Rumen microbiome nutriomics: Harnessing omics technologies for enhanced understanding of rumen microbiome functions and ruminant nutrition

Abstract

Abstract The rumen microbiome has attracted tremendous interest among microbiologists and ruminant nutritionists because of its crucial role in mediating feed digestion and fermentation and supplying most of the energy, nutrients, and precursors for producing ruminant products. The application of various omics technologies, including metataxonomics, metagenomics, metatranscriptomics, metaproteomics, and metabolomics, have enabled unprecedented investigations into this ecosystem, shedding new light on its interactions with diet and animals and its relationships with key production traits. Despite the valuable insights these omics technologies provide, each has its unique utility and inherent limitations. Achieving a holistic characterization of the rumen microbiome and deciphering its causal relationship with diet and key animal production traits remain an ongoing endeavor. In this perspective review paper, we highlight the limitations of individual technologies and advocate for an integrated multi-omics approach and data analyses in studying the intricate relationships between diet, rumen microbes, and ruminant nutrition. This approach, termed “rumen microbiome nutriomics,” aims to comprehensively understand the rumen microbiome in the context of diets and animal productivity. Our emphasis lies in recognizing the necessity of integrated analysis across multiple data layers, encompassing data of diet, rumen microbiome features, animal genotypes, and production traits and identifying the causal relationship among them. We also call for collaborative efforts to develop a comprehensive rumen microbiome genome database, including prokaryotes, protozoa, fungi, and viruses. Furthermore, standardization of processes and analyses is crucial to address the variability observed in the literature, facilitating comparison of results among future studies and enabling robust data reanalysis through advanced data analytics.