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Analysing the cow mammary gland microbiome: How does the microbiome drive disease?

Principal Supervisor: Dr Kevin Purdy - SLS

Co-supervisor: Prof Laura Green - SLS

PhD project title: Analysing the cow mammary gland microbiome: How does the microbiome drive disease?

University of Registration: Warwick

Project outline:

It is clear that the interaction between animals and their multiple microbiomes are critical to health and welfare and, for farmed animals, linked to productivity and food security. However, understanding these interactions is a significant technical and intellectual challenge, requiring practical and analytical skills and insight to make progress in understanding how the microbiome and host function.

Previous work in the Purdy/Green collaboration has investigated cow mammary gland (MG) microbiome dynamics in the context of intramammary health and disease. This work has produced a resource of ~10,000 MG quarter milk samples from approximately 200 cows sampled ~10 times from 2 herds. All samples are linked to a clinical record, along with somatic cell count (SCC), a measure of host immune response to the MG microbiome. Epidemiological analysis of SCC produced 9 latent classes of SCC dynamics (a proxy measure of “disease state”) within the dataset. These latent classes were used to select 45 animals for microbiome analysis (~180 quarter samples taken at 10 time points = ~1,800 analysed milk samples).

This work has shown that all MGs, irrespective of their SCC, have a diverse microbiome, that these vary in composition and diversity and have differing dynamics over time within and between individual cows and between SCC latent classes. Discriminant analysis has shown SCC latent classes have significantly different microbiomes and community dynamics over time and may suggest that there are microbiomes that are resistant to and sensitive to infection leading to mastitis. These data and analyses indicate how powerful these types of datasets are when under-pinned by a clearly defined disease phenotype, in this case SCC. This initial dataset raises many questions including; is a dynamic microbiome more or less susceptible to disease? Are there members of the microbiome that appear to consistently confer resistance to mastitis? Does disease develop primarily through a resident or invading pathogen? Are microbiomes different in heifers compared to more mature cows and does age lead to a more diversity and more stable microbiome?

This PhD project will initially utilise this large existing microbiome dataset to develop specific hypotheses and questions such as those posed above. These hypotheses will then be tested by microbiome analysis of specifically selected milk samples from the remaining ~8000 milk samples and analysed using epidemiological and ecological statistical modelling to address the hypotheses.

The student will work directly with both Dr Purdy and Prof Green throughout the project and also interact with Dr Purdy and Prof Green’s collaborators Prof M Green and Dr A Bradley (University of Nottingham), world-leading experts in the epidemiology and control of mastitis.

The data collected to date, from a well replicated longitudinal study has already shown itself to be one of the most clearly defined microbiome datasets available with a clear disease phenotype that enables the development of hypotheses that will produce a step-change in our understanding of microbiomes and their influence on health and well-being.

BBSRC Strategic Research Priority: Food Security

Techniques that will be undertaken during the project:

  • Epidemiological analysis of data – statistical modelling to detect trends and relevance to disease of community members
  • DNA extraction from milk samples
  • PCR amplification and next generation sequence analysis of the bacterial community

Contact: Dr Kevin Purdy, University of Warwick