Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference

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作者
Naouel Zerrouk
Quentin Miagoux
Aurelien Dispot
Mohamed Elati
Anna Niarakis
机构
[1] GenHotel,
[2] Univ. Évry,undefined
[3] Université Paris-Saclay,undefined
[4] University Lille,undefined
[5] CNRS,undefined
[6] Inserm,undefined
[7] CHU Lille,undefined
[8] Centre Oscar Lambret,undefined
[9] UMR9020,undefined
[10] UMR1277,undefined
[11] Canther,undefined
[12] Cancer Heterogeneity,undefined
[13] Plasticity and Resistance To Therapies,undefined
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摘要
Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.
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