Regulation of the Tyrosine Kinase Pyk2 by Calcium Is through Production of Reactive Oxygen Species in Cytotoxic T Lymphocytes
Bibliographic record
Abstract
Pyk2 was identified as a Ca(2+)-dependent kinase, however, the regulation of Pyk2 by Ca(2+) in T cells remains controversial. We found that Ca(2+) mobilization preferentially induced Pyk2 phosphorylation in cytotoxic T lymphocytes (CTL). Furthermore, Pyk2 phosphorylation in CTL was not absolutely Ca(2+) dependent but relied on the strength of T cell receptor stimulation. Ionomycin-stimulated Pyk2 phosphorylation did not require calmodulin activity, because phosphorylation was not inhibited by the calmodulin inhibitor W7, and we detected no Ca(2+)-regulated association between Pyk2 and calmodulin. Ca(2+)-stimulated Pyk2 phosphorylation was dependent on Src-family kinase activity, even at the Pyk2 autophosphorylation site. We sought to identify a Ca(2+)-regulated pathway that could trigger Pyk2 phosphorylation in T cells and found that ionomycin stimulated the production of reactive oxygen species and an H(2)O(2) scavenger inhibited ionomycin-induced Pyk2 phosphorylation. Additionally, H(2)O(2) induced strong Erk activation and ionomycin-stimulated Pyk2 phosphorylation was Erk dependent. These data support the conclusion that Ca(2+) mobilization induces the production of reactive oxygen species, which in turn activate the Erk pathway, leading to Src-family kinase-dependent Pyk2 phosphorylation. Our data demonstrate that Pyk2 is not a Ca(2+)-dependent kinase in T cells but instead, increased intracellular Ca(2+) induces Pyk2 phosphorylation through production of reactive oxygen species. These findings are consistent with the possibility that Pyk2 acts as an early sensor of numerous extracellular signals that trigger a Ca(2+) flux and/or reactive oxygen species to amplify tyrosine phosphorylation signaling events.
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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".