The induction of SHP-1 degradation by TAOK3 ensures the responsiveness of T cells to TCR stimulation
Bibliographic record
Abstract
Thousand-and-one–amino acid kinase 3 (TAOK3) is a serine and threonine kinase that belongs to the STE-20 family of kinases. Its absence reduces T cell receptor (TCR) signaling and increases the interaction of the tyrosine phosphatase SHP-1, a major negative regulator of proximal TCR signaling, with the kinase LCK, a component of the core TCR signaling complex. Here, we used mouse models and human cell lines to investigate the mechanism by which TAOK3 limits the interaction of SHP-1 with LCK. The loss of TAOK3 decreased the survival of naïve CD4 + T cells by dampening the transmission of tonic and ligand-dependent TCR signaling. In mouse T cells, Taok3 promoted the secretion of interleukin-2 (IL-2) in response to TCR activation in a manner that depended on Taok3 gene dosage and on Taok3 kinase activity. TCR desensitization in Taok3 −/− T cells was caused by an increased abundance of Shp-1, and pharmacological inhibition of Shp-1 rescued the activation potential of these T cells. TAOK3 phosphorylated threonine-394 in the phosphatase domain of SHP-1, which promoted its ubiquitylation and proteasomal degradation. The loss of TAOK3 had no effect on the abundance of SHP-2, which lacks a residue corresponding to SHP-1 threonine-394. Modulation of SHP-1 abundance by TAOK3 thus serves as a rheostat for TCR signaling and determines the activation threshold of T lymphocytes.
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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) | 0.001 | 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".