The flavonoid galangin induces release of neutrophil extracellular traps and enhances resolution of <i>E. coli</i> pneumonia
Bibliographic record
Abstract
Abstract Killing of invading bacteria is essential for efficient control of infection and return to homeostasis. Phagocytosis of E. coli by neutrophils induces neutrophil apoptosis and subsequent efferocytosis, critical control points of the outcome of inflammation. Recently, we found that E. coli DNA impairs phagocytosis of E. coli by human neutrophils, delays neutrophil apoptosis and prolongs E. coli-evoked lung injury in mice. Here, we investigated whether the flavonoid galangin, which was shown to exert anti-inflammatory actions in several inflammation models, could also affect neutrophil function and the outcome of bacterial infection. In human neutrophils, galangin (100μM) did not affect viability or apoptosis; it efficiently countered the potent apoptosis-delaying cue from E. coli DNA by decreasing activation of ERK 1/2 and Akt, leading to Mcl-1 degradation and to collapse of mitochondrial trans-membrane potential. Galangin alone did not affect phagocytosis of live E. coli by neutrophils, and did not restore diminished phagocytosis in the presence of E. coli DNA. Consequently, phagocytosis-mediated bacterial killing was unaffected. By contrast, galangin triggered release of neutrophil extracellular traps (NETs). This was associated with enhanced bacterial killing, which was prevented in the presence of DNAse-I. In mice, galangin administered at the peak of inflammation, accelerated clearance of bacteria and the resolution of pulmonary injury evoked by intratracheal instillation of live E. coli. Collectively, these results identify novel mechanism, overriding survival cues from E. coli DNA and inducing release of NETs, by which galangin could facilitate bacterial clearance and the resolution of inflammation.
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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) | 0.001 | 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".