Lactobacillus Gasseri in the Upper Small Intestine Impacts an ACSL3-Dependent Fatty Acid Sensing Pathway That Regulates Whole-Body Glucose Homeostasis
Bibliographic record
Abstract
Long chain acyl CoA synthetase (ACSL)-dependent upper small intestinal lipid metabolism activates preabsorptive pathways to regulate metabolic homeostasis, but whether changes in the upper small intestinal microbiota alter specific fatty acid-dependent pathways to impact glucose homeostasis remains unknown. We here first find that upper small intestinal infusion of Intralipid, oleic acid, or linoleic acid preabsorptively increases glucose tolerance and lowers glucose production in rodents. High-fat feeding impairs preabsorptive fatty acid sensing and reduces upper small intestinal Lactobacillus gasseri levels and ACSL3 expression. Transplantation of healthy upper small intestinal microbiota to high-fat fed rodents restores Lactobacillus gasseri levels and fatty acid sensing via increased ACSL3 expression, while Lactobacillus gasseri probiotic administration to non-transplanted high-fat fed rodents is sufficient to restore upper small intestinal ACSL3 expression and fatty acid sensing. In summary, we unveil a glucoregulatory role of upper small intestinal Lactobacillus gasseri that impacts an ACSL3-dependent glucoregulatory fatty acid sensing pathway. Disclosure P.V. Bauer: None. F. Duca: None. T. Waise: None. H.J. Dranse: None. B.A. Rasmussen-Small: None. A. Puri: None. M. Rasti: None. T.K.T. Lam: None.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".