Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat Genet

1] Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. [2] Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel. [3] Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
Nature Genetics (Impact Factor: 29.35). 10/2012; 44(11). DOI: 10.1038/ng.2442
Source: PubMed


DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. Here, we show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modification and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical to understanding methylation dynamics in normal and cancer cells.

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Available from: Adi Zundelevich, Oct 10, 2015
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    • "Another essential feature of hTERT-immortalized cells is time-dependent acquisition of large-scale changes in gene expression (42,46). Given the stable diploid karyotype of MRC-5hTERT cells, these expression patterns must be epigenetic by nature, as they cannot be explained by aneuploidy or alterations in DNA sequence. "
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    ABSTRACT: Tumourigenic transformation of normal cells into cancer typically involves several steps resulting in acquisition of unlimited growth potential, evasion of apoptosis and non-responsiveness to growth inhibitory signals. Both genetic and epigenetic changes can contribute to cancer development and progression. Given the vast genetic heterogeneity of human cancers and difficulty to monitor cancer-initiating events in vivo, the precise relationship between acquisition of genetic mutations and the temporal progression of epigenetic alterations in transformed cells is largely unclear. Here, we use an in vitro model system to investigate the contribution of cellular immortality and oncogenic transformation of primary human cells to epigenetic reprogramming of DNA methylation and gene expression. Our data demonstrate that extension of replicative life span of the cells is sufficient to induce accumulation of DNA methylation at gene promoters and large-scale changes in gene expression in a time-dependent manner. In contrast, continuous expression of cooperating oncogenes in immortalized cells, although essential for anchorage-independent growth and evasion of apoptosis, does not affect de novo DNA methylation at promoters and induces subtle expression changes. Taken together, these observations imply that cellular immortality promotes epigenetic adaptation to highly proliferative state, whereas transforming oncogenes confer additional properties to transformed human cells.
    Nucleic Acids Research 12/2013; 42(6). DOI:10.1093/nar/gkt1351 · 9.11 Impact Factor
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    • "For instance, multiple passages of the cell lines IMR90 (human fetal lung fibroblast) and H1 (human embryonic stem cells) showed high concordance [17]. The methylome is not immutable, however, and can be modulated by the environment, different developmental stages [18], or prolonged tissue culture [19]. For example, dynamic changes in methylome occur in human embryonic stem cells, a fibroblastic differentiated derivative of the human embryonic stem cells, and neonatal fibroblasts. "
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    ABSTRACT: Background A DNA methylation signature has been characterized that distinguishes rheumatoid arthritis (RA) fibroblast like synoviocytes (FLS) from osteoarthritis (OA) FLS. The presence of epigenetic changes in long-term cultured cells suggest that rheumatoid FLS imprinting might contribute to pathogenic behavior. To understand how differentially methylated genes (DMGs) might participate in the pathogenesis of RA, we evaluated the stability of the RA signature and whether DMGs are enriched in specific pathways and ontology categories. Methods To assess the RA methylation signatures the Illumina HumanMethylation450 chip was used to compare methylation levels in RA, OA, and normal (NL) FLS at passage 3, 5, and 7. Then methylation frequencies at CpGs within the signature were compared between passages. To assess the enrichment of DMGs in specific pathways, DMGs were identified as genes that possess significantly differential methylated loci within their promoter regions. These sets of DMGs were then compared to pathway and ontology databases to establish enrichment in specific categories. Results Initial studies compared passage 3, 5, and 7 FLS from RA, OA, and NL. The patterns of differential methylation of each individual FLS line were very similar regardless of passage number. Using the most robust analysis, 20 out of 272 KEGG pathways and 43 out of 34,400 GO pathways were significantly altered for RA compared with OA and NL FLS. Most interestingly, we found that the KEGG 'Rheumatoid Arthritis' pathway was consistently the most significantly enriched with differentially methylated loci. Additional pathways involved with innate immunity (Complement and Coagulation, Toll-like Receptors, NOD-like Receptors, and Cytosolic DNA-sensing), cell adhesion (Focal Adhesion, Cell Adhesion Molecule), and cytokines (Cytokine-cytokine Receptor). Taken together, KEGG and GO pathway analysis demonstrates non-random epigenetic imprinting of RA FLS. Conclusions The DNA methylation patterns include anomalies in key genes implicated in the pathogenesis of RA and are stable for multiple cell passages. Persistent epigenetic alterations could contribute to the aggressive phenotype of RA synoviocytes and identify potential therapeutic targets that could modulate the pathogenic behavior.
    Genome Medicine 04/2013; 5(4):40. DOI:10.1186/gm444 · 5.34 Impact Factor
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    • "This 450k comparison indicates that non-random overlap can be seen with the same technology even though the samples were produced at different times by different laboratories. This is not trivial because methylation patterns can diverge over time (52,53), so an accurate method of analysis would still show some inconsistency between samples. Similarly, the data from this study also shows non-random overlap with COHCAP regions detected using BS-Seq (Figure 4B). "
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    ABSTRACT: COHCAP (City of Hope CpG Island Analysis Pipeline) is an algorithm to analyze single-nucleotide resolution DNA methylation data produced by either an Illumina methylation array or targeted bisulfite sequencing. The goal of the COHCAP algorithm is to identify CpG islands that show a consistent pattern of methylation among CpG sites. COHCAP is currently the only DNA methylation package that provides integration with gene expression data to identify a subset of CpG islands that are most likely to regulate downstream gene expression, and it can generate lists of differentially methylated CpG islands with ∼50% concordance with gene expression from both cell line data and heterogeneous patient data. For example, this article describes known breast cancer biomarkers (such as estrogen receptor) with a negative correlation between DNA methylation and gene expression. COHCAP also provides visualization for quality control metrics, regions of differential methylation and correlation between methylation and gene expression. This software is freely available at
    Nucleic Acids Research 04/2013; 41(11). DOI:10.1093/nar/gkt242 · 9.11 Impact Factor
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