Furanoid F-Acid F6 Uniquely Induces NETosis Compared to C16 and C18 Fatty Acids in Human Neutrophils
Bibliographic record
Abstract
Various biomolecules induce neutrophil extracellular trap (NET) formation or NETosis. However, the effect of fatty acids on NETosis has not been clearly established. In this study, we focused on the NETosis-inducing ability of several lipid molecules. We extracted the lipid molecules present in Arabian Gulf catfish (Arius bilineatus, Val) skin gel, which has multiple therapeutic activities. Gas chromatography–mass spectrometry (GC-MS) analysis of the lipid fraction-3 from the gel with NETosis-inducing activity contained fatty acids including a furanoid F-acid (F6; 12,15-epoxy-13,14-dimethyleicosa-12,14-dienoic acid) and common long-chain fatty acids such as palmitic acid (PA; C16:0), palmitoleic acid (PO; C16:1), stearic acid (SA; C18:0), and oleic acid (OA; C18:1). Using pure molecules, we show that all of these fatty acids induce NETosis to different degrees in a dose-dependent fashion. Notably, F6 induces a unique form of NETosis that is rapid and induces reactive oxygen species (ROS) production by both NADPH oxidase (NOX) and mitochondria. F6 also induces citrullination of histone. By contrast, the common fatty acids (PA, PO, SA, and OA) only induce NOX-dependent NETosis. The activation of the kinases such as ERK (extracellular signal-regulated kinase) and JNK (c-Jun N-terminal kinase) is important for long-chain fatty acid-induced NETosis, whereas, in F-acid-induced NETosis, Akt is additionally needed. Nevertheless, NETosis induced by all of these compounds requires the final chromatin decondensation step of transcriptional firing. These findings are useful for understanding F-acid- and other fatty acid-induced NETosis and to establish the active ingredients with therapeutic potential for regulating diseases involving NET formation.
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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".