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      Impact of the rumen microbiome on milk fatty acid composition of Holstein cattle

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          Abstract

          Background

          Fatty acids (FA) in bovine milk derive through body mobilization, de novo synthesis or from the feed via the blood stream. To be able to digest feedstuff, the cow depends on its rumen microbiome. The relative abundance of the microbes has been shown to differ between cows. To date, there is little information on the impact of the microbiome on the formation of specific milk FA. Therefore, in this study, our aim was to investigate the impact of the rumen bacterial microbiome on milk FA composition. Furthermore, we evaluated the predictive value of the rumen microbiome and the host genetics on the composition of individual FA in milk.

          Results

          Our results show that the proportion of variance explained by the rumen bacteria composition (termed microbiability or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_{B}^{2}$$\end{document} ) was generally smaller than that of the genetic component (heritability), and that rumen bacteria influenced most C15:0, C17:0, C18:2 n-6, C18:3 n-3 and CLA cis-9, trans-11 with estimated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h_{B}^{2}$$\end{document} ranging from 0.26 to 0.42. For C6:0, C8:0, C10:0, C12:0, C16:0, C16:1 cis-9 and C18:1 cis-9, the variance explained by the rumen bacteria component was close to 0. In general, both the rumen microbiome and the host genetics had little value for predicting FA phenotype. Compared to genetic information only, adding rumen bacteria information resulted in a significant improvement of the predictive value for C15:0 from 0.22 to 0.38 (P = 9.50e−07) and C18:3 n-3 from 0 to 0.29 (P = 8.81e−18).

          Conclusions

          The rumen microbiome has a pronounced influence on the content of odd chain FA and polyunsaturated C18 FA, and to a lesser extent, on the content of the short- and medium-chain FA in the milk of Holstein cattle. The accuracy of prediction of FA phenotypes in milk based on information from either the animal’s genotypes or rumen bacteria composition was very low.

          Electronic supplementary material

          The online version of this article (10.1186/s12711-019-0464-8) contains supplementary material, which is available to authorized users.

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          Most cited references24

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          Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

          Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB) were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI) were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e.g. human metabolism, health and behaviour, as well as to understand the genetic link between host and microbiome.
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            Fatty acids in bovine milk fat

            Milk fat contains approximately 400 different fatty acid, which make it the most complex of all natural fats. The milk fatty acids are derived almost equally from two sources, the feed and the microbial activity in the rumen of the cow and the lipids in bovine milk are mainly present in globules as an oil-in-water emulsion. Almost 70% of the fat in Swedish milk is saturated of which around 11% comprises short-chain fatty acids, almost half of which is butyric acid. Approximately 25% of the fatty acids in milk are mono-unsaturated and 2.3% are poly-unsaturated with omega-6/omega-3 ratio around 2.3. Approximately 2.7% are trans fatty acids.
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              Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows

              Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host’s rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host’s genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.
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                Author and article information

                Contributors
                Bart.buitenhuis@mbg.au.dk
                Jan.lassen@mbg.au.dk
                Samantha.noel@anis.au.dk
                damian@broadinstitute.org
                pso@mbg.au.dk
                Gareth.Difford@nofima.no
                nina.poulsen@food.au.dk
                Journal
                Genet Sel Evol
                Genet. Sel. Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                29 May 2019
                29 May 2019
                2019
                : 51
                : 23
                Affiliations
                [1 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, , Aarhus University, ; Blichers Alle 20, P.O. Box 50, 8830 Tjele, Denmark
                [2 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Department of Animal Science, , Aarhus University, ; Blichers Alle 20, P.O. Box 50, 8830 Tjele, Denmark
                [3 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, Center for Biological Sequence Analysis, , Denmark Technical University, ; 2800 Lyngby, Denmark
                [4 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Department of Food Science, , Aarhus University, ; Blichers Alle 20, P.O. Box 50, 8830 Tjele, Denmark
                Author information
                http://orcid.org/0000-0002-4953-3081
                Article
                464
                10.1186/s12711-019-0464-8
                6542034
                31142263
                0425a092-7d1a-4227-8143-1c783a12e818
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 September 2018
                : 14 May 2019
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Genetics
                Genetics

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