Integrated meta-omics reveals new ruminal microbial features associated with feed efficiency in dairy cattle

• DOI: 10.1186/s40168-022-01228-9
• Publication Date: 2022-02-16T10:02:39Z
• Authors: Ming-Yuan Xue; Yun-Yi Xie; Yifan Zhong; Xiao-Jiao Ma; Hui-Zeng Sun; Jian-Xin Liu
• Keywords: 0301 basic medicine; 2. Zero hunger; Rumen; Feed efficiency; Research; Rumen microbiome; QR100-130; Animal Feed; Diet; Microbial ecology; 03 medical and health sciences; Fermentation; Dairy cattle; Metabolomics; Animals; Lactation; Cattle; Female; Metagenomics; Metatranscriptomics

AbstractBackgroundAs the global population continues to grow, competition for resources between humans and livestock has been intensifying. Increasing milk protein production and improving feed efficiency are becoming increasingly important to meet the demand for high-quality dairy protein. In a previous study, we found that milk protein yield in dairy cows was associated with the rumen microbiome. The objective of this study was to elucidate the potential microbial features that underpins feed efficiency in dairy cows using metagenomics, metatranscriptomics, and metabolomics.ResultsComparison of metagenomic and metatranscriptomic data revealed that the latter was a better approach to uncover the associations between rumen microbial functions and host performance. Co-occurrence network analysis of the rumen microbiome revealed differential microbial interaction patterns between the animals with different feed efficiency, with high-efficiency animals having more and stronger associations than low-efficiency animals. In the rumen of high-efficiency animals,Selenomonasand members of theSuccinivibrionaceaefamily positively interacted with each other, functioning as keystone members due to their essential ecological functions and active carbohydrate metabolic functions. At the metabolic level, analysis using random forest machine learning suggested that six ruminal metabolites (all derived from carbohydrates) could be used as metabolic markers that can potentially differentiate efficient and inefficient microbiomes, with an accuracy of prediction of 95.06%.ConclusionsThe results of the current study provided new insights into the new ruminal microbial features associated with feed efficiency in dairy cows, which may improve the ability to select animals for better performance in the dairy industry. The fundamental knowledge will also inform future interventions to improve feed efficiency in dairy cows.