Tyrosine Phosphorylation in Immune Cells: Direct and Indirect Effects on Toll-Like Receptor-Induced Proinflammatory Cytokine Production
Bibliographic record
Abstract
Tyrosine phosphorylation is a key means of signal transduction in the immune system, initiating signals from antigen receptors, integrins, and cytokine receptors. Tyrosine phosphorylation is regulated by the balance of tyrosine kinase and tyrosine phosphatase activities. Src family kinases are prevalent in leukocytes and play critical roles in many signaling pathways present in immune cells. For example, they are the key kinases that phosphorylate both immunoreceptor tyrosinebased activation and inhibitory motifs. CD45 is a leukocyte-specific, transmembrane protein tyrosine phosphatase and an important regulator of Src family kinase activity. Here, we briefly review the importance of tyrosine phosphorylation in key signaling pathways in immune cells and then review the accumulating evidence for tyrosine phosphorylation in Toll-like receptor (TLR) signaling leading to proinflammatory cytokine and type I interferon production. We examine how tyrosine phosphorylation directly impacts TLR signaling pathways and review the involvement of specific tyrosine kinases and phosphatases. Finally, we consider how tyrosine phosphorylation signals from other signaling pathways integrate with the TLR signaling pathway to modulate proinflammatory cytokine production.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".