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An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways.

ABSTRACT: Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotypes. Importantly, we discover tissue independent age-associated hotspots targeting stem-cell differentiation pathways, which we validate in independent DNA methylation data sets, encompassing over 1000 samples from different tissue types. We further show that these pathways would not have been discovered had we used a non-network based approach and that the use of the protein interaction network improves the overall robustness of the inference procedure. The proposed algorithm will be useful to any study seeking to identify interactome hotspots associated with common phenotypes.

SUBMITTER: West J

PROVIDER: S-EPMC3620664 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways.

West James J Beck Stephan S Wang Xiangdong X Teschendorff Andrew E AE

Scientific reports 20130101

Epigenetic changes have been associated with ageing and cancer. Identifying and interpreting epigenetic changes associated with such phenotypes may benefit from integration with protein interactome models. We here develop and validate a novel integrative epigenome-interactome approach to identify differential methylation interactome hotspots associated with a phenotype of interest. We apply the algorithm to cancer and ageing, demonstrating the existence of hotspots associated with these phenotyp ...[more]

PMID: 23568264

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Project description:Background Neuropsychiatric disorders such as Schizophrenia (SCZ) and Bipolar disorder (BPD) pose a broad range of problems with different symptoms mainly characterized by some combination of abnormal thoughts, emotions, behaviour, etc. However, in depth molecular and pathophysiological mechanisms among different neuropsychiatric disorders have not been clearly understood yet. We have used RNA-seq data to investigate unique and overlapping molecular signatures between SCZ and BPD using an integrative network biology approach. Methods RNA-seq count data were collected from NCBI-GEO database generated on post-mortem brain tissues of controls (n = 24) and patients of BPD (n = 24) and SCZ (n = 24). Differentially expressed genes (DEGs) were identified using the consensus of DESeq2 and edgeR tools and used for downstream analysis. Weighted gene correlation networks were constructed to find non-preserved (NP) modules for SCZ, BPD and control conditions. Topological analysis and functional enrichment analysis were performed on NP modules to identify unique and overlapping expression signatures during SCZ and BPD conditions. Results We have identified four NP modules from the DEGs of BPD and SCZ. Eleven overlapping genes have been identified between SCZ and BPD networks, and they were found to be highly enriched in inflammatory responses. Among these eleven genes, TNIP2, TNFRSF1A and AC005840.1 had higher sum of connectivity exclusively in BPD network. In addition, we observed that top five genes of NP module from SCZ network were downregulated which may be a key factor for SCZ disorder. Conclusions Differential activation of the immune system components and pathways may drive the common and unique pathogenesis of the BPD and SCZ. Highlights • Common and unique molecular signatures were identified between BPD and SCZ using integrative network biology approach.• Non-preserved (NP) modules for SCZ, BPD and control conditions are found to be associated with the differential regulation of neuroinflammatory process.• Overlapping and disease-specific genes and pathways may provide therapeutic intervention or prevention of BPD and SCZ.

Dataset Information

An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways.

Publications

An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways.

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