Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods

Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, α_S1-casein (CN), α_S2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.     

Predicting milk protein fractions using infrared spectroscopy and a gradient boosting machine for breeding purposes in Holstein cattle

In recent years, increasing attention has been focused on the genetic evaluation of protein fractions in cow milk with the aim of improving milk quality and technological characteristics. In this context, advances in high-throughput phenotyping by Fourier transform infrared (FTIR) spectroscopy offer the opportunity for large-scale, efficient measurement of novel traits that can be exploited in breeding programs as indicator traits. We took milk samples from 2,558 Holstein cows belonging to 38 herds in northern Italy, operating under different production systems. Fourier transform infrared spectra were collected on the same day as milk sampling and stored for subsequent analysis. Two sets of data (i.e., phenotypes and FTIR spectra) collected in 2 different years (2013 and 2019–2020) were compiled. The following traits were assessed using HPLC: true protein, major casein fractions [α_S1-casein (CN), α_S2-CN, β-CN, κ-CN, and glycosylated-κ-CN], and major whey proteins (β-lactoglobulin and α-lactalbumin), all of which were measured both in grams per liter (g/L) and proportion of total nitrogen (% N). The FTIR predictions were calculated using the gradient boosting machine technique and tested by 3 different cross-validation (CRV) methods. We used the following CRV scenarios: (1) random 10-fold, which randomly split the whole into 10-folds of equal size (9-folds for training and 1-fold for validation); (2) herd/date-out CRV, which assigned 80% of herd/date as the training set with independence of 20% of herd/date assigned as the validation set; (3) forward/backward CRV, which split the data set in training and validation set according with the year of milk sampling (FTIR and gold standard data assessed in 2013 or 2019–2020) using the “old” and “new” databases for training and validation, and vice-versa with independence among them; (4) the CRV for genetic parameters (CRV-gen), where animals without pedigree as assigned as a fixed training population and animals with pedigree information was split in 5-folds, in which 1-fold was assigned to the fixed training population, and 4-folds were assigned to the validation set (independent from the training set). The results (i.e., measures and predictions) of CRV-gen were used to infer the genetic parameters for gold standard laboratory measurements (i.e., proteins assessed with HPLC) and FTIR-based predictions considering the CRV-gen scenario from a bi-trait animal model using single-step genomic BLUP. We found that the prediction accuracies of the gradient boosting machine equations differed according to the way in which the proteins were expressed, achieving higher accuracy when expressed in g/L than when expressed as % N in all CRV scenarios. Concerning the reproducibility of the equations over the different years, the results showed no relevant differences in predictive ability between using “old” data as the training set and “new” data as the validation set and vice-versa. Comparing the additive genetic variance estimates for milk protein fractions between the FTIR predicted and HPLC measures, we found reductions of ?19.7% for milk protein fractions expressed in g/L, and ?21.19% expressed as % N. Although we found reductions in the heritability estimates, they were small, with values ranging from ?1.9 to ?7.25% for g/L, and ?1.6 to ?7.9% for % N. The posterior distributions of the additive genetic correlations (r_a) between the FTIR predictions and the laboratory measurements were generally high (>0.8), even when the milk protein fractions were expressed as % N. Our results show the potential of using FTIR predictions in breeding programs as indicator traits for the selection of animals to enhance milk protein fraction contents. We expect acceptable responses to selection due to the high genetic correlations between HPLC measurements and FTIR predictions.     

Milk protein genetic variants and isoforms identified in bovine milk representing extremes in coagulation properties

A gel-based proteomic approach consisting of 2-dimensional gel electrophoresis coupled with mass spectrometry was applied for detailed protein characterization of a subset of individual milk samples with extreme rennet coagulation properties. A milk subset with either good or poor coagulation abilities was selected from 892 Danish Holstein-Friesian and Jersey cows. Screening of genetic variants of the major milk proteins resulted in the identification of common genetic variants of β-casein (CN; A(1), A(2), B), κ-CN (A, B), and β-lactoglobulin (LG; A, B), as well as a low frequency variant, κ-CN variant E, and variants not previously reported in Danish breeds (i.e., β-CN variant I and β-LG variant C). Clear differences in the frequencies of the identified genetic variants were evident between breeds and, to some extent, between coagulation groups within breeds, indicating that an underlying genetic variation of the major milk proteins affects the overall milk coagulation ability. In milk with good coagulation ability, a high prevalence of the B variants of all 3 analyzed proteins were identified, whereas poorly coagulating milk was associated with the β-CN variant A(2), κ-CN variant A or E, and β-LG variant A or C. The β-CN variant I was identified in milk with both good and poor coagulation ability, a variant that has not usually been discriminated from β-CN variant A(2) in other studied cow populations. Additionally, a detailed characterization of κ-CN isoforms was conducted. Six κ-CN isoforms varying in phosphorylation and glycosylation levels from each of the genetic variants of κ-CN were separated and identified, along with an unmodified κ-CN form at low abundance. Relative quantification showed that around 95% of total κ-CN was phosphorylated with 1 or 2 phosphates attached, whereas approximately 35% of the identified κ-CN was glycosylated with 1 to 3 tetrasaccharides. Comparing isoforms from individual samples, we found a very consistent κ-CN isoform pattern, with only minor differences in relation to breed, κ-CN genetic variant, and milk coagulation ability.     

Effects of genetic variants and sialylation on in vitro digestibility of purified κ-casein

Milk with different κ-casein (CN) phenotypes has previously been found to influence its gastric digestion rate. Therefore, the aim of the present study is to disentangle contributions of genetic variation and its related sialylation on the in vitro digestion process of κ-CN. Accordingly, κ-CN was purified from milk representing homozygous cows with κ-CN phenotypes AA, BB, or EE and used as substrate molecules in model studies using the INFOGEST 2.0 in vitro static digestion model. Furthermore, the effect of removal of the terminal sialic acids present on the O-linked oligosaccharides of the purified κ-CN A, B, and E protein variants were studied by desialylation enzymatic assays. The κ-CN proteins were purified by reducing anion exchange chromatography with purities of variants A, B, and E of 93.0, 97.1, and 90.0%, respectively. Protein degradations of native and desialylated κ-CN isolates in gastric and intestinal phases were investigated by sodium dodecyl sulfate-PAGE, degree of hydrolysis (DH), and liquid chromatography electrospray ionization mass spectrometry. It was shown that after purification, the κ-CN molecules reassembled into multimer states, which then constituted the basis for the digestion studies. As assessed by DH, purified variants A and E were found to exhibit faster in vitro digestion rates in both gastric and intestinal phases compared with variant B. Desialylation increased both gastric and intestinal digestion rates for all variants, as measured by DH. In the gastric phase, desialylation promoted digestion of variant B at a rate comparable with native variants A and E, whereas in the intestinal phase, desialylation of variant B promoted better digestion than native A or E. Taken together, the results confirm that low glycosylation degree of purified κ-CN promotes faster in vitro digestion rates, and that desialylation of the O-linked oligosaccharides further promotes digestion. This finding could be applied to produce dairy products with enhanced digestibility.     

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.     

Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CF_t)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R²) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CF_t (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R² averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CF_t, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CF_t, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.     

Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia

Increased concentrations of some serum biomarkers are known to be associated with impaired health of dairy cows. Therefore, being able to predict these biomarkers, especially in the early stage of lactation, would enable preventive management decision. Some health biomarkers may also be used as phenotypes for genetic improvement for improved animal health. In this study, we validated the accuracy and robustness of models for predicting serum concentrations of β-hydroxybutyrate (BHB), fatty acids, and urea nitrogen, using milk mid-infrared (MIR) spectroscopy. The data included 3,262 blood samples of 3,027 lactating Holstein-Friesian cows from 19 dairy herds in Southeastern Australia, collected in the period from July 2017 to April 2020. The models were developed using partial least squares regression and were validated using 10-fold random cross-validation, herd-year by herd-year external validation, and year by year validation. The coefficients of determination (R²) for prediction of serum BHB, fatty acids, and urea obtained through random cross-validation were 0.60, 0.42, and 0.87, respectively. For the herd-year by herd-year external validation, the prediction accuracies held up comparatively well, with R² values of 0.49, 0.33, and 0.67 for of serum BHB, fatty acids, and urea, respectively. When the models were developed using data from a single year to predict data collected in future years, the R² remained comparable, however, the root mean squared errors increased substantially (4–10 times larger than compared with that of herd-year by herd-year external validation) which could be due to machine differences in spectral response, the change in spectral response of individual machines over time, or other differences associated with farm management between seasons. In conclusion, the mid-infrared equations for predicting serum BHB, fatty acids, and urea have been validated. The prediction equations could be used to help farmers detect cows with metabolic disorders in early lactation in addition to generating novel phenotypes for genetic improvement purposes.     

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.     

Short communication: An observational study investigating inter-observer agreement for variation over time of body condition score in dairy cows

Body condition score (BCS) is strongly correlated with energy reserves. The ease, rapidity of scoring, and high intra- and inter-observer repeatability make it a widely used herd management tool in bovine practice and in scientific studies. Loss or gain of BCS, rather than a single BCS measurement, is frequently used to monitor energy balance in dairy cows. It is unknown if the difference between 2 BCS measures taken at different moments (ΔBCS) would demonstrate inter-observer agreement similar to that of a single BCS measurement. The objective of this study was to compare inter-observer agreement of BCS and ΔBCS in dairy cows when multiple observers perform data collection. An observational study was conducted between April and September 2015; 3 observers independently assessed BCS of 73 Holstein cows from 1 commercial dairy herd. Body condition score assessments of the animals were performed between 1 and 20 d in milk (early lactation; exam 1) and again between 41 and 60 d in milk (peak of milk production; exam 2). Quadratic weighted kappa (κ_w) was computed to quantify agreement between observers for single BCS measurements and ΔBCS. For single BCS measurements, κ_w of 0.79 (95% CI: 0.69, 0.85) and 0.84 (95% CI: 0.77, 0.89) were obtained for exam 1 and exam 2, respectively. Such values would be interpreted as strong agreement and are consistent with the available literature on BCS repeatability. When computing agreement for ΔBCS, a κ_w value of 0.49 (95% CI: 0.32, 0.63) was obtained, suggesting moderate agreement between observers. These findings suggest that studies investigating single BCS measures could use many observers with a high degree of accuracy in the results. When ΔBCS is the parameter of interest, more reliable results would be obtained if one observer conducts all assessments.     

Differential in vitro digestion rates in gastric phase of bovine milk with different κ-casein phenotypes

Casein (CN) micelles will coagulate in the stomach after ingestion, which is similar to the cheesemaking process. Although genetic variants of bovine proteins, especially κ-CN, have been confirmed to influence the coagulation properties of the CN micelle, its influence on milk digestibility has not been revealed yet. This study aimed to investigate how genetic variants, glycosylation degree of κ-CN, and CN micelle size influence digestion rates during in vitro gastrointestinal digestion. Three milk pools, representing κ-CN phenotypes of either AA, BB, or AB composition were prepared from milk of individual Danish Holstein cows representing these different genotypes. In vitro digestion of the 3 milk pools, AA, BB, or AB, was investigated by sodium dodecyl sulfate–PAGE, liquid chromatography–mass spectrometry, and degree of hydrolysis. The results showed that κ-CN AA milk had faster digestion rate in the gastric phase compared with BB and AB milks, whereas only small differences were apparent in the intestinal digestion phase. The results further documented that the milk pools representing κ-CN phenotypes BB and AB had comparable overall glycosylation degrees (50.9% and 50.0%, respectively) and higher than that of the AA milk pool (46.9%). Further, the AA milk pool was associated with larger CN micelles. These differences in CN micelle sizes and glycosylation degrees can be part of underlying explanations for the differential in vitro digestion rates observed between the AA, BB, and AB κ-CN milk pools.     

Determination of acetone in cow milk by Fourier transform infrared spectroscopy for the detection of subclinical ketosis

Fourier transform infrared analysis (FTIR) was used in combination with partial least squares regression (PLS) to predict the concentration of acetone in milk. FTIR spectra were compared with results of a gas-chromatographic head space method. Principal component analysis of whole spectra (3000 to 1000 cm(-1)) suggested to reduce the spectrum of analysis for acetone to 1450 to 1200 cm(-1). A second derivative was applied to the spectra to remove baseline effects and further enhance the spectral features. Full cross-validation was used to compare the reference with predicted acetone concentrations of samples not included in model development. PLS applied to the full spectral range resulted in a complex 19-factor model with a cross-validation error of 0.22 mM. After reducing the spectrum and taking the second derivative, we obtained a model with seven factors that yielded a cross-validation error of 0.21 mM. This compares favorably with a previously reported model with 20 factors and an error of 0.25 mM. Using PLS predictions to identify cows with subclinical ketosis resulted in 95 to 100% sensitivity and 96 to 100% specificity when the threshold for subclinical ketosis was 0.4 to 1.0 mM. The corresponding positive predictive values were > or = 76% and the negative predictive values > 98% throughout an assumed range of subclinical ketosis prevalence of 10 to 30%.     

Effect of κ-casein B relative content in bulk milk κ-casein on Montasio, Asiago, and Caciotta cheese yield using milk of similar protein composition

The aim of this study was to investigate the effect exerted by the relative content of κ-casein (κ-CN) B in bulk milk κ-CN on coagulation properties and cheese yield of 3 Italian cheese varieties (Montasio, Asiago, and Caciotta). Twenty-four cheese-making experiments were carried out in 2 industrial and 1 small-scale dairy plant. Detailed protein composition of bulk milk of 380 herds providing milk to these dairies was analyzed by reversed-phase HPLC. To obtain 2 experimental milks differing in the relative content of κ-CN B in κ-CN, herds were selected on the basis of bulk milk protein composition and relative content of κ-CN genetic variants. Milk was collected and processed separately for the 2 groups of selected herds. A difference of 20% in the relative content of κ-CN B in κ-CN was obtained for the 2 experimental milks for Montasio and a difference of 15% for Asiago and Caciotta. The 2 experimental milks were of similar protein and CN content, casein number, pH, CN composition, and β-CN genetic composition. For each cheese-making trial, amounts of milk, ranging from 2,000 to 6,000 kg, were manufactured. Each vat contained milk collected at least from 4 dairy herds. Cheese yield after brining and at the end of the aging was recorded. Milk with a greater proportion of κ-CN B in κ-CN (HIGHB) exhibited similar coagulation properties and greater cheese yield compared with milk with a lower proportion of κ-CN B in κ-CN (LOWB). The increased cheese yield observed for HIGHB when manufacturing Montasio cheese was ascribed to a greater fat content compared with LOWB. The probability of HIGHB giving a cheese yield 5% greater than that of LOWB ranged from 51 to 67% for Montasio cheese, but was less than 21% for Asiago and Caciotta cheeses. Variation in relative content of κ-CN B in κ-CN content did not relevantly affect industrial cheese yield when milks of similar CN composition were processed. An indirect effect due to the increased κ-CN content of κ-CN B milk is thought to explain the favorable effects of κ-CN B on cheese yield reported in the literature.     

Relationships between body condition,body condition loss,and serum metabolites during the transition period in primiparous and multiparous cows

In the transition period from late gestation to early lactation, dairy cows undergo tremendous metabolic changes. Insulin is a relevant antilipolytic factor. Decreasing serum concentrations of insulin and glucose, increasing serum concentrations of nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHB), and changes in body condition score (BCS) reflect the negative energy balance around calving. This study investigated peripartum metabolic adaptation in 359 primiparous and 235 multiparous German Holstein cows from a commercial dairy herd under field conditions. Body condition score was recorded and blood samples were taken 10 to 1 d prepartum, 2 to 4 d postpartum, and 12 to 20 d postpartum. Generalized mixed models and generalized estimation equations were applied to assess associations between prepartum BCS; BCS changes during the transition period; insulin, glucose, NEFA, and BHB serum concentrations; and milk yield, which was taken from an electronic milk meter from d 6 of lactation. Serum insulin concentrations of multiparous postpartum cows were lower compared with prepartum, and compared with primiparous cows. In general, primiparous cows had lower postpartum NEFA and BHB concentrations than multiparous cows. In primiparous cows, we identified a positive association between prepartum BCS and prepartum serum insulin concentration. Prepartum obese multiparous cows, but not primiparous cows, were characterized by higher postpartum serum NEFA and BHB concentrations and lower milk yield than other cows in the same parity class. Primiparous cows with a smaller degree of BCS loss during the transition period had higher postpartum insulin and lower NEFA concentrations and lower milk yield than other primiparous cows. In conclusion, primiparous cows had less lipolysis and lower milk yield than multiparous cows, associated with higher insulin concentrations. Avoiding high body condition loss during the transition period is a main factor in preventing peripartal metabolic imbalances of glucose and fat metabolism.     

Genetic variation in serum protein pattern and blood β-hydroxybutyrate and their relationships with udder health traits,protein profile,and cheese-making properties in Holstein cows

The aim of this study was to investigate in Holstein cows the genetic basis of blood serum metabolites [i.e., total protein, albumin, globulin, albumin:globulin ratio (A:G), and blood β-hydroxybutyrate (BHB)], a set of milk phenotypes related to udder health, milk quality technological characteristics, and genetic relationships among them. Samples of milk were collected from 498 Holstein cows belonging to 28 herds. All animal welfare and milk phenotypes were assessed using standard analytical methodology. A set of Bayesian univariate and bivariate animal models was implemented via Gibbs sampling, and statistical inference was based on the marginal posterior distributions of parameters of concern. We observed a small additive genetic influence for serum albumin concentrations, moderate heritability (≥0.20) for total proteins, globulins, and A:G, and high heritability (0.37) for blood BHB. Udder health traits (somatic cell score, milk lactose, and milk pH) showed low or moderate heritabilities (0.15–0.20), whereas variations in milk protein fraction concentrations were confirmed as mostly under genetic control (heritability: 0.21–0.71). The moderate and high heritabilities observed for milk coagulation properties and curd firming modeling parameters provided confirmation that genetic background exerts a strong influence on the cheese-making ability of milk, largely due to genetic polymorphisms in the major milk protein genes. Blood BHB showed strong negative genetic correlations with globulins (?0.619) but positive correlations with serum albumin (0.629) and A:G (0.717), which suggests that alterations in the serum protein pattern and BHB blood levels are likely to be genetically related. Strong relationships were found between albumin and fat percentages (?0.894), between globulin and α_S2-CN (?0.610), and, to a lesser extent, between serum protein pattern and milk technological characteristics. Genetic relationships between blood BHB and traits related to udder health and milk quality and technological characteristics were mostly weak. This study provides evidence that there is exploitable additive genetic variation for traits related to animal health and welfare and throws light on the shared genetic basis of these traits and the phenotypes related to the quality and cheese-making ability of milk.     

Yak milk whey protein denaturation and casein micelle disaggregation/aggregation at different pH and temperature

At the natural pH of yak milk (pH 6.6), a low level (<30%) of κ-casein (κ-CN) was found in the serum phase after heating at 95 °C for 30 min, indicating that as much as 70% of the β-lactoglobulin (β-Lg) and κ-CN complexes is associated with the micelle colloidal particles. The β-Lg and κ-CN levels increased from 13.2% and 2.6% at pH 6.0 to 35.2% and 60.1% at pH 7.0, respectively, when yak milk was heated at 95 °C for 30 min. At pH 6.0–6.4, the denatured whey proteins were associated with the caseins in the colloidal phase, resulting in milk gelation upon heating. The distribution of β-Lg and κ-CN complexes increased in the serum phase, demonstrated by the increasing levels of both β-Lg and κ-CN with increasing pH; at high pH (6.6–7.0), large proportions of β-Lg and α-lactalbumin were lost, presumably forming complexes in the colloidal phase.     

乳蛋白多态性和盐分布对牛乳凝乳性能的影响

研究不同基因型乳蛋白对牛乳凝乳特性的影响规律。采集1 071 头荷斯坦奶牛血样,分析κ-酪蛋白（κ-casein,κ-CN）和β-乳球蛋白（β-lactoglobulin,β-LG）的基因型,在明确基因型的基础上,采集样品开展牛乳凝乳能力评价。在初步筛选的基础上,选择凝乳性能好、凝乳性能差和不凝乳样品各至少30 份,重复3 次,开展凝乳流变学特性、蛋白多态性及矿物离子分布分析。通过动态流变仪、电感应耦合等离子体质谱仪、毛细管电泳、高效液相色谱技术分析不同凝乳等级牛乳的凝乳时间,胶体钙、镁、磷含量差异,不同基因型导致蛋白多态性及含量对牛乳凝乳能力的影响。结果显示,在所有奶牛组中,β-LG的AB基因型（占比48.48%）最常见,但AA型基因（30.97%）的原料乳凝乳效果较好;κ-CN的BB基因型（12.00%）凝乳效果较好,较AA、AB等其凝乳时间更短和凝胶强度更强。凝乳性能好的样品中CN含量及胶体钙含量较高,pH值较凝乳性能差和不凝乳样品低,凝乳时间与κ-CN含量呈反比,酪蛋白和乳清蛋白组成和基因频率的变化会影响牛乳凝乳性能的变化。     

Phosphorylation and glycosylation isoforms of bovine κ-casein variant E in homozygous Swedish Red cow milk detected by liquid chromatography-electrospray ionization mass spectrometry

Variations in the phosphorylation and glycosylation patterns of the common κ-casein (CN) variants A and B have been explored, whereas studies on variant E heterogeneity are scarce. This study reports for the first time the detailed phosphorylation and glycosylation pattern of the κ-CN variant E in comparison with variants A and B. Individual cow milk samples representing κ-CN genotype EE (n = 12) were obtained from Swedish Red cows, and the natural posttranslational modifications of its κ-CN were identified and quantified by liquid chromatography-electrospray mass spectrometry. In total, 12 unique isoform masses of κ-CN variant E were identified. In comparison, AA and BB milk consisted of 14 and 17 unique isoform masses, respectively. The most abundant κ-CN E isoform detected in the EE milk was the monophosphorylated, unglycosylated [1P 0G, ～70%; where P indicates phosphorylation from single to triple phosphorylation (1–3P), and G indicates glycosylation from single to triple glycosylation (1–3G)] form, followed by diphosphorylated, unglycosylated (2P 0G, ～12%) form, resembling known patterns from variants A and B. However, a clear distinction was the presence of the rare triphosphorylated, nonglycosylated (3P 0G, ～0.05%) κ-CN isoform in the EE milk. All isoforms detected in variant E were phosphorylated, giving a phosphorylation degree of 100%. This is comparable with the phosphorylation degree of variants A and B, being also almost 100%, though with very small amounts of nonphosphorylated, glycosylated isoforms detected. The glycosylation degree of variant E was found to be around 17%, a bit higher than observed for variant B (around 14%), and higher than variant A (around 7%). Among glycosylation, the glycan e was the most common type identified for all 3 variants, followed by c/d (straight and branched chain trisaccharides, respectively), and b. In contrast to κ-CN variants A and B, no glycan of type a was found in variant E. Taken together, this study shows that the posttranslational modification pattern of variant E resembles that of known variants to a large extent, but with subtle differences.     

Bovine κ-casein inhibits human rotavirus (HRV) infection via direct binding of glycans to HRV

Human rotavirus (HRV) is a major etiologic agent of severe infantile gastroenteritis. κ-Casein (κ-CN) from both human and bovine mature milk has been reported to have anti-HRV activity; however, the mechanism of this activity is poorly understood. The present study examined the molecular basis for the protective effect of bovine κ-CN derived from late colostrum (6–7 d after parturition) and from mature milk. Among the components of casein, κ-CN is the only glycosylated protein that has been identified. Therefore, we investigated whether the glycan residues in κ-CN were involved in the anti-HRV activity. Desialylated CN obtained by neuraminidase treatment exhibited anti-HRV activity, whereas deglycosylated CN obtained by o-glycosidase treatment lacked antiviral activity, indicating that glycans were responsible for the antiviral activity of CN. Furthermore, an evanescent-field fluorescence-assisted assay showed that HRV particles directly bound to heated casein (at 95°C for 30 min) in a viral titer–dependent manner. Although the heated κ-CN retained inhibitory activity in a neutralization assay, the activity was weaker than that observed before heat treatment. Our findings indicate that the inhibitory mechanism of bovine κ-CN against HRV involves direct binding to viral particles via glycan residues. In addition, heat-labile structures in κ-CN may play an important role in maintenance of κ-CN binding to HRV.     

Short communication: Characterization of a monoclonal antibody for κ-casein B of cow's milk

A monoclonal antibody (antik-B) against an oligopeptide of 23 AA corresponding to the region 131-153 of bovine κ-casein (κ-CN) B was generated using the Human Combinatorial Antibody Library (HuCAL) technology. Both AA substitutions distinguishing κ-CN A and B are located in that region (positions 136 and 148). In this study, the reactivity of antik-B to milk samples collected from cows previously genotyped as CSN3*AA, CSN3*AB, and CSN3*BB was tested. According to Western blot results, antik-B recognized κ-CN B and it showed no cross-reactivity toward κ-CN A and other milk proteins. Furthermore, a modified Western blot method, urea-PAGE Western blot, was set up to assess the reactivity of antik-B toward all isoforms of κ-CN B. In conclusion, antik-B was specific to κ-CN B in milk and it seemed to be reactive toward all its isoforms.