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