Validating the use of bovine buccal sampling as a proxy for the rumen microbiota using a time course and random forest classification approach

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 Simpson9s diversity, Shannon9s evenness and Bray-Curtis dissimilarities between methods were significantly less apparent when sampling was performed prior to morning feeding (P 60%) taxa in buccal and rumen samples had significant variance in absolute abundance between sampling methods, but could 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.