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An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver.

ABSTRACT: Many groups, including our own, have proposed the use of DNA methylation profiles as biomarkers for various disease states. While much research has been done identifying DNA methylation signatures in cancer vs. normal etc., we still lack sufficient knowledge of the role that differential methylation plays during normal cellular differentiation and tissue specification. We also need thorough, genome level studies to determine the meaning of methylation of individual CpG dinucleotides in terms of gene expression.In this study, we have used (insert statistical method here) to compile unique DNA methylation signatures from normal human heart, lung, and kidney using the Illumina Infinium 27 K methylation arrays and compared those to gene expression by RNA sequencing. We have identified unique signatures of global DNA methylation for human heart, kidney and liver, and showed that DNA methylation data can be used to correctly classify various tissues. It indicates that DNA methylation reflects tissue specificity and may play an important role in tissue differentiation. The integrative analysis of methylation and RNA-Seq data showed that gene methylation and its transcriptional levels were comprehensively correlated. The location of methylation markers in terms of distance to transcription start site and CpG island showed no effects on the regulation of gene expression by DNA methylation in normal tissues.This study showed that an integrative analysis of methylation array and RNA-Seq data can be utilized to discover the global regulation of gene expression by DNA methylation and suggests that DNA methylation plays an important role in normal tissue differentiation via modulation of gene expression.

SUBMITTER: Xie L

PROVIDER: S-EPMC3287572 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver.

Xie Linglin L Weichel Brent B Ohm Joyce Ellen JE Zhang Ke K

BMC systems biology 20111223

<h4>Background</h4>Many groups, including our own, have proposed the use of DNA methylation profiles as biomarkers for various disease states. While much research has been done identifying DNA methylation signatures in cancer vs. normal etc., we still lack sufficient knowledge of the role that differential methylation plays during normal cellular differentiation and tissue specification. We also need thorough, genome level studies to determine the meaning of methylation of individual CpG dinucle ...[more]

PMID: 22784623

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Project description:ObjectiveTo identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data.MethodsThe transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software.ResultsA total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10- 05), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10- 16. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA.ConclusionsRGS1 can be used as novel methylated biomarkers for RA.

Dataset Information

An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver.

Publications

An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver.

Similar Datasets

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