Mouse EphrinB3 Augments T-cell Signaling and Responses to T-cell Receptor Ligation
Bibliographic record
Abstract
Ephrins (EFN) are cell-surface ligands of Ephs, the largest family of cell-surface receptor tyrosine kinases. The function of EFNs in the immune system has not been well studied, although some EFNs and Ephs are expressed at high levels on certain leukocytes. We report here that EFNB3 and its receptors (collectively called EFNB3Rs, as EFNB3 binds to multiple EphBs) were expressed in peripheral T cells and monocytes/macrophages, with T cells being the dominant EFNB3+ and EFNB3R+ cell type. Solid-phase EFNB3-Fc in the presence of suboptimal anti-CD3 crosslinking enhanced T-cell responses in terms of proliferation, activation marker expression, interferon-gamma but not interleukin-2 production, and cytotoxic T-cell activity. EFNB3R costimulation in the presence of phorbol 12-myristate 13- acetate was insensitive to cyclosporin A, similar to CD28 costimulation, suggesting they might share a part of the signaling pathway. After crosslinking, T-cell receptor and EFNB3R congregated into aggregated rafts, and this provided a morphological basis for signaling pathways of T-cell receptor and EFNB3R to interact. Solid-phase EFNB3-Fc augmented p38 and p44/42 MAPK activation further downstream of the signaling pathway. These data suggest that EFNB3 is important in T-cell/T-cell and T-cell/antigen-presenting cell collaboration to enhance T-cell activation and function.
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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 | 0.000 | 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".