Associations between differential somatic cell count and milk yield, quality, and technological characteristics in Holstein cows

The aim of this study was to investigate the associations between differential somatic cell count (DSCC) and milk quality and udder health traits, and for the first time, between DSCC and milk coagulation properties and cheesemaking traits in a population of 1,264 Holstein cows reared in northern Italy. Differential somatic cell count represents the combined proportions of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in the total somatic cell count (SCC), with macrophages (MAC) making up the remaining proportion. The milk traits investigated in this study were milk yield (MY), 8 traits related to milk composition and quality (fat, protein, casein, casein index, lactose, urea, pH, and milk conductivity), 9 milk coagulation traits [3 milk coagulation properties (MCP) and 6 curd firming (CF) traits], 7 cheesemaking traits, 3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits. A linear mixed model was fitted to explore the associations between SCS combined with DSCC and the aforementioned milk traits. An additional model was run, which included DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the percentage of PMN-LYM and MAC by SCC in the milk for each cow in the data set. The unfavorable association between SCS and milk quality and technological traits was confirmed. Increased DSCC was instead associated with a linear increase in MY, casein index, and lactose proportion and a linear decrease in milk fat and milk conductivity. Accordingly, DSCC was favorably associated with all MCP and CF traits (with the exception of the time needed to achieve maximum, CF), particularly with rennet coagulation time, and it always displayed linear relationships. Differential somatic cell count was also positively associated with the recovery of milk nutrients in the curd (protein, fat, and energy), which increased linearly with increasing DSCC. The PMN-LYM count was rarely associated with milk traits, even though the pattern observed confirmed the results obtained when both SCS and DSCC were included in the model. The MAC count, however, showed the opposite pattern: MY, casein index, and lactose percentage decreased and milk conductivity increased with an increasing MAC count. No significant association was found between PMN-LYM count and MCP, CF, CY, and REC traits, whereas MAC count was unfavorably associated with MCP, CF traits, some CY traits, and all REC traits. Our results showed that the combined information derived from SCS and DSCC might be useful to monitor milk quality and cheesemaking-related traits.

1H NMR Metabolic Profile to Discriminate Pasture Based Alpine Asiago PDO Cheeses

Simple Summary

Nowadays, alpine cheese from grazing dairy herds has a premium market value because consumers perceive its higher degree of healthiness and sustainability. The authenticity of pasture-based cheese should be safeguarded from local hay-based milk analogues. The study aimed at assessing the reliability of proton nuclear magnetic resonance (¹H NMR) to discriminate pasture-based alpine Asiago PDO cheeses of different ripening time from similar hay-based samples processed in the same dairy plant. Cheeses were produced from raw milk collected from grazing or hay-fed alpine dairy herds and they were ripened for 2 (Pressato), 4 (Allevo_4), and 6 (Allevo_6) months. Samples of the cheeses were submitted to wet chemistry and nuclear magnetic resonance analysis. The outcomes of the ¹H NMR spectroscopy were used in a multivariate discriminant procedure. Choline, 2,3-butanediol, lysine, and tyrosine and some residual sugar-like compounds were water-soluble biomarkers of cows’ feeding system. However, the application of ¹H NMR based metabolomics was an effective fingerprinting method to correctly identify only cheese samples with the shortest ripening period. The classification of more aged cheese samples according to the cows’ feeding system was less accurate likely due to the chemical and biochemical changes induced by a prolonged maturation process.

Abstract

The study was carried out in an alpine area of North-Eastern Italy to assess the reliability of proton nuclear magnetic resonance ¹H NMR to fingerprint and discriminate Asiago PDO cheeses processed in the same dairy plant from upland pasture-based milk or from upland hay-based milk. Six experimental types of Asiago cheese were made from raw milk considering 2 cows’ feeding systems (pasture- vs. hay-based milk) and 3 ripening times (2 months, Pressato vs. 4 months, Allevo_4 vs. 6 months, Allevo_6). Samples (n = 55) were submitted to chemical analysis and to ¹H NMR coupled with multivariate canonical discriminant analysis. Choline, 2,3-butanediol, lysine, tyrosine, and some signals of sugar-like compounds were suggested as the main water-soluble metabolites useful to discriminate cheese according to cows’ feeding system. A wider pool of polar biomarkers explained the variation due to ripening time. The validation procedure based on a predictive set suggested that ¹H NMR based metabolomics was an effective fingerprinting tool to identify pasture-based cheese samples with the shortest ripening period (Pressato). The classification to the actual feeding system of more aged cheese samples was less accurate likely due to their chemical and biochemical changes induced by a prolonged maturation process.

Pyrrolizidine alkaloids in bee pollen identified by LC-MS/MS analysis and colour parameters using multivariate class modeling

Toxic pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) can be present in bee pollen depending on the plants visited by bees. A liquid chromatography-mass spectrometry (LC-MS/MS) method was developed and validated to monitor 17 PAs/PANOs in 44 bee pollens. The CIE-L∗a∗b∗ colour coordinates with the specular component either included or excluded were recorded in pellets and ground aliquots. Lightness (L∗) and yellowness (b∗) of ground bee pollen were significantly correlated to PAs/PANOs content. The L∗ and b∗ cut-offs sorted by a receiver operating characteristic analysis to predict PAs/PANOs presence showed a significant increase in the relative risk to detect amounts higher than 84 μg kg-1. Two supervised canonical discriminant analyses confirmed that pollen without PAs could be distinguished from those containing PAs/PANOs. The data suggest that instrumental colour coupled with supervised models could be used as a screening test for PAs/PANOs in bee pollen, before the confirmatory LC-MS/MS analysis.

Quarter-level analyses of the associations among subclinical intramammary infection and milk quality, udder health, and cheesemaking traits in Holstein cows

In this study, we investigated associations among subclinical intra-mammary infection (IMI) and quarter-level milk composition, udder health indicators, and cheesemaking traits. The dataset included records from 450 Holstein cows belonging to three dairy herds. After an initial screening (T0) to identify animals infected by Streptococcus agalactiae, Streptococcus uberis, Staphylococcus aureus, and Prototheca spp., 613 quarter milk samples for 2 different sampling times (T1 and T2, 1 mo after T1) were used for analysis. Milk traits were analyzed using a hierarchical linear mixed model including the effects of days in milk, parity and herd, and bacteriological and inflammatory category [culture negative with somatic cell count (SCC) <200,000 cells/mL; culture negative with SCC ≥200,000 cells/mL; or culture positive]. All udder health indicators were associated with increased SCC and IMI at both sampling times. The largest effects were detected at T2 for milk lactose (-7% and -5%) and milk conductivity (+9% and +8%). In contrast, the increase in differential SCC (DSCC) in samples with elevated SCC was larger at T1 (+17%). Culture-negative samples with SCC ≥200,000 cells/mL had the highest SCC and greatest numbers of polymorphonuclear-neutrophils-lymphocytes and macrophages at both T1 and T2. Regarding milk cheesemaking ability, samples with elevated SCC showed the worst pattern of curd firmness at T1 and T2. At T2, increased SCC and IMI induced large decreases in recoveries of nutrients into the curd, in particular recovered protein (-14% and -16%) and recovered fat (-12% and -14%). Different behaviors were observed between Strep. agalactiae and Prototheca spp., especially at T2. In particular, samples that were positive for Strep. agalactiae had higher proportions of DSCC (+19%) compared with negative samples with low SCC, whereas samples that were positive for Prototheca spp. had lower DSCC (-11%). Intramammary infection with Prototheca spp. increased milk pH compared with culture-negative samples (+3%) and negative samples that had increased SCC (+2%). The greatest impairment in curd firmness at 30 min from rennet addition was observed for samples that were positive for Prototheca spp. (-99% compared with negative samples, and -98% compared with negative samples with high SCC). These results suggest that IMI caused by Prototheca spp. have detrimental effects on milk technological traits that deserve further investigation of the mechanisms underlying animals' responses to infection.

Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows

Mastitis is one of the most prevalent diseases in dairy cattle and is the cause of considerable economic losses. Alongside somatic cell count (SCC), differential somatic cell count (DSCC) has been recently introduced as a new indicator of intramammary infection. The DSCC is expressed as a count or a proportion (%) of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in milk somatic cells. These numbers are complemented to total somatic cell count or to 100 by macrophages (MAC). The aim of this study was to investigate the genetic variation and heritability of DSCC, and its correlation with milk composition, udder health indicators, milk composition, and technological traits in Holstein cattle. Data used in the analysis consisted in single test-day records from 2,488 Holstein cows reared in 36 herds located in northern Italy. Fourier-transform infrared (FTIR) spectroscopy was used to predict missing information for some milk coagulation and cheese-making traits, to increase sample size and improve estimation of the genetic parameters. Bayesian animal models were implemented via Gibbs sampling. Marginal posterior means of the heritability estimates were 0.13 for somatic cell score (SCS); 0.11 for DSCC, MAC proportion, and MAC count; and 0.10 for PMN-LYM count. Posterior means of additive genetic correlations between SCS and milk composition and udder health were low to moderate and unfavorable. All the relevant genetic correlations between the SCC traits considered and the milk traits (composition, coagulation, cheese yield and nutrients recovery) were unfavorable. The SCS showed genetic correlations of -0.30 with the milk protein proportion, -0.56 with the lactose proportion and -0.52 with the casein index. In the case of milk technological traits, SCS showed genetic correlations of 0.38 with curd firming rate (k₂₀), 0.45 with rennet coagulation time estimated using the curd firming over time equation (RCT_eq), -0.39 with asymptotic potential curd firmness, -0.26 with maximum curd firmness (CF_max), and of -0.31 with protein recovery in the curd. Differential somatic cell count expressed as proportion was correlated with SCS (0.60) but had only 2 moderate genetic correlations with milk traits: with lactose (-0.32) and CF_max (-0.33). The SCS was highly correlated with the log PMN-LYM count (0.79) and with the log MAC count (0.69). The 2 latter traits were correlated with several milk traits: fat (-0.38 and -0.43 with PMN-LYM and MAC counts, respectively), lactose percentage (-0.40 and -0.46), RCT_eq (0.53 and 0.41), t_max (0.38 and 0.48). Log MAC count was correlated with k₂₀ (+0.34), and log PMN-LYM count was correlated with CF_max (-0.26) and weight of water curd as percentage of weight of milk processed (-0.26). The results obtained offer new insights into the relationships between the indicators of udder health and the milk technological traits in Holstein cows.

Clinical pharmacokinetics of a dexmedetomidine-methadone combination in dogs undergoing routine anaesthesia after buccal or intramuscular administration

This study aimed to define the pharmacokinetic profiles of dexmedetomidine and methadone administered simultaneously in dogs by either an oral transmucosal route or intramuscular route and to determine the bioavailability of the oral transmucosal administration relative to the intramuscular one of both drugs, so as the applicability of this administration route in dogs. Twelve client-owned dogs, scheduled for diagnostic procedures, were treated with a combination of dexmedetomidine hydrochloride (10 μg/kg) and methadone hydrochloride (0.4 mg/kg) through an oral transmucosal route or intramuscularly. Oral transmucosal administration caused ptyalism in most subjects, and intramuscular administration caused transient peripheral vasoconstriction. The results showed reduced and delayed absorption of both dexmedetomidine and methadone when administered through an oral transmucosal route, with median (range) C_max values of 0.82 (0.42-1.49) ng/ml and 13.22 (2.80-52.30) ng/ml, respectively. The relative bioavailability was low: 16.34% (dexmedetomidine) and 15.5% (methadone). Intramuscular administration resulted in a more efficient absorption profile, with AUC and C_max values for both drugs approximately 10 times higher. Dexmedetomidine and methadone administered simultaneously by an oral transmucosal route using injectable formulations were not well absorbed through the oral mucosa. Nevertheless, additional studies on these drugs combination using alternative administration routes are recommended.

Associations between the detailed milk mineral profile, milk composition, and metabolic status in Holstein cows

The causes of variation in the milk mineral profile of dairy cattle during the first phase of lactation were studied under the hypothesis that the milk mineral profile partially reflects the animals' metabolic status. Correlations between the minerals and the main milk constituents (i.e., protein, fat, and lactose percentages), and their associations with the cows' metabolic status indicators were explored. The metabolic status indicators (MET) that we used were blood energy-protein metabolites [nonesterified fatty acids, β-hydroxybutyrate (BHB), glucose, cholesterol, creatinine, and urea], and liver ultrasound measurements (predicted triacylglycerol liver content, portal vein area, portal vein diameter and liver depth). Milk and blood samples, and ultrasound measurements were taken from 295 Holstein cows belonging to 2 herds and in the first 120 d in milk (DIM). Milk mineral contents were determined by ICP-OES; these were considered the response variable and analyzed through a mixed model which included DIM, parity, milk yield, and MET as fixed effects, and the herd/date as a random effect. The MET traits were divided in tertiles. The results showed that milk protein was positively associated with body condition score (BCS) and glucose, and negatively associated with BHB blood content; milk fat was positively associated with BHB content; milk lactose was positively associated with BCS; and Ca, P, K and S were the minerals with the greatest number of associations with the cows' energy indicators, particularly BCS, predicted triacylglycerol liver content, glucose, BHB and urea. We conclude that the protein, fat, lactose, and mineral contents of milk partially reflect the metabolic adaptation of cows during lactation and within 120 DIM. Variations in the milk mineral profile were consistent with changes in the major milk constituents and the metabolic status of cows.

Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle

The aims of this study were to investigate potential functional relationships among milk protein fractions in dairy cattle and to carry out a structural equation model (SEM) GWAS to provide a decomposition of total SNP effects into direct effects and effects mediated by traits that are upstream in a phenotypic network. To achieve these aims, we first fitted a mixed Bayesian multitrait genomic model to infer the genomic correlations among 6 milk nitrogen fractions [4 caseins (CN), namely κ-, β-, α_S1-, and α_S2-CN, and 2 whey proteins, namely β-lactoglobulin (β-LG) and α-lactalbumin (α-LA)], in a population of 989 Italian Brown Swiss cows. Animals were genotyped with the Illumina BovineSNP50 Bead Chip v.2 (Illumina Inc.). A Bayesian network approach using the max-min hill-climbing (MMHC) algorithm was implemented to model the dependencies or independence among traits. Strong and negative genomic correlations were found between β-CN and α_S1-CN (-0.706) and between β-CN and κ-CN (-0.735). The application of the MMHC algorithm revealed that κ-CN and β-CN seemed to directly or indirectly influence all other milk protein fractions. By integrating multitrait model GWAS and SEM-GWAS, we identified a total of 127 significant SNP for κ-CN, 89 SNP for β-CN, 30 SNP for α_S1-CN, and 14 SNP for α_S2-CN (mostly shared among CN and located on Bos taurus autosome 6) and 15 SNP for β-LG (mostly located on Bos taurus autosome 11), whereas no SNP passed the significance threshold for α-LA. For the significant SNP, we assessed and quantified the contribution of direct and indirect paths to total marker effect. Pathway analyses confirmed that common regulatory mechanisms (e.g., energy metabolism and hormonal and neural signals) are involved in the control of milk protein synthesis and metabolism. The information acquired might be leveraged for setting up optimal management and selection strategies aimed at improving milk quality and technological characteristics in dairy cattle.

Natural contaminants in bee pollen: DNA metabarcoding as a tool to identify floral sources of pyrrolizidine alkaloids and fungal diversity

The use of bee pollen as a food supplement has increased in recent years as it contains several nutrients and phytochemicals. However, depending on floral composition, bee pollen can be contaminated by pyrrolizidine alkaloids (PAs), PA N-oxides (PANOs) and toxigenic fungi found in plants, which may pose a potential health risk for consumers. Thus, a DNA metabarcoding approach based on internal transcribed spacer 2 (ITS2) region was used to identify the plant sources of 17 PAs/PANOs detected by a validated method in liquid chromatography coupled to mass spectrometry (LC-MS/MS), as well as floral and fungal diversity in 61 bee pollen samples. According to LC-MS/MS analysis, 67% of the samples contained PAs/PANOs with mean concentration of 339 µg/kg. The contamination pattern was characterised by lycopsamine- and senecionine-type PAs/PANOs. PA/PANO-producing plants were identified in 54% of the PA/PANO-contaminated samples analysed by DNA metabarcoding, which also allowed identifying the overall floral and fungal composition of 56 samples. To evaluate the performance of the molecular approach, a subset of 25 samples was analysed by classical palynology. Palynological analysis partially confirmed the results of DNA metabarcoding, which had a better performance in distinguishing pollens of different genera from Asteraceae (76%) and Brassicaceae (88%). However, the molecular analysis did not identify pollens from Castanea, Eucalyptus, Hedera and Salix, which were abundant in 11 samples according to palynology. On the other hand, the molecular analysis allowed identifying several fungal genera in 33 samples, including the toxigenic fungi Alternaria and Aspergillus, which were positively correlated to the plant genus Hypericum. Despite limitations in identifying some pollen types, these preliminary results suggest that the DNA metabarcoding could be applied in a multidisciplinary approach to give a picture of floral and fungal diversity, which can be sources of natural contaminants in bee pollen and would help to control its safety.

Genomic Analysis of Milk Protein Fractions in Brown Swiss Cattle

Simple Summary

Milk protein fractions are hugely important in the dairy industry because of the key role they play in milk technological properties. The selection of cows for milk protein fractions may, therefore, improve both the nutritional and technological characteristics of milk, and, consequently, the processing efficiency and value of the dairy product. This study estimated the genetic parameters of the major milk protein fractions (four caseins, and two whey proteins) determined variously as: (i) milk content (g/100g milk), (ii) percentage of milk nitrogen (%N) and (iii) daily yield (g/d) in Brown Swiss dairy cattle. The results showed that the (co)variances and genetic parameter estimates differed according to how the proteins were measured. These results provide useful information for developing selection strategies in dairy cattle breeding programs aimed at improving both the nutritional and technological properties of milk.

Abstract

Depending on whether milk protein fractions are evaluated qualitatively or quantitatively, different genetic outcomes may emerge. In this study, we compared the genetic parameters for the major milk protein fractions—caseins (α_S1-, α_S2-, β-, and к-CN), and whey proteins (β-lactoglobulin, β-LG; α-lactalbumin, α-LA)—estimated using the multi-trait genomic best linear unbiased prediction method and expressed variously as milk content (g/100g milk), percentage of milk nitrogen (%N) and daily yield per cow (g/d). The results showed that the genetic parameter estimates varied according to how the milk protein fractions were expressed. Heritability estimates for the caseins and whey protein fractions expressed as daily yields were lower than when they were expressed as proportions and contents, revealing important differences in genetic outcomes. The proportion and the content of β-CN were negatively correlated with the proportions and contents of α_S1-CN, α_S2-CN, and к-CN, while the daily yield of β–CN was negatively correlated with the daily yields of α_S1-CN and α_S2-CN. The Spearman’s rank correlations and the coincidence rates between the various predicted genomic breeding values (GEBV) for the milk protein fractions expressed in different ways indicated that these differences had a significant effect on the ranking of the animals. The results suggest that the way milk protein fractions are expressed has implications for breeding programs aimed at improving milk nutritional and technological characteristics.

Transcriptome-wide mapping of milk somatic cells upon subclinical mastitis infection in dairy cattle

BACKGROUND

Subclinical intramammary infection (IMI) represents a significant problem in maintaining dairy cows' health. Disease severity and extent depend on the interaction between the causative agent, environment, and host. To investigate the molecular mechanisms behind the host immune response, we used RNA-Seq for the milk somatic cells (SC) transcriptome profiling in healthy cows (n = 9), and cows naturally affected by subclinical IMI from Prototheca spp. (n = 11) and Streptococcus agalactiae (S. agalactiae; n = 11). Data Integration Analysis for Biomarker discovery using Latent Components (DIABLO) was used to integrate transcriptomic data and host phenotypic traits related to milk composition, SC composition, and udder health to identify hub variables for subclinical IMI detection.

RESULTS

A total of 1,682 and 2,427 differentially expressed genes (DEGs) were identified when comparing Prototheca spp. and S. agalactiae to healthy animals, respectively. Pathogen-specific pathway analyses evidenced that Prototheca's infection upregulated antigen processing and lymphocyte proliferation pathways while S. agalactiae induced a reduction of energy-related pathways like the tricarboxylic acid cycle, and carbohydrate and lipid metabolism. The integrative analysis of commonly shared DEGs between the two pathogens (n = 681) referred to the core-mastitis response genes, and phenotypic data evidenced a strong covariation between those genes and the flow cytometry immune cells (r² = 0.72), followed by the udder health (r² = 0.64) and milk quality parameters (r² = 0.64). Variables with r ≥ 0.90 were used to build a network in which the top 20 hub variables were identified with the Cytoscape cytohubba plug-in. The genes in common between DIABLO and cytohubba (n = 10) were submitted to a ROC analysis which showed they had excellent predictive performances in terms of discriminating healthy and mastitis-affected animals (sensitivity > 0.89, specificity > 0.81, accuracy > 0.87, and precision > 0.69). Among these genes, CIITA could play a key role in regulating the animals' response to subclinical IMI.

CONCLUSIONS

Despite some differences in the enriched pathways, the two mastitis-causing pathogens seemed to induce a shared host immune-transcriptomic response. The hub variables identified with the integrative approach might be included in screening and diagnostic tools for subclinical IMI detection.

Predicting milk protein fractions using infrared spectroscopy and a gradient boosting machine for breeding purposes in Holstein cattle

In recent years, increasing attention has been focused on the genetic evaluation of protein fractions in cow milk with the aim of improving milk quality and technological characteristics. In this context, advances in high-throughput phenotyping by Fourier transform infrared (FTIR) spectroscopy offer the opportunity for large-scale, efficient measurement of novel traits that can be exploited in breeding programs as indicator traits. We took milk samples from 2,558 Holstein cows belonging to 38 herds in northern Italy, operating under different production systems. Fourier transform infrared spectra were collected on the same day as milk sampling and stored for subsequent analysis. Two sets of data (i.e., phenotypes and FTIR spectra) collected in 2 different years (2013 and 2019-2020) were compiled. The following traits were assessed using HPLC: true protein, major casein fractions [α_S1-casein (CN), α_S2-CN, β-CN, κ-CN, and glycosylated-κ-CN], and major whey proteins (β-lactoglobulin and α-lactalbumin), all of which were measured both in grams per liter (g/L) and proportion of total nitrogen (% N). The FTIR predictions were calculated using the gradient boosting machine technique and tested by 3 different cross-validation (CRV) methods. We used the following CRV scenarios: (1) random 10-fold, which randomly split the whole into 10-folds of equal size (9-folds for training and 1-fold for validation); (2) herd/date-out CRV, which assigned 80% of herd/date as the training set with independence of 20% of herd/date assigned as the validation set; (3) forward/backward CRV, which split the data set in training and validation set according with the year of milk sampling (FTIR and gold standard data assessed in 2013 or 2019-2020) using the "old" and "new" databases for training and validation, and vice-versa with independence among them; (4) the CRV for genetic parameters (CRV-gen), where animals without pedigree as assigned as a fixed training population and animals with pedigree information was split in 5-folds, in which 1-fold was assigned to the fixed training population, and 4-folds were assigned to the validation set (independent from the training set). The results (i.e., measures and predictions) of CRV-gen were used to infer the genetic parameters for gold standard laboratory measurements (i.e., proteins assessed with HPLC) and FTIR-based predictions considering the CRV-gen scenario from a bi-trait animal model using single-step genomic BLUP. We found that the prediction accuracies of the gradient boosting machine equations differed according to the way in which the proteins were expressed, achieving higher accuracy when expressed in g/L than when expressed as % N in all CRV scenarios. Concerning the reproducibility of the equations over the different years, the results showed no relevant differences in predictive ability between using "old" data as the training set and "new" data as the validation set and vice-versa. Comparing the additive genetic variance estimates for milk protein fractions between the FTIR predicted and HPLC measures, we found reductions of -19.7% for milk protein fractions expressed in g/L, and -21.19% expressed as % N. Although we found reductions in the heritability estimates, they were small, with values ranging from -1.9 to -7.25% for g/L, and -1.6 to -7.9% for % N. The posterior distributions of the additive genetic correlations (r_a) between the FTIR predictions and the laboratory measurements were generally high (>0.8), even when the milk protein fractions were expressed as % N. Our results show the potential of using FTIR predictions in breeding programs as indicator traits for the selection of animals to enhance milk protein fraction contents. We expect acceptable responses to selection due to the high genetic correlations between HPLC measurements and FTIR predictions.

Effects of Supplementation of a Mycotoxin Mitigation Feed Additive in Lactating Dairy Cows Fed Fusarium Mycotoxin-Contaminated Diet for an Extended Period

Fusarium mycotoxins are inactivated by rumen flora; however, a certain amount can pass the rumen and reticulum or be converted into biological active metabolites. Limited scientific evidence is available on the impact and mitigation of Fusarium mycotoxins on dairy cows' performance and health, particularly when cows are exposed for an extended period (more than 2 months). The available information related to these mycotoxin effects on milk cheese-making parameters is also very poor. The objective of this study was to evaluate a commercially available mycotoxin mitigation product (MMP, i.e., TOXO® HP-R, Selko, Tilburg, The Netherlands) in lactating dairy cows fed a Fusarium mycotoxin-contaminated diet, and the repercussions on the dry matter intake, milk yield, milk quality, cheese-making traits and health status of cows. The MMP contains smectite clays, yeast cell walls and antioxidants. In the study, 36 lactating Holstein cows were grouped based on the number of days of producing milk, milk yield, body condition score and those randomly assigned to specific treatments. The study ran over 2 periods (March/May-May/July 2022). In each period, six animals/treatment were considered. The experimental periods consisted of 9 days of adaptation and 54 days of exposure. The physical activity, rumination time, daily milk production and milk quality were measured. The cows were fed once daily with the same total mixed ration (TMR) composition. The experimental groups consisted of a control (CTR) diet, with a TMR with low contamination, high moisture corn (HMC), and beet pulp; a mycotoxins (MTX) diet, with a TMR with highly contaminated HMC, and beet pulp; and an MTX diet supplemented with 100 g/cow/day of the mycotoxin mitigation product (MMP). The trial has shown that the use of MMP reduced the mycotoxin's negative effects on the milk yield and quality (protein, casein and lactose). The MTX diet had a lower milk yield and feed efficiency than the CTR and MMP HP-R diets. The MMP limited the negative effect of mycotoxin contamination on clotting parameters, mitigating the variations on some coagulation properties; however, the MMP inclusion tended to decrease the protein and apparent starch digestibility of the diet. These results provide a better understanding of mycotoxin risk on dairy cows' performances and milk quality. The inclusion of an MMP product mitigated some negative effects of the Fusarium mycotoxin contamination during this trial. The major effects were on the milk yield and quality in both the experimental periods. These results provide better insight on the effects of mycotoxins on the performance and quality of milk, as well as the cheese-making traits. Further analyses should be carried out to evaluate MMP's outcome on immune-metabolic responses and diet digestibility.

Novel insights into the associations between immune cell population distribution in mammary glands and milk minerals in Holstein cows

Udder health has a crucial role in sustainable milk production, and various reports have pointed out that changes in udder condition seem to affect milk mineral content. The somatic cell count (SCC) is the most recognized indicator for the determination of udder health status. Recently, a new parameter, the differential somatic cell count (DSCC), has been proposed for a more detailed evaluation of intramammary infection patterns. Specifically, the DSCC is the combined proportions of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) on the total SCC, with macrophages (MAC) representing the remainder proportion. In this study, we evaluated the association between DSCC in combination with SCC on a detailed milk mineral profile in 1,013 Holstein-Friesian cows reared in 5 herds. An inductively coupled plasma-optical emission spectrometry was used to quantify 32 milk mineral elements. Two different linear mixed models were fitted to explore the associations between the milk mineral elements and first, the DSCC combined with SCC, and second, DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the proportion of PMN-LYM and MAC by SCC. We observed a significant positive association between SCC and milk Na, S, and Fe levels. Differential somatic cell count showed an opposite behavior to the one displayed by SCC, with a negative association with Na and positive association with K milk concentrations. When considering DSCC as count, Na and K showed contrasting behavior when associated with PMN-LYM or MAC counts, with decreasing of Na content and increasing K when associated with increasing PMN-LYM counts, and increasing Na and decreasing K when associated with increasing MAC count. These findings confirmed that an increase in SCC is associated with altered milk Na and K amounts. Moreover, MAC count seemed to mirror SCC patterns, with the worsening of inflammation. Differently, PMN-LYM count exhibited patterns of associations with milk Na and K contents attributable more to LYM than PMN, given the nonpathological condition of the majority of the investigated population. An interesting association was observed for milk S content, which increased with increasing of inflammatory conditions (i.e., increased SCC and MAC count) probably attributable to its relationship with milk proteins, especially whey proteins. Moreover, milk Fe content showed positive associations with the PMN-LYM population, highlighting its role in immune regulation during inflammation. Further studies including individuals with clinical condition are needed to achieve a comprehensive view of milk mineral behavior during udder health impairment.

Subclinical Mastitis from Streptococcus agalactiae and Prototheca spp. Induces Changes in Milk Peptidome in Holstein Cattle

Early detection of bovine subclinical mastitis may improve treatment strategies and reduce the use of antibiotics. Herein, individual milk samples from Holstein cows affected by subclinical mastitis induced by S. agalactiae and Prototheca spp. were analyzed by untargeted and targeted mass spectrometry approaches to assess changes in their peptidome profiles and identify new potential biomarkers of the pathological condition. Results showed a higher amount of peptides in milk positive on the bacteriological examination when compared with the negative control. However, the different pathogens seemed not to trigger specific effects on the milk peptidome. The peptides that best distinguish positive from negative samples are mainly derived from the most abundant milk proteins, especially from β- and αs1-casein, but also include the antimicrobial peptide casecidin 17. These results provide new insights into the physiopathology of mastitis. Upon further validation, the panel of potential discriminant peptides could help the development of new diagnostic and therapeutic tools.

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle

Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R2 ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R2 = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R2 from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties.

Observational study on the associations between milk yield, composition, and coagulation properties with blood biomarkers of health in Holstein cows

The considerable increase in the production capacity of individual cows owing to both selective breeding and innovations in the dairy sector has posed challenges to management practices in terms of maintaining the nutritional and metabolic health status of dairy cows. In this observational study, we investigated the associations between milk yield, composition, and technological traits and a set of 21 blood biomarkers related to energy metabolism, liver function or hepatic damage, oxidative stress, and inflammation or innate immunity in a population of 1,369 high-yielding Holstein-Friesian dairy cows. The milk traits investigated in this study included 4 production traits (milk yield, fat yield, protein yield, daily milk energy output), 5 traits related to milk composition (fat, protein, casein, and lactose percentages and urea), 11 milk technological traits (5 milk coagulation properties and 6 curd-firming traits). All milk traits (i.e., production, composition, and technological traits) were analyzed according to a linear mixed model that included the days in milk, the parity order, and the blood metabolites (tested one at a time) as fixed effects and the herd and date of sampling as random effects. Our findings revealed that milk yield and daily milk energy output were positively and linearly associated with total cholesterol, nonesterified fatty acids, urea, aspartate aminotransferase, γ-glutamyl transferase, total bilirubin, albumin, and ferric-reducing antioxidant power, whereas they were negatively associated with glucose, creatinine, alkaline phosphatase, total reactive oxygen metabolites, and proinflammatory proteins (ceruloplasmin, haptoglobin, and myeloperoxidase). Regarding composition traits, the protein percentage was negatively associated with nonesterified fatty acids and β-hydroxybutyrate (BHB), while the fat percentage was positively associated with BHB, and negatively associated with paraoxonase. Moreover, we found that the lactose percentage increased with increasing cholesterol and albumin and decreased with increasing ceruloplasmin, haptoglobin, and myeloperoxidase. Milk urea increased with an increase in cholesterol, blood urea, nonesterified fatty acids, and BHB, and decreased with an increase in proinflammatory proteins. Finally, no association was found between the blood metabolites and milk coagulation properties and curd-firming traits. In conclusion, this study showed that variations in blood metabolites had strong associations with milk productivity traits, the lactose percentage, and milk urea, but no relationships with technological traits of milk. Specifically, increasing levels of proinflammatory and oxidative stress metabolites, such as ceruloplasmin, haptoglobin, myeloperoxidase, and total reactive oxygen metabolites, were shown to be associated with reductions in milk yield, daily milk energy output, lactose percentage, and milk urea. These results highlight the close connection between the metabolic and innate immunity status and production performance. This connection is not limited to specific clinical diseases or to the transition phase but manifests throughout the entire lactation. These outcomes emphasize the importance of identifying cows with subacute inflammatory and oxidative stress as a means of reducing metabolic impairments and avoiding milk fluctuations.

Genome-wide landscape of genetic diversity, runs of homozygosity, and runs of heterozygosity in five Alpine and Mediterranean goat breeds

BACKGROUND

Goat breeds in the Alpine area and Mediterranean basin exhibit a unique genetic heritage shaped by centuries of selection and adaptability to harsh environments. Understanding their adaptive traits can aid breeding programs target enhanced resilience and productivity, especially as we are facing important climate and agriculture challenges. To this aim the genomic architecture of 480 goats belonging to five breeds (i.e., Saanen [SAA], Camosciata delle Alpi [CAM], Murciano-Granadina [MUR], Maltese [MAL], Sarda [SAR]) reared in the Sardinia Island were genotyped and their genomic architecture evaluated to find molecular basis of adaptive traits. Inbreeding, runs of homozygosity (ROH) and runs of heterozygosity (ROHet) were identified. Finally, candidate genes in the ROH and ROHet regions were explored through a pathway analysis to assess their molecular role.

RESULTS

In total, we detected 10,341 ROH in the SAA genome, 11,063 ROH in the CAM genome, 12,250 ROH in the MUR genome, 8,939 ROH in the MAL genome, and 18,441 ROH in the SAR genome. Moreover, we identified 4,087 ROHet for SAA, 3,360 for CAM, 2,927 for MUR, 3,701 for MAL, and 3,576 for SAR, with SAR having the highest heterozygosity coefficient. Interestingly, when computing the inbreeding coefficient using homozygous segment (FROH), SAA showed the lowest value while MAL the highest one, suggesting the need to improve selecting strategies to preserve genetic diversity within the population. Among the most significant candidate genes, we identified several ones linked to different physiological functions, such as milk production (e.g., DGAT1, B4GALT1), immunity (GABARAP, GPS2) and adaptation to environment (e.g., GJA3, GJB2 and GJB6).

CONCLUSIONS

This study highlighted the genetic diversity within and among five goat breeds. The high levels of ROH identified in some breeds might indicate high levels of inbreeding and a lack in genetic variation, which might negatively impact the animal population. Conversely, high levels of ROHet might indicate regions of the genetic diversity, beneficial for breed health and resilience. Therefore, these findings could aid breeding programs in managing inbreeding and preserving genetic diversity.

Impact of somatic cell count combined with differential somatic cell count on milk protein fractions in Holstein cattle

Udder health in dairy herds is a very important issue given its implications for animal welfare and the production of high-quality milk. Somatic cell count (SCC) is the most widely used means of assessing udder health status. However, differential somatic cell count (DSCC) has recently been proposed as a new and more effective means of evaluating intramammary infection dynamics. Differential SCC represents the combined percentage of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) in the total SCC, with macrophages (MAC) accounting for the remaining proportion. The aim of this study was to evaluate the association between SCC and DSCC and the detailed milk protein profile in a population of 1,482 Holstein cows. A validated reversed-phase HPLC method was used to quantify 4 caseins (CN), namely α_S1-CN, α_S2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin, which were expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, %N). A linear mixed model was fitted to explore the associations between somatic cell score (SCS) combined with DSCC and the protein fractions expressed quantitatively and qualitatively. We ran an additional model that included DSCC expressed as PMN-LYM and MAC counts, obtained by multiplying the percentages of PMN-LYM and MAC by SCC for each cow in the data set. When the protein fractions were expressed as grams per liter, SCS was significantly negatively associated with almost all the casein fractions and positively associated with the whey protein α-lactalbumin, while DSCC was significantly associated with α_S1-CN, β-CN, and α-lactalbumin, but in the opposite direction to SCS. We observed the same pattern with the qualitative data (i.e., %N), confirming opposite effects of SCS and DSCC on milk protein fractions. The PMN-LYM count was only slightly associated with the traits of concern, although the pattern observed was the same as when both SCS and DSCC were included in the model. The MAC count, however, generally had a greater impact on many casein fractions, in particular decreasing both β-CN content (g/L) and proportion (%N), and exhibited the opposite pattern to the PMN-LYM count. Our results show that information obtained from both SCS and DSCC may be useful in assessing milk quality and protein fractions. They also demonstrate the potential of MAC count as a novel udder health trait.

Streptococcus agalactiae and Prototheca spp. induce different mammary gland leukocyte responses in Holstein cows

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Streptococcus agalactiae increased polymorphonuclear neutrophils in milk samples.

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Prototheca infection greatly increased total T lymphocytes and T-helper lymphocytes in milk samples.

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Prototheca spp. trigger an adaptive immune response and chronic inflammation.

In this study, we investigated the association between natural subclinical intramammary infection (IMI) caused by Streptococcus agalactiae and Prototheca spp. and milk differential cell counts assessed by cytofluorimetric analysis in Holstein cows. After an initial bacteriological screening on 188 animals and a second assessment carried out 2 wk later aimed at confirming the bacteriological status, we collected milk samples from 47 animals and performed (1) milk composition analyses; (2) somatic cell counts and differential somatic cell counts (DSCC); and (3) cytofluorimetric analyses. Before statistical analyses, animals with co-infections were filtered out. Bacteriological status (negative, positive for Strep. agalactiae, or positive for Prototheca spp.) significantly affected the investigated traits. Compared with culture-negative samples, those that were positive for Strep. agalactiae and Prototheca spp. had higher SCS (+61% and +49%, respectively), DSCC (+4% and +19%, respectively), log polymorphonuclear neutrophil (PMN)-lymphocyte (LYM) counts (+59% and +71%, respectively), and log macrophage (MAC) counts (+63% and +72%, respectively). The individual leukocyte populations determined by cytofluorimetric analysis confirmed that mastitis infection increased the proportion of PMN in the milk samples compared with culture-negative samples, particularly when caused by Strep. agalactiae (+51%). In the case of MAC, the 2 pathogens behaved in opposite ways: Strep. agalactiae increased MAC by 41%, whereas Prototheca decreased MAC by 25%. Prototheca infection strongly increased the proportion of total T lymphocytes (TL; +87%) and T-helper lymphocytes (+83%). Accordingly, the (PMN+MAC):TL ratio increased with Strep. agalactiae infection (+95%) and decreased with Prototheca infection (−43%) compared with culture-negative samples. These results suggest the prevalence of an adaptive immune response and chronic inflammation in Prototheca infection, and an innate immune response to Strep. agalactiae. This knowledge might provide an important contribution to the development of novel and effective diagnostics and therapeutics.

Methylome-wide analysis of milk somatic cells upon subclinical mastitis in dairy cattle

Better understanding of the molecular mechanisms behind bovine mastitis is fundamental for improving the management of this disease, which continues to be of major concern for the dairy industry, especially in its subclinical form. Disease severity and progression depend on numerous aspects, such as livestock genetics, and the interaction between the causative agent, the host, and the environment. In this context, epigenetic mechanisms have proven to have a role in controlling the response of the animal to inflammation. Therefore, in this study we aimed to explore genome-wide DNA methylation of milk somatic cells (SC) in healthy cows (n = 15) and cows affected by naturally occurring subclinical mastitis by Streptococcus agalactiae (n = 12) and Prototheca spp. (n = 11), to better understand the role of SC methylome in the host response to disease. Differentially methylated regions (DMR) were evaluated comparing: (1) Strep. agalactiae-infected versus healthy; (2) Prototheca-infected versus healthy, and (3) mastitis versus healthy and (4) Strep. agalactiae-infected versus Prototheca-infected. The functional analysis was performed at 2 levels. To begin with, we extracted differentially methylated genes (DMG) from promoter DMR, which were analyzed using the Cytoscape ClueGO plug-in. Coupled with this DMG-driven approach, all the genes associated with promoter-methylated regions were fed to the Pathifier algorithm. From the DMR analysis, we identified 1,081 hypermethylated and 361 hypomethylated promoter regions in Strep. agalactiae-infected animals, while 1,514 hypermethylated and 358 hypomethylated promoter regions were identified in Prototheca-infected animals, when compared with the healthy controls. When considering infected animals as a whole group (regardless of the pathogen), we found 1,576 hypermethylated and 460 hypomethylated promoter regions. Both pathogens were associated with methylation differences in genes involved in pathways related to meiosis, reproduction and tissue remodeling. Exploring the whole methylome, in subclinically infected cows we observed a strong deregulation of immune-related pathways, such as nuclear factor kB and toll-like receptors signaling pathways, and of energy-related pathways such as the tricarboxylic acid cycle and unsaturated fatty acid biosynthesis. In conclusion, no evident pathogen-specific SC methylome signature was detected in the present study. Overall, we observed a clear regulation of host immune response driven by DNA methylation upon subclinical mastitis. Further studies on a larger cohort of animals are needed to validate our results and to possibly identify a unique SC methylome that signifies pathogen-specific alterations.