Suilysin-induced Platelet-Neutrophil Complexes Formation is Triggered by Pore Formation-dependent Calcium Influx
Bibliographic record
Abstract
Abstract Platelet activation and platelet–neutrophil interactions have been found to be involved in inflammation, organ failure and soft-tissue necrosis in bacterial infections. Streptococcus suis, an emerging human pathogen, can cause streptococcal toxic-shock syndrome (STSS) similarly to Streptococcus pyogenes. Currently, S. suis–platelet interactions are poorly understood. Here, we found that suilysin (SLY), the S. suis cholesterol-dependent cytolysin (CDC), was the sole stimulus of S. suis that induced platelet-neutrophil complexes (PNC) formation. Furthermore, P-selectin released in α-granules mediated PNC formation. This process was triggered by the SLY-induced pore forming-dependent Ca2+ influx. Moreover, we demonstrated that the Ca2+ influx triggered an MLCK-dependent pathway playing critical roles in P-selectin activation and PNC formation, however, PLC-β-IP3/DAG-MLCK and Rho-ROCK-MLCK signalling were not involved. Additionally, the “outside-in” signalling had a smaller effect on the SLY-induced P-selectin release and PNC formation. Interestingly, other CDCs including pneumolysin and streptolysin O have also been found to induce PNC formation in a pore forming-dependent Ca2+ influx manner. It is possible that the bacterial CDC-mediated PNC formation is a similar response mechanism used by a wide range of bacteria. These findings may provide useful insight for discovering potential therapeutic targets for S. suis-associated STSS.
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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".