An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants

• DOI: 10.1186/s40168-021-01078-x
• Publication Date: 2021-06-12T13:02:53Z
• Authors: Fei Xie; Wei Jin; Huazhe Si; Yuan Yuan; Ye Tao; Junhua Liu; Xiaoxu Wang; Chengjian Yang; Qiushuang Li; Xiaoting Yan; Limei Lin; Qian Jiang; Lei Zhang; Changzheng Guo; Chris Greening; Rasmus Heller; Le Luo Guan; Phillip B. Pope; Zhiliang Tan; Weiyun Zhu; Min Wang; Qiang Qiu; Zhipeng Li; Shengyong Mao
• Keywords: EFFICIENCY; Metagenome-assembled genomes; Ruminant; Feed efficiency; RUMEN MICROBIOME; Microbial ecology; REVEALS; TOOL; Animals; Gastrointestinal microbiome; Phylogeny; Alphaproteobacteria; 2. Zero hunger; COMPLEX; Bacteria; Research; COW; QR100-130; Ruminants; EVOLUTION; READ ALIGNMENT; Gastrointestinal Microbiome; INSIGHTS; Metagenome; RNA-SEQ DATA

Abstract Background Gastrointestinal tract (GIT) microbiomes in ruminants play major roles in host health and thus animal production. However, we lack an integrated understanding of microbial community structure and function as prior studies are predominantly biased towards the rumen. In this study, we used shotgun metagenomics to profile the microbiota of 370 samples that represent 10 GIT regions of seven ruminant species.

Results Our analyses reconstructed a GIT microbial reference catalog with > 154 million nonredundant genes and identified 8745 uncultured candidate species from over 10,000 metagenome-assembled genomes. The integrated gene catalog across the GIT regions demonstrates spatial associations between the microbiome and physiological adaptations, and 8745 newly characterized genomes substantially expand the genomic landscape of ruminant microbiota, particularly those from the lower gut. This substantially expands the previously known set of endogenous microbial diversity and the taxonomic classification rate of the GIT microbiome. These candidate species encode hundreds of enzymes and novel biosynthetic gene clusters that improve our understanding concerning methane production and feed efficiency in ruminants. Overall, this study expands the characterization of the ruminant GIT microbiota at unprecedented spatial resolution and offers clues for improving ruminant livestock production in the future.

Conclusions Having access to a comprehensive gene catalog and collections of microbial genomes provides the ability to perform efficiently genome-based analysis to achieve a detailed classification of GIT microbial composition and function. Our study will bring unprecedented power in future association studies to investigate the impact of the GIT microbiota in ruminant health and production.