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.

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.

BackgroundBovine mastitis is one of the most costly and prevalent diseases affecting dairy cows worldwide. In order to develop new strategies to prevent Escherichia coli-induced mastitis, a detailed understanding of the molecular mechanisms underlying the host immune response to an E. coli infection is necessary. To this end, we performed a global gene-expression analysis of mammary gland tissue collected from dairy cows that had been exposed to a controlled E. coli infection. Biopsy samples of healthy and infected utter tissue were collected at T = 24 h post-infection (p.i.) and at T = 192 h p.i. to represent the acute phase response (APR) and chronic stage, respectively. Differentially expressed (DE) genes for each stage were analyzed and the DE genes detected at T = 24 h were also compared to data collected from two previous E. coli mastitis studies that were carried out on post mortem tissue.ResultsNine-hundred-eighty-two transcripts were found to be differentially expressed in infected tissue at T = 24 (P < 0.05). Up-regulated transcripts (699) were largely associated with immune response functions, while the down-regulated transcripts (229) were principally involved in fat metabolism. At T = 192 h, all of the up-regulated transcripts were associated with tissue healing processes. Comparison of T = 24 h DE genes detected in the three E. coli mastitis studies revealed 248 were common and mainly involved immune response functions. KEGG pathway analysis indicated that these genes were involved in 12 pathways related to the pro-inflammatory response and APR, but also identified significant representation of two unexpected pathways: natural killer cell-mediated cytotoxicity pathway (KEGG04650) and the Rig-I-like receptor signalling pathway (KEGG04622).ConclusionsIn E. coli-induced mastitis, infected mammary gland tissue was found to significantly up-regulate expression of genes related to the immune response and down-regulate genes related to fat metabolism. Up to 25% of the DE immune response genes common to the three E. coli mastitis studies at T = 24 h were independent of E. coli strain and dose, cow lactation stage and number, tissue collection method and gene analysis method used. Hence, these DE genes likely represent important mediators of the local APR against E. coli in the mammary gland.

BackgroundThe milk fat profile of the Danish Holstein (DH) and Danish Jersey (DJ) show clear differences. Identification of the genomic regions, genes and biological pathways underlying the milk fat biosynthesis will improve the understanding of the biology underlying bovine milk fat production and may provide new possibilities to change the milk fat composition by selective breeding. In this study a genome wide association scan (GWAS) in the DH and DJ was performed for a detailed milk fatty acid (FA) profile using the HD bovine SNP array and subsequently a biological pathway analysis based on the SNP data was performed.ResultsThe GWAS identified in total 1,233 SNPs (FDR < 0.10) spread over 18 chromosomes for nine different FA traits for the DH breed and 1,122 SNPs (FDR < 0.10) spread over 26 chromosomes for 13 different FA traits were detected for the DJ breed. Of these significant SNPs, 108 SNP markers were significant in both DH and DJ (C14-index, BTA26; C16, BTA14; fat percentage (FP), BTA14). This was supported by an enrichment test. The QTL on BTA14 and BTA26 represented the known candidate genes DGAT and SCD. In addition we suggest ACSS3 to be a good candidate gene for the QTL on BTA5 for C10:0 and C15:0. In addition, genetic correlations between the FA traits within breed showed large similarity across breeds. Furthermore, the biological pathway analysis revealed that fat digestion and absorption (KEGG04975) plays a role for the traits FP, C14:1, C16 index and C16:1.ConclusionThere was a clear similarity between the underlying genetics of FA in the milk between DH and DJ. This was supported by the fact that there was substantial overlap between SNPs for FP, C14 index, C14:1, C16 index and C16:1. In addition genetic correlations between FA showed a similar pattern across DH and DJ. Furthermore the biological pathway analysis suggested that fat digestion and absorption KEGG04975 is important for the traits FP, C14:1, C16 index and C16:1.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1112) contains supplementary material, which is available to authorized users.

The present study explored the effectiveness of Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The partial least squares regression method was used to develop the prediction models. The models were validated using different external test sets, one randomly leaving out 20% of the records (validation A), the second randomly leaving out 20% of cows (validation B), and a third (for DMI prediction models) randomly leaving out one cow (validation C). The data included 1,044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Results showed better accuracies for validation A compared with other validation methods. Milk yield (MY) contributed largely to DMI prediction; MY explained 59% of the variation and the validated model error root mean square error of prediction (RMSEP) was 2.24kg. The model was improved by adding live weight (LW) as an additional predictor trait, where the accuracy R increased from 0.59 to 0.72 and error RMSEP decreased from 2.24 to 1.83kg. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, with R=0.30 and RMSEP=2.91kg. However, once the spectral information was added, along with MY and LW as predictors, model accuracy improved and R increased to 0.81 and RMSEP decreased to 1.49kg. Prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages, with R=0.46 and RMSEP=1.70. The most important spectral wavenumbers that contributed to DMI and RFI prediction models included fat, protein, and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. For DMI, the milk fat region was responsible for the major variation in milk spectra; for RFI, the major variation in milk spectra was within the milk protein region.