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.     

Effects of milk protein polymorphism and composition,casein micelle size and salt distribution on the milk coagulation properties in Norwegian Red cattle

Effects of milk protein polymorphism and composition, casein micelle size and salts distribution on the coagulation properties of milk from 99 Norwegian Red cattle (NRF) were studied. Genetic variants of α_S1-casein (CN), β-CN, κ-CN and β-lactoglobulin (LG) affected rennet coagulation properties of milk. Significant effects of κ-CN and the composite genotype α_S1-β-κ-CN were observed on acid coagulation properties. Relative concentrations of milk proteins were significantly affected by individual casein genotypes and the composite genotype of α_S1-β-κ-CN while, the relative concentration of β-LG was only affected by β-LG genotypes. The salts distribution in milk and the concentration of milk proteins affected both rennet and acid coagulation properties. Milk protein genotypes associated with better rennet coagulation, impaired the acid coagulation properties. However, α_S1-β-κ-CN BB-A¹A²-BE and BB-A²A²-BB were associated with poor rennet and acid coagulation properties. Breeding programs should focus on decreasing these genotypes in NRF cattle.     

Effects of the detailed protein composition of milk on curd yield and composition measured by model micro-cheese curd making of individual milk samples

The effect of the contents of casein (CN) and whey protein fractions on curd yield (CY) and composition was estimated using 964 individual milk samples. Contents of α_S1-CN, α_S2-CN, β-CN, γ-CN, glycosylated κ-CN (Gκ-CN), unglycosylated κ-CN, β-LG, and α-LA of individual milk samples were measured using reversed-phase HPLC. Curd yield and curd composition were measured by model micro-cheese curd making using 25 mL of milk. Dry matter CY (DMCY) was positively associated with all casein fractions but especially with α_S1-CN and β-CN. Curd moisture decreased at increasing β-CN content and increased at increasing γ-CN and Gκ-CN content. Due to their associations with moisture, Gκ-CN and β-CN were the fractions with the greatest effect on raw CY, which decreased by 0.66% per 1-standard deviation (SD) increase in the content of β-CN and increased by 0.62% per 1-SD increase in the content of Gκ-CN. The effects due to variation in percentages of the casein fractions in total casein were less marked than those exerted by contents. A 1-SD increase in β-CN percentage in casein (+3.8% in casein) exerted a slightly negative effect on DMCY (β = ?0.05%). Conversely, increasing amounts of α_S1-CN percentage were associated with a small increase in DMCY. Hence, results suggest that, at constant casein and whey protein contents in milk, the DMCY depends to a limited extent on the variation in the α_S1-CN:β-CN ratio. κ-Casein percentage did not affect DMCY, indicating that the positive relationship detected between the content of κ-CN and DMCY can be attributed to the increase in total casein resulting from the increased amount of κ-CN and not to variation in κ-CN relative content. However, milk with increased Gκ-CN percentage in κ-CN also shows increased raw CY and produces curds with increased moisture content. Curd yield increased at increasing content and relative proportion of β-LG in whey protein, but this is attributable to an improved capacity of the curd to retain water. Results obtained in this study support the hypothesis that, besides variation in total casein and whey protein contents, variation in protein composition might affect the cheese-making ability of milk, but this requires further studies.     

Comparison of milk protein composition and rennet coagulation properties in native Swedish dairy cow breeds and high-yielding Swedish Red cows

Recent studies have reported a very high frequency of noncoagulating milk in Swedish Red cows. The underlying factors are not fully understood. In this study, we explored rennet-induced coagulation properties and relative protein profiles in milk from native Swedish Mountain and Swedish Red Polled cows and compared them with a subset of noncoagulating (NC) and well-coagulating (WC) milk samples from modern Swedish Red cows. The native breeds displayed a very low prevalence of NC milk and superior milk coagulation properties compared with Swedish Red cows. The predominant variants in both native breeds were α_S1-casein (α_S1-CN) B, β-CN A² and β-lactoglobulin (β-LG) B. For κ-CN, the B variant was predominant in the Swedish Mountain cows, whereas the A variant was the most frequent in the Swedish Red Polled. The native breeds displayed similar protein composition, but varied in content of α_S1-CN with 9 phosphorylated serines (9P) form. Within the Swedish Mountain cows, we observed a strong inverse correlation between the relative concentration of κ-CN and micelle size and a positive correlation between ionic calcium and gel firmness. For comparison, we investigated a subset of 29 NC and 28 WC milk samples, representing the extremes with regard to coagulation properties based on an initial screening of 395 Swedish Red cows. In Swedish Red, NC milk properties were found to be related to higher frequencies of β-CN A², κ-CN E and A variants, as well as β-LG B, and the predominant composite genotype of β- and κ-CN in the NC group was A²A²/AA. Generally, the A²A²/AA composite genotype was related to lower relative concentrations of κ-CN isoforms and higher relative concentrations of α_S1-, α_S2-, and β-CN. Compared with the group of WC milk samples, NC milk contained a higher fraction of α_S2-CN and α-lactalbumin (α-LA) but a lower fraction of α_S1-CN 9P. In conclusion, milk from native Swedish breeds has good characteristics for cheese milk, which could be exploited in niche dairy products. In milk from Swedish Mountain cows, levels of ionic calcium seemed to be more important for rennet-induced gel firmness than variation in the relative protein profile. In Swedish Red, lower protein content as well as higher fraction of α_S2-CN and lower fraction of α_S1-CN 9P were related to NC milk. Further, a decrease in the frequency of the composite β-κ-CN genotype A²A²/AA through selective breeding could have a positive effect on milk coagulation properties.     

Whole-genome association study for milk protein composition in dairy cattle

Our objective was to perform a genome-wide association study for content in bovine milk of α_S1-casein (α_S1-CN), α_S2-casein (α_S2-CN), β-casein (β-CN), κ-casein (κ-CN), α-lactalbumin (α-LA), β-lactoglobulin (β-LG), casein index, protein percentage, and protein yield using a 50K single nucleotide polymorphism (SNP) chip. In total, 1,713 Dutch Holstein-Friesian cows were genotyped for 50,228 SNP and a 2-step association study was performed. The first step involved a general linear model and the second step used a mixed model accounting for all family relationships. Associations with milk protein content and composition were detected on 20 bovine autosomes. The main genomic regions associated with milk protein composition or protein percentage were found on chromosomes 5, 6, 11, and 14. The number of chromosomal regions showing significant (false discovery rate <0.01) effects ranged from 3 for β-CN and 3 for β-LG to 12 for α_S2-CN. A genomic region on Bos taurus autosome (BTA) 6 was significantly associated with all 6 major milk proteins, and a genomic region on BTA 11 was significantly associated with the 4 caseins and β-LG. In addition, regions were detected that only showed a significant effect on one of the milk protein fractions: regions on BTA 13 and 22 with effects on α_S1-CN; regions on BTA 1, 9, 10, 17, 19, and 28 with effects on α_S2-CN; a region on BTA 6 with an effect on β-CN; regions on BTA 13 and 21 with effects on κ-CN; regions on BTA 1, 5, 9, 16, 17, and 26 with effects on α-LA; and a region on BTA 24 with an effect on β-LG. The proportion of genetic variance explained by the SNP showing the strongest association in each of these genomic regions ranged from <1% for α_S1-CN on BTA 22 to almost 100% for casein index on BTA 11. Variation associated with regions on BTA 6, 11, and 14 could in large part but not completely be explained by known protein variants of β-CN (BTA 6), κ-CN (BTA 6), and β-LG (BTA 11) or DGAT1 variants (BTA 14). Our results indicate 3 regions with major effects on milk protein composition, in addition to several regions with smaller effects involved in the regulation of 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 (a₃₀) 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 a₃₀ 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.     

Short communication: Effect of kefir grains on proteolysis of major milk proteins

The effect of kefir grains on the proteolysis of major milk proteins in milk kefir and in a culture of kefir grains in pasteurized cheese whey was followed by reverse phase-HPLC analysis. The reduction of κ-, α-, and β-caseins (CN), α-lactalbumin (α-LA), and β-lactoglobulin (β-LG) contents during 48 and 90 h of incubation of pasteurized milk (100 mL) and respective cheese whey with kefir grains (6 and 12 g) at 20°C was monitored. Significant proteolysis of α-LA and κ-, α-, and β-caseins was observed. The effect of kefir amount (6 and 12 g/100 mL) was significant for α-LA and α- and β-CN. α-Lactalbumin and β-CN were more easily hydrolyzed than α-CN. No significant reduction was observed with respect to β-LG concentration for 6 and 12 g of kefir in 100 mL of milk over 48 h, indicating that no significant proteolysis was carried out. Similar results were observed when the experiment was conducted over 90 h. Regarding the cheese whey kefir samples, similar behavior was observed for the proteolysis of α-LA and β-LG: α-LA was hydrolyzed between 60 and 90% after 12 h (for 6 and 12 g of kefir) and no significant β-LG proteolysis occurred. The proteolytic activity of lactic acid bacteria and yeasts in kefir community was evaluated. Kefir milk prepared under normal conditions contained peptides from proteolysis of α-LA and κ-, α-, and β-caseins. Hydrolysis is dependent on the kefir:milk ratio and incubation time. β-Lactoglobulin is not hydrolyzed even when higher hydrolysis time is used. Kefir grains are not appropriate as adjunct cultures to increase β-LG digestibility in whey-based or whey-containing foods.     

Milk nutrition and childhood epilepsy: An ex vivo study on cytokines and oxidative stress in response to milk protein fractions

We present a pilot study on the effects of milk protein fractions [α_S1-casein (CN), α_S2-CN, κ-CN, β-CN, and a mix of α-lactalbumin (α-LA) and β-lactoglobulin (β-LG)] from different animal species (bovine, ovine, and caprine) on pro- and anti-inflammatory cytokines and oxidative status in cultured peripheral blood mononuclear cells from children with generalized epilepsy. Peripheral blood mononuclear cells (PBMC) were obtained by density gradient from blood of 10 children with generalized epilepsy (5 males; mean age 33.6 ± 5.4 mo) and 10 controls (5 males; mean age 35.6 ± 6.8 mo). Children with epilepsy were grouped according to cytokine levels as follows: children with epilepsy having low levels of cytokines not different from those of control children (LL-EC); children with epilepsy having cytokine levels at least 5-fold higher (medium levels) than those of control children (ML-EC); and children with epilepsy having cytokine levels at least 10-fold higher (high levels) than those of control children (HL-EC). The production of tumor necrosis factor-α (TNF-α), IL-10, IL-6, and IL-1β was studied in cultured PBMC incubated with α_S1-CN, α_S2-CN, κ-CN, β-CN, and a mix of α-LA and β-LG from bovine, caprine, and ovine milks. The levels of reactive oxygen and nitrogen species (ROS/RNS) and catalase activity were assessed in cultured supernatant. In the HL-EC group, β-CN from small ruminant species (ovine and caprine) induced the highest levels of TNF-α, whereas PBMC incubated with α_S2-CN from ovine milk and the mix of β-LG and α-LA from all tested milk species had the lowest levels of TNF-α. Within the HL-EC group, production of IL-1β was higher for bovine and ovine α_S2-CN fractions and lower for caprine and ovine β-CN and κ-CN. In the HL-EC group, IL-6 was higher in cultured PBMC incubated with α_S2-CN from bovine and ovine milk than from caprine milk. The cytokine IL-10 did not differ among milking species. The highest levels of ROS/RNS were found after incubation of PBMC with the β-CN fraction in bovine milk. Catalase activity was higher in PBMC cultured with β-CN isolated from bovine and caprine milk and with α_S1-CN from ovine milk.     

Interactions of milk proteins during heat and high hydrostatic pressure treatments — A Review

Pressure treatment of β-lactoglobulin (β-LG), whey protein concentrate (WPC), whey protein isolate and skim milk has been explored by many groups using a wide range of techniques. In general terms, heat treatment and pressure treatment have similar effects: denaturing and aggregating the whey proteins and diminishing the number of viable microorganisms. However, there are significant differences between the effects of the two treatments on protein unfolding and the subsequent thiol-catalysed disulfide-bond interchanges that lead to different structures and product characteristics. Application of a range of techniques has given insight into the subtle differences between the pathways from native proteins to the final product mix. This review covers some of the techniques used and their strengths, and the probable pathways from native protein to the final products. β-LG is one of the most pressure-sensitive proteins and α-lactalbumin (α-LA) is one of the most pressure resistant. In a heated WPC system, bovine serum albumin is very sensitive and β-LG is more resistant. In a heated milk system, β-LG reacts with κ-casein (κ-CN) and not with α_S2-CN, but, in pressure-treated milk, β-LG forms adducts with either κ-CN or α_S2-CN. In both treatments, the role of β-LG is central to the ongoing reactions, involving α-LA and κ-CN in heated systems but involving κ-CN, α_S2-CN and α-LA in pressurized systems.Industrial relevanceHigh hydrostatic pressure (HHP) processing, as opposed to heat treatment, has received much attention recently as a means of processing milk proteins. This review examines the differences in the denaturation pathways that give rise to different final products.     

Effects of breed and casein genetic variants on protein profile in milk from Swedish Red,Danish Holstein,and Danish Jersey cows

In selecting cows for higher milk yields and milk quality, it is important to understand how these traits are affected by the bovine genome. The major milk proteins exhibit genetic polymorphism and these genetic variants can serve as markers for milk composition, milk production traits, and technological properties of milk. The aim of this study was to investigate the relationships between casein (CN) genetic variants and detailed protein composition in Swedish and Danish dairy milk. Milk and DNA samples were collected from approximately 400 individual cows each of 3 Scandinavian dairy breeds: Swedish Red (SR), Danish Holstein (DH), and Danish Jersey (DJ). The protein profile with relative concentrations of α-lactalbumin, β-lactoglobulin, and α_S1-, α_S2-, κ-, and β-CN was determined for each milk sample using capillary zone electrophoresis. The genetic variants of the α_S1- (CSN1S1), β- (CSN2), and κ-CN (CSN3) genes for each cow were determined using TaqMan SNP genotyping assays (Applied Biosystems, Foster City, CA). Univariate statistical models were used to evaluate the effects of composite genetic variants, α_S1-β-κ-CN, on the protein profile. The 3 studied Scandinavian breeds differed from each other regarding CN genotypes, with DH and SR having similar genotype frequencies, whereas the genotype frequencies in DJ differed from the other 2 breeds. The similarities in genotype frequencies of SR and DH and differences compared with DJ were also seen in milk production traits, gross milk composition, and protein profile. Frequencies of the most common composite α_S1-β-κ-CN genotype BB/A²A²/AA were 30% in DH and 15% in SR, and cows that had this genotype gave milk with lower relative concentrations of κ- and β-CN and higher relative concentrations of α_S-CN, than the majority of the other composite genotypes in SR and DH. The effect of composite genotypes on relative concentrations of the milk proteins was not as pronounced in DJ. The present work suggests that a higher frequency of BB/A¹A²/AB, together with a decrease in BB/A²A²/AA, could have positive effects on DH and SR milk regarding, for example, the processing of cheese.     

Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, β-, α_S1-, and α_S2-CN, and 2 whey proteins, namely β-lactoglobulin (β-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between β-CN and α_S1-CN (?0.706) and between β-CN and κ-CN (?0.735). The application of the MMHC algorithm revealed that κ-CN and β-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for β-CN, 30 SNP for α_S1-CN, and 14 SNP for α_S2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for β-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.     

Distinct composition of bovine milk from Jersey and Holstein-Friesian cows with good,poor, or noncoagulation properties as reflected in protein genetic variants and isoforms

The objective of this study was to examine variation in overall milk, protein, and mineral composition of bovine milk in relation to rennet-induced coagulation, with the aim of elucidating the underlying causes of milk with impaired coagulation abilities. On the basis of an initial screening of 892 milk samples from 42 herds with Danish Jersey and Holstein-Friesian cows, a subset of 102 samples was selected to represent milk with good, poor, or noncoagulating properties (i.e., samples that within each breed represented the most extremes in regard to coagulation properties). Milk with good coagulation characteristics was defined as milk forming a strong coagulum based on oscillatory rheology, as indicated by high values for maximum coagulum strength (G′_max) and curd firming rate (CFR) and a short rennet coagulation time. Poorly coagulating milk formed a weak coagulum, with a low G′_max and CFR and a long rennet coagulation time. Noncoagulating milk was defined as milk that failed to form a coagulum, having G′_max and CFR values of zero at measurements taken within 1 h after addition of rennet. For both breeds, a lower content of total protein, total casein (CN) and κ-CN, and lower levels of minerals (Ca, P, Mg) were identified in poorly coagulating and noncoagulating milk in comparison with milk with good coagulation properties. Liquid chromatography/electrospray ionization-mass spectrometry revealed the presence of a great variety of genetic variants of the major milk proteins, namely, α_S1-CN (variants B and C), α_S2-CN (A), β-CN (A¹, A², B, I, and F), κ-CN (A, B, and E), α-lactalbumin (B), and β-lactoglobulin (A, B, and C). In poorly coagulating and noncoagulating milk samples of both breeds, the predominant composite genotype of α_S1-, β-, and κ-CN was BB-A²A²-AA, which confirmed a genetic contribution to impaired milk coagulation. Interestingly, subtle variations in posttranslational modification of CN were observed between the coagulation classes in both breeds. Poorly coagulating and noncoagulating milk contained a lower fraction of the least phosphorylated α_S1-CN form, α_S1-CN 8P, relative to total α_S1-CN, along with a lower fraction of glycosylated κ-CN relative to total κ-CN. Thus, apparent variation was observed in the milk and protein composition, in the genetic makeup of the major milk proteins, and in the posttranslational modification level of CN between milk samples with either good or impaired coagulation ability, whereas the composition of poorly coagulating and noncoagulating milk was similar.     

Effect of protein composition on the cheese-making properties of milk from individual dairy cows

The objective of this study was to evaluate the effect of variations in milk protein composition on milk clotting properties and cheese yield. Milk was collected from 134 dairy cows of Swedish Red and White, Swedish Holstein, and Danish Holstein-Friesian breed at 3 sampling occasions. Concentrations of α_S1-, β-, and κ-casein (CN), α-lactalbumin, and β-lactoglobulin (LG) A and B were determined by reversed phase liquid chromatography. Cows of Swedish breeds were genotyped for genetic variants of β- and κ-CN. Model cheeses were produced from individual skimmed milk samples and the milk clotting properties were evaluated. More than 30% of the samples were poorly coagulating or noncoagulating, resulting in weak or no coagulum, respectively. Poorly and noncoagulating samples were associated with a low concentration of κ-CN and a low proportion of κ-CN in relation to total CN analyzed. Furthermore, the κ-CN concentration was higher in milk from cows with the AB genotype than the AA genotype of κ-CN. The concentrations of α_S1-, β-, and κ-CN and of β-LG B were found to be significant for the cheese yield, expressed as grams of cheese per one hundred grams of milk. The ratio of CN to total protein analyzed and the β-LG B concentration positively affected cheese yield, expressed as grams of dry cheese solids per one hundred grams of milk protein, whereas β-LG A had a negative effect. Cheese-making properties could be improved by selecting milk with high concentrations of α_S1-, β-, and κ-CN, with high κ-CN in relation to total CN and milk that contains β-LG B.     

Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (α_S1-CN, α_S2-CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the β-CN, κ-CN, and β-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the β-LG B variant was associated with a lower concentration of β-LG compared with the β-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the β-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health.     

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 (β-κ-casein) haplotypes and BLG (β-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 β-lactoglobulin (β-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 κ-CN content and percentage of κ-CN to total CN and with decreased percentages of α_S1- and γ-CN to total CN. Allele CSN2 B had the effect of increasing β-CN content and decreasing content of α_S1-CN. Haplotypes including allele CSN2 A¹ exhibited decreased β-, α_S2-, and γ-CN concentrations and increased α_S1- and κ-CN contents, whereas CSN2 I had positive effects on β-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 β-LG concentration and percentage of β-LG to total whey protein and with decreased content of other milk proteins, namely β-CN and α_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.     

pH-dependent sedimentation and protein interactions in ultra-high-temperature-treated sheep skim milk

Sheep milk is considered unstable to UHT processing, but the instability mechanism has not been investigated. This study assessed the effect of UHT treatment (140°C/5 s) and milk pH values from 6.6 to 7.0 on the physical properties of sheep skim milk (SSM), including heat coagulation time, particle size, sedimentation, ionic calcium level, and changes in protein composition. Significant amounts of sediment were found in UHT-treated SSM at the natural pH (～6.6) and pH 7.0, whereas lower amounts of sediment were observed at pH values of 6.7 to 6.9. The proteins in the sediment were mainly κ-casein (CN)–depleted casein micelles with low levels of whey proteins regardless of the pH. Both the pH and the ionic calcium level of the SSM at all pH values decreased after UHT treatment. The dissociation levels of κ-, β-, and α_S2-CN increased with increasing pH of the SSM before and after heating. The protein content, ionic calcium level, and dissociation level of κ-CN were higher in the SSM than values reported previously in cow skim milk. These differences may contribute to the high amounts of sediment in the UHT-treated SSM at natural pH (～6.6). Significantly higher levels of κ-, β-, and α_S2-CN were detected in the serum phase after heating the SSM at pH 7.0, suggesting that less κ-CN was attached to the casein micelles and that more internal structures of the casein micelles may have been exposed during heating. This could, in turn, have destabilized the casein micelles, resulting in the formation of protein aggregates and high amounts of sediment after UHT treatment of the SSM at pH 7.0.     

Effect of milk protein genetic polymorphisms on rennet and acid coagulation properties after standardisation of protein content

The effects of milk protein genetic polymorphisms on the rennet and acid coagulation properties of milk after protein standardisation were investigated. Skim milk samples were adjusted to a protein concentration of 6.07 ± 0.06% by ultrafiltration (UF) before evaluating rennet coagulation and acid coagulation properties. Only the β-lactoglobulin (β-LG) genotypes influenced the rennet-clotting time before standardisation for the total protein concentration by UF; however, this effect was confounded with the β-LG concentration. After UF-concentration, a similar protein concentration between the samples was achieved in the retentate, then the rennet clotting time and rennet curd firmness at 30 min were significantly influenced by both the κ-casein (κ-CN) and β-LG genotypes. κ-CN genotypes significantly influenced the acid coagulation properties of both skim milk and retentate. Variations in the concentration of milk proteins (mostly α_S2-CN-12P) explained most of the differences in the rennet and acid coagulation properties of milk after protein standardisation by UF.     

Variations in milk protein fractions affect the efficiency of the cheese-making process

The aim of this study was to assess the influence of the amounts of the α_S1-, α_S2-, β-, and κ-casein (CN) and the α-lactalbumin and β-lactoglobulin protein fractions on the efficiency of the cheese-making process independently of their genetic polymorphisms. The study was carried out on milk samples from 1,271 Brown Swiss cows from 85 herds classified into 4 categories according to management, feeding, and housing characteristics (traditional and modern systems). To assess the efficiency of the cheese-making process, we processed the milk samples according to a laboratory cheese-making procedure (1,500 mL/sample) and obtained the following measures: (1) 3 percentage cheese yields (%CY_curd, %CY_solids, %CY_water), (2) 2 daily cheese yields obtained by multiplying %CY (curd and total solids) by daily milk yields (dCY_curd, dCY_solids), (3) 4 measures of nutrient recovery in the curd (REC_fat, REC_protein, REC_solids, REC_energy), and (4) 2 measures of cheese-making efficiency in terms of the ratio between the observed and theoretical %CY (Ef-%CY_curd, Ef-%CY_solids). All the aforementioned traits were analyzed by fitting 2 linear mixed models with protein fractions as fixed effects expressed as percentage in the milk (model M-%milk) and as percentage of the total casein content (model M-%cas) together with the effects of total casein content (only in model M-%cas), daily milk yield (only in model M-%milk; not for dCY traits), dairy system, herd (random effect), days in milk, parity, and vat. The efficiency of overall cheese yield (Ef-%CY_curd) was mostly positively associated with β-CN content in the milk, whereas Ef-%CY_solids was greater with higher amounts of κ-CN and α_S1-CN (M-%milk) due to the strong influence of both fractions on the recovery rate of milk components in the curd (fat and total solids, protein with α_S1-CN only) when expressed as percentage of milk and of total casein; only β-CN was more important for REC_protein. In contrast, we found β-lactoglobulin to be highly negatively related to all the traits related to the cheese-making process and to the daily cheese yield per cow, whereas α-lactalbumin was positively associated with the latter traits. Additional research on this topic is needed, with particular focus on the genetic and genomic aspects of the role of protein fractions in the cheese-making process and on the associations between genetic polymorphisms in milk protein and milk nutrient recovery in the curd.     

Effect of parity, days in milk, and milk yield on detailed milk protein composition in Mediterranean water buffalo

The effects of some nongenetic factors on milk protein fraction contents and relative proportions were estimated in 606 individual milk samples of Mediterranean water buffalo. Content of α(S1)-casein (CN), α(S2)-CN, β-CN, γ-CN, κκ-CN, glycosylated κ-CN (glyco-κ-CN), α-lactalbumin, and β-lactoglobulin was measured by reversed-phase HPLC. Relative contents of α(S1)-CN%, α(S2)-CN%, β-CN%, and κ-CN% were, respectively, 32.1, 17.1, 34.5, and 15.7%, whereas γ-CN% accounted for 0.6% of total casein content. Increasing total casein content in milk would result in a greater proportion of β-CN% at the expense of all of the other major casein fractions, especially of κ-CN%. Values of α(S2)-CN%, β-CN%, and γ-CN% tended to decrease with parity, although their variations were not significant, whereas α(S1)-CN% and glyco-κ-CN% showed the opposite trend. Contents of most protein fractions showed the typical trends observed for milk components as lactation progressed, with high contents in early lactation, a minimum in midlactation, followed by a gradual increase toward the latter part of lactation. Values of α(S1)-CN% increased during lactation, whereas α(S2)-CN% decreased. The proportion of β-CN% had its maximum value between 60 and 160 d of lactation, followed by a decrease, whereas κ-CN% had its minimum value in early lactation (<60 d) and remained relatively constant in the period of mid and late lactation. Glyco-κ-CN% and β-lactoglobulin% decreased in the first part of lactation, to reach their minimum values in midlactation, followed by an increase. Milk of top-producing buffaloes, compared with that of low-producing ones, had a significantly greater value of β-CN% and glyco-κ-CN%, and lower proportion of α(S1)-CN%. The possible effect exerted by protein genetic variants in affecting variation of milk protein fraction contents and relative proportions should be further considered to better get insight into buffalo milk protein composition.     

Qualitative and Quantitative Determination of Caseins with Reverse-Phase and Anion-Exchange HPLC

Whole native caseinate (WNC) and casein (CN) fractions from preparative DE-52 cellulose urea columns were chromatographed using C-8 reverse-phase (RP) and DEAE-type anion-exchange (AEx) HPLC systems. With RP, α_S2-CN and κ-CN eluted first as several small peaks; α_S1-CN eluted later as two peaks, followed by β-CN peaks. With AEx, κ-CN eluted early as a group of peaks, β-CN eluted next, and α_S1-CN and α_S2-CN coeluted last. Standard curves were prepared for α_S1-CN and β-CN using RP-PHLC and showed correlation coefficients of 0.99 and 0.98, respectively. The caseins in WNC, nonfat dry milk casein, commercial casein(ates) and caseins from milks of individual cows were determined.