A multidisciplinary approach involving sensor dynamic systems & bioinformatics to predict cattle health - Walton, E.K. (2020)
Dairy herds contribute largely to the agricultural economy of the UK. However, with the demand for cheap dairy products and pressure to reduce the environmental impact of farming practices, there is a growing interest in increasing production efficiency. One way to achieve this is by understanding the relationship between disease and growth. This pilot study looked at the trend and correlation of weight, metagenome and core temperature of calves along time on UK farms. The aim of the study was to assess the use of weight, metagenome and core temperature in combination to aid in monitoring calf health. A total of 22 calves were used in the study that spanned over 6 weeks. 20 calves aged between 7 and 8 months old were followed over 6 weeks. A further 12 animals around 14 weeks of age were included in the final week of the study. Core temperature was measured via ruminal boluses while weight was recorded manually using a weigh band. The metagenome was determined via shotgun sequencing of DNA extracted from rectal swab samples. From our preliminary results, it appears that the metagenome is highly enriched with organisms that are taxonomically unclassified (~50% of reads). The classified metagenomics component reflects a more dynamic and constantly changing pattern at an individual level than at a farm level. No major differences could be identified between either healthy and unhealthy or young and old classified calves using the taxonomically classified reads representing over half of the overall data. However, interestingly it was found that the Lactobacillales order was only found in detectable abundances in the older calves. Lactobacillalles have been an important order of bacteria in probiotic research in recent years and one found to be very dependent on diet. Similarly, only a weak pattern was observed linking the calves demonstrating abnormal or poor weight gain. Our approach and preliminary results demonstrate a novel approach for the study of health indicators integrating large sets of heterogeneous data such as field, clinical, sensor and genomic data. Likewise, the findings highlight the importance of discovering dedicated pipelines to analyse the unclassified reads in an attempt to provide a more complete and comprehensive overview of the metagenomics data.