GTPase-activating protein (RASAL1) associates with ZAP-70 of the TCR and negatively regulates T-cell proliferation and anti-tumor immunity
Bibliographic record
Abstract
Abstract T-cell activation is needed for responses to antigen and controls responses to foreign antigen and cancer neo-antigens in immunotherapy. The full range of signaling events that inhibit T-cell activation and limit anti-tumor reactivity is unclear. Previous studies have identified the importance of phosphatases and E-3 ligases as inhibitors of activation. In this study, we show by a combination of tandem affinity chromatography, co-precipitation and proximity ligation analysis (PLA) that RASAL1, a novel GTPase-activating protein (GAP), binds to ZAP-70 of the TCR complex and inhibits anti-CD3 activation of ERKs and proliferation in T-cells. RASAL1 inhibited via two pathways where it binds and inhibits ZAP-70 activity, and acts as a GAP to inhibit the p21ras-ERK pathway. As a negative regulator, its expression is induced as a consequence of T-cell activation, where it reduced in vitro responses to anti-CD3 and to antigenic peptides presented by dendritic cells (DCs), while having no effect on T-cell dwell times. Further, we show that siRNA knock-down of RASAL1 expression increased in vivo CD4+ T-cell responses to peptide antigen while reducing the pulmonary metastasis of B16 melanoma and the growth of solid EL-4 lymphoma tumors. This anti-tumor effect was accompanied by a marked increase in CD8+ tumor-infiltrating T-cells (TILs) expressing effector molecules granzyme B (GZMB) and interferon γ-1 (IFNγ1). These findings identify RASAL1 as a new negative regulator of T-cell activation and tumor immunity.
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) | 0.000 | 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) | 0.001 | 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".