HPK1 competes with ADAP for SLP‐76 binding and <i>via</i> Rap1 negatively affects T‐cell adhesion
Bibliographic record
Abstract
The hematopoietic progenitor kinase 1 (HPK1) signals into MAPK and NFκB pathways downstream of immunoreceptors, but enigmatically is a negative regulator of leukocytes. Here, we report a novel role for HPK1 in regulating the activation of the adhesion molecule leukocyte function-associated antigen-1 (LFA-1). Upon TCR stimulation, mediated by binding of adhesion and degranulation promoting adaptor protein (ADAP) to SLP-76, a ternary complex composed of ADAP/55-kDa src kinase associated phosphoprotein (SKAP-55) and RIAM translocates to the membrane and causes membrane recruitment of the active small GTPase Ras-related protein 1 (Rap1). Active Rap1, via its binding to RapL (regulator for cell adhesion and polarization enriched in lymphoid tissues), mediates LFA-1 integrin activation. We show here that HPK1, which also binds SLP-76, compete with ADAP for SLP-76 binding. In addition, HPK1 dampens Rap1 activation, resulting in decreased LFA-1 activity. Analysis of HPK1-deficient T cells revealed increased ADAP recruitment to SLP-76 and elevated Rap1 activation in those cells, leading to increased adhesion to ICAM-1 and cell spreading. Altogether, these results describe a novel function for HPK1 in linking TCR signaling to cell adhesion regulation and provide a mechanistic explanation for the negative regulatory role of HPK1 in T-cell biology.
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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".