Hinchcliff, M. E. | bioRxiv Science

bioRxiv2024

RUNX1 is Expressed in a Subpopulation of Dermal Fibroblasts and Higher RUNX1 Levels are Associated with the Severity of Systemic Sclerosis

The activation of Runt-related transcription factor 1 (RUNX1) in fibroblasts has been implicated in wound healing and fibrosis; however, the role of RUNX1 in the fibrotic progression of the autoimmune disease systemic sclerosis (SSc) is not known. Through gene expression analysis, we have demonstrated an association between the severity of dermal fibrosis and the expression levels of RUNX1 in the skin of patients with SSc. Additionally, we identified hypomethylated CpG sites proximal to the RUNX1 gene, implicating their potential role in the increased expression of RUNX1. Analysis of single-cell RNA-seq data from skin biopsies of individuals with SSc revealed that RUNX1 is higher in subpopulations of fibroblasts enriched in SSc, which are believed to contribute to fibrosis. Lastly, modulation of RUNX1 activity using an inhibitor caused a reduction in fibroblast contraction and proliferation rates. Altogether, this study is the first to demonstrate a potential role for RUNX1 in the pathogenesis of systemic sclerosis dermal fibrosis.

genomics↗

bioRxiv2017

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

BackgroundCardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups.\n\nMethodsWe used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters.\n\nResultsFour SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5-33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1-52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters.\n\nConclusionsMachine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.

genomics↗