Understanding rumen function using (meta)genome-guided metaproteomics

Enteric methane production in ruminants is a major anthropogenic source of methane emissions and contributor to global climate change. The metabolic functions carried out by the rumen microbiome are of scientific and industrial interest, as increased understanding of this complex microbial community can contribute to increased feed efficiency and production of important human food sources (meat and dairy), as well as development of methane mitigation strategies, without compromising livestock. The microbial community present in the rumen is composed of a dense and complex mixture of anaerobic bacteria, protozoa, archaea, fungi and phages. In close synergetic relationships, the rumen microbiota specializes in degradation and fermentation of complex lignocellulosic biomass into volatile fatty acids (VFAs), which is utilized for host energy metabolism. In addition to VFAs, anaerobic fermentation produces carbon dioxide and hydrogen as byproducts, which subsequently can be converted to methane by archaeal populations known as methanogens. While our knowledge regarding rumen microbiology has been built from studying isolated microorganisms in pure culture, advances in culture-independent “multiomics” approaches are making increasingly larger contributions to a greater understanding of previously understated environmental microbial communities in the rumen. In this study, the rumen metaproteome of samples from cows and goats subjected to different diets supplemented with different lipid sources and complex carbohydrates was analyzed. To do this, we utilized a cow-rumen specific protein sequence database consisting of metagenomeassembled genomes (MAGs) and genomes from cultivated rumen bacteria and fungi. We illustrate that database design has a major influence in the identification rate of proteins that are expressed by complex microbial communities. By combining genomic data with metaproteomics, downstream analysis using high accuracy mass spectrometry and advanced proteomics software enabled a detailed portrait of the rumen microbial community and its metabolic functions. Our “multi-omic” analysis reflected a change in rumen microbial composition in cases where cows were subjected to a high starch diet (COS), in addition to an indirect shift in metabolic pathways that suggested lower hydrogen production, which is hypothesized to be connected to the (lower) activity of methanogenic archaea. These observations were supported by lower methane measurements in the host animals feed COS. Moving forward, improvements to database design, which include using sample-specific metagenomes, will transform our ability to extract maximum understanding into complex rumen microbial populations, their metabolic functions and their interactions with the host. Ultimately, this will create greater opportunities to manipulate the rumen microbiome and to decrease greenhouse gas emissions while ensuring animal health