4.6 Article

Prepartum and Postpartum Rumen Fluid Microbiomes: Characterization and Correlation with Production Traits in Dairy Cows

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APPLIED AND ENVIRONMENTAL MICROBIOLOGY
卷 81, 期 4, 页码 1327-1337

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AMER SOC MICROBIOLOGY
DOI: 10.1128/AEM.03138-14

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  1. National Institute of Food and Agriculture at the New York State Federal Formula Funds [NYC-478452]

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Microbes present in the rumen of dairy cows are essential for degradation of cellulosic and nonstructural carbohydrates of plant origin. The prepartum and postpartum diets of high-producing dairy cows are substantially different, but in what ways the rumen microbiome changes in response and how those changes may influence production traits are not well elucidated. Here, we sequenced the 16S and 18S rRNA genes using the MiSeq platform to characterize the prepartum and postpartum rumen fluid microbiomes in 115 high-producing dairy cows, including both primiparous and multiparous animals. Discriminant analysis identified differences between the microbiomes of prepartum and postpartum samples and between primiparous and multiparous cows. 18S rRNA sequencing revealed an overwhelming dominance of the protozoan class Litostomatea, with over 90% of the eukaryotic microbial population belonging to that group. Additionally, fungi were relatively more prevalent and Litostomatea relatively less prevalent in prepartum samples than in postpartum ones. The core rumen microbiome (common to all samples) consisted of 64 bacterial taxa, of which members of the genus Prevotella were the most prevalent. The Chao1 richness index was greater for prepartum multiparous cows than for postpartum multiparous cows. Multivariable models identified bacterial taxa associated with increased or reduced milk production, and general linear models revealed that a metagenomically based prediction of productivity is highly associated with production of actual milk and milk components. In conclusion, the structure of the rumen fluid microbiome shifts between the prepartum and first-week postpartum periods, and its profile within the context of this study could be used to accurately predict production traits.

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