Histidine-rich glycoprotein binds DNA and RNA and attenuates their capacity to activate the intrinsic coagulation pathway
Bibliographic record
Abstract
When triggered by factor (F) XII and nucleic acids, we showed that thrombosis in HRG-deficient mice is accelerated compared with that in wild-type mice. In this study, we set out to identify the mechanisms by which nucleic acids promote contact activation, and to determine whether HRG attenuates their effects. DNA or RNA addition to human plasma enhances thrombin generation via the intrinsic pathway and shortens the clotting time. Their effect on the clotting time is seven- to 14-fold greater in HRG-deficient plasma than in control plasma. Investigations into the mechanisms of activation reveal that nucleic acids a) promote FXII activation in the presence of prekallikrein- and high molecular weight kininogen (HK), and b) enhance thrombin-mediated FXI activation by 10- to 12-fold. Surface plasmon resonance studies show that DNA and RNA bind FXII, FXIIa, HK, FXI, FXIa and thrombin with high affinity. HRG attenuates DNA- and RNA-mediated FXII activation, and FXI activation by FXIIa or by thrombin, suggesting that HRG down regulates the capacity of DNA and RNA to activate the intrinsic pathway. Therefore, HRG attenuates the procoagulant activity of nucleic acids at multiple levels.
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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".