In the Presence of Phospholipids, Glycosaminoglycans Potentiate Factor Xa-Mediated Protein C Activation by Modulating Factor Xa Activity
Bibliographic record
Abstract
Although the thrombin/thrombomodulin complex is considered the physiological activator of protein C, factor Xa (f.Xa) can also activate protein C in a reaction that is potentiated by glycosaminoglycans. To explore this phenomenon, we first examined the effect of glycosaminoglycans of varying degrees of sulfation on the kinetics of protein C activation by f.Xa in the presence of Ca2+ and phosphatidylcholine-phosphatidylserine vesicles (PCPS). Heparin increased the rate of protein C activation by f.Xa by 4-fold. In contrast, N-desulfated heparin had no effect on activation, whereas dextran sulfate, which is more sulfated than heparin, increased catalytic efficiency 21-fold. These data suggest that the capacity of glycosaminoglycans to catalyze protein C activation by f.Xa depends on their degree of sulfation. The affinities of individual glycosaminoglycans for protein C and f.Xa were measured in the absence or presence of PCPS by monitoring changes in extrinsic fluorescence when fluorescein-labeled f.Xa or protein C was titrated with the various glycosaminoglycans. Heparin binds protein C with low affinity in the absence or presence of PCPS. In contrast, the affinity of heparin for f.Xa is 86-fold higher in the presence of PCPS compared to that in the absence of PCPS. Similar results were obtained using surface plasmon resonance. These findings suggest that a high affinity glycosaminoglycan binding site is exposed when f.Xa binds to PCPS. The observation that heparin promotes f.Xa-mediated activation of prethrombin 1 only in the presence of phospholipid suggests that glycosaminoglycan binding modulates the active site of f.Xa. This study reveals that when f.Xa interacts with anionic phospholipids, glycosaminoglycans bind f.Xa more tightly, allosterically modulate its active site, and enhance its capacity to activate protein C.
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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) | 0.000 | 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 | 0.001 |
| 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".