The Epigenome: Molecular Hide and Seek. Stephan Beck and Alexander Olek, editors. Weinheim, Germany: Wiley-VCH GmbH Co. KGaA, 2003, 188 pp., $35.00, softcover. ISBN 3-527-30494-0.
Bibliographic record
Abstract
The Epigenome, edited by Beck and Olek with contributions from leading experts in DNA methylation, provides an excellent introduction to the unrealized potential of the fifth base in DNA—5-methyl-cytosine—for understanding and diagnosis of human disease. The mapping of the human genome generated unprecedented excitement and anticipation that understanding of the molecular basis of disease will be revolutionized and that a new world of diagnostic predictors of human disease will be unraveled. However, many are unaware of the fact that the published map of the human genome lacks the pattern of distribution of the fifth base. The fifth base of DNA is generated after the genetic information is replicated. Methyl groups are added to the fifth position on cytosines residing at the dinucleotide sequence CpG by an enzymatic process after DNA replication. What makes the fifth base unique is that it is distributed in specific patterns in different cell types; it thus confers on each cell in the body its unique identity, whereas the DNA sequence is identical (with a few exceptions) in all tissues. DNA methylation is tightly linked to other elements of the chromatin structure that regulate gene expression programs. Methylated cytosines in promoter regions mark silenced genes. However, whereas all of the components involved in gene regulation are removed when DNA is extracted from tissues, the methylation pattern, which is part of the covalent structure of the genome, remains. Thus, the methylation pattern of a genome is a stable image of the functional living genome and has diagnostic value long after other markers of gene expression status are lost and can be analyzed in old specimens. A methylation map of a specific tissue or a pathologic specimen reflects the profile of gene expression of this tissue. Differing from the rigid genome, which changes rarely and irreversibly by mutagenic events, the DNA methylation pattern is dynamic and responsive to normal physiologic processes such as aging, as discussed by Issa in chapter 8, as well as nutritional and environmental factors, as discussed in chapter 7 by Fenech. Chapter 9, by Olek et al., discusses how changes in methylation might be involved in gene-silencing events in cancer, type II diabetes, and cardiovascular disease. Chapter 6 provides a good synopsis of human diseases caused by epimutations, such as Rett syndrome, ICF syndrome, and fragile X syndrome. That profiling DNA methylation patterns might provide superb diagnostic and predictive markers of stages of cancer as well as potentially other diseases is argued convincingly by Millar et al. in the first chapter, Doerfler in the second chapter, and Olek et al. in the last chapter. The first two chapters present the rationale for whole-genome and high-throughput approaches to DNA methylation, and the last chapter presents current approaches to the digitizing of DNA methylation information and to its unrealized potential in molecular diagnostics. DNA methylation analysis was limited in the past by a candidate gene approach as well as by the low throughput of available methodologies. However, this is rapidly changing, and chapter 3, by Huang et al., lucidly describes novel whole-genome approaches to mapping disease-specific DNA methylation patterns and high-throughput approaches to concurrently profiling the state of methylation of multiple CG sites. Profiling of DNA methylation patterns could potentially produce characterizing stage- and disease-specific DNA methylation signatures that could serve as superb diagnostic tools. This chapter provides a good review of these new methodologies and could serve as an excellent introduction for those interested in using DNA methylation analyses in clinical diagnosis as well as developing new diagnostic markers. Other chapters of the book discuss the biology of DNA methylation. Chapter 4, by Reik and Dean, presents the relationship between DNA methylation and other epigenomic mechanisms, and chapter 9 provides a thoughtful analysis of the possible causal relationship between gene expression and DNA methylation with heavy emphasis on parental imprinting. The main strengths of this book include its strong emphasis on the diagnostic potential of methylation analyses and its current review of different methodologies for high-throughput DNA methylation mapping. This book will be of interest to those using genomics, gene expression arrays, and profiling approaches. They might be convinced by the interesting chapters of this book to consider DNA methylation profiling in their practice. It is also of interest to graduate students, clinicians, and researchers in the fields of molecular oncology and molecular medicine and to all interested in the molecular basis of disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".