EphB6 crosslinking results in costimulation of T cells
Bibliographic record
Abstract
Erythropoietin-producing hepatocyte (Eph) kinases represent the largest receptor tyrosine kinase family. Some of them are expressed in the T cell compartment, but their function in T cells is unknown. In peripheral blood, EphB6 was predominantly expressed on T cells, and was upregulated after culture. EphB6 crosslinking by anti-EphB6 mAb or ephrinB2 in the presence of suboptimal T cell receptor (TCR) stimulation led to drastic T cell proliferation, suggesting that EphB6 can co-stimulate T cells. The proliferation was accompanied by enhanced production of several lymphokines, such as IFN-gamma, IL-6, IL-10, TGF-beta, TNF-alpha, and GM-CSF, but not IL-2 and IL-4. Sorted EphB6(+) T cells had significantly stronger response to anti-CD3 and anti-CD28 stimulation than EphB6(-) T cells had. Taken together, these data suggest an important role of EphB6 in normal T cell activation. Within two minutes of anti-CD3 and anti-CD28 stimulation, EphB6 aggregated and colocalized with TCR, and this provides a morphological basis for EphB6 to enhance TCR signaling. The capping was followed by p38 MAPK activation, showing that EphB6 is capable of signaling, in spite of its lack of intrinsic kinase activity. This study demonstrates that interaction between EphB6 and its ligands facilitates T cell responses to antigen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".