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. 2023 Aug 10;11(10):6271-6287.
doi: 10.1002/fsn3.3567. eCollection 2023 Oct.

Dioscoreae Rhizoma starch improves chronic diarrhea by regulating the gut microbiotas and fecal metabolome in rats

Qing Zhang  1 , Xu Zhang  1 , Qing Wang  1 , Suiqing Chen  1
Affiliations

Dioscoreae Rhizoma starch improves chronic diarrhea by regulating the gut microbiotas and fecal metabolome in rats

Qing Zhang et al. Food Sci Nutr. .

Abstract

Chinese yam (Dioscorea opposite Thunb.) has been used as food and medicine to treat diarrhea for thousands of years. This article aimed to elucidate the potential mechanism of Dioscoreae Rhizoma starch in alleviating chronic diarrhea induced by rhubarb based on gut microbiotas and fecal metabolome. The administration of the Dioscoreae Rhizoma aqueous extracts, crude polysaccharides, and starch could improve diarrhea and alleviate intestinal injury in chronic diarrhea rats. The Dioscoreae Rhizoma starch displayed the most apparent effect on regulating intestinal microbiotas by increasing the abundance and diversity of microbiotas. At the genus level, there were 17 changed intestinal microbiotas in model rats, and the treatment with Dioscoreae Rhizoma starch regulated 11 microbiotas. Metabolomics analysis revealed that Dioscoreae Rhizoma starch could regulate abnormal fecal metabolites to alleviate diarrhea, and these metabolites are involved in phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; vitamin B6 metabolism; and purine metabolism. This study will contribute to the further research and development of Dioscoreae Rhizoma starch.

Keywords: 16s rRNA; Dioscorea opposita Thunb.; chronic diarrhea; metabolomics; starch.

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Conflict of interest statement

The authors declared that they have no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
The body weight (a) and the HE staining (b) of colon tissue of rats in each group. **p < .01 compared with group B, #p < .05 compared with group M.
FIGURE 2
FIGURE 2
Sequencing depth analysis and α‐diversity analysis (a: rarefaction curve of each group, b: α‐diversity analysis of intestinal flora in each group). *p < .05 compared with group B, **p < .01 compared with group B, #p < .05 compared with group M, ##p < .01 compared with group M.
FIGURE 3
FIGURE 3
The β‐diversity analysis and histogram of gut microbiotas (a: PCoA analysis, b: NMDS analysis, c: histogram of gut microbiotas distribution at the phylum level, d: histogram of gut microbiotas distribution at the genus level).
FIGURE 4
FIGURE 4
The change of gut microbiotas of rats in each group (a: heatmap of the top 10 gut microbiotas of rats at the phylum level, b: ratio of Firmicutes to Bacteroidetes of rats in each group, c: heatmap of the top 30 gut microbiotas of rats at the genus level). *p < .05 compared with group B, **p < .01 compared with group B; #p < .05 compared with group M, ##p < .01 compared with group M.
FIGURE 5
FIGURE 5
Typical chromatograms of QC samples (a: positive mode, b: negative mode).
FIGURE 6
FIGURE 6
PCA and OPLS‐DA score plots of the metabolites in different groups (PCA score plot in [a] positive and [b] negative ion modes, OPLS‐DA score plot in [c] positive and [d] negative ion modes).
FIGURE 7
FIGURE 7
Metabolic pathway analysis of potential biomarkers (a: model group compared with the blank group, b: yam starch group compared with the model group).
FIGURE 8
FIGURE 8
Heatmap of correlation coefficients between the potential metabolic biomarkers and the microbiological biomarkers.
See this image and copyright information in PMC

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