Integrated rumen microbiome and serum metabolome analysis responses to feed type that contribution to meat quality in lambs.

ABSTRACT

BACKGROUND: Lifestyle factors, such as diet, are known to be a driver on the meat quality, rumen microbiome and serum metabolites. Rumen microbiome metabolites may be important for host health, the correlation between rumen microbiome and production of rumen metabolites are reported, while the impact of rumen microbiome on the serum metabolome and fatty acid of meat are still unclear. This study was designed to explore the rumen microbiome, serum metabolome and fatty acid of meat in response to the grass diet and concentrate diet to lambs, and the relationship of which also investigated. METHODS: In the present study, 12 lambs were randomly divided into two groups a grass diet (G) and a concentrate diet (C). Here, multiple physicochemical analyses combined with 16S rRNA gene sequences and metabolome analysis was performed to reveal the changes that in response to feed types. RESULTS: The concentrate diet could improve the growth performance of lambs compared to that fed with the grass diet. The microbiome composition was highly individual, compared to the concentrate group, the abundance of Rikenellaceae_RC9_gut_group, F082_unclassified, Muribaculaceae_unclassified, Ruminococcaceae_NK4A214_group, Bacteroidetes_unclassified, and Bacteroidales_UCG-001_unclassified were significantly (P < 0.05) lower in the grass group, while, the abundance of Succinivibrio, Succinivibrionaceae_UCG-002, Fibrobacter and Christensenellaceae_R-7_group were significantly (P < 0.05) higher in the grass group. Serum metabolomics analysis combined with enrichment analysis revealed that serum metabolites were influenced by feed type as well as the metabolic pathway, and significantly affected serum metabolites involved in amino acids, peptides, and analogues, bile acids, alcohols and derivatives, linoleic acids derivatives, fatty acids and conjugates. Most of the amino acids, peptides, and analogues metabolites were positively associated with the fatty acid contents. Among the bile acids, alcohols and derivatives metabolites, glycocholic was positively associated with all fatty acid contents, except C180, while 25-Hydroxycholesterol and lithocholic acid metabolites were negatively associated with most of the fatty acid contents. CONCLUSION: Correlation analysis of the association of microbiome with metabolite features, metabolite features with fatty acid provides us with comprehensive understanding of the composition and function of microbial communities. Associations between utilization or production were widely identified among affected microbiome, metabolites and fatty acid, and these findings will contribute to the direction of future research in lamb.