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

Dielectric relaxation parameters combing raw milk compositions to improve the prediction performance of milk somatic cell count

BACKGROUND

Milk somatic cell count (SCC) is an international standard for identifying mastitis in dairy cows and measuring raw milk quality. Milk SCC can be predicted based on dielectric relaxation parameters (DRPs). We noted a high correlation between DRPs and the milk composition content (MCC), and so we hypothesized that combining DRPs with MCC could improve the prediction accuracy of milk SCC. The present study aimed to analyze the relationship between milk SCC, DRPs and MCC, as well as to investigate the potential of combining DRPs with MCC to improve the prediction accuracy of milk SCC.

RESULTS

The dielectric spectra (20-4500 MHz) of 276 milk samples were measured, and their DRPs (εl, εh, Δε, τ and σ) were solved by the modified Debye equation. The SCC prediction models were developed using dielectric full spectra, DRPs and DRPs combined with MCC. The results showed the correlations between DRPs (εl, εh, Δε and σ) and MCC (fat, protein, lactose and total solids) were high, and SCC exhibited a non-linear relationship with DRPs and MCC. The 5DRPs + MCC-generalized regression neural network model had the best prediction, with a standard error of prediction for prediction of 0.143 log SCC mL-1 and residual of the prediction bias of 2.870, which was superior to the models based on full spectra, DRPs and near-infrared or visible/near-infrared.

CONCLUSION

The present study has improved the prediction accuracy of milk SCC based on the DRPs combing MCC and provides a new method for dairy farming and milk quality assessment. © 2024 Society of Chemical Industry.

Presence of pathogen DNA in milk harvested from quarters is associated to changes in cows' milk yield and composition

BACKGROUND

Intramammary infection is the result of invasion and multiplication of microorganisms in the mammary gland and commonly leads to mastitis in dairy animals. Although much has been done to improve cows' udder health, mastitis remains a significant and costly health issue for dairy farmers, especially if subclinical. In this study, quarter milk samples from clinically healthy cows were harvested to detect pathogens via quantitative PCR (qPCR) and evaluate changes in individual milk traits according to the number of quarters infected and the type of microorganism(s). A commercial qPCR kit was used for detection of Mycoplasma bovis, Mycoplasma spp., Staphylococcus aureus, coagulase-negative staphylococci (CNS), Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Prototheca spp., Escherichia coli, Klebsiella spp., Enterococcus spp. and Lactococcus lactis ssp. lactis. Quarter and pooled milk information of 383 Holstein, 132 Simmental, 129 Rendena, and 112 Jersey cows in 9 Italian single-breed herds was available.

RESULTS

Among the cows with pathogen(s) present in at least 1 quarter, CNS was the most commonly detected DNA, followed by Streptococcus uberis, Mycoplasma bovis, and Streptococcus agalactiae. Cows negative to qPCR were 206 and had the lowest milk somatic cell count. Viceversa, cows with DNA isolated in ≥ 3 quarters were those with the highest somatic cell count. Moreover, when major pathogens were isolated in ≥ 3 quarters, milk had the lowest casein index and lactose content. In animals with pathogen(s) DNA isolated, the extent with whom milk yield and major solids were impaired did not significantly differ between major and minor pathogens.

CONCLUSIONS

The effect of the number of affected quarters on the pool milk quality traits was investigated in clinically healthy cows using a commercial kit. Results remark the important negative effect of subclinical udder inflammations on milk yield and quality, but more efforts should be made to investigate the presence of untargeted microorganisms, as they may be potentially dangerous for cows. For a smarter use of antimicrobials, analysis of milk via qPCR is advisable - especially in cows at dry off - to identify quarters at high risk of inflammation and thus apply a targeted/tailored treatment.

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.

Effect of intramammary infection and inflammation on milk protein profile assessed at the quarter level in Holstein cows

In this study we wanted to investigate the associations between naturally occurring subclinical intramammary infection (IMI) caused by different etiological agents (i.e., Staphylococcus aureus, Streptococcus agalactiae, Streptococcus uberis, and Prototheca spp.), in combination with somatic cell count (SCC), on the detailed milk protein profile measured at the individual mammary gland quarter. An initial bacteriological screening (time 0; T0) conducted on individual composite milk from 450 Holstein cows reared in 3 herds, was performed to identify cows with subclinical IMI. We identified 78 infected animals which were followed up at the quarter level at 2 different sampling times: T1 and T2, 2 and 6 wk after T0, respectively. A total of 529 quarter samples belonging to the previously selected animals were collected at the 2 sampling points and analyzed with a reversed phase HPLC (RP-HPLC) validated method. Specifically, we identified and quantified 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin (LF), which were later expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, % N). Data were analyzed with a hierarchical linear mixed model with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT), and the SCC × BACT interaction. The random effect of individual cow, nested within herd, DIM and parity was used as the error term for the latter effects. Both IMI (i.e., BACT) and SCC significantly reduced the proportion of β-CN and αS1-CN, ascribed to the increased activity of both milk endogenous and microbial proteases. Less evident alterations were found for whey proteins, except for LF, which being a glycoprotein with direct and undirect antimicrobial activity, increased both with IMI and SCC, suggesting its involvement in the modulation of both the innate and adaptive immune response. Finally, increasing SCC in the positive samples was associated with a more marked reduction of total caseins at T1, and αS1-CN at T2, suggesting a synergic effect of infection and inflammation, more evident at high SCC. In conclusion, our work helps clarify the behavior of protein fractions at quarter level in animals having subclinical IMI. The inflammation status driven by the increase in SCC, rather the infection, was associated with the most significant changes, suggesting that the activity of endogenous proteolytic enzymes related to the onset of inflammation might have a pivotal role in directing the alteration of the milk protein profile.

Serum metabolome differences associated with subclinical intramammary infection caused by Streptococcus agalactiae and Prototheca spp. in multiparous dairy cows

Mastitis is one of the most significant diseases in dairy cows and causes several economic losses. Somatic cell count (SCC) is often used as an indirect diagnostic tool for mastitis, especially for subclinical mastitis (SCM) where no symptoms or signs can be detected. Streptococcus agalactiae is one of the main causes of contagious mastitis, and Prototheca spp. is an alga-inducing environmental mastitis that is not always correlated with increased milk SCC. The aim of this study was to evaluate the changes in the metabolomic profile of blood in relation to subclinical intramammary infection (IMI) in dairy cows. In addition, differences resulting from the etiologic agent causing mastitis were also considered. Forty Holstein-Friesian dairy cows in mid and late lactation were enrolled in this cross-sectional design study. Based on the bacteriological examination of milk, the animals were divided into 3 groups: group CTR (control group; n = 16), group A (affected by SCM with IMI caused by Strep. agalactiae; n = 17), and group P (affected by SCM with IMI caused by Prototheca spp.; n = 7). Blood samples from the jugular vein were collected in tubes containing clot activator; the serum aliquot was stored until metabolomic analysis by 1H-nuclear magnetic resonance spectroscopy. Statistical analysis was conducted by fitting a linear model with the group as the fixed effect and SCC as the covariate. Forty-two metabolites were identified, and among them 10 were significantly different among groups. Groups A and P showed greater levels of His and lactose and lower levels of acetate, Asn, and dimethylamine compared with group CTR. Group A showed high levels of Val, and group P showed high levels of Cit and methylguanidine, as well as lower levels of 3-hydroxybutyrate, acetone, allantoin, carnitine, citrate, and ethanol. These metabolites were related to ruminal fermentations, energy metabolism, urea synthesis and metabolism, immune and inflammatory response, and mammary gland permeability. These results suggest systemic involvement with subclinical IMI and that the metabolic profile of animals with SCM undergoes changes related to the etiological agent of mastitis.

Pathogen and severity-dependent immune responses in bovine mastitis: highlight the dynamics of differential somatic cell count

Immune responses in bovine clinical mastitis (CM) probably differ depending on the causative pathogen and disease severity. The observational study aimed to investigate whether both factors are associated with the dynamics of immune indicators, including somatic cell score (SCS), white blood cell count (WBC), serum albumin/globulin (A/G) ratio, and differential somatic cell count (DSCC). We collected blood and milk samples 0, 3, 5, 7, 14, and 21 days after CM occurred in 38 cows, and grouped the cases (n=49) by disease severity and pathogen. We analyzed data using a linear mixed model considering the effects of pathogens and severity, calculated estimated-marginal means for indicators at each time point, and compared the means between groups. The dynamics of WBC varied depending on both pathogen and severity. WBC changed drastically in either severe or coliform-caused CM, slightly elevated in streptococcal mastitis, but unchanged in staphylococcal mastitis. This possibly relates to the deficiency in innate immune response toward staphylococci. The A/G ratio also changed depending on severity, as it dropped sharply only in severe CM. We observed a non-linear relationship between DSCC and SCS, possibly due to mammary epithelial cells shedding in milk when CM occurred. When cows recovering from Streptococcus dysgalatiae mastitis, DSCC decreased while SCS remained high, suggesting a healing process requiring more macrophages. Our results demonstrate that both the severity and pathogen are associated with immune responses in CM, providing insights into mastitis pathogenesis.

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.

Estimating the nonlinear interaction between somatic cell score and differential somatic cell count on milk production by parity using generalized additive models

This observational study aimed to use somatic cell score (SCS) and differential somatic cell count (DSCC), the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells, to investigate how mastitis affected milk production. Using generalized additive models, we analyzed 50,618 test-day records from 8,081 lactations from 7,912 cows in 197 herds between January 2021 and March 2022 to estimate the nonlinear interaction between SCS and DSCC, and the effects of lactation stages and seasons on milk yield, milk component percentages, and milk component yields by parity of cows. The results show that the interaction between SCS and DSCC on these traits was significant, nonlinear, and complex. When DSCC was high, the negative effects of SCS were minimal, even when SCS reached 8 (i.e., 3,200,000 somatic cells/mL). Cows with high DSCC could have milk yields similar to healthy cows, implying that these cows may have been in the early stages of mastitis and that the milk yield had yet to be affected. Contrastingly, when DSCC was low, milk loss due to high SCS was drastic, especially for cows in third or later lactations, whose milk yield could reduce from more than 35 kg/d to less than 15 kg/d (-59.9%). This tremendous milk loss in high-parity cows was likely due to their higher milk yield and higher risks of chronic mastitis. High SCS and low DSCC also led to a pronounced change in milk composition. The decrease in the percentage of lactose can be directly related to the damage of inflammation to the mammary gland, while the increase in fat and protein percentages was more attributable to the concentration effect resulting from the reduced milk yield. Compared with analyses based on categorized SCS and DSCC values, modeling these 2 indices directly helps us more precisely assess mastitis effects on milk yield and milk composition. For efficient milk production, our results indicate that we should prevent high-parity cows from entering a state of high SCS and low DSCC.

Associations between subclinical intramammary infection and milk fatty acid profile at the quarter level in Holstein cattle

Mastitis, especially the subclinical form, is the most common economic and health problem in dairy cows. Little is known about changes in milk fatty acid (FA) composition according to infection/inflammation status of the mammary gland. The aim of this study was to investigate the associations between naturally occurring subclinical intramammary infection (IMI) from different pathogens, i.e. Streptococcus agalactiae, Staphylococcus aureus, Streptococcus uberis and Prototheca spp., and the detailed milk FA profile assessed at quarter level in Holstein cows. After an initial bacteriological screening (T0) on 450 Holstein cows reared in three dairy herds, we identified 78 cows positive at the bacteriological examination. These animals were followed up at the quarter level two weeks (T1) and six weeks (T2) after T0. In total, 600 single-quarter samples were obtained at T1 and T2. Individual FAs were determined using the gas chromatography analytical method. Investigated traits were 70 individual FAs, 12 FA groups, and six desaturation indices. The associations between subclinical IMI combined with somatic cell count (SCC) and milk FA profile were investigated using a hierarchical linear mixed model (i.e., observational unit was quarter within cow) with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT, positive and negative), and the SCC × BACT interaction. The random effect of individual cow nested within herd, DIM and parity was used as the error term for the latter effects. The most significant associations were detected at T2. Notably, IMI reduced the proportions of individual short-chain FA, especially 4:0 and 6:0 (-14%), but increased the proportion of the most abundant medium-chain FA (MCFA), 16:0 (+4%). A reduction in the desaturation indices was observed mostly for 14:1 index (-9%), in line with the reduction in 14:1 (-10%). Somatic cell count significantly affected 14 individual FAs. In particular, samples with high SCC (≥200 000) had significantly lower proportions of 8:0, 10:0, 11:0, 12:0, and 13:0 compared with samples with low SCC (<200 000). Increasing SCC in animals positive at the bacteriological examination were associated with a reduction in total MCFA at T2 (while in negative animals, they remained constant across SCC classes), possible evidence that elongation of the FA chain from 11 to 16 carbons is affected by a combination of infection and SCC. This study showed that subclinical IMI and SCC are mainly associated with reductions in the synthesis of FA and the desaturation process in the mammary gland.

Blood biochemical changes upon subclinical intramammary infection and inflammation in Holstein cattle

The aim of this study was to investigate the associations between subclinical intramammary infection (IMI) from different pathogens combined with inflammation status and a set of blood biochemical traits including energy-related metabolites, indicators of liver function or hepatic damage, oxidative stress, inflammation, innate immunity, and mineral status in 349 lactating Holstein cows. Data were analyzed with a linear model including the following fixed class effects: days in milk, parity, herd, somatic cell count (SCC), bacteriological status (positive and negative), and the SCC × bacteriological status interaction. Several metabolites had significant associations with subclinical IMI or SCC. Increased SCC was associated with a linear decrease in cholesterol concentrations which ranged from -2% for the class ≥50,000 and <200,000 cells/mL to -11% for the SCC class ≥400,000 cells/mL compared with the SCC class <50,000 cells/mL. A positive bacteriological result was associated with an increase in bilirubin (+24%), paraoxonase (+11%), the ratio paraoxonase/cholesterol (+9%), and advanced oxidation protein product concentration (+23%). Increased SCC were associated with a linear decrease in ferric reducing antioxidant power concentrations ranging from -3% for the class ≥50,000 and <200,000 cells/mL to -9% for the SCC class ≥400,000 cells/mL (respect to the SCC class <50,000 cells/mL). A positive bacteriological result was associated with an increase in haptoglobin concentrations (+19%). Increased SCC were also associated with a linear increase in haptoglobin concentrations, which ranged from +24% for the class ≥50,000 and <200,000 cells/mL (0.31 g/L) to +82% for the SCC class ≥400,000 cells/mL (0.45 g/L), with respect to the SCC class <50,000 cells/mL (0.25 g/L). Increased SCC were associated with a linear increase in ceruloplasmin concentrations (+15% for SCC ≥50,000 cells/mL). The observed changes in blood biochemical markers, mainly acute phase proteins and oxidative stress markers, suggest that cows with subclinical IMI may experience a systemic involvement.

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.

Biochemical and Computational Assessment of Acute Phase Proteins in Dairy Cows Affected with Subclinical Mastitis

Subclinical mastitis (SCM) is a predominant form of mastitis wherein major visible signs of disease are absent. The present study aimed to determine acute phase proteins (APPs) like ferritin, C-reactive protein (CRP), and microalbumin (Malb) in 135 composite milk and serum samples of healthy (n = 25) and SCM (n = 110) cows. As bovine mastitis is an inflammatory disease, the present study also aimed at finding novel anti-inflammatory compounds from natural sources by repurposing approach using computational studies. The findings of the present study revealed substantial elevation (p < 0.001) in milk SCC and an increase in ferritin, CRP, and Malb (p < 0.001) in milk and sera of the SCM group as compared to healthy animals. Receiver operating characteristics of milk SCC, milk, and serum APPs unraveled statistically substantial alteration (p < 0.001). Further, SCC was correlated with milk APPs ferritin (r = 0.26 **, p < 0.002), CRP (r = 0.19 *, p < 0.02), and Malb (r = 0.21 *, p < 0.01). Additionally, milk SCC was correlated with serum ferritin (r = 0.28 **, p < 0.001), CRP (r = 0.16, p > 0.05), and Malb (r = 0.16, p > 0.05). The findings of molecular docking revealed that Chaetoglobosin U was the most effective molecule that showed the highest binding affinity (kcal/mol) of -10.1 and -8.5 against ferritin and albumin. The present study concluded that the estimation of cow-side tests, SCC, and APPs in milk/serum is suitable to detect SCM and screening herd community. Furthermore, Chaetoglobosin U could be developed as a promising anti-inflammatory inhibitor; however, further studies are required to validate these findings.

High-concentrate diet elevates histone lactylation mediated by p300/CBP through the upregulation of lactic acid and induces an inflammatory response in mammary gland of dairy cows

High-concentrate diet can cause metabolic diseases, such as subacute ruminal acidosis (SARA), and secondary mastitis. To investigate the effect of SARA induced by high-concentrate diet on the lysine lactylation (Kla) and inflammatory responses in the mammary gland of dairy cows and the mechanism between them, we selected twelve mid-lactation Holstein cows with similar body conditions for modelling. They were randomly divided into two groups, fed a low-concentrate diet (LC) and a high-concentrate diet (HC) for 21 days. Our results showed that high-concentrate diet feeding significantly reduced ruminal pH, and the pH was below 5.6 for more than 3 h per day, indicating successful induction of the SARA model. Lactic acid concentrations in mammary gland and plasma were higher in the HC group than that in the LC group. HC diet feeding significantly up-regulated the expression levels of the Pan Kla, H3K18la, p300/CBP and monocarboxylate transporter 1 (MCT1) in the mammary gland. In addition, the mRNA expression levels of inflammatory factors were significantly regulated, including IL-1β, IL-1α, IL-6, IL-8, SAA3, and TNF-α, while the anti-inflammatory factor IL-10 was down-regulated. The mammary gland of HC group was structurally disorganized with incomplete glandular vesicles, with a large number of detached mammary epithelial cells and inflammatory cells infiltration. The up-regulation of TLR4, TNF-α, p-p65, and p-IκBα indicated that the TLR4/NF-κB signaling pathway was activated. In conclusion, this study found that HC diet feeding can induce SARA and increase the concentration of lactic acid in mammary gland and plasma. Then, lactic acid could be transported into cells by MCT1 and up-regulate the expression level of histone lactylation mediated by p300/CBP, and subsequently promote the activation of TLR4/NF-κB signaling pathway, ultimately causing inflammatory responses in the mammary gland.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CV_r) and herd-out (CV_h). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R² value from 5% to 75% for CV_r and 2% to 139% for CV_h, whereas the stacking ensemble increased the R² value from 4% to 70% for CV_r and 4% to 150% for CV_h. Considering the best model, with the CV_r scenario, good prediction accuracies were obtained for glucose (R² = 0.81), urea (R² = 0.73), albumin (R² = 0.75), total reactive oxygen metabolites (R² = 0.79), total thiol groups (R² = 0.76), ceruloplasmin (R² = 0.74), total proteins (R² = 0.81), globulins (R² = 0.87), and Na (R² = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Metabolic and physiological adaptations to first and second lactation in Holstein dairy cows

Huge differences exist between cow yields and body sizes during their first and second lactations. The transition period is the most critical and investigated phase of the lactation cycle. We compared metabolic and endocrine responses between cows at different parities during the transition period and early lactation. Eight Holstein dairy cows were monitored at their first and second calving during which they were reared under the same conditions. Milk yield, dry matter intake (DMI), and body weight (BW) were regularly measured, and energy balance, efficiency, and lactation curves were calculated. Blood samples were collected on scheduled days from -21 d relative to calving (DRC) to 120 DRC for the assessment of metabolic and hormonal profiles (biomarkers of metabolism, mineral status, inflammation, and liver function). Large variations in the period in question for almost all variables investigated were observed. Compared with their first lactation, cows during their second lactation had higher DMI (+15%) and BW (+13%), their milk yield was greater (+26%), lactation peak was higher and earlier (36.6 kg/d at 48.8 DRC vs. 45.0 kg/d at 62.9 DRC), but persistency was reduced. Milk fat, protein, and lactose contents were higher during the first lactation and coagulation properties were better (higher titratable acidity, faster and firmer curd formation). Postpartum negative energy balance was more severe the during the second lactation (1.4-fold at 7 DRC) and plasma glucose was lower. Circulating insulin and insulin-like growth factor-1 were lower in second-calving cows during the transition period. At the same time, markers of body reserve mobilization (β-hydroxybutyrate and urea) increased. Moreover, albumin, cholesterol, and γ-glutamyl transferase were higher during second lactation, whereas bilirubin and alkaline phosphatase were lower. The inflammatory response after calving was not different, as suggested by the similar haptoglobin concentrations and only transient differences in ceruloplasmin. Blood growth hormone did not differ during the transition period but was lower during the second lactation at 90 DRC, whereas circulating glucagon was higher. These results agree with the differences in milk yield and confirmed the hypothesis of a different metabolic and hormonal status between the first and second lactation partly related to different degrees of maturity.

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.