Induction of neutrophil degranulation by S100A9 via a MAPK-dependent mechanism
Bibliographic record
Abstract
S100A9 is a proinflammatory protein, expressed abundantly in the cytosol of neutrophils and monocytes. High extracellular S100A9 concentrations have been correlated with chronic inflammatory diseases such as rheumatoid arthritis and Crohn's disease, as well as with phagocyte extravasation. This study tested the hypothesis that S100A9 induces degranulation in human neutrophils. S100A9 was found to up-regulate the surface expression of CD35 and CD66b, proteins contained in secretory vesicles and specific/gelatinase granules, respectively. In addition, gelatinase and albumin, stored, respectively, in specific/gelatinase granules and secretory vesicles, were detected in the supernatants of neutrophils stimulated with S100A9. In contrast, stimulation with S100A9 had no effect on CD63 expression or MPO secretion, two proteins contained in azurophilic granules. S100A9 induced the phosphorylation of the MAPKs, ERK1/2, p38, and JNK. Inhibition of p38 and JNK but not ERK1/2, with specific inhibitors (SB203580, JNKII, and PD98059, respectively), blocked neutrophil degranulation induced by S100A9. Taken together, these results support the hypothesis and clearly indicate that S100A9 induces the degranulation of secretory and specific/gelatinase granules but not of azurophilic granules in a process involving p38 and JNK and further support its classification as a DAMP.
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 | 0.001 |
| 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".