Dissecting ARL15 Function in Rheumatoid Arthritis: Insights from Ex Vivo and In Vitro Synovial Fibroblast Models
KASHYAP, S.; PANDEY, A. K.; Paritosh, K.; Kanjilal, M.; Kumar, U.; BK, T.
Show abstract
ARL15, coding for a small GTPase was identified as a non-HLA susceptibility gene in rheumatoid arthritis (RA) through a GWAS in a North Indian cohort. Serum adiponectin and ARL15 levels were higher in RA patients with the associated genotype. The present study aimed to delineate the functional role of ARL15 in RA pathobiology using gene knockdown (KD) combined with transcriptomic profiling in both ex-vivo RA synovial fibroblasts (RASF) and in vitro MH7A cell lines. In RASF, ARL15 KD led to the downregulation of COMP-an extracellular matrix stabilizer linked to severe RA-alongside upregulation of adiponectin and IFN response genes such as IFI6 and USP18. Furthermore, upregulation of NPTX1 and MX1, previously associated with disease modulation and treatment response was observed. Downregulation of CTGF, CD248, and PTX3 suggested involvement of ARL15 in inflammation and RA-associated cardiovascular risk. In contrast, ARL15 KD in MH7A cells displayed distinct gene signatures with upregulated cytokines (IL1A, IL8, CXCLs) and downregulated inflammatory regulators (DOCK2, TLR4, TGFB2), reflecting an inflammatory bias distinct from the patient-derived RASF. This divergence highlights the limitations of immortalized cell models in capturing patient heterogeneity and disease complexity. However, the dual-system approach underscores the multifaceted role of ARL15 in regulating connective tissue architecture, inflammation, and immune response. These key findings position ARL15 as a promising therapeutic target, warranting further investigation in RA animal models and genomic medicine. Taken together, this work provides a compelling rationale to pursue ARL15 targeted interventions in RA management.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interleukin-17A causes osteoarthritis-like transcriptional changes in human osteoarthritis-derived chondrocytes and synovial fibroblasts in vitro 95%
- Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis 94%
- The macrophage reprogramming ability of antifolates reveals soluble CD14 as a potential biomarker for methotrexate response in rheumatoid arthritis 94%
Similar papers in this journal
- RORβ modulates a gene program that is protective against articular cartilage damage 95%
- Anti-inflammatory role of APRIL by modulating regulatory B cells in antigen-induced arthritis 94%
- High-Throughput Analysis of Lung Immune Cells in a Murine Model of Rheumatoid Arthritis-Associated Lung Disease 93%
Similar papers in this journal
- Reduction of pro-inflammatory effector functions through remodeling of fatty acid metabolism in CD8 + T-cells from Rheumatoid Arthritis patients 95%
- Identification and Evaluation of Serum Protein Biomarkers Which Differentiate Psoriatic from Rheumatoid Arthritis 91%
- The DNA methylation Profile of Undifferentiated Arthritis Patients Anticipates their Subsequent Differentiation to Rheumatoid Arthritis 91%
Similar papers in this journal
- Mast cells differentiated in synovial fluid and resident in osteophytes exalt the inflammatory pathology of osteoarthritis 94%
- Berberine delays onset of collagen induced arthritis through T cell suppression. 93%
- Joint degeneration in a mouse model of pseudoachondroplasia: ER stress, inflammation and autophagy blockage 92%
Similar papers in this journal
- Novel insights into the regulation of chemerin expression: role of acute-phase cytokines and DNA methylation 93%
- Effect of a retinoic acid analogue on BMP-driven pluripotent stem cell chondrogenesis 92%
- The Heparan Sulfate Proteoglycan Syndecan-1 Influences Local Bone Cell Communication via the RANKL/OPG Axis 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.