Platelet activation by<i>Streptococcus sanguinis</i>is accompanied by MAP kinase phosphorylation
Bibliographic record
Abstract
There is increasing interest in the role of infections in atherothrombotic conditions. In particular, bacteria, notably those of oral origin, have been shown to activate platelets using a variety of mechanisms. Previous studies have shown that S. sanguinis strain 2017-78 induces platelet aggregation which requires the presence of both vWF and IgG. This aggregation is accompanied by the consecutive phosphorylation/desphosphorylation/rephosphorylation of several signalling proteins. The first two phases are thromboxane-dependent whereas the rephosphorylation phase is mediated by engagement of the αIIbβ3 integrin. Here signalling events, specifically the potential role of MAP kinases, associated with S. sanguinis strain 2017-78-induced platelet activation have been further examined using an immunoblotting approach. The addition of S. sanguinis strain 2017-78 caused a similar triphasic phosphorylation profile of the platelet MAP kinase Erk2 to that seen with other phosphoproteins. Pretreatment with aspirin or RGDS did not affect 2017-78-induced Erk2 phosphorylation or desphosphorylation but both inhibited the rephosphorylation phase. In contrast the level of 2017-78-induced platelet MAP kinase p38 phosphorylation remained at an elevated level, and this was unaffected by aspirin. Similarly, 2017-78-induced cPLA(2) phosphorylation remained above basal levels during the aggregation process. The p38 inhibitor SB203580 inhibited S. sanguinis-induced aggregation with no effect on the phosphorylation of either p38 or cPLA(2). Thus the current study demonstrates the activation of both the Erk2 and p38 forms of MAP kinases, and of cPLA(2), in platelets stimulated with S. sanguinis strain 2017-78, and is consistent with a role for Erk2, but not for p38, in the cPLA(2) phosphorylation in response to S. sanguinis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".