Morphea is an inflammatory fibrotic disorder of the skin that has been likened to systemic sclerosis (SSc). We sought to examine the molecular landscape of morphea by examining lesional skin gene expression and blood biomarkers and comparing the gene expression profiles with those from site-matched nonlesional and SSc lesional skin. We found the morphea transcriptome is dominated by IFN-γ-mediated T helper 1 immune dysregulation, with a relative paucity of fibrosis pathways. Specifically, expression profiles of morphea skin clustered with the SSc inflammatory subset and were distinct from the those of SSc fibroproliferative subset. Unaffected morphea skin also differed from unaffected SSc skin because it did not exhibit pathological gene expression signatures. Examination of downstream IFN-γ-mediated chemokines, CXCL9 and CXCL10, revealed increased transcription in the skin but not in circulation. In contrast to transcriptional activity, CXCL9 was elevated in serum and was associated with active, widespread cutaneous involvement. Taken together, these results indicate that morphea is a skin-directed process characterized by T helper 1 immune-mediated dysregulation, which contrasts with fibrotic signatures and systemic transcriptional changes associated with SSc. The similarity between morphea and the inflammatory subset of SSc on transcriptional profiling indicates that therapies under development for this subset of SSc are also promising for treatment of morphea.

Table S2. Full clinical data for all patients included in this study. (XLSX 14 kb)

Table S5. Single-sample gene set enrichment analysis in SSc patients. Single-sample gene set enrichment analysis (ssGSEA) [26] was run as a module in GenePattern, using relevant KEGG pathways as the query gene sets. Raw pathway enrichment scores are shown for each sample in our dataset. (XLSX 102 kb)

BackgroundInfectious 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.MethodsWe 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).ResultsWe 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.ConclusionsMicrobiome 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.

Systemic sclerosis is an autoimmune disease characterized by fibrosis of skin and multiple organs of which the pathogenesis is poorly understood. We studied differentially expressed coding and non-coding genes in relation to systemic sclerosis pathogenesis with a specific focus on antisense non-coding RNAs. Skin biopsy-derived RNAs from 14 early systemic sclerosis patients and six healthy individuals were sequenced with ion-torrent and analyzed using DEseq2. Overall, 4,901 genes with a fold change >1.5 and a false discovery rate <5% were detected in patients versus controls. Upregulated genes clustered in immunologic, cell adhesion, and keratin-related processes. Interestingly, 676 deregulated non-coding genes were detected, 257 of which were classified as antisense genes. Sense genes expressed opposite of these antisense genes were also deregulated in 42% of the observed sense-antisense gene pairs. The majority of the antisense genes had a similar effect sizes in an independent North American dataset with three genes (CTBP1-AS2, OTUD6B-AS1, and AGAP2-AS1) exceeding the study-wide Bonferroni-corrected P-value (PBonf < 0.0023, Pcombined = 1.1 × 10-9, 1.4 × 10-8, 1.7 × 10-6, respectively). In this study, we highlight that together with coding genes, (antisense) long non-coding RNAs are deregulated in skin tissue of systemic sclerosis patients suggesting a novel class of genes involved in pathogenesis of systemic sclerosis.

Background: Autoantibody profiles represent important patient stratification markers in systemic sclerosis (SSc). Here, we performed serum-immunoprecipitations with patient antibodies followed by mass spectrometry (LC-MS/MS) to obtain an unbiased view of all possible autoantibody targets and their associated molecular complexes recognized by SSc.Methods: HeLa whole cell lysates were immunoprecipitated (IP) using sera of patients with SSc clinically positive for autoantibodies against RNA polymerase III (RNAP3), topoisomerase 1 (TOP1), and centromere proteins (CENP). IP eluates were then analyzed by LC-MS/MS to identify novel proteins and complexes targeted in SSc. Target proteins were examined using a functional interaction network to identify major macromolecular complexes, with direct targets validated by IP-Western blots and immunofluorescence.Results: A wide range of peptides were detected across patients in each clinical autoantibody group. Each group contained peptides representing a broad spectrum of proteins in large macromolecular complexes, with significant overlap between groups. Network analyses revealed significant enrichment for proteins in RNA processing bodies (PB) and cytosolic stress granules (SG) across all SSc subtypes, which were confirmed by both Western blot and immunofluorescence.Conclusions: While strong reactivity was observed against major SSc autoantigens, such as RNAP3 and TOP1, there was overlap between groups with widespread reactivity seen against multiple proteins. Identification of PB and SG as major targets of the humoral immune response represents a novel SSc autoantigen and suggests a model in which a combination of chronic and acute cellular stresses result in aberrant cell death, leading to autoantibody generation directed against macromolecular nucleic acid-protein complexes.

Genome-wide expression profiling in systemic sclerosis (SSc) has identified four 'intrinsic' subsets of disease (fibroproliferative, inflammatory, limited, and normal-like), each of which shows deregulation of distinct signaling pathways; however, the full set of pathways contributing to this differential gene expression has not been fully elucidated. Here we examine experimentally derived gene expression signatures in dermal fibroblasts for thirteen different signaling pathways implicated in SSc pathogenesis. These data show distinct and overlapping sets of genes induced by each pathway, allowing for a better understanding of the molecular relationship between profibrotic and immune signaling networks. Pathway-specific gene signatures were analyzed across a compendium of microarray datasets consisting of skin biopsies from three independent cohorts representing 80 SSc patients, 4 morphea, and 26 controls. IFNα signaling showed a strong association with early disease, while TGFβ signaling spanned the fibroproliferative and inflammatory subsets, was associated with worse MRSS, and was higher in lesional than non-lesional skin. The fibroproliferative subset was most strongly associated with PDGF signaling, while the inflammatory subset demonstrated strong activation of innate immune pathways including TLR signaling upstream of NF-κB. The limited and normal-like subsets did not show associations with fibrotic and inflammatory mediators such as TGFβ and TNFα. The normal-like subset showed high expression of genes associated with lipid signaling, which was absent in the inflammatory and limited subsets. Together, these data suggest a model by which IFNα is involved in early disease pathology, and disease severity is associated with active TGFβ signaling.

Systemic sclerosis (SSc) is characterized by inflammation, vascular dysfunction, and ultimately fibrosis. Progress in understanding disease pathogenesis and developing effective disease treatments has been hampered by an incomplete understanding of SSc heterogeneity. To clarify this, we have used genomic approaches to identify distinct patient subsets based on gene expression patterns in SSc skin and other end-target organs. Here, we review what is known about the gene expression-based subsets in SSc, currently defined as the inflammatory, fibroproliferative, limited, and normal-like subsets. The inflammatory subset of patients is characterized by infiltrating immune cells that include T cells, macrophages, and possibly dendritic cells, although little is known about the mediators these cells secrete and the pathways that govern cell activation. Prior studies have suggested a role for pathogens as a trigger of immune responses in SSc, and recent data have identified viral and mycobiome components as potential environmental triggers. We present a model based on analyses of gene expression data and a review of the literature, which suggests that the gene expression subsets observed in patients possibly represent distinct, interconnected molecular states of disease, to which an innate immune response is central that results in the generation of clinical disease.

Previous studies have suggested a role for pathogens as a trigger of systemic sclerosis (SSc), although neither a pathogen nor a mechanism of pathogenesis is known. Here we show enrichment of Rhodotorula sequences in the skin of patients with early, diffuse SSc compared with that in normal controls. RNA-seq was performed on four SSc patients and four controls, to a depth of 200 million reads per patient. Data were analyzed to quantify the nonhuman sequence reads in each sample. We found little difference between bacterial microbiome and viral read counts, but found a significant difference between the read counts for a mycobiome component, R. glutinis. Normal samples contained almost no detected R. glutinis or other Rhodotorula sequence reads (mean score 0.021 for R. glutinis, 0.024 for all Rhodotorula). In contrast, SSc samples had a mean score of 5.039 for R. glutinis (5.232 for Rhodotorula). We were able to assemble the D1-D2 hypervariable region of the 28S ribosomal RNA (rRNA) of R. glutinis from each of the SSc samples. Taken together, these results suggest that R. glutinis may be present in the skin of early SSc patients at higher levels than in normal skin, raising the possibility that it may be triggering the inflammatory response found in SSc.