Udder health in dairy cattle: association with milk composition, cheese-making traits, and blood serum proteins

The main objective of this PhD thesis was to study the association between udder health [focusing on subclinical cases of bovine mastitis identified by somatic cell count (SCC) and bacteriological analyses] and several milk quality and technological traits related to the cheese-making process, and blood serum proteins, as possible immune response indicators. To achieve our goal, the work was splitted in 4 chapters. Two datasets were used: for the 1st chapter, milk samples from 1,271 Brown Swiss cows from 85 herds were used. In the subsequent 3 chapters, milk and blood samples were collected from 1,508 dairy specialized and dual-purpose cows of 6 different breeds (Holstein Friesian, Brown Swiss, Jersey, Simmental, Rendena and Grey Alpine) housed in 41 multi-breed herds. The aim of the 1st chapter was to determine the effects of very low to very high SCC on milk yield, composition, coagulation properties [including traditional milk coagulation properties (MCP) and new curd firming model parameters (CFt)], cheese yield (CY) and recovery of milk nutrients in the curd (REC) at the individual cow level. The objective of the 2nd chapter was to investigate the association between blood serum proteins [i.e., total protein, albumin, globulin and the ratio of albumin-to-globulin (A:G)] and milk SCC. Since several factors should be considered to appropriately interpret serum proteins concentration in blood, we explored the effect of herd productivity (defined according to the average net energy of milk yielded daily by the cows), breed, and individual cow factors (i.e., stage of lactation and parity) on blood traits. In chapters 3 and 4, pathogen-specific information was included in the analysis to gain a better understanding of the specific changes in the traits previously investigated. Subclinical cases of mastitis were confirmed by bacteriological analysis and multiplex-PCR assays. In particular, in the 3rd chapter we investigated the association between pathogen-specific cases of subclinical mastitis and several milk quality and technological traits (i.e., milk yield, composition, detailed protein profile, coagulation properties and cheese-related traits). Based on the results of the 2nd chapter, the 4th chapter studied the association between pathogen-specific cases of subclinical mastitis and blood serum proteins, that in chapter 2 showed a correlation with SCC in milk. Results of chapter 1 confirmed the negative effect of high SCC on milk yield, composition, MCP, CFt, CY and REC traits. As somatic cell score (SCS) increased, a linear loss of milk production and variations in milk composition (e.g., casein-to-protein ratio, lactose and pH) were observed. These changes decreased the quality and clotting ability of the processed milk, which showed a slower coagulation time and a weaker curd firmness. This, in turn, affected the cheese processing (as confirmed by reductions in the CY and the recovery of milk nutrients in the curd). Our findings showed nonlinear trends for some milk traits with respect to SCS, highlighting the negative effect of very low SCC on some milk technological traits. Our 2nd chapter showed that cows in high producing herds had greater serum albumin concentrations. Breed differences in serum protein profile could be associated with individual genetic variation and could also be explained by the different selective breeding programs to which breeds have been subjected. Changes in blood serum proteins were observed throughout the entire lactation and according to the parity order. Linear relationships between blood serum proteins and SCS confirmed the importance of SCC as an indicator of mammary gland inflammation. Moreover, our results highlighted the potential use of blood serum proteins as indicators of immune response of the mammary gland to infections and their analysis represents a possible initial screening test to identify animals which need further clinical investigations. Such non-genetic factors affecting variation in blood serum proteins should also be considered in future genetics/genomics investigations. Results of the 3rd chapter revealed that compared with normal milk, all culture-positive samples and culture-negative samples with medium to high SCC presented significant variations in the casein-to-protein ratio and lactose content. Given that no differences were observed comparing milk infected by contagious, environmental and opportunistic pathogens, our findings suggested an effect of inflammation rather than infection. The greatest impairment in milk yield and composition, clotting ability and cheese production was observed for milk samples with the highest SCC (i.e., culture-positive samples where contagious pathogens were recovered, and culture-negative samples with high SCC), revealing a discrepancy between inflammatory status and bacteriological results, and thus confirming the important role of SCC as udder health indicator. Culture-negative samples with high SCC were possibly undergoing a strong inflammatory response and pathogens could not be isolated because engulfed by macrophages. In the 4th chapter, culture-negative samples with high milk SCC, which we hypothesized to be infected by contagious bacteria engulfed by neutrophils, and milk samples infected by contagious and environmental bacteria were associated with greater globulin content (and lower A:G) in blood. In accordance with the results in chapter 3 for milk traits, variation in blood serum proteins seemed to be associated with inflammation rather than infection, as globulin significantly increased in the blood of cows whose milk samples had the highest SCC, independently from intramammary infection pathogens

Cross-species meta-analysis of transcriptomic data in combination with supervised machine learning models identifies the common gene signature of lactation process

Lactation, a physiologically complex process, takes place in mammary gland after parturition. The expression profile of the effective genes in lactation has not comprehensively been elucidated. Herein, meta-analysis, using publicly available microarray data, was conducted identify the differentially expressed genes (DEGs) between pre- and post-peak milk production. Three microarray datasets of Rat, Bos Taurus, and Tammar wallaby were used. Samples related to pre-peak (n = 85) and post-peak (n = 24) milk production were selected. Meta-analysis revealed 31 DEGs across the studied species. Interestingly, 10 genes, including MRPS18B, SF1, UQCRC1, NUCB1, RNF126, ADSL, TNNC1, FIS1, HES5 and THTPA, were not detected in original studies that highlights meta-analysis power in biosignature discovery. Common target and regulator analysis highlighted the high connectivity of CTNNB1, CDD4 and LPL as gene network hubs. As data originally came from three different species, to check the effects of heterogeneous data sources on DEGs, 10 attribute weighting (machine learning) algorithms were applied. Attribute weighting results showed that the type of organism had no or little effect on the selected gene list. Systems biology analysis suggested that these DEGs affect the milk production by improving the immune system performance and mammary cell growth. This is the first study employing both meta-analysis and machine learning approaches for comparative analysis of gene expression pattern of mammary glands in two important time points of lactation process. The finding may pave the way to use of publically available to elucidate the underlying molecular mechanisms of physiologically complex traits such as lactation in mammals.Mohammad Farhadian, Seyed A. Rafat, Karim Hasanpur, Mansour Ebrahimi and Esmaeil Ebrahimi