Effects of oral liquiritigenin inoculation on gut microbiota and gene expression in intestinal and extraintestinal tissues of mice
Bibliographic record
Abstract
Introduction: Liquiritigenin (LQ), a natural flavonoid found in traditional Chinese medicine and often administered orally, holds potential to affect both the gut and its microbiota, that potentially mediating or influencing its biological and pharmacological effects. However, the effects of LQ on gut microbiota composition and intestinal function remain poorly understood. In this study, we aimed to explore the impact of LQ on gut microbiota and gene expression in both intestinal and extraintestinal tissues. Methods: We orally inoculated six-week-old SPF C57BL/6 mice with either LQ (a concentration of 4 mg/ml diluted in dimethylsulfoxide, (DMSO)) or DMSO, and administered daily for a duration of 2 weeks. At the end of the experimental period, all mice were euthanized. Fresh fecal samples, as well as samples from the intestine, lung, and liver, were collected for subsequent microbiota analysis, RNA-seq, or histochemical and immunohistochemical (IHC) staining. Results: Findings show that LQ alters gut microbiota composition, enhancing microbial correlations in the colon but causing some dysbiosis, evidenced by increased pathobionts, decreased beneficial bifidobacteria, and reduced microbiota diversity. Gene expression analysis reveals LQ upregulates mucosal immune response genes and antiinfection genes in both the intestine and lung, with histology confirming increased Paneth cells and antimicrobial peptides in the intestine. Additionally, LQ affects tissue-specific gene expression, triggering hypersensitivity genes in the colon, downregulating metabolic genes in the small intestine, and reducing cell motility and adhesion genes in the lung. Discussion: These results suggest LQ's potential to modulate common mucosal immunity but also highlight possible risks of gut dysbiosis and hypersensitivity, particularly in vulnerable individuals. Our study, while informative about the effects of LQ on gut health, lacks direct evidence on whether changes in gut microbiota and gene expression caused by LQ impact inflammatory diseases or are causally linked. Future research should investigate this through fecal microbiota transplantation to explore the causal relationships and LQ's potential effects on immune responses and disease outcomes in relevant models.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".