Methane is one of the major contributors to global warming. The rumen microbiota is directly involved in methane production in cattle. The link between variation in rumen microbial communities and host genetics has important applications and implications in bioscience. Having the potential to reveal the full extent of microbial gene diversity and complex microbial interactions, integrated metagenomics and network analysis holds great promise in this endeavour. This study investigates the rumen microbial community in cattle through the integration of metagenomic and network-based approaches. Based on the relative abundance of 1570 microbial genes identified in a metagenomics analysis, the co-abundance network was constructed and functional modules of microbial genes were identified. One of the main contributions is to develop a random matrix theory-based approach to automatically determining the correlation threshold used to construct the co-abundance network. The resulting network, consisting of 549 microbial genes and 3349 connections, exhibits a clear modular structure with certain trait-specific genes highly over-represented in modules. More specifically, all the 20 genes previously identified to be associated with methane emissions are found in a module (hypergeometric test, p <10−11). One third of genes are involved in methane metabolism pathways. The further examination of abundance profiles across 8 samples of genes highlights that the revealed pattern of metagenomics abundance has a strong association with methane emissions. Furthermore, the module is significantly enriched with microbial genes encoding enzymes that are directly involved in methanogenesis (hypergeometric test, p <10−9).
- Rumen microbial community
- Network-based approaches
- Random matrix theory
Wang, H. . HY., Zheng, H., Browne, F., Roeheb, R., Dewhurst, R. J., Engel, F., Hemmje, M., Lu, X., & Walsh, P. (2017). Integrated metagenomic analysis of the rumen microbiome of cattle reveals key biological mechanisms associated with methane traits. Methods, 124, 108-119. https://doi.org/10.1016/j.ymeth.2017.05.029