 Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune and Profibrotic Pathways Connected with Genetic PolymorphismsJ Matthew Mahoney, Jaclyn Taroni, Viktor Martyanov, Tammara A Wood, Casey S Greene, Patricia A Pioli, Monique E Hinchcliff and Michael L Whitfield PLOS Computational Biology, 2015, vol. 11, issue 1, 1-20 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.Author Summary: Systemic sclerosis (SSc) is a rare autoimmune disease characterized by skin thickening (fibrosis) and progressive organ failure. Previous studies of SSc skin biopsies have identified molecular subsets of SSc based upon gene expression termed the inflammatory, fibroproliferative, normal-like, and limited intrinsic subsets. These gene expression signatures are large and although the biological processes are conserved, the exact list of genes can vary across datasets due to random variation, as well as minor differences in the composition of the study cohorts (e.g. early vs. late disease). We developed a computational tool to identify the consensus genes underlying the subsets across heterogeneous data and characterized the biological role of the consensus genes in SSc in order to obtain a systems level perspective of the SSc subsets. Our analysis reveals a complex network of genes connecting two of the major SSc intrinsic subsets, inflammatory and fibroproliferative. Many genetic loci associated with SSc risk show connections with the consensus genes of the intrinsic subsets, indicating that differential expression of genes defining the subsets may be related to genetic risk for SSc, thus for the first time placing the genetic risk factors in the context of, and showing putative relationships with, the intrinsic gene expression subsets. Date: 2015 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004005 DOI: 10.1371/journal.pcbi.1004005 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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