The objective of this study was to examine variation in overall milk, protein, and mineral composition of bovine milk in relation to rennet-induced coagulation, with the aim of elucidating the underlying causes of milk with impaired coagulation abilities. On the basis of an initial screening of 892 milk samples from 42 herds with Danish Jersey and Holstein-Friesian cows, a subset of 102 samples was selected to represent milk with good, poor, or noncoagulating properties (i.e., samples that within each breed represented the most extremes in regard to coagulation properties). Milk with good coagulation characteristics was defined as milk forming a strong coagulum based on oscillatory rheology, as indicated by high values for maximum coagulum strength (G'(max)) and curd firming rate (CFR) and a short rennet coagulation time. Poorly coagulating milk formed a weak coagulum, with a low G'(max) and CFR and a long rennet coagulation time. Noncoagulating milk was defined as milk that failed to form a coagulum, having G'(max) and CFR values of zero at measurements taken within 1h after addition of rennet. For both breeds, a lower content of total protein, total casein (CN) and κ-CN, and lower levels of minerals (Ca, P, Mg) were identified in poorly coagulating and noncoagulating milk in comparison with milk with good coagulation properties. Liquid chromatography/electrospray ionization-mass spectrometry revealed the presence of a great variety of genetic variants of the major milk proteins, namely, α(S1)-CN (variants B and C), α(S2)-CN (A), β-CN (A(1), A(2), B, I, and F), κ-CN (A, B, and E), α-lactalbumin (B), and β-lactoglobulin (A, B, and C). In poorly coagulating and noncoagulating milk samples of both breeds, the predominant composite genotype of α(S1)-, β-, and κ-CN was BB-A(2)A(2)-AA, which confirmed a genetic contribution to impaired milk coagulation. Interestingly, subtle variations in posttranslational modification of CN were observed between the coagulation classes in both breeds. Poorly coagulating and noncoagulating milk contained a lower fraction of the least phosphorylated α(S1)-CN form, α(S1)-CN 8P, relative to total α(S1)-CN, along with a lower fraction of glycosylated κ-CN relative to total κ-CN. Thus, apparent variation was observed in the milk and protein composition, in the genetic makeup of the major milk proteins, and in the posttranslational modification level of CN between milk samples with either good or impaired coagulation ability, whereas the composition of poorly coagulating and noncoagulating milk was similar.
The aim of the present investigation was to study the underlying causes of noncoagulating (NC) milk. Based on an initial screening in a herd of 53 Danish Holstein-Friesians, 20 individual Holstein-Friesian cows were selected for good and poor chymosin-induced coagulation properties; that is, the 10 cows producing milk with the poorest and best coagulating properties, respectively. These 20 selected cows were followed and resampled on several occasions to evaluate possible changes in coagulation properties. In the follow-up study, we found that among the 10 cows with the poorest coagulating properties, 4 cows consistently produced poorly coagulating (PC) or NC milk, corresponding to a frequency of 7%. Noncoagulating milk was defined as milk that failed to form a coagulum, defined as increase in the storage modulus (G') in oscillatory rheometry, within 45min after addition of chymosin. Poorly coagulating milk was characterized by forming a weak coagulum of low G'. Milk proteomic profiling and contents of different casein variants, ionic contents of Ca, P and Mg, κ-casein (CN) genotypes, casein micelle size, and coagulation properties of the 4 NC or PC samples were compared with milk samples of 4 cows producing milk with good coagulation properties. The studies included determination of production of caseinomacropeptide to ascertain whether noncoagulation could be ascribed to the first or second phase of chymosin-induced coagulation. Caseinomacropeptide was formed in all 8 milk samples after addition of chymosin, indicating that the first step (cleavage of κ-CN) was not the cause of inability to coagulate. Furthermore, the effect of mixing noncoagulating and well-coagulating milk was studied. By gradually blending NC with well-coagulating milk, the coagulation properties of the well-coagulating samples were compromised in a manner similar to titration. Milk samples from cows that consistently produced NC milk were further studied at the udder quarter level. The coagulation properties of the quarter milk samples were not significantly different from those of the composite milk sample, showing that poor coagulation traits and noncoagulation traits of the composite milk were not caused by the milk quality of a single quarter. The milk samples exhibiting PC or NC properties were all of the κ-CN variant AA genotype, and contained casein micelles with a larger mean diameter and a lower fraction of κ-CN relative to total CN than milk with good coagulation properties. Interestingly, the relative proportions of different phosphorylation forms of α-CN differed between well-coagulating milk and PC or NC milk samples. The PC and NC milk samples contained a lower proportion of the 2 less-phosphorylated variants of α-CN (α(S1)-CN-8P and α(S2)-CN-11P) compared with samples of milk that coagulated well.
Somatic cell count (SCC) is associated with changes in milk composition, including changes in proteins, lipids, and milk metabolites. Somatic cell count is normally used as an indicator of mastitis infection. The compositional changes in protein and fat affect milk coagulation properties, and also the metabolite composition is thought to contribute to differential milk properties. Milk somatic cells comprise different cell types, which may contribute to differential milk metabolite fingerprints. In this study, milk from a relatively large number of individual cows, representing significant differences in SCC, were analyzed by nuclear magnetic resonance (NMR)-based metabonomics, and the milk metabolite profiles were analyzed for differences related to SCC. Global principal component analysis performed on 876 samples from 2 Danish dairy breeds and orthogonal projection of latent structures discriminant analysis performed on a smaller subset (n=70) representing high (SCC >7.2×10(5) cells/mL) and low (SCC <1.4×10(4) cells/mL) milk SCC identified latent variables, which could be attributed to milk with elevated SCC. In addition, partial least squares regression between the NMR milk metabolite profiles and SCC revealed a strong correlation. The orthogonal projection of latent structures discriminant analysis and partial least squares regressions pinpointed specific NMR spectral regions and thereby identification of milk metabolites that differed according to SCC. Relative quantification of the identified metabolites revealed that lactate, butyrate, isoleucine, acetate, and β-hydroxybutyrate were increased, whereas hippurate and fumarate were decreased in milk with high levels of somatic cells.
Substantial variation in milk coagulation properties has been observed among dairy cows. Consequently, raw milk from individual cows and breeds exhibits distinct coagulation capacities that potentially affect the technological properties and milk processing into cheese. This variation is largely influenced by protein composition, which is in turn affected by underlying genetic polymorphisms in the major milk proteins. In this study, we conducted a large screening on 3 major Scandinavian breeds to resolve the variation in milk coagulation traits and the frequency of milk with impaired coagulation properties (noncoagulation). In total, individual coagulation properties were measured on morning milk collected from 1,299 Danish Holstein (DH), Danish Jersey (DJ), and Swedish Red (SR) cows. The 3 breeds demonstrated notable interbreed differences in coagulation properties, with DJ cows exhibiting superior coagulation compared with the other 2 breeds. In addition, milk samples from 2% of DH and 16% of SR cows were classified as noncoagulating. Furthermore, the cows were genotyped for major genetic variants in the αS1- (CSN1S1), β- (CSN2), and κ-casein (CSN3) genes, revealing distinct differences in variant frequencies among breeds. Allele I of CSN2, which had not formerly been screened in such a high number of cows in these Scandinavian breeds, showed a frequency around 7% in DH and DJ, but was not detected in SR. Genetic polymorphisms were significantly associated with curd firming rate and rennet coagulation time. Thus, CSN1S1 C, CSN2 B, and CSN3 B positively affected milk coagulation, whereas CSN2 A(2), in particular, had a negative effect. In addition to the influence of individual casein genes, the effects of CSN1S1-CSN2-CSN3 composite genotypes were also examined, and revealed strong associations in all breeds, which more or less reflected the single gene results. Overall, milk coagulation is under the influence of additive genetic variation. Optimal milk for future cheese production can be ensured by monitoring the frequency of unfavorable variants and thus preventing an increase in the number of cows producing milk with impaired coagulation. Selective breeding for variants associated with superior milk coagulation can potentially increase raw milk quality and cheese yield in all 3 Scandinavian breeds.
Studies have suggested that nanoscale extracellular vesicles (EV) in human and bovine milk carry immune modulatory properties which could provide beneficial health effects to infants. In order to assess the possible health effects of milk EV, it is essential to use isolates of high purity from other more abundant milk structures with well-documented bioactive properties. Furthermore, gentle isolation procedures are important for reducing the risk of generating vesicle artefacts, particularly when EV subpopulations are investigated. In this study, we present two isolation approaches accomplished in three steps based on size-exclusion chromatography (SEC) resulting in effective and reproducible EV isolation from raw milk. The approaches do not require any EV pelleting and can be applied to both human and bovine milk. We show that SEC effectively separates phospholipid membrane vesicles from the primary casein and whey protein components in two differently obtained casein reduced milk fractions, with one of the fractions obtained without the use of ultracentrifugation. Milk EV isolates were enriched in lactadherin, CD9, CD63 and CD81 compared to minimal levels of the EV-marker proteins in other relevant milk fractions such as milk fat globules. Nanoparticle tracking analysis and electron microscopy reveals the presence of heterogeneous sized vesicle structures in milk EV isolates. Lipid analysis by thin layer chromatography shows that EV isolates are devoid of triacylglycerides and presents a phospholipid profile differing from milk fat globules surrounded by epithelial cell plasma membrane. Moreover, the milk EV fractions are enriched in RNA with distinct and diverging profiles from milk fat globules. Collectively, our data supports that successful milk EV isolation can be accomplished in few steps without the use of ultracentrifugation, as the presented isolation approaches based on SEC effectively isolates EV in both human and bovine milk.
In selecting cows for higher milk yields and milk quality, it is important to understand how these traits are affected by the bovine genome. The major milk proteins exhibit genetic polymorphism and these genetic variants can serve as markers for milk composition, milk production traits, and technological properties of milk. The aim of this study was to investigate the relationships between casein (CN) genetic variants and detailed protein composition in Swedish and Danish dairy milk. Milk and DNA samples were collected from approximately 400 individual cows each of 3 Scandinavian dairy breeds: Swedish Red (SR), Danish Holstein (DH), and Danish Jersey (DJ). The protein profile with relative concentrations of α-lactalbumin, β-lactoglobulin, and α(S1)-, α(S2)-, κ-, and β-CN was determined for each milk sample using capillary zone electrophoresis. The genetic variants of the α(S1)- (CSN1S1), β- (CSN2), and κ-CN (CSN3) genes for each cow were determined using TaqMan SNP genotyping assays (Applied Biosystems, Foster City, CA). Univariate statistical models were used to evaluate the effects of composite genetic variants, α(S1)-β-κ-CN, on the protein profile. The 3 studied Scandinavian breeds differed from each other regarding CN genotypes, with DH and SR having similar genotype frequencies, whereas the genotype frequencies in DJ differed from the other 2 breeds. The similarities in genotype frequencies of SR and DH and differences compared with DJ were also seen in milk production traits, gross milk composition, and protein profile. Frequencies of the most common composite α(S1)-β-κ-CN genotype BB/A(2)A(2)/AA were 30% in DH and 15% in SR, and cows that had this genotype gave milk with lower relative concentrations of κ- and β-CN and higher relative concentrations of αS-CN, than the majority of the other composite genotypes in SR and DH. The effect of composite genotypes on relative concentrations of the milk proteins was not as pronounced in DJ. The present work suggests that a higher frequency of BB/A(1)A(2)/AB, together with a decrease in BB/A(2)A(2)/AA, could have positive effects on DH and SR milk regarding, for example, the processing of cheese.
Milk is a key component in infant nutrition worldwide and, in the Western parts of the world, also in adult nutrition. Milk of bovine origin is both consumed fresh and processed into a variety of dairy products including cheese, fermented milk products, and infant formula. The nutritional quality and processing capabilities of bovine milk is closely associated to milk composition. Metabolomics is ideal in the study of the low-molecular-weight compounds in milk, and this review focuses on the recent nuclear magnetic resonance (NMR)-based metabolomics trends in milk research, including applications linking the milk metabolite profiling with nutritional aspects, and applications which aim to link the milk metabolite profile to various technological qualities of milk. The metabolite profiling studies encompass the identification of novel metabolites, which potentially can be used as biomarkers or as bioactive compounds. Furthermore, metabolomics applications elucidating how the differential regulated genes affects milk composition are also reported. This review will highlight the recent advances in NMR-based metabolomics on milk, as well as give a brief summary of when NMR spectroscopy can be useful for gaining a better understanding of how milk composition is linked to nutritional or quality traits.
Predicting individual fatty acids (FA) in bovine milk from Fourier transform infrared (FT-IR) measurements is desirable. However, such predictions may rely on covariance structures among individual FA and total fat content. These covariance structures may change with factors such as breed and feed, among others. The aim of this study was to estimate how spectral variation associated with total fat content and breed contributes to predictions of individual FA. This study comprised 890 bovine milk samples from 2 breeds (455 Holstein and 435 Jersey). Holstein samples were collected from 20 Danish dairy herds from October to December 2009; Jersey samples were collected from 22 Danish dairy herds from February to April 2010. All samples were from conventional herds and taken while cows were housed. Moreover, in a spiking experiment, FA (C14:0, C16:0, and C18:1 cis-9) were added (spiked) to a background of commercial skim milk to determine whether signals specific to those individual FA could be obtained from the FT-IR measurements. This study demonstrated that variation associated with total fat content and breed was responsible for successful FT-IR-based predictions of FA in the raw milk samples. This was confirmed in the spiking experiment, which showed that signals specific to individual FA could not be identified in FT-IR measurements when several FA were present in the same mixture. Hence, predicted concentrations of individual FA in milk rely on covariance structures with total fat content rather than absorption bands directly associated with individual FA. If covariance structures between FA and total fat used to calibrate partial least squares (PLS) models are not conserved in future samples, these samples will show incorrect and biased FA predictions. This was demonstrated by using samples of one breed to calibrate and samples of the other breed to validate PLS models for individual FA. The 2 breeds had different covariance structures between individual FA and total fat content. The results showed that the validation samples yielded biased predictions. This may limit the usefulness of FT-IR-based predictions of individual FA in milk recording as indirect covariance structures in the calibration set must be valid for future samples. Otherwise, future samples will show incorrect predictions.
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