How Do ROS Induce NETosis? Oxidative DNA Damage, DNA Repair, and Chromatin Decondensation
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) are intricate, DNA-based, web-like structures adorned with cytotoxic proteins. They play a crucial role in antimicrobial defense but are also implicated in autoimmune diseases and tissue injury. The process of NET formation, known as NETosis, is a regulated cell death mechanism that involves the release of these structures and is unique to neutrophils. NETosis is heavily dependent on the production of reactive oxygen species (ROS), which can be generated either through NADPH oxidase (NOX) or mitochondrial pathways, leading to NOX-dependent or NOX-independent NETosis, respectively. Recent research has revealed an intricate interplay between ROS production, DNA repair, and NET formation in different contexts. UV radiation can trigger a combined process of NETosis and apoptosis, known as apoNETosis, driven by mitochondrial ROS and DNA repair. Similarly, in calcium ionophore-induced NETosis, both ROS and DNA repair are key components, but only play a partial role. In the case of bacterial infections, the early stages of DNA repair are pivotal. Interestingly, in serum-free conditions, spontaneous NETosis occurs through NOX-derived ROS, with early-stage DNA repair inhibition halting the process, while late-stage inhibition increases it. The intricate balance between DNA repair processes and ROS production appears to be a critical factor in regulating NET formation, with different pathways being activated depending on the nature of the stimulus. These findings not only deepen our understanding of the mechanisms behind NETosis but also suggest potential therapeutic targets for conditions where NETs contribute to disease pathology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".