Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA
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ABSTRACT:
PROVIDER: PRJNA315380 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR3225610_1.fastq.gz | Fastqsanger.gz | |||
| SRR3225610_2.fastq.gz | Fastqsanger.gz | |||
| SRR3225620_1.fastq.gz | Fastqsanger.gz | |||
| SRR3225620_2.fastq.gz | Fastqsanger.gz | |||
| SRR3225630_1.fastq.gz | Fastqsanger.gz |
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Project description:Adjacent CpG sites in mammalian genomes tend to be co-methylated due to the processivity of enzymes responsible for adding or removing the methyl group. Yet discordant methylation patterns have also be observed, and found to be related to stochastic or uncoordinated molecular processes. Here we focused on a systematic search and investigation of regions in the human genome that exhibit highly coordinated methylation. By examining the co-methylation patterns of multiple adjacent CpG sites, termed methylation haplotypes, in single bisulfite sequencing reads, we applied a greedy-searching strategy to defined blocks of tightly coupled CpG sites, called Methylation Haplotype Blocks (MHBs), based on 53 sets of whole genome bisulfite sequencing (WGBS) data, including 43 published sets from human adult tissues, ESC and in vitro differentiated cell lines, as well as 10 sets from human adult tissues generated in this study. The MHBs were then further validated with 101 sets of RRBS ENCODE data, and 637 sets of Illumina 450k methylation array data from TCGA tumor and normal samples. Globally, MHBs are enriched in but only partially overlap with several well-known genomic features, including CpG islands, promoters, enhancers and VMRs. To perform quantitative analysis of the MHBs, we defined a metric called Methylation Haplotype Load (MHL), which is covered both average methylation level and methylation complexity and therefore more informative than average methylation level or Shannon entropy. Using a feature selection strategy, we identified a set of tissue-specific MHBs that cluster by developmental germ-layers. Interestingly, examination of these MHBs revealed two distinct mechanisms for fate commitment during development: epigenetic silencing of pluripotent genes, such as NANOG, for mesoderm induction; and epigenetic induction (or de-suppression) of lineage-specific factors for ectoderm commitment.
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Project description:Adjacent CpG sites in mammalian genomes tend to be co-methylated due to the processivity of enzymes responsible for adding or removing the methyl group. Yet discordant methylation patterns have also be observed, and found to be related to stochastic or uncoordinated molecular processes. Here we focused on a systematic search and investigation of regions in the human genome that exhibit highly coordinated methylation. By examining the co-methylation patterns of multiple adjacent CpG sites, termed methylation haplotypes, in single bisulfite sequencing reads, we applied a greedy-searching strategy to defined blocks of tightly coupled CpG sites, called Methylation Haplotype Blocks (MHBs), based on 53 sets of whole genome bisulfite sequencing (WGBS) data, including 43 published sets from human adult tissues, ESC and in vitro differentiated cell lines, as well as 10 sets from human adult tissues generated in this study. The MHBs were then further validated with 101 sets of RRBS ENCODE data, and 637 sets of Illumina 450k methylation array data from TCGA tumor and normal samples. Globally, MHBs are enriched in but only partially overlap with several well-known genomic features, including CpG islands, promoters, enhancers and VMRs. To perform quantitative analysis of the MHBs, we defined a metric called Methylation Haplotype Load (MHL), which is covered both average methylation level and methylation complexity and therefore more informative than average methylation level or Shannon entropy. Using a feature selection strategy, we identified a set of tissue-specific MHBs that cluster by developmental germ-layers. Interestingly, examination of these MHBs revealed two distinct mechanisms for fate commitment during development: epigenetic silencing of pluripotent genes, such as NANOG, for mesoderm induction; and epigenetic induction (or de-suppression) of lineage-specific factors for ectoderm commitment.
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Project description:Adjacent CpG sites in mammalian genomes tend to be co-methylated due to the processivity of enzymes responsible for adding or removing the methyl group. Yet discordant methylation patterns have also be observed, and found to be related to stochastic or uncoordinated molecular processes. Here we focused on a systematic search and investigation of regions in the human genome that exhibit highly coordinated methylation. By examining the co-methylation patterns of multiple adjacent CpG sites, termed methylation haplotypes, in single bisulfite sequencing reads, we applied a greedy-searching strategy to defined blocks of tightly coupled CpG sites, called Methylation Haplotype Blocks (MHBs), based on 53 sets of whole genome bisulfite sequencing (WGBS) data, including 43 published sets from human adult tissues, ESC and in vitro differentiated cell lines, as well as 10 sets from human adult tissues generated in this study. The MHBs were then further validated with 101 sets of RRBS ENCODE data, and 637 sets of Illumina 450k methylation array data from TCGA tumor and normal samples. Globally, MHBs are enriched in but only partially overlap with several well-known genomic features, including CpG islands, promoters, enhancers and VMRs. To perform quantitative analysis of the MHBs, we defined a metric called Methylation Haplotype Load (MHL), which is covered both average methylation level and methylation complexity and therefore more informative than average methylation level or Shannon entropy. Using a feature selection strategy, we identified a set of tissue-specific MHBs that cluster by developmental germ-layers. Interestingly, examination of these MHBs revealed two distinct mechanisms for fate commitment during development: epigenetic silencing of pluripotent genes, such as NANOG, for mesoderm induction; and epigenetic induction (or de-suppression) of lineage-specific factors for ectoderm commitment.
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Project description:Adjacent CpG sites in mammalian genomes can be co-methylated owing to the processivity of methyltransferases or demethylases, yet discordant methylation patterns have also been observed, which are related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that show highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks, after analysis of 61 whole-genome bisulfite sequencing data sets and validation with 101 reduced-representation bisulfite sequencing data sets and 637 methylation array data sets. Using a metric called methylation haplotype load, we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, using methylation haplotypes we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 patients with lung or colorectal cancer.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2017-02-19 | GSE79279 | GEO
Project description:Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA [WGBS]
| PRJNA315194 | ENA
Project description:Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA [Plasma RRBS]
| PRJNA315379 | ENA
Project description:Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA [Tumor RRBS]
| PRJNA315188 | ENA
Project description:Kaposi's Sarcoma (KS) is a proliferation of aberrant vascular structures lined by spindle cells, and is caused by a gammaherpes virus (HHV8/KSHV). Its course is aggravated by co-infection with HIV-1, where the timing of infection with HIV-1 and HHV8 is important for the clinical outcome. METHODS: In order to better understand the pathogenesis of KS, we have analysed tissue from two AIDS-KS lesions, and from normal skin by serial analysis of gene expression (SAGE). Semi-quantitative RT-PCR was then used to validate the results. RESULTS: The expression profile of AIDS-related KS (AIDS-KS) reflects an active process in the skin. Transcripts of HHV8 were found to be very low, and HIV-1 mRNA was not detected by SAGE, although it could be found using RT-PCR. Comparing the expression profile of AIDS-KS tissue with publicly available SAGE libraries suggested that AIDS-KS mRNA levels are most similar to those in an artificially mixed library of endothelial cells and leukocytes, in line with the description of KS lesions as containing spindle cells with endothelial characteristics, and an inflammatory infiltrate. At least 64 transcripts were found to be significantly elevated, and 28 were statistically downregulated in AIDS-KS compared to normal skin. Five of the upregulated mRNAs, including Tie 1 and sialoadhesin/CD169, were confirmed by semi-quantitative PCR to be elevated in additional AIDS-KS biopsies. Antibodies to sialoadhesin/CD169, a known marker of activated macrophages, were shown to specifically label tumour macrophages. CONCLUSION: The expression profile of AIDS-KS showed 64 genes to be significantly upregulated, and 28 genes downregulated, compared with normal skin. One of the genes with increased expression was sialoadhesin (CD169). Antibodies to sialoadhesin/CD169 specifically labelled tumour-associated macrophages, suggesting that macrophages present in AIDS-KS lesions belong to a subset of human CD169+ macrophages. Keywords: other
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