Recent advances in the bovine β-casein gene mutants on functional characteristics and nutritional health of dairy products: Status, challenges, and prospects

β-casein is the second most abundant form of casein in milk. Changes in amino acid sequence at specific positions in the primary structure of β-casein in milk will produce gene mutations that affect the physicochemical properties of dairy products and the hydrolysis site of digestive enzymes. The screening method of β-casein allele frequency detection in dairy products also has attracted the extensive attention of scientists and farmers. The A1 and A2 β-casein is the two usual mutation types, distinguished by histidine and proline at position 67 in the peptide chain. This paper summarizes the effects of A1 and A2 β-casein on the physicochemical properties of dairy products and evaluates the effects on human health, and the genotyping methods were also concluded. Impressively, this review presents possible future opportunities and challenges for the promising field of A2 β-casein, providing a valuable reference for the development of the functional dairy market.

A discussion on A1-free milk: Nuances and comments beyond implications to the health

This chapter provides an overarching view of the multifaceted aspects of milk β-casein, focusing on its genetic variants A1 and A2. The work examines the current landscape of A1-free milk versus regular milk, delving into health considerations, protein detection methods, technological impacts on dairy production, non-bovine protein, and potential avenues for future research. Firstly, it discussed ongoing debates surrounding categorizing milk based on A1 and A2 β-casein variants, highlighting challenges in establishing clear regulatory standards and quality control methods. The chapter also addressed the molecular distinction between A1 and A2 variants at position 67 of the amino acid chain. This trait affects protein conformation, casein micelle properties, and enzymatic susceptibility. Variations in β-casein across animal species are acknowledged, casting doubt on non-bovine claims of "A2-like" milk due to terminology and genetic differences. Lastly, this work explores the burgeoning field of biotechnology in milk production.

Observational study on the associations between milk yield, composition, and coagulation properties with blood biomarkers of health in Holstein cows

The considerable increase in the production capacity of individual cows owing to both selective breeding and innovations in the dairy sector has posed challenges to management practices in terms of maintaining the nutritional and metabolic health status of dairy cows. In this observational study, we investigated the associations between milk yield, composition, and technological traits and a set of 21 blood biomarkers related to energy metabolism, liver function or hepatic damage, oxidative stress, and inflammation or innate immunity in a population of 1,369 high-yielding Holstein-Friesian dairy cows. The milk traits investigated in this study included 4 production traits (milk yield, fat yield, protein yield, daily milk energy output), 5 traits related to milk composition (fat, protein, casein, and lactose percentages and urea), 11 milk technological traits (5 milk coagulation properties and 6 curd-firming traits). All milk traits (i.e., production, composition, and technological traits) were analyzed according to a linear mixed model that included the days in milk, the parity order, and the blood metabolites (tested one at a time) as fixed effects and the herd and date of sampling as random effects. Our findings revealed that milk yield and daily milk energy output were positively and linearly associated with total cholesterol, nonesterified fatty acids, urea, aspartate aminotransferase, γ-glutamyl transferase, total bilirubin, albumin, and ferric-reducing antioxidant power, whereas they were negatively associated with glucose, creatinine, alkaline phosphatase, total reactive oxygen metabolites, and proinflammatory proteins (ceruloplasmin, haptoglobin, and myeloperoxidase). Regarding composition traits, the protein percentage was negatively associated with nonesterified fatty acids and β-hydroxybutyrate (BHB), while the fat percentage was positively associated with BHB, and negatively associated with paraoxonase. Moreover, we found that the lactose percentage increased with increasing cholesterol and albumin and decreased with increasing ceruloplasmin, haptoglobin, and myeloperoxidase. Milk urea increased with an increase in cholesterol, blood urea, nonesterified fatty acids, and BHB, and decreased with an increase in proinflammatory proteins. Finally, no association was found between the blood metabolites and milk coagulation properties and curd-firming traits. In conclusion, this study showed that variations in blood metabolites had strong associations with milk productivity traits, the lactose percentage, and milk urea, but no relationships with technological traits of milk. Specifically, increasing levels of proinflammatory and oxidative stress metabolites, such as ceruloplasmin, haptoglobin, myeloperoxidase, and total reactive oxygen metabolites, were shown to be associated with reductions in milk yield, daily milk energy output, lactose percentage, and milk urea. These results highlight the close connection between the metabolic and innate immunity status and production performance. This connection is not limited to specific clinical diseases or to the transition phase but manifests throughout the entire lactation. These outcomes emphasize the importance of identifying cows with subacute inflammatory and oxidative stress as a means of reducing metabolic impairments and avoiding milk fluctuations.

Effect of intramammary infection and inflammation on milk protein profile assessed at the quarter level in Holstein cows

In this study we wanted to investigate the associations between naturally occurring subclinical intramammary infection (IMI) caused by different etiological agents (i.e., Staphylococcus aureus, Streptococcus agalactiae, Streptococcus uberis, and Prototheca spp.), in combination with somatic cell count (SCC), on the detailed milk protein profile measured at the individual mammary gland quarter. An initial bacteriological screening (time 0; T0) conducted on individual composite milk from 450 Holstein cows reared in 3 herds, was performed to identify cows with subclinical IMI. We identified 78 infected animals which were followed up at the quarter level at 2 different sampling times: T1 and T2, 2 and 6 wk after T0, respectively. A total of 529 quarter samples belonging to the previously selected animals were collected at the 2 sampling points and analyzed with a reversed phase HPLC (RP-HPLC) validated method. Specifically, we identified and quantified 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin (LF), which were later expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, % N). Data were analyzed with a hierarchical linear mixed model with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT), and the SCC × BACT interaction. The random effect of individual cow, nested within herd, DIM and parity was used as the error term for the latter effects. Both IMI (i.e., BACT) and SCC significantly reduced the proportion of β-CN and αS1-CN, ascribed to the increased activity of both milk endogenous and microbial proteases. Less evident alterations were found for whey proteins, except for LF, which being a glycoprotein with direct and undirect antimicrobial activity, increased both with IMI and SCC, suggesting its involvement in the modulation of both the innate and adaptive immune response. Finally, increasing SCC in the positive samples was associated with a more marked reduction of total caseins at T1, and αS1-CN at T2, suggesting a synergic effect of infection and inflammation, more evident at high SCC. In conclusion, our work helps clarify the behavior of protein fractions at quarter level in animals having subclinical IMI. The inflammation status driven by the increase in SCC, rather the infection, was associated with the most significant changes, suggesting that the activity of endogenous proteolytic enzymes related to the onset of inflammation might have a pivotal role in directing the alteration of the milk protein profile.

A2 milk consumption and its health benefits: an update

Milk is a widely consumed nutrient-rich food containing protein variants such as casein A2 and A1. A1 differs from A2 in an amino acid at position 67 (Pro67 to His67). The breakdown of β-casein yields β-casomorphins (BCM), among which BCM-7 is extensively studied for its effects on the human body. Animal studies have shown that A1 β-casein milk increases digestive transit time and enhances myeloperoxidase activity. Individuals with lactose intolerance prefer A2 milk to conventional A1 milk, as BCM-7 in A1 milk can lead to inflammation and discomfort in sensitive individuals. A2 milk, which contains A2 β-casein, is believed to be more easily digestible than A1 β-casein. Its popularity has grown owing to reports linking A1 casein to diseases such as type 1 diabetes, heart disease, and autism. A2 milk has gained popularity as an alternative to A1 milk, primarily because of its potential benefits for individuals with certain diseases. This review aims to provide an updated understanding of A2 milk consumption and its health benefits. This review aims to provide an updated understanding of A2 milk consumption and its health benefits.

Influence of β-casein genotype on physicochemical properties and functionality of bovine milk

Several studies have been focused on the effect of milk protein genetic variants on milk physicochemical properties and functionality in recent years. β-casein, an important protein related to milk processibility, has been reported to have 2 main genetic variants A1 and A2, for which cows may be homozygous or heterozygous. In this study, several physicochemical properties of milk with β-casein variants A1A1, A1A2, and A2A2 from 3 collection occasions were analyzed. Higher manganese content and lower pH were found to be associated with the A1A1 variant compared with the other 2 genotypes. Better rennet and acid coagulation were found in A1A1 milk compared with A1A2 and A2A2 milk (although P > 0.05), whereas A2A2 milk was more stable to creaming compared with the other 2 genotypes, which may be linked to its smaller fat globule size. Thus, milk from cows with A1A1 genotype could be preferable for cheese making, while that with A2A2 variant can be used in formulations requiring good stability against creaming, and for example, yogurt making, where the softer yogurt texture may be easier to digest.

Bifenthrin induces cell death in bovine mammary epithelial cells via ROS generation, calcium ion homeostasis disruption, and MAPK signaling cascade alteration

Bifenthrin is one of the widely used synthetic pyrethroid insecticides, employed for various purposes worldwide. As lipophilic pyrethroids can easily bind to soil particles, which is why their residues are detected in various environments. Consequently, the toxicity of bifenthrin to non-target organisms can be regarded as an environmental concern. The toxic effects of bifenthrin have been studied in various animal models and cell lines; however, its toxic effects on cattle remain unclear. In particular, gaining insights into the toxic effects of bifenthrin on the mammary lactation system is crucial for the dairy industry. Therefore, we proceeded to investigate the toxic effects of bifenthrin on the bovine mammary epithelial cells (MAC-T cells). We established that bifenthrin inhibited cell proliferation and triggered apoptosis in MAC-T cells. Additionally, bifenthrin induced mitochondrial dysfunction and altered inflammatory gene expression by disrupting mitochondrial membrane potential (MMP) and generating excessive reactive oxygen species (ROS). We also demonstrated that bifenthrin disrupted both cytosolic and mitochondrial calcium ion homeostasis. Furthermore, bifenthrin altered mitogen-activated protein kinase (MAPK) signaling cascades and downregulated casein-related genes. Collectively, we confirmed the multiple toxic effects of bifenthrin on MAC-T cells, which could potentially reduce milk yield and quality.

Bifenox induces programmed cell death in bovine mammary epithelial cells by impairing calcium homeostasis, triggering ER stress, and altering the signaling cascades of PI3K/AKT and MAPK

Bifenox (methyl 5-(2,4-dichlorophenoxy)-2-nitrobenzoate), a nitrophenyl ether herbicide, was first introduced in the 1980s to control broadleaf weeds. As a result of its wide and frequent application in diverse agricultural settings and reports on residual traces, potential adverse effects of bifenox have been studied extensively in rat hepatocytes, bovine peripheral lymphocytes, and mice. Despite the reported risks of bifenox exposure in dairy cows, the toxicity of bifenox on bovine lactation system has not been extensively investigated. Therefore, we used bovine mammary epithelial (MAC-T) cells to study the toxic effects of bifenox on mammary glands. We found that bifenox inhibited MAC-T cells proliferation and disturbed the cell cycle, especially in the sub-G1 and G1 phases. Bifenox also disrupted the calcium homeostasis within the cell and impaired mitochondrial membrane potential. We also examined phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase (MAPK) signaling cascades. The findings indicated hyperactivation of phosphorylated protein kinase B (AKT), p70 ribosomal S6 kinase (p70S6K), S6, extracellular signal-regulated kinases 1 and 2 (ERK1/2), p38, c-Jun N-terminal kinase (JNK), and c-Jun, as well as endoplasmic reticulum (ER) stress caused by bifenox treatment. In conclusion, based on our in vitro study employing MAC-T cells, we report that bifenox can induce damage to the bovine mammary glands, potentially impacting milk production.

Relationships of beta-casein genetics with production, fertility, and survival of purebred organic Holstein dairy cows

The objective of this study was to compare β-casein genotype of purebred certified-organic Holstein cows, and their effect on production, fertility, and survival. Holstein cows (n = 1,982) from 13 certified-organic dairy herds from the western, midwestern, and northeastern United States were genomically tested with CLARIFIDE Plus (Zoetis) for β-casein genotype. Two hundred fourteen cows were A1A1 (11%), 848 cows were A1A2 (43%), and 920 cows were A2A2 (46%). In total, 2,249 lactation records, 1,025 from the first parity and 1,224 records during second and greater parities were used. Test-day milk, fat, and protein production (305-d) and somatic cell score were obtained from the Dairy Herd Improvement Association. A lower limit of 50 d for days open was applied, and cows with more than 250 d open had days open set to 250 d. Independent variables for statistical analysis were the fixed effects of herd, parity, β-casein genotype (A1A1, A1A2, A2A2), and β-casein genotype by parity interaction. Cow nested within parity was the random effect in the statistical models for fertility and production traits. Herd had a significant effect on all fertility, production, and survival variables. Parity affected the number of times bred per pregnancy and days open, milk, fat, and protein production, and somatic cell score. Beta-casein genotype and herd influenced the percentage of cows surviving to first and second lactation. Results indicate no difference in production and fertility regarding β-casein genotype for organic dairy herds. Survival was biased against the A1 allele, which is indicated by lower survival rates during first lactation. These results may offer organic producers more flexibility in breeding and culling decisions to produce A2A2 milk.

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.

Mating allocations in Holstein combining genomic information and linear programming optimization at the herd level

In this study, we explored mating allocation in Holstein using genomic information for 24,333 Holstein females born in Denmark, Finland, and Sweden. We used 2 data sets of bulls: the top 50 genotyped bulls and the top 25 polled genotyped bulls on the Nordic total merit scale. We used linear programming to optimize economic scores within each herd, considering genetic level, genetic relationship, semen cost, the economic impact of genetic defects, polledness, and β-casein. We found that it was possible to reduce genetic relationships and eliminate expression of genetic defects with minimal effect on the genetic level in total merit index. Compared with maximizing only Nordic total merit index, the relative frequency of polled offspring increased from 13.5 to 22.5%, and that of offspring homozygous for β-casein (A2A2) from 66.7 to 75.0% in one generation, without any substantial negative impact on other comparison criteria. Using only semen from polled bulls, which might become necessary if dehorning is banned, considerably reduced the genetic level. We also found that animals carrying the polled allele were less likely to be homozygous for β-casein (A2A2) and more likely to be carriers of the genetic defect HH1. Hence, adding economic value to a monogenic trait in the economic score used for mating allocation sometimes negatively affected another monogenetic trait. We recommend that the comparison criteria used in this study be monitored in a modern genomic mating program.

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.

An approach on detection, quantification, technological properties, and trends market of A2 cow milk

The genetic variant A2 β-casein integrates the casein protein group in milk and has been often associated with positive health outcomes. Therefore, this review explores the present understanding of A2 β-casein, including detection methods and the market trends for dairy from A2 milk. Also, the interaction of A2 β-casein with αs₁-casein and κ-casein genotypes was examined in terms of technological impacts on A2 milk. A limited number of preliminary studies has aimed to investigate the sensorial and technological impacts of β-casein variants in milk matrices, for instance, in yogurt and other derivatives. Nevertheless, considering studies carried out so far, it is concluded that the manufacture of dairy products from A2 milk is perfectly feasible, as the products presented slight differences when compared to those derived from traditional milk. In one of the works, sensitive drops in rennet coagulation time and curd firmness values were observed in cheese traits. However, it is relevant to point out that variant A of κ-casein plays a negative role in the coagulation features of milk. Therefore, alterations in the pattern of cheese-making properties are not uniquely related to β-casein variants. Attempts to produce A2 β-casein in laboratory (non-natural source), through biosynthesis, for example, have not been found so far. This knowledge gap offers a promising area for future studies concerning proteins and bioactive peptide production.

What is the impact of amino acid mutations in the primary structure of caseins on the composition and functionality of milk and dairy products?

The impact of amino acid mutations within the peptide structure of bovine milk protein is important to understand as it can effect processability and subsequently effect its physiological properties. Genetic polymorphisms of bovine caseins can influence the chemical, structural, and technological properties, including casein micelle morphology, calcium distribution, network creation upon gelation, and surface activity. The A1 and A2 genetic variants of β-casein have recently acquired growing attention from both academia and industry, prompting new developments in the area. The difference between these two genetic variants is the inclusion of either proline in β-casein A2 or histidine in β-casein A1 at position 67 in the peptide chain. The aim of this review was to examine the extent to which milk and ingredient functionality is influenced by β-casein phenotype. One of the main findings of this review was although β-casein A1 was found to be the dominant variant in milks with superior acid gelation and rennet coagulation properties, milks comprised of β-casein A2 possessed greater emulsion and foam formation capabilities. The difference in the casein micelle assembly, hydrophobicity, and chaperone activity of caseins may explain the contrast in the functionality of milks containing β-casein from either A1 or A2 families. This review provides new insights into the subtle variations in the physicochemical properties of bovine milks, which could potentially support dairy producers in the development of new dairy products with different functional properties.

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Impact of β- and other caseins on the casein micelle structure and functionality.

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Proline and histidine in β-caseins play a key role in casein micelle conformation.

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Chaperone activity of β-casein A2 towards heat-induced aggregation of whey protein.

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Gels prepared of milks with β-casein A1 possess a denser and firmer structure.

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Ordered structure of β-casein A2 led to improved emulsion and foam formation.

A2 Milk: New Perspectives for Food Technology and Human Health

Although milk consumption is increasing worldwide, in some geographical regions, its consumption has persistently declined in recent decades. This fact, together with the increase in milk production prices, has caused both milk producers and the dairy industry to be immersed in a major crisis. Some possible solutions to this problem are to get people who do not currently consume milk to start drinking it again, or to market milk and dairy products with a higher added value. In this context, a type of milk called A2 has recently received attention from the industry. This type of milk, characterized by a difference in an amino acid at position 67 of the β-casein polypeptide chain, releases much smaller amounts of bioactive opioid peptide β-casomorphin 7 upon digestion, which has been linked to harmful effects on human health. Additionally, A2 milk has been attributed worse technological properties in the production of some dairy products. Thus, doubts exist about the convenience for the dairy industry to bet on this product. The aim of this review is to provide an update on the effects on human health of A2 milk, as well as its different technological properties to produce dairy products.

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.