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

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 inv...

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Autores principales: Zerrouk, Naouel, Miagoux, Quentin, Dispot, Aurelien, Elati, Mohamed, Niarakis, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529794/
https://www.ncbi.nlm.nih.gov/pubmed/33004899
http://dx.doi.org/10.1038/s41598-020-73147-4
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author Zerrouk, Naouel
Miagoux, Quentin
Dispot, Aurelien
Elati, Mohamed
Niarakis, Anna
author_facet Zerrouk, Naouel
Miagoux, Quentin
Dispot, Aurelien
Elati, Mohamed
Niarakis, Anna
author_sort Zerrouk, Naouel
collection PubMed
description 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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spelling pubmed-75297942020-10-02 Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference Zerrouk, Naouel Miagoux, Quentin Dispot, Aurelien Elati, Mohamed Niarakis, Anna Sci Rep Article 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. Nature Publishing Group UK 2020-10-01 /pmc/articles/PMC7529794/ /pubmed/33004899 http://dx.doi.org/10.1038/s41598-020-73147-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zerrouk, Naouel
Miagoux, Quentin
Dispot, Aurelien
Elati, Mohamed
Niarakis, Anna
Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
title Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
title_full Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
title_fullStr Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
title_full_unstemmed Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
title_short Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
title_sort identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529794/
https://www.ncbi.nlm.nih.gov/pubmed/33004899
http://dx.doi.org/10.1038/s41598-020-73147-4
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