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SHEEP MAMMARY GLAND MICROBIOME Emma Monaghan MMEG 16/12/13 Complexity and importance of microbial communities – what we see in humans Cho and Blaser, 2012 Hunt el al, 2011 Intramammary infections (IMI) in sheep • Impact negatively on sheep health and productivity • Difficult for farmers to treat as detection is difficult and treatment is often of limited success • Over 120 species of bacteria have been linked to IMI • Is the mammary gland a site where bacteria live in a microbial community? Mastitis research at Warwick Cooper, 2011 and Smith, 2012 Research hypotheses and aims Hypotheses: 1. A natural microbial community forms in the sheep mammary gland 2. Changes in the community lead to development of IMI Aims: • To develop an understanding of the bacteria present • Investigate community structure over time and age of sheep i.e. do the species of bacteria colonising the udder increase? • Determine how colonising bacterial species influence mammary gland health using SCC Mastitis research at Warwick Bacterial community analysis study Parity 1 Parity 2 Parity 3 Parity 4 Parity 10 30 sheep, 1 farm DNA extraction (Purdy, 2005) 5 age groups 16S bacterial PCR (Hunt et al, 2011) Milk collected from each half for 8 weeks DGGE Modelling and Sequencing Mastitis research at Warwick Challenges Milk as a substrate contains fats, proteins, calcium ions milk consistency can vary between sheep bacteria residing in host cells low levels of bacteria variable optimisation between sample sets required Mastitis research at Warwick DGGE of milk samples from one sheep L 1 2 3 4 5 6 1 2 3 4 5 6 L Left half of mammary gland Right half of mammary gland Left mammary gland Right mammary gland Mastitis research at Warwick DGGE of milk samples from a second sheep L 1 2 3 4 5 Left mammary gland 6 1 2 3 4 5 6 L Right mammary gland Mastitis research at Warwick GelCompar II – MDS plot Mastitis research at Warwick GelCompar II – PCA Mastitis research at Warwick Summary of GelCompar results • Left and right halves cluster separately • Early and late weeks cluster together • Samples from same time point but different half cluster • Similar number of DGGE bands per sample when sheep grouped by age: Average no. of bands Average no. of bands per milk sample when grouped by sheep parity (age) 7 6 5 4 3 2 1 0 1 2 3 4 10 Sheep parity (age) Mastitis research at Warwick Modelling of sheep and DGGE data Mixed effects 4 level regression model Dependent variables Log(SCC) Independent variables Sheep ID Sheep age Half Week 2,068 bands were identified and each 1 was classified into 1 of 35 band classes. Both binary and numerical data were analysed separately in the model. 15 band classes have been identified as significant, with 8 linked to an increase and 7 a decrease in SCC. Significant bands being identified on DGGEs and sequenced. Mastitis research at Warwick High-throughput Illumina sequencing Using Illumina MiSeq on study samples: Data per sample and accuracy in base calls Good depth of coverage for study fragment size Cost –all study samples with controls and replicates Using paired-end sequencing with two-step PCR protocol Sequencing data will be fed into the model alongside data on identity of bacteria associated with changes in SCC Mastitis research at Warwick What’s next? SEQUENCING Analysis of data from Illumina sequencing test run Submit all study samples for Illumina sequencing to: Investigate community composition over time, within and between sheep and with sheep age Support PCR-DGGE approach and GelCompar II analysis Provide further data for analysis and incorporation into the model DGGE Re-DGGE selected samples to identify bacterial species associated with changes in SCC Mastitis research at Warwick Acknowledgements Supervisors: • Professor Laura Green • Dr Kevin Purdy Data collection and support: • Dr Selene Huntley • Dr Ed Smith Funders: • BBSRC, EBLEX, QMMS and BioScience KTN Bob Jagendorf, 2012