Can microbe analysis of effluent be used as a proxy for bovine herd health? : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Genetics at Massey University, Albany, New Zealand

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The microbial community within the bovine gut has been shown to have major impacts on the health of a cow. Most recent studies have focused on identifying and quantifying the gut microbiome of humans, and more recently of agricultural animals. Microbiome studies come with significant challenges, especially around quantifying bias. In this study, careful validation of controls was undertaken to ensure that the method that was followed was appropriate. These controls included a mock community, technical replicates and a spike-in for comparison of methods that could be used to classify a microbiome. Variation in these methods can be caused by sequencing type, classification tools and databases, as well as open-source pipelines. Using this information, the best pipeline was determined and then used to identify genera in previously uncharacterized bovine effluent microbiome samples. This pipeline consists of 16S rRNA gene Illumina sequencing with the USEARCH-UNOISE classification tool. The dairy effluent system analysed here is interconnected and shows a high degree of similarity in its microbiome composition across sampling locations. Samples have similar microbial communities caused most strongly by collection date, followed by location. The most abundant microbes present are those involved in breaking down faeces (i.e., Corynebacterium). I found that sequencing depth has a large impact on microbiome classification accuracy. Determining the core taxa of a microbiome will enable analysis of any changes from the expected microbes. These changes can be due to normal fluctuations, such as age. However, microbial dysbiosis can be due to pathogenic microbes. I also explored the effect of pathogenic microbes on microbial community composition as it can have a big influence on animal health. Early identification of infections can minimise the financial and bovine welfare impact on farms.
Figure 1.1 is re-used under a CC Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.