Mechanistic Studies of DNase I Activity: Impact of Heparin Variants and PAD4
Bibliographic record
Abstract
ABSTRACT Background: Excessive production of neutrophil extracellular traps (NETs) in sepsis contributes to vascular occlusion by acting as a scaffold and stimulus for thrombus formation. Removal of extracellular DNA, the major structural component of NETs, by DNase I may reduce host injury. Objectives: Methods: DNA–histone complexes were treated with DNase I ± heparin variants and visualized via agarose gels. We compared the ability of DNase I ± heparin variants to digest NETs released by phorbol 12-myristate 13-acetate-stimulated neutrophils versus DNA–histone complexes released by necrotic HEK293 cells. Plasma DNA and DNase I levels were measured longitudinally in 76 septic patients. Results: Heparin enhances DNase I-mediated digestion of DNA–histone complexes in a size-dependent manner that does not require the antithrombin-binding region. In contrast, DNase I alone was able to degrade the DNA–histone component of NETs presumably due to peptidylarginine deiminase 4 (PAD4)-mediated histone citrullination that weakens DNA–histone interactions. In purified systems, PAD4 treatment of DNA–histone complexes enhanced the ability of DNase I to degrade histone-bound DNA. In septic patients, endogenous DNase I levels remained persistently low over 28 days, and there were no significant correlations between DNA and DNase I levels. Conclusion: Heparin enhances DNA-mediated digestion of DNA–histone complexes in a size-dependent manner that is independent of its anticoagulant properties. Citrullination of histones by PAD4 renders DNA–histone complexes susceptible to DNase I digestion. Endogenous DNase I levels are persistently decreased in septic patients, which supports the potential utility of DNase I as a therapy for sepsis.
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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".