Release of the pre-assembled naRNA-LL37 composite DAMP re-defines neutrophil extracellular traps (NETs) as intentional DAMP webs
Bibliographic record
Abstract
Abstract Neutrophil extracellular traps (NETs) are a key antimicrobial feature of cellular innate immunity mediated by polymorphonuclear neutrophils (PMNs), the primary human leukocyte population. NETs trap and kill microbes but have also been linked to inflammation, e.g. atherosclerosis, arthritis or psoriasis by unknown mechanisms. We here characterize naRNA (NET-associated RNA), as a new canonical, abundant, and unexplored inflammatory NET component. naRNA, upon release by NET formation, drove further NET formation in naïve PMN, and induced macrophage and keratinocyte activation via TLR8 in humans and Tlr13 in mice, in vitro and in vivo. Importantly, in vivo naRNA strongly drove skin inflammation, whereas genetic ablation of RNA sensing drastically ameliorated psoriatic skin inflammation. Rather than accidentally assembling with LL37 on the NET, naRNA was intracellularly pre-associated in resting neutrophils as a ‘composite DAMP’, thus highlighting NET formation as a DAMP release process. This re-defines sterile NETs as an intentionally inflammatory agent, signaling and amplifying neutrophil activation. Moreover, in the many conditions previously linked to NETs and extracellular RNA, TLR-mediated naRNA sensing emerges as both potential cause and new intervention target. Graphical abstract Created with biorender.com
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".