate cancer, 280–282 Hypomethylation, in prostate cancer, 282–285 I ICF syndrome, 39, 50–53, 58 miRNAs, 226t ncRNAs, 229–230 IDH1/2 mutations, 99 IFNG, 165t–166t, 342t–345t IGF2/H19, 320 IGF-binding protein (IGFBP3), 165t–166t, 168, 266t, 365–368, 366t–367t IKBKG, 335t–341t IKZF3, 342t–345t IL-1R2, 335t–341t IL-2, 342t–345t IL-2RA, 335t–341t IL-4, 335t–345t IL-4R, 342t–345t IL-5RA, 335t–341t IL-6, 342t–345t IL-10, 335t–341t IL-13, 342t–345t Immunodeficiency, 51t–52t Immunoglobulin E (IgE), 332–333 Immunohistochemistry (IHC), 82t prostate cancer, 452t, 453–454 672 Impaired glucose tolerance (IGT), 365–368 Imprinting control regions (ICR), 181 Inclusion bodies (IBs), and ALS, 526–528 Infertility and prenatal diagnostic, presequencing, 181–182 Infinium 450K, 122t Information management system (IMS), biobanks with, 26 Informed consent (IC), 22–23 and governance, 25 Ingenuity pathway analysis (IPA), 365–368 iNOS (NOS2), 342t–345t Institutional review board (IRB), 25–26 Insulin receptor (INSR), 181–182 Insulin-like growth factor (IGF1), 181–182 Insulin-like growth factor receptor (IGF1R), 181–182, 365–368, 366t–367t Insulin-like growth factor (IGF2), 47–48, 157, 285, 366t–367t DNA methylation of, 144–145, 148 Insulin-like growth factor receptor (IGF2R), 47 Insulin-like growth factor binding protein (IGFBP3), 181–182 Intensive care unit (ICU) stay, sepsis during, 499 Inter-alpha-trypsin inhibitor heavy chain family, member (ITIH5), 185t–186t, 188–189 Intergenic hypomethylated regions (HMRS), 263 International Prognostic Scoring System (IPSS), 92–93, 96–97, 96t, 98t, 100t, 103t Intratumoral androgen biosynthesis, 276 Intrauterine growth restriction (IUGR), 361–362 In vitro diagnostics (IVD) market, 9–10 Ion exchange chromatography (SCX), 197 IRAK3, 165t–166t, 168 IRS1, 321–322, 322t IRX4, 388–389 Isocitrate dehydrogenase (IDH), 44–45 INDEX Isolated chromosome abnormalities, 98t ITGA2B, 335t–341t J Jumonji C (JMJC), 422t–424t, 424, 450 K KCNN3, 335t–341t KCNQ1, 321–322, 322t KCNQ1OT1, 226t KCNQ4, 335t–341t KCSN2, 321–322, 322t KEL, 335t–341t Kinase domain receptor (KDR), 362–363, 366t–367t KLF1, 335t–341t KLF8, 287 KLK10, 164 KRAS, 225 L L1-MET, 165t–166t, 168 L2HGDH, 335t–341t LAMA3, 282, 305t–306t LAMB3, 305t–306t LAMC2, 305t–306t Large cell carcinoma, 263–264 Latent-associated transcript (LAT), 379t–384t, 391–392 Late-onset Alzheimer’s disease (LOAD), 149–150 LDLRAP1, 305t–306t Leiomyosarcoma (LMS), miRNAs in, 625t–627t, 628, 631–632 LEP, 320 Leptin, 366t–367t Let-7i, 573t–578t Leucine-rich repeat kinase (LRRK2), 406 Leukemia, prosequencing, 189 LHX2, 229–230 Lifestyle, 45–47 linc-DMRT2, 226t lincRNA-BC4, 226t lincRNA-BC5, 226t lincRNA-BC8, 226t linc-TP53I13, 226t Linoleic acid (LA), 359 Lipopolysaccharide (LPS), and SIRS, 502, 502t Liposarcoma, miRNAs in, 628–629 Liquid chromatography-mass spectrometry (LC-MS), 197, 199–200, 205 Liver cancer, prosequencing, 188 LMX1B, 321–322, 322t lncRNAs in cancer, 227–229 epigenome and, 222–223 LOC283487, 335t–341t Locked nucleic acid (LNA), 109–110, 111t, 653 Locus-specific oligo (LSO), 123–124 Long intergenic noncoding RNAs (lincRNAs), 216–218, 219f, 220–221, 224f, 228–229 Long interspersed elements (LINE), 178–184 Long interspersed elements (LINE-1), 105–107, 110–111, 163–164, 282–283 Long ncRNAs, 80–81 Long noncoding RNA (lncRNA), 41–42, 79, 156, 169, 216–217, 220–221, 240, 242t, 245–249, 245t associated diseases, 226t obesity, 314–316, 315f Long terminal repeat (LTR), 430 Long-chain polyunsaturated fatty acids (LCPUFA), 357, 358f and DNA methylation, 359–360 Loss of heterogeneity (LOH), of tumor suppressor, 550 LPCAT2, 335t–341t LRP1B, 321–322, 322t LRRC8C, 335t–341t LSD1, 452t LTB4R, 335t–341t Luminometric methylation assay (LUMA), 110–111 obesity, 322t Lung adenocarcinoma, 263–264 Lung cancer, 543 diagnosis, 545 miRNA signatures, 545–547 DNA methylation, 263–269 biomarkers for drug response prediction, 268 biomarkers with diagnostic values, 265–267 biomarkers with prognostic values, 267–268 epigenetic therapy, 268–269 ... of Epigenetics in Diagnostics 14 Epigenetic Biomarkers and In Vitro Diagnostics 8 List of Abbreviations 15 References15 Epigenetic Biomarkers and the Clinical Laboratory10 Epigenetic Biomarkers. .. Specifically, epigenetic biomarkers are coevolving and have reached a critical point 10 1.  EPIGENETIC BIOMARKERS AND DIAGNOSTICS FIGURE 3  Number of clinical trials using epigenetic drugs and biomarkers. .. technologies and their potential in clinical diagnostics it is important to validate new technologies and epigenetic biomarkers for diagnostics and prognostics and to provide professional standards and