{"doi":"10.3389/fimmu.2024.1428773","title":"Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues","abstract":"<jats:p>Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.</jats:p>","journal":"Frontiers in Immunology","year":2024,"id":645621,"datarank":0.37273599746820013,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.0,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":541259,"name":"Teresina Laragione","orcid":null,"position":1,"is_corresponding":false},{"id":1668346,"name":"Percio S. Gulko","orcid":null,"position":2,"is_corresponding":false},{"id":583366,"name":"María Rodríguez Martínez","orcid":"0000-0003-3766-4233","position":3,"is_corresponding":false},{"id":1681195,"name":"Aurelien Pelissier","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues","abstract":"<jats:p>Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.</jats:p>","is_dataset_classified":null,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39161769","pmcid":"PMC11330812","openalex_id":"https://openalex.org/W4401345924","authors":[],"funders":[{"funder_name":"HORIZON EUROPE European Innovation Council","grant_id":"765158","title":"COmbatting disorders of adaptive immunity with Systems MedICine"},{"funder_name":"European Commission","grant_id":"826121","title":"individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology"}],"total_grants":2,"fwci":1.8248,"citation_percentile":0.87954589,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2024,"count":1},{"year":2025,"count":7},{"year":2026,"count":2}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1428773/pdf","host_type":"journal"},{"url":"https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1428773/pdf","host_type":"publisher"},{"url":"https://www.frontiersin.org/articles/10.3389/fimmu.2024.1428773/full","host_type":"publisher"},{"url":"https://doi.org/10.3389/fimmu.2024.1428773","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39161769","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11330812","host_type":"repository"},{"url":"https://hdl.handle.net/11475/32378","host_type":"repository"},{"url":"https://doaj.org/article/429edf280d684d168175af9d2ccd6b43","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11330812/pdf/fimmu-15-1428773.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11330812","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11330812?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1101/2023.12.28.573505","host_type":""},{"url":"https://dx.doi.org/10.3929/ethz-b-000690812","host_type":""},{"url":"https://dx.doi.org/10.21256/zhaw-32378","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/38234732","host_type":""},{"url":"http://dx.doi.org/10.1101/2023.12.28.573505","host_type":""},{"url":"http://dx.doi.org/10.3389/fimmu.2024.1428773","host_type":""},{"url":"http://hdl.handle.net/20.500.11850/690812","host_type":""}],"fields_of_study":["Cytokine Signaling Pathways and Interactions","Rheumatoid Arthritis Research and Therapies","Lymphoma Diagnosis and Treatment","0301 basic medicine","03 medical and health sciences","0303 health sciences","Humans","Arthritis, Rheumatoid","Gene Regulatory Networks","Synovial Membrane","Transcription Factors","Gene Expression Profiling","Computational Biology","Synoviocytes","Osteoarthritis","Gene Expression Regulation","B-Lymphocytes","Transcriptome"],"mesh_terms":["Synoviocytes","Arthritis, Rheumatoid","B-Lymphocytes","Gene Expression Regulation","Humans","Osteoarthritis","Synovial Membrane","Transcription Factors","Computational Biology","Gene Expression Profiling","Gene Regulatory Networks","Transcriptome"],"keywords":["Cell type","Transcription factor","Immunology","Synovial membrane","Phenotype","Biology","Inflammation","Rheumatoid arthritis","Cell","Medicine","Gene","Genetics","T cell","Monocyte","Co-regulation","Gene Regulatory Network (Grn)","Fls","Key Driver","Transcriptomic Factor","Article","Arthritis, Rheumatoid","Osteoarthritis","Humans","Gene Regulatory Networks","610.28: Biomedizin, Biomedizinische Technik","rheumatoid arthritis; key driver; gene regulatory network (GRN); co-regulation; transcriptomic factor; FLS; monocyte; T cell","B-Lymphocytes","Gene Expression Profiling","Computational Biology","RC581-607","Synoviocytes","Gene Expression Regulation","Immunologic diseases. Allergy","Transcriptome","Transcription Factors"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. Good health"},{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T09:34:10.047239Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}