A Novel Multi-Network Approach Reveals Tissue-Specific Cellular Modulators of Fibrosis in Systemic Sclerosis

Systemic sclerosis (SSc) is a multi-organ autoimmune disease characterized by skin fibrosis. Internal organ involvement is heterogeneous. It is unknown whether disease mechanisms are common across all involved affected tissues or if each manifestation has a distinct underlying pathology.We used consensus clustering to compare gene expression profiles of biopsies from four SSc-affected tissues (skin, lung, esophagus, and peripheral blood) from patients with SSc, and the related conditions pulmonary fibrosis (PF) and pulmonary arterial hypertension, and derived a consensus disease-associate signature across all tissues. We used this signature to query tissue-specific functional genomic networks. We performed novel network analyses to contrast the skin and lung microenvironments and to assess the functional role of the inflammatory and fibrotic genes in each organ. Lastly, we tested the expression of macrophage activation state-associated gene sets for enrichment in skin and lung using a Wilcoxon rank sum test

Gene Expression Changes Reflect Clinical Response in a Placebo-Controlled Randomized Trial of Abatacept in Patients with Diffuse Cutaneous Systemic Sclerosis

Systemic sclerosis is an autoimmune disease characterized by inflammation and fibrosis of the skin and internal organs. We sought to assess the clinical and molecular effects associated with response to intravenous abatacept in patients with diffuse cutaneous systemic

Identification of Cell Cycle–Regulated Genes Periodically Expressed in U2OS Cells and their Regulation by FOXM1 and E2F Transcription Factors

We identify the cell cycle–regulated mRNA transcripts genome-wide in the osteosarcoma-derived U2OS cell line. This results in 2140 transcripts mapping to 1871 unique cell cycle–regulated genes that show periodic oscillations across multiple synchronous cell cycles. We identify genomic loci bound by the G2/M transcription factor FOXM1 by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) and associate these with cell cycle–regulated genes. FOXM1 is bound to cell cycle–regulated genes with peak expression in both S phase and G2/M phases. We show that ChIP-seq genomic loci are responsive to FOXM1 using a real-time luciferase assay in live cells, showing that FOXM1 strongly activates promoters of G2/M phase genes and weakly activates those induced in S phase. Analysis of ChIP-seq data from a panel of cell cycle transcription factors (E2F1, E2F4, E2F6, and GABPA) from the Encyclopedia of DNA Elements and ChIP-seq data for the DREAM complex finds that a set of core cell cycle genes regulated in both U2OS and HeLa cells are bound by multiple cell cycle transcription factors. These data identify the cell cycle–regulated genes in a second cancer-derived cell line and provide a comprehensive picture of the transcriptional regulatory systems controlling periodic gene expression in the human cell division cycle

Regulator combinations identify systemic sclerosis patients with more severe disease

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 “intrinsic” subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36–54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease

Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression in systemic sclerosis skin

Abstract Background Infectious agents have long been postulated to be disease triggers for systemic sclerosis (SSc), but a definitive link has not been found. Metagenomic analyses of high-throughput data allows for the unbiased identification of potential microbiome pathogens in skin biopsies of SSc patients and allows insight into the relationship with host gene expression. Methods We examined skin biopsies from a diverse cohort of 23 SSc patients (including lesional forearm and non-lesional back samples) by RNA-seq. Metagenomic filtering and annotation was performed using the Integrated Metagenomic Sequencing Analysis (IMSA). Associations between microbiome composition and gene expression were analyzed using single-sample gene set enrichment analysis (ssGSEA). Results We find the skin of SSc patients exhibits substantial changes in microbial composition relative to controls, characterized by sharp decreases in lipophilic taxa, such as Propionibacterium, combined with increases in a wide range of gram-negative taxa, including Burkholderia, Citrobacter, and Vibrio. Conclusions Microbiome dysbiosis is associated with disease duration and increased inflammatory gene expression. These data provide a comprehensive portrait of the SSc skin microbiome and its association with local gene expression, which mirrors the molecular changes in lesional skin

Bridges between components of the network.

<p>Several genes bridge the component subnetworks of the molecular network. <i>PLAUR</i> is a gene that contains SSc-associated polymorphisms that forms a bridge between the interferon subnetwork and TGFβ/ECM subnetwork. The gene <i>RAC2</i> is a bridge between the interferon and M2 macrophage subnetworks. The genes <i>LCP2</i> and <i>CXCR4</i> are bridges between the M2 macrophage subnetwork and the adaptive immunity subnetwork. There are also several paths through <i>GRB10</i> to <i>ADAP2</i> between the M2 macrophage subnetwork and the adaptive immunity subnetwork. The genes <i>CD14</i> and <i>THY1</i> (<i>CD90</i>) are bridges between the M2 macrophage subnetwork and the TGFβ/ECM subnetwork. The genes <i>IRAK1</i> and <i>PXK</i> are bridges between the TGFβ/ECM subnetwork and the cell proliferation subnetwork.</p

Model of interactions among the components of the network.

<p>The molecular network of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004005#pcbi-1004005-g004" target="_blank">Fig. 4</a> is densely interconnected, implicating many possible interactions between the core molecular processes (interferon activation, M2 macrophage activation, adaptive immunity, ECM remodeling, and cell proliferation). Stepping back from the granular detail of single genes, we see a system of distinct parts through which SSc could be initiated and maintained. Among these are paths of particular interest. The interferon subnetwork and the M2 macrophage subnetwork are connected by RAC2. The M2 macrophage subnetwork in turn is connected to the ECM subnetwork through paths through <i>CD14</i> and <i>THY1</i>. Suggesting macrophages may influence or drive ECM abnormalities in skin. The interferon subnetwork and the ECM subnetwork are connected through paths containing the pleiotropic and polymorphic gene <i>PLAUR</i>. The M2 macrophage subnetwork is connected to the adaptive immunity subnetwork through several distinct sets of paths through the genes <i>GRB10</i>, <i>LCP2</i>, and <i>CXCR4</i>. The ECM subnetwork is connected to the cell proliferation cluster through TGFβ pathway genes and paths containing the polymorphic genes <i>IRAK1</i> and <i>PXK</i>, which suggests that ECM remodeling modulates cell proliferation through the TGFβ pathway. The interferon node may negatively regulate proliferation via the ERK/MAPK pathway resulting in the general mutual exclusivity of the inflammatory and fibroproliferative subsets. Thus we see a set of interconnected, balancing feedback loops that can enforce subset homeostasis, but also allow for patients to transition between the subsets, possibly in response to therapy.</p

Molecular network of inflammatory and fibroproliferative consensus genes.

<p>The consensus genes for the inflammatory and fibroproliferative subsets are connected in the IMP functional network. Inflammatory genes are colored purple, while fibroproliferative genes are colored red. Genes with polymorphisms are colored in green and MRSS biomarker genes are colored yellow. One MRSS biomarker gene (<i>IFI44</i>) was also an inflammatory consensus gene (pink), while three polymorphic genes were inflammatory consensus genes (turquoise). Note the five distinct subnetworks corresponding to type I interferons, M2 macrophages, ECM proteins and TGFβ signaling, adaptive immunity, and cell proliferation. The interferon, M2 macrophage, and adaptive immunity subnetworks are composed almost exclusively of inflammatory genes, while the ECM subnetwork shares genes from both intrinsic subsets. Furthermore, the polymorphic genes interact primarily with inflammatory subset genes indicating that the genetic risk in SSc is related to immune abnormalities.</p

Composition of study cohorts.

<p>Composition of study cohorts.</p