Selective disruption of Traf1/cIAP2 interaction attenuates inflammatory responses and rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: Tumor necrosis factor receptor-associated factor 1 (TRAF1) is an immune signaling adapter protein linked to increased susceptibility to rheumatoid arthritis (RA). TRAF1 has dual roles in regulating NF-κB and MAPK signaling: it promotes signaling through its association with cellular inhibitor of apoptosis 2 (cIAP2) downstream of certain tumor necrosis factor receptor (TNFR) family members but inhibits Toll-like receptor (TLR) signaling by limiting linear ubiquitination of key signaling proteins. In this study, we investigated whether selectively targeting TRAF1/cIAP2 interaction would lower inflammation and reduce severity of RA. METHODS: We employed CRISPR/Cas9-mediated mediated gene editing to modify TRAF1 and specifically abrogate its interaction with cIAP2 in human macrophage cell lines and in mice. Biochemical studies were then employed to assess inflammatory signaling and cytokine production in gene edited macrophages. The collagen antibody-induced arthritis (CAIA) model of RA was used to trigger joint inflammation in mice. RESULTS: We identify a critical mutation in TRAF1 (V203A in humans, V196A in mice) that disrupts its interaction with cIAP2, leading to a significant reduction in TLR signaling and downstream inflammation in human and murine macrophages. We demonstrate that TRAF1 is recruited to the TLR4 complex and is indispensable for the recruitment of cIAP2, facilitating TAK1 phosphorylation and the activation of NF-κB and MAPK signaling pathways. Remarkably, mice harboring the TRAF1 V196A mutation are protected from LPS-induced septic shock and exhibit markedly reduced joint inflammation and disease severity in the CAIA model of RA. CONCLUSION: These findings reveal a previously unrecognized and crucial role for the TRAF1/cIAP2 axis in promoting inflammation and offer a promising foundation for the development of novel therapeutic strategies for inflammatory conditions, such as RA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".