Lactation is a dynamic process characterised by a production peak at 6–8 weeks, followed by a steady decline. To understand the molecular drivers of these phases and the influence of production systems, this study aims to provide a transcriptomic characterisation of bovine milk somatic cells (BMSCs) in Holstein (HO), Simmental (SM), Simmental × Holstein crossbreed (SM × HO), and Podolica (POD) cows at 60 and 120 days in milk (DIM). Total RNA was sequenced at high coverage, and differential expression and functional enrichment analyses were performed. While a core set of milk protein and fatty acid genes was identified, breed-specific analysis showed SM × HO had the highest variation (677 differentially expressed genes, DEGs). Genes upregulated at 120 DIM involved mitochondrial metabolism and oxidative phosphorylation, while downregulated genes were associated with nuclear transcriptional regulation. At 60 DIM, SM × HO vs. HO showed 66 DEGs, with upregulated genes linked to chromatin remodelling and immune regulation. Comparing production systems, 28 DEGs between POD and HO/SM highlighted differences in mitochondrial activity and transcriptional regulation. This study bridges a knowledge gap by profiling the milk transcriptome of unexplored cattle breeds, providing novel insights into the molecular regulation of lactation.

At the individual cow level, sub-optimum fertility, mastitis, negative energy balance and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were: 1) to assess the potential of milk Mid Infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6P, free glucose), ketosis (BHB and acetone), mastitis (NAGase and LDH), and fertility (progesterone); 2) to test alternative methodologies to partial least square regression (PLS) to better account for the specific asymmetric distribution of the biomarkers; and 3) to create robust models by merging large data sets from 5 international or national projects. Benefiting from this international collaboration, the data set comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents while the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. PLS was used as the reference basis, and compared with a random modification of distribution associated with PLS (Random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS) and Support Vector Machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low vs high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation data set. The remaining 80% of herds were used as the calibration data set. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose and LDH (R2v = 0.48, 0.58, 0.28, and 0.24). For other molecules, PLS-Random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15). Hence, PLS and SVM based on the entire data set provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis and mastitis in dairy cows, which in turn have major influences on their fertility and survival.

Milk fat depression (MFD) syndrome, a consistent decrease in milk fat content, is related to important changes in fatty acid composition due to feed imbalances and the consequent ruminal metabolism alteration. Milk produced in two different farming systems was compared: Holstein Friesian fed with unified in intensive production and Podolica raised on a pasture in an extensive system. Milk chemical characteristics and fatty acid composition were determined comparing milk with a normal fat level (>3.8%) to milk with a low fat level (<3.2%) in each breeding system. Holstein Friesian milk showed the decrease in trans-11 and increase in trans-10 C18:1 (shift from trans-11 to trans-10 C18:1) in low fat with respect to normal fat milk with a consequent decrease in the trans-11/trans-10 C18:1 ratio. Even conjugated linoleic acid (CLA), C18:2 cis-9, trans-11, was lower while CLA trans-10, cis-12 was higher in low fat milk than in normal fat milk from Holstein Friesian. These changes, that are indicators of MFD syndrome, were not found in Podolica milk between fat levels. Holstein Friesian milk showed less short-chain fatty acids (9.48 % vs. 11.05%, p < 0.001), trans vaccenic acid (C18:1 trans-11, 0.51% vs. 3.39%, p < 0.001), rumenic acid (CLA C18:2 cis-9, trans-11, 0.32% vs. 1.45%, p < 0.001) and total CLA (0.53% vs. 1.91%, p < 0.001) contents than Podolica milk. Further losses of these human healthy nutrients in low fat Friesian milk reduced the nutritional quality of the milk, while the milk from animals raised on the pasture was of better quality even when the level of fat was low.

Sialyloligosaccharides (SOS) are bioactive molecules that play an important role in brain development and the increase in immunity in infants. In adults, they act as prebiotics, enhancing protection against microbial pathogens. In the present work, we aimed to analyze the levels of SOS in mature milk, at days 60 and 120 after calving in four cow breeds: Holstein (HO), Simmental × Holstein (SM × HO), Simmental (SM), all fed with total mixed ration (TMR) in intensive production, and Podolica (POD) raised on pasture in an extensive system. The concentrations of SOS (3′-sialyllactose = 3′-SL, 6′-sialyllactose = 6′-SL, 6′-Sialyl-N-acetyllactosamine = 6′-SLN, disialyllactose = DSL, expressed in mg/L) were determined using HPAEC-PAD, a high-performance anion-exchange chromatography with pulsed amperometric detection. Results showed both breed and lactation effects. The contents of 3′-SL, 6′-SL, 6′-SLN, and DSL were higher at 60 than 120 days (p < 0.001), as well as in POD, as compared to the other breeds (p < 0.001). Furthermore, SM showed a significantly greater level of 3′-SL than HO (p < 0.001), as well as a significantly higher level of 6′-SLN in SM than HO (p < 0.001) and SM × HO (p < 0.001). Our findings may have implications for several areas of sustainability that might be used in the cattle management system.

Among the dairy sector's current concerns, the assessment of global animal health status is a complex challenge. Its multidimensionality means that global monitoring tools are rarely considered. Instead, specific disease detection is often studied separately and, due to financial and ethical issues, uses small-scale data sets focusing on few biomarkers. Several studies have already been conducted using milk Fourier transform mid-infrared (FT-MIR) spectroscopy to detect mastitis and lameness or to quantify health-related biomarkers in milk or blood. Those studies are relevant but they focus mainly on one biomarker or disease. To solve this issue and the small-scale data set, in this study, we proposed a holistic approach using big data obtained from milk recording, including milk yield, somatic cell count, and 27 FT-MIR-based predictors related to milk composition and animal health status. Using 740,454 records collected from 114,536 first-parity Holstein cows in southern Belgium, we performed repeated unsupervised learning algorithms based on Ward's agglomerative hierarchical clustering method to find potential interesting patterns. A divide-and-conquer approach was used to overcome the limitation of computational resources in clustering a relatively large data set. Five groups of records were identified. Differences observed in the fourth group suggested a relationship to metabolic disorders. The fifth group seemed to be related to mastitis. In a second step, we performed a partial least squares discriminant analysis (PLS-DA) to predict the probability of belonging to those specific groups for the entire data set. The obtained global accuracy was 0.77 and the balanced accuracy (i.e., the mean between sensitivity and specificity) of discriminating the fourth and fifth groups was 0.88 and 0.96, respectively. Then, a validation of the interpretation of those groups was performed using 204 milk and blood reference records. The predicted probability associated with the metabolic disorders issue had significant correlations of 0.54 with blood β-hydroxybutyrate, 0.44 with blood nonesterified fatty acids, -0.32 with blood glucose, -0.23 with milk glucose-6-phosphate, and 0.38 with milk isocitrate. In contrast, the predicted probability of belonging to the mastitis group had correlations of 0.69 with milk lactate dehydrogenase, 0.46 with milk N-acetyl-β-d-glucosaminidase, -0.18 with milk free glucose, and 0.16 with milk glucose-6-phosphate. Consequently, these results suggest that the obtained quantitative traits indirectly reflect some of the main health disorders in dairy farming and could be used to monitor dairy cows on a large scale. By using unsupervised learning on large-scale milk recording data and then validating the pattern using reference laboratory measures, we propose a new approach to quickly assess dairy cow health status.

This research communication reports concentrations of two sialic acids (SA), N-acetylneuraminic (Neu5Ac) and N-glycolylneuraminic (Neu5Gc), in fresh milk from different cow breeds throughout lactation. According to published studies, the two SA types found in animal-derived products have diverse and conflicting effects on human health, but SA content is not routinely analysed in individual milk cows samples. We measured the content of Neu5Ac and Neu5Gc in milk from Holstein Friesian (HO), Simmental (SM), Simmental × Holstein crossbred (SM×HO), and Podolica (POD) cows at 60 and 120 d following calving. HO, SM and SM×HO were reared in an intensive production while POD were raised in an extensive system. Results showed that total Neu5Ac was overall thirty times more abundant than Neu5Gc, and their concentrations were higher at 120 d than at 60 d (P < 0.001). Neu5Gc values were greater in HO, SM, and SM × HO than in POD (P < 0.001), while HO had a higher Neu5Ac value than the other three breeds (P < 0.001). These findings shed light on the differences in SA content among cow breeds and lay the groundwork for future research to select animals that produce milk with desirable characteristics for human health.

Objectives. Milk of mammal origin is an important food in the diet during infancy as well as in adulthood. It contains lipids, proteins, carbohydrates, essential nutrients, bioactive compounds (immunoglobulins, oligosaccharides), organic acids, nucleotides, micronutrients, and metabolites. Milk metabolites may originate from multiple cell types or metabolisms. The milk quality is affected by different factors, such as lactation stage, feed, genetic background, and cow health. Somatic cell count (SCC) is normally used as an indicator of mastitis. Here we proposed to use milk metabolites as indicators of mammary gland health status. Methods. A total of 44 dairy cows of four different breeds (Holstein, Simmenthal, Simmenthal x Holstein crossbred, and Podolica) were enrolled for this study. Immediately after morning milking, milk samples (around 60 days after calving) were collected and placed on ice for transport to the laboratory. Once in the laboratory, the milk samples were aliquoted, and frozen at −80 ℃. Nuclear magnetic resonance (NMR) spectroscopy was used to determine milk metabolites. Samples were classified as “high” (≥400.000) or “low” (≤400.000) SCC content. The metabolite profiles in the two classes were analysed to identify differences. A General Linear Model (GLM) procedure was used to identify the statistically significant differences of metabolites content. Results. NMR spectra confirmed milk metabolites associated with a high number of SCC, already identified in previous studies, where many milk samples were analysed. In this study, four new metabolites (N-acetyl-lysine, N-acetyl-glutamine, creatinine, phosphocreatine,) were associated to high somatic cell count and could be used as biomarkers of mammary gland health status.

Peripartum dairy cows commonly experience negative energy balance (EB) and immunosuppression together with high incidences of infectious and metabolic disease. This study investigated mechanisms linking EB status with immune defense in early lactation. Data were collected from multiparous Holstein cows from six herds and leukocyte transcriptomes were analyzed using RNA sequencing. Global gene expression was related to circulating IGF-1 (as a biomarker for EB) by subdividing animals into three groups, defined as IGF-1 LOW (<35 ng/mL, n = 35), MODERATE (35–100 ng/mL, n = 92) or HIGH (>100 ng/mL, n = 43) at 14 ± 4 days in milk (DIM). Differentially expressed genes between groups were identified using CLC Genomics Workbench V21, followed by cluster and KEGG pathway analysis, focusing on the comparison between LOW and HIGH IGF-1 cows. LOW cows were older and had significantly lower dry matter intakes and EB values, whereas HIGH cows produced more milk. During the first 35 DIM, 63% of LOW cows had more than one health problem vs. 26% HIGH cows, including more with clinical mastitis and uterine infections. Gene expression analysis indicated that leukocytes in LOW cows switched energy metabolism from oxidative phosphorylation to aerobic glycolysis (PGM, LDH, and PDK4). Many antimicrobial peptides were up-regulated in LOW cows (e.g., PTX3, DMBT1, S100A8, and S100A9) together with genes associated with inflammation, platelet activation and the complement cascade. HIGH cows had greater expression of genes regulating T and B cell function and the cytoskeleton. Overall, results suggested an ongoing cycle of poor EB and higher infection rates in LOW IGF-1 cows which was reflected in altered leukocyte functionality and reduced milk production.

Bovine mastitis, an inflammatory disease of the mammary gland, is classified as subclinical or clinical. Circulating neutrophils are recruited to the udder to combat infection. We compared the transcriptomic profiles in circulating leukocytes between healthy cows and those with naturally occurring subclinical or clinical mastitis. Holstein Friesian dairy cows from six farms in EU countries were recruited. Based on milk somatic cell count and clinical records, cows were classified as healthy (n = 147), subclinically (n = 45) or clinically mastitic (n = 22). Circulating leukocyte RNA was sequenced with Illumina NextSeq single end reads (30 M). Differentially expressed genes (DEGs) between the groups were identified using CLC Genomics Workbench V21, followed by GO enrichment analysis. Both subclinical and clinical mastitis caused significant changes in the leukocyte transcriptome, with more intensive changes attributed to clinical mastitis. We detected 769 DEGs between clinical and healthy groups, 258 DEGs between subclinical and healthy groups and 193 DEGs between clinical and subclinical groups. Most DEGs were associated with cell killing and immune processes. Many upregulated DEGs in clinical mastitis encoded antimicrobial peptides (AZU1, BCL3, CAMP, CATHL1, CATHL2, CATHL4,CATHL5, CATHL6, CCL1, CXCL2, CXCL13, DEFB1, DEFB10, DEFB4A, DEFB7, LCN2, PGLYRP1, PRTN3, PTX3, S100A8, S100A9, S100A12, SLC11A1, TF and LTF) which were not upregulated in subclinical mastitis. The use of transcriptomic profiles has identified a much greater up-regulation of genes encoding antimicrobial peptides in circulating leukocytes of cows with naturally occurring clinical compared with subclinical mastitis. These could play a key role in combatting disease organisms.

The main objective of this study was to test the efficiency of a management system combining metabolic clustering of cows based on Fourier-transform mid-infrared (FT-MIR) spectra of milk and targeted treatment of metabolically imbalanced cows with propylene glycol drench. We hypothesized that cows identified in a metabolically imbalanced status during early lactation were associated with subsequent impaired health, reproduction, and production, and that treatment with propylene glycol treatment would improve health, reproduction, and production relatively more in these cows than in control cows. We completed a prospective, randomized controlled trial with 356 early-lactation cows in 2 private dairy herds in Denmark from December 2017 to April 2018. Milk samples of cows were collected before treatment, from 4 to 9 d in milk, and after treatment, from 22 to 27 d in milk. Milk samples were analyzed using FT-MIR spectroscopy. We also measured 4 milk metabolites (β-hydroxybutyrate, isocitrate, malate, and glutamate) and fat and protein contents. Based on FT-MIR spectra and cluster analyses, cows were clustered into groups of metabolically imbalanced and healthy cows. Within each group, cows were allocated randomly to treatment with propylene glycol (500 mL for 5 d) or no treatment. We analyzed the effect of the treatment on cow-level variables: metabolic cluster, milk metabolites, fat and protein contents, and fat-to-protein ratio at a milk sampling after the treatment. Furthermore, we analyzed daily milk yield, calving to first service interval, and disease occurrence. Results showed only a few effects of propylene glycol treatment and few interactions between treatment and metabolic clusters. We found no significant main effects of propylene glycol treatment in any of these analyses. A negative effect of the imbalanced metabolic cluster was found for the outcome of calving to first service interval for multiparous cows. In conclusion, we found a longer calving to first service interval in metabolically imbalanced cows, but we were not able to demonstrate overall benefits from the applied detection of cows in imbalanced metabolic status in early lactation and follow-up by treatment with propylene glycol.

AbstractThe aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

The identification of genes involved in phenotypes related to milk quality is important for both economic and health aspects in livestock production. The aim of this study was to assess the level of gelsolin gene expression in two breeds of dairy sheep - Sarda and Gentile - with pronounced differences in quantitative and qualitative milk traits. Gelsolin, a type of actin-modulating proteins is involved in the processes of actin remodeling during cell growth and apoptosis; therefore a role of this protein in mammary changes during lactation was here hypothesized. Individual milk samples were collected three times during lactation from 26 ewes of the two breeds. The differential gene expression of gelsolin in the two breeds and the three lactation times was estimated by quantitative PCR on RNA extracted from milk somatic cells. Correlations of gelsolin gene expression with milk yield and quality and days of lactation were also estimated. The results showed that gelsolin gene expression was significantly higher in the Sarda compared to the Gentile at each lactation stage, in agreement with the longer lactation duration and the higher daily milk yield of the first breed. Significant correlations of gelsolin gene expression were found with milk fat content in Sarda breed (-0.46, P<0.05). Gelsolin expression analysis confirmed the link between gelsolin gene function and milk fat content of sheep.

Background Milk sialylated oligosaccharides (SOS) play crucial roles in many biological processes. The most abundant free SOS in goat’s milk are 3’sialyllactose (3′-SL), 6’sialyllactose (6′-SL) and disialyllactose (DSL). The production of these molecules is determined genetically by the expression of glycosyltransferases and by the availability of nucleotide sugar substrates, but the precise mechanisms regulating the differential patterns of milk oligosaccharides are not known. We aimed to identify the complete cDNAs of candidate genes implicated in SOS biosynthesis ( B4GALT1, LALBA, ST3GAL5, ST6GAL1 ) and to analyse their expression during lactation in the Garganica and Maltese goat breeds. Moreover, we analysed the colostrum and milk contents of 3′-SL, 6′-SL and disialyllactose (DSL) and the possible correlations between expressed genes and SOS. Results We identified the complete coding cDNAs of B4GALT1 (HQ700335.1), ST3GAL5 (KF055858.2), and ST6GAL1 (HQ709167.1), the single nucleotide polymorphism (SNPs) of these genes and 2 splicing variants of the ST6GAL1 cDNA. RT-qPCR analysis showed that LALBA and ST6GAL1 were the genes with the highest and lowest expression in both breeds, respectively. The interaction effects of the breeds and sampling times were associated with higher levels of B4GALT1 and ST3GAL5 gene expression in Garganica than in Maltese goats at kidding. B4GALT1, LALBA, and ST3GAL5 gene expression changed from kidding to 60 and 120 days in Maltese goats, while in Garganica goats, a difference was observed only for the LALBA gene. Breed and lactation effects were also found for SOS contents. Positive correlations of B4GALT1 , LALBA, ST3GAL5 , and ST6GAL1 with 3′-SL/6′SL and DSL were found. Conclusions The genetic effect on the oligosaccharide content of milk was previously highlighted in bovines, and this study is the first to investigate this effect in two goat breeds (Garganica and Maltese) during lactation. The genetic variability of candidate genes involved in SOS biosynthesis highlights their potential role in affecting gene expression and ultimately biological function. The investigation of gene regulatory regions as well as the examination of other sialyltransferase genes will be needed to identify the genetic pattern leading to a higher SOS content in the autochtonous Garganica breed and to protect it using a focused breeding strategy.

The objective of this study was to develop a generic risk management system based on the Hazard Analysis and Critical Control Point (HACCP) principles for the prevention of critical negative energy balance (NEB) in dairy herds using an expert panel approach. In addition, we discuss the advantages and limitations of the system in terms of implementation in the individual dairy herd. For the expert panel, we invited 30 researchers and advisors with expertise in the field of dairy cow feeding and/or health management from eight European regions. They were invited to a Delphi-based set-up that included three inter-correlated questionnaires in which they were asked to suggest risk factors for critical NEB and to score these based on 'effect' and 'probability'. Finally, the experts were asked to suggest critical control points (CCPs) specified by alarm values, monitoring frequency and corrective actions related to the most relevant risk factors in an operational farm setting. A total of 12 experts (40 %) completed all three questionnaires. Of these 12 experts, seven were researchers and five were advisors and in total they represented seven out of the eight European regions addressed in the questionnaire study. When asking for suggestions on risk factors and CCPs, these were formulated as 'open questions', and the experts' suggestions were numerous and overlapping. The suggestions were merged via a process of linguistic editing in order to eliminate doublets. The editing process revealed that the experts provided a total of 34 CCPs for the 11 risk factors they scored as most important. The consensus among experts was relatively high when scoring the most important risk factors, while there were more diverse suggestions of CCPs with specification of alarm values and corrective actions. We therefore concluded that the expert panel approach only partly succeeded in developing a generic HACCP for critical NEB in dairy cows. We recommend that the output of this paper is used to inform key areas for implementation on the individual dairy farm by local farm teams including farmers and their advisors, who together can conduct herd-specific risk factor profiling, organise the ongoing monitoring of herd-specific CCPs, as well as implement corrective actions when CCP alarm values are exceeded.

Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites' levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R 2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R 2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

Although buffaloes (Bubalus bubalis) are a major component of the livestock industry worldwide, limited progress has been made in the study of the mechanisms regulating the immune response to pathogens and parasites affecting their health and productivity. This has been, in part, attributable to the limited availability of reagents to study immune responses in buffalo. As reported here, a set of cross-reactive monoclonal antibodies (mAbs), developed against bovine, ovine and caprine leukocyte differentiation molecules (LDM) and major histocompatibility complex (MHC) molecules, were identified and used to compare expression of LDM in Italian and Egyptian buffalo. The results show most of the epitopes identified with the mAbs are conserved on LDM and MHC I and II molecules in both lineages of buffalo. Comparison of the composition of lymphocyte subsets between buffalo and cattle revealed they are similar except for expression of CD2 and CD8 on workshop cluster one (WC1) positive γδ T cells. In cattle, CD8 is expressed on a subset of CD2+/WC1- γδ T cells that are present in low frequency in blood of young and old animals, whereas, CD8-/CD2-/WC1+ γδ T cells are present in high frequency in young animals, decreasing with age. In the buffalo, CD2 is expressed on a subset of WC1+ γδ T cells and CD8 is expressed on all WC1+ γδ T cells. The availability of this extensive set of mAbs provides opportunities to study the immunopathogenesis of pathogens and parasites affecting the health of buffalo.

This study seeks to verify the feasibility of increasing twinning in a herd of the Italian autochtonous Maremmana breed. The data set included 1260 individuals born from 1963 to 2014, 527 males and 733 females, 402 of them calving at least once from 1983 through 2015. Breeding values for twinning were estimated by a single-trait linear animal model. However, since twinning is a dichotomous trait and the frequency of twins is far smaller than the frequency of single births, breeding values were also estimated by a single-trait animal threshold model. Heritability of twinning was 0.014±0.018 and 0.062±0.093 for the linear and the threshold models, respectively. Repeatability was 0.071±0.004 and 0.286± 0.012, respectively, for the two models. Genotyping with the Illumina BovineSNP54 BeadChip was performed for cows living on farm in 2012 (119 cows) and a genome-wide association analysis was performed on the corrected phenotype of all calving during the lifespan of each cow, using the GenABEL package in R and a three step GRAMMAR-GC approach. Genomic heritability, calculated from the genomic kinship matrix estimated through genomic marker data, was 0.29±0.021. The most significant detected single nucleotide polymorphisms (Hapmap22923-BTA-129564) was located in proximity of two genes, ARHGAP8 and TMEM200C, which might be potential functional candidates for twinning rate in cattle.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is an important cause of acute food- borne zoonoses worldwide, typically carried by pigs. It is well known that Salmonella has evolved a wide array of strategies enabling it to invade the host, but little information is available on the specific host responses to Salmonella infections. In the present study, we used an in vivo approach (involving piglets infected with a virulent or an attenuated S. Typhimurium strain) coupled to histological and proteomic analysis of the cecum mucosa, to highlight the host pathways activated during S. Typhimurium infection. We confirm the complex host-pathogen interaction. Our data showed that the metabolic and the cytoskeleton organization functions were the most significantly altered. In particular, the modifications of energy metabolic pathway could suggest a “nutriprive” mechanism, in which the host reduce its metabolic and energetic status to limit Salmonella infection. This study could represent a preliminary approach, providing information useful to better understand the host-Salmonella interaction.