Platelet Src family kinases: A tale of reversible phosphorylation
Bibliographic record
Abstract
Sarcoma (Src) family kinases (SFKs) have occupied a central place in platelet research for over 40 years. Discovered by virologists and oncologists as the proto proto-oncogene, Src tyrosine kinase spurred a phenomenal burst of research on reversible tyrosine phosphorylation and signal transduction. For a time, platelets were adopted as the model of choice for studying the biological functions of Src, owing to their ease of isolation, high Src expression, and lack of a nucleus, only to be abandoned due to challenges of culturing and manipulating using common molecular biology-based techniques. For platelet biologists, SFKs have remained an important area of investigation, initiating and amplifying signals from all major adhesion, activation, and inhibitory receptors, including the integrin αIIbβ3, the collagen receptor complex glycoprotein VI–Fc receptor γ-chain, the G protein–coupled ADP receptor P2Y12 and the inhibitory receptors platelet endothelial cell adhesion molecule-1 and G6b-B. The vital roles of SFKs in platelets is highlighted by the severe phenotypes of null and gain-of-function mutations in SFKs in mice and humans, and effects of pharmacologic inhibitors on platelet activation, thrombosis, and hemostasis. The recent description of critical regulators of SFKs in platelets, namely, C-terminal Src kinase (Csk), Csk homologous kinase (Chk), the receptor-type protein-tyrosine phosphatase receptor type J (PTPRJ) helps explain some of the bleeding side effects of tyrosine kinase inhibitors and are novel therapeutic targets for regulating the thrombotic and hemostatic capacity of platelets. Recent findings from Chk, Csk, and PTPRJ knockout mouse models highlighted that SFKs are able to autoinhibit by phosphorylating their C-terminal tyrosine residues, providing fundamental insights into SFK autoregulation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".