Biomarker and proteome analysis of milk from dairy cows with clinical mastitis: Determining the effect of different bacterial pathogens on the response to infection

Antimicrobial usage (AMU) could be reduced by differentiating the causative bacteria in cases of clinical mastitis (CM) as either Gram-positive or Gram-negative bacteria or identifying whether the case is culture-negative (no growth, NG) mastitis. Immunoassays for biomarker analysis and a Tandem Mass Tag (TMT) proteomic investigation were employed to identify differences between samples of milk from cows with CM caused by different bacteria. A total of 94 milk samples were collected from cows diagnosed with CM across seven farms in Scotland, categorized by severity as mild (score 1), moderate (score 2), or severe (score 3). Bovine haptoglobin (Hp), milk amyloid A (MAA), C-reactive protein (CRP), lactoferrin (LF), α-lactalbumin (LA) and cathelicidin (CATHL) were significantly higher in milk from cows with CM, regardless of culture results, than in milk from healthy cows (all P-values <0.001). Milk cathelicidin (CATHL) was evaluated using a novel ELISA technique that utilises an antibody to a peptide sequence of SSEANLYRLLELD (aa49-61) common to CATHL 1-7 isoforms. A classification tree was fitted on the six biomarkers to predict Gram-positive bacteria within mastitis severity scores 1 or 2, revealing that compared to the rest of the samples, Gram-positive samples were associated with CRP < 9.5 μg/ml and LF ≥ 325 μg/ml and MAA < 16 μg/ml. Sensitivity of the tree model was 64%, the specificity was 91%, and the overall misclassification rate was 18%. The area under the ROC curve for this tree model was 0.836 (95% bootstrap confidence interval: 0.742; 0.917). TMT proteomic analysis revealed little difference between the groups in protein abundance when the three groups (Gram-positive, Gram-negative and no growth) were compared, however when each group was compared against the entirety of the remaining samples, 28 differentially abundant protein were identified including β-lactoglobulin and ribonuclease. Whilst further research is required to draw together and refine a suitable biomarker panel and diagnostic algorithm for differentiating Gram- positive/negative and NG CM, these results have highlighted a potential panel and diagnostic decision tree. Host-derived milk biomarkers offer significant potential to refine and reduce AMU and circumvent the many challenges associated with microbiological culture, both within the lab and on the farm, while providing the added benefit of reducing turnaround time from 14 to 16 h of microbiological culture to just 15 min with a lateral flow device (LFD).

Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models

Mastitis has a substantial impact on the dairy industry across the world, causing dairy producers to suffer losses due to the reduced quality and quantity of produced milk. A further problem, related to this issue, is the excessive use of antibiotics that leads to the development of resistance in different bacterial strains. The growing consumer awareness oriented toward food safety and rational use of antibiotics has promoted the search for new methods of early identification of cows that may be at risk of developing the disease. Subclinical mastitis does not cause any visible changes to the udder or milk, and therefore it is more difficult to detect than clinical mastitis. The collection of large amounts of data related to milk performance of cows allows using machine learning (ML) methods to build models that could be used for classifying cows into healthy and at risk of subclinical mastitis. The data used for the purpose of this study included information from routine milk recording procedures. The dataset consisted of 19,856 records of 2,227 Polish Holstein-Friesian cows from 3 herds. The authors decided to use the approach of building ensemble ML models, in particular bagging, boosting, stacking, and super-learner models, and comparing them for accuracy of identification of disease-affected cows against single ML models based on the support vector machines, logistic regression, Gaussian Naive Bayes, k-nearest neighbors, and decision tree algorithms. The models were trained and evaluated based on the information recorded for herd 1 and using an 80:20 train-test split ratio according to animal ID (to avoid data leakage). The information recorded for herds 2 and 3 was only used to evaluate on unseen data models developed using the herd 1 dataset. Among the single ML models, the support vector machines model was found to be the most accurate in predicting subclinical mastitis at subsequent test day when used both for the training set (mean F1-score of 0.760) and the testing sets containing data for herds 1, 2, and 3 (F1-score of 0.778, 0.790, and 0.741 respectively). The gradient boosting model was found to be the best performing model among the ensemble ML models (F1-score of 0.762, 0.779, 0.791, and 0.723 for the training set and the testing sets, respectively). The super-learner model, featuring the most advanced design and logistic regression in the meta layer, achieved the highest mean F1-score of 0.775 during the cross validation; however, it was characterized by a slightly worse prediction accuracy of the testing sets (mean F1-score of 0.768, 0.790, and 0.693 for herds 1, 2 and 3 respectively). The study findings confirm the promising role of ensemble ML methods, which were found to be slightly superior with respect to most of the single ML models.

Comparison of bacteriological culture method and multiplex real-time PCR for detection of mastitis

This study includes the evaluation of multiplex real-time PCR (rPCR) kit, which was developed to provide rapid diagnosis of mastitis infections, by working with milk samples of 2 different sources of mastitis and comparing the results with the classical bacteriological culture method (BC). A total of 273 bacteria were isolated in 226 samples (47.88%) out of 472 samples by BC. These were 139 (50.91%) Staphylococcus spp., 61 (22.34%) Streptococcus spp., 15 (5.49%) E. coli, 8 (2.93%) Enterococcus spp., 50 (18.31%) other bacteria. When we look at the multiplex rPCR results; 1052 positive were obtained for the gene regions of 14 different bacteria, 1 yeast, and 1 β-lactamase gene examined in 472 samples. While no searched gene region was found by rPCR in 78 (16.5%) of the 472 samples studied, at least 1 gene was detected in 394 (83.5%) samples. These 1052 positive samples by rPCR were; 263 (28.43%) Staphylococcus spp., 51 (5.51%) S. aureus, 57 (6.16%) Enterococcus spp., 49 (5.29%) C. bovis, 16 (1.73%) S. dysgalactiae, 84 (9.08%) S. agalactiae, 71 (7.67%) S. uberis, 73 (7.89%) E. coli, 14 (1.51%) Prototheca spp., 39 (4.21%) T. pyogenes/P. indolicus, 5 (0.54%) S. marcescens, 15 (1.62%) K. oxytoca/pneumonia, 117 (12.64%) Mycoplasma spp., 31 (3.35%) M. bovis, 40 (4.32%) yeast, and 127 samples (26.90%) were β-lactamase positive. When the antibiotic resistance of the isolates was evaluated, 78 (31.96%) tetracycline, 72 (29.5%) penicillin, and 60 (24.59%) clindamycin resistance were observed predominantly in Gram-positive isolates, while 6 (23.07%) tigecycline, 6 (23.07%) netilmicin, 6 (23.07%) pipercillin resistance was found in gram-negative isolates. While a bacteria and/or yeast gene was found by rPCR in 187 of 246 (76.01%) samples with no bacterial growth, a bacterium was isolated with BC in only 20 (8.84%) samples whose gene region was not found by rPCR. As a result, the multiplex rPCR system used in the diagnosis of mastitis has been found to be quite reliable as it can detect a large number of bacteria in a very short time compared to classical methods. Therefore, we advise the use of rPCR and/or culture for confirmation of clinical signs in mastitis and at routine mastitis surveillance.

Performance evaluation of a rapid immunochromatographic test kit in detecting bovine mastitis-causing streptococci

Mastitis causes significant economic losses to the dairy industry due to decreased milk production in infected cows. Identification of mastitis-causing pathogens, such as streptococci, is necessary for selecting an effective antibiotic for treating mastitis. Although bacterial cultivation is widely used for pathogen identification, it requires more than 24 hr to complete. Contrarily, Lateral flow assays are simple, rapid, and inexpensive testing procedures. In this study, the effectiveness of an immunochromatographic test kit for detecting streptococci in milk samples from cows with clinical mastitis was evaluated as an alternative to bacterial cultivation. The performance of the immunochromatographic test kit for detecting mastitis-causing pathogens was compared with that of bacterial cultivation and real-time quantitative polymerase chain reaction (qPCR). The sensitivity and specificity of the immunochromatographic test kit were 0.800 and 0.875, respectively, compared with bacterial cultivation. Additionally, the κ statistic values of the immunochromatographic test kit was 0.667, indicating substantial agreement with the results of bacterial cultivation. Statistically, sensitivity and specificity of the immunochromatographic kit and real-time qPCR did not differ significantly; thus, the immunochromatographic test kit detected mastitis-causing streptococci as effectively as real-time qPCR. Therefore, the immunochromatographic kit is a rapid, inexpensive, and simple method for detecting streptococci and contributes to the timely selection of appropriate antibiotics for treatment and promotes early recovery from mastitis.

Development of a prototypic, field-usable diagnostic tool for the detection of gram-positive cocci-induced mastitis in cattle

BACKGROUND

Bovine mastitis is one of the most widespread diseases affecting cattle, leading to significant losses for the dairy industry. Currently, the so-called gold standard in mastitis diagnosis involves determining the somatic cell count (SCC). Apart from a number of advantages, this method has one serious flaw: It does not identify the etiological factor causing a particular infection, making it impossible to introduce targeted antimicrobial therapy. This can contribute to multidrug-resistance in bacterial species. The diagnostic market lacks a test that has the advantages of SCC and also recognizes the species of pathogen causing the inflammation. Therefore, the aim of our study was to develop a lateral flow immunoassay (LFIA) based on elongation factor Tu for identifying most prevalent Gram-positive cocci responsible for causing mastitis including Streptococcus uberis, Streptococcus agalactiae and Staphylococcus aureus.

RESULTS

As a result, we showed that the assay for S. uberis detection demonstrated a specificity of 89.02%, a sensitivity of 43.59%, and an accuracy of 80.3%. In turn, the second variant - assay for Gram-positive cocci reached a specificity of 95.59%, a sensitivity of 43.28%, and an accuracy of 78.33%.

CONCLUSIONS

Our study shows that EF-Tu is a promising target for LFIA and we have delivered evidence that further evaluation could improve test parameters and fill the gap in the mastitis diagnostics market.

Milk miRNA expression in buffaloes as a potential biomarker for mastitis

BACKGROUND

Buffaloes have the highest potential for production due to a promising gene pool that is being enhanced and upgraded. Mastitis is a significant health impediment that greatly diminishes milk yield and quality, affecting rural farmers' livelihoods. The traditional gold standard used for diagnosing mastitis or subclinical mastitis is CMT, but it has the drawback of false positive or negative results. Subclinical mastitis, if not treated promptly, can lead to mammary tumors. To address the gap in early diagnosis of subclinical mastitis in CMT-negative milk of buffaloes, we performed a retrospective analysis and evaluated the milk miRNA expression profiles as potential biomarkers.

RESULTS

Thirty buffalo milk samples based on clinical signs and CMT were divided into normal, subclinical, and clinical mastitis. SCC evaluation showed significant differences between the groups. The data analysis demonstrated that the elevation of miR-146a and miR-383 differed substantially between normal, subclinical, and clinical mastitis milk of buffaloes with 100% sensitivity and specificity. The relationship of SCC with miR-146a and miR-383 in normal/healthy and subclinical mastitis was positively correlated.

CONCLUSION

The overexpression of miR-146a and miR-383 is associated with inflammation. It can be a valuable prognostic and most sensitive biomarker for early mastitis detection in buffaloes with SCC below 2 lakhs and CMT-ve, enhancing the accuracy of subclinical mastitis diagnosis.

[Investigations of a controlled, decision tree based procedure for Selective Dry Cow Treatment in Bavarian dairy farms]

OBJECTIVE

Four parameters of a decision tree for Selective Dry Cow Treatment (SDCT), examined in a previous study, were analyzed regarding their efficacy in detecting cows for dry cow treatment (DCT, use of intramammary antimicrobials). This study set out to review wether all parameters (somatic cell count [SCC≥ 200 000 SC/ml 3 months' milk yield recordings prior dry off (DO)], clinical mastitis history during lactation [≥1 CM], culturing [14d prior DO, detection of major pathogens] and California-Mastitis-Test [CMT, > rate 1/+ at DO]) are necessary for accurate decision making, whether there are possible alternatives to replace culturing, and whether a simplified model could replace the decision tree.

MATERIAL AND METHODS

Records of 18 Bavarian dairy farms from June 2015 to August 2017 were processed. Data analysis was carried out by means of descriptive statistics, as well as employing a binary cost sensitive classification tree and logit-models. For statistical analyses the outcomes of the full 4-parameter decision tree were taken as ground truth.

RESULTS

848 drying off procedures in 739 dairy cows (CDO) were included. SCC and CMT selected 88.1%, in combination with CM 95.6% of the cows that received DCT (n=494). Without culturing, 22 (4.4%) with major pathogens (8x Staphylococcus [S.] aureus) infected CDO would have been misclassified as not needing DCT. The average of geometric mean SCC (within 100 d prior DO) for CDO with negative results in culturing was<100 000 SC/ml milk, 100 000-150 000 SC/ml for CDO infected with minor pathogens, and ≥ 150 000 SC/ml for CDO infected with major pathogens (excluding S.aureus). Using SCC during lactation (at least 1x > 200 000 SC/ml) and positive CMT to select CDO for DCT, contrary to the decision tree, 37 CDO (4.4%) would have been treated "incorrectly without" and 43 CDO (5.1%) "unnecessarily with" DCT. Modifications were identified, such as SCC<131 000 SC/ml within 100 d prior to DO for detecting CDO with no growth or minor pathogens in culturing. The best model for grading CDO for or against DCT (CDO without CM and SCC<200 000 SC/ml [last 3 months prior DO]) had metrics of AUC=0.74, Accuracy=0.778, balanced Accuracy=0.63, Sensitivity=0.92 and Specificity=0.33.

CONCLUSIONS

Combining the decision tree's parameters SCC, CMT and CM renders suitable selection criteria under the conditions of this study. When omitting culturing, lower thresholds for SCC should be considered for each farm individually to select CDO for DCT. Nonetheless, the most accurate model could not replace the full decision tree.

Reliability of udder infrared thermography as a non-invasive technology for early detection of sub-clinical mastitis in Sahiwal (Bos indicus) cows under semi-intensive production system

The present study focused on Sahiwal cows, a prominent milch breed in tropical India, to correlate udder temperature with physiological markers of stress and inflammation during subclinical mastitis (SCM). The primary goal was to assess the potential of udder infrared thermography for the early detection of SCM under the semi-intensive production. Cows were categorized based on milk somatic cell counts (SCC), with healthy (H) cows having SCC <2 × 105 cells/mL and no history of mastitis, and cows with subclinical mastitis (SCM) and initial stages of clinical mastitis (CM) having quarter milk SCC of 2-5 × 105 and >5 × 105 cells/mL, respectively. Firstly, udder thermograms were analysed for udder skin surface temperature (USST), teat skin surface temperature (TSST), and teat apex temperature (TAT) using Fluke software to determine the optimal site for temperature measurement during intramammary infection. Secondly, milk samples were collected for automatic estimation of compositional changes, electrical conductivity, and pH. Thirdly, milk whey was separated for quantifying stress and inflammatory indicators, including cortisol, prolactin, and acute-phase proteins (APPs): milk amyloid A and milk haptoglobin using bovine-specific ELISA kits. Significant increases (p < 0.01) in USST, TSST, TAT, cortisol, and APPs were observed in SCM and CM compared to healthy cows, while prolactin levels decreased (p < 0.01). The correlation matrix revealed strong positive correlations of SCC with USST (r = 0.84, p < 0.01). In ROC analysis, USST demonstrated cut-off values of 37.74 and 39.58 °C, with accuracy (p < 0.05) of 98% for SCM and 95% for CM, surpassing both TAT and TSST. Therefore, the combination of these non-invasive methods increases the reliability and accuracy of infrared thermography for early detection of SCM, providing valuable insights for the development of a protocol for routine screening and udder health monitoring in indigenous dairy cows.

Factors affecting N-acetyl-β-D-glucosaminidase as an indicator for mastitis detection in dairy sheep

The study of new indirect methods for mastitis detection is of great relevance both at the economic level of the farm and dairies, and in terms of consumer health, and animal welfare. These methods help us to monitor the disease and speed up the decision-making process on treatment of the affected animal and the destination of the milk. The main aim of this work was to study the effect of intramammary infection and other non-infectious factors on the activity of the enzyme N-acetyl-β-D-glucosaminidase (NAGase) in milk, in order to evaluate its use as an indicator for the early diagnosis of mastitis in sheep that could be less expensive, easier to measure and a better marker of inflammation or complementary to existing methods such as somatic cell count (SCC). Seven biweekly samplings were carried out, in which NAGase activity, SCC and milk were analyzed. Glands were classified according to their sanitary status based on the results of the SCC and bacteriological analysis. Non-infectious factors such as lactation stage, parity number and milking session had a statistically significant effect on NAGase values, finding the highest NAGase values at the onset and end of the study, in infectious mastitic glands of multiparous females and at morning milking. However, among the NAGase variation factors studied, the health status of the gland was the factor that caused the highest variation in enzyme levels, with infectious mastitic glands showing higher values than healthy glands. The predictive ability of NAGase was also studied by means of several logistic regression models, with the one that included NAGase together with lactation stage and parity obtaining the best results if sensitivity is to be prioritized, or the model that included NAGase, lactation stage, parity, milking and production if specificity is to be prioritized. From the results obtained, it can be concluded that the use of NAGase as an intramammary infection detection method in sheep can be useful when non-infectious factors that cause changes in the concentration of the enzyme are also considered.

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.

Using registry data to identify individual dairy cows with abnormal patterns in routinely recorded somatic cell counts

Data from the Danish milk recording system routinely enter the Danish Cattle Database, including somatic cell counts (SCC) for individual animals. Elevated SCC can signal intramammary inflammation, suggesting subclinical mastitis. Detecting mastitis is pivotal to limit severity, prevent pathogen spread, and target treatment or culling. This study aimed to differentiate normal and abnormal SCC patterns using recorded registry data. We used registry data from 2010 to 2020 for dairy cows in herds with 11 annual milk recordings. To create consistency across herds, we used data from 13,996 unique animals and eight different herds, selected based on the amount of data available, only selecting Holstein animals and conventional herds. We fitted log10-transformed SCC to days in milk (DIM) using the Wilmink and Wood's curve functions, originally developed for milk yield over the lactation. We used Nonlinear Least Square and Nonlinear Mixed Effect models to fit the log10-transformed SCC observations to DIM at animal level. Using mean squared residuals (MSR), we found a consistently better fit using a Wood's style function. Detection of MSR outliers in the model fitting process was used to identify animals with log10(SCC) curves deviating from the expected "normal" curve for that same animal. With this study, we propose a method to identify single animals with SCC patterns that indicate abnormalities, such as mastitis, based on registry data. This method could potentially lead to a registry data-based detection of mastitis cases in larger dairy herds.

[Haptoglobin as an indicator for diseases during early lactation of dairy cows, with particular consideration of udder health]

s an acute-phase protein Haptoglobin (HP) is part of the non-specific immune response and represents a strong indicator for inflammatory conditions in cattle. The purpose of this article is to provide an overview of previous study results on serum and milk HP related to diseases in early lactation with special consideration of udder health. During inflammatory diseases of the reproductive tract, metabolism and musculoskeletal system, HP increases in the serum and may serve as a non-specific indicator for diseases during early lactation. Threshold values are available for the differentiation of healthy from diseased animals. A correlation exists between HP in blood and milk. The HP concentration in milk is not only influenced by systemic disorders, as the udder epithelium is also independently capable of synthesizing HP in case of an infection. In mastitis, HP concentration may be used to estimate the severity of the disease. In addition, HP may provide certain suspicions regarding the causative pathogen. Threshold values for milk HP are available for the differentiation of healthy individuals from subclinically resp. clinically affected animals.

Genetic identification of Staphylococcus aureus isolates from cultured milk samples of bovine mastitis using isothermal amplification with CRISPR/Cas12a-based molecular assay

Bovine mastitis, a common and costly disease in dairy cattle, is primarily caused by Staphylococcus aureus. Timely and accurate detection of this pathogen is crucial for effective disease management. In this study, we developed and validated a novel molecular diagnostic assay based on the CRISPR/Cas12a system coupled with Recombinase Polymerase Amplification (RPA) and Loop-Mediated Isothermal Amplification (LAMP). We utilized specific primers targeting the nucleotide sequences of the S.aureus genes of interest, such as nuc and sea. RPA/LAMP reactions were performed under optimized conditions, and the resulting products were subsequently subjected to CRISPR/Cas12a detection. The CRISPR/Cas12a assay successfully detected the target nuc and sea genes, with a limit of detection of 104 and 102 gene copies per reaction, respectively. All 13 S.aureus clinical isolates were identified by RPA-CRISPR/Cas12a assay. The total reaction time is approximately 1 h. The assay demonstrated high sensitivity for the detection of S.aureus in both laboratory and clinical samples.

Development of Loop-Mediated Isothermal Amplification for the Detection of Prototheca bovis Directly from Milk Samples of Dairy Cattle

Prototheca bovis is an algal emerging pathogen in dairy farms causing refractory protothecal mastitis with increasing incidence worldwide and significant economic impact. P. bovis infects cows throughout the lactation cycle, including dry periods, and can persist in the udder and the environment for a long time. Since P. bovis does not respond to treatments with antibiotics, the suggested sanitary measure to restrict the spread is culling infected animals. A point-of-care test for early detection of the causative agent is critically needed to guide farm management and the appropriate treatment of mastitis. Loop-mediated isothermal amplification (LAMP) is a highly specific molecular method, time-saving, cost-effective and easy to perform in limited settings. This study aimed to develop a LAMP assay for P. bovis detection directly from milk samples; it was employed in conjunction with a commercial DNA extraction kit which was previously used to extract DNA from milk specimens containing microbes. The LAMP assay detected P. bovis DNA within 1 h in milk samples spiked with P. bovis at a concentration of 50 cells/μL, enabling on-farm disease monitoring and decision-making based on a reliable diagnosis. The LAMP method will contribute to the accurate and rapid identification of P. bovis in asymptomatic or recurrent mastitis cases and consequently aid the implementation of targeted control measures and the reduction of losses in milk production.

Milk as diagnostic fluid for udder health management

BACKGROUND

Mastitis is the major disease affecting milk production of dairy cattle, and milk is an obvious substrate for the detection of both the inflammation and its causative infectious agents at quarter, cow, or herd levels. In this review, we examine the use of milk to detect inflammation based on somatic cell count (SCC) and other biomarkers, and for the detection of mastitis pathogens through culture-based and culture-free methods.

FINDINGS

The use of SCC at a cow or bulk milk level to guide udder health management in lactation is well-established, and SCC is increasingly used to guide selective dry cow treatment. Other markers of inflammation include electrical conductivity, which is used commercially, and markers of disease severity such as acute phase proteins but are not pathogen-specific. Some pathogen-specific markers based on humoral immune responses are available, but their value in udder health management is largely untested. Commercial pathogen detection is based on culture or polymerase chain reaction, with other tests, for example, loop-mediated isothermal amplification or 16S microbiome analysis still at the research or development stage. Matrix-assisted laser desorption ionisation time of flight (MALDI-ToF) is increasingly used for the identification of cultured organisms whilst application directly to milk needs further development. Details of test sensitivity, specificity, and use of the various technologies may differ between quarter, cow, and bulk milk applications.

CONCLUSIONS

There is a growing array of diagnostic assays that can be used to detect markers of inflammation or infection in milk. The value of some of these methods in on-farm udder health improvement programs is yet to be demonstrated whilst methods with proven value may be underutilised.

Seasonal assessment of mastitis using thermogram analysis in Sahiwal cows

"India is the world's leading producer of milk" and demands a non-invasive diagnostic tool like infrared thermography (IRT) to identify the costliest production disease, mastitis. It can form the basis of precision dairy farming. Therefore, the present study focuses on thermal imaging of the udder and teat quarters of Sahiwal cows during different seasons to identify subclinical (SCM) and clinical mastitis (CM) cases using the Darvi DTL007 camera. A total of 24-69 lactating Sahiwal cows were screened out using IRT regularly throughout the year. The intramammary infection status was further assessed using the CMT. The receiver operating characteristic analysis was carried out to develop the current study's cut-off for various thermographic parameters. The incidence for SCM and CM ranged from 26.47 to 38.75% and 17.83-22.79%, respectively during different seasons in Sahiwal udder quarters. The thermogram analysis revealed a significant difference (p < 0.01) in the mean values of the udder and teat surface temperature of Sahiwal cows between healthy, SCM, and CM during different seasons. The mean values of udder skin surface temperature (USST) during different seasons ranged between 29.07 and 36.91 °C, 31.51 to 37.88 °C and 32.42 to 38.79 °C among healthy, SCM, and CM-affected quarters, and correspondingly, the mean values of teat skin surface temperature (TSST) were 28.28 to 36.77 °C, 30.68 to 37.88 °C and 31.70 to 38.73 °C, respectively. Further results revealed an increase (p < 0.01) in the mean values of USST during winter, summer, rainy, and autumn as 2.44, 3.35; 0.97, 1.88; 1.06, 1.83; 1.29, 2.39 °C and TSST as 2.4, 3.42; 1.11, 1.96; 1.21, 2.19, 1.3, 2.4 °C of SCM, CM-affected quarters to healthy quarters, respectively, in Sahiwal cows. Thermograms showed a strong positive correlation with the CMT scores of SCM, CM cases, and healthy samples. Henceforth, irrespective of the seasons studied in the present work, IRT is an efficient, supportive tool for the early identification of subclinical mastitis.

Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes

In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.

Considering potential roles of selected MicroRNAs in evaluating subclinical mastitis and Milk quality in California mastitis test (+) and infected bovine milk

This study investigates the relationships between subclinical mastitis and milk quality with selected microRNAs in cow milk. California Mastitis Test (CMT)-positive (n = 20) and negative (n = 20) samples were compared (Experiment I). Additionally, samples with CMT-positive but microbiological-negative, as well as positive for only Staphylococcus subspecies (Staph spp.) and only Streptococcus subspecies (Strep spp.) were examined (Experiment II). Four groups were formed in Experiment II: Group I (CMT and microbiological-negative) (n = 20), Group II (CMT-positive but microbiological-negative) (n = 10), Group III (Staph spp.) (n = 5), Group IV (Strep spp.) (n = 5). While electrical conductivity, somatic cell count (SCC), malondialdehyde (MDA) increased, miR-27a-3p and miR-223 upregulated and miR-125b downregulated in the CMT-positive group in Experiment I. SCC and MDA were higher in CMT-positive groups. miR-27a-3p and miR-223 upregulated in Groups III and IV. While miR-155 is upregulated, miR-125b downregulated in Group IV. Milk fat is positively correlated with miR-148a and miR-223. As miR-27a-3p positively correlated with SCC and MDA, miR-125b negatively correlated with electrical conductivity and SCC. miR-148a and MDA were positively correlated. miR-155 was correlated with fat-free dry matter, protein, lactose, and freezing point. miR-223 was positively correlated with SCC and miR-148a. Results particularly highlight miR-27a-3p and miR-223 as potential biomarkers in subclinical mastitis, especially those caused by Staph spp. and Strep spp., while miR-148a, miR-155, and miR-223 stand out in determining milk quality.

Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis

Mastitis is one of the most widespread infectious diseases that adversely affects the profitability of the dairy industry worldwide. Accurate diagnosis and identification of pathogens early to cull infected animals and minimize the spread of infection in herds is critical for improving treatment effects and dairy farm welfare. The major pathogens causing mastitis and pathogenesis are assessed first. The most recent and advanced strategies for detecting mastitis, including genomics and proteomics approaches, are then evaluated . Finally, the advantages and disadvantages of each technique, potential research directions, and future perspectives are reported. This review provides a theoretical basis to help veterinarians select the most sensitive, specific, and cost-effective approach for detecting bovine mastitis early.

Multi-criteria decision analysis for supporting the selection of subclinical mastitis screening tests to use in large- and small-scale dairy farms in Türkiye

The production of high-quality and safe milk is closely associated with the udder health of dairy cows. While there are many mastitis diagnostic tests/methods available, choosing the most appropriate diagnostic test for a sustainable udder health control program could be a challenge. This study was aimed at selecting tests for the screening of subclinical mastitis on small- and large-scale dairy farms in Türkiye, using multi-criteria decision-making methods. An integrated approach employing the analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) together was used to select subclinical mastitis screening tests for on-farm use. While the AHP determines the weights of the evaluation criteria, the TOPSIS provides a final ranking. Nine different subclinical mastitis screening (SCM) methods (DeLaval somatic cell counter, PortaSCC test, California mastitis test (CMT), rapid culture, portable/hand-held electrical conductivity meter, infrared thermography, leukocyte esterase strip test, milk pH, UdderCheck test) were analyzed on the basis of five selection criteria (the market availability of the test, the diagnostic accuracy of the test, the cost of the test, the cow-side use of the test, and the practicality of the test). The selection criteria were determined based on literature review and stakeholder input. The weighting of the criteria with the AHP was based on the pairwise comparison of the criteria by stakeholders. The criteria were weighted from 1 to 9 according to their relative importance as follows: "1: equally important," "3: moderately important," "5: strongly important," "7: very strongly important," "9: extremely important," and "2, 4, 6, 8: intermediate values." Final ranking of SCM tests with the TOPSIS was based on the stakeholder evaluations of fulfillment of the criteria by the alternatives. The most appropriate screening test for both large- and small-scale dairy farms was determined to be the CMT. The CMT is a very useful, easy to perform, and low-cost tool for detecting subclinical mastitis. Being a major element of udder health control programs, the CMT, if regularly used on dairy farms in Türkiye, would enable the culling of chronically infected animals and the reduction of mastitis-associated economic losses. Furthermore, regular CMTs would contribute to reducing milk SCC and improving milk quality. In conclusion, multi-criteria decision-making methods not only provide a systematic approach that may assist both veterinarians and farmers in deciding on the best choice among the different tests available for the screening of subclinical mastitis but also offer potential benefits to policymakers, researchers, and other industry stakeholders.

Assessing the relationship between somatic cell count and the milk mid-infrared spectrum in Chinese Holstein cows

BACKGROUND

Milk produced by dairy cows is a complex combination of many components, but the effect of mastitis has only been investigated for a few of these components. Milk mid-infrared (MIR) spectra can reflect the global composition of milk, and this study aimed to detect the relationships between milk MIR spectral wavenumbers and milk somatic cell count (SCC)-a sensitive biomarker for mastitis.

METHODS

Pearson correlation analysis was used to calculate the correlation coefficient between somatic count score (SCS) and spectral wavenumbers. A general linear mixed model was applied to investigate the effect of three different classes of SCC (low, middle and high) on spectral wavenumbers.

RESULTS

The mean correlation coefficient between the 'fingerprint region' (wavenumbers 925-1582 cm-1 ) and the SCS was higher than that for other regions of the MIR spectrum, and the specific wavenumber with the strongest correlation with the SCS was within the 'fingerprint region'. SCC class had a significant (p < 0.05) effect on 639 spectral wavenumbers. In particular, some spectral wavenumbers within the 'fingerprint region' were highly affected by the SCC class.

LIMITATION

The data were collected from only one province in China, so the generalisability of the findings may be limited.

CONCLUSION

SCC had close relationships with milk spectral wavenumbers related to important milk components or chemical bonds.

Short-milking-tube thermograms: An alternative to udder thermograms for mastitis detection in Sahiwal cows

Mastitis is a multi-etiological production disease that causes substantial financial loss to dairy farmers. In this context, early detection of mastitis using thermograms can aid the dairy sector in managing mastitis efficiently, and this technology could be a supportive tool in precision dairy farming. Infrared cameras can detect minor temperature changes on the udder surface by taking multiple images of the udder and teat. In the current study, a thermogram of the short milking tube (SMT) of the milking machine, as well as the udder and teat of lactating Sahiwal cow (n = 100 quarters of 25 Sahiwal cows), was captured using a hand-held digital infrared thermal camera (DarviDTL007) during morning milking to assess the mastitis status. CMT and SCC of milk samples were carried out for further confirmatory diagnosis of healthy, sub-clinical (SCM), and clinical mastitis (CM). Cut-offs for short milking tube temperature were developed using the receiver operating characteristics analysis. Results of thermal image analysis revealed that the pre-milking, milking, and post-milking parameters of the udder and the teat skin surface temperatures showed a significant difference in the healthy, SCM, and CM-affected quarters. The thermogram analysis showed a significant increase (p < 0.05) of 1.11 and 2.04°C in the mean values of SMT surface temperature among SCM and CM quarters compared to healthy quarters, respectively. In addition, the values of CMT and SCC revealed a significant increase (p < 0.05) in SCM and CM samples and a positive correlation to SMT surface temperatures. Short milking tube thermograms can be a useful assessment tool for detecting sub-clinical mastitis in dairy animals.

Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms

Mastitis is the most common disease among dairy cows and is known to have negative effects on both animal welfare and the profitability of dairy farms. Early detection of clinical mastitis cases is considered the best option for preventing cows from developing mastitis. In this study, we developed clinical mastitis prediction models that only required inputting common indicators from the automatic milking system. We utilized multidimensional data from the cow mastitis database of Afimilk (China) Agricultural Technology Co., Ltd. to predict mastitis in dairy cows. All data were screened for the period of 0-150 days of lactation. The data included parity, lactation day, period, mean and standard deviation of milk yield, of electrical conductivity, and of lying time, which were taken as input features. The classification of whether cows suffer from clinical mastitis was determined as output. We analyzed 426 cows with clinical mastitis and 2087 healthy cows by using four machine learning algorithms: Decision Tree, Random Forest, Back Propagation neural networks, and Support Vector Machines. In these four algorithms, the accuracy ranged from 94% to 98%, while the running times varied widely from seconds to minutes. The decision tree prediction model achieved an accuracy of 98% and the precision rate for healthy cows was 99%, while for mastitis cows it was 97%. Machine learning algorithms have played an important role in predicting cow mastitis, with the Decision Tree algorithm showing great performance and higher accuracy in our research.

Estimation of the performance of two real-time polymerase chain reaction assays for detection of Staphylococcus aureus, Streptococcus agalactiae, and Streptococcus dysgalactiae in pooled milk samples in a field study

The early detection of major mastitis pathogens is crucial for the udder health management of dairy herds. Testing of pooled milk samples, either individual test-day cow samples (TDCS) or aseptically collected pre-milk quarter samples (PMQS) may provide an easy to use and cost-effective group level screening tool. Therefore, the aim of this study was (1) to evaluate the sensitivity (Se) and specificity (Sp) of 2 commercial multiplex real-time PCR test kits applied to pooled milk samples using a Bayesian latent class analysis and (2) to estimate the probability of detection in relation to the pool size and the number of cows positively tested by bacteriological culture (BC) within a pool. Pools of 10, 20 and 50 cows were assembled from 1,912 test-day samples and 7,336 PMQS collected from a total of 2,045 cows from 2 commercial dairy farms. Two commercial quantitative real-time PCR kits were applied to detect Staphylococcus aureus, Streptococcus agalactiae, and Streptococcus dysgalactiae in the pooled samples, and a BC was applied to PMQS yielding a cumulative pool result. A pool was considered BC-positive if it contained at least one BC-positive PMQS. Pathogens were more frequently detected in the PMQS pools than in the TDCS pools. Pools of 10 cows showed the highest probability of detection irrespective of sample type or type of PCR kit compared with larger pool sizes. Estimation with a Bayesian latent class analysis resulted in a median Se in PMQS pools of 10 cows for Staph. aureus of 63.3% for PCR kit I, 78.1% for PCR kit II, and 95.5% for BC; the Sp values were 97.0%, 97.6%, and 89.1%, respectively. The estimated median Se for Strep. species for PCR kits ranged between 77.5 and 85.6% and for BC between 73.7% and 79.2%; the median Sp values ranged between 93.6 and 99.2% for PCR kits, and between 96.9% and 97.4% for BC. In addition, the probability of detection increased with an increasing number of BC-positive cows per pool. To achieve a probability of detection of 90%, the estimated number of positive cows in PMQS pools of 10 cows for kit I was 4.1 for Staph. aureus, 1.5 for Strep. agalactiae, and 1.3 for Strep. dysgalactiae; for the equivalent TDCS pools and pathogens, 6.9, 1.9, and 2.0 positive cows were required, respectively. For Kit II and PMQS pools, the number of positive cows required was 2.8 for Staph. aureus, 1.4 for Strep. agalactiae, and 1.2 for Strep. dysgalactiae; for the equivalent TDCS pools and pathogens, 5.3, 1.8, and 2.0 positive cows were required, respectively. In conclusion, the type of samples used for pooling, the pool size and the number of infected cows per pool determine the probability of detecting an infection with major mastitis pathogens within a pool by PCR testing.

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.

MALDI-TOF bacterial subtyping for rapid detection of biomarkers in Staphylococcus aureus from subclinical bovine mastitis

AIMS

This study aimed to evaluate matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) bacterial subtyping for the rapid detection of biomarkers in Staphylococcus aureus from subclinical bovine mastitis.

METHODS AND RESULTS

A total of 229 S. aureus isolates were obtained from milk samples collected from cows with subclinical mastitis using microbiological culture. Staphylococcus aureus isolates were also submitted to PCR analysis targeting the mecA and mecC genes, which are indicative of methicillin resistance. Confirmation of the species was achieved through MALDI-TOF MS analysis. To analyze antimicrobial resistance patterns, the MALDI BioTyper Compass Explorer and ClinProTools Bruker software were employed, and dendrograms were generated using Bionumerics software.

CONCLUSIONS

MALDI-TOF MS successfully identified S. aureus at the species level, but no methicillin resistance was observed. Moreover, spectral typing displayed limited similarity when compared to pulsed-field gel electrophoresis (PFGE).

Usefulness of the total and differential somatic cell count based udder health group concept for evaluating herd management practices and udder health in dairy herds

Subclinical mastitis and associated economic losses are a steady challenge in the dairy industry. The combination of the well-established somatic cell count (SCC) parameter and the new differential SCC (DSCC) opens up the possibility to categorise cows into four different udder health groups (UHG) based on results from a single milk recording/dairy herd improvement (DHI) test: UHG A: healthy/normal, ≤ 200,000 cells/mL and DSCC ≤ 65 %; B: suspicious, ≤ 200,000 cells/mL and DSCC > 65 %; C: (subclinical) mastitis, > 200,000 cells/mL and DSCC > 65 %; D: chronic/persistent mastitis, > 200,000 cells/mL and DSCC ≤ 65 %. The objectives of this study were to investigate 1) herd management practises among herds in different UHG categories and 2) herd performance parameters depending on the proportion of cows in UHG A. A total number of 41 herds in Styria, Austria, and Thuringia, Germany, were visited and interviewed for the first part of the study. The herds were categorised into 3 UHG categories depending on the proportion of cows in UHG A: I = >65 %; II = 55-65 %; and III = <55 %. Those with good udder health and best herd performance (+9 % milk yields, +11 % longevity, -35 % antibiotic treatments) applied distinct preventive measures, in particular excellent cubicle management and early antibiotic treatment (P < 0.05 each). However, preventive measures were applied to a lower extent in other herds. Herds were categorised differently using the UHG concept compared to SCC alone as the UHG-based categorisation allowed to clearer distinguish herds with medium-good from those with good udder health. A total number of 129,812 regular milk recording/DHI test day results of 890 Austrian and 183 German herds was used for the second part of the study. Results revealed a trend of increasing daily production as proportions of cows in UHG A increase. In conclusion, the UHG concept allowed clearer distinction of herds with good, medium-good, and poor udder health and could be used to promote practises leading to better animal health, less antibiotic treatments, and higher milk quality.

A sample-preparation-free, point-of-care testing system for in situ detection of bovine mastitis

We present a highly integrated point-of-care testing (POCT) device capable of immediately and accurately screening bovine mastitis infection based on somatic cell counting (SCC). The system primarily consists of a homemade cell-counting chamber and a miniature fluorescent microscope. The cell-counting chamber is pre-embedded with acridine orange (AO) in advance, which is simple and practical. And then SCC is directly identified by microscopic imaging analysis to evaluate the bovine mastitis infection. Only 4 μL of raw bovine milk is required for a simple sample testing and accurate SCC. The entire assay process from sampling to result in presentation is completed quickly within 6 min, enabling instant "sample-in and answer-out." Under laboratory conditions, we mixed bovine leukocyte suspension with whole milk and achieved a detection limit as low as 2.12 × 104 cells/mL on the system, which is capable of screening various types of clinical standards of bovine milk. The fitting degrees of the proposed POCT system with manual fluorescence microscopy were generally consistent (R2 > 0.99). As a proof of concept, four fresh milk samples were used in the test. The average accuracy of somatic cell counts was 98.0%, which was able to successfully differentiate diseased cows from healthy ones. The POCT system is user-friendly and low-cost, making it a potential tool for on-site diagnosis of bovine mastitis in resource-limited areas.

[Selective dry cow therapy on dairy farms in Southern Germany - a case series]

The aim of this case series was to describe how the selective dry-off therapy affected the udder health of Bavarian dairy farms under field conditions as well as to record whether a long-term reduction in antibiotic dry-off therapy was feasible. Between 2016 and 2021, 90 herds participated. A subset of dairy herds participated over a period of several years. Quarter milking samples were taken annually from all lactating cows in the herds, and treatment and test day results were evaluated. Major pathogens were detected during the initial whole herd testing (e. g., Streptococcus agalactiae, Streptococcus canis) and the treatment regimen needed to be adjusted. Even though the median treatment risk decreased, at least numerically, from 63% to 50%, the treatment rate in individual herds could vary greatly between years (-60% to+40%). Selective dry-off therapy can be implemented without endangering the udder health of the herd.

Relationship between total and differential quarter somatic cell counts at dry-off and early lactation

Mastitis is a most common disease of dairy cows and causes tremendous economic loss to the dairy industry worldwide. Somatic cell counts (SCC) reflect the inflammatory response to infections and is a metric used as key indicator in mastitis screening programs, typically within the framework of national milk recording schemes. Besides the determination of total SCC, the differentiation of cell types has been described to be beneficial for a more definite description of the actual udder health status of dairy cows. Differential somatic cell count (DSCC) represents the combined proportion of polymorphonuclear leukocytes (PMN) and lymphocytes expressed as a percentage of the total. The aim of this study was to investigate the relationship between SCC and differential somatic cell count (DSCC) in individual quarter milk samples collected at different time points: at dry-off, after calving and at the lactation peak. We used individual quarter data from farms representing the specialized production system of Parmigiano Reggiano cheese in Northern Italy. Average DSCC values ranged between 44.9% and 56.3%, with higher values (60.4%-72.1%) in milk samples with ≥ 1 million SCC/ml (where the proportion of samples with DSCC > 70% can be as high as 0.73). Moderate overall correlations between DSCC and log(SCC) were estimated (Pearson = 0.42, Spearman = 0.38), with a clear increasing trend with parity and around the lactation peak (e.g. Pearson = 0.59 at 60 DIM in parity 4). Taking SCC values as indicators of subclinical mastitis, DSCC would diagnose mastitis with 0.75 accuracy. Data editing criteria do have an impact on results, with stricter filtering leading to lower correlations between log(SCC) and DSCC. In conclusion DSCC and SCC provide different descriptions of the udder health status of dairy cows which, at least to some extent, are independent. DSCC alone doesn't provide more accurate information than SCC at quarter level but, used in combination with SCC, can be of potential interest within the framework of milk recording programs, especially in the context of selective dry-cow therapy (SDCT). However, this needs further investigation and updated threshold values need to be selected and validated.

Expression of anti-inflammatory markers IL-2, IL-10, TGF-β1, βDEF-2, βDEF-3 and Cathelicidin LL37 in dairy cattle milk with different health status of the udder

Great economic losses to the dairy industry are associated with bovine mastitis, which results in poor milk quality and high treatment costs. Anti-inflammatory proteins play an important role in the suppression of the immune response against invading pathogenic microorganisms and are therefore being studied for possible use in the early diagnosis of mastitis. In our study, we used milk samples from 15 cows of Holstein Friesian breed with different health status (5 healthy, 5 subclinical, and 5 clinical animals), and tested them using immunohistochemical (IHC) analysis to evaluate the presence of IL-2, IL-10, TGF-β1, βDEF-2, DEF-3, and Cathelicidin LL37 proteins. The calculation of positively and negatively stained cells for each biomarker was performed using the semiquantitative counting method. We found the presence of all factors with the exception of Cathelicidin LL37, which was almost absent in milk samples of all animal groups. The significant decrease of IL-10, β-def2, and β-def3 expression levels within the 3 days of sampling, found in the milk of animals with sub- and clinical mastitis, indicates the loss of antiinflammatory protection of the affected cow's udder. In contrast, the stable increase of IL-2 and TGF-β1 positive cells observed in the milk of mastitis-affected cows, and the similar expression of these factors in the milk of healthy animals, indicate the possible lack of involvement of these cytokines at an early stage of udder inflammation.

Milk proteins as mastitis markers in dairy ruminants - a systematic review

Mastitis is one of the most impacting diseases in dairy farming, and its sensitive and specific detection is therefore of the greatest importance. The clinical evaluation of udder and mammary secretions is typically combined with the milk Somatic Cell Count (SCC) and often accompanied by its bacteriological culture to identify the causative microorganism. In a constant search for improvement, several non-enzymatic milk proteins, including milk amyloid A (M-SAA), haptoglobin (HP), cathelicidin (CATH), and lactoferrin (LF), have been investigated as alternative biomarkers of mastitis for their relationship with mammary gland inflammation, and immunoassay techniques have been developed for detection with varying degrees of success. To provide a general overview of their implementation in the different dairy species, we carried out a systematic review of the scientific literature using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. Our review question falls within the type “Diagnostic test accuracy questions” and aims at answering the diagnostic question: “Which are the diagnostic performances of mastitis protein biomarkers investigated by immunoassays in ruminant milk?”. Based on 13 keywords combined into 42 searches, 523 manuscripts were extracted from three scientific databases. Of these, 33 passed the duplicate removal, title, abstract, and full-text screening for conformity to the review question and document type: 78.8% investigated cows, 12.1% sheep, 9.1% goats, and 6.1% buffaloes (some included more than one dairy species). The most frequently mentioned protein was M-SAA (48.5%), followed by HP (27.3%), CATH (24.2%) and LF (21.2%). However, the large amount of heterogeneity among studies in terms of animal selection criteria (45.5%), index test (87.9%), and standard reference test (27.3%) resulted in a collection of data not amenable to meta-analysis, a common finding illustrating how important it is for case definitions and other criteria to be standardized between studies. Therefore, results are presented according to the SWiM (Synthesis Without Meta-analysis) guidelines. We summarize the main findings reported in the 33 selected articles for the different markers and report their results in form of comparative tables including sample selection criteria, marker values, and diagnostic performances, where available. Finally, we report the study limitations and bias assessment findings.

Management of high cows-share-contribution of SCC to the bulk milk tank by acoustic pulse technology (APT)

A cow with mastitis has a high somatic cell count (SCC) in its milk. Cow-share-contribution of somatic cells to the bulk milk tank (BMTSCC) refers to the relative addition made by each cow's milk to the bulk tank's SCC. Since bulk milk is graded and priced according to the BMTSCC, high-yielding cows with mastitis are the main contributors to penalizations in milk price. The benefits of acoustic pulse technology (APT) application to tissues are well documented, including its anti-inflammatory effect and restoration of tissue function by triggering natural healing processes. An APT-based device was developed specifically for treating mastitis in dairy cows. It enables rapid and deep penetration of the acoustic pulses over a large area of the udder in a single session. A study was performed on six farms with a total of 3,900 cows. One unit of cow-share-contribution equaled the addition of 1,000 cells to each mL of the bulk milk volume above the mean BMTSCC. A total of 206 cows were selected: 103 were treated with APT and 103 served as controls. All of the cows contributed over 1.5 units to the BMTSCC at the time of treatment. Seventy-five days after APT treatment, 2 of the 103 treated cows (1.9%) were culled, compared to 19 (18.5%) of the 103 control cows, as well as infected quarter dry-off in 5 others (4.85%). Overall success was defined as a decrease of >75% in cow-share-contribution from treatment time in two of the three monthly milk recordings following treatment. Results indicated 57.3% success for the APT-treated cows vs. 14.6% for the untreated control groups. Highest share-contribution provide an additional tool for the farmer's decision of how to control BMTSCC. Because the cow-share-contribution value is relative to herd size and BMTSCC, this study included a similar number of cows, with similar SCC and milk yield from each of the six herds.

Bovine mastitis inflammatory assessment using silica coated ZnO-NPs induced fluorescence of NAGase biomarker assay

Bovine mastitis (BM) is the most common inflammatory disease in the dairy sector worldwide, originated from bacterial invasion onto the mammary gland. Early BM detection is crucial for identifying new pathogenic infections within the dairy herd, which can be alleviated by antimicrobial therapy. N-acetyl-β-D-glucosaminidase (NAGase) is a prominent BM inflammatory biomarker secreted onto the blood circulation upon pathogenesis and then released into milk, capable of separating healthy quarters from subclinical and clinical BM cases. Herein, we report on a sensitive differentiation assay of BM severity based on enhanced fluorescence emission of a conventional NAGase activity assay. The addition of silica-coated zinc oxide nanoparticles induces non-radiative energy transfer to the lysosomal reaction products, thus leading to enhanced fluorescence (above 3-fold). Various milk qualities within the entire inflammatory spectrum were evaluated by the modified fluorescence assay with respect to non-infected milk. The amplified emission values differentiate between two predominant BM causative pathogens (Streptococcus dysgalactiae and Escherichia coli) at various somatic cell counts. In general, the presented concept offers an efficient, simple, cost-effective fluorescence signal augmentation for mastitis identification, thus offering means to diagnose the severity of the associated disease.

Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis

Staphylococcus aureus is a serious human and animal pathogen threat exhibiting extraordinary capacity for acquiring new antibiotic resistance traits in the pathogen population worldwide. The development of fast, affordable and effective diagnostic solutions capable of discriminating between antibiotic-resistant and susceptible S. aureus strains would be of huge benefit for effective disease detection and treatment. Here we develop a diagnostics solution that uses Matrix-Assisted Laser Desorption/Ionisation-Time of Flight Mass Spectrometry (MALDI-TOF) and machine learning, to identify signature profiles of antibiotic resistance to either multidrug or benzylpenicillin in S. aureus isolates. Using ten different supervised learning techniques, we have analysed a set of 82 S. aureus isolates collected from 67 cows diagnosed with bovine mastitis across 24 farms. For the multidrug phenotyping analysis, LDA, linear SVM, RBF SVM, logistic regression, naïve Bayes, MLP neural network and QDA had Cohen's kappa values over 85.00%. For the benzylpenicillin phenotyping analysis, RBF SVM, MLP neural network, naïve Bayes, logistic regression, linear SVM, QDA, LDA, and random forests had Cohen's kappa values over 85.00%. For the benzylpenicillin the diagnostic systems achieved up to (mean result ± standard deviation over 30 runs on the test set): accuracy = 97.54% ± 1.91%, sensitivity = 99.93% ± 0.25%, specificity = 95.04% ± 3.83%, and Cohen's kappa = 95.04% ± 3.83%. Moreover, the diagnostic platform complemented by a protein-protein network and 3D structural protein information framework allowed the identification of five molecular determinants underlying the susceptible and resistant profiles. Four proteins were able to classify multidrug-resistant and susceptible strains with 96.81% ± 0.43% accuracy. Five proteins, including the previous four, were able to classify benzylpenicillin resistant and susceptible strains with 97.54% ± 1.91% accuracy. Our approach may open up new avenues for the development of a fast, affordable and effective day-to-day diagnostic solution, which would offer new opportunities for targeting resistant bacteria.

Staphylococcus aureus Isolated from Ruminants with Mastitis in Northern Greece Dairy Herds: Genetic Relatedness and Phenotypic and Genotypic Characterization

Staphylococcus aureus is the most common mastitis-related pathogen in dairy cattle, goats, and sheep worldwide. However, the population structure and genomic characteristics of mastitis-associated S. aureus in small ruminants are limited. Furthermore, the genotypic and phenotypic characteristics involved in the pathogenicity of S. aureus have been thoroughly defined, yet their association with the severity of mastitis is not fully established. Here, we performed genotyping by pulsed-field gel electrophoresis (PFGE) and spa analyses to assess the genetic diversity and relatedness of 162 S. aureus strains recovered from clinical mastitis (CM) and subclinical mastitis (SCM) cases from goats, sheep, and bovines. PFGE analysis revealed 108 distinguishable pulsotypes and 3 main clusters that comprised isolates from the three host species, while according to spa typing, 32 different spa types were identified. Genotypic analysis revealed a spreading of genetically related or indistinguishable S. aureus strains among ovine, caprine, and bovine farms of distant geographical regions. In total, 28 different staphylococcal enterotoxin (SE) gene profiles were observed, revealing a diverse range of SE genes among isolates. By evaluating the antimicrobial resistance, we found low phenotypic antimicrobial resistance among all ruminant isolates. We also performed multiple correspondence analysis, which indicated that the presence of the sec gene, biofilm production, and high autoaggregation ability are associated with CM cases.

Facile construction of a molecularly imprinted polymer-based electrochemical sensor for the detection of milk amyloid A

A molecularly imprinted electrochemical sensor for the detection of serum amyloid A (MAA) in milk was established for early diagnosis of subclinical mastitis in dairy cows. The electrochemical sensor was initially constructed using a nanocomposite material (reduced graphene oxide/gold nanoparticles, AuNPs@rGO) to modify the working electrode. The template protein, MAA, was then immobilized using pyrrole as the functional monomer to carry out the electropolymerization. Finally, the template protein was removed to form a molecular imprint film with the capability to qualitatively and quantitatively signaling of MAA. Cyclic voltammetry (CV), differential pulse voltammetry (DPV), and scanning electron microscopy (SEM) were used to characterize the modification process of the molecularly imprinted electrochemical sensors. Under optimized conditions, the sensor shows two well-behaved linear relationships in the MAA concentration range 0.01 to 200 ng/mL. A lower detection limit was estimated to be 5 pg/mL (S/N = 3). Other parameters including the selectivity, reproducibility (RSD 3.2%), and recovery rate (96.1-103%) are all satisfactory. Compared with the traditional methods, detection of MAA to determine the subclinical mastitis of dairy cows can efficiently be diagnosed and hence prevent an outbreak of dairy cow mastitis. The electrochemical sensor can detect MAA more rapidly, sensitively, and inexpensively than the ELISA-based MAA detection. These advantages indicate that the method is promising for early diagnosis of dairy cows.

Sensitive and rapid lateral-flow assay for early detection of subclinical mammary infection in dairy cows

Detection of subclinical mastitis (SCM) in its initial stage can save great economic losses, improve milk quality and animal welfare. We have developed a semiquantitative lateral flow assay for the detection of SCM in dairy cows targeting myeloperoxidase (MPO) enzyme of milk neutrophils. A competitive immunoassay format was used, and colloidal gold nanoparticles (GNP) were prepared and used as a labelling agent. Monoclonal anti-MPO antibodies were used and assessed for its quality by enzyme-linked immunosorbent assay and dot blot. Conjugation method for GNP and anti-MPO antibodies was standardised, and the conjugate was placed over the conjugate pad. MPO coupled with a carrier protein (OVA) and the species-specific secondary antibodies were placed on test and control lines, respectively. The developed assay was verified with 75 milk samples collected from healthy, SCM and clinical mastitis cows. It displayed a high sensitivity as it could detect MPO as low as 1.5 ng/ml, an accuracy greater than 97% and showed no crossreactivity when crosschecked with other milk proteins. The developed assay can be used as an alternative for SCM diagnostic tests where lab structure are available for obtaining the lysate of milk SCC.

Multiple Bacteria Identification in the Point-of-Care: an Old Method Serving a New Approach

The accurate diagnosis of bacterial infections is of critical importance for effective treatment decisions. Due to the multietiologic nature of most infectious diseases, multiplex assays are essential for diagnostics. However, multiplexability in nucleic acid amplification-based methods commonly resorts to multiple primers and/or multiple reaction chambers, which increases analysis cost and complexity. Herein, a polymerase chain reaction (PCR) offer method based on a universal pair of primers and an array of specific oligonucleotide probes was developed through the analysis of the bacterial 16S ribosomal RNA gene. The detection system consisted of DNA hybridization over an array of magnetoresistive sensors in a microfabricated biochip coupled to an electronic reader. Immobilized probes interrogated single-stranded biotinylated amplicons and were obtained using asymmetric PCR. Moreover, they were magnetically labelled with streptavidin-coated superparamagnetic nanoparticles. The benchmarking of the system was demonstrated to detect five major bovine mastitis-causing pathogens: Escherichia coli, Klebsiella sp., Staphylococcus aureus, Streptococcus uberis, and Streptococcus agalactiae. All selected probes proved to specifically detect their respective amplicon without significant cross reactivity. A calibration curve was performed for S. agalactiae, which demonstrates demonstrating a limit of detection below 30 fg/µL. Thus, a sensitive and specific multiplex detection assay was established, demonstrating its potential as a bioanalytical device for point-of-care applications.

Evaluation of rapid culture, a predictive algorithm, esterase somatic cell count and lactate dehydrogenase to detect intramammary infection in quarters of dairy cows at dry-off

Our objective was to compare four tests to standard milk culture followed by MALDI-ToF in quarters of cows at dry-off. Cows (n = 432) were randomly selected from seven U.S. dairy herds already participating in a multi-site clinical trial in summer 2018. Aseptic foremilk samples were collected from quarters (n = 1728) two days prior to dry-off, and subjected to index and reference tests. The four index tests included rapid culture, a predictive algorithm, an esterase strip test measuring somatic cell count (SCC) and a cow-side lactate dehydrogenase (LDH) test. Rapid culture was performed by inoculating quarter milk samples onto a commercial rapid culture plate. Plates were evaluated by technicians after 30-40 h of incubation at 37 ± 2 °C. Quarters were classified as infected if any bacterial growth was observed. For the algorithm test method, all quarters were classified as infected if the cow met any of the following criteria: 1) any Dairy Herd Improvement Association (DHIA) test with a SCC > 200,000 cells / ml during the current lactation or 2) two or more clinical mastitis cases during the current lactation. Esterase-SCC and cow-side LDH tests involved adding milk to the test strip and reading for color changes. For esterase-SCC and cow-side LDH tests, low (≥250 cells / ml and ≥100 U / L) and high (≥500 cells / ml and ≥200 U / L) thresholds were used to classify quarters as infected or not. Composite samples (4 × 2 mL quarter-milk samples commingled) were also tested for rapid culture, esterase-SCC and cow-side LDH tests, such that if a composite sample was positive, then all quarters contributing to that sample were classified as infected. The reference test was traditional aerobic culture conducted in an accredited laboratory using MALDI-ToF for identification of isolates. Traditional culture was only conducted on quarter-milk samples, and consequently, IMI was always considered at the quarter-level. Unconditional logistic regression was used to estimate sensitivity (SE), specificity (SP), apparent prevalence, positive predictive values (PPV) and negative predictive values (NPV) for each index test. Cohen's Kappa (κ) was used to measure agreement between tests. Algorithm, esterase-SCC and cow-side LDH tests had poor agreement with the reference test (κ ranging from 0.01 to 0.12), while rapid culture had fair agreement (κ = 0.28). No test had concurrently high SE and SP. Negative predictive values were moderate to high for all tests.

Investigation of differential somatic cell count as a potential new supplementary indicator to somatic cell count for identification of intramammary infection in dairy cows at the end of the lactation period

The objective of this study was to investigate the new differential somatic cell count (DSCC) as a supplementary indicator to SCC for the identification of intramammary infection (IMI) in dairy cows at the end of the lactation period. Different approaches for identification of cows with IMI (i.e. often based on SCC) and targeted antimicrobial treatment of those rather than of all cows have been developed (i.e. selective dry cow treatment). Recently, DSCC representing the proportion of polymorphonuclear neutrophils and lymphocytes, has been introduced as an additional indicator for the presence of IMI. We used the last dairy herd improvement (DHI) samples taken within 42 d prior to dry-off as well as hand-stripped samples collected within 5 days prior to dry-off to measure DSCC and SCC. The bacteriological status was determined using quarter foremilk samples collected close to drying off. In total, 582 cows were dried off during our study but not all of them could be included in the data analysis for different reasons (e.g. incomplete data, samples too old for reliable determination of SCC and DSCC, contamination). Eventually, the final data set comprised of 310 cows of which 64 and 149 were infected with major and minor pathogens, respectively, and 97 were uninfected. The area under receiver-operating characteristics curves (AUC) were calculated to compare the diagnostic abilities of the different parameters. The AUC for identification of IMI by major pathogens when using the combination of DSCC and SCC was 0.64 compared to 0.62 for SCC alone and 0.62 for DSCC alone. The different parameters were further compared based on test characteristics and predictive values. For example, classifying cows as infected based on a cut-off of 200,000 cells/ml for SCC alone and in terms of using DSCC combined with SCC based on either >60% and/or >200,000 cells/ml, the sensitivity changed from 47 to 66% and the specificity from 74 to 54%. At the same time, the negative predictive value changed from 84 to 86% and the positive predictive value from 32 to 27%. Test characteristics and predictive values of the parameters DSCC and SCC were similar using DHI and hand-stripped samples. In conclusion, our study provides first indications on test characteristics and predictive values for the combination of DSCC and SCC. However, more work on this subject and the actual practical application is needed.

The detection of intramammary infections using online somatic cell counts

Timely and accurate identification of cows with intramammary infections is essential for optimal udder health management. Various sensor systems have been developed to provide udder health information that can be used as a decision support tool for the farmer. Among these sensors, the DeLaval Online Cell Counter (DeLaval, Tumba, Sweden) provides somatic cell counts from every milking at cow level. Our aim was to describe and evaluate diagnostic sensor properties of these online cell counts (OCC) for detecting an intramammary infection, defined as an episode of subclinical mastitis or a new case of clinical mastitis. The predictive abilities of a single OCC value, rolling averages of OCC values, and an elevated mastitis risk (EMR) variable were compared for their accuracy in identifying cows with episodes of subclinical mastitis or new cases of clinical mastitis. Detection of subclinical mastitis episodes by OCC was performed in 2 separate groups of different mastitis pathogens, Pat 1 and Pat 2, categorized by their known ability to increase somatic cell count. The data for this study were obtained in a field trial conducted in the dairy herd of the Norwegian University of Life Sciences. Altogether, 173 cows were sampled at least once during a 17-mo study period. The total number of quarter milk cultures was 5,330. The most common Pat 1 pathogens were Staphylococcus epidermidis, Staphylococcus aureus, and Streptococcus dysgalactiae. The most common Pat 2 pathogens were Corynebacterium bovis, Staphylococcus chromogenes, and Staphylococcus haemolyticus. The OCC were successfully recorded from 82,182 of 96,542 milkings during the study period. For episodes of subclinical mastitis the rolling 7-d average OCC and the EMR approach performed better than a single OCC value for detection of Pat 1 subclinical mastitis episodes. The EMR approach outperformed the OCC approaches for detection of Pat 2 subclinical mastitis episodes. For the 2 pathogen groups, the sensitivity of detection of subclinical mastitis episodes was 69% (Pat 1) and 31% (Pat 2), respectively, at a predefined specificity of 80% (EMR). All 3 approaches were equally good at detecting new cases of clinical mastitis, with an optimum sensitivity of 80% and specificity of 90% (single OCC value).

Diagnostic value of composite milk sample vs single quarter milk sample for the detection of Staphylococcus aureus intra-mammary infections in cattle

Staphylococcus aureus (S. aureus) is one of the most important causes of mastitis in dairy cattle. Control and eradication programs of S. aureus intra-mammary infections (IMI) are based on different factors included the correct detection and management of the infected cows. The present study aimed at evaluating the efficacy of composite milk samples (CMS) analysis, compared to quarter milk samples (QMS) analysis, for the bacteriological detection of S. aureus intra-mammary infections. During 2016, 661 CMS (hygienically collected) and 2644 QMS (aseptically collected) were obtained from 661 cows in 5 herds. All the samples were submitted to S. aureus bacteriological culture and somatic cell count (SCC) analysis. QMS bacteriological analysis on blood agar plates was able to detect 236 cows excreting S. aureus, while the bacteriological analysis of CMS, using selective agar, identified 229 positive cows. The concordance was 95% with an excellent Cohen's κ (0.89). Relative sensitivity and specificity of CMS vs QMS, considered as the reference test, were 91.5% ± 2.1 and 96.9% ± 1.3 (CI 95%), respectively. In addition, the relative sensitivity of CMS improved as the number of infected quarters per cow and the number of colony forming units (cfu) per sample increased. The predictive value of CMS results was better when paired with SCC data, in particular CMS showed better negative predictive value when SCC was <200,000 cells/mL and better positive predictive value when SCC was>200,000 cells/mL. The probability for a cow to be S. aureus positive was 56.4% in case of SCC > 200,000 cells/mL, while it was 18.6% in case of SCC < 200,000 cells/mL. The average SCC in CMS was significantly higher in positive cows and the value rose as the number of infected quarters per cow increased. Given the intermittent excretion of S. aureus in milk from dairy cows, it could be more advantageous to carry out several serial CMS, rather than few QMS, being CMS an easier to collect and less expensive milk sampling method. Thus, bacteriological examination of CMS, combined with SCC data of the same sample, could be extremely useful for the success of S. aureus IMI control plans, because repeated CMS are easier to be performed and could be more easily proposed to the farmers.

An Update on the Effect of Clinical Mastitis on the Welfare of Dairy Cows and Potential Therapies

Despite the widespread implementation of mastitis control programs, mastitis is the most common and one of the costliest diseases in the dairy industry, with broad-ranging impacts and consequences. Recent technological advances have allowed researchers to assess the effects of mastitis on animal behavior and welfare, and the efficacy of mastitis treatments. Several nonsteroidal anti-inflammatory drugs are available as supportive therapies for clinical mastitis. This article focuses on recent advances in the assessment, therapy, and effects of mastitis on cow behavior and welfare.

Transcriptomics and iTRAQ-Proteomics Analyses of Bovine Mammary Tissue with Streptococcus agalactiae-Induced Mastitis

Mastitis is a highly prevalent disease in dairy cows that causes large economic losses. Streptococcus agalactiae is a common contagious pathogen and a major cause of bovine mastitis. The immune response to intramammary infection with S. agalactiae in dairy cows is a very complex biological process. To understand the host immune response to S. agalactiae-induced mastitis, mammary gland of lactating Chinese Holstein cows was challenged with S. agalactiae via nipple tube perfusion. Visual inspection, analysis of milk somatic cell counts, histopathology, and transmission electron microscopy of mammary tissue were performed to confirm S. agalactiae-induced mastitis. Microarray and isobaric tags for relative and absolute quantitation (iTRAQ) were used to compare the transcriptomes and proteomes of healthy and mastitic mammary tissue. Compared with healthy tissue, a total of 129 differentially expressed genes (DEGs, fold change >2, p < 0.05) and 144 differentially expressed proteins (DEPs, fold change >1.2, p < 0.05) were identified in mammary tissue from S. agalactiae-challenged cows. Among the concordant 18 DEGs/DEPs, immunoglobulin M precursor, cathelicidin-7 precursor, integrin alpha-5, and complement C4-A-like isoform X1 were associated with mastitis. Intramammary infection with S. agalactiae triggered a complex host innate immune response that involved complement and coagulation cascades, ECM-receptor interaction, focal adhesion, and phagosome and bacterial invasion of epithelial cells pathways. These results provide candidate genes or proteins for further studies in the context of prevention and targeted treatment of bovine mastitis.

Evaluation of milk sample fractions for characterization of milk microbiota from healthy and clinical mastitis cows

Amplicon sequencing technique has been increasingly applied to the clinical setting as a sensitive diagnostic tool. Therefore, it is of great importance to develop a DNA extraction method that accurate isolates DNA from complex host-associated microbiota. Given the multifactorial etiology of clinical mastitis and the diversified lifestyle of bacterial species harboring in milk, here four distinct milk sample fractions: raw whole milk, milk fat, casein-pellet, and casein-pellet + fat from healthy cows and cows with clinical mastitis, were subjected to bead-beating DNA extraction, followed by high-throughput sequencing. We aimed to identify the best approach for characterization of the milk microbiota and detection of mastitis pathogens (Klebsiella spp., Streptococcus spp. and Escherichia coli). DNA from each milk fraction tested was extracted by two commercial kits, which include physical, mechanical and chemical lysis; in total 280 DNA samples from 35 cows were analyzed. Milk-health-status were categorized into four groups (healthy group; E. coli-mastitis group; Klebsiella spp.-mastitis group; and Streptococcus spp.–mastitis group). Bacterial phyla and families were described for each milk-health-status group across milk sample fractions and DNA extraction kits. For the mastitis groups the relative abundance of f__Enterobacteriaceae and f__Streptococcaceae were compared to determine the efficacy of procedures in detecting the mastitis pathogens. The four milk fractions used allowed efficiently and uniformly detection of the causative agent of mastitis. Only 27% of the families detected in healthy milk were shared among the samples extracted from all fractions of milk samples; followed by 3, 4, and 12% for the samples from E. coli-mastitis, Klebsiella spp.-mastitis and Streptococcus spp-mastitis, respectively. However, the shared families comprised a mean relative abundance greater than 85%, regardless of milk-health-status, milk fraction and DNA isolation method. Taxonomic data at the family level showed that sequences from mastitis milk samples cultured positive for E. coli and Klebsiella spp. were predominantly affiliated with f__Enterobacteriaceae, while for Streptococcus spp. were dominated by f__Streptococcacea, followed by f__Pseudomonadaceae and f__Enterococcaceae. Microbial community analysis revealed that most of the microbial community composition corresponded to milk bacterial species irrespective of the DNA isolation method and milk fraction evaluated.

Comparative analysis of four commercial on-farm culture methods to identify bacteria associated with clinical mastitis in dairy cattle

Several multiple-media culture systems have become commercially available for on-farm identification of mastitis-associated pathogens. However, the accuracy of these systems has not been thoroughly and independently validated against microbiological evaluations performed by referral laboratories. Therefore, the purpose of the present study was to evaluate the performance of commercially available culture plates (Accumast, Minnesota Easy System, SSGN and SSGNC Quad plates) to identify pathogens associated with clinical mastitis in dairy cows. Milk samples from the affected quarter with clinical mastitis were aerobically cultured with the on-farm culture systems and by two additional reference laboratories. Agreeing results from both standard laboratories were denoted as the reference standard (RS). Accuracy (Ac), sensitivity (Se), specificity (Sp), positive and negative predictive values (PPV and NPV, respectively) and Cohen’s kappa coefficient (k) of on-farm plates were determined based on the RS culture of 211 milk samples. All four plate-systems correctly identified ≥ 84.9% of milk samples with no bacterial growth. Accumast had greater values for all overall predictive factors (Ac, Se, Sp, PPV and NPV) and a substantial agreement (k = 0.79) with RS. The inter-rater agreements of Minnesota, SSGN, and SSGNC with RS were moderate (0.45 ≤ k ≤ 0.55). The effectiveness to categorize bacterial colonies at the genus and species was numerically different amongst the commercial plates. Our findings suggest that Accumast was the most accurate on-farm culture system for identification of mastitis-associated pathogens of the four systems included in the analysis.