Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune and Profibrotic Pathways Connected with Genetic Polymorphisms

Abstract

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Integrated, multicohort analysis of systemic sclerosis identifies robust transcriptional signature of disease severity

“…However, recent analyses suggest there may be stages in the progressive disease (24). Our results in longitudinal serial skin biopsy samples in the Northwestern cohort also suggest that patients may go through different intrinsic subsets as part of their progressive diseases.…”

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

“…Genes with nominally significant decrease post-treatment (uncorrected p <0.05, paired t-test) in clinically significant improvers were supplied as positive examples to a SVM classifier (Greene et al, 2015) (Figure 1); genes that showed no evidence of differential expression are used as negative examples (0.95<uncorrected p≤1). The classifier learned the connectivity patterns of the DEGs in the Genome-scale Integrated Analysis of gene Networks in Tissues (GIANT) skin network and returned a ranking of all genes in the genome (Greene et al, 2015). Genes with high positive scores are most functionally similar to the nominally significant DEGs from the expression analysis (Greene et al, 2015), but are not required to be differentially expressed.…”

“…The classifier learned the connectivity patterns of the DEGs in the Genome-scale Integrated Analysis of gene Networks in Tissues (GIANT) skin network and returned a ranking of all genes in the genome (Greene et al, 2015). Genes with high positive scores are most functionally similar to the nominally significant DEGs from the expression analysis (Greene et al, 2015), but are not required to be differentially expressed. Thus, top-ranked genes may be unregulated at the mRNA level or 'missed' due to small sample sizes, but are highly relevant to response.…”

“…Thus, top-ranked genes may be unregulated at the mRNA level or 'missed' due to small sample sizes, but are highly relevant to response. Similar approaches have been applied to GWAS re-analysis (Greene et al, 2015) and to DEGs for novel viral antagonism mechanism detection (Gorenshteyn et al, 2015).…”

“…These publicly available, tissue-specific networks were constructed using curated biological information (i.e., process annotations), as well as raw biological data (Greene et al, 2015). In our meta-analyses, we used linear support vector machine (SVM) classifiers to identify the connectivity patterns of .…”