Milk, Molecules and Microbiomes Metabolomic and proteomic insights into milk biochemistry: genetic, seasonal and feed induced changes in milk Simone Rochfort Molecular Phenomics Biosciences Research, AgriBio Department of Economic Development, Jobs, Transport and Resources School of Applied Systems Biology, La Trobe University
40
Embed
Milk, Molecules and Microbiomes...Milk, Molecules and Microbiomes Metabolomic and proteomic insights into milk biochemistry: genetic, seasonal and feed induced changes in milk Simone
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Milk, Molecules and Microbiomes
Metabolomic and proteomic insights into milk biochemistry: genetic, seasonal and feed induced
changes in milk
Simone Rochfort
Molecular Phenomics
Biosciences Research, AgriBio
Department of Economic Development, Jobs, Transport and Resources
School of Applied Systems Biology, La Trobe University
Biosciences Research DivisionOutline
• Project background• Techniques – microbiome, OS, proteins, non-polar and polar lipids, NMR• Time course data• Within herd variation – how different are individual animals at one time point?• Seasonal variation – do profiles change over a milking season• Between breed variation – Holstein vs. Jersey• Feed induced variation• Data correlations• Next Steps
Biosciences Research DivisionAgriBio, Centre for AgriBioscience
- grains, dairy, meat and horticulture
• Supporting the agriculture and food sector
• Bioscience innovation for high impact outcomes in plant and animal industries
- driving productivity growth
- supporting market access
- enhancing farm business profitability
• Training the knowledge workforce of thefuture
• Two nationally recognised dairy research centres
- AgriBio
- Ellinbank
• Research priorities are improved;
- forage and feeding systems
- animal performance
- soil, water and nutrient use
- processor innovation
Major investment in dairy R&D
Gippsland - 38ºS latitude, 1100 mm rainfall, 500 cows
Melbourne area – glasshouses and laboratories
• Characterisation of milk for processing improvements• How does on-farm variation and manufacturing processes contribute to milk
characteristics and how does this affect product quality
Milk Systems Biology Project
Products
Plant SystemRumen Microbes
Animal
Milk quality
ProcessorMilk composition
• Components important for quality and processing are known, but not measured systematically
• New tools provide a unique opportunity to understand and better control milk quality in the supply chain
– Metagenomic sequencing – 16s– Sensitive mass spectrometry (lipids, intact protein, bottom up proteomics, OS)– NMR (Nuclear Magnetic Resonance)
• How do milk constituents vary – between animals, in a season, by diet?
Knowledge gap in the dairy supply chain
O P O
O−
O
H2C
CH
H2C
OCR1
O O C
O
R2
X
glycerophospholipid
Structure of phospholipids
H2CHC
OH
CH
N+ CH
C
CH2
CH3
H
H3
OH
( )12
sphingosine
H2CHC
O
CH
NH CH
C
CH2
CH3
H
OH
( )12
C
R
O
PO O
OH2C
H2CN+
CH3
H3C
CH3
Sphingomyelin
phosphocholine
sphingosine
fatty acid
−
Structure of glycosphingolipids
H2CHC
OH
CH
NH CH
C
CH2
CH3
H
OH
( )12
C
R
O
ceramide
Structure of sphingolipids
LCMS and GCMS Lipidomics Approaches Structure of triacylglycerols
LCMS Proteomics ApproachesFTMS full scan 15000 resolution TICAcclaim PepMap100, 75μm x 2cm, C18 3μm 100Å, Dionex50 min nLC run, 3-40%ACN separation in 35min
3%
40%
90%
Bottom up proteomics: nano LCMSMS
Identify 100s of proteins including caseins, beta-lactoglobulin, alpha-lactalbumin, lactoferrin, and minor proteins including glycoproteins and enzymes.
bCN_15k_sm_45C_200uLmin_P1C-9_01_3550.d
1 2 3 4 5
6
7
8
9
10
11
PR0566pH4_15k_sm_200uLmin_7_PC-1_01_3565.d
7.5
10.0
12.5
15.0
17.5
20.0
22.5
25.0
27.5
Time [min]
1092.9335
1414.07351602.4837
1716.95251848.7864 2002.9597
1000 1250 1500 1750 2000
LCMS Proteomics ApproachesTop down/intact proteomics: Adapting LC methods for intact protein analysis for LCMS analysis
Isoforms of casein, glycosylation κ casein, phosphorylation variants
Spectral deconvolution
β-casein standard A1
A2
Raw milkA1
no A2 β-casein
NMR (Nuclear Magnetic Resonance)
1H NMR Spectrum of Skim Milk
Citrate
Lactose
Aromatic compounds Organic acids
Linking biochemistry to structure and function
Which proteins and what modifications affect casein micelle size? What about minor interacting constituents?
Which lipids and proteins affect milk fat globule size?
How are physical factors affected e.g. UHT shelf life by microbial content, protein and lipid interaction in cheese making and spray drying
Deeper knowledge to underpin on farm or in factory manipulation
Time course analysis shows increase in protein, decrease in some lipids, increase in choline, betaine, decrease in PC, GPC
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 24 48 72 24 48 72 24 28 72 time (hours)
4 10 25 temp ( oC )
4oC Profiles are comparable: lactobacillales, bifidobacteriales10oC pseudomonadales25oC enterobacteriales, steptococcaceae
Time course
Within herd variation: microbial diversityoligosaccharides
lipids
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Lactobacillales
Enterobacteriales
Clostridiales
Pseudomonadales
BacillalesActinomycetales
Within herd variation: microbial diversity
12 animals on the same farm at the same time• Largely similar complexity and composition• Variation in relative abundances
Order
Within herd variation: microbial diversity
Family
Oligosaccharide – low abundance compared to lactose but nutritionally important . E.g. sialyllactose for brain development
LC-MS profiles of some of the OS
Rela
tive
amou
nt(io
n in
tens
ity 1
e6)
Content of 3-sialyllactose in milk
Cow number
Within herd variation: OS diversity
Methods: Liu et al. 2014. Journal of Agricultural and Food Chemistry 62: 11568-11574
Correlation between fames profile and total fat r=0.90
2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4
2.6
2.8
3
3.2
3.4
3.6
3.8
4
4.2
Y Measured 3 Protein (%)
Y C
V P
redi
cted
3 P
rote
in (%
)
Samples/Scores Plot of FAMES
Class Set: study
R 2̂ = 0.660
Correlation between fames profile and total protein r=0.77
Data Correlations/Prediction
2.5 3 3.5 4 4.5 5 5.52.5
3
3.5
4
4.5
5
5.5
Y Measured 1 MFGY
CV
Pre
dict
ed 1
MFG
Samples/Scores Plot of FAMES
R 2̂ = 0.650
Correlation between fames profile MFG size r=0.81
2 3 4 5 6 7 8 93.8
4
4.2
4.4
4.6
4.8
5
5.2
Y Measured 4 Lactose (%)
Y C
V P
redi
cted
4 L
acto
se (%
)
Samples/Scores Plot of FAMES
Class Set: study
R 2̂ = 0.011
Correlation between fames profile and lactose r=0.1
Data Correlations/Prediction
2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.42
2.5
3
3.5
4
4.5
Y Measured 3 Protein (%)
Y C
V P
redi
cted
3 P
rote
in (%
)
Samples/Scores Plot of taxa
R 2̂ = 0.300
Fit1:1AprilDecFebJanMarMayNovOct
Correlation between metagenomicsprofile and protein r=0.55
Next Steps
• Finalise on going studies (e.g. cow heat stress, UHT longevity)• Complete analysis • Reanalyse and integrate datasets• Identify relationship between physiochemical traits and biochemistry
(e.g. Lipid content and MFG size)• A subset (420 animals) have been genotyped – GWAS
Future Steps
• Breeding values for specific products (e.g. cheese, spray drying)• On farm or In plant options for product optimisation• In plant tools to identify problems (e.g. microbial contamination)
How else are can these measurements be used?
• Indicators of animal health and fertility
• GWAS studies of metabolite heritability
• Milk and cheese provenance determination (adulteration)
• Metabolic changes in product manufacture e.g. cheese ripining
Some recent literature:• Curtis, S.D.; Curini, R.; Delfini, M.; Brosio, E.; D’Ascenzo, F.; Bocca, B. Amino acid profile in the ripening of Grana Padano cheese: a NMR
study. Food Chem. 2000, 71, 495–502.• Brescia, M.A.; Monfreda, M.; Buccolieri, A.; Carrino, C. Characterisation of the geographical origin of buffalo milk and mozzarella cheese
by means of analytical and spectroscopic determinations. Food Chem. 2005, 89, 139–147.• Mulas, G.; Roggio, T.; Uzzau, S.; Anedda, R. A new magnetic resonance imaging approach for discriminating Sardinian sheep milk cheese
made from heat-treated or raw milk. J. Dairy Sci. 2013, 96, 7393–7403.