Early and Late Processes Driving NET Formation, and the Autocrine/Paracrine Role of Endogenous RAGE Ligands
Bibliographic record
Abstract
Neutrophil extracellular trap (NET) formation has emerged as an important response against various pathogens; it also plays a role in chronic inflammation, autoimmunity, and cancer. Despite a growing understanding of the mechanisms underlying NET formation, much remains to be elucidated. We previously showed that in human neutrophils activated with different classes of physiological stimuli, NET formation features both early and late events that are controlled by discrete signaling pathways. However, the nature of these events has remained elusive. We now report that PAD4 inhibition only affects the early phase of NET generation, as do distinct signaling intermediates (TAK1, MEK, p38 MAPK). Accordingly, the inducible citrullination of residue R2 on histone H3 is an early neutrophil response that is regulated by these kinases; other arginine residues on histones H3 and H4 do not seem to be citrullinated. Conversely, elastase blockade did not affect NET formation by several physiological stimuli, though it did so in PMA-activated cells. Among belated events in NET formation, we found that chromatin decondensation is impaired by the inhibition of signaling pathways controlling both early and late stages of the phenomenon. In addition to chromatin decondensation, other late processes were uncovered. For instance, unstimulated neutrophils can condition themselves to be poised for rapid NET induction. Similarly, activated neutrophils release endogenous proteic factors that promote and largely mediate NET generation. Several such factors are known RAGE ligands and accordingly, RAGE inbibition largely prevents both NET formation and the conditioning of neutrophils to rapidly generate NETs upon stimulation. Our data shed new light on the cellular processes underlying NET formation, and unveil unsuspected facets of the phenomenon that could serve as therapeutic targets. In view of the involvement of NETs in both homeostasis and several pathologies, our findings are of broad relevance.
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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".