Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE41050 | GEO | 2012/09/24
SECONDARY ACCESSION(S): PRJNA175630
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE41048: Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues (ChIP-Seq and MeDIP-Seq) GSE41049: Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues (Gene Expression data) Refer to individual Series
2012-09-24 | E-GEOD-41050 | biostudies-arrayexpress
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. 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 modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells.
2012-09-24 | GSE41049 | GEO
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. 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 modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells. Gene expression profiled in duplicate throughout the microevolutionary timecourse using Affymetrix Gene 1.0 ST arrays.
2012-09-24 | E-GEOD-41049 | biostudies-arrayexpress
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. 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 modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells.
2012-09-24 | GSE41048 | GEO
Project description:DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. 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 modifications and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical for understanding methylation dynamics in normal and cancer cells. Genomewide sequencing data is included herein: H3K27me3 profiled throughout the microevolutionary timecourse using ChIP-seq, DNA methylation profiled through the timecourse using MeDIP-seq, and H3K4me3 and CTCF profiled in late and early timepoints using ChIP-seq. Deep Bisulfite sequencing amplicon data and results can be obtained at: http://compgenomics.weizmann.ac.il/tanay/?page_id=99 .
2012-09-24 | E-GEOD-41048 | biostudies-arrayexpress
Project description:AimTo identify and analyze the differentially expressed proteins in normal and cancerous tissues of four patients suffering from colon cancer.MethodsColon tissues (normal and cancerous) were homogenized and the proteins were extracted using three protein extraction buffers. The extraction buffers were used in an orderly sequence of increasing extraction strength for proteins with hydrophobic properties. The protein extracts were separated using the SDS-PAGE method and the images were captured and analyzed using Quantity One software. The target protein bands were subjected to in-gel digestion with trypsin and finally analyzed using an ESI-ion trap mass spectrometer.ResultsA total of 50 differentially expressed proteins in colonic cancerous and normal tissues were identified.ConclusionMany of the identified proteins have been reported to be involved in the progression of similar or other types of cancers. However, some of the identified proteins have not been reported before. In addition, a number of hypothetical proteins were also identified.
| S-EPMC4087399 | biostudies-literature
Project description:BackgroundDuring the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature.MethodsNew statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions.ResultsWe illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions.ConclusionsOur framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.
| S-EPMC3304533 | biostudies-literature
Project description:Epigenetic plasticity in relation to in utero exposures may mechanistically explain observed differences in the likelihood of developing common complex diseases including hypertension, diabetes and cardiovascular disease through the cumulative effects of subtle alterations in gene expression. Imprinted genes are essential mediators of growth and development and are characterized by differentially methylated regulatory regions (DMRs) that carry parental allele-specific methylation profiles. This theoretical 50% level of methylation provides a baseline from which endogenously- or exogenously-induced deviations in methylation can be detected. We quantified DNA methylation at imprinted gene DMRs in a large panel of human conceptal tissues, in matched buccal cell specimens collected at birth and at one year of age, and in the major cell fractions of umbilical cord blood to assess the stability of methylation at these regions. DNA methylation was measured using validated pyrosequencing assays at seven DMRs regulating the IGF2/H19, DLK1/MEG3, MEST, NNAT and SGCE/PEG10 imprinted domains. DMR methylation did not significantly differ for the H19, MEST and SGCE/PEG10 DMRs across all conceptal tissues analyzed (ANOVA p>0.10). Methylation differences at several DMRs were observed in tissues from brain (IGF2 and MEG3-IG DMRs), liver (IGF2 and MEG3 DMRs) and placenta (both DLK1/MEG3 DMRs and NNAT DMR). In most infants, methylation profiles in buccal cells at birth and at one year of age were comparable, as was methylation in the major cell fractions of umbilical cord blood. Several infants showed temporal deviations in methylation at multiple DMRs. Similarity of inter-individual and intra-individual methylation at some, but not all of the DMRs analyzed supports the possibility that methylation of these regions can serve as useful biosensors of exposure.
| S-EPMC3396645 | biostudies-literature
Project description:Genomic imprinting arises from allele-specific epigenetic modifications that are established during gametogenesis and that are maintained throughout somatic development. These parental-specific modifications include DNA methylation and post-translational modifications to histones, which create allele-specific active and repressive domains at imprinted regions. Through the use of a high-density genomic tiling array, we generated DNA and histone methylation profiles at 11 imprinted gene clusters in the mouse from DNA and from chromatin immunoprecipitated from sperm, heart, and cerebellum. Our analysis revealed that despite high levels of differential DNA methylation at non-CpG islands within these regions, imprinting control regions (ICRs) and secondary differentially methylated regions (DMRs) were identified by an overlapping pattern of H3K4 trimethylation (active chromatin) and H3K9 trimethylation (repressive chromatin) modifications in somatic tissue, and a sperm differentially methylated region (sDMR; sperm not equal somatic tissue). Using these features as a common signature of DMRs, we identified 11 unique regions that mapped to known imprinted genes, to uncharacterized genes, and to intergenic regions flanking known imprinted genes. A common feature among these regions was the presence of a CpG island and an array of tandem repeats. Collectively, this study provides a comprehensive analysis of DNA methylation and histone H3K4me3 and H3K9me3 modifications at imprinted gene clusters, and identifies common epigenetic and genetic features of regions regulating genomic imprinting.
| S-EPMC2720189 | biostudies-literature
Project description:BACKGROUND: Aberrant methylation at imprinted differentially methylated regions (DMRs) in human 11p15.5 has been reported in many tumors including hepatoblastoma. However, the methylation status of imprinted DMRs in imprinted loci scattered through the human genome has not been analyzed yet in any tumors. METHODS: The methylation statuses of 33 imprinted DMRs were analyzed in 12 hepatoblastomas and adjacent normal liver tissue by MALDI-TOF MS and pyrosequencing. Uniparental disomy (UPD) and copy number abnormalities were investigated with DNA polymorphisms. RESULTS: Among 33 DMRs analyzed, 18 showed aberrant methylation in at least 1 tumor. There was large deviation in the incidence of aberrant methylation among the DMRs. KvDMR1 and IGF2-DMR0 were the most frequently hypomethylated DMRs. INPP5Fv2-DMR and RB1-DMR were hypermethylated with high frequencies. Hypomethylation was observed at certain DMRs not only in tumors but also in a small number of adjacent histologically normal liver tissue, whereas hypermethylation was observed only in tumor samples. The methylation levels of long interspersed nuclear element-1 (LINE-1) did not show large differences between tumor tissue and normal liver controls. Chromosomal abnormalities were also found in some tumors. 11p15.5 and 20q13.3 loci showed the frequent occurrence of both genetic and epigenetic alterations. CONCLUSIONS: Our analyses revealed tumor-specific aberrant hypermethylation at some imprinted DMRs in 12 hepatoblastomas with additional suggestion for the possibility of hypomethylation prior to tumor development. Some loci showed both genetic and epigenetic alterations with high frequencies. These findings will aid in understanding the development of hepatoblastoma.
| S-EPMC3880457 | biostudies-literature