Genetic analysis of orotic acid predicted with Fourier transform infrared milk spectra.

Fourier transform infrared spectral analysis is a cheap and fast method to predict milk composition. A not very well studied milk component is orotic acid. Orotic acid is an intermediate in the biosynthesis pathway of pyrimidine nucleotides and is an indicator for the metabolic cattle disorder deficiency of uridine monophosphate synthase. The function of orotic acid in milk and its effect on calf health, health of humans consuming milk or milk products, manufacturing properties of milk, and its potential as an indicator trait are largely unknown. The aims of this study were to determine if milk orotic acid can be predicted from infrared milk spectra and to perform a large-scale phenotypic and genetic analysis of infrared-predicted milk orotic acid. An infrared prediction model for orotic acid was built using a training population of 292 Danish Holstein and 299 Danish Jersey cows, and a validation population of 381 Danish Holstein cows. Milk orotic acid concentration was determined with nuclear magnetic resonance spectroscopy. For genetic analysis of infrared orotic acid, 3 study populations were used: 3,210 Danish Holstein cows, 3,360 Danish Jersey cows, and 1,349 Dutch Holstein Friesian cows. Using partial least square regression, a prediction model for orotic acid was built with 18 latent variables. The error of the prediction for the infrared model varied from 1.0 to 3.2 mg/L, and the accuracy varied from 0.68 to 0.86. Heritability of infrared orotic acid predicted with the standardized prediction model was 0.18 for Danish Holstein, 0.09 for Danish Jersey, and 0.37 for Dutch Holstein Friesian. We conclude that milk orotic acid can be predicted with moderate to good accuracy based on infrared milk spectra and that infrared-predicted orotic acid is heritable. The availability of a cheap and fast method to predict milk orotic acid opens up possibilities to study the largely unknown functions of milk orotic acid.

Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk.

Small components and metabolites in milk are significant for the utilization of milk, not only in dairy food production but also as disease predictors in dairy cattle. This study focused on estimation of genetic parameters and detection of quantitative trait loci for metabolites in bovine milk. For this purpose, milk samples were collected in mid lactation from 371 Danish Holstein cows in first to third parity. A total of 31 metabolites were detected and identified in bovine milk by using (1)H nuclear magnetic resonance (NMR) spectroscopy. Cows were genotyped using a bovine high-density single nucleotide polymorphism (SNP) chip. Based on the SNP data, a genomic relationship matrix was calculated and used as a random factor in a model together with 2 fixed factors (herd and lactation stage) to estimate the heritability and breeding value for individual metabolites in the milk. Heritability was in the range of 0 for lactic acid to >0.8 for orotic acid and ß-hydroxybutyrate. A single SNP association analysis revealed 7 genome-wide significant quantitative trait loci [malonate: Bos taurus autosome (BTA)2 and BTA7; galactose-1-phosphate: BTA2; cis-aconitate: BTA11; urea: BTA12; carnitine: BTA25; and glycerophosphocholine: BTA25]. These results demonstrate that selection for metabolites in bovine milk may be possible.

Nuclear magnetic resonance metabonomics reveals strong association between milk metabolites and somatic cell count in bovine milk.

Somatic cell count (SCC) is associated with changes in milk composition, including changes in proteins, lipids, and milk metabolites. Somatic cell count is normally used as an indicator of mastitis infection. The compositional changes in protein and fat affect milk coagulation properties, and also the metabolite composition is thought to contribute to differential milk properties. Milk somatic cells comprise different cell types, which may contribute to differential milk metabolite fingerprints. In this study, milk from a relatively large number of individual cows, representing significant differences in SCC, were analyzed by nuclear magnetic resonance (NMR)-based metabonomics, and the milk metabolite profiles were analyzed for differences related to SCC. Global principal component analysis performed on 876 samples from 2 Danish dairy breeds and orthogonal projection of latent structures discriminant analysis performed on a smaller subset (n=70) representing high (SCC >7.2×10(5) cells/mL) and low (SCC <1.4×10(4) cells/mL) milk SCC identified latent variables, which could be attributed to milk with elevated SCC. In addition, partial least squares regression between the NMR milk metabolite profiles and SCC revealed a strong correlation. The orthogonal projection of latent structures discriminant analysis and partial least squares regressions pinpointed specific NMR spectral regions and thereby identification of milk metabolites that differed according to SCC. Relative quantification of the identified metabolites revealed that lactate, butyrate, isoleucine, acetate, and ß-hydroxybutyrate were increased, whereas hippurate and fumarate were decreased in milk with high levels of somatic cells.