GEF-H1 Is Required for Colchicine Inhibition of Neutrophil Rolling and Recruitment in Mouse Models of Gout
Bibliographic record
Abstract
Abstract Gout is a painful arthritic inflammatory disease caused by buildup of monosodium urate (MSU) crystals in the joints. Colchicine, a microtubule-depolymerizing agent that is used in prophylaxis and treatment of acute gout flare, alleviates the painful inflammatory response to MSU crystals. Using i.p. and intra-articular mouse models of gout-like inflammation, we found that GEF-H1/GEF-H1/AHRGEF2, a microtubule-associated Rho-GEF, was necessary for the inhibitory effect of colchicine on neutrophil recruitment. GEF-H1 was required for neutrophil polarization in response to colchicine, characterized by uropod formation, accumulation of F-actin and myosin L chain at the leading edge, and accumulation of phosphorylated myosin L chain, flotillin-2, and P-selectin glycoprotein ligand-1 (PSGL-1) in the uropod. Wild-type neutrophils that were pre-exposed to colchicine failed to roll or accumulate on activated endothelial monolayers, whereas GEF-H1 knockout (GEF-H1−/−) neutrophils were unaffected by treatment with colchicine. In vivo, colchicine blocked MSU-induced recruitment of neutrophils to the peritoneum and the synovium in wild-type mice, but not in GEF-H1−/− mice. Inhibition of macrophage IL-1β production by colchicine was independent of GEF-H1, supporting a neutrophil-intrinsic mode of action. Our results suggest that the anti-inflammatory effects of colchicine in acute gout-like inflammation can be accounted for by inhibition of neutrophil-rolling interactions with the inflamed vasculature and occurs through GEF-H1–dependent neutrophil stimulation by colchicine. These results contribute to our understanding of the therapeutic action of colchicine, and could inform the application of this drug in other conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".