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. 2024 Dec;108(1):125.
doi: 10.1007/s00253-023-12841-5. Epub 2024 Jan 15.

Integrated omics analysis reveals the alteration of gut microbiota and fecal metabolites in Cervus elaphus kansuensis

Zhenxiang Zhang  1   2 , Changhong Bao  1 , Zhaonan Li  1 , Caixia He  1 , Wenjie Jin  1 , Changzhong Li  3 , Yanxia Chen  4
Affiliations

Integrated omics analysis reveals the alteration of gut microbiota and fecal metabolites in Cervus elaphus kansuensis

Zhenxiang Zhang et al. Appl Microbiol Biotechnol. 2024 Dec.

Abstract

The gut microbiota is the largest and most complex microecosystem in animals. It is influenced by the host's dietary habits and living environment, and its composition and diversity play irreplaceable roles in animal nutrient metabolism, immunity, and adaptation to the environment. Although the gut microbiota of red deer has been studied, the composition and function of the gut microbiota in Gansu red deer (Cervus elaphus kansuensis), an endemic subspecies of red deer in China, has not been reported. In this study, the composition and diversity of the gut microbiome and fecal metabolomics of C. elaphus kansuensis were identified and compared for the first time by using 16S rDNA sequencing, metagenomic sequencing, and LC-MS/MS. There were significant differences in gut microbiota structure and diversity between wild and farmed C. elaphus kansuensis. The 16S rDNA sequencing results showed that the genus UCRD-005 was dominant in both captive red deer (CRD) and wild red deer (WRD). Metagenomic sequencing showed similar results to those of 16S rDNA sequencing for gut microbiota in CRD and WRD at the phylum and genus levels. 16S rDNA and metagenomics sequencing data suggested that Bacteroides and Bacillus might serve as marker genera for CRD and WRD, respectively. Fecal metabolomics results showed that 520 metabolites with significant differences were detected between CRD and WRD and most differential metabolites were involved in lipid metabolism. The results suggested that large differences in gut microbiota composition and fecal metabolites between CRD and WRD, indicating that different dietary habits and living environments over time have led to the development of stable gut microbiome characteristics for CRD and WRD to meet their respective survival and reproduction needs. KEY POINTS: • Environment and food affected the gut microbiota and fecal metabolites in red deer • Genera Bacteroides and Bacillus may play important roles in CRD and WRD, respectively • Flavonoids and ascorbic acid in fecal metabolites may influence health of red deer.

Keywords: Cervus elaphus kansuensis; Environmental adaptation; Gansu red deer; Gut microbiota; Metabolomics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
ASVs detected in CRD and WRD based on 16S rDNA sequencing. A Bar plot of gut microbiota at phylum level. B F/B values of gut microbiota detected by 16S rDNA sequencing. C Bar plot of gut microbiota at genus level
Fig. 2
Fig. 2
Differential analysis of gut microbiota detected by 16S rDNA sequencing. A T-test analysis of gut microbiota at phylum level. B Volcano plot of differential microbiota. C T-test analysis of gut microbiota at genus level. D LDA score diagram for different species. E Evolutionary branching diagram for different species
Fig. 3
Fig. 3
Bar plot of relative species abundance of gut microbiota detected by metagenomic sequencing. A Bar plot of gut microbiota at phylum level. B Bar plot of gut microbiota at genus level
Fig. 4
Fig. 4
Differential analysis of gut microbiota detected by metagenomic sequencing. A LDA score diagram for different species. B Evolutionary branching diagram for different species. C Heat map of KEGG pathway annotation of gut microbiota detected in CRD and WRD based on metagenomic sequencing
Fig. 5
Fig. 5
Annotation of all fecal metabolites detected in CRD and WRD. A P HMDB annotation of all metabolites. B Lipidmaps annotation of all metabolites. C KEGG annotation of all metabolites
Fig. 6
Fig. 6
Differential analysis of fecal metabolites detected in CRD and WRD based on LC-MS/MS. A PLSDA score scatter plot of differential metabolites. B PLSDA validation diagram of differential metabolites
Fig. 7
Fig. 7
Bubble map of the enriched KEGG pathways of differential metabolites
Fig. 8
Fig. 8
Correlation analysis of the gut microbiome and fecal metabolites. Correlation analysis of significantly different metabolites with significantly different gut microbiota at genus level. The results of were presented as a heatmap. *P < 0.05, **P < 0.01, ***P < 0.001 denoted statistical significance between bacterial taxa and metabolites
See this image and copyright information in PMC

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