Effect of Protein Composition on the Cheese-Making Properties of Milk from Individual Dairy Cows

Evidence of additive genetic variation for major milk proteins in dairy cows: A meta-analysis

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, β-lactoglobulin, sum of whey proteins, casein, αs1-casein, αs2-casein, β-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were -0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between β-lactoglobulin and κ-casein) to 0.892 (between αs1-casein and αs2-casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

Emerging Parameters Justifying a Revised Quality Concept for Cow Milk

Milk has become a staple food product globally. Traditionally, milk quality assessment has been primarily focused on hygiene and composition to ensure its safety for consumption and processing. However, in recent years, the concept of milk quality has expanded to encompass a broader range of factors. Consumers now also consider animal welfare, environmental impact, and the presence of additional beneficial components in milk when assessing its quality. This shifting consumer demand has led to increased attention on the overall production and sourcing practices of milk. Reflecting on this trend, this review critically explores such novel quality parameters, offering insights into how such practices meet the modern consumer's holistic expectations. The multifaceted aspects of milk quality are examined, revealing the intertwined relationship between milk safety, compositional integrity, and the additional health benefits provided by milk's bioactive properties. By embracing sustainable farming practices, dairy farmers and processors are encouraged not only to fulfill but to anticipate consumer standards for premium milk quality. This comprehensive approach to milk quality underscores the necessity of adapting dairy production to address the evolving nutritional landscape and consumption patterns.

Effects of feeding whole-cracked rapeseeds, nitrate, and 3-nitrooxypropanol on protein composition, minerals, and vitamin B in milk from Danish Holstein cows

The present study was conducted to assess the individual or combined effects of feeding dietary fat (whole-cracked rapeseed), nitrate, and 3-nitrooxypropanol (3-NOP) on protein profile, mineral composition, B vitamins, and nitrate residues in milk from dairy cows. Forty-eight Danish Holstein cows used in an 8 × 8 incomplete Latin square design were fed 8 factorially arranged diets ((30 or 63 g crude fat/kg DM) × (0 or 10 g nitrate/kg DM) × (0 or 80 mg 3-NOP/kg DM)) over 6 periods of 21 d each. In each period, milk samples were collected from individual cows during the third week by pooling milk obtained from 4 consecutive milkings, and analyzed for protein profile including protein modifications, mineral composition, riboflavin, cobalamin, and presence of nitrate residues. Fat supplementation led to an increase in the phosphorylation degree of αS1-CN by 8.5% due to a decreased relative proportion of αS1-CN 8P and an increased relative proportion of αS1-CN 9P and further to a decrease in the relative proportion of αS2-CN by 2.4%. Additionally, fat supplementation decreased the relative proportions of glycosylated and unglycosylated forms of κ-CN, consequently leading to a 3.6% decrease in total κ-CN. In skim milk, K, Ca, P, and Mg concentrations were altered by individual use of fat, nitrate, and 3-NOP. Feeding nitrate resulted in a 5.4% increase in riboflavin concentration in milk while supplementing 3-NOP increased cobalamin concentration in milk by 21.1%. The nitrate concentration in milk was increased upon feeding nitrate however, this increased concentration was well below the maximum permissible limit of nitrate in milk (<50 mg/L). In conclusion, no major changes were observed in milk protein, and mineral compositions by feeding fat, nitrate, and 3-NOP to dairy cows while the increased riboflavin and cobalamin by nitrate and 3-NOP, respectively, could be of beneficial nutritional value for milk consumers.

Estimating the Effect of the Kappa Casein Genotype on Milk Coagulation Properties in Israeli Holstein Cows

In Israel, about 26% of produced milk is used to produce hard cheeses and 29% for soft cheeses. Milk with preferred coagulation properties requires a shorter coagulation time and yields a higher curd firmness than milk with inferior coagulation properties. Studies have shown that milk from cows with the B allele of kappa casein (κ-CN) produces more cheese than milk from those with A and E alleles. There is evidence that milk from AE or EE genotype cows is unsuitable for cheese production. In the early 1990s, the proportion of the B allele in Israeli Holstein cattle was about 17%, similar to its prevalence in the Holstein population worldwide. In recent years, however, its proportion has increased to about 40%. We analyzed milk coagulation properties as a function of the cow's κ-CN genotype, including time in minutes until the beginning of coagulation and curd firmness after 60 min-measured in volts via an optigraph device and scored on a scale of 0-4 by a laboratory technician. Cow selection was based on their sire's genotype, so that there would be sufficient genotypes that include the rare E allele. A total of 359 cows were sampled from 15 farms: 64 with genotype AA, 142 with AB, 41 with AE, 65 with BB, and 47 with BE. Data were analyzed via the general linear model procedure of SAS. We found the following: (a) There were significant differences between genotypes for optigraph-measured curd firmness. In a multi-comparison test, the BB genotype gave the highest curd firmness, and AB and BE showed a significant advantage compared to AA and AE (9.4, 8.6, 8.4, 6.9, 6.8 V, respectively). Assuming a frequency of about 55% for the A allele, about 30% of the milk delivered to dairy plants comes from AA cows. (b) There was a significant difference between the genotypes in technician-observed curd firmness, with BB scoring significantly higher than AA and AE. (c) The optigraph-measured curd firmness was significantly higher for milk from primiparous cows as compared to milk from second, third, or fourth lactation cows (8.9, 7.8, 7.9, 7.7 V, respectively). The technician-observed curd firmness was significantly higher for primiparous vs. multiparous cows. There was a clear advantage in curd firmness for genotypes that included the B allele compared to those with AA and AE genotypes. We can increase the proportion of the B allele in the population by insemination of cows using bulls with the genotypes AB and BB. This factor should therefore be included in the selection index.

Genetic variants of CSN1S1, CSN2, CSN3, and BLG genes and their association with dairy production traits in Sahiwal cattle and Nili-Ravi buffaloes

Milk protein genes are associated with milk yield and composition in dairy animals. The present study aimed to identify milk protein genes (CSN1S1, CSN2, CSN3, and BLG) genetic variants and their association with milk yield in Sahiwal cattle and Nili-Ravi buffaloes. One hundred animals from each species were selected to collect blood samples and milk production records. Primers were designed for these milk protein genes for PCR amplification. Sequencing of resultant PCR products revealed a higher number of SNPs (13 vs. 7, 5 vs. 1, and 6 vs. 2) in Sahiwal as compared to Nili-Ravi animals in CSN1S1, CSN2, and CSN3 genes, respectively. However, a single SNP was observed in BLG gene of both species. Association analysis revealed that one SNP in BLG gene of Nili-Ravi was associated (p < 0.05) with 305-day milk yield. Two SNPs at CSN1S1 gene in Sahiwal were associated with dry-period. Similarly, one SNP at CSN1S1 and two SNPs at CSN3 gene showed significant association (p < 0.05) with average calving-interval in Sahiwal while two SNPs in CSN1S1 gene were associated (p < 0.05) with this trait in Nili-Ravi. These SNPs could be helpful as candidate variants for marker-assisted selection in cattle and buffaloes for improvement of lactation performance.

Autoimmune amelogenesis imperfecta in patients with APS-1 and coeliac disease

Ameloblasts are specialized epithelial cells in the jaw that have an indispensable role in tooth enamel formation-amelogenesis1. Amelogenesis depends on multiple ameloblast-derived proteins that function as a scaffold for hydroxyapatite crystals. The loss of function of ameloblast-derived proteins results in a group of rare congenital disorders called amelogenesis imperfecta2. Defects in enamel formation are also found in patients with autoimmune polyglandular syndrome type-1 (APS-1), caused by AIRE deficiency3,4, and in patients diagnosed with coeliac disease5-7. However, the underlying mechanisms remain unclear. Here we show that the vast majority of patients with APS-1 and coeliac disease develop autoantibodies (mostly of the IgA isotype) against ameloblast-specific proteins, the expression of which is induced by AIRE in the thymus. This in turn results in a breakdown of central tolerance, and subsequent generation of corresponding autoantibodies that interfere with enamel formation. However, in coeliac disease, the generation of such autoantibodies seems to be driven by a breakdown of peripheral tolerance to intestinal antigens that are also expressed in enamel tissue. Both conditions are examples of a previously unidentified type of IgA-dependent autoimmune disorder that we collectively name autoimmune amelogenesis imperfecta.

Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Cheese yield and nutrients recovery in the curd predicted by Fourier-transform spectra from individual sheep milk samples

The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% validation set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as "water" and "fingerprint" regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.

β-Casein A1 and A2: Effects of polymorphism on the cheese-making process

Of late, "A2 milk" has gained prominence in the dairy sector due to its potential implications in human health. Consequently, the frequency of A2 homozygous animals has considerably increased in many countries. To elucidate the potential implications that beta casein (β-CN) A1 and A2 may have on cheese-making traits, it is fundamental to investigate the relationships between the genetic polymorphisms and cheese-making traits at the dairy plant level. Thus, the aim of the present study was to evaluate the relevance of the β-CN A1/A2 polymorphism on detailed protein profile and cheese-making process in bulk milk. Based on the β-CN genotype of individual cows, 5 milk pools diverging for presence of the 2 β-CN variants were obtained: (1) 100% A1; (2) 75% A1 and 25% A2; (3) 50% A1 and 50% A2; (4) 25% A1 and 75% A2; and (5) 100% A2. For each cheese-making day (n = 6), 25 L of milk (divided into 5 pools, 5 L each) were processed, for a total of 30 cheese-making processes. Cheese yield, curd nutrient recovery, whey composition, and cheese composition were assessed. For every cheese-making process, detailed milk protein fractions were determined through reversed-phase HPLC. Data were analyzed by fitting a mixed model, which included the fixed effects of the 5 different pools, the protein and fat content as a covariate, and the random effect of the cheese-making sessions. Results showed that the percentage of κ-CN significantly decreased up to 2% when the proportion of β-CN A2 in the pool was ≥25%. An increase in the relative content of β-CN A2 (≥50% of total milk processed) was also associated with a significantly lower cheese yield both 1 and 48 h after cheese production, whereas no effects were observed after 7 d of ripening. Concordantly, recovery of nutrients reflected a more efficient process when the inclusion of β-CN A2 was ≤75%. Finally, no differences in the final cheese composition obtained by the different β-CN pools were observed.

Efficiency of Manchega Sheep Milk Intended for Cheesemaking and Determination of Factors Causing Inefficiency

Understanding the factors that determine and regulate cheese yield would allow, through deterministic parametric efficiency models, the determination of the most appropriate milk characteristics for the industry, and the estimation of a technological value for these characteristics. The present study aims to evaluate coagulation performance of Manchega sheep milk intended for cheesemaking and explores two models to determine milk technological efficiency. For this purpose, 1200 Manchega sheep milk samples were collected, and analyses were performed for composition, milk coagulation properties (MCP), somatic cell count (SCC), and milk color values. A first model was built based on curd yield (CE) and a second one based on dry curd yield (DCE). GLM and MANCOVA analyses were used to identify the factors that determine curd yield efficiency, which mainly depended on pH, casein, and lactose content and, to a lesser extent, on the speed of coagulation and curd firmness. When comparing both models, differences were linked to the water retention capacity of the curd. Based on this, the DCE model was considered much more accurate for prediction of coagulation efficiency in a wider variety of cheeses, as it does not seem to be affected by moisture loss.

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from -0.61 to -0.41 and from -0.55 to -0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

Obtaining of Recombinant Camel Chymosin and Testing Its Milk-Clotting Activity on Cow's, Goat's, Ewes', Camel's and Mare's Milk

In the cheese-making industry, commonly chymosin is used as the main milk-clotting enzyme. Bactrian camel (Camelus bactrianus) chymosin (BacChym) has a milk-clotting activity higher than that of calf chymosin for cow's, goat's, ewes', mare's and camel's milk. A procedure for obtaining milk-clotting reagent based on recombinant camel chymosin is proposed here. Submerged fermentation by a recombinant yeast (Pichia pastoris GS115/pGAPZαA/ProchymCB) was implemented in a 50 L bioreactor, and the recombinant camel chymosin was prepared successfully. The activity of BacChym in yeast culture was 174.5 U/mL. The chymosin was concentrated 5.6-fold by cross-flow ultrafiltration and was purified by ion exchange chromatography. The activity of the purified BacChym was 4700 U/mL. By sublimation-drying with casein peptone, the BacChym powder was obtained with an activity of 36,000 U/g. By means of this chymosin, cheese was prepared from cow's, goat's, ewes', camel's and mare's milk with a yield of 18%, 17.3%, 15.9%, 10.4% and 3%, respectively. Thus, the proposed procedure for obtaining a milk-clotting reagent based on BacChym via submerged fermentation by a recombinant yeast has some prospects for biotechnological applications. BacChym could be a prospective milk-clotting enzyme for different types of milk and their mixtures.

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.

Impact of somatic cell count combined with differential somatic cell count on milk protein fractions in Holstein cattle

Udder health in dairy herds is a very important issue given its implications for animal welfare and the production of high-quality milk. Somatic cell count (SCC) is the most widely used means of assessing udder health status. However, differential somatic cell count (DSCC) has recently been proposed as a new and more effective means of evaluating intramammary infection dynamics. Differential SCC represents the combined percentage of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) in the total SCC, with macrophages (MAC) accounting for the remaining proportion. The aim of this study was to evaluate the association between SCC and DSCC and the detailed milk protein profile in a population of 1,482 Holstein cows. A validated reversed-phase HPLC method was used to quantify 4 caseins (CN), namely α_S1-CN, α_S2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin, which were expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, %N). A linear mixed model was fitted to explore the associations between somatic cell score (SCS) combined with DSCC and the protein fractions expressed quantitatively and qualitatively. We ran an additional model that included DSCC expressed as PMN-LYM and MAC counts, obtained by multiplying the percentages of PMN-LYM and MAC by SCC for each cow in the data set. When the protein fractions were expressed as grams per liter, SCS was significantly negatively associated with almost all the casein fractions and positively associated with the whey protein α-lactalbumin, while DSCC was significantly associated with α_S1-CN, β-CN, and α-lactalbumin, but in the opposite direction to SCS. We observed the same pattern with the qualitative data (i.e., %N), confirming opposite effects of SCS and DSCC on milk protein fractions. The PMN-LYM count was only slightly associated with the traits of concern, although the pattern observed was the same as when both SCS and DSCC were included in the model. The MAC count, however, generally had a greater impact on many casein fractions, in particular decreasing both β-CN content (g/L) and proportion (%N), and exhibited the opposite pattern to the PMN-LYM count. Our results show that information obtained from both SCS and DSCC may be useful in assessing milk quality and protein fractions. They also demonstrate the potential of MAC count as a novel udder health trait.

Effect of Antioxidant Supplementation on Milk Yield and Quality in Italian Mediterranean Lactating Buffaloes

Buffaloes are raised mainly to obtain milk that is nutritionally very rich. The technological characteristics of buffalo milk are optimal for processing into cheese, and it is mainly used to produce mozzarella cheese. Under stressful conditions, buffaloes, like other animals, produce milk qualitatively poorly. The stressors that can affect the quality of production are, in addition to other factors, deficiencies in nutrients such as vitamins, antioxidants, and minerals. In this study, we evaluated the effect of antioxidant supplementation on the quality of buffalo milk. Sixty-six buffaloes were enrolled and subdivided into two balanced groups of 33 each. The ZnSe group received 0.2 kg/head/day of Bufalo Plus® containing antioxidants and barley meal, CaCO3 and MgCO3 mix; the control group was supplemented with 0.2 kg/head/day of barley meal, CaCO3 and MgCO3 mix. The two groups were fed ad libitum with a total mixed ration (TMR). The amount of diet distributed was recorded daily, and the residue in the trough manger was recorded three times per week. TMR samples were taken every two weeks for each group. Daily milk yield was recorded twice a week. Milk samples were collected every four weeks and analysed for chemical and technological properties. Furthermore, milk total antioxidant capacity was determined. The results obtained showed that the antioxidant supplement had no effect on feed intake, feeding behaviour, and feed efficiency. The treatment positively influenced milk production while it did not affect the chemical characteristics of the milk. In addition, the supplement of antioxidants improved the milk clotting properties (MCP). The supplement did not affect the antioxidant activity of the milk.

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.

Breeding for improved protein fractions and free amino acids concentration in bovine milk

Considerable resources are required to routinely measure detailed milk compositional traits. Hence, an insufficient volume of phenotypic data can hinder genetic progress in these traits within dairy cow breeding programmes. The objective of the present study was to quantify the opportunities for breeding for improved milk protein and free amino acid (FAA) composition by exploiting mid‐infrared spectroscopy (MIRS) predictions routinely recorded from milk samples. Genetic parameters for protein fractions and FAA composition were estimated using 134,546 test‐day records from 16,166 lactations on 9,572 cows using linear mixed models. Heritability of MIRS‐predicted protein fractions ranged from 0.19 (α‐lactalbumin) to 0.55 (β‐lactoglobulin A), while heritability of MIRS‐predicted FAA ranged from 0.08 for glycine to 0.29 for glutamic acid. Genetic correlations among the MIRS‐predicted FAA were moderate to strong ranging from −0.44 (aspartic acid and lysine) to 0.97 (glutamic acid and total FAA). Adjustment of the genetic correlations for the genetic merit of 24‐h milk yield did not greatly affect the correlations. Results from the current study highlight the presence of exploitable genetic variation for both protein fractions and FAA in dairy cow milk. Besides, the direction of genetic correlations reveals that breeding programmes directly selecting for greater milk protein concentration carry with them favourable improvement in casein and whey fractions.

The Texture of Camel Milk Cheese: Effects of Milk Composition, Coagulants, and Processing Conditions

Numerous people in African, Middle Asian, Middle Eastern, and Gulf Cooperation Council (GCC) countries highly value camel milk (CM) as it plays a vital role in their diet. The protein composition of CM as well as the structure of its casein micelles differs significantly from bovine milk (BM). Cheeses made from CM have a weak curd and soft texture compared to those made from BM. This review article presents and discusses the effect of milk protein composition, processing conditions (pasteurization and high-pressure treatment), and coagulants (camel chymosin, organic acids, plant proteases) on the quality of CM cheeses. CM cheese's weak texture is due to compositional characteristics of the milk, including low κ-casein-to-β-casein ratio (≈0.05 in CM vs. ≈0.33 in BM), large micelle size, different whey protein components, and higher proteolytic activity than BM. CM cheese texture can be improved by preheating the milk at low temperatures or by high pressure. Supplementing CM with calcium has shown inconsistent results on cheese texture, which may be due to interactions with other processing conditions. Despite their structure, CM cheeses are generally well liked in sensory studies.

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.

Identification of rare genetic variants of the αS-caseins in milk from native Norwegian dairy breeds and comparison of protein composition with milk from high-yielding Norwegian Red cows

Several factors influence the composition of milk. Among these, genetic variation within and between cattle breeds influences milk protein composition, protein heterogeneity, and their posttranslational modifications. Such variations may further influence technological properties, which are of importance for the utilization of milk into dairy products. Furthermore, these potential variations may also facilitate the production of differentiated products (e.g., related to specific breeds or specific genetic variants). The objective of this study was to investigate the genetic variation and relative protein composition of the major proteins in milk from 6 native Norwegian dairy breeds representing heterogeneity in geographical origin, using the modern Norwegian breed, Norwegian Red, as reference. In total, milk samples from 144 individual cows were collected and subjected to liquid chromatography-electrospray ionization/mass spectrometry-based proteomics for identification of genetic and posttranslational modification isoforms of the 4 caseins (α_S1-CN, α_S2-CN, β-CN, κ-CN) and the 2 most abundant whey proteins (α-lactalbumin and β-lactoglobulin). Relative quantification of these proteins and their major isoforms, including phosphorylations of α_S1-CN and glycosylation of κ-CN, were determined based on UV absorbance. The presence and frequency of genetic variants of the breeds were found to be very diverse and it was possible to identify rare variants of the CN, which, to our knowledge, have not been identified in these breeds before. Thus, α_S1-CN variant D was identified in low frequency in 3 of the 6 native Norwegian breeds. In general, α_S1-CN was found to be quite diverse between the native breeds, and the even less frequent A and C variants were furthermore detected in 1 and 5 of the native breeds, respectively. The α_S1-CN variant C was also identified in samples from the Norwegian Red cattle. The variant E of κ-CN was identified in 2 of the native Norwegian breeds. Another interesting finding was the identification of α_S2-CN variant D, which was found in relatively high frequencies in the native breeds. Diversity in more common protein genetic variants were furthermore observed in the protein profiles of the native breeds compared with milk from the high-yielding Norwegian Reds, probably reflecting the more diverse genetic background between the native breeds.

Integrated Small RNA Sequencing, Transcriptome and GWAS Data Reveal microRNA Regulation in Response to Milk Protein Traits in Chinese Holstein Cattle

Milk protein is one of the most important economic traits in the dairy industry. Yet, the regulatory network of miRNAs for the synthesis of milk protein in mammary is poorly understood. Samples from 12 Chinese Holstein cows with three high ( ≥ 3.5%) and three low ( ≤ 3.0%) phenotypic values for milk protein percentage in lactation and non-lactation were examined through deep small RNA sequencing. We characterized 388 known and 212 novel miRNAs in the mammary gland. Differentially expressed analysis detected 28 miRNAs in lactation and 52 miRNAs in the non-lactating period with a highly significant correlation with milk protein concentration. Target prediction and correlation analysis identified some key miRNAs and their targets potentially involved in the synthesis of milk protein. We analyzed for enrichments of GWAS signals in miRNAs and their correlated targets. Our results demonstrated that genomic regions harboring DE miRNA genes in lactation were significantly enriched with GWAS signals for milk protein percentage traits and that enrichments within DE miRNA targets were significantly higher than in random gene sets for the majority of milk production traits. This integrated study on the transcriptome and posttranscriptional regulatory profiles between significantly differential phenotypes of milk protein concentration provides new insights into the mechanism of milk protein synthesis, which should reveal the regulatory mechanisms of milk secretion.

Effect of Protein Genotypes on Physicochemical Properties and Protein Functionality of Bovine Milk: A Review

Milk protein comprises caseins (CNs) and whey proteins, each of which has different genetic variants. Several studies have reported the frequencies of these genetic variants and the effects of variants on milk physicochemical properties and functionality. For example, the C variant and the BC haplotype of αS1-casein (αS1-CN), β-casein (β-CN) B and A1 variants, and κ-casein (κ-CN) B variant, are favourable for rennet coagulation, as well as the B variant of β-lactoglobulin (β-lg). κ-CN is reported to be the only protein influencing acid gel formation, with the AA variant contributing to a firmer acid curd. For heat stability, κ-CN B variant improves the heat resistance of milk at natural pH, and the order of heat stability between phenotypes is BB > AB > AA. The A2 variant of β-CN is more efficient in emulsion formation, but the emulsion stability is lower than the A1 and B variants. Foaming properties of milk with β-lg variant B are better than A, but the differences between β-CN A1 and A2 variants are controversial. Genetic variants of milk proteins also influence milk yield, composition, quality and processability; thus, study of such relationships offers guidance for the selection of targeted genetic variants.

Graduate Student Literature Review: Current understanding of the influence of on-farm factors on bovine raw milk and its suitability for cheesemaking

Relationships between dairy farm practices, the composition and properties of raw milk, and the quality of the resulting cheese are complex. In this review, we assess the effect of farm factors on the quality of bovine raw milk intended for cheesemaking. The literature reports several prominent farm-related factors that are closely associated with milk quality characteristics. We describe their effects on the composition and technological properties of raw milk and on the quality of the resulting cheese. Cow breed, composite genotype, and protein polymorphism all have noticeable effects on milk coagulation, cheese yield, and cheese composition. Feed and feeding strategy, dietary supplementation, housing and milking system, and seasonality of milk production also influence the composition and properties of raw milk, and the resulting cheese. The microbiota in raw milk is influenced by on-farm factors and by the production environment, and may influence the technological properties of the milk and the sensory profile of certain cheese types. Advances in research dealing with the technological properties of raw milk have undoubtedly improved understanding of how on-farm factors affect milk quality attributes, and have refuted the concept of one milk for all purposes. The specific conditions for milk production should be considered when the milk is intended for the production of cheese with unique characteristics. The scientific identification of these conditions would improve the current understanding of the complex associations between raw milk quality and farm and management factors. Future research that considers dairy landscapes within broader perspectives and develops multidimensional approaches to control the quality of raw milk intended for long-ripening cheese production is recommended.

Análisis genético de cinco polimorfismos de nucleótido simple de caseínas lácteas obtenidos con chips genómicos en ganado Holstein de Antioquia, Colombia

Los polimorfismos genéticos asociados con las caseínas de la leche son de gran importancia, ya que pueden ser usados como marcadores genéticos para mejorar el rendimiento productivo en los hatos lecheros. El objetivo de este estudio fue evaluar la diversidad y estructura genética de 5 SNP de caseínas de la leche, obtenidos con chips genómicos en vacas y toros de raza Holstein en Antioquia (Colombia). Fueron muestreados 113 animales de raza Holstein en 3 regiones del departamento de Antioquia (norte, centro y oriente) y un cuarto grupo de sementales comerciales. Los animales fueron genotipificados con chips genómicos de alta densidad (Illumina BovineHD e Illumina SNP50 v2), a partir de los cuales se identificaron 5 SNP (ARS-BFGL-NGS-8140, BTA-77380-no-rs, BTA-32346-no-rs, BTB-00821654 y ARS-BFGL-NGS-15809). Para cada SNP se realizó un análisis genético mediante un análisis de varianza molecular (Amova) usando el software GenAIEx 6.501. Los SNP con mayor heterocigosidad total (HT) fueron ARS-BFGL-NGS-8140 y BTA-32346-no-rs, con resultados cercanos al 45%; sin embargo, la HT para ARS-BFGL-NGS-15809, BTA-77380-no-rs y BTB-00821654 estuvo por debajo del 15%. El SNP con mayor diversidad genética fue BTA-32346-no-rs (Ho – He = 0,06; p < 0,05). En esta investigación se evaluó una subpoblación de toros comerciales extranjeros, en la cual se obtuvieron frecuencias alélicas y genotípicas similares a las obtenidas para las subpoblaciones locales, sugiriendo que los alelos de los toros muy posiblemente están fijados en dichas subpoblaciones, por lo que la estructura y diversidad genética tienden a ser bajas en la muestra de estudio.

Nonlinear modeling to describe the pattern of 15 milk protein and nonprotein compounds over lactation in dairy cows

The protein profile of milk includes several caseins, whey proteins, and nonprotein nitrogen compounds, which influence milk's value for human nutrition and its cheesemaking properties for the dairy industry. To fill in the gap in current knowledge of the patterns of these individual nitrogenous compounds throughout lactation, we tested the ability of a parametric nonlinear lactation model to describe the pattern of each N compound expressed qualitatively (as % of total milk N), quantitatively (in g/L milk), and as daily yield (in g/d). The lactation model was tested on a data set of detailed milk nitrogenous compound profiles (15 fractions-12 protein traits and 3 nonproteins-for each expression mode: 45 traits) obtained from 1,342 cows reared in 41 multibreed herds. Our model was a modified version of Wilmink's model, often used for describing milk yield during lactation because of its reliability and ease of parameter interpretation from a biological point of view. We allowed the sign of the persistency coefficient (parameter c) that explained the variation in the long-term milk component (parameter a) to be positive or negative. We also allowed the short-term milk component (parameter b) to be positive or negative, and we estimated a specific speed of adaptation parameter (parameter k) for each trait rather than assumed a value a priori, as in the original model (k = 0.05). These 4 parameters were included in a nonlinear mixed model with cow breed and parity order as fixed effects, and herd-date as random. Combinations of the positive and negative signs of the b and c parameters allowed us to identify 4 differently shaped lactation curves, all found among the patterns exhibited by the nitrogenous fractions as follows: the "zenith" curve (with a maximum peak; for milk yield and 10 other N traits), the "nadir" curve (with a minimum point; for 20 traits, including almost all those expressed in g/L of milk), the "downward" curve (continuously decreasing; for 14 traits, including almost all those in g/d), and the "upward" curve (continuously increasing; only for κ-casein, in % N). Direct estimation of the k parameters specific to each trait showed the large variability in the adaptation speed of fresh cows and greatly increased the model's flexibility. The results indicated that nonlinear parametric mathematical models can effectively describe the different and complex patterns exhibited by individual nitrogenous fractions during lactation; therefore, they could be useful tools for interpreting milk composition variations during lactation.

Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows

Mastitis is one of the most prevalent diseases in dairy cattle and is the cause of considerable economic losses. Alongside somatic cell count (SCC), differential somatic cell count (DSCC) has been recently introduced as a new indicator of intramammary infection. The DSCC is expressed as a count or a proportion (%) of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in milk somatic cells. These numbers are complemented to total somatic cell count or to 100 by macrophages (MAC). The aim of this study was to investigate the genetic variation and heritability of DSCC, and its correlation with milk composition, udder health indicators, milk composition, and technological traits in Holstein cattle. Data used in the analysis consisted in single test-day records from 2,488 Holstein cows reared in 36 herds located in northern Italy. Fourier-transform infrared (FTIR) spectroscopy was used to predict missing information for some milk coagulation and cheese-making traits, to increase sample size and improve estimation of the genetic parameters. Bayesian animal models were implemented via Gibbs sampling. Marginal posterior means of the heritability estimates were 0.13 for somatic cell score (SCS); 0.11 for DSCC, MAC proportion, and MAC count; and 0.10 for PMN-LYM count. Posterior means of additive genetic correlations between SCS and milk composition and udder health were low to moderate and unfavorable. All the relevant genetic correlations between the SCC traits considered and the milk traits (composition, coagulation, cheese yield and nutrients recovery) were unfavorable. The SCS showed genetic correlations of -0.30 with the milk protein proportion, -0.56 with the lactose proportion and -0.52 with the casein index. In the case of milk technological traits, SCS showed genetic correlations of 0.38 with curd firming rate (k₂₀), 0.45 with rennet coagulation time estimated using the curd firming over time equation (RCT_eq), -0.39 with asymptotic potential curd firmness, -0.26 with maximum curd firmness (CF_max), and of -0.31 with protein recovery in the curd. Differential somatic cell count expressed as proportion was correlated with SCS (0.60) but had only 2 moderate genetic correlations with milk traits: with lactose (-0.32) and CF_max (-0.33). The SCS was highly correlated with the log PMN-LYM count (0.79) and with the log MAC count (0.69). The 2 latter traits were correlated with several milk traits: fat (-0.38 and -0.43 with PMN-LYM and MAC counts, respectively), lactose percentage (-0.40 and -0.46), RCT_eq (0.53 and 0.41), t_max (0.38 and 0.48). Log MAC count was correlated with k₂₀ (+0.34), and log PMN-LYM count was correlated with CF_max (-0.26) and weight of water curd as percentage of weight of milk processed (-0.26). The results obtained offer new insights into the relationships between the indicators of udder health and the milk technological traits in Holstein cows.

The effects of camel chymosin and Withania coagulans extract on camel and bovine milk cheeses

Withania coagulans (W. coagulans) extract and camel chymosin have aspartic protease capable of coagulating milk for cheese production. This study investigated the quality of camel and bovine milk cheeses coagulated using Withania extracts, came chymosin, and their mixture in two experiments. In Experiment (1), a factorial design with four factors (W. coagulans, camel chymosin, incubation time, and incubation temperature) was performed. The effect of these factors on cheese's yield and hardness were assessed. An enzyme concentration corresponding to a 36 µg/L of milk of W. coagulans, 50 IMCU/L of camel chymosin, holding time of 4 h, and incubation temperature of 60 °C provided the optimal textural hardness for both camel and bovine milk cheeses. Seven treatments were analyzed in experiment (2) were analyzed for physicochemical properties, yield, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGEitation). The results showed that pure Withania extract exhibited the lower coagulating effect resulting in cheeses with low yield, hardness, fat, protein, and total solids. The SDS-PAGE electropherograms of camel cheese showed several low molecular weight bands as compared to bovine cheese. This phenomenon is due to excessive proteolysis in camel cheese, which we believed is caused by the presence of endogenous enzymes.

Variation in milk fat globule size and composition: A source of bioactives for human health

Milk fat globules (MFGs) are secreted from the mammalian gland and are composed of a triacylglycerol core surrounded by a triple membrane structure, the milk fat globule membrane (MFGM). The MFGM contains complex lipids and proteins reported to have nutritional, immunological, neurological and digestive functions. Human and ruminant milk are shown to share a similar MFG structure but with different size, profile and abundance of protein and polar lipids. This review summarizes the reported data on human, bovine, caprine and ovine MFG composition and concentration of bioactive components in different MFG-size fractions. A comprehensive understanding of compositional variations between milk from different species and MFG size fractions may help promote various milk sources as targeted supplements to improve human development and health. MFG size and MFGM composition are species-specific and affected by lactation, diet and breed (or maternal origin). Purification and enrichment methods for some bioactive proteins and lipids present in the MFGM have yet to be established or are not scaled sufficiently to be used to supplement human diets. To overcome this problem, MFG size selection through fractionation or herd selection may provide a convenient way to pre-enrich the MFG fraction with specific protein and lipid components to fulfill human dietary and health requirements.

The mineral profile affects the coagulation pattern and cheese-making efficiency of bovine milk

Natural variations in milk minerals, their relationships, and their associations with the coagulation process and cheese-making traits present an opportunity for the differentiation of milk destined for high-quality natural products, such as traditional specialties or Protected Designation of Origin (PDO) cheeses. The aim of this study was to quantify the effects of the native contents of Ca, P, Na, K, and Mg on 18 traits describing traditional milk coagulation properties (MCP), curd firming over time (CF_t) equation parameters, cheese yield (CY) measures, and nutrient recoveries in the curd (REC) using models that either included or omitted the simultaneous effects of milk fat and casein contents. The results showed that, by including milk fat and casein and the minerals in the statistical model, we were able to determine the specific effects of each mineral on coagulation and cheese-making efficiency. In general, about two-thirds of the apparent effects of the minerals on MCP and the CF_t equation parameters are actually mediated by their association with milk composition, especially casein content, whereas only one-third of the effects are direct and independent of milk composition. In the case of cheese-making traits, the effects of the minerals were mediated only negligibly by their association with milk composition. High Ca content had a positive effect on the coagulation pattern and cheese-making traits, favoring water retention in the curd in particular. Phosphorus positively affected the cheese-making traits in that it was associated with an increase in CY in terms of curd solids, and in all the nutrient recovery traits. However, a very high P content in milk was associated with lower fat recovery in the curd. The variation in the Na content in milk only mildly affected coagulation, whereas with regard to cheese-making, protein recovery was negatively associated with high concentrations of this mineral. Potassium seemed not to be actively involved in coagulation and the cheese-making process. Magnesium content tended to slow coagulation and reduce CY measures. Further studies on the relationships of minerals with casein and protein fractions could deepen our knowledge of the role of all minerals in coagulation and the cheese-making process.

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.

The Impact of Seasonality in Pasture-Based Production Systems on Milk Composition and Functionality

Seasonal calving, pasture-based dairy systems are widely practiced in countries with a temperate climate and plentiful rainfall such as Ireland and New Zealand. This approach maximizes milk production from pasture and, consequently, is a low-cost, low-input dairy production system. On the other hand, the majority of global milk supply is derived from high input indoor total mixed ration systems where seasonal calving is not practiced due to the dependence on ensiled silages, grains and concentrated feeds, which are available year-round. Synchronous changes in the macro and micronutrients in milk are much more noticeable as lactation progresses through early, mid and late stages in seasonal systems compared to non-seasonal systems-which can have implications on the processability and functionality of milk.

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from -0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.

Effect of Heat Stress on Dairy Cow Performance and on Expression of Protein Metabolism Genes in Mammary Cells

Simple Summary

Environmental temperatures are increasing, and consequent global warming also has negative effects on dairy cattle farms, which may result in reduced production and poorer milk quality. The protein content of casein, in particular, is important in influencing the coagulation properties of milk and, therefore, the production and quality of cheese. The aim of this study was to assess the effect of heat stress on animal performance and on the expression of selected genes involved in milk protein metabolism. Eight dairy cows were kept under thermoneutral conditions for 8 days. The same animals were then maintained under mild heat stress conditions for an additional 8 days. The results of this study revealed that mild heat stress reduced the feed intake and performance of dairy cows in terms of milk and protein yield, but not the expression of the target genes involved in milk protein metabolism, such as those coding for caseins.

Abstract

The aim of this study was to assess the effect of heat stress on dairy cow performance and on the expression of selected genes involved in milk protein metabolism. Eight Italian Holstein Friesian cows were kept under thermoneutral conditions (temperature–humidity index (THI) < 72, CON) for 8 days and under mild heat stress conditions (72 < THI < 78, HS) for an additional 8 days. The rectal temperature, feed intake, and milk yield were recorded during the last 3 days of the CON and HS periods. During the same time period, milk samples were collected to assess the composition and expression of selected genes involved in milk protein metabolism. Gene expression analyses were performed on somatic cells from milk, which are representative of mammary tissue. In terms of dairy cow performance, HS resulted in lower milk and protein yields and feed intake but higher rectal temperature than for CON (p < 0.05). Under HS, there were greater abundances of HSPA1A (p < 0.05) and BCL2 (p < 0.05), compared to CON, but similar levels of CSN2 (p > 0.05), CSN3 (p > 0.05), HSPA8 (p > 0.05), and STAT5B (p > 0.05) mRNA. Mild heat stress reduced the performance of dairy cows without affecting the expression of genes coding for caseins.

Short communication: Caseins and α-lactalbumin content of camel milk (Camelus dromedarius) determined by capillary electrophoresis

Camel milk has unique physical, nutritional, and technological properties when compared with other milks, especially bovine. Because proteins confer many of the properties of milk and its products, this study aimed to determine the proteins of camel milk, their correlations, and relative distribution. Raw milk samples were collected from 103 dromedary camels in the morning and evening. Capillary electrophoresis results showed wide variation in the concentrations (g/L) of proteins between samples as follows: α-lactalbumin, 0.3 to 2.9; α_S1-casein, 2.4 to 10.3; α_S2-casein, 0.3 to 3.9; β-casein, 5.5 to 29.0; κ-casein, 0.1 to 2.4; unknown casein protein 1, 0.0 to 3.4; and unknown casein protein 2, 0.0 to 4.6. The range in percent composition of the 4 caseins were as follows: α_S1, 12.7 to 35.3; α_S2, 1.8 to 20.8; β, 42.3 to 77.4; and κ, 0.6 to 17.4. The relative proportion of α_S1-, α_S2-, β-, and κ-caseins in camel milk (26:4:67:3, wt/wt) differed from that of bovine milk (38:10:36:12, wt/wt). This difference might explain the dissimilarity between the 2 milks with respect to technical and nutritional properties.

β-Galactosidase gene codon optimization results in post-transcriptional enhancement of expression

OBJECTIVE

lacZ encodes for β-galactosidase within the galactose operon of bacterial cells. When used as a reporter gene, bacterial "β-galactosidase" expression is often insufficient for detection in mammalian cells. We intended to optimize the lacZ codon usage according to the most frequently used codons for the seven major proteins in cow's milk, in order to pave a way for the enhancement of transgenic genes expression in eukaryotes.

RESULTS

We constructed modified lacZ (named olacZ) according to optional codons used for proteins expressed in cow's milk. The expression of lacZ and olacZ was then compared in HC11 (a murine mammary gland epithelial line), 293T, HeLa, Cos7, and NIH 3T3 cells. While there was no significant difference at the mRNA level between lacZ and olacZ (P > 0.05). The quantification of β-galactosidase activity and in situ staining experiments showed a 1.2-fold to 3.3-fold expression improvement when comparing olacZ with lacZ. The levels of β-galactosidase expression at the protein levels from olacZ were approximately 9.2-fold and 2.4-fold respectively for Cos7 and HC11 cells. Furthermore, a 1.9-fold tendency of enhanced expression of olacZ in mammary gland during lactation was observed in transgenic-olacZ mice.

CONCLUSION

This study demonstrates an alternative choice for improving lacZ reporter expression in eukaryotes, especially in the mammary gland of cattle or goats.

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.

Gelation of milks of different species (dairy cattle, goat, sheep, red deer, and water buffalo) using glucono-δ-lactone and pepsin

Dynamic low-amplitude oscillatory rheology was used to study the gelation properties of skim milk gels made at 37°C, using glucono-δ-lactone alone (acid gels) or a combination of glucono-δ-lactone and porcine pepsin ("combination gels"). The protein contents of the skim milks increased in the order goat milk < cattle milk < buffalo milk < sheep milk < deer milk, whereas the average casein micelle diameters increased in the order cattle milk < buffalo milk < goat milk < sheep milk ≃ deer milk. The gelation pH (4.55-4.73) of all milks were close to the isoelectric pH (4.6) of casein, except for buffalo milk, which had a significantly higher gelation pH (5.72). The storage moduli (G') of the acid gels increased with time in the milks of all species except for buffalo milk, for which a double peak in G' was observed. The final storage moduli after 6 h (G'_final) increased in the order goat milk < cattle milk < sheep milk < deer milk < buffalo milk. In general, for the combination gels, the G'_final values and the gelation pH increased to variable extents, except for goat milk. Confocal scanning laser microscopy showed that goat milk and cattle milk formed gels with more open protein networks compared with the dense clustered protein networks of the milks with high protein content (buffalo, sheep, and deer milks). This study indicates that milks from different species respond differently under the action of an acid precursor and pepsin. These results can be used to provide a better understanding of curd making and the digestion properties of noncattle milks.

Genomic Analysis of Milk Protein Fractions in Brown Swiss Cattle

Simple Summary

Milk protein fractions are hugely important in the dairy industry because of the key role they play in milk technological properties. The selection of cows for milk protein fractions may, therefore, improve both the nutritional and technological characteristics of milk, and, consequently, the processing efficiency and value of the dairy product. This study estimated the genetic parameters of the major milk protein fractions (four caseins, and two whey proteins) determined variously as: (i) milk content (g/100g milk), (ii) percentage of milk nitrogen (%N) and (iii) daily yield (g/d) in Brown Swiss dairy cattle. The results showed that the (co)variances and genetic parameter estimates differed according to how the proteins were measured. These results provide useful information for developing selection strategies in dairy cattle breeding programs aimed at improving both the nutritional and technological properties of milk.

Abstract

Depending on whether milk protein fractions are evaluated qualitatively or quantitatively, different genetic outcomes may emerge. In this study, we compared the genetic parameters for the major milk protein fractions—caseins (α_S1-, α_S2-, β-, and к-CN), and whey proteins (β-lactoglobulin, β-LG; α-lactalbumin, α-LA)—estimated using the multi-trait genomic best linear unbiased prediction method and expressed variously as milk content (g/100g milk), percentage of milk nitrogen (%N) and daily yield per cow (g/d). The results showed that the genetic parameter estimates varied according to how the milk protein fractions were expressed. Heritability estimates for the caseins and whey protein fractions expressed as daily yields were lower than when they were expressed as proportions and contents, revealing important differences in genetic outcomes. The proportion and the content of β-CN were negatively correlated with the proportions and contents of α_S1-CN, α_S2-CN, and к-CN, while the daily yield of β–CN was negatively correlated with the daily yields of α_S1-CN and α_S2-CN. The Spearman’s rank correlations and the coincidence rates between the various predicted genomic breeding values (GEBV) for the milk protein fractions expressed in different ways indicated that these differences had a significant effect on the ranking of the animals. The results suggest that the way milk protein fractions are expressed has implications for breeding programs aimed at improving milk nutritional and technological characteristics.

An improved LC-MS method to profile molecular diversity and quantify the six main bovine milk proteins, including genetic and splicing variants as well as post-translationally modified isoforms

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Identification of the main milk proteins isoforms are inferred from a mass database.

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Quantification of the six main milk proteins is achieved from corrected UV at 214 nm.

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Multiple isoforms can be quantified from mass signal, within each protein family.

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Glycosylation isoforms of κ-casein which impact micelle stability are quantified.

Here we describe a method based on Liquid Chromatography coupled with Mass Spectrometry (LC-MS) that provides an accurate determination of the six main bovine milk proteins, including allelic and splicing variants, as well as isoforms resulting from post-translational modifications, with an unprecedented level of resolution. Proteins are identified from observed molecular masses in comparison with theoretical masses of intact proteins indexed in an “in-house” database that includes nearly 3000 entries. Quantification was performed either from UV (214 nm) or mass signals. Thus, up to one hundred molecules, derived from the six major milk proteins, can be identified and quantified from an individual milk sample. This powerful and reliable method, initially developed as an anchoring method to estimate the composition of the six main bovine milk proteins from MIR spectra, is transferable to several mammalian species, including small ruminants, camels, equines, rabbits, etc., for which specific mass databases are available.