Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci

ABSTRACT: Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions.We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. Here we report numerous looping interactions and provide evidence that only a minority of interactions are common to both B- and T-cell lines, suggesting interactions may be highly cell-type specific; some disease-associated SNPs do not interact with the nearest gene but with more compelling candidate genes (for example, FOXO1, AZI2) often situated several megabases away; and finally, regions associated with different autoimmune diseases interact with each other and the same promoter suggesting common autoimmune gene targets (for example, PTPRC, DEXI and ZFP36L1). Capture Hi-C was carried out to characterise the interactions of confirmed susceptibility loci for four autoimmune diseases: rheumatoid arthritis (RA), type 1 diabetes (T1D), psoriatic arthritis (PsA) and juvenile idiopathic arthritis (JIA) with the aim of linking associated variants with disease-causing genes. We have tested the interactions in two complementary experiments: the first, Region Capture, targets regions associated with disease; the second, Promoter Capture, provides independent validation through capturing all known promoters within 500kb of lead associated disease SNPs. All experiments were performed in human T-cell (Jurkat) and B-cell (GM12878) lines in duplicate.

ORGANISM(S): Homo sapiens

SUBMITTER: Paul Martin

PROVIDER: E-GEOD-69600 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Rheumatoid arthritis is a prototypical autoimmune arthritis affecting nearly 1% of the world population and is a significant cause of worldwide disability. Though prior studies have demonstrated the appearance of RA-related autoantibodies years before the onset of clinical RA, the pattern of immunologic events preceding the development of RA remains unclear. To characterize the evolution of the autoantibody response in the preclinical phase of RA, we used a novel multiplex autoantigen array to evaluate development of the anti-citrullinated protein antibodies (ACPA) and to determine if epitope spread correlates with rise in serum cytokines and imminent onset of clinical RA. To do so, we utilized a cohort of 81 patients with clinical RA for whom stored serum was available from 1-12 years prior to disease onset. We evaluated the accumulation of ACPA subtypes over time and correlated this accumulation with elevations in serum cytokines. We then used logistic regression to identify a profile of biomarkers which predicts the imminent onset of clinical RA (defined as within 2 years of testing). We observed a time-dependent expansion of ACPA specificity with the number of ACPA subtypes. At the earliest timepoints, we found autoantibodies targeting several innate immune ligands including citrullinated histones, fibrinogen, and biglycan, thus providing insights into the earliest autoantigen targets and potential mechanisms underlying the onset and development of autoimmunity in RA. Additionally, expansion of the ACPA response strongly predicted elevations in many inflammatory cytokines including TNF-α, IL-6, IL-12p70, and IFN-γ. Thus, we observe that the preclinical phase of RA is characterized by an accumulation of multiple autoantibody specificities reflecting the process of epitope spread. Epitope expansion is closely correlated with the appearance of preclinical inflammation, and we identify a biomarker profile including autoantibodies and cytokines which predicts the imminent onset of clinical arthritis. A total of 559 human sera were profiled using a custom made panel of putative rheumatoid arthritis associated autoantigens. The cohort was comprised of 79 patients with clinical RA for whom stored serum was available from 1-10 years prior to disease onset and 80 control subjects without RA that were matched to cases based on age, gender, race, region of assignment, and time of serum sampling.

Project description:Objective: Production of pathogenic autoantibodies by self-reactive plasma cells (PC) is a hallmark of autoimmune diseases. Investigating the prevalence of PC in autoimmune disease and their relationship with known pathogenic pathways may increase our understanding of the role of PC in disease progression and treatment response. Methods: We developed a sensitive gene expression based method to overcome the challenges of measuring PC using flow cytometry. Whole genome microarray analysis of sorted cellular fractions identified a panel of genes, IGHA, IGJ, IGKC, IGKV, and TNFRSF17, expressed predominantly in PC. The sensitivity of the PC signature score created from the combined expression levels of these genes was assessed through ex vivo experiments with sorted cells. This PC gene expression signature was used for monitoring changes in PC levels following anti-CD19 therapy; evaluating the relationship between PC and other autoimmune disease-related genes; and estimating PC levels in affected blood and tissue from multiple autoimmune diseases. Results: The PC signature was highly sensitive and capable of detecting as few as 300 PCs. The PC signature was reduced over 90% in scleroderma patients following anti-CD19 treatment and this reduction was highly correlated (r = 0.77) with inhibition of collagen gene expression. Evaluation of multiple autoimmune diseases revealed 30-35% of lupus, rheumatoid arthritis, and scleroderma patients with increased PC levels. Conclusion: This newly developed PC signature provides a robust and accurate method to measure PC levels in the clinic. Our results highlight subsets of patients across multiple autoimmune diseases that may benefit from PC depleting therapy. To examine gene expression in purified cellular fractions, normal human blood was collected from 2 donors as per institutional policy. The granulocyte (CD15+), monocyte (CD14+), T cell (CD3+), B cell (Non-PC gated, CD19+), and PC (CD27++CD38++) fractions from peripheral blood were separated. White blood cells were washed with FACS buffer (PBS + 0.5%BSA + 2mM EDTA (Gibco)) and incubated with 20% heat-inactivated FBS for 10-15 minutes on ice. The following mAbs were added directly to the cells: CD15 (HI98); CD14 (M5E2), CD3 (UCHT1), CD27 (M-T271), CD38 (HB7), and DAPI (Molecular probes). Cells were sorted on a Becton Dickinson FACS Aria II flow cytometer. All sorted fractions were collected in FACS buffer, centrifuged, and the resulting cell pellet was suspended in RNA lysis buffer (Ambion).

Dataset Information

Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure