We hypothesize that, through milk composition and different milk metabolites, it is possible to characterize the technological properties (e.g., coagulation) of milk. In this research, liquid chromatography mass spectrometry was used to obtain profiles of low molecular weight organic compounds in 143 milk samples. The metabolic profiles of milk from cows were correlated with their coagulation properties. Using multivariate data analysis methods, we demonstrated that the metabolic profiles of the milk were correlated with coagulation ability. Several marker ions responsible for differential coagulation were found. Although not all affected metabolites could be identified, the most significant differences were found for carnitine and oligosaccharides. Exploitation of these results may increase the use of biomarkers to assess the coagulation ability of milk. This study represents the first large-scale metabolomic profiling of noncoagulating and coagulating bovine milk samples in Estonia.

Glucose uptake in tissues is mediated by insulin receptor (INSR) and glucose transporter 4 (GLUT4). The aim of this study was to examine the effect of body condition during the dry period on adipose tissue mRNA and protein expression of INSR and GLUT4, and on the dynamics of glucose and insulin following the i.v. glucose tolerance test in Holstein cows 21 d before (d -21) and after (d 21) calving. Cows were grouped as body condition score (BCS) ≤3.0 (thin, T; n = 14), BCS = 3.25 to 3.5 (optimal, O; n = 14), and BCS ≥3.75 (overconditioned, OC; n = 14). Blood was analyzed for glucose, insulin, fatty acids, and β-hydroxybutyrate concentrations. Adipose tissue was analyzed for INSR and GLUT4 mRNA and protein concentrations. During the glucose tolerance test 0.15 g/kg of body weight glucose was infused; blood was collected at -5, 5, 10, 20, 30, 40, 50, and 60 min, and analyzed for glucose and insulin. On d -21 the area under the curve (AUC) of glucose was smallest in group T (1,512 ± 33.9 mg/dL × min) and largest in group OC (1,783 ± 33.9 mg/dL × min), and different between all groups. Basal insulin on d -21 was lowest in group T (13.9 ± 2.32 µU/mL), which was different from group OC (24.9 ± 2.32 µU/mL. On d -21 the smallest AUC 5-60 of insulin in group T (5,308 ± 1,214 µU/mL × min) differed from the largest AUC in group OC (10,867 ± 1,215 µU/mL × min). Time to reach basal concentration of insulin in group OC (113 ± 14.1 min) was longer compared with group T (45 ± 14.1). The INSR mRNA abundance on d 21 was higher compared with d -21 in groups T (d -21: 3.3 ± 0.44; d 21: 5.9 ± 0.44) and O (d -21: 3.7 ± 0.45; d 21: 4.7 ± 0.45). The extent of INSR protein expression on d -21 was highest in group T (7.3 ± 0.74 ng/mL), differing from group O (4.6 ± 0.73 ng/mL), which had the lowest expression. The amount of GLUT4 protein on d -21 was lowest in group OC (1.2 ± 0.14 ng/mL), different from group O (1.8 ± 0.14 ng/mL), which had the highest amount, and from group T (1.5 ± 0.14 ng/mL). From d -21 to 21, a decrease occurred in the GLUT4 protein levels in both groups T (d -21: 1.5 ± 0.14 ng/mL; d 21: 0.8 ± 0.14 ng/mL) and O (d -21: 1.8 ± 0.14 ng/mL; d 21: 0.8 ± 0.14 ng/mL). These results demonstrate that in obese cows adipose tissue insulin resistance develops prepartum and is related to reduced GLUT4 protein synthesis. Regarding glucose metabolism, body condition did not affect adipose tissue insulin resistance postpartum.

The genetic variation of ás1-, â- and ê-caseins and b-lactoglobulin was determined and their effects on the rennet coagulation properties were examined using 335 milk samples from 118 Estonian Native (EN) cows. We found 16 aggregate casein genotypes (ás1-, â-, ê-caseins), of which four . namely, BB A2A2 AA (21.2%), BB A1A2 AB (16.9%), BB A1A2 AA (14.4%), and BB A2A2 AB (10.2%) – occurred among nearly two-thirds of the analysed cows. Aggregate casein genotype had a significant overall effect on rennet coagulation parameters. Better rennet coagulation properties were found for aggregate casein genotypes CC A2A2 AB and BC A1A2 BB, among frequent genotypes for BB A1A2 AB. Of the cattle breeds raised in Estonia, milk from EN had the best coagulation properties and highest frequency of favourable ê-Cn B allele.;

Milk coagulation properties (MCP) analysis is performed using a wide range of methodologies in different countries and laboratories, using different instruments, coagulant activity in the milk, and type of coagulant. This makes it difficult to compare results and data from different research. The aims of this study were to propose a method for the transformation of values of rennet coagulation time (RCT) and curd firmness (a(30)) and to predict the noncoagulation (NC) probability of milk samples analyzed using different methodologies. Individual milk samples were collected during the morning milking in October 2010 from each of 165 Holstein-Friesian dairy cows in 2 freestall barns in Italy, and sent to 3 laboratories for MCP analysis. For each laboratory, MCP analysis was performed using a different methodology: A, with a computerized renneting meter instrument using 0.051 international milk clotting units (IMCU)/mL of coagulant activity; B, with a Lattodinamografo (Foss-Italia, Padova, Italy) using 0.051 IMCU/mL of coagulant activity; and C, with an Optigraph (Ysebaert, Frépillon, France) using 0.120 IMCU/mL of coagulant activity. The relationships between MCP traits were analyzed with correlation and regression analyses for each pair of methodologies. For each MCP trait, 2 regression models were applied: model 1 was a single regression model, where the dependent and independent variables were the same MCP trait determined by 2 different methodologies; in model 2, both a(30) and RCT were included as independent variables. The NC probabilities for laboratories with the highest number of NC samples were predicted based on the RCT and a(30) values measured in the laboratories with lower number of NC samples using logistic regression and receiver operating characteristic analysis. The percentages of NC samples were 4.2, 11.5, and 0.6% for A, B, and C, respectively. The transformation of MCP traits was more precise with model 1 for RCT (R(2): 0.77-0.82) than for a(30) (R(2): 0.28-0.63). The application of model 2 was needed when the C measurements were transformed into the other scales. The analyses of NC probabilities of milk samples showed that NC samples from one methodology were well distinguishable (with an accuracy of 0.972-0.996) based on the rennet coagulation time measured with the other methodology. A standard definition for MCP traits analysis is needed to enable reliable comparisons between MCP traits recorded in different laboratories and in different animal populations and breeds.