EphrinB1 Is Essential in T-cell-T-cell Co-operation during T-cell Activation
Bibliographic record
Abstract
Eph kinases are the largest family of receptor tyrosine kinases, and their ligands are ephrins (EFNs), which are also cell surface molecules. We have very limited knowledge about the expression and function of these kinases and their ligands in the immune system. In this study we investigated the effect of EFNB1 on mouse T-cells. EFNB1 and the Eph kinases it interacts with (collectively called EFNB1 receptors (EFNB1R)) were expressed on T-cells, B cells, and monocytes/macrophages. Some T-cells were double positive for EFNB1 and EFBB1R. Solid phase EFNB1 in the presence of suboptimal TCR ligation augmented T-cell responses in terms interferon-gamma secretion, proliferation, and cytotoxic T lymphocyte activity but not interleukin-2 production. After T-cell receptor (TCR) ligation, EFNB1R congregated to TCR caps, and then both of them translocated to raft caps. This provides a morphological basis for EFNB1R to enhance TCR signaling. Further downstream of the signaling pathway, EFNB1R stimulation led to increased LAT (linker for activation of T-cells) phosphorylation and p44/42 and p38 MAPK activation. Similar to CD28 costimulation, EFNB1R costimulation was insensitive to cyclosporin A inhibition. On the other hand, unlike the former, EFNB1R costimulation failed to activate Akt, which is essential in triggering interleukin-2 production. Our study suggests that EFNB1 is pivotal in T-cell-T-cell costimulation and in reducing T-cell response threshold to antigen stimulation.
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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 | 0.001 |
| 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".