1. Jan – Dec. 2015
RuminOmics
Connecting the animal genome, the intestinal microbiome and nutrition to enhance the efficiency of ruminant digestion and to mitigate the environmental impacts of ruminant livestock production, Coordinator: John Wallace
Budget: EU contribution 5,95 M
Participant no.
Participant organisation name
1 (Coordinator)
University of Aberdeen 2
Parco Tecnologico Padano, Lodi 3
Natural Resources Institute Finland, Jokioinen 4
Swedish University of Agricultural Sciences, Umeå 5
University of Nottingham 6
Institute of Animal Physiology & Genetics, Prague 7
Università Cattolica del Sacro Cuore, Piacenza 8
Centre National de la Recherche Scientifique, Grenoble 9
European Association of Animal Production 10
European Forum of Farm Animal Breeders 11
Quality Meat Scotland
1,5,11 3
2,7,9 6 4 8 10

2. RuminOmics - Aims of project
Emissions
Animal genetics
Ruminal microbiome
Tools
Training
Dissemination
Does the animal itself determine its ruminal microbiome?
If so, is this a heritable trait?
How does nutrition affect this relationship?
How do these interact to produce the emission phenotype?

3. Objectives - Experiments
1000 cows in UK, Italy, Sweden, Finland
Methane
N emissions Ruminal Animal
FCE microbiome genotype
Milk quality
20 cows in Sweden, Finland
Impact of N, CHO, lipid nutrition
50 cows in UK, Italy, Sweden, Finland
Full metagenome analysis of high
and low emitters
Rumen digesta transfer experiments
Identical twins – unrelated cows
Interspecies: cow-reindeer

4. Main achievements
Extensive nutrition/performance/genetics/microbiome dataset; resource for researchers – still being analysed
Tools:
International ring test -> will lead to harmonization of methodology for microbiome studies
Tapio I. et al.: Oral samples as non-invasive proxies for assessing the composition of the rumen microbial community – PLoS ONE 2016, March 17
Metaproteomics’ practical usefulness is limited
Milk fatty acids not strongly correlated with rumen microbiome or emissions (preliminary)

5. Main achievements
Host effect on rumen microbiome not predictable in digesta exchange studies (composition in identical twins not more similar, large between animal variation in response), however some microbes show interesting pattern of host genetic control (e.g. Firmicutes)
Potential to reduce emissions by nutrition rather limited (costs, ethics)
GWAS results on cow genome
regions associated with CH4
emissions, archaea/bacteria
proportions, individual phyla
(+ milk FAs) -> GEBV

6. Conclusions, 1000 cows study
Relationships between emissions, digestion and efficiency are complex
Generally more efficient cows produce more methane per unit of feed intake
Evidence that methane emissions, feed efficiency and nitrogen efficiency are related to rumen microorganisms
Relationship between milk production efficiency (kg energy-corrected milk per kg dry matter intake) and methane production (g methane per kg dry matter intake). Cows in the lower right quadrant are more efficient than average and produce less methane than average.
-> opportunities to select cows with both lower methane emissions and higher feed efficiency

7. The systems biology challenge
Genome
sequence variation
modification (epigenetic)
Environment
nutrition
dieseases
stress
Genome expression
transcription to mRNA
ncRNA, miRNA
translation to proteins
protein modifications
Proteome
Metabolome
Phenotype ?
Rumen microbiome
composition (taxa; genes)
function (metatranscriptome, metaproteome)

8. Future challenges
Feed efficiency and feed intake traits still challenging to improve, because difficulties in getting accurate phenotype measurements from large numbers of individuals
Understanding the role of the microbiome
Including biological background information and causal variants to genomic evaluation would improve predictions, reduce dependence on LD (-> across-breed)
Difficult to pinpoint causative variants, most SNPs in GWAS studies map outside protein-coding regions
Cattle functional regulatory elements have not been well annotated (-> FAANG)
Epigenetic effects (-> FAANG)

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