Role of <i>EFNB1</i> and <i>EFNB2</i> in Mouse Collagen‐Induced Arthritis and Human Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: EFNB1 and EFNB2 are ligands for Eph receptor tyrosine kinases. This study was undertaken to investigate how the expression of Efnb1 and Efnb2 on murine T cells influences the pathogenesis of collagen-induced arthritis (CIA) and to assess correlations between the T cell expression of these 2 molecules and measures of disease activity in patients with rheumatoid arthritis (RA). METHODS: CIA was studied in mice with T cell-specific deletion (double gene knockout [dKO]) of both Efnb1 and Efnb2. Expression of EFNB1 and EFNB2 messenger RNA (mRNA) in peripheral blood T cells from patients with RA was determined by quantitative reverse transcription- polymerase chain reaction. RESULTS: In dKO mice, clinical scores of arthritis were reduced compared to those in wild-type (WT) control mice. Serum collagen-specific antibody titers in dKO mice were lower than those in WT mice. In analyses based on equal cell numbers, dKO mouse T cells, as compared to WT mouse T cells, provided vastly inferior help to B cells in the production of collagen-specific antibodies in vitro. T cells from dKO mice were compromised in their ability to migrate to the arthritic paws in vivo and in their ability to undergo chemotaxis toward CXCL12 in vitro. Deletion mutation of Efnb1 and Efnb2 intracellular tails revealed critical regions in controlling T cell chemotaxis. T cells from RA patients expressed higher EFNB1 mRNA levels, which correlated with RA symptoms and laboratory findings. CONCLUSION: Efnb1 and Efnb2 in T cells are essential for pathogenic antibody production and for T cell migration to the inflamed paws in mice with CIA. These findings suggest that the expression of EFNB1 in T cells might be a useful parameter for monitoring RA disease activity and treatment responses.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".