JNK Activation Turns on LPS- and Gram-Negative Bacteria-Induced NADPH Oxidase-Dependent Suicidal NETosis
Bibliographic record
Abstract
Neutrophils cast neutrophil extracellular traps (NETs) to ensnare microbial pathogens. Nevertheless, the molecular rheostats that regulate NETosis in response to bacteria are not clearly established. We hypothesized that stress-activated protein kinase or c-Jun N-terminal Kinase (SAPK/JNK) is a molecular switch that turns on NETosis in response to increasing concentrations of lipopolysaccharide (LPS)- and Gram-negative bacteria. Here we show that Escherichia coli LPS (0111:B4; 10-25 μg/ml), but not phorbol myristate acetate (PMA), activates JNK in human neutrophils in a dose-dependent manner. JNK inhibitors SP600125 and TCSJNK6o, and a TLR4 inhibitor TAK242 suppress reactive oxygen species production and NETosis in LPS-, but not PMA-treated neutrophils. Diphenyleneiodonium suppresses LPS-induced NETosis, confirming that endotoxin induces NADPH oxidase-dependent NETosis. Immunoblots, Sytox Green assays, and confocal microscopy of cleaved caspase-3 and nuclear morphology show that JNK inhibition does not induce apoptosis in LPS-stimulated neutrophils. JNK inhibition also suppresses NETosis induced by two typical Gram-negative bacteria, E. coli and Pseudomonas aeruginosa. Therefore, we propose that neutrophils use a TLR4-dependent, JNK-mediated molecular sensing mechanism to initiate NADPH oxidase-dependent suicidal NETosis in response to increasing concentrations of LPS, and Gram-negative bacteria. The LPS-TLR4-JNK activation axis determines the fate of these cells: to be or not to be NETotic neutrophils.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".