Metabolic capabilities of key rumen microbiota drive methane emissions in cattle.

The rumen microbiome plays a critical role in determining feed conversion and methane emissions in cattle, with significant implications for both agricultural productivity and environmental sustainability. In this study, we applied a hierarchical joint species distribution model to predict directional associations between biotic factors and abundances of microbial populations determined via metagenome-assembled genomes (MAGs). Our analysis revealed distinct microbial differences, including 191 MAGs significantly more abundant in animals with a higher methane yield (above 24 g/kg dry matter intake [DMI]; high-emission cattle), and 220 MAGs more abundant in low-emission cattle. Interestingly, the microbiome community of the low-methane-emission rumen exhibited higher metabolic capacity but with lower functional redundancy compared to that of high-methane-emission cattle. Our findings also suggest that microbiomes associated with low methane yields are prevalent in specific functionalities such as active fiber hydrolysis and succinate production, which may enhance their contributions to feed conversion in the host animal. This study provides an alternate genome-centric means to investigate the microbial ecology of the rumen and identify microbial and metabolic intervention targets that aim to reduce greenhouse gas emissions in livestock production systems. IMPORTANCE: Ruminant livestock are major contributors to global methane emissions, largely through microbial fermentation in the rumen. Understanding how microbial communities vary between high- and low-methane-emitting animals is critical for identifying mitigation strategies. This study leverages a genome-centric approach to link microbial metabolic traits to methane output in cattle. By reconstructing and functionally characterizing hundreds of microbial genomes, we observe that a low-methane-emission rumen harbors well-balanced, "streamlined" microbial communities characterized by high metabolic capacity and minimal metabolic overlap across populations (low functional redundancy). Our results demonstrate the utility of genome-level functional profiling in uncovering microbial community traits tied to climate-relevant phenotypes.