TNF-like Ligand 1A (TL1A) Gene Knockout Leads to Ameliorated Collagen-Induced Arthritis in Mice: Implication of TL1A in Humoral Immune Responses
Bibliographic record
Abstract
TNF-like ligand 1A (TL1A), also known as TNFSF15, is a member of the TNF superfamily. Its known receptor is death receptor 3 (DR3). In humans, TL1A also binds to a secreted TNF family member called decoy receptor 3, which interferes with the interaction between TL1A and DR3. TL1A/DR3 signal has been implicated in several autoimmune diseases in animal models as well as in clinical conditions. We generated TL1A gene knockout (KO) mice to assess its role in collagen-induced arthritis (CIA), a mouse model of human rheumatoid arthritis. The KO mice were fertile and had no visible anomalies. Their lymphoid organ size and cellularity, T and B cell subpopulations, Th cell and regulatory T cell development in vivo and in vitro, and antiviral immune responses were comparable to those of wild-type mice. However, the KO mice presented ameliorated CIA in terms of clinical scores, disease incidence, and pathological scores. The KO mice had reduced titers of pathogenic anti-collagen Abs in the sera. No apparent defect was found in the function of follicular Th cells. We revealed that plasma cells but not B cells expressed high levels of DR3 and were direct targets of TL1A. In the presence of TL1A, they survived better and produced more pathogenic Ab. This study presented novel knowledge about the role of TL1A in humoral immune responses and its mechanism of action in CIA pathogenesis.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".