DNA Is an Antimicrobial Component of Neutrophil Extracellular Traps
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs) comprise an ejected lattice of chromatin enmeshed with granular and nuclear proteins that are capable of capturing and killing microbial invaders. Although widely employed to combat infection, the antimicrobial mechanism of NETs remains enigmatic. Efforts to elucidate the bactericidal component of NETs have focused on the role of NET-bound proteins including histones, calprotectin and cathepsin G protease; however, exogenous and microbial derived deoxyribonuclease (DNase) remains the most potent inhibitor of NET function. DNA possesses a rapid bactericidal activity due to its ability to sequester surface bound cations, disrupt membrane integrity and lyse bacterial cells. Here we demonstrate that direct contact and the phosphodiester backbone are required for the cation chelating, antimicrobial property of DNA. By treating NETs with excess cations or phosphatase enzyme, the antimicrobial activity of NETs is neutralized, but NET structure, including the localization and function of NET-bound proteins, is maintained. Using intravital microscopy, we visualized NET-like structures in the skin of a mouse during infection with Pseudomonas aeruginosa. Relative to other bacteria, P. aeruginosa is a weak inducer of NETosis and is more resistant to NETs. During NET exposure, we demonstrate that P. aeruginosa responds by inducing the expression of surface modifications to defend against DNA-induced membrane destabilization and NET-mediated killing. Further, we show induction of this bacterial response to NETs is largely due to the bacterial detection of DNA. Therefore, we conclude that the DNA backbone contributes both to the antibacterial nature of NETs and as a signal perceived by microbes to elicit host-resistance strategies.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".