Kerstin Brügemann

Flow cytometric differential cell counts in milk for the evaluation of inflammatory reactions in clinically healthy and subclinically infected bovine mammary glands

Somatic cell counts (SCC) are generally used as an indicator of udder health. In Germany, a cutoff value of 100,000 cells/mL is currently used to differentiate between healthy and diseased mammary glands. In addition to SCC, differential cell counts (DCC) can be applied for a more detailed evaluation of the udder health status. The aim of this study was to differentiate immune cells in milk of udder quarters classified as healthy based on SCC values of <100,000 cells/mL. Twenty cows were selected and 65 healthy udder quarters were compared with a control group of 15 diseased udder quarters (SCC>100,000 cells/mL). Cells were isolated from milk of all quarters to measure simultaneously percentages of lymphocytes, macrophages, and polymorphonuclear neutrophilic leukocytes (PMNL) by flow cytometric analysis. The bacteriological status of all 80 quarters was also determined. Differential cell count patterns of milk samples (n = 15) with extreme low SCC values of ≤ 6,250 cells/mL revealed high lymphocyte proportions of up to 88%. Milk cell populations in samples (n = 42) with SCC values from >6,250 to ≤ 25,000 cells/mL were also dominated by lymphocytes, whereas DCC patterns of 6 out of 41 milk samples with SCC values from ≥ 9,000 to ≤ 46,000 cells/mL indicated already inflammatory reactions based on the predominance of PMNL (56-75%). In 13 of 15 milk samples of the diseased udder quarters (SCC >100,000 cells/mL), PMNL were categorically found as dominant cell population with proportions of ≥ 49%. Macrophages were the second predominant cell population in almost all samples tested in relation to lymphocytes and PMNL. Further analysis of the data demonstrated significant differences of the cellular components between udder quarters infected by major pathogens (e.g., Staphylococcus aureus; n = 5) and culture-negative udder quarters (n = 56). Even the percentages of immune cells in milk from quarters infected by minor pathogens (e.g., coagulase-negative staphylococci; n = 19) differed significantly from those in milk of culture-negative quarters. Our flow cytometric analysis of immune cells in milk of udder quarters classified as healthy by SCC <100,000 cells/mL revealed inflammatory reactions based on DCC.

Microscopic differential cell counts in milk for the evaluation of inflammatory reactions in clinically healthy and subclinically infected bovine mammary glands.

Somatic cell count (SCC) is generally regarded as an indicator of udder health. A cut-off value of 100×10(3) cells/ml is currently used in Germany to differentiate between normal and abnormal secretion of quarters. In addition to SCC, differential cell counts (DCC) can be applied for a more detailed analysis of the udder health status. The aim of this study was to differentiate somatic cells in foremilk samples of udder quarters classified as normal secreting by SCC <100×10(3) cells/ml. Twenty cows were selected and 72 normal secreting udder quarters were compared with a control group of six diseased quarters (SCC >100×10(3) cells/ml). In two severely diseased quarters of the control group (SCC of 967×10(3) cells/ml and 1824×10(3) cells/ml) Escherichia coli and Staphylococcus aureus were detected. DCC patterns of milk samples (n = 25) with very low SCC values of ≤6·25×10(3)cells/ml revealed high lymphocyte proportions of up to 92%. Milk cell populations in samples (n = 41) with SCC values of (>6·25 to ≤25)×10(3) cells/ml were also dominated by lymphocytes (mean value 47%), whereas DCC patterns of milk from udder quarters (n = 6) with SCC values (>25 to ≤100)×10(3)cells/ml changed. While in samples (n = 3) with SCC values of (27-33)×10(3) cells/ml macrophages were predominant (35-40%), three milk samples with (43-45)×10(3) cells/ml indicated already inflammatory reactions based on the predominance of polymorphonuclear leucocytes (PMN) (54-63%). In milk samples of diseased quarters PMN were categorically found as dominant cell population with proportions of ≥65%. Macrophages were the second predominant cell population in almost all samples tested in relationship to lymphocytes and PMN. To our knowledge, this is the first study evaluating cell populations in low SCC milk in detail. Udder quarters classified as normal secreting by SCC <100×10(3) cells/ml revealed already inflammatory processes based on DCC.

Inferring relationships between clinical mastitis, productivity and fertility: a recursive model application including genetics, farm associated herd management, and cow-specific antibiotic treatments.

A dataset of test-day records, fertility traits, and one health trait including 1275 Brown Swiss cows kept in 46 small-scale organic farms was used to infer relationships among these traits based on recursive Gaussian-threshold models. Test-day records included milk yield (MY), protein percentage (PROT-%), fat percentage (FAT-%), somatic cell score (SCS), the ratio of FAT-% to PROT-% (FPR), lactose percentage (LAC-%), and milk urea nitrogen (MUN). Female fertility traits were defined as the interval from calving to first insemination (CTFS) and success of a first insemination (SFI), and the health trait was clinical mastitis (CM). First, a tri-trait model was used which postulated the recursive effect of a test-day observation in the early period of lactation on liability to CM (LCM), and further the recursive effect of LCM on the following test-day observation. For CM and female fertility traits, a bi-trait recursive Gaussian-threshold model was employed to estimate the effects from CM to CTFS and from CM on SFI. The recursive effects from CTFS and SFI onto CM were not relevant, because CM was recorded prior to the measurements for CTFS and SFI. Results show that the posterior heritability for LCM was 0.05, and for all other traits, heritability estimates were in reasonable ranges, each with a small posterior SD. Lowest heritability estimates were obtained for female reproduction traits, i.e. h(2)=0.02 for SFI, and h(2)≈0 for CTFS. Posterior estimates of genetic correlations between LCM and production traits (MY and MUN), and between LCM and somatic cell score (SCS), were large and positive (0.56-0.68). Results confirm the genetic antagonism between MY and LCM, and the suitability of SCS as an indicator trait for CM. Structural equation coefficients describe the impact of one trait on a second trait on the phenotypic pathway. Higher values for FAT-% and FPR were associated with a higher LCM. The rate of change in FAT-% and in FPR in the ongoing lactation with respect to the previous LCM was close to zero. Estimated recursive effects between SCS and CM were positive, implying strong phenotypic impacts between both traits. Structural equation coefficients explained a detrimental impact of CM on female fertility traits CTFS and SFI. The cow-specific CM treatment had no significant impact on performance traits in the ongoing lactation. For most treatments, beta-lactam-antibiotics were used, but test-day SCS and production traits after the beta-lactam-treatment were comparable to those after other antibiotic as well as homeopathic treatments.

Simulation, prediction, and genetic analyses of daily methane emissions in dairy cattle

This study presents an approach combining phenotypes from novel traits, deterministic equations from cattle nutrition, and stochastic simulation techniques from animal breeding to generate test-day methane emissions (MEm) of dairy cows. Data included test-day production traits (milk yield, fat percentage, protein percentage, milk urea nitrogen), conformation traits (wither height, hip width, body condition score), female fertility traits (days open, calving interval, stillbirth), and health traits (clinical mastitis) from 961 first lactation Brown Swiss cows kept on 41 low-input farms in Switzerland. Test-day MEm were predicted based on the traits from the current data set and 2 deterministic prediction equations, resulting in the traits labeled MEm1 and MEm2. Stochastic simulations were used to assign individual concentrate intake in dependency of farm-type specifications (requirement when calculating MEm2). Genetic parameters for MEm1 and MEm2 were estimated using random regression models. Predicted MEm had moderate heritabilities over lactation and ranged from 0.15 to 0.37, with highest heritabilities around DIM 100. Genetic correlations between MEm1 and MEm2 ranged between 0.91 and 0.94. Antagonistic genetic correlations in the range from 0.70 to 0.92 were found for the associations between MEm2 and milk yield. Genetic correlations between MEm with days open and with calving interval increased from 0.10 at the beginning to 0.90 at the end of lactation. Genetic relationships between MEm2 and stillbirth were negative (0 to -0.24) from the beginning to the peak phase of lactation. Positive genetic relationships in the range from 0.02 to 0.49 were found between MEm2 with clinical mastitis. Interpretation of genetic (co)variance components should also consider the limitations when using data generated by prediction equations. Prediction functions only describe that part of MEm which is dependent on the factors and effects included in the function. With high probability, there are more important effects contributing to variations of MEm that are not explained or are independent from these functions. Furthermore, autocorrelations exist between indicator traits and predicted MEm. Nevertheless, this integrative approach, combining information from dairy cattle nutrition with dairy cattle genetics, generated novel traits which are difficult to record on a large scale. The simulated data basis for MEm was used to determine the size of a cow calibration group for genomic selection. A calibration group including 2,581 cows with MEm phenotypes was competitive with conventional breeding strategies.

Genetic line comparisons and genetic parameters for endoparasite infections and test-day milk production traits

Keeping dairy cows in grassland systems relies on detailed analyses of genetic resistance against endoparasite infections, including between- and within-breed genetic evaluations. The objectives of this study were (1) to compare different Black and White dairy cattle selection lines for endoparasite infections and (2) the estimation of genetic (co)variance components for endoparasite and test-day milk production traits within the Black and White cattle population. A total of 2,006 fecal samples were taken during 2 farm visits in summer and autumn 2015 from 1,166 cows kept in 17 small- and medium-scale organic and conventional German grassland farms. Fecal egg counts were determined for gastrointestinal nematodes (FEC-GIN) and flukes (FEC-FLU), and fecal larvae counts for the bovine lungworm Dictyocaulus viviparus (FLC-DV). The lowest values for gastrointestinal nematode infections were identified for genetic lines adopted to pasture-based production systems, especially selection lines from New Zealand. Heritabilities were low for FEC-GIN (0.05-0.06 ± 0.04) and FLC-DV (0.05 ± 0.04), but moderate for FEC-FLU (0.33 ± 0.06). Almost identical heritabilities were estimated for different endoparasite trait transformations (log-transformation, square root). The genetic correlation between FEC-GIN and FLC-DV was 1.00 ± 0.60, slightly negative between FEC-GIN and FEC-FLU (-0.10 ± 0.27), and close to zero between FLC-DV and FEC-FLU (0.03 ± 0.30). Random regression test-day models on a continuous time scale [days in milk (DIM)] were applied to estimate genetic relationships between endoparasite and longitudinal test-day production traits. Genetic correlations were negative between FEC-GIN and milk yield (MY) until DIM 85, and between FEC-FLU and MY until DIM 215. Genetic correlations between FLC-DV and MY were negative throughout lactation, indicating improved disease resistance for high-productivity cows. Genetic relationships between FEC-GIN and FEC-FLU with milk protein content were negative for all DIM. Apart from the very early and very late lactation stage, genetic correlations between FEC-GIN and milk fat content were negative, whereas they were positive for FEC-FLU. Genetic correlations between FEC-GIN and somatic cell score were positive, indicating similar genetic mechanisms for susceptibility to udder and endoparasite infections. The moderate heritabilities for FEC-FLU suggest inclusion of FEC-FLU into overall organic dairy cattle breeding goals to achieve long-term selection response for disease resistance.

Phenotypic, genetic, and single nucleotide polymorphism marker associations between calf diseases and subsequent performance and disease occurrences of first-lactation German Holstein cows

A total of 31,396 females born from 2010 to 2013 in 43 large-scale Holstein-Friesian herds were phenotyped for calf and cow disease traits using a veterinarian diagnosis key. Calf diseases were general disease status (cGDS), calf diarrhea (cDIA), and calf respiratory disease (cRD) recorded from birth to 2 mo of age. Incidences were 0.48 for cGDS, 0.28 for cRD, and 0.21 for cDIA. Cow disease trait recording focused on the early period directly after calving in first parity, including the interval from 10 d before calving to 200 d in lactation. For cows, at least one entry for the respective disease implied a score = 1 (sick); otherwise, score = 0 (healthy). Corresponding cow diseases were first-lactation general disease status (flGDS), first-lactation diarrhea (flDIA), and first-lactation respiratory disease (flRD). Additional cow disease categories included mastitis (flMAST), claw disorders (flCLAW), female fertility disorders (flFF), and metabolic disorders (flMET). A further cow trait category considered first-lactation test-day production traits from official test-days 1 and 2 after calving. The genotype data set included 41,256 single nucleotide polymorphisms (SNP) from 9,388 females with phenotypes. Linear and generalized linear mixed models with a logit link-function were applied to Gaussian and categorical cow traits, respectively, considering the calf disease as a fixed effect. Most of the calf diseases were not significantly associated with the occurrence of any cow disease. By trend, increasing risks for the occurrence of cow diseases were observed for healthy calves, indicating mechanisms of disease resistance with aging. Also by trend, occurrence of calf diseases was associated with decreasing milk, protein, and fat yields. Univariate linear and threshold animal models were used to estimate heritabilities and breeding values (EBV) for all calf and cow traits. Heritabilities for cGDS and cRD were 0.06 and 0.07 for cDIA. Genetic correlations among all traits were estimated using linear-linear animal models in a series of bivariate runs. The genetic correlation between cDIA and cRD was 0.29. Apart from the genetic correlation between flRD with cGDS (-0.38), EBV correlations and genetic correlations between calf diseases with all cow traits were close to zero. Genome-wide association studies were applied to estimate SNP effects for cRD and cDIA, and for the corresponding traits observed in cows (flRD and flDIA). Different significant SNP markers contributed to cDIA and flDIA, or to cRD and flRD. The average correlation coefficient between cRD and flRD considering SNP effects from all chromosomes was 0.01, and between cDIA and flDIA was -0.04. In conclusion, calf diseases are not appropriate early predictors for cow traits during the early lactation stage in parity 1.

Alternative strategies for genetic analyses of milk flow in dairy cattle

Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components using a variety of statistical models. For bivariate linear-threshold model applications, Gaussian-distributed AMF (linear sire model) was categorized into 2 distinct classes (threshold sire model) by setting arbitrary thresholds for extremely slow or extremely fast milking cows. In different bivariate runs with the 2 traits, Gaussian AMF and binary AMF, within a Bayesian framework, thresholds for the binary trait were 1.2, 1.6, 2.6, and 2.8 kg/min. Posterior heritabilities for AMF from the linear and the threshold models in all runs were in a narrow range and close to 0.26, and the posterior genetic correlation between AMF, defined as either a Gaussian or binary trait, was 0.99. A data subset was used to infer genetic and phenotypic relationships between AMF with test-day traits milk yield, fat percentage, protein percentage, somatic cell score (SCS), fat-to-protein ratio, and energy-corrected milk using recursive linear sire models, standard multiple trait linear sire models, and multiple trait linear sire models accounting for the effect of a trait 1 on a trait 2, and of trait 2 on trait 3, via linear regressions. The time-lagged 3-trait system focused on the first test-day trait after calving (trait 1), on AMF (trait 2), and on the test-day trait (trait 3) after the AMF measurement. Posterior means for heritabilities for AMF from linear and recursive linear models used for the reduced data set ranged between 0.29 and 0.38, and were slightly higher than heritabilities from the threshold models applied to the full data set. Genetic correlations from the recursive linear model and the linear model were similar for identical trait combinations including AMF and test-day traits 1 and 3. The largest difference was found for the genetic correlation between AMF and fat percentage from the first test day (i.e., -0.31 from the recursive linear model vs. -0.26 from the linear model). Genetic correlations from the linear model, including an additional regression coefficient, partly differed, especially when comparing correlations between AMF and SCS and between AMF and fat-to-protein ratio recorded after the AMF measurement data. Structural equation coefficients from the recursive linear model and corresponding regression coefficients from the linear model with additional regression, both depicting associations on the phenotypic scale, were quite similar. From a physiological perspective, all models confirmed the antagonistic relationship between SCS with AMF on genetic and phenotypic scales. A pronounced recursive relationship was also noted between productivity (milk yield and energy-corrected milk) and AMF, suggesting further research using physiological parameters as indicators for cow stress response (e.g., level of hormones) should be conducted.

Patent gastrointestinal nematode infections in organically and conventionally pastured dairy cows and their impact on individual milk and fertility parameters

Infections with gastrointestinal nematodes (GIN) can lead to production losses and impacts on product quality in affected cows, which has mainly been demonstrated during deworming experiments or via herd-level measurements. Here, a field study was carried out to explore the association between GIN infection status and milk production as well as fertility parameters in individual dairy cows. Different selection lines of Black and White cows were included in the study, which were distributed among 17 small and medium-sized organic and conventional German grassland farms. Faecal samples of 1166 dairy cows were examined twice, in July and September 2015. Nematode eggs were found in the faeces of 473 (40.6%) cows. As expected, strongylid eggs (Trichostrongylidae or Oesophagostomum and Bunostomum spp., respectively) were the predominant morphotype, followed by Strongyloides papillosus and Capillaria spp. eggs. In July, cows kept under organic conditions had a significantly lower GIN prevalence in comparison to cows kept on conventional farms. Faecal egg counts were generally low, with the highest value in September and an arithmetic mean of 11.3 eggs per gram faeces (EPG) for all observations. The relationships between GIN infection status and milk yield (kg milk/cow/day), milk protein content (%) and milk fat content (%) for each first test-day record after parasitological assessment were estimated by using linear mixed models. Milk protein content was estimated 0.05% lower in GIN positive compared to GIN negative cows, whereas no significant effect on milk yield or milk fat content was observed. The impact of GIN infection status on success in first insemination (SFI) was estimated by using a threshold model. No significant association was demonstrated between GIN infection status and SFI. Unexpectedly, the fertility parameter days from calving-to-first-service (CTFS) showed a significantly shorter average interval in GIN positive cows. However, these data on reproductive performance need to be considered preliminary as long-term studies are needed to allow a firm prediction of the impact of GIN infection status on dairy cow fertility parameters.