The tyrosine kinase FER is responsible for the capacitation-associated increase in tyrosine phosphorylation in murine sperm
Bibliographic record
Abstract
Sperm capacitation is required for fertilization. At the molecular level, this process is associated with fast activation of protein kinase A. Downstream of this event, capacitating conditions lead to an increase in tyrosine phosphorylation. The identity of the tyrosine kinase(s) mediating this process has not been conclusively demonstrated. Recent experiments using stallion and human sperm have suggested a role for PYK2 based on the use of small molecule inhibitors directed against this kinase. However, crucially, loss-of-function experiments have not been reported. Here, we used both pharmacological inhibitors and genetically modified mice models to investigate the identity of the tyrosine kinase(s) mediating the increase in tyrosine phosphorylation in mouse sperm. Similar to stallion and human, PF431396 blocks the capacitation-associated increase in tyrosine phosphorylation. Yet, sperm from Pyk2(-/-) mice displayed a normal increase in tyrosine phosphorylation, implying that PYK2 is not responsible for this phosphorylation process. Here, we show that PF431396 can also inhibit FER, a tyrosine kinase known to be present in sperm. Sperm from mice targeted with a kinase-inactivating mutation in Fer failed to undergo capacitation-associated increases in tyrosine phosphorylation. Although these mice are fertile, their sperm displayed a reduced ability to fertilize metaphase II-arrested eggs in vitro.
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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) | 0.001 | 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) | 0.002 | 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".