Epigenomics in Health and Disease

 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 12. Oktober 2015
  • |
  • 328 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-12-800496-8 (ISBN)
 

Epigenomics in Health and Disease discusses the next generation sequencing technologies shaping our current knowledge with regards to the role of epigenetics in normal development, aging, and disease.

It includes the consequences for diagnostics, prognostics, and disease-based therapies made possible by the study of the complete set of epigenetic modifications to the genetic material of human cells.

With coverage pertinent to both basic biology and translational research, the book will be of particular interest for medical and bioscience researchers and students seeking current translational knowledge in epigenesis and epigenomics.

Coverage includes the latest findings on epigenome-wide research in disease-based profiling, epidemiological implications, epigenome-wide epigenetic studies, the cancer epigenome, and other pervasive disease categories.

  • Presents critical reviews that provide the means for reviewing and analyzing the epigenome as a whole, also discussing its translational potential
  • Combines basic epigenomic knowledge with methodological and biostatistical topics related to technology and data analysis
  • Includes coverage of relatively new topics, including DNA methylation dynamics during development and differentiation, genome-wide histone post-translational modifications during development and differentiation, and genome-wide DNA methylation changes during aging
  • Englisch
  • USA
Elsevier Science
  • 4,33 MB
978-0-12-800496-8 (9780128004968)
0128004967 (0128004967)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Epigenomics in Health and Disease
  • Copyright Page
  • Contents
  • List of Contributors
  • Preface
  • 1 The Role of the Genetic Code in the DNA Methylation Landscape Formation
  • 1.1 Bringing the Genetic Code to Life
  • 1.2 Intrinsic Properties of DNA
  • 1.3 Sequence-Pattern-Dependent DNA Methylation Profiles
  • 1.4 DNA-Binding Factors Shaping the Epigenetic Landscape
  • 1.5 The Dynamics of Transcription-Factor-Mediated DNA hypomethylation
  • 1.6 Translational Potential of Demethylation Dynamics
  • 1.7 DNA Methylation Quantitative Trait Loci
  • 1.8 Genotype-Driven Variance in Human DNA Methylation Profiles
  • 1.9 Epigenetic Mediator Function for Human Risk Phenotype Formation
  • 1.10 Epitype Association Guiding the Interpretation of Cancer Risk Polymorphisms
  • 1.11 Closing Remarks
  • References
  • 2 DNA Methylation Microarrays
  • 2.1 Introduction
  • 2.2 Infinium DNA Methylation Technology
  • 2.2.1 BeadArray Platform
  • 2.2.2 Infinium Assay
  • 2.3 HumanMethylation450 Array Design and Performance
  • 2.3.1 Design Challenges
  • 2.3.2 Content Selection
  • 2.3.3 Gene Coverage
  • 2.3.4 CGI Coverage
  • 2.3.5 Correlation with WGBS Data
  • 2.4 HumanMethylation450 Array Advantages and Limitations
  • 2.5 DNA Methylation Data Analysis
  • 2.6 Use of Methylation Arrays in Epigenetic Studies
  • 2.6.1 DNA Methylation and Cancer
  • 2.6.2 DNA Methylation and Aging
  • 2.6.3 Epigenome-Wide Association Studies
  • 2.7 Conclusion
  • References
  • 3 Ultra-Deep Sequencing of Bisulfite-Modified DNA
  • 3.1 Introduction
  • 3.2 Sample Preparation and Study Design Considerations for Ultra-Deep Bisulfite Sequencing
  • 3.3 Technical Considerations for Ultra-Deep Bisulfite Sequencing Approaches
  • 3.3.1 Sequencing Depth
  • 3.3.2 Read Length
  • 3.3.3 Quality Controls
  • 3.3.4 Approaches to Mapping to a Reference Genome
  • 3.4 Testing for Differential Methylation
  • 3.5 Identifying Enriched or Differentially Methylated Transcription Factor-Binding Sites
  • 3.6 Data Visualization and Annotation
  • 3.7 Gene Set Enrichment Strategies
  • 3.8 Published Applications
  • 3.8.1 Using ERRBS to Study Leukemia
  • 3.8.2 WGBS in Cancers
  • 3.9 Conclusions and Future Directions
  • References
  • 4 Bioinformatics Tools in Epigenomics Studies
  • 4.1 Introduction
  • 4.2 Types of Experiments and Data Characteristics
  • 4.2.1 DNA Methylation
  • 4.2.1.1 Whole-Genome Bisulfite Sequencing
  • 4.2.1.2 Reduced-Representation Bisulfite Sequencing
  • 4.2.1.3 Methylated DNA Immunoprecipitation
  • 4.2.1.4 DNA Methylation Microarrays
  • 4.2.2 Histone Modifications
  • 4.2.2.1 Chromatin Immunoprecipitation
  • 4.3 Bioinformatics Tools
  • 4.3.1 R/Bioconductor
  • 4.3.1.1 Whole-Genome Bisulfite Sequencing
  • 4.3.1.2 MeDIP-seq
  • 4.3.1.3 Reduced-Representation Bisulfite Sequencing
  • 4.3.1.4 ChIP-chip
  • 4.3.1.5 ChIP-seq
  • 4.3.1.6 DNA Methylation
  • 4.3.1.7 Linear Models
  • 4.3.1.8 Annotation
  • 4.3.1.9 Visualization
  • 4.3.1.10 Machine Learning
  • 4.3.2 Open Bioinformatics Foundation
  • 4.3.2.1 BioPerl
  • 4.3.2.2 BioJava
  • 4.3.2.3 Biopython
  • 4.3.2.4 The European Molecular Biology Open Software Suite
  • 4.3.3 Online Tools
  • 4.3.3.1 Databases
  • 4.3.3.2 Annotation
  • 4.3.3.3 Visualization
  • 4.3.4 Software Tools
  • 4.3.4.1 Aligners
  • 4.3.4.2 Peak Finders
  • 4.3.4.3 Motif Discovery
  • 4.3.4.4 Workflow Definition
  • 4.3.4.5 Visualization
  • 4.4 Reproducible Research
  • 4.4.1 Literate Programming
  • 4.4.2 Orthogonal Validation
  • 4.4.3 Provenance
  • 4.4.4 Open Source
  • 4.5 Conclusion
  • References
  • 5 Noncoding RNA Regulation of Health and Disease
  • 5.1 Introduction
  • 5.2 Noncoding RNAs
  • 5.2.1 Long Noncoding RNAs
  • 5.2.1.1 Origin and Epigenetic Signatures
  • 5.2.2 Mechanisms of Action
  • 5.2.2.1 LncRNAs as Chromatin Modifiers
  • 5.2.2.2 LncRNAs as Decoy
  • 5.2.2.3 LncRNAs in Disease
  • 5.2.3 Enhancer RNAs
  • 5.3 Circulating Noncoding RNAs
  • 5.4 Exosomes: Biogenesis and Cancer Biomarkers
  • 5.5 Conclusion
  • References
  • 6 Genome-Wide DNA Methylation Changes During Aging
  • 6.1 Introduction
  • 6.2 Observed Differences in DNA Methylation Patterns with Aging
  • 6.3 Causes of Age-Related DNA Methylation Changes
  • 6.4 Tissue-Specific and Tissue-Independent Age-Associated DNA Methylation
  • 6.5 Implications: Age-Associated DNA Methylation and Disease Risk
  • 6.6 Environmental Factors that Influence DNA Methylation Patterns Over Time
  • 6.7 DNA Methylation as an Epigenetic/Biologic Clock
  • 6.8 Summary and Future Studies
  • References
  • 7 The Dynamics of Histone Modifications During Aging
  • 7.1 Introduction
  • 7.2 Part 1: Nucleosome Density and Aging
  • 7.3 Part 2: Histone Variants
  • 7.3.1 Histone H3
  • 7.3.2 Histone H2A
  • 7.3.3 Senescence and Histone Variants
  • 7.4 Part 3: Histone Modifications
  • 7.4.1 Activating Histone Marks
  • 7.4.1.1 H3K4me3
  • 7.4.1.2 Acetylation
  • H4K16ac
  • H3K56ac
  • 7.4.2 Repressive Histone Marks
  • 7.4.2.1 H3K27me3
  • 7.4.2.2 H3K9me3
  • 7.4.2.3 H4K20me3
  • 7.4.2.4 H3K36me3
  • 7.4.3 Senescence, DNA Damage, and Histone Modifications
  • 7.5 Progeria: Accelerated Aging Due to Nuclear Architecture Dysfunction
  • 7.6 Conclusion
  • References
  • 8 Epigenomic Studies in Epidemiology
  • 8.1 Introduction: From Classical Epidemiology to Epigenomic Epidemiology
  • 8.2 The Choice of an Appropriate Study Design
  • 8.2.1 Cohort Studies
  • 8.2.2 Birth Cohort Studies
  • 8.2.3 Cross-Sectional Studies
  • 8.2.4 Case-Control Studies
  • 8.2.5 Nested Case-Control Studies
  • 8.2.6 Twin Studies
  • 8.3 Environmental Epigenetics
  • 8.4 Validation of Results
  • 8.5 Biologic Sample Selection
  • 8.6 Methods Selection
  • 8.6.1 DNA Methylation
  • 8.6.1.1 Bisulfite-Conversion-Based Assays
  • 8.6.1.2 Enrichment-Based Methods
  • 8.6.2 Histone Modifications
  • 8.6.3 miRNAs
  • 8.7 Extracellular Nucleic Acid Markers
  • 8.8 Sample Size Selection and Statistics
  • 8.9 Confounding Factors and Effect Modifiers: Dealing with Complex Systems
  • 8.10 Conclusions and Perspectives
  • References
  • 9 The DNA Methylomes of Cancer
  • 9.1 Introduction to the Epigenetic Language
  • 9.2 Definition and Classical Roles of DNA Methylation
  • 9.3 DNA Methylation in Cancer: A Historical Perspective
  • 9.4 High-Throughput Approaches to Detect DNA Methylation Changes
  • 9.5 The Genome-Wide DNA Methylome of Cancer Cells: Overview and General Insights
  • 9.6 DNA Methylation Changes Outside Promoters Is a Major Finding in Cancer
  • 9.7 Altered DNA Methylation in Cancer Is Biased Toward Particular Chromatin States
  • 9.8 Normal Reference Samples of the Cancer Epigenome
  • 9.9 DNA Methylation Changes: Cause or Consequence of Cancer?
  • 9.10 Clinical Use of DNA Methylation in Cancer
  • 9.11 Conclusions and Future Directions
  • Acknowledgments
  • References
  • 10 Genome-Wide Epigenetic Studies in Neurologic Diseases
  • 10.1 Introduction
  • 10.2 Neuroepigenetics in the "OMICS" Era
  • 10.2.1 Alzheimer Disease
  • 10.2.2 Parkinson Disease
  • 10.2.3 Huntington Disease
  • 10.2.4 Multiple Sclerosis
  • 10.2.5 Major Psychosis
  • 10.2.6 Epilepsy
  • 10.2.7 Diabetic Neuropathy
  • 10.3 Conclusion
  • References
  • 11 Epigenetic Deregulation in Autoimmune Disease
  • 11.1 The Loss of Immune Tolerance: Breaking Bad
  • 11.2 Epigenetic Regulation
  • 11.2.1 DNA Methylation
  • 11.2.2 Histone Modifications
  • 11.3 Local and Systemic Autoimmune Disorders: Importance of the Environment
  • 11.4 Epigenetic Regulation in Autoimmune Disorders
  • 11.4.1 Systemic or Rheumatoid Autoimmune Disorders
  • 11.4.1.1 Systemic Lupus Erythematosus
  • 11.4.1.2 Rheumatoid Arthritis
  • 11.4.1.3 Systemic Sclerosis
  • 11.4.1.4 Sjögren Syndrome
  • 11.4.2 Tissue-Specific AIDs
  • 11.4.2.1 Type I Diabetes
  • 11.4.2.2 Psoriasis
  • 11.4.2.3 Inflammatory Bowel Diseases
  • 11.5 Molecular Characterization of Common Pathogenic Routes: Autoimmunity in the Twenty-First Century
  • 11.5.1 Toward Accurate Diagnosis
  • 11.5.2 Toward Individual-Based Treatment
  • 11.6 Conclusions and Perspectives
  • References
  • 12 Genome-Wide DNA and Histone Modification Studies in Metabolic Disease
  • 12.1 Introduction
  • 12.2 Type 2 Diabetes and Epigenetic Modifications
  • 12.3 Obesity and Epigenetic Modifications
  • 12.4 Do Diet and Exercise Interventions Alter the Epigenetic Pattern and Potentially Risk for Metabolic Disease?
  • 12.5 Does the Intrauterine Environment Alter the Epigenetic Pattern and Potentially Risk for Metabolic Disease?
  • 12.6 Conclusions
  • References
  • 13 Clinical Applications of Epigenomics
  • 13.1 Introduction
  • 13.2 Cancer Is an Epigenetic Disease
  • 13.2.1 Methylation as Qualitative Disease Markers: Illuminating Disease Biology and Facilitating Accurate Diagnosis
  • 13.2.1.1 Colorectal Cancer
  • 13.2.1.1.1 Gene Panels
  • 13.2.1.1.2 Global (Epigenomic) Analyses for Disease Classification
  • 13.3 Epigenomics as a Tool to Unravel Cancer Mechanisms
  • 13.4 Methylation Profiling in Other Cancers
  • 13.5 Methylation Markers as Clinical Predictors of Disease Progression and the Potential Power of Quantitative Biomarkers
  • 13.6 Genetic Mutation in Epigenetic Regulators
  • 13.7 Other Epigenetic Methylation Patterns in Cancer
  • 13.7.1 Gene Body
  • 13.7.2 Hypomethylation Regions, LOCKs, and LADs
  • 13.7.3 Shores
  • 13.8 Other Recurrent Epigenomic Patterns in Cancer (Nonmethylation)
  • 13.8.1 Genome-Scale Epigenetic Reprogramming During Epithelial-to-Mesenchymal Transition
  • 13.8.2 Polycomb Complexes and Bivalent Chromatin
  • 13.8.3 Post-Translational Histone Modifications and Nucleosomes
  • 13.8.4 RNA-Mediated Epigenetic Regulation
  • 13.8.5 miRNA
  • 13.8.6 LncRNA
  • 13.9 Epigenomics and Epigenetic Therapy
  • 13.10 Potential Applications of Epigenetic and Genetic Biomarkers with Epigenetic Therapies
  • 13.10.1 TET2 Mutations and AZA Responses
  • 13.10.2 MTIs and Solid Tumors
  • 13.11 Epigenome-Wide Association Studies for Common Human Diseases
  • 13.12 Summary and Future Directions Related to Clinical Applications of Epigenomics
  • References
  • Index
  • Back Cover

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