Computational Epigenetics and Diseases

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• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128145135
• eBook ISBN: 9780128145142
• Topic: Medicine
• Subtopic: Pharmaceutical, Biotechnology & Healthcare Industry

Computational Epigenetics and Diseases, written by leading scientists in this evolving field, provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases. In particular, the major computational tools, databases, and strategies for computational epigenetics analysis, for example, DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA, are summarized, in the context of human diseases. This book discusses bioinformatics methods for epigenetic analysis specifically applied to human conditions such as aging, atherosclerosis, diabetes mellitus, schizophrenia, bipolar disorder, Alzheimer disease, Parkinson disease, liver and autoimmune disorders, and reproductive and respiratory diseases. Additionally, different organ cancers, such as breast, lung, and colon, are discussed. This book is a valuable source for graduate students and researchers in genetics and bioinformatics, and several biomedical field members interested in applying computational epigenetics in their research. - Provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases - Summarizes the major computational tools, databases, and strategies for computational epigenetics analysis, such as DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA - Covers the major milestones and future directions of computational epigenetics in various kinds of human diseases such as aging, atherosclerosis, diabetes, heart disease, neurological disorders, cancers, blood disorders, liver diseases, reproductive diseases, respiratory diseases, autoimmune diseases, human imprinting disorders, and infectious diseases

Chapter 1

Computational Epigenetics and Disease

Loo Keat Wei Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia

Abstract

Epigenetics represents a rapidly growing and promising field for the discovery of novel disease biomarkers and understanding the pathophysiology and mechanism of complex diseases. The central objectives of writing this book are to provide theoretical insight, summarize practical implications, and draw attention to the emerging area of computational epigenetics and disease. There are 23 chapters in this book, covering the theories, frameworks, pipelines, and methods of computational epigenetics analyses and discussing the development of new software and databases and integration of these tools in analyzing noncommunicable diseases, neurological disorders, and autoimmune diseases as well as several important types of cancers. The emerging field of computational epigenetics has been moving from a hypothesis-driven approach toward a holistic data-driven modeling approach. Hence, we hope that reader gains pertinent insight after reading this book.

Keywords

Analysis; Cancers; Computational; Disease; Disorders; DNA methylation; Epigenetics; Histone modification; miRNA

Introduction

Epigenetics represents a rapidly growing and promising field for the discovery of novel disease biomarkers and understanding the pathophysiology of complex diseases. Epigenetic modifications regulate gene expression and gene activity without altering the underlying DNA sequence, but instead modifying the chromatin structure via DNA methylation, histone modifications, miRNAs, and noncoding RNAs [1]. These epigenetic mechanisms play important roles in embryonic development, transcriptional regulation, chromatin structure, genomic imprinting, and maintenance of genome integrity. While epigenetic changes are required for normal development and cell function, they can also be responsible for disease initiation and progression, especially cancer. Technological advances such as high-throughput technologies (e.g., next-generation sequencing [NGS] and microarray) and modern bioinformatics tools have enabled the profiling and mapping of large-scale epigenomic data [1]. Thus, computational approaches are required as part of the epigenomic research, especially during experimental design, data visualization, hypothesis validation, and result interpretation. Moreover, a computational modeling is required to facilitate the integration of variable data sources, including differentially methylated regions, miRNA binding, chromatin modifications, gene expressions, genetic variations, genomic regions, phenotypic characteristics, etc. Although the field of computational epigenetics is still in its infancy, the potential payoffs are enormous. It is possible to understand the mechanistic basis of human diseases by using computational approaches, even without a deep understanding of the fundamental pathophysiologic mechanisms behind the illness. By writing this book, we aim to provide theoretical insight, summarize practical implications, and draw attention to the emerging area of computational epigenetics and disease.

Computational Approaches in DNA Methylation

DNA methylation is one of the most intensely studied epigenetic modifications in humans. A methyl group is covalently added at the fifth position of cytosine (C) to form 5-methylcytosine (5mC), which is catalyzed by DNA methyltransferases (DNMTs). DNMTs are a group of enzymes that involved in the regulation of DNA methylation patterns, especially during normal development and diseases [2]. For instance, DNMT3a and DNMT3b play important roles in de novo methylation and embryonic development, while DNMT1 maintains DNA methylation patterns during gene duplication and mitosis. Methyl-CpG-binding domain proteins (MBDs) recruit the specific components of the epigenetic machinery to read and interpret the genetic information encoded by the methylated DNA. DNA methylation can be occurred in the repetitive genomic regions, including satellite DNA and parasitic elements (e.g., long interspersed transposable elements [LINES], short interspersed transposable elements [SINES], and endogenous retroviruses), which contained CpG dinucleotides for cytosine to be methylated. In humans, methylation of cytosine occurs predominantly at 5′-CpG-3′ dinucleotides, and to a lesser extent at non-CpG sites (e.g., CpA, CpT, and CpC). The CpG dinucleotides are highly concentrated in CpG islands (CGIs), which are often located in the gene promoters, near the transcription start sites, and the enhancer regions [3–5]. CGIs are typically unmethylated and may undergo dynamic methylation changes during development, differentiation, and disease [5,6]. Methylated or unmethylated CGIs could affect the gene expression patterns through regulation of chromatin structure and transcription factor binding [7]. Therefore, it is crucial to measure the differential DNA methylation in the context of CG. Numerous approaches have been proposed to study DNA methylation, including bisulfite PCR sequencing, PyroMark CpG assay, Illumina's Infinium Methylation assay, quantitative MethyLight assay, luminometric methylation assay, methylated DNA immunoprecipitation (MeDIP), MeDIP coupled with high-throughput sequencing (MeDIP-seq), methyl-CpG-binding domain coupled with high-throughput sequencing (MBD-seq), methylation-sensitive restriction enzyme sequencing (MRE-seq), reduced representation bisulfite sequencing (RRBS), and whole genome bisulfite sequencing (WGBS) [8–11].

Bisulfite sequencing remains the gold standard method for the detection of DNA methylome, due to the increasing throughput of NGS technologies and the decrement in cost. The mapping and alignment of bisulfite reads from NGS (e.g., RRBS, Agilent SureSelect Human Methyl-Seq, NimbleGen SeqCap Epi CpGiant, and whole genomic bisulfite sequencing) are more complicated than the regular sequence reads. However, this massive task can become less burdensome via computational tools, which can be filtered and quality controlled by using BALM, Bismark, BRAT-nova, BS-seeker, BSMAP, MAQ, MOABS, MACAU, MEDIPS, RMAP, PASH, TAMeBS, WALT, etc. [1]. Bisulfite treatment converts the unmethylated cytosines to uracils, and subsequently recognized as thymines in the sequencing reads. The degree of DNA methylation can be calculated from the frequency of cytosines and thymines at a specific CpG locus, by aligning the raw reads against cytosines in the reference genomic sequence [1]. In brief, wild card aligners (e.g., BSMAP, RMAP, and Pash 3.0) substitute cytosines with IUPAC letter “Y” and then align with hashing extension method, in order to match to thymines in the bisulfite reads [1]. Alternatively, three-letter aligners (e.g., Bismark, BS-seeker, and BRAT-nova) can be used to convert all cytosines to lower case “t” in both reference sequence and reads, followed by short read alignment (e.g., Bowtie or Bowtie 2) based on the three-letter code of DNA (A, G, and T) [1]. Upon obtaining the processed data, DNA methylation regions can be highly predictive based on the transcriptional activity of downstream genes, transcription start sites, transcription factor binding sites, presence or absence of TATA box, and/or RNA polymerase II occupancy on DNA [3]. Such computational predictions [3] are useful, particularly where experimental data are still lacking [11,12], which represent the first step toward quantitative analysis of DNA methylation data. When no a priori knowledge is available on a candidate gene methylation, it is more acceptable to assess the DNA methylated regions comprising a number of cytosines or known as “CpG island.” Although several statistical methods have been applied in the detection of differential DNA methylated regions [13], Fisher's exact test or paired nonparametric tests are the most common methods for comparing the methylation levels of the cytosines within the regions of interest. The false discovery rate is required to be corrected for multiple testing, based on the Benjamini–Hochberg procedure. Alternatively, probabilistic and more unbiased methods such as Hidden Markov Models (HMM) can be used for this segmentation problem. Additionally, a multivariate statistical model has been proposed for analyzing epigenetic data [14]. Such approaches are much more realistic than marginal models, in order to optimize the interpretation of the resulting epigenetic data.

Computational Approaches in Histone Modifications

In addition to DNA methylation, histone modifications are also widely studied epigenetic mechanisms. DNA is wrapped around by an octamer of histone core to form nucleosomes, and subsequently organized into chromatin. Each nucleosome is composed of two copies of four histone proteins H2A, H2B, H3, and H4. Overall structure of chromatin can be altered through the posttranslational modifications of histone N-terminal tails, such as methylation, phosphorylation, acetylation, ubiquitination, SUMOylation, ADP ribosylation, biotinylation, deamination, and proline isomerization [15]. Notably, histone acetylation, methylation, phosphorylation, and ubiquitination are involved in gene activation, whereas methylation, ubiquitination, SUMOylation, biotinylation, deamination, and proline isomerization are involved in gene repression. These histone modifications act as the docking sites for chromatin to recruit histone chaperones and nucleosome remodellers, and subsequently alter the chromatin architecture for transcriptional activity and ge...

Table of contents

• Cover image
• Title page
• Table of Contents
• Translational Epigenetics Series
• Copyright
• Contributors
• Chapter 1. Computational Epigenetics and Disease
• Chapter 2. Computational Methods for Epigenomic Analysis
• Chapter 3. Statistical Approaches for Epigenetic Data Analysis
• Chapter 4. Bioinformatics Methodology Development for the Whole Genome Bisulfite Sequencing
• Chapter 5. Data Analysis of ChIP-Seq Experiments: Common Practice and Recent Developments
• Chapter 6. Computational Tools for microRNA Target Prediction
• Chapter 7. Integrative Analysis of Epigenomics Data
• Chapter 8. Differential DNA Methylation and Network Analysis in Schizophrenia
• Chapter 9. Epigenome-Wide DNA Methylation and Histone Modification of Alzheimer's Disease
• Chapter 10. Epigenomic Reprogramming in Cardiovascular Disease
• Chapter 11. Bioinformatic and Biostatistic Methods for DNA Methylome Analysis of Obesity
• Chapter 12. Epigenomics of Diabetes Mellitus
• Chapter 13. Epigenetic Profiling in Head and Neck Cancer
• Chapter 14. Epigenome-Wide DNA Methylation Profiles in Oral Cancer
• Chapter 15. Computational Epigenetics for Breast Cancer
• Chapter 16. Integrative Epigenomics of Prostate Cancer
• Chapter 17. Network Analysis of Epigenetic Data for Bladder Cancer
• Chapter 18. Epigenome-Wide Analysis of DNA Methylation in Colorectal Cancer
• Chapter 19. Integrative Omic Analysis of Neuroblastoma
• Chapter 20. Computational Analysis of Epigenetic Modifications in Melanoma
• Chapter 21. DNA Methylome of Endometrial Cancer
• Chapter 22. Epigenetics and Epigenomics Analysis for Autoimmune Diseases
• Chapter 23. Computational Epigenetics in Lung Cancer
• Index