Rumen Bacteria and Serum Metabolites Predictive of Feed Efficiency Phenotypes in Beef Cattle

Clemmons, Brooke A.; Martino, Cameron; Powers, Joshua B.; Campagna, Shawn R.; Voy, Brynn H.; Donohoe, Dallas R.; Gaffney, James; Myer, Phillip R.

doi:10.1101/701300

Cited by 1 publication

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“…As has been noted previously (9), the presence of the commensal oral microbial community in buccal swab samples prevents direct comparisons between rumen content samples and buccal swabs and must be filtered from buccal swab samples prior to analysis using manual and mathematical methods (9, 10). By using a random forest classifier, we were able to assign importance estimates to individual microbial taxa based on their use as a feature in our classification models, as has been done previously (46, 47). The top OTUs, after variable importance analysis, consisted of microbes that were oral-specific (oral, n = 10), rumen-biased (rumen, n = 12), and those with high prevalence regardless of sample type but varied based on relative abundance (core, n = 2).…”

Section: Discussionmentioning

confidence: 99%

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

Young

Skarlupka

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et al. 2020

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24Analysis of the cow microbiome, as well as host genetic influences on the establishment and 25 colonization of the rumen microbiota, is critical for development of strategies to manipulate 26 ruminal function toward more efficient and environmentally friendly milk production. To this 27 end, the development and validation of noninvasive methods to sample the rumen microbiota at 28 a large-scale is required. Here, we further optimized the analysis of buccal swab samples as a 29 proxy for direct microbial samples of the rumen of dairy cows. To identify an optimal time for 30 sampling, we collected buccal swab and rumen samples at six different time points relative to 31 animal feeding. We then evaluated several biases in these samples using a machine learning 32 classifier (random forest) to select taxa that discriminate between buccal swab and rumen 33 samples. Differences in the Simpson's diversity, Shannon's evenness and Bray-Curtis 34 dissimilarities between methods were significantly less apparent when sampling was performed 35 prior to morning feeding (P<0.05), suggesting that this time point was optimal for representative 36 sampling. In addition, the random forest classifier was able to accurately identify non-rumen 37 taxa, including 10 oral and feed-associated taxa. Two highly prevalent (> 60%) taxa in buccal 38 and rumen samples had significant variance in absolute abundance between sampling methods, 39 but could be qualitatively assessed via regular buccal swab sampling. This work not only 40 provides new insights into the oral community of ruminants, but further validates and refines 41 buccal swabbing as a method to assess the rumen microbiota in large herds. 42 43 44 45 46 47 48 The gastrointestinal tract of ruminants harbors a diverse microbial community that coevolved 49 symbiotically with the host, influencing its nutrition, health and performance. While the 50 influence of environmental factors on rumen microbes is well-documented, the process by which 51 host genetics influences the establishment and colonization of the rumen microbiota still needs 52 to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen 53 microbiota. There are three common methods for rumen sampling but all of them present at least 54 one disadvantage, including animal welfare, sample quality, labor, and scalability. The 55 development and validation of non-invasive methods, such as buccal swabbing, for large-scale 56 rumen sampling is needed to support studies that require large sample sizes to generate reliable 57 results. The validation of buccal swabbing will also support the development of molecular tools 58 for the early diagnosis of metabolic disorders associated with microbial changes in large herds. 59 KEYWORDS 60 Bacteria, oral community, rumen microbiota, buccal swab, machine learning, random forest 61 62 63 64 65 66 67 68 69 microorganisms from all three super kingdoms (for a review see (1, 2)). Essential to the digestion 72 of complex plant polymers by the host, the r...

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