Human Gut Microbes Produce EPA‐ and DHA‐Derived Oxylipins, but not <i>N</i> ‐Acyl‐Ethanolamines, From Fish Oil
Bibliographic record
Abstract
We recently reported that human fecal microbiota colonized in a simulator of the human intestinal microbiome ecosystem (SHIME) process dietary oil from Buglossoides arvensis (Ahiflower), rich in the n-3 polyunsaturated fatty acid (PUFA) stearidonic acid, to the endocannabinoid (eCB)-like N-stearidonoyl-ethanolamine. Here, we assess if human fecal microbiota collected in summer and winter and inoculated in the SHIME (simulating four different intestinal sections; ileum, and ascending, transverse and descending colon) and then treated with fish oil (FO) rich in triglyceride-esterified docosahexaenoic and eicosapentaenoic acids (DHA and EPA, respectively) result in the formation of the corresponding anti-inflammatory and anticancer n-3 PUFA metabolites, including N-acylethanolamines and oxylipins. Effluents were collected every day and analyzed by LC-MS/MS for PUFA metabolites and bacterial and short-chain fatty acids (SCFA) composition during an initial 7-day treatment with vehicle and then a further 7-day treatment with FO (DHA and EPA in equal amounts, 910 mg/day each). A time-dependent increase of DHA and EPA was observed, particularly with the summer microbiota in all intestinal sections except for the duodenum, which does not contain gut microbiota. The formation of 17-hydroxy-docosahexaenoic acid from DHA, and 15-hydroxy- and 18-hydroxy-eicosapentaenoic acid from EPA was observed within the summer samples. However, no accumulation of N-docosahexaenoyl-ethanolamine or N-eicosapentaenoyl-ethanolamine, nor any other N-acyl-ethanolamines, was detected in any intestinal section and season. These data suggest that a human fecal microbiome cultivated in a SHIME processes DHA and EPA-containing triglycerides to oxylipins with known activity on the host, but not necessarily to N-acyl-ethanolamines.
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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".