PDI AND ERP57 CO-CLUSTER IN PLATELETS AND THEIR MOVEMENT IS REGULATED BY ACTIN POLYMERIZATION
Bibliographic record
Abstract
Introduction The thiolisomerases PDI and ERp57 are released to the platelet surface and contribute to several cellular responses. PDI is present in subcellular structures, not corresponding to α - and δ -granules, and lysosomes. It was, however, shown to co-localise with TLR9 in T-granules. The subcellular localisation of other platelet thiolisomerases or their mechanisms of translocation to the cell surface have not been established. Hypothesis and Methods Using spinning disk confocal microscopy we explored whether ERp57 and PDI co-localise in resting and activated platelets and whether actin polymerization regulates their translocation to the platelet surface. Results In resting platelets, PDI and ERp57 were organized in granular structures both on the platelet surface and in the cytoplasm where they co-localised (Pearson's correlation coefficient=0.597±0.08, mean±SD). They neither co-distributed with P-selectin, an α -granule membrane marker, nor with TLR9, a marker of T-granules. Upon platelet activation, the thiolisomerases still co-distributed and migrated to the surface (Pearson's correlation coefficient=0.533±0.08, mean±SD). Inhibiting actin polymerization with latrunculin A, decreased P-selectin exposure on the platelet surface and prevented agonist-induced platelet shape change, as shown by tubulin staining. Importantly, latrunculin A also blocked the translocation of PDI and ERp57 from internal granular structures to the platelet surface. Conclusions In resting platelets PDI and ERp57 are organized, and partially co-localised, in punctuate structures different from α- or T-granules. Actin polymerization during platelet activation exerts a fundamental role in the relocalisation of PDI and ERp57 to the platelet surface, thus suggesting that thiolisomerases undergo a bonfide secretory mechanism.
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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".