Background Genetics studies have now identified over 80 SLE risk loci that influence predisposition to SLE with the majority of risk variants altering regulatory elements that govern gene expression. Precise understanding of how risk variants in regulatory elements influence gene expression in different cell types and cell states is critical for defining the molecular networks leading to autoimmunity. To begin to address this issue, we profiled the chromatin accessibility landscape of three distinct, albeit heterogeneous, compartments of the immune system across three clinical states.
Materials and methods Primary B and T lymphocytes and monocytes from 5 SLE subjects with high disease activity (SLEDAI ≥3) and 4 SLE subjects with low disease activity (SLEDAI ≤2) and 5 healthy controls were collected and processed for high-throughput open chromatin profiling by ATAC-seq. Reads were aligned to the hg19 genome and regions of enriched chromatin accessibility “peaks” were identified with MACS2. For each cell type, we identified the consensus set of epigenetically active peaks across all 14 subjects. We conducted enrichment tests of identified loci using the GREAT tool and performed differential accessibility analysis using the edgeR package in R. Transcription factor binding motif enrichment and overlaps with know SLE risk haplotypes were also determined.
Results Chromatin accessibility profiles among the three cell types shared common features as well as peaks specific to each cell-type profile. The peaks unique to each profile were enriched in genomic loci specific to their cellular function as well as their known immunologic molecular signatures in SLE. Quantitative analysis of differential chromatin accessibility loci which discriminate between individuals with SLE and healthy controls patients with high versus low disease activity. Motif analysis revealed that many consensus peaks occupy binding sites of cohesion complex subunits, suggesting that long-range chromatin interactions may mediate immune responses that drive SLE progression. In addition, 320 SLE risk SNPs were located within an open chromatin peak suggesting these as SNPs candidates for functional impact.
Conclusions Our analysis suggests that chromatin profiling may have power to differentiate patients from controls as well varying extremes of disease activity and can pinpoint putative functional SNPs. Additional insight will be gained from further refinement of immune cell compartments. Future studies will focus on long-range interactions driving differences in chromatin accessibility and integrating these data with transcriptome data. We expect this approach to exapnd our knowledge of how regulatory networks in specific cells and cell states drive SLE progression.
Acknowledgements This work was supported by the following grants from the National Institutes of Health: NIAID: U19AI082714; NIAMS: AR056360, AR063124; NIGMS: GM110766
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