|SKARLUPKA, JOSEPH - University Of Wisconsin|
|RESENDE, RAFAEL - Federal University Of Goias|
|FISCHER, AMELIE - The French Livestock Institute|
|SUEN, GARRET - University Of Wisconsin|
Submitted to: Applied and Environmental Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/13/2020
Publication Date: 6/26/2020
Citation: Young, J.N., Skarlupka, J.H., Resende, R.T., Fischer, A., Kalscheur, K., Mcclure, J.C., Cole, J.B., Suen, G., Bickhart, D.M. 2020. Validating the use of bovine buccal sampling as a proxy for the rumen microbiota using a time course and random forest classification approach. Applied and Environmental Microbiology. 86(17). Article e00861-20.. https://doi.org/10.1128/AEM.00861-20 .
Interpretive Summary: The microbes that colonize the cattle rumen are what allows the cow to digest plant material. There is evidence that these microbes also influence the fat content of milk produced by dairy breeds. However, it is very difficult to identify and measure these microbes so that they can be selected for optimal milk production. This study explores the use of non-invasive swabbing of the cow's mouth to identify rumen microbe communities. It was found that this method is efficient and was suitable for routine analysis. This may be the beginning of a new industrial application for selecting for cows with better rumen microbial profiles.
Technical Abstract: Analysis of the cow microbiome, as well as host genetic influences on the establishment and colonization of the rumen microbiota, is critical for development of strategies to manipulate ruminal function toward more efficient and environmentally friendly milk production. To this end, the development and validation of noninvasive methods to sample the rumen microbiota at a large-scale is required. Here, we further optimized the analysis of buccal swab samples as a proxy for direct microbial samples of the rumen of dairy cows. To identify an optimal time for sampling, we collected buccal swab and rumen samples at six different time points relative to animal feeding. We then evaluated several biases in these samples using a machine learning classifier (random forest) to select taxa that discriminate between buccal swab and rumen samples. Differences in the Simpson’s diversity, Shannon’s evenness and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P<0.05), suggesting that this time point was optimal for representative sampling. In addition, the random forest classifier was able to accurately identify non-rumen taxa, including 10 oral and feed-associated taxa. Two taxa found in high abundance in both buccal and rumen samples had significant variance in relative abundance between sampling methods, but could feasibly be qualitatively assessed via regular buccal swab sampling. This work not only provides new insights into the oral community of ruminants, but further validates and refines buccal swabbing as a method to assess the rumen microbiota in large herds.