Download Sheep mammary gland microbiome

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