ROS and DNA repair in spontaneous versus agonist-induced NETosis: Context matters
Bibliographic record
Abstract
Reactive oxygen species (ROS) is essential for neutrophil extracellular trap formation (NETosis). Nevertheless, how ROS induces NETosis at baseline and during neutrophil activation is unknown. Although neutrophils carry DNA transcription, replication and repair machineries, their relevance in the short-lived mature neutrophils that carry pre-synthesized proteins has remained a mystery for decades. Our recent studies show that (i) NETosis-inducing agonists promote NETosis-specific kinase activation, genome-wide transcription that helps to decondense chromatin, and (ii) excess ROS produced by NADPH oxidase activating agonists generate genome-wide 8-oxy-guanine (8-OG), and the initial steps of DNA repair are needed to decondense chromatin in these cells. These steps require DNA repair proteins necessary for the assembly and nicking at the damaged DNA sites (poly ADP ribose polymerase PARP, apurinic endonuclease APE1 and DNA ligase), but not the enzymes that mediate the repair DNA synthesis (Proliferating cell nuclear antigen (PCNA) and DNA Polymerases). In this study, we show that (i) similar to agonist-induced NETosis, inhibition of early steps of oxidative DNA damage repair proteins suppresses spontaneous NETosis, but (ii) the inhibition of late stage repair proteins DNA polymerases and PCNA drastically promotes baseline NETosis. Hence, in the absence of excessive ROS generation and neutrophil activation, DNA repair mediated by PCNA and DNA polymerases is essential to prevent chromatin decondensation and spontaneous NETosis. These findings indicate that ROS, oxidative DNA damage, transcription and DNA repair differentially regulate spontaneous and agonist-induced NETosis. Therefore, context matters.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".