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Deciphering functional groups of rumen microbiome and their underlying potentially causal relationships in shaping host traits.

Ming-Yuan XueYun-Yi XieXin-Wei ZangYi-Fan ZhongXiao-Jiao MaHui-Zeng SunJian-Xin Liu
Published in: iMeta (2024)
Over the years, microbiome research has achieved tremendous advancements driven by culture-independent meta-omics approaches. Despite extensive research, our understanding of the functional roles and causal effects of the microbiome on phenotypes remains limited. In this study, we focused on the rumen metaproteome, combining it with metatranscriptome and metabolome data to accurately identify the active functional distributions of rumen microorganisms and specific functional groups that influence feed efficiency. By integrating host genetics data, we established the potentially causal relationships between microbes-proteins/metabolites-phenotype, and identified specific patterns in which functional groups of rumen microorganisms influence host feed efficiency. We found a causal link between Selenomonas bovis and rumen carbohydrate metabolism, potentially mediated by bacterial chemotaxis and a two-component regulatory system, impacting feed utilization efficiency of dairy cows. Our study on the nutrient utilization functional groups in the rumen of high-feed-efficiency dairy cows, along with the identification of key microbiota functional proteins and their potentially causal relationships, will help move from correlation to causation in rumen microbiome research. This will ultimately enable precise regulation of the rumen microbiota for optimized ruminant production.
Keyphrases
  • dairy cows
  • gene expression
  • machine learning
  • big data
  • transcription factor
  • deep learning
  • data analysis