Paolo Carnier

Validation of a new reversed-phase high-performance liquid chromatography method for separation and quantification of bovine milk protein genetic variants

A new RP-HPLC method for the separation and quantification of the most common genetic variants of bovine milk proteins is described. A reversed-phase analytical column C8 (Zorbax 300SB-C8 RP, 3.5 microm, 300A, 150 x 4.6 I.D.) was used. All the most common casein (CN) and whey protein genetic variants, including beta-CN(I) were detected and separated simultaneously in less then 40 min, with the exception of alpha(S1)-CN(B) and CN(C) variants. Purified protein genetic variants were employed in calibration and showed different absorbances at 214 nm. The procedure was developed using 40 raw individual milk samples of cows belonging to four different breeds and certified skim milk powder BCR-063R. Method validation consisted in testing linearity, repeatability, reproducibility and accuracy. A linear relationship (R(2)>0.99) between the concentrations of proteins and peak areas was observed over the concentration range, with low detection limits. Repeatability and reproducibility were satisfactory for both retention times and peak areas. The RSD of peak areas ranged from 0.92 to 4.32% within analytical day and from 0.85 to 9.52% across analytical days. The recoveries, calculated using mixtures of samples previously quantified, ranged from 98.1 to 103.7%.

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk.

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a(30), mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Prediction models for RCT and a(30) based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a(30), respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a(30) were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a(30) ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.

Reproducibility and Repeatability of Measures of Milk Coagulation Properties and Predictive Ability of Mid-Infrared Reflectance Spectroscopy

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a(30), mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6 h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a(30), respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a(30). Repeatabilities of RCT and a(30) measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a(30.) Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.

Effects of Composite β- and κ-Casein Genotypes on Milk Coagulation, Quality, and Yield Traits in Italian Holstein Cows

The aim of the study was to estimate the effect of the composite CSN2 and CSN3 genotypes on milk coagulation, quality, and yield traits in Italian Holstein cows. A total of 1,042 multiparous Holstein cows reared on 34 commercial dairy herds were sampled once, concurrently with monthly herd milk recording. The data included the following traits: milk coagulation time; curd firmness; pH and titratable acidity; fat, protein, and casein contents; somatic cell score; and daily milk, fat, and protein yields. A single-trait animal model was assumed with fixed effects of herd, days in milk, parity, composite casein genotype of CSN2 and CSN3 (CSN2-CSN3), and random additive genetic effect of an animal. The composite genotype of CSN2-CSN3 showed a strong effect on both milk coagulation traits and milk and protein yields, but not on fat and protein contents and other milk quality traits. For coagulation time, the best CSN2-CSN3 genotypes were those with at least one B allele in both the CSN2 and CSN3 loci. The CSN3 locus was associated more strongly with milk coagulation traits, whereas the CSN2 locus was associated more with milk and protein yields. However, because of the tight linkage between the 2 loci, the composite genotypes, or haplotypes, are more appropriate than the single-locus genotypes if they were considered for use in selection.

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a(30)) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a(30) with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h(2))=0.240 and h(2)=0.210 for HF and BS, respectively] than a(30) (h(2)=0.148 and h(2)=0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h(2)=0.103 and h(2)=0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h(2)=0.108). A negative genetic correlation, lower than -0.85, was estimated between RCT and a(30) for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were those of RCT with pH and titratable acidity in both breeds, ranging from 0.80 to 0.94. Including NC milk information in the data affected the estimated correlations and decreased the uncertainty associated with the estimation process. On the basis of the estimated heritabilities and genetic correlations, enhancement of MCP through selective breeding with no detrimental effects on yield and composition seems feasible in both breeds. Milk acidity may play a role as an indicator trait for indirect enhancement of MCP.

Effects of β-κ-casein (CSN2-CSN3) haplotypes, β-lactoglobulin (BLG) genotypes, and detailed protein composition on coagulation properties of individual milk of Simmental cows

The aim of this study was to investigate the effects of CSN2-CSN3 (beta-kappa-casein) haplotypes, BLG (beta-lactoglobulin) genotypes, content of milk protein fractions, and protein composition on coagulation properties of milk (MCP). Rennet coagulation time (RCT) and curd firmness (a(30)) were measured using a computerized renneting meter, and the contents of major milk protein fractions were quantified by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Cow genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Phenotypes for MCP were regressed on CSN2-CSN3 haplotype probabilities using linear models that also included the effects of herd-test-day, parity, days in milk, pH, somatic cell score, renneting meter sensor, sire of the cow, BLG genotype, and content of major protein fractions or, alternatively, protein composition. When the statistical model did not account for protein fraction contents or protein composition, haplotypes carrying CSN3 B were associated with shorter RCT and greater a(30) compared with those carrying CSN3 A. Haplotypes carrying CSN2 B had the effect of decreasing RCT and increasing a(30) relative to haplotype A(2)A. When effects of protein fractions content or protein composition were added to the model, no difference across haplotypes due to CSN3 and CSN2 alleles was observed for MCP, with the exception of the effect of CSN2 B on RCT, which remained markedly favorable. Hence, the effect of CSN3 B on MCP is related to a variation in protein composition caused by the allele-specific expression of kappa-casein, rather than to a direct role of the protein variant on the coagulation process. In addition, the favorable effect exerted by CSN2 B on a(30) was caused by the increased beta-casein content in milk. Conversely, CSN2 B is likely to exert a direct genetic effect on RCT, which does not depend upon variation of beta-casein content associated with CSN2 B. Increased RCT was observed for milk yielded by BLG BB cows, even when models accounted for protein composition. Rennet clotting time was favorably affected by kappa-casein content and percentage of kappa-casein to total casein, whereas a(30) increased when contents and percentages of beta-CN and kappa-CN increased. Changes of milk protein composition and allele frequency at casein and whey protein genes affect variation of MCP.

Effects of β-κ-casein (CSN2-CSN3) haplotypes and β-lactoglobulin (BLG) genotypes on milk production traits and detailed protein composition of individual milk of Simmental cows

The aim of this study was to investigate the effects of CSN2-CSN3 (beta-kappa-casein) haplotypes and BLG (beta-lactoglobulin) genotypes on milk production traits, content of protein fractions, and detailed protein composition of individual milk of Simmental cows. Content of the major protein fractions was measured by reversed-phase HPLC in individual milk samples of 2,167 cows. Protein composition was measured as percentage of each casein (CN) fraction to total CN and as percentage of beta-lactoglobulin (beta-LG) to total whey protein. Genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Traits were analyzed by using a linear model including the fixed effects of herd-test-day, parity, days in milk, and somatic cell score class, linear regressions on haplotype probabilities, class of BLG genotype, and the random effect of the sire of the cow. Effects of haplotypes and BLG genotypes on yields were weak or trivial. Genotype BB at BLG and haplotypes carrying CSN2 B and CSN3 B were associated with increased CN content and CN number. Haplotypes including CSN3 B were associated with increased kappa-CN content and percentage of kappa-CN to total CN and with decreased percentages of alpha(S1)- and gamma-CN to total CN. Allele CSN2 B had the effect of increasing beta-CN content and decreasing content of alpha(S1)-CN. Haplotypes including allele CSN2 A(1) exhibited decreased beta-, alpha(S2)-, and gamma-CN concentrations and increased alpha(S1)- and kappa-CN contents, whereas CSN2 I had positive effects on beta-CN concentration and trivial effects on content of other protein fractions. Effects of haplotypes on CN composition were similar to those exerted on content of CN fractions. Allele BLG A was associated with increased beta-LG concentration and percentage of beta-LG to total whey protein and with decreased content of other milk proteins, namely beta-CN and alpha(S1)-CN. Estimated additive genetic variance for investigated traits ranged from 14 to 39% of total variance. Increasing the frequency of specific genotypes or haplotypes by selective breeding might be an effective way to change milk protein composition.

Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle

The objective of this study was to estimate genetic parameters for milk protein fraction contents, milk protein composition, and milk coagulation properties (MCP). Contents of α(S1)-, α(S2)-, β-, γ-, and κ-casein (CN), β-lactoglobulin (β-LG), and α-lactalbumin (α-LA) were measured by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Milk protein composition was measured as percentage of each CN fraction in CN (α(S1)-CN%, α(S2)-CN%, β-CN%, γ-CN%, and κ-CN%) and as percentage of β-LG in whey protein (β-LG%). Rennet clotting time (RCT) and curd firmness (a(30)) were measured by a computerized renneting meter. Heritabilities for contents of milk proteins ranged from 0.11 (α-LA) to 0.52 (κ-CN). Heritabilities for α(S1)-CN%, κ-CN%, and β-CN% were similar and ranged from 0.63 to 0.69, whereas heritability of α(S2)-CN%, γ-CN%, and β-LG% were 0.28, 0.18, and 0.34, respectively. Effects of CSN2-CSN3 haplotype and BLG genotype accounted for more than 80% of the genetic variance of α(S1)-CN%, β-CN%, and κ-CN% and 50% of the genetic variance of β-LG%. The genetic correlations among the contents of CN fractions and between CN and whey protein fractions contents were generally low. When the data were adjusted for milk protein gene effects, the magnitude of the genetic correlations among the contents of milk protein fractions markedly increased, indicating that they undergo a common regulation. The proportion of β-CN in CN correlated negatively with κ-CN% (r=-0.44). The genetic relationships between CN and whey protein composition were trivial. Low milk pH correlated with favorable MCP. Genetically, contents and proportions of α(S1)- and α(S2)-CN in CN were positively correlated with RCT. The relative proportion of β-CN in CN exhibited a genetic correlation with RCT of -0.26. Both the content and the relative proportion of κ-CN in CN did not correlate with RCT. Weak curds were genetically associated with increased proportions in CN of α(S1)- and α(S2)-CN, decreased contents of β-CN and κ-CN, and decreased proportion of κ-CN in CN. Negligible effects on the estimated correlations between a(30) and κ-CN contents or proportion in CN were observed when the model accounted for milk protein gene effects. Increasing β-CN and κ-CN contents and relative proportions in CN and decreasing the content and proportions of α(S1)-CN and α(S2)-CN and milk pH through selective breeding exert favorable effects on MCP.

Effectiveness of mid-infrared spectroscopy for the prediction of detailed protein composition and contents of protein genetic variants of individual milk of Simmental cows.

Mid-infrared (MIR) spectroscopy was used to predict the detailed protein composition of 1,517 milk samples of Simmental cows. Contents of milk protein fractions and genetic variants were quantified by reversed-phase HPLC. The most accurate predictions were those obtained for total protein, casein (CN), α(S1)-CN, β-lactoglobulin (LG), glycosylated κ-CN, and whey protein content, which exhibited coefficients of determination between predicted and measured values in cross-validation (1-VR) ranging from 0.61 to 0.78. Less favorable were results for β-CN (1-VR=0.53), α(S2)-CN, and κ-CN (1-VR=0.49). Neither the content of α-LA nor that of γ-CN was accurately predicted by MIR. Predicting the content of the most common milk protein genetic variants (κ-CN A and B; β-CN A¹, A², and B; and β-LG A and B) was unfeasible (1-VR <0.15 for the content of κ-CN genetic variants and 1-VR <0.01 for the content of β-CN variants). The best predictions were obtained for β-LG A and β-LG B contents (1-VR of 0.60 and 0.44, respectively). Results indicated that MIR is not applicable for predicting individual milk protein composition with high accuracy. However, MIR spectroscopy predictions may play a role as indicator traits in selective breeding to enhance milk protein composition. The genetic correlation between MIR spectroscopy predictions and measures of milk protein composition needs to be investigated, as it affects the suitability of MIR spectroscopy predictions as indicator traits in selective breeding.

Prediction of protein composition of individual cow milk using mid-infrared spectroscopy.

Abstract This study investigated the application of mid-infrared spectroscopy for the prediction of protein composition in individual milk samples (n=1,336) of Simmental cows. Protein fractions were quantified by RP-HPLC and MIR data were recorded over the spectral range from 4,000 to 900 cm-1. Models were developed by partial least squares regression using untreated spectra. The most successful predictions were for protein, casein, αS1-casein, whey protein, and β-lactoglobulin contents. The models could discriminate between high and low values of protein composition (R2=0.50 to 0.58). The root mean square errors of cross-validation were 3.11g/l for protein (range 39.91g/l), 2.76g/l for casein (range 35.16g/l), 1.07 g/l for αS1-casein (range 12.82g/l), 0.51 g/l for whey protein (range 4.97g/l), and 0.43 for β-lactoglobulin (range 4.37). Application of MIR spectroscopy is possible for the routinely assessment of protein, casein, αS1-casein, whey protein, and β-lactoglobulin and its implementation might be in future a tool for impro ving protein composition of bovine milk through breeding programs.