IL-17A and TNF synergistically drive expression of proinflammatory mediators in synovial fibroblasts via IκBζ-dependent induction of ELF3
Bibliographic record
Abstract
OBJECTIVE: IL-17A and TNF act in synergy to induce proinflammatory mediators in synovial fibroblasts thus contributing to diseases associated with chronic arthritis. Many of these factors are regulated by transcription factor E74-like factor-3 (ELF3). Therefore, we sought to investigate ELF3 as a downstream target of IL-17A and TNF signalling and to characterize its role in the molecular mechanism of synergy between IL-17A and TNF. METHODS: Regulation of ELF3 expression by IL-17A and TNF was studied in synovial fibroblasts of RA and OA patients and RA synovial explants. Signalling leading to ELF3 mRNA induction and the impact of ELF3 on the response to IL-17A and TNF were studied using siRNA, transient overexpression and signalling inhibitors in synovial fibroblasts and HEK293 cells. RESULTS: ELF3 was marginally affected by IL-17A or TNF alone, but their combination resulted in high and sustained expression. ELF3 expression was regulated by the nuclear factor-κB (NF-κB) pathway and CCAAT/enhancer-binding protein β (C/EBPβ), but its induction required synthesis of the NF-κB co-factor IκB (inhibitor of NF-κB) ζ. siRNA-mediated depletion of ELF3 attenuated the induction of cytokines and matrix metalloproteinases by the combination of IL-17A and TNF. Overexpression of ELF3 or IκBζ showed synergistic effect with TNF in upregulating expression of chemokine (C-C motif) ligand 8 (CCL8), and depletion of ELF3 abrogated CCL8 mRNA induction by the combination of IκBζ overexpression and TNF. CONCLUSION: Altogether, our results establish ELF3 as an important mediator of the synergistic effect of IL-17A and TNF in synovial fibroblasts. The findings provide novel information of the pathogenic mechanisms of IL-17A in chronic arthritis and implicate ELF3 as a potential therapeutic target.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".