Network analyses unraveled the complex interactions in the rumen microbiota associated with methane emission in dairy cattle.

Abstract

Background: Methane emissions from livestock, particularly from dairy cattle, represent a significant source of greenhouse gas, contributing to the global climate crisis. Understanding the complex interactions within the rumen microbiota that influence methane emissions is crucial for developing effective mitigation strategies.

Results: This study employed Weighted Gene Co-expression Network Analysis to investigate the complex interactions within the rumen microbiota that influence methane emissions. By integrating extensive rumen microbiota sequencing data with precise methane emission measurements in 750 Holstein dairy cattle, our research identified distinct microbial communities and their associations with methane production. Key findings revealed that the blue module from network analysis was significantly correlated (0.45) with methane emissions. In this module, taxa included the genera Prevotella and Methanobrevibactor, along with species such as Prevotella brevis, Prevotella ruminicola, Prevotella baroniae, Prevotella bryantii, Lachnobacterium bovis, and Methanomassiliicoccus luminyensis are the key components to drive the complex networks. However, the absence of metagenomics sequencing is difficult to reveal the deeper taxa level and functional profiles.

Conclusions: The application of Weighted Gene Co-expression Network Analysis provided a comprehensive understanding of the microbiota-methane emission relationship, serving as an innovative approach for microbiota-phenotype association studies in cattle. Our findings underscore the importance of microbiota-trait and microbiota-microbiota associations related to methane emission in dairy cattle, contributing to a systematic understanding of methane production in cattle. This research offers key information on microbial management for mitigating environmental impact on the cattle population.