Age-related alterations in metabolome and microbiome provide insights in dietary transition in giant pandas

Author:

Liu Fangyuan1,Li Rengui23,Zhong Yi4,Liu Xu1,Deng Wenwen23,Huang Xiaoyu23,Price Megan1,Li Jing1

Affiliation:

1. Key Laboratory of Bio-resources and Eco-environment (Ministry of Education), College of Life Sciences, Sichuan University , Chengdu, Sichuan, China

2. China Conservation and Research Center for the Giant Panda , Dujiangyan, Sichuan, China

3. Key Laboratory of State Forestry and Grassland Administration on Conservation Biology for Rare Animals of the Giant Panda State Park , Dujiangyan, Sichuan, China

4. China Wildlife Conservation Association , Beijing, China

Abstract

ABSTRACT We conducted UPLC-MS-based metabolomics, 16S rRNA, and metagenome sequencing on the fecal samples of 44 captive giant pandas ( Ailuropoda melanoleuca ) from four age groups (i.e., Cub, Young, Adult, and Old) to comprehensively understand age-related changes in the metabolism and gut microbiota of giant pandas. We characterized the metabolite profiles of giant pandas based on 1,376 identified metabolites, with 152 significantly differential metabolites (SDMs) found across the age groups. We found that the metabolites and the composition/function of the gut microbiota changed in response to the transition from a milk-dominant diet in panda cubs to a bamboo-specific diet in young and adult pandas. Lipid metabolites such as choline and hippuric acid were enriched in the Cub group, and many plant secondary metabolites were significantly higher in the Young and Adult groups, while oxidative stress and inflammatory related metabolites were only found in the Old group. However, there was a decrease in the α-diversity of gut microbiota in adult and old pandas, who exclusively consume bamboo. The abundance of bacteria related to the digestion of cellulose-rich food, such as Firmicutes, Streptococcus, and Clostridium, significantly increased from the Cub to the Adult group, while the abundance of beneficial bacteria such as Faecalibacterium , Sarcina, and Blautia significantly decreased. Notably, several potential pathogenic bacteria had relatively high abundances, especially in the Young group. Metagenomic analysis identified 277 CAZyme genes including cellulose degrading genes, and seven of the CAZymes had abundances that significantly differed between age groups. We also identified 237 antibiotic resistance genes (ARGs) whose number and diversity increased with age. We also found a significant positive correlation between the abundance of bile acids and gut bacteria, especially Lactobacillus and Bifidobacterium . Our results from metabolome, 16S rRNA, and metagenome data highlight the important role of the gut microbiota-bile acid axis in the regulation of age-related metabolism and provide new insights into the lipid metabolism of giant pandas. IMPORTANCE The giant panda is a member of the order Carnivora but is entirely herbivorous. The giant panda’s specialized diet and related metabolic mechanisms have not been fully understood. It is therefore crucial to investigate the dynamic changes in metabolites as giant pandas grow and physiologically adapt to their herbivorous diet. This study conducted UPLC-MS-based metabolomics 16S rRNA, and metagenome sequencing on the fecal samples of captive giant pandas from four age groups. We found that metabolites and the composition/function of gut microbiota changed in response to the transition from a milk-dominant diet in cubs to a bamboo-specific diet in young and adult pandas. The metabolome, 16S rRNA, and metagenome results highlight that the gut microbiota-bile acid axis has an important role in the regulation of age-related metabolism, and our study provides new insights into the lipid metabolism of giant pandas.

Funder

National Forestry and Grassland Administration

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

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