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Comparative Epigenomics Reveals Hierarchical Regulation of Non-CG Methylation in Arabidopsis

ABSTRACT: Background: Genome-wide characterization by next-generation sequencing has greatly improved our understanding of the “landscape” of epigenetic modifications. Since 2008, whole-genome bisulfite sequencing (WGBS) has become the gold standard for DNA methylation analysis and a tremendous amount of WGBS data has been generated by the research community. However, methods for the systematic comparison of DNA methylation profiles to identify novel regulatory mechanisms have yet to be established.Results: Here we developed a standardized pipeline and re-analyzed over three hundred publicly available Arabidopsis WGBS libraries from various mutant backgrounds, tissue types, and stress treatments. In total this collection included more than 3,700 Gb (Giga base-pairs) of sequencing data and a large number of wild-type controls. This enabled us to identify “high-confidence Differentially Methylated Regions” (hcDMRs) with high reliability by comparing each ‘test’ library to each of the 54 controls. We adopted two statistical methods, Statistical Measurements on Overlapping of DMRs (S-MOD) followed by Quantitative Measurements on Overlapping of DMRs (Q-MOD), to compare and cluster libraries based on their impacts on DNA methylation. In addition to confirming existing relationships using this unbiased approach, we revealed novel connections between methylation pathways. For instance, MET1 and CMT3 were found to be required for maintenance of asymmetric CHH methylation at non-overlapping regions of CMT2 targeted heterochromatin.Conclusions: Our comparative methylome approach has established a framework for extracting biological insights via large-scale comparison of methylomes, and can also be adopted for other omics datasets. Together, the results demonstrate the effectiveness of data-driven, hypothesis-generating epigenetic research.

ORGANISM(S): Arabidopsis thaliana

PROVIDER: GSE98872 | GEO | 2017/12/31

SECONDARY ACCESSION(S): PRJNA386485

REPOSITORIES: GEO

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Project description:DNA methylation is one of the key epigenetic modifications involved in gene regulation. While whole-genome bisulfite sequencing (WGBS) allows the genome-wide quantification of DNA methylation at the single-base pair resolution, the contribution of DNA methylation in disease susceptibility remains to be fully understood. Here, we present a novel Bayesian smoothing approach (called ABBA) to accurately detect differentially methylated regions (DMRs) from WGBS data, and employ ABBA to map DNA methylation changes in disease. ABBA enables the genome-wide modeling of single-base resolution DNA methylation data, automatically adapting to different correlation structures in CpG methylation across the genome without requiring user-defined parameters and accounts for covariates and non-genetic confounding factors. First, we demonstrate the approach by an extensive simulation study and benchmarked ABBA against recently proposed alternative methods. We show the better performance of ABBA across a range of relevant parameters, including sequencing depth, number of replicates and noise level originating from incomplete bisulfite conversion. Second, we generated WGBS data from primary macrophages derived from an experimental rat model of glomerulonephritis and control rats, and employed ABBA to identify DMRs (>1,000 at 5% FDR) genome-wide that were associated with disease susceptibility. Investigation of the DMRs revealed differential DNA methylation localized to the promoter of the Ifitm3 gene and comparisons with existing DMR detection methods shows the enhanced interpretability of the results provided by ABBA. Additional ChIP-seq and RNA-seq analyses in the same experimental system showed differential transcription factor binding at the Ifitm3 promoter by JunD (an established determinant of glomerulonephritis) and consistent differential expression of Ifitm3. Taken together, these data suggest a role for DNA methylation alteration of the Ifitm3 gene in the pathogenesis of glomerulonephritis, and provide a proof of concept of using ABBA for differential methylation analysis of WGBS data.

Project description:DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances we still have a too limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in depth analysis of 42 whole genome bisulfite sequencing (WGBS) data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, a majority of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription factor binding sites (TFBS), which allow identification of key lineage specific regulators. In addition, differentially methylated regions (DMRs) often harbor SNPs associated with cell type related diseases as determined by GWAS. The results also highlight the general inefficiency of WGBS as 70-80% of the sequencing reads across these data sets provided little or no relevant information regarding CpG methylation. To further demonstrate the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results confirm that only a fraction does so as part of coordinated regulatory programs. Therefore our selected DMRs can serve as a starting point to help guide novel, more effective reduced representation approaches to capture the most informative fraction of CpGs as well as further pinpoint putative regulatory elements. Analysis of 42 human WGBS libraries comprising 30 distinct primary cell lines, tissues, in vitro derived cell types and cell lines. BiSeq raw sequencing reads were aligned using maq in bisulfite mode (Li et al. 2008) or bsmap 2.7 (Xi et al. 2009) against human genome version hg19/GRCh37, discarding duplicate reads. DNA methylation calling was performed based on an extended custom software pipeline published previously for RRBS (Gu et al., 2010). The bed files contain all seen CpGs within the given library. The number of methylated reads/number of total reads is listed in the score column. This study includes a re-analysis of Samples from the NIH Roadmap Epigenomics Mapping Consortium (REMC; GSE16256, GSE17312), Hodges et al. 2011 (GSE31971), Molaro et al. 2011 (GSE30340), and Xi et al. 2013. WARNING: This submission is incomplete pending further deposits of raw data files from Meissner via EDACC.

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Project description:Purpose: Generate genome-wide methylation profiles of non-small cell lung carcinomas (NSCLC) and their matching lung tissues for detection of hypermethylated and hypomethylated regions present in the tumors. Methods: MethylCapture followed by next-generation sequencing (Illumina GAIIx) of 7 nsclc tumor samples and paired lung tissues in replicated, plus one cell line, 2 fully artificially methylated and 2 fully artificially unmethylated controls. Normalization of methylation reads based on CpG coupling factor–method. Relative methylation scores (rms) in 500bp non-overlapping windows. 90th percentile of rpm (reads per million) values for all 500bp genome-wide windows, with rpm <1.33 were considered. Distributions of 10bp bins rms values within each 500bp genomic region were compared using both one-sided Student’s t-test and one-sided Wilcoxon rank-sum test. Testing was done separately for hypo- and hypermethylation and p-value threshold of 10-18. Results: MethylCap-seq data revealed strong positive correlation between replicate experiments and between paired tumor/lung samples. 14472 differentially methylated regions (DMR) with non-overlapping 500 bp windows were found. 57 DMRs were present in all NSCLC tumors. 287 were unique for squamous-cell carcinomas and 26 unique for adenocarcinomas. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. Conclusion: We provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC. The DMRs can be in further studies to develop sensitive biological markers for NSCLC, which may enable non-invasive diagnosis and early detection of the disease, and potentially allow histological classification. MethylCap-seq of 7 nsclc tumor samples and paired lung tissues, plus 2 fully methylated and 2 fully unmethylated controls.

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Comparative Epigenomics Reveals Hierarchical Regulation of Non-CG Methylation in Arabidopsis

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