Small intestinal taurochenodeoxycholic acid-FXR axis alters local nutrient-sensing glucoregulatory pathways in rats
Bibliographic record
Abstract
OBJECTIVE: The mechanism of nutrient sensing in the upper small intestine (USI) and ileum that regulates glucose homeostasis remains elusive. Short-term high-fat (HF) feeding increases taurochenodeoxycholic acid (TCDCA; an agonist of farnesoid X receptor (FXR)) in the USI and ileum of rats, and the increase of TCDCA is prevented by transplantation of microbiota obtained from the USI of healthy donors into the USI of HF rats. However, whether changes of TCDCA-FXR axis in the USI and ileum alter nutrient sensing remains unknown. METHODS: Intravenous glucose tolerance test was performed in rats that received USI or ileal infusion of nutrients (i.e., oleic acids or glucose) via catheters placed toward the lumen of USI and/or ileum, while mechanistic gain- and loss-of-function studies targeting the TCDCA-FXR axis or bile salt hydrolase activity in USI and ileum were performed. RESULTS: USI or ileum infusion of nutrients increased glucose tolerance in healthy but not HF rats. Transplantation of healthy microbiome obtained from USI into the USI of HF rats restored nutrient sensing and inhibited FXR via a reduction of TCDCA in the USI and ileum. Further, inhibition of USI and ileal FXR enhanced nutrient sensing in HF rats, while inhibiting USI (but not ileal) bile salt hydrolase of HF rats transplanted with healthy microbiome activated FXR and disrupted nutrient sensing in the USI and ileum. CONCLUSIONS: We reveal a TCDCA-FXR axis in both the USI and ileum that is necessary for the upper small intestinal microbiome to govern local nutrient-sensing glucoregulatory pathways in rats.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".