Phenotypic and genotypic assessment of iron acquisition in diverse bovine-associated non-aureus staphylococcal strains.

Although the role of iron in bacterial infections has been well described for Staphylococcus (S.) aureus, iron acquisition in (bovine-associated) non-aureus staphylococci and mammaliicocci (NASM) remains insufficiently mapped. This study aimed at elucidating differences between four diverse bovine NASM field strains from two species, namely S. chromogenes and S. equorum, in regards to iron uptake (with ferritin and lactoferrin as an iron source) and siderophore production (staphyloferrin A and staphyloferrin B) by investigating the relationship between the genetic basis of iron acquisition through whole genome sequencing (WGS) with their observed phenotypic behavior. The four field strains were isolated in a previous study from composite cow milk (CCM) and bulk tank milk (BTM) in a Flemish dairy herd. Additionally, two well-studied S. chromogenes isolates originating from a persistent intramammary infection and from a teat apex were included for comparative purpose in all assays. Significant differences between species and strains were identified. In our phenotypical iron acquisition assay, while lactoferrin had no effect on growth recovery for all strains in iron deficient media, we found that ferritin served as an effective source for growth recovery in iron-deficient media for S. chromogenes CCM and BTM strains. This finding was further corroborated by analyzing potential ferritin iron acquisition genes using whole-genome sequencing data, which showed that all S. chromogenes strains contained hits for all three proposed ferritin reductive pathway genes. Furthermore, a qualitative assay indicated siderophore production by all strains, except for S. equorum. This lack of siderophore production in S. equorum was supported by a quantitative assay, which revealed significantly lower or negligible siderophore amounts compared to S. aureus and S. chromogenes. The WGS analysis showed that all tested strains, except for S. equorum, possessed complete staphyloferrin A (SA)-synthesis and export operons, which likely explains the phenotypic absence of siderophore production in S. equorum strains. While analyzing the staphyloferrin A and staphyloferrin B operon landscapes for all strains, we noticed some differences in the proteins responsible for iron acquisition between different species. However, within strains of the same species, the siderophore-related proteins remained conserved. Our findings contribute valuable insights into the genetic elements associated with bovine NASM pathogenesis.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms.

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6 ± 5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81 ± 0.47. The PMC caused an average milk loss of 0.77 ± 0.26 kg per lactating cow per day, which corresponded to an average production loss of 2.38 ± 0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring.

Udder health problems are often associated with milk losses. These losses are different between quarters, as infected quarters are affected both by systemic and pathogen-specific local effects, whereas noninfected quarters are only subject to systemic effects. To gain insight in these losses and the milk yield dynamics during disease, it is essential to have a reliable reference for quarter-level milk yield in an unperturbed state, mimicking its potential yield. We developed a novel methodology to predict this quarter milk yield per milking session, using an historical data set of 504 lactations collected on a test farm by an automated milking system from DeLaval (Tumba, Sweden). Using a linear mixed model framework in which covariates associated with the linearized Wood model and the milking interval are included, we were able to describe quarter-level yield per milking session with a proportional error below 10%. Applying this model enables us to predict the milk yield of individual quarters 1 to 50 d ahead with a mean prediction error ranging between 8 and 20%, depending on the amount of historical data available to estimate the random effect covariates for the predicted lactation. The developed methodology was illustrated using 2 examples for which quarter-level milk losses are calculated during clinical mastitis. These showed that the quarter-level mixed model allows us to gain insight in quarter lactation dynamics and enables to calculate milk losses in different situations.