A Model of Platelet Aggregation Involving Multiple Interactions of Thrombospondin-1, Fibrinogen, and GPIIbIIIa Receptor
Bibliographic record
Abstract
Thrombospondin-1 (TSP) may, after secretion from platelet alpha granules, participate in platelet aggregation, but its mode of action is poorly understood. We evaluated the capacity of TSP to form inter-platelet cross-bridges through its interaction with fibrinogen (Fg), using either Fg-coated beads or Fg bound to the activated GPIIbIIIa integrin (GPIIbIIIa*) immobilized on beads or on activated fixed platelets (AFP), i.e. in a system free of platelet signaling and secretion mechanisms. Aggregation at physiological shear rates (100-2000 s(-1)) was studied in a microcouette device and monitored by flow cytometry. Soluble TSP bound to and induced aggregation of Fg-coated beads dose-dependently, which could be blocked by the amino-terminal heparin-binding domain of TSP, TSP18. Soluble TSP did not bind to GPIIbIIIa*-coated beads or AFP, unless they were preincubated with Fg. The interaction of soluble TSP with Fg-GPIIbIIIa*-coated beads or Fg-AFP resulted in the formation of aggregates via Fg-TSP-Fg cross-bridges, as demonstrated in a system where direct cross-bridges mediated by GPIIbIIIa*-Fg on one particle and free GPIIbIIIa* on a second particle were blocked by the RGD mimetic Ro 44-9883. Soluble TSP increased the efficiency of Fg-mediated aggregation of AFP by 30-110% over all shear rates and GPIIbIIIa* occupancies evaluated. Surprisingly, TSP binding to Fg already bound to its GPIIbIIIa* receptor appears to block the ability of this occupied Fg to recognize another GPIIbIIIa* receptor, but this TSP can indeed cross-bridge to another Fg molecule on a second platelet. Finally, TSP-coated beads could directly coaggregate at shear rates from 100 to 2000 s(-1). Our studies provide a model for the contribution of secreted TSP in reinforcing inter-platelet interactions in flowing blood, through direct Fg-TSP-Fg and TSP-TSP cross-bridges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".