Human gut microbiota and their production of endocannabinoid-like mediators are directly affected by a dietary oil
Bibliographic record
Abstract
Dietary omega-3 polyunsaturated fatty acids (n-3 PUFAs) and the gut microbiome affect each other. We investigated the impact of supplementation with Buglossoides arvensis oil (BO), rich in stearidonic acid (SDA), on the human gut microbiome. Employing the Mucosal Simulator of the Human Intestinal Microbial Ecosystem (M-SHIME), we simulated the ileal and ascending colon microbiomes of four donors. Our results reveal two distinct microbiota clusters influenced by BO, exhibiting shared and contrasting shifts. Notably, Bacteroides and Clostridia abundance underwent similar changes in both clusters, accompanied by increased propionate production in the colon. However, in the ileum, cluster 2 displayed a higher metabolic activity in terms of BO-induced propionate levels. Accordingly, a triad of bacterial members involved in propionate production through the succinate pathway, namely Bacteroides, Parabacteroides, and Phascolarctobacterium, was identified particularly in this cluster, which also showed a surge of second-generation probiotics, such as Akkermansia, in the colon. Finally, we describe for the first time the capability of gut bacteria to produce N-acyl-ethanolamines, and particularly the SDA-derived N-stearidonoyl-ethanolamine, following BO supplementation, which also stimulated the production of another bioactive endocannabinoid-like molecule, commendamide, in both cases with variations across individuals. Spearman correlations enabled the identification of bacterial genera potentially involved in endocannabinoid-like molecule production, such as, in agreement with previous reports, Bacteroides in the case of commendamide. This study suggests that the potential health benefits on the human microbiome of certain dietary oils may be amenable to stratified nutrition strategies and extend beyond n-3 PUFAs to include microbiota-derived endocannabinoid-like mediators.
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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 | 0.001 |
| 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".