Understanding the functional and genomic architecture of the rumen microbiome affecting performance traits in bovines

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By 2050, the human population will grow to over 9 billion people, and in the same time frame, global meat production is set to increase by 73%. There is a need to increase the efficiency and sustainability of animal production, reduce waste in the food chain and ensure safe and nutritious diets in order to address this challenge. Rumen microbes confers a unique ability to convert human inedible high-fibre forage into nutrients the animal can absorb to produce high-quality proteins as meat and milk. However, intensive food production puts a strain on the environment, and there is a need to produce more food ethically and in a way that does not harm the environment. The project addresses these challenges by unravelling the functional and genomic architecture of the ruminal microbiome affecting performance traits of cattle. This information will be used to identify fundamental associations between the microbiome or its genes with animal performance traits and methane emissions. In this study we will sequence all microbial genomes - the metagenome - to describe the composition of the microbial community and its functional genes. The analysis will be based on a unique dataset of 288 experimental beef cattle, with rumen DNA samples and a large array of performance information (e.g. feed conversion efficiency, growth, body composition and meat quality) available. These data are structured by breeds and sire progeny groups to estimate the animal host genetic effects on the microbiome and microbial genes. The experimental data have been the basis of numerous publications in which it was shown that at the animal performance level, and for methane emissions, there are large differences between breeds, sire progeny groups and diets. Preliminary analysis for 8 of these animals suggests that there is a link between the abundance of the microbial community or microbial genes and animal performance traits and methane emissions. However, to understand the function and genomic architecture of the ruminal microbiome, analysis of the full sample set is necessary. Algorithms will be developed to predict animal performance, e.g. feed conversion efficiency and methane emissions from the abundance of the microbial community and genes. These high value, but costly-to-measure traits could then be predicted by analysing the rumen microbiome (sampled via stomach tube on live animals or in the abattoir). However, to verify the associations between the rumen microbiome and performance traits, we need basic knowledge about the functional and genomic architecture of the microbiome. Additionally, microbial biomarkers to predict e.g. feed conversion efficiency could be identified. Due to the unique structure of the data in sire progeny groups and diets, we will be able to predict the host genetic and nutritional effect on the microbial community and microbial genes. This structure can also be used in the network analysis to identify animal genetic effects on the functional and genomic architecture of the microbiome. The project will provide unprecedented new knowledge of the genomic and functional architecture of the microbiome and its impact on performance traits and methane emissions as well as the interaction with animal genetics and nutrition. We will compare the functional and genetic architecture of the microbiome in beef cattle with that of other species to provide insights about the microbiome of different species, in particular humans. By understanding host genetic effects on the rumen microbiota and associations with body composition, we expect to provide new insights for human personalised medicine approaches to reduce obesity.