Hyphenated gas chromatography-mass spectrometry (GC-MS) and multivariate data analysis techniques were used to uncover milk metabolite differences in different αS1-casein genotypes of goats. By a discriminant GC-MS metabolomics approach, we characterized milk polar metabolites of 28 goats. Animals were selected on the basis of their genotypes as 7 goats classified heterozygous for weak or null alleles, 5 for the genotype EE, 9 for the genotypes AE and BE, and finally 7 for the strong genotype AA. Low molecular weight polar metabolite profile was tightly related to the different goat genotypes, milk production, and protein levels. Results of multivariate statistical analysis of GC-MS data demonstrate that different heterozygous and homozygous genotypes expressed different metabolites such as citric and aconitic acid for the strong allele class with different sugars and polyols for the weak class.
Gas chromatography-mass spectrometry metabolomics of goat milk with different polymorphism at the αS1-casein genotype locus.
CABONI, PIERLUIGI;MURGIA, ANTONIO;
2016-01-01
Abstract
Hyphenated gas chromatography-mass spectrometry (GC-MS) and multivariate data analysis techniques were used to uncover milk metabolite differences in different αS1-casein genotypes of goats. By a discriminant GC-MS metabolomics approach, we characterized milk polar metabolites of 28 goats. Animals were selected on the basis of their genotypes as 7 goats classified heterozygous for weak or null alleles, 5 for the genotype EE, 9 for the genotypes AE and BE, and finally 7 for the strong genotype AA. Low molecular weight polar metabolite profile was tightly related to the different goat genotypes, milk production, and protein levels. Results of multivariate statistical analysis of GC-MS data demonstrate that different heterozygous and homozygous genotypes expressed different metabolites such as citric and aconitic acid for the strong allele class with different sugars and polyols for the weak class.File | Dimensione | Formato | |
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GOAT milk polymorphismJDS2016.pdf
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