Milk protein profile: measure from mid infrared spectra and identification of influence factors
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1 EAAP 2016, Belfast, 1 Sept Milk protein profile: measure from mid infrared spectra and identification of influence factors Session 71 «Milk and meat quality highlighting knowledge gaps in the supply chain» M. Gelé (marine.gele@idele.fr) M. Ferrand-Calmels, G. Miranda, N. Ballot, L. Bianchi, M. Brochard, P. Martin
2 Milk protein profile: a strategic issue for the dairy sector Fat content Protein content Lactose Minerals Vitamines high market value sulfur amino acids, tryptophan, lysine β-casein κ-casein α-lactalbumin 1. How to measure concentrations of major milk proteins in routine? 2. What are the nutrition and physiology-related factors that influence their concentration? 2
3 The methodology at a glance MIR spectroscopy Data collection (from nov 2009 to oct 2010) 1000 French dairy farms Calibration set LC-MS PREDICTION EQUATIONS Estimated protein profile MIR analysis Spectral data Genetic determinism / QTL DNA samples Genotypes Non-genetic influence factors Field surveys Management MIR: mid infrared ~ LC-MS: liquid chromatography mass spectrometry ~ QTL : quantitative trait locus 3
4 The methodology at a glance MIR spectroscopy Calibration set LC-MS PREDICTION EQUATIONS MIR: mid infrared ~ LC-MS: liquid chromatography mass spectrometry ~ QTL : quantitative trait locus 4
5 1 st step: quantifying protein profile 280 milk samples 3 breeds: Holstein, Normande & Montbeliarde Several French regions Large diversity of lactation stages and feeding systems Milk analysis labs (Foss machines only) INRA Jouy-en-Josas Dionex «Ultimate 3000» Chromatography column linked with a Brucker Daltonics «microtof II» mass spectrometer Proteolysis kinetics proteolysis attributed to each protein MIR: mid infrared ~ LC-MS: liquid chromatography mass spectrometry 5
6 1 st step: quantifying protein profile Partial Least Square regression Cross-validation (LOO) PREDICTION EQUATIONS Relative S y,x (%) PC Total cn β-cn α s1 -cn α s2 -cn β-lg+α-la κ-cn α-la LOO: leave one out ~ PC: protein content ~ cn: casein ~ la: lactalbumin ~ lg: lactoglobulin β-lg R² Possible use Routine, any application Analytic use, quantitative information Screening, high or low levels Not recommended 6
7 The methodology at a glance MIR spectroscopy Data collection (from nov 2009 to oct 2010) 1000 French dairy farms Calibration set LC-MS PREDICTION EQUATIONS Estimated protein profile MIR analysis Spectral data Genetic determinism / QTL DNA samples Genotypes Non-genetic influence factors Field surveys Management MIR: mid infrared ~ LC-MS: liquid chromatography mass spectrometry ~ QTL : quantitative trait locus 7
8 2 nd step: screening a population 220,000 data Very similar protein profile between breeds Very low variability of protein contents (Std <1.2 point) MIR: mid infrared spectrometry ~ cn: casein ~ la: lactalbumin ~ lg: lactoglobulin 8
9 The methodology at a glance MIR spectroscopy Data collection (from nov 2009 to oct 2010) 1000 French dairy farms Calibration set LC-MS PREDICTION EQUATIONS Estimated protein profile MIR analysis Spectral data Genetic determinism / QTL DNA samples Genotypes Non-genetic influence factors Field surveys Management MIR: mid infrared ~ LC-MS: liquid chromatography mass spectrometry ~ QTL : quantitative trait locus 9
10 3 rd step: identifying influence factors Sanchez et al publications from 2013 QTL : quantitative trait locus 10
11 3 rd step: identifying influence factors Nutrition-related factors FOOD TYPE αs1-cn αs2-cn β-cn κ-cn β-lg Hay + = =/+ + - Grass silage + = = = - Mais silage & Grass silage Mais silage Mais silage & Pasture = = = = = Baseline = = = = = SAS proc mixed Pasture + + = + - More caseins with hay and pasture Very limited impact of food type on protein profile (<1 point) cn: casein ~ lg: lactoglobulin 11
12 3 rd step: identifying influence factors Physiology-related factors SAS proc mixed +1.5 point -1 point Peak lactation No significant impact of the number of lactation Impact of udder health Impact of lactation stage during the first 2 months on β-cn and β-lg cn: casein ~ lg: lactoglobulin 12
13 Some conclusions Quantifying protein profile in routine IS possible Protein profile is not (a lot) dependent on breed or food type Lactation stage is the most impactful non-genetic factor β-cn and β-lg are responsible for the change in protein profile What about future prospects? Improving LC-MS method Improving the equations Using the equations to phenotype new traits such as cheesemaking properties of milk TO BE CONTINUED LC-MS: liquid chromatography mass spectrometry ~ cn: casein ~ lg: lactoglobulin 13
14 MANY THANKS TO Farmers Financial partners Technical partners You! 14
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