Dataset Information

Chromosome contacts in activated T cells identify autoimmune disease candidate genes.

ABSTRACT:

Background

Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4⁺ T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes.

Results

Within 4 h, activation of CD4⁺ T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach.

Conclusions

Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes.

SUBMITTER: Burren OS

PROVIDER: S-EPMC5584004 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Chromosome contacts in activated T cells identify autoimmune disease candidate genes.

Burren Oliver S OS Rubio García Arcadio A Javierre Biola-Maria BM Rainbow Daniel B DB Cairns Jonathan J Cooper Nicholas J NJ Lambourne John J JJ Schofield Ellen E Castro Dopico Xaquin X Ferreira Ricardo C RC Coulson Richard R Burden Frances F Rowlston Sophia P SP Downes Kate K Wingett Steven W SW Frontini Mattia M Ouwehand Willem H WH Fraser Peter P Spivakov Mikhail M Todd John A JA Wicker Linda S LS Cutler Antony J AJ Wallace Chris C

Genome biology 20170904 1

<h4>Background</h4>Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4<sup>+</sup> T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes.<h4>Results</h4>Within 4 h, activation of CD4<sup>+</sup> T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes i ...[more]

PMID: 28870212

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Project description:BackgroundAlzheimer's disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.ResultsOur approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.ConclusionsWith the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Project description:BackgroundInadequate immunity caused by poor immune surveillance leads to tumorigenesis, while excessive immunity due to breakdown of immune tolerance causes autoimmune genesis. Although the function of immunity during the onset of these two processes appears to be distinct, the underlying mechanism is shared. To date, gene expression data for large bodies of clinical samples are available, but the resemblances of tumorigenesis and autoimmune genesis in terms of immune responses remains to be summed up.MethodsConsidering the high disease prevalence, we chose invasive ductal carcinoma (IDC) and systemic lupus erythematosus (SLE) to study the potential commonalities of immune responses. We obtained gene expression data of IDC/SLE patients and normal controls from five IDC databases (GSE29044, GSE21422, GSE22840, GSE15852, and GSE9309) and five SLE databases (GSE154851, GSE99967, GSE61635, GSE50635, and GSE17755). We intended to identify genes differentially expressed in both IDC and SLE by using three bioinformatics tools including GEO2R, the limma R package, and Weighted Gene Co-expression Network Analysis (WGCNA) to perform function enrichment, protein-protein network, and signaling pathway analyses.ResultsThe mRNA levels of signal transducer and activator of transcription 1 (STAT1), 2'-5'-oligoadenylate synthetase 1 (OAS1), 2'-5'-oligoadenylate synthetase like (OASL), and PML nuclear body scaffold (PML) were found to be differentially expressed in both IDC and SLE by using three different bioinformatics tools of GEO2R, the limma R package and WGCNA. From the combined databases in this study, the mRNA levels of STAT1 and OAS1 were increased in IDC while reduced in SLE. And the mRNA levels of OASL and PML were elevated in both IDC and SLE. Based on Kyoto Encyclopedia of Genes and Genomes pathway analysis and QIAGEN Ingenuity Pathway Analysis, both IDC and SLE were correlated with the changes of multiple components involved in the Interferon (IFN)-Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway.ConclusionThe expression levels of STAT1 and OAS1 manifest the opposite expression tendency across cancer and autoimmune disease. They are components in the IFN-JAK-STAT signaling pathway related to both tumorigenesis and autoimmune genesis. STAT1 and OAS1-associated IFN-JAK-STAT signaling could explain the commonalities during tumorigenesis and autoimmune genesis and render significant information for more precise treatment from the point of immune homeostasis.

Dataset Information

Chromosome contacts in activated T cells identify autoimmune disease candidate genes.

Background

Results

Conclusions

Publications

Chromosome contacts in activated T cells identify autoimmune disease candidate genes.

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