Systemic inflammatory biomarkers and somatic cell count in dairy cows with subclinical mastitis

Overview of Bovine Mastitis: Application of Metabolomics in Screening Its Predictive and Diagnostic Biomarkers

Bovine mastitis is an inflammatory disease of the mammary glands, and its pathogenesis and diagnosis are complicated. Through qualitative and quantitative analysis of small-molecule metabolites, the metabolomics technique plays an important role in finding biomarkers and studying the metabolic mechanism of bovine mastitis. Therefore, this paper reviews the predictive and diagnostic biomarkers of bovine mastitis that have been identified using metabolomics techniques and that are present in samples such as milk, blood, urine, rumen fluid, feces, and mammary tissue. In addition, the metabolic pathways of mastitis-related biomarkers in milk and blood were analyzed; it was found that the tricarboxylic acid (TCA) cycle was the most significant (FDR = 0.0015767) pathway in milk fluid, and glyoxylate and dicarboxylate metabolism was the most significant (FDR = 0.0081994) pathway in blood. The purpose of this review is to provide useful information for the prediction and early diagnosis of bovine mastitis.

Comparative analysis of Streptococcus agalactiae serotypes Ia and II isolates from China and Pakistan in a murine model: A focus on pathogenesis and immune response

Bovine mastitis, caused by Streptococcus agalactiae (Group B Streptococcus; GBS), poses significant economic challenges to the global dairy industry. Mouse models serves as valuable tools for assessing GBS-induced infections as an alternative to large animals. This study aimed to investigate the LD50 dose, organ bacterial load, and quantification of peritoneal leukocyte populations for GBS serotypes Ia and II isolates from China and Pakistan. Additionally, we measured indicators such as lactoferrin, albumin, and myeloperoxidase (MPO) activity. Pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-2) and anti-inflammatory cytokines (IL-10 and TGF-β) in serum and tissue samples were evaluated using ELISA and qPCR, respectively. BALB/c mice (4 mice per group) received individual intraperitoneal injections of 100 μl containing specific bacterial inoculum concentrations (ranging from 105 to 109 CFU per mouse) of Chinese and Pakistani GBS isolates (serotypes Ia and II). Control groups received 100 μL of sterile PBS. Results revealed that the LD50 bacterial dose causing 50 % mortality in mice was 107 CFU. The highest bacterial load in all experimental groups was quantified in the peritoneum, followed by blood, mammary gland, liver, spleen, lungs, and brain. The most significant bacterial dissemination was observed in mice inoculated with Pakistani serotype Ia at 24 h, with a subsequent notable decline in bacterial counts at day 3. Notably, infection with Pakistani serotype Ia showed a trend of increased total leukocyte counts, significantly higher than Pakistani serotype II, Chinese Serotype Ia, and Chinese serotype II. A substantial influx of neutrophils and lymphocytes was observed in response to all tested serotypes, with Pakistani serotype Ia inducing a significantly higher influx compared to other groups (Pakistani serotype II, Chinese serotype Ia, and Chinese serotype II). Furthermore, TNF-α, IL-1β, IL-2, and IL-6 expressions were significantly increased in mice one day after infection with the Pakistani serotype Ia. Compared to mice infected with the Pakistani serotype II, Chinese Serotype Ia, and Chinese serotype II, those infected with the Pakistani serotype Ia isolate exhibited the highest production of IL-10 and TGF-β, along with significantly increased concentrations of lactoferrin, albumin, and MPO. These findings suggest that the persistence and severity of infection caused by the Pakistani serotype Ia may be linked to its ability to spread to deeper tissues. This study enhances our understanding of the clinical characteristics of bovine mastitis caused by S. agalactiae in China and Pakistan.

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.

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.

Individual genomic loci, transcript level, and biochemical profile of immune and antioxidant markers associated with genetically identified bacterial mastitis in Shami goats in Egypt

Background

Mastitis in goats is unquestionably a grave concern, with far-reaching implications for both animal well-being and productivity, while also presenting a potential threat to public health.

Aim

The study aimed to compare culture methods and multiplex PCR (m-PCR) in the detection of the most three common mastitis-causing pathogens (Staphylococcus aureus, Escherichia coli, and Streptococcus spp.) and investigate the gene expression, single nucleotide polymorphisms (SNPs), serum concentrations of immunological and antioxidant indicators linked to mastitis in Shami goats.

Methods

One hundred Shami do (50 Shami goats with clinical mastitis and 50 normal goats taken as control group). The culture methods and m-PCR analysis were used to find the bacteria in the milk samples. Blood samples were obtained to assess some hemato-biochemical parameters, detect SNPs, and determine the expression of certain immunological and antioxidant indicators in the genes.

Results

The culture method detected the pathogens causing mastitis in 90% of the milk samples, but m-PCR detected them in 100% of the milk samples. SNPs linked to mastitis resistance/susceptibility in examined does were detected through DNA sequencing of immunological and antioxidant indicators. The magnitude of gene expression varied significantly between the resistant and mastitis-affected groups. Significant (P ˂ 0.05) elevations were noticed in WBCs count, mainly neutsrophils count, serum levels of BHB, NEFA, triglycerides, LDL-C, AST, ALT, ALP, creatinine, total protein, globulin, Ca, K, GPx, MDA, acute phase proteins, and cytokines in mastitis affected does as compared to control. While RBCs count, PCV%, lymphocytes count, serum concentration of glucose, cholesterol, HDL-C, albumin, Na, Cl, P, GSH, SOD, and catalase significantly (P ˂ 0.05) diminished in mastitis affected does compared to healthy ones. APPs and pro-inflammatory cytokines scored high sensitivities and NPVs but TNF-α and serum amyloid A (SAA) had the highest percentages of increase.

Conclusion

The study confirmed that m-PCR is the most sensitive method for bacteria identification (S. aureus, E. coli, and Strept. spp.) while SNPs in antioxidant and immunological genes may be important genetic indicators for mastitis risk or resistance in Shami does. To establish an effective management plan and forecast the most sensitive risk time for illness onset, gene expression profiles of the tested genes may also be employed as proxy biomarkers. TNF-α and SAA may be precious indicators for the detection of caprine mastitis.

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.

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.

Assessment of Mastitis Patterns in Serbian Dairy Cows: Blood Serum Metabolic Profile and Milk Composition Parameters

Mastitis is one of the most important diseases in dairy cows, leading to substantial economic losses associated with decreased milk production and quality. Early detection of changes in metabolic and milk parameters is crucial for maintaining animal welfare and milk quality. This study aimed to detect patterns in metabolic and milk composition parameters in Serbian dairy cows affected by mastitis. It also examined the relationship between these factors in cows with clinical and subclinical mastitis, as well as in healthy cows. This study included 60 Holstein-Friesian cows with the same body score condition that were in the same lactation phase. They were divided into three groups of 20: clinical and subclinical mastitis and a control group of healthy cows. The categorization was based on clinical udder health and the California mastitis test. Blood serum metabolic profiles were measured using a Rayto spectrophotometer (Shenzhen, China), and milk composition was determined using MilcoScanTM (Foss, Hilleroed, Denmark) and FossomaticTM (Foss, Hilleroed, Denmark) instruments. Significant increases in non-esterified fatty acids (NEFAs), beta-hydroxybutyrate (BHB), total protein, globulin, urea, total bilirubin, magnesium, and enzyme activity were noted in mastitis-affected cows compared to healthy ones. Additionally, mastitis-affected cows had higher total protein and globulin levels and increased somatic cell counts (SCCs), while albumin concentrations were decreased. Furthermore, a negative correlation between total protein and lactose suggested inflammation leading to reduced lactose levels due to cell damage, infection, and lactose use by mastitis pathogens. Hence, indicators of the energy and protein status of the metabolic profile, together with the chemical composition of milk, may be significant diagnostic tools for detecting, monitoring, and predicting the outcome of mastitis in cows.

Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.

Multiple-Vessel-Based Blood Gas Profiles Analysis Revealed the Potential of Blood Oxygen in Mammary Vein as Indicator of Mammary Gland Health Risk of High-Yielding Dairy Cows

The blood gas profile is a routine method in the rapid disease diagnosis of farm animals, yet its potential in evaluating mammary health status of dairy cows remains to be investigated. This study was conducted to learn the potential of the blood gas parameter regarding the mammary gland health status in lactating dairy cows. Twenty animals were divided into two groups, the H-SCC group (milk SCC > 122 k/mL) and L-SCC group (milk SCC < 73.8 k/mL), to compare blood gas profiles from different blood vessels and to identify the key parameters associated with milk somatic cell count. H-SCC cows are higher in malondialdehyde content, but lower in SOD and T-AOC activities in the milk, compared to the L-SCC group. In terms of blood gas parameters, most differ across the three vessels, including K+, CO2 pressure, O2 pressure, HCO3−, base excess in the extracellular fluid compartment, and saturation of O2. The Pearson correlation analysis showed that oxygen-related variables in the mammary vein, including oxygen concentrations, O2 pressure, and saturation of O2, are negatively correlated with levels of malondialdehyde, lactate dehydrogenase, and plasmin in the milk. Our study revealed that oxygen-related variables in the mammary vein can be a marker in suggesting mammary-gland health status in high-yielding cows.

Nanocurcumin alleviates inflammation and oxidative stress in LPS-induced mastitis via activation of Nrf2 and suppressing TLR4-mediated NF-κB and HMGB1 signaling pathways in rats

Coliform mastitis is a worldwide serious disease of the mammary gland. Curcumin is a pleiotropic polyphenol obtained from turmeric, but it is hydrophobic and rapidly eliminated from the body. However, nanoformulation of curcumin significantly improves its pharmacological activity by enhancing its hydrophobicity and oral bioavailability. Our study aimed to investigate the possible antioxidant and anti-inflammatory effects of nanocurcumin as a prophylactic against LPS-induced coliform mastitis in rat model, where LPS was extracted from a field strain of Escherichia coli (bovine mastitis isolate). The study was conducted on twenty lactating Wistar female rats divided into four equal groups, and the mastitis model was initiated by injection of LPS through the duct of the mammary gland. The results showed that nanocurcumin significantly attenuated the lipid peroxidation (MDA), oxidized glutathione, the release of pro-inflammatory cytokines (TNF-α and IL-1β), and the gene expression of TLR4, NF-κB p65, and HMGB1. Meanwhile, it improved the reduced glutathione level and Nrf2 activity and preserved the normal alveolar architecture. These findings suggested that nanocurcumin supplementation can be a promising potential protective approach for coliform mastitis.