Physiological Stimuli Induce PAD4-Dependent, ROS-Independent NETosis, With Early and Late Events Controlled by Discrete Signaling Pathways
Why this work is in the frame
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Bibliographic record
Abstract
Neutrophils are known to extrude decondensed chromatin, thus forming NETs (neutrophil extracellular traps). These structures immobilize pathogens, thereby preventing their spreading, and are also adorned with antimicrobial molecules. NETs can also influence pathogenesis in chronic inflammation, autoimmunity, and cancer. Despite the importance of NETs, the molecular mechanisms underlying their formation, as well as the upstream signaling pathways involved, are only partially understood. Likewise, current methodological approaches to quantify NETs suffer from significant drawbacks, not the least being the inclusion of a significant non-specific signal. In this study, we used novel, fluorescent polymers that only bind extruded chromatin, allowing a specific and standardized quantification of NETosis. This allowed us to reliably rank the relative potency of various physiologic NET inducers. In neutrophils activated with such stimuli, inhibition of the Syk or PI3K pathways blocked NETosis by acting upon late events in NET formation. Inhibition of the TAK1, p38 MAPK, or MEK pathways also hindered NETosis, but by acting on early events. By contrast, inhibiting PKC, Src family kinases, or JNK failed to prevent NETosis; cycloheximide or actinomycin D were also ineffective. Expectedly, NET formation was deeply compromised following inhibition of the NADPH oxidase in PMA-activated neutrophils, but was found to be ROS-independent in response to physiological agonists. Conversely, we show for the first time in human neutrophils that selective inhibition of PAD4 potently prevents NETosis by all stimuli tested. Our data substantially extends current knowledge of the signaling pathways controlling NETosis, and reveals how they affect early or late stages of the phenomenon. In view of the involvement of NETs in several pathologies, our findings also identify molecular targets that could be exploited for therapeutic intervention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it