Effects of protein genetic variants on their phosphorylation levels, milk composition, milk proteome, and milk coagulation ability in Chinese Holstein bovine milk

Milk samples were collected from 3625 Chinese Holstein cows to assess the effects of κ-casein (κ-CN) and β-lactoglobulin (β-LG) genetic variants on its milk coagulation properties. The results show that Chinese Holstein cows have a higher frequency of the κ-CN AA and AB variants, and β-LG of the AB and AA variants. Of these, κ-CN B variants, the β-LG AA and BB variants were more frequent in milk showing good coagulation. The effects of the genetic variants on milk composition, milk proteome, and protein phosphorylation sites were studied. The results showed that higher concentrations of protein and dry matter were found in κ-CN BE variant. Moreover, large variations in milk proteome among different κ-CN and β-LG variants were observed. Highly phosphorylated for κ-CN, especially Ser97, was observed in cows with the κ-CN BE variant, but no effect of β-LG variants on phosphorylation site was found. Of the various factors examined, variation of κ-CN phosphorylation sites Ser97 may be the most important in affecting casein structure and milk coagulation ability. Some milk protein contents were found to be negative factors for milk coagulation. In summary, this study showed that κ-CN genetic variants contained different milk compositions and phosphorylation site Ser97 influenced milk coagulation.

Bovine Milk Proteome: Milk Fat Globule Membrane Protein Is the Most Sensitive Fraction in Response to High Somatic Cell Count

The impacts of high milk somatic cell count (SCC) on different milk fractions are not well understood. In this study, proteins in milk exosomes, milk fat globule membrane (MFGM), and whey from cows with low (<105 cells/mL, CG) and high SCC (>5 × 105 cells/mL, HSG) were identified using a tandem mass tag proteomic approach. In total, 1568, 2160, and 1002 proteins were identified, with 65, 552, and 98 proteins being altered by high SCC in exosomes, MFGM, and whey, respectively. With high SCC, the exosome marker (ACTB) was increased in the exosomes of HSG. The main MFGM proteins (BTN1A1, PLIN3, FABP3, and MFGE8) and functional proteins (MUC1, IGSF5, TLR5, and CD36/14) were decreased, while the lipid/energy metabolism-related proteins were increased in the MFGM of HSG. The glycolysis-related proteins were increased in the whey of HSG. Also, the host defense/inflammation-related proteins were changed in three fractions under high SCCs. MFGM was the most sensitive fraction to a high SCC, followed by whey. These findings provide guidance for the early detection of unhealthy mammary glands.

CSN1S1 and LALBA Polymorphisms and Other Factors Influencing Yield, Composition, Somatic Cell Score, and Technological Properties of Cow's Milk

We evaluated the influence of CSN1S1 and LALBA polymorphisms on cow's milk yield and quality. The analysis was done on Czech Simmental and Holstein cows. Non-genetic factors were included as well. CSN1S1 did not influence the milk performance in the first lactation. In the second lactation, cows with the BB genotype had significantly higher milk, protein, and fat yields than BC. The differences between LALBA genotypes were non-significant in the first lactation, while in the second lactation, the fat percentage was significantly higher in BB than in AB. The farm significantly influenced milk, protein, and fat yields in both the first and second lactations and fat percentage in the first lactation. The effect of CSN1S1 and LALBA genotypes on the milk technological quality was non-significant. Breed did not influence any of the evaluated technological traits and SCS. The ethanol test was not influenced by farm, season, lactation phase, protein percentage, breed, or non-fat solids percentage. Farm, season, and protein percentage significantly influenced milk fermentation ability, renneting, and SCS. The lactose content is a good indicator of udder health.

Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows

The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CY_CURD), total solids and water retained in the fresh cheese (%CY_SOLIDS and %CY_WATER), and 60-days ripened cheese (%CY_RIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

Modeling weight loss of cheese during ripening and the influence of dairy system, parity, stage of lactation, and composition of processed milk

The yield, flavor, and texture of ripened cheese result from numerous interrelated microbiological, biochemical, and physical reactions that take place during ripening. The aims of the present study were to propose a 2-compartment first-order kinetic model of cheese weight loss over the ripening period; to test the variation in new informative phenotypes describing this process; and to assess the effects on these traits of dairy farming system, individual farms within dairy system, animal factors, and milk composition. A total of 1,211 model cheeses were produced in the laboratory using individual 1.5-L milk samples from Brown Swiss cows reared on 83 farms located in Trento Province. During ripening (60 d; temperature 15°C, relative humidity 85%), the weight of all model cheeses was measured, and cheese yield (cheese weight/processed milk weight, %CY) was calculated at 7 intervals from cheese-making (0, 1, 7, 14, 28, 42, and 60 d). Using these measures, a 2-compartment first-order kinetic model (3-parameter equation) was developed for modeling %CY during the ripening period, as follows: [Formula: see text] , where %CY_t is the %CY at ripening time t; %CY_i and %CY_f are the modeled %CY traits at time 0 d (%CY_i = initial %CY) and at the end of a ripening period sufficient to reach a constant wheel weight (%CY_f = final %CY after 60 d ripening in the case of small model cheeses); k_CY is the instant rate constant for cheese weight loss (%/d). Cheese weight and protein and fat losses were calculated as the % difference between the model cheeses at 0 and after 60 d of ripening. The variation in cheese pH was calculated as the % difference between pH at 0 and after 60 d. Dairy system, individual herd within dairy system, and the cow's parity and lactation stage (tested with a linear mixed model) strongly affected almost all the traits collected during model cheese ripening. Milk fat, protein, lactose, pH, and somatic cell score also greatly affected almost all the traits, although k_CY was affected only by milk protein. After including milk composition in the linear mixed model, the importance of all the herd and animal sources of variation was greatly reduced for all traits. The proposed model and novel traits could be tested, first, with the aim of establishing new monitoring procedures enabling the dairy industry to improve milk quality-based payment systems at the herd level and, second, with a view to exploring possible genetic improvements to dairy cow populations.