<i>Saccharomyces boulardii</i> CNCM I‐745 modulates the microbiota–gut–brain axis in a humanized mouse model of Irritable Bowel Syndrome
Bibliographic record
Abstract
BACKGROUND: Gnotobiotic mice colonized with microbiota from patients with irritable bowel syndrome (IBS) and comorbid anxiety (IBS+A) display gut dysfunction and anxiety-like behavior compared to mice colonized with microbiota from healthy volunteers. Using this model, we tested the therapeutic potential of the probiotic yeast Saccharomyces boulardii strain CNCM I-745 (S. bou) and investigated underlying mechanisms. METHODS: Germ-free Swiss Webster mice were colonized with fecal microbiota from an IBS+A patient or a healthy control (HC). Three weeks later, mice were gavaged daily with S. boulardii or placebo for two weeks. Anxiety-like behavior (light preference and step-down tests), gastrointestinal transit, and permeability were assessed. After sacrifice, samples were taken for gene expression by NanoString and qRT-PCR, microbiota 16S rRNA profiling, and indole quantification. KEY RESULTS: Mice colonized with IBS+A microbiota developed faster gastrointestinal transit and anxiety-like behavior (longer step-down latency) compared to mice with HC microbiota. S. bou administration normalized gastrointestinal transit and anxiety-like behavior in mice with IBS+A microbiota. Step-down latency correlated with colonic Trpv1 expression and was associated with altered microbiota profile and increased Indole-3-acetic acid (IAA) levels. CONCLUSIONS & INFERENCES: Treatment with S. bou improves gastrointestinal motility and anxiety-like behavior in mice with IBS+A microbiota. Putative mechanisms include effects on pain pathways, direct modulation of the microbiota, and indole production by commensal bacteria.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".