LACC1 Regulates TNF and IL-17 in Mouse Models of Arthritis and Inflammation
Bibliographic record
Abstract
Abstract Both common and rare genetic variants of laccase domain-containing 1 (LACC1, previously C13orf31) are associated with inflammatory bowel disease, leprosy, Behcet disease, and systemic juvenile idiopathic arthritis. However, the functional relevance of these variants is unclear. In this study, we use LACC1-deficient mice to gain insight into the role of LACC1 in regulating inflammation. Following oral administration of Citrobacter rodentium, LACC1 knockout (KO) mice had more severe colon lesions compared with wildtype (WT) controls. Immunization with collagen II, a collagen-induced arthritis (CIA) model, resulted in an accelerated onset of arthritis and significantly worse arthritis and inflammation in LACC1 KO mice. Similar results were obtained in a mannan-induced arthritis model. Serum and local TNF in CIA paws and C. rodentium colons were significantly increased in LACC1 KO mice compared with WT controls. The percentage of IL-17A–producing CD4+ T cells was elevated in LACC1 KO mice undergoing CIA as well as aged mice compared with WT controls. Neutralization of IL-17, but not TNF, prevented enhanced mannan-induced arthritis in LACC1 KO mice. These data provide new mechanistic insight into the function of LACC1 in regulating TNF and IL-17 during inflammatory responses. We hypothesize that these effects contribute to immune-driven pathologies observed in individuals carrying LACC1 variants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".