Arthritis & RheumatologyGenomicsPeer-reviewed journal article
RNA-Seq analysis identifies novel roles for the primary cilia gene SPAG17 and the SOX9 locus non-coding RNAs in systemic sclerosis
Elisha D. O. RobersoniD, Mary A. CarnsiD, Li Hua CaoiD, Kathleen AreniD, Isaac A. GoldbergiD, David J. Morales-HeiliD, Benjamin D. KormaniD, John P. AtkinsoniD, John VargaiD
Abstract
Systemic sclerosis (SSc) is characterized by immune activation, vasculopathy, and unresolving fibrosis in the skin, lungs, and other organs. We performed RNA-Seq analysis on skin biopsies and peripheral blood mononuclear cells (PBMCs) from SSc patients and controls to better understand SSc pathogenesis. We analyzed these data to 1) test for case-control differences, and 2) identify genes whose expression levels correlate with SSc severity as measured by local skin score, modified Rodnan skin score (MRSS), forced vital capacity (FVC), or diffusion capacity for carbon monoxide (DLCO). We found that PBMCs from SSc patients showed a strong type 1 interferon signature. This signal replicated in the skin, with additional signals for increased extracellular matrix (ECM) genes, classical complement pathway activation, and the presence of B cells. Notably, we observed a marked decrease in the expression of SPAG17, a cilia component, in SSc skin. We identified genes that correlated with MRSS, DLCO, and FVC in SSc PBMCs and skin using weighted gene co-expression analysis (WGCNA). These genes were largely distinct from the case/control differentially expressed genes. In PBMCs, type 1 interferon signatures negatively correlated with DLCO. In SSc skin, ECM gene expression positively correlated with MRSS. Network analysis of SSc skin genes correlated with clinical features identified the non-coding RNAs SOX9-AS1 and ROCR, both near the SOX9 locus, as highly connected, "hub-like" genes in the network. These results identify non-coding RNAs and SPAG17 as novel factors potentially implicated in SSc pathogenesis.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 15
- FWCI
- 1.70
- Field percentile
- top 17%
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