Associations between different udder health groups defined based on a combination of total and differential somatic cell count and the future udder health status of dairy cows

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, termed ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss (ML) estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of the daily heritability estimates over lactation were 0.086 (0.075-0.095) for SCS, 0.104 (0.073-0.127) for ML_SCS_DSCC, 0.137 (0.014-0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115-0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

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

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

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

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

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.

Association between differential somatic cell count and California Mastitis Test results in Holstein cattle

The California Mastitis Test (CMT) has been used to estimate total somatic cell count (SCC) levels in milk; however, milk with similar SCC levels occasionally shows inconsistent CMT results, which limits the use of the CMT. This observational study aimed to investigate how differential cell counts in milk influence the CMT in Holstein cattle through the novel parameters differential somatic cell count (DSCC) and macrophage proportion (MAC). We performed the CMT on d 0, 3, 5, 7, 14, and 21 after identifying mastitis, and simultaneously measured SCC, DSCC, and MAC at the quarter level. We followed 58 mastitis events occurring in 41 cows and obtained 307 quarter-level records after data cleaning. We transformed SCC to somatic cell score (SCS) and MAC to its logarithm to fit the normal distribution and analyzed the data using the cumulative logit mixed model. Results showed that both an increase in SCS (odds ratio: 3.66, 95% confidence interval: 2.89-4.64) and the logarithm of MAC (odds ratio: 4.35, 95% confidence interval: 1.91-9.91) can contribute to a higher CMT score. During the healing process of mastitis, MAC tends to increase as SCC decreases; thus, even samples with low SCC can cause positive CMT reactions. We recommend that practitioners avoid making treatment decisions based on the CMT alone. We also noted that the CMT is sensitive to subclinical mastitis with high MAC, hence it could be considered an alternative to detecting high MAC (chronic) mastitis.

Testing Two Somatic Cell Count Cutoff Values for Bovine Subclinical Mastitis Detection Based on Milk Microbiota and Peripheral Blood Leukocyte Transcriptome Profile

Somatic cell count (SCC) is an important indicator of the health state of bovine udders. However, the exact cut-off value used for differentiating the cows with healthy quarters from the cows with subclinical mastitis remains controversial. Here, we collected composite milk (milk from four udder quarters) and peripheral blood samples from individual cows in two different dairy farms and used 16S rRNA gene sequencing combined with RNA-seq to explore the differences in the milk microbial composition and transcriptome of cows with three different SCC levels (LSCC: <100,000 cells/mL, MSCC: 100,000−200,000 cells/mL, HSCC: >200,000 cells/mL). Results showed that the milk microbial profiles and gene expression profiles of samples derived from cows in the MSCC group were indeed relatively easily discriminated from those from cows in the LSCC group. Discriminative analysis also uncovered some differentially abundant microbiota at the genus level, such as Bifidobacterium and Lachnospiraceae_AC2044_group, which were more abundant in milk samples from cows with SCC below 100,000 cells/mL. As for the transcriptome profiling, 79 differentially expressed genes (DEGs) were found to have the same direction of regulation in two sites, and functional analyses also showed that biological processes involved in inflammatory responses were more active in MSCC and HSCC cows. Overall, these results showed a similarity between the milk microbiota and gene expression profiles of MSCC and HSCC cows, which presented further evidence that 100,000 cells/ml is a more optimal cut-off value than 200,000 cells/mL for intramammary infection detection at the cow level.

Using High-Resolution Differential Cell Counts (HRDCCs) in Bovine Milk and Blood to Monitor the Immune Status over the Entire Lactation Period

Differential cell counts in milk offer a deeper insight into the immunology of the mammary gland and might even provide information about the systemic health status of a dairy cow. Consequently, their potential as a diagnostic method to identify biomarkers has been a subject of research for quite some time. The objective of our study was to closely monitor the immune status of eight healthy dairy cows throughout their whole lactation. For this, high-resolution differential cell counts in milk and blood were determined by means of flow cytometry, which included 10 subpopulations of the 3 main populations of immune cells and their viability. Milk and blood samples were taken twice a week in the first 100 days after calving and once a week during the remaining lactation period: in total, 55 (52–57) blood and 55 (52–57) milk samples per animal. In addition, six well-established routine laboratory biomarkers, i.e., haptoglobin, calcium, and different metabolic parameters (non-esterified fatty acids, β-hydroxybutyric acid, bilirubin, and glutamate dehydrogenase), were analyzed in all blood samples. Furthermore, a standard differential blood cell count was performed on all blood samples. We found substantial differences between cell count progressions in the blood and milk. The distribution of cell populations in the blood remained mostly stable throughout the lactation, albeit at different individual levels. Several cell populations in the milk showed a noticeable dynamic over time, which caused a separation of different lactation stages in clustering analyses. Gamma delta T cells and CD4+ T cells in the milk stood out as they showed characteristic fluctuations during the course of lactation as well as minor changes in the case of inflammation. The determination of a differential cell count has the potential to be a sensitive diagnostic and prognostic tool in bovine milk. Further studies need to show to what extent the method is suitable for routine diagnostics and how to deal with animal-specific differences.