Multi-omics insights into the response of the gut microbiota and metabolites to albendazole deworming in captive Rhinopithecus brelichi

Abstract

Background: Parasite infection and deworming treatment affect the host gut microbiota. Exploring the response mechanism of the gut microbiota in Rhinopithecus brelichi (R. brelichi) to albendazole deworming treatment is of great value for protecting this critically endangered species.

Methods and results: This study used metataxonomics and metabolomics to explore the responses of the gut microbiota and metabolites of R. brelichi to albendazole deworming treatment. The results showed that deworming significantly reduced the eggs per gram of feces (EPG). The 16S rRNA gene sequencing results showed that the richness and diversity of the gut microbiota in R. brelichi after deworming were significantly increased. Meanwhile, deworming treatment also changed the composition of the gut microbiota. At the genus level, the Christensenellaceae R7 group, UCG 002, UCG 005, uncultured rumen bacterium, and Rikenellaceae RC9 gut group were significantly enriched in the pre-deworming samples. Unclassified Muribaculaceae, Prevotella 9, and Bacteroides were significantly enriched in the post-deworming samples. Metabolomics analysis revealed that the relative abundance of 382 out of 1,865 metabolites showed significant differences between the pre- and post-deworming samples. Among them, 103 metabolites were annotated based on the HMDB and mainly classified into Prenol lipids, Carboxylic acids and derivatives, and Organooxygen compounds, etc. The KEGG enrichment analysis result indicated that these metabolites were mainly involved in energy, amino acid, lipid, and purine metabolism. Correlation analysis showed that Bacteroides and unclassified Muribaculaceae, whose relative abundances were upregulated after deworming treatment, were positively correlated with Kaempferol, 5,7-Dihydroxy-3-methoxy-4'-prenyloxyflavone, Purpurin, and Rhein, which have anti-parasitic activities. The Christensenellaceae R7 group, with a downregulated relative abundance after deworming treatment, was not only negatively correlated with the above four metabolites, but also positively correlated with Retinyl beta-glucuronide, which is a storage form of vitamin A, and positively correlated with CDP-Choline, which increases the host's susceptibility to Entamoeba histolytica and Plasmodium falciparum.

Conclusion: This study emphasizes that deworming treatment has an impact on the gut microbiota and metabolic functions of R. brelichi. By exploiting the correlations between differential microbiota and metabolites, potential probiotics or prebiotics can be explored, thereby enhancing the efficiency of deworming and reducing its side effects.

Keywords: 16S rRNA gene; R. brelichi; correlations; gut microbiota; non-targeted metabolomics.

FIGURE 1

Eggs per gram of feces before and after deworming treatment with albendazole. pre-DW: fecal samples before deworming treatment; post-DW: fecal samples after deworming treatment. Paired t-test: **P < 0.01.

FIGURE 2

Effect of deworming treatment on microbiota diversity in the gut of R. brelichi. (A) Shared and unique ASVs between the pre- and post-deworming groups were visualized by Venn diagram. (B) ACE index. (C) Shannon index. (D) PCoA plot of beta diversity of gut microbiota before and after deworming. pre-DW: fecal samples before deworming treatment; post-DW: fecal samples after deworming treatment. Wilcoxon rank sum test, false discovery rate (FDR) correction: *P < 0.05, **P < 0.01.

FIGURE 3

Effect of deworming treatment on gut microbiota composition of R. brelichi. (A) Species distribution of the top ten phyla pre- and post-deworming treatment. (B) Species distribution of the top ten genera pre- and post-deworming treatment. (C) LEfSe histogram showing differential biomarkers of gut microbiota in pre- and post-deworming groups. pre-DW: fecal samples before deworming treatment; post-DW: fecal samples after deworming treatment.

FIGURE 4

Correlation network analysis of gut microbiota. Construction criteria: The absolute value of the correlation coefficient is greater than 0.1 and the P-value is less than 0.05. The color of the connecting line represents the correlation between two nodes. Red indicates a positive correlation, while green indicates a negative correlation. The thickness of the connecting line represents the weight value derived from the correlation analysis. The thicker the line, the larger the weight value. The numbers outside the nodes indicate the bacterial genus represented by each node. The size of the nodes represents the average abundance of the bacterial genus.

FIGURE 5

OPLS-DA analysis and permutation test of OPLS-DA of fecal metabolites between pre- and post-deworming groups. (A) OPLS-DA score plot in positive ion mode. (B) Permutation test of the OPLS-DA model in positive ion mode. (C) OPLS-DA score plot in negative ion mode. (D) Permutation test of the OPLS-DA model in negative ion mode. pre-DW: fecal samples before deworming treatment; post-DW: fecal samples after deworming treatment.

FIGURE 6

Distribution of the differential metabolites and enrichment analysis of their involved metabolic pathways. (A) Volcano plot of differential metabolites in positive ion mode. (B) Volcano plot of differential metabolites in negative ion mode. (C) KEGG pathway enrichment dotplot in postive ion mode. (D) KEGG pathway enrichment dotplot in negative ion mode.

FIGURE 7

The AUC of each metabolite in the KEGG pathways that enriched the most differential metabolites. AUC: Area Under Curve.

FIGURE 8

Correlation heatmap of the differential microbiota and differential metabolites with an AUC over 0.9. Correlations are indicated by colors and numbers. Among the colors, red represents positive correlation, blue represents negative correlation, and green represents no correlation. The number in the color block represents the degree of correlation. The closer the absolute value is to 1, the higher the correlation between the two factors; the closer the absolute value is to 0, the lower the correlation between the two factors.