Adaptor SKAP-55 Binds p21ras Activating Exchange Factor RasGRP1 and Negatively Regulates the p21ras-ERK Pathway in T-Cells
Bibliographic record
Abstract
While the adaptor SKAP-55 mediates LFA-1 adhesion on T-cells, it is not known whether the adaptor regulates other aspects of signaling. SKAP-55 could potentially act as a node to coordinate the modulation of adhesion with downstream signaling. In this regard, the GTPase p21(ras) and the extracellular signal-regulated kinase (ERK) pathway play central roles in T-cell function. In this study, we report that SKAP-55 has opposing effects on adhesion and the activation of the p21(ras) -ERK pathway in T-cells. SKAP-55 deficient primary T-cells showed a defect in LFA-1 adhesion concurrent with the hyper-activation of the ERK pathway relative to wild-type cells. RNAi knock down (KD) of SKAP-55 in T-cell lines also showed an increase in p21(ras) activation, while over-expression of SKAP-55 inhibited activation of ERK and its transcriptional target ELK. Three observations implicated the p21(ras) activating exchange factor RasGRP1 in the process. Firstly, SKAP-55 bound to RasGRP1 via its C-terminus, while secondly, the loss of binding abrogated SKAP-55 inhibition of ERK and ELK activation. Thirdly, SKAP-55-/- primary T-cells showed an increased presence of RasGRP1 in the trans-Golgi network (TGN) following TCR activation, the site where p21(ras) becomes activated. Our findings indicate that SKAP-55 has a dual role in regulating p21(ras)-ERK pathway via RasGRP1, as a possible mechanism to restrict activation during T-cell adhesion.
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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) | 0.003 | 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".