Neutrophil Extracellular Traps Promote Thrombin Generation Through Platelet-Dependent and Platelet-Independent Mechanisms
Bibliographic record
Abstract
OBJECTIVE: Activation of neutrophils by microbial or inflammatory stimuli results in the release of neutrophil extracellular traps (NETs) that are composed of DNA, histones, and antimicrobial proteins. In purified systems, cell-free DNA (CFDNA) activates the intrinsic pathway of coagulation, whereas histones promote thrombin generation through platelet-dependent mechanisms. However, the overall procoagulant effects of CFDNA/histone complexes as part of intact NETs are unknown. In this study, we examined the procoagulant potential of intact NETs released from activated neutrophils. We also determined the relative contribution of CFDNA and histones to thrombin generation in plasmas from patients with sepsis. APPROACH AND RESULTS: NETs released from phorbyl myristate-activated neutrophils enhance thrombin generation in platelet-poor plasma. This effect was DNA dependent (confirmed by DNase treatment) and occurred via the intrinsic pathway of coagulation (confirmed with coagulation factor XII- and coagulation factor XI-depleted plasma). In platelet-rich plasma treated with corn trypsin inhibitor, addition of phorbyl myristate-activated neutrophils increased thrombin generation and shortened the lag time in a toll-like receptor-2- and toll-like receptor-4-dependent mechanism. Addition of DNase further augmented thrombin generation, suggesting that dismantling of the NET scaffold increases histone-mediated, platelet-dependent thrombin generation. In platelet-poor plasma samples from patients with sepsis, we found a positive correlation between endogenous CFDNA and thrombin generation, and addition of DNase attenuated thrombin generation. CONCLUSIONS: These studies examine the procoagulant activities of CFDNA and histones in the context of NETs. Our studies also implicate a role for the intrinsic pathway of coagulation in sepsis pathogenesis.
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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) | 0.002 | 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".