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Host control of methane emissions from sheep

Project start date: 01 July 2013
Project end date: 09 February 2017
Publication date: 09 February 2017
Project status: Completed
Livestock species: Sheep, Lamb

Summary

​The “Host control of methane in sheep” project improved our understanding of the underlying biology contributing to variation in methane emissions between sheep, and linked this variation to the expression of genes in the rumen wall and the microbiome of the rumen in individual sheep. A diverse range of datasets, including phenotype, genotype, microbiome, transcriptome and metabolome, were generated from a unique set of animals. Our results confirm that variation in DMI, rumen volume, distribution of digesta components and MRT, all of which influence fermentation rate, are the likely sources of variation in MY between animals and suggests this underpins the genetic basis of difference in MY. There was also an association between increased live weight and lower density of ventral papillae, which suggests the capacity to absorb energy yielding nutrients arising from microbial fermentation may vary between animals.

We have shown that the rumen evolved as a specialised outpouching of the oesophagus and that the epithelial gene expression has many features of the gene expression in the skin, including potential regulatory mechanisms. The number/proliferation rate of the epithelial basal cells appears to be the most responsive of the rumen wall components to differences in diet. The expression of a cluster of genes that includes the ketone body synthesis pathway is the most correlated with the methane phenotypes across the animals tested and we identified genes correlated with particle retention time that are potentially controlling the contraction rate of the muscle compartment. The Genome Wide Association Analysis provided a list of the top 20 SNPs showing suggestive association with methane yield, rumen volume and mean retention time, but the whole genome association analysis was limited by the number of animals. The major objective of the genotyping data was to link gene expression to genotype and phenotype to prioritise candidate genes for causation of phenotypic differences. Despite a number of different approaches being applied no convincing candidate genes correlated with mean retention time or methane yield have yet been identified.

Growth and development have a major impact on the gut flora of the rumen and is the major driver of variance between the samples. Despite this, there are a small group of identifiable species that we found to be associated consistently with methane traits. These include an increase in species associated with Sharpea and Kandleria and an increase in the methylotophic methanogenic species. There was also some evidence of associations between microbial species, metabolites and methane status of the animals.  The results from assessing the microbiome using a massively parallel sequencing approach found the overall rumen microbiome has limited repeatability. Despite this we found a ‘core’ group of species amongst the microbial populations profiled were reasonably repeatable. The results show that the core microbiome explains a reasonable proportion of the phenotypic variation in methane emission between animals. A modest accuracy of prediction of methane traits from the ‘core’ rumen microbiome profiles was encouraging.

A full integration of all datasets (phenotype, genotype, microbiome, metabolome and transcriptome) has yet to be achieved, but we have integrated across paired and triplet datasets.  In doing this we have improved our understanding of the underlying biological and genetic basis of variation in methane emissions and this will facilitate ways to improve animal efficiency and production and contribute to developing better measurement procedures to select sheep varying in methane emission for commercial breeding programs. Our results are the first step in devising strategies for the effective use of pasture nutrients by rumen microbes and the host animal to better utilize existing agricultural land, reduce pressure to clear land, reduce the use of inputs such as water and fertilizers and the amount of agricultural waste such as manure.