V. Bonfatti

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%.

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.

Genetic parameters of carcass and meat quality traits of double muscled Piemontese cattle

Genetic parameters of meat quality (MQ) were estimated on Longissimus thoracis muscle of 1208 Piemontese young bulls, progeny of 109 AI sires. Carcass weight (CW), conformation (EUS) and pH (pH24h) were recorded at 24h and lightness (L*), redness (a*), yellowness (b*), pH (pH8d), drip loss (DL), cooking loss (CL) and shear force (SF) were assessed. The heritability (h(2)) of pH24h was very low (0.06), but h2 of pH8d was markedly higher (0.42). Heritability was 0.32, 0.33, and, 0.14, for L*, a* and b*, respectively, whereas was 0.24, 0.07 and 0.14, for DL, CL, and SF, respectively. The two pH measures showed opposite genetic relationships with color measures. Genetic correlations of DL and CL were positive with L* and b* and negative with a*. Genetic correlations between carcass traits and MQ suggest that animals with superior growth potential tend to exhibit reduced EUS scores and pale meat with lower tenderness and water holding capacity. Conversely, improvement of EUS score through selection would lead to light, bright, and tender meat with enhanced water holding capacity.

Survival analysis of preweaning piglet survival in a dry-cured ham-producing crossbred line

The aim of this study was to investigate piglet preweaning survival and its relationship with a total merit index (TMI) used for selection of Large White terminal boars for dry-cured ham production. Data on 13,924 crossbred piglets (1,347 litters), originated by 189 Large White boars and 328 Large White-derived crossbred sows, were analyzed under a frailty proportional hazards model, assuming different baseline hazard functions and including sire and nursed litter as random effects. Estimated hazard ratios (HR) indicated that sex, cross-fostering, year-month of birth, parity of the nurse sow, size of the nursed litter, and class of TMI were significant effects for piglet preweaning survival. Female piglets had less risk of dying than males (HR = 0.81), as well as cross-fostered piglets (HR = 0.60). Survival increased when piglets were nursed by sows of third (HR = 0.85), fourth (HR = 0.76), and fifth (HR = 0.79) parity in comparison with first and second parity sows. Piglets of small (HR = 3.90) or very large litters (HR >1.60) had less chance of surviving in comparison with litters of intermediate size. Class of TMI exhibited an unfavorable relationship with survival (HR = 1.20 for the TMI top class). The modal estimates of sire variance under different baseline hazard functions were 0.06, whereas the variance for the nursed litter was close to 0.7. The estimate of the nursed litter effect variance was greater than that of the sire, which shows the importance of the common environment generated by the nurse sow. Relationships between sire rankings obtained from different survival models were high. The heritability estimate in equivalent scale was low and reached a value of 0.03. Nevertheless, the exploitable genetic variation for this trait justifies the inclusion of piglet preweaning survival in the current breeding program for selection of Large White terminal boars for dry-cured ham production.

Direct and social genetic effects on body weight at 270 days and carcass and ham quality traits in heavy pigs

The aims of this study were to estimate covariance components for BW at 270 d (BW270) and carcass and ham quality traits in heavy pigs using models accounting for social effects and to compare the ability of such models to fit the data relative to models ignoring social interactions. Phenotypic records were from 9,871 pigs sired by 293 purebred boars mated to 456 crossbred sows. Piglets were born and reared at the same farm and randomly assigned at 60 d of age to groups (6.1 pigs per group on average) housed in finishing pens, each having an area of 6 m(2). The average additive genetic relationship among group mates was 0.11. Pigs were slaughtered at 277 ± 3 d of age and 169.7 ± 13.9 kg BW in groups of nearly 70 animals each. Four univariate animal models were compared: a basic model (M1) including only direct additive genetic effects, a model (M2) with nonheritable social group (pen) effects in addition to effects in M1, a model (M3) accounting for litter effects in addition to M2, and a model (M4) accounting for social genetic effects in addition to effects in M3. Restricted maximum likelihood estimates of covariance components were obtained for BW270; carcass backfat depth; carcass lean meat content (CLM); iodine number (IOD); and linoleic acid content (LIA) of raw ham subcutaneous fat; subcutaneous fat depth in the proximity of semimembranosus muscle (SFD1) and quadriceps femoris muscle (SFD2); and linear scores for ham round shape (RS), subcutaneous fat (SF), and marbling. Likelihood ratio tests indicated that, for all traits, M2 fit the data better than M1 and that M3 was superior to M2 except for SFD1 and SFD2. Model M4 was significantly better than M3 for BW270 (P < 0.001) and CLM, IOD, RS, and SF (P < 0.05). The contribution of social genetic effects to the total heritable variance was large for CLM and BW270, ranging from 33.2 to 35%, whereas the one for ham quality traits ranged from 6.8 (RS) to 11.2% (SF). Direct and social genetic effects on BW270 were uncorrelated, whereas there was a negative genetic covariance between direct and social effects on CLM, IOD, RS, and SF, which reduced the total heritable variance. This variance, measured relative to phenotypic variance, ranged from 21 (CLM) to 54% (BW270). Results indicate that social genetic effects affect variation in traits relevant for heavy pigs used in dry-cured hams manufacturing. Such effects should be exploited and taken into account in design of breeding programs for heavy pigs.

Separation and quantification of water buffalo milk protein fractions and genetic variants by RP-HPLC

A RP-HPLC method, developed for the separation and quantification of the most common genetic variants of bovine milk proteins, was successfully applied to the analysis of water buffalo milk. All the most common buffalo casein and whey proteins fractions, as well as their genetic variants, were detected and separated simultaneously in 40 min. Purified buffalo proteins were used as calibration standards and a total of 536 individual milk samples were analysed for protein composition. α(S1)-, α(S2)-, βγ-, and κ-casein were 32.2%, 15.8%, 36.5%, and 15.5%, respectively, of total casein content, whereas content of β-Lactoglobulin was approximately 1.3 times as high as that of α-Lactalbumin. The existence of a polymorphism of κ-casein was demonstrated in Mediterranean water buffalo and α(S1)- and κ-casein genetic variants were successfully detected by RP-HPLC.