Modulation of Human Neutrophil Responses to CD32 Cross-Linking by Serine/Threonine Phosphatase Inhibitors: Cross-Talk Between Serine/Threonine and Tyrosine Phosphorylation
Bibliographic record
Abstract
The interplay between serine/threonine and tyrosine phosphorylation was studied in human neutrophils. The direct effects of calyculin and okadaic acid, potent inhibitors of PP1 and PP2A serine/threonine phosphatases, on the patterns of neutrophil phosphorylation, and their effects on the responses of neutrophils to CD32 cross-linking were monitored. After a 2-min incubation with 10-6 M calyculin, a transient tyrosine phosphorylation of a subset of proteins, among which Cbl and Syk, was observed. After a longer incubation (>5 min) with calyculin, concomitant with an accumulation of serine and threonine phosphorylation, neutrophil responses to CD32 cross-linking were selectively altered. Tyrosine phosphorylation of Cbl in response to CD32 cross-linking was inhibited by calyculin, and this inhibition was linked with a slower electrophoretic mobility of Cbl as a consequence of its phosphorylation on serine/threonine residues. However, tyrosine phosphorylation of Syk and of the receptor itself were not affected. Furthermore, the mobilization of intracellular calcium stimulated by CD32 cross-linking was totally abrogated by calyculin. Finally, the stimulation of superoxide production observed in response to CD32 cross-linking was enhanced in calyculin-treated cells. These results suggest that serine/threonine phosphorylation events regulate the signaling pathways activated by CD32 cross-linking in neutrophils and identify a novel mechanism of modulation of the functional responsiveness of human neutrophils to CD32 cross-linking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".