Chemotactic Factor-Induced Recruitment and Activation of Tec Family Kinases in Human Neutrophils. II. Effects of LFM-A13, a Specific Btk Inhibitor
Bibliographic record
Abstract
Tyrosine phosphorylation events play major roles in the initiation and regulation of several functional responses of human neutrophils stimulated by chemotactic factors such as the bacterially derived tripeptide formylmethionyl-leucyl-phenylalanine (fMet-Leu-Phe). However, the links between the G protein-coupled receptors, the activation of the tyrosine kinases, and the initiation of neutrophil functional responses remain unclear. In the present study we assessed the effects of a Btk inhibitor, leflunomide metabolite analog (LFM-A13), on neutrophils. LFM-A13 decreased the tyrosine phosphorylation induced by fMet-Leu-Phe and inhibited the production of superoxide anions and the stimulation of adhesion, chemotaxis, and phospholipase D activity. We observed a decreased accumulation of phosphatidylinositol-3,4,5-trisphosphate in response to fMet-Leu-Phe in LFM-A13-pretreated cells even though the inhibitor had no direct effect on the lipid kinase activity of the p110 gamma or p85/p110 phosphatidylinositol 3-kinases or on the activation of p110 gamma by fMet-Leu-Phe. The phosphorylation of Akt and of extracellular signal-regulated kinases 1/2 and p38 were similarly inhibited by LFM-A13. LFM-A13 also negatively affected the translocation of Rac-2, RhoA, ADP ribosylation factor-1, Tec, Bmx, and Btk induced by fMet-Leu-Phe. The results of this study provide evidence for an involvement of Btk and possibly other Tec kinase family members in the regulation of the functional responsiveness of human neutrophils and link these events, in part at least, to the modulation of levels of phosphatidylinositol-3,4,5-trisphosphate.
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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".