Regulation of RasGRP1 Function in T Cell Development and Activation by Its Unique Tail Domain
Bibliographic record
Abstract
The Ras-guanyl nucleotide exchange factor RasGRP1 plays a critical role in T cell receptor-mediated Erk activation. Previous studies have emphasized the importance of RasGRP1 in the positive selection of thymocytes, activation of T cells, and control of autoimmunity. RasGRP1 consists of a number of well-characterized domains, which it shares with its other family members; however, RasGRP1 also contains an ~200 residue-long tail domain, the function of which is unknown. To elucidate the physiological role of this domain, we generated knock-in mice expressing RasGRP1 without the tail domain. Further analysis of these knock-in mice showed that thymocytes lacking the tail domain of RasGRP1 underwent aberrant thymic selection and, following TCR stimulation, were unable to activate Erk. Furthermore, the deletion of the tail domain led to enhanced CD4(+) T cell expansion in aged mice, as well as the production of autoantibodies. Mechanistically, the tail-deleted form of RasGRP1 was not able to traffic to the cell membrane following stimulation, indicating a potential reason for its inability to activate Erk. While the DAG-binding C1 domain of RasGRP1 has long been recognized as an important factor mediating Erk activation, we have revealed the physiological relevance of the tail domain in RasGRP1 function and control of Erk signaling.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".