Chaihu-shugan-san alleviates depression-like behavior in mice exposed to chronic unpredictable stress by altering the gut microbiota and levels of the bile acids hyocholic acid and 7-ketoDCA
Bibliographic record
Abstract
Chaihu-Shugan-San (CSS) is a traditional botanical drug formula often prescribed to treat depression in oriental countries, but its pharmacotherapeutic mechanism remains unknown. It was recently reported that CSS alters the composition of intestinal microflora and related metabolites such as bile acids (BAs). Since the intestinal microflora affects physiological functions of the brain through the gut-microbiota-brain axis, herein we investigated whether CSS altered BA levels, gut microflora, and depression-like symptoms in chronic unpredictable mild stress (CUMS) mice, a well-established mouse model of depression. Furthermore, we determined whether BA manipulation and fecal microbiota transplantation altered CSS antidepressant actions. We found that the BA chelator cholestyramine impaired the antidepressant effects of CSS, which was partially rescued by dietary cholic acid. CSS increased the relative abundance of Parabacteroides distasonis in the colon of CUMS mice, and increased serum levels of various BAs including hyocholic acid (HCA) and 7-ketodeoxycholic acid (7-ketoDCA). Furthermore, gut bacteria transplantation from CSS-treated mice into untreated or cholestyramine-treated CUMS mice restored serum levels of HCA and 7-ketoDCA, alleviating depression-like symptoms. In the hippocampus, CSS-treated mice had decreased expression of genes associated with BA transport (Bsep and Fxr) and increased expression of brain-derived neurotrophic factor and its receptor, TrkB. Overall, CSS increases intestinal P. distasonis abundance, leading to elevated levels of secondary BAs in the circulation and altered expression of hippocampal genes implicated in BA transport and neurotrophic signaling. Our data strongly suggest that the gut microbiota-brain axis contributes to the potent antidepressant action of CSS by modulating BA metabolism.
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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 | 0.000 |
| 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 | 0.002 |
| 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".