Abstract 17411: De Novo Protein Synthesis of Alpha-Toxin Activated Platelets
Bibliographic record
Abstract
Introduction: Endovascular infections with bacteria are often devastating with subsequent high morbidity and mortality. Exo- and or endotoxins of bacteria can activate endothelial cells, leukocytes and platelets. Platelets are first line defence they accumulate at sites of vascular injury or infection. Platelet activation is a necessary step in thrombus formation. Nevertheless, stimulation of platelets will result in de novo protein synthesis despite missing nucleus since platelets armed with translational equipment. Methods: In the present study we determined the effect of staphylococcus aureus α-toxin on platelet activation and de novo protein synthesis analysed with 2-D gels, proteomics and phosphorylation analysis. Results: α-toxin induced platelet activation resulted in modulation of de novo protein synthesis of DJ-1 Protein, ras suppressor protein1, PLEK protein, fumaryl aceto acetase sowie das coronin actin binding protein. This synthesis was time- and concentration-dependent and was markedly increased when platelets adhered to collagen or fibrinogen and required ligation of α IIb β 3 . Accumulation of protein synthesis in platelets was blocked by global translational inhibitors and attenuated by inhibitors that regulate signalling through the mammalian Target of Rapamycin (mTOR). In addition with phosphorylation analysis we were able demonstrate modulation of threonine phosphorylation of fumaryl aceto acetase, phosphor threonin signal of coronin actin binding protein, phosphorylation of peroxiredoxin-6, phosphorylation of tropomyosin-2, phosphothreonin signal of H + transporting two sector ATPase upon α-toxin stimulation. Conclusion: Interactions with staphylococcus aureus α-toxin and platelets might lead to their activation and de novo protein synthesis. These results suggest that platelets have an important role in inflammation besides their aggregating duties in inflammatory disease.
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) | 0.003 | 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; 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".