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.

Periparturient blood T-lymphocyte PD-1 and CTLA-4 expression as potential predictors of new intramammary infections in dairy cows during early lactation (short communication).

BACKGROUND: The periparturient period in dairy cows is marked by immunosuppression which increases the likelihood of infectious disorders, particularly also mastitis. An in-depth understanding of peripartum leukocyte biology is vital for the implementation of highly successful post-partum disease prevention measures. Immune checkpoint molecules, such as programmed death 1 (PD-1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4), are critical inhibitory receptors expressed on immune cells, particularly T cells, that drive immunosuppressive signaling pathways. However, the potential role of immune checkpoint molecules expression in T-cells on udder health has never been explored. Thus, the association between the occurrence of new postpartum intramammary infections (IMIs) and the expression of programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) on blood T-cells during the peripartum period was investigated. RESULTS: In this study, the incidence of IMIs by any pathogen in early lactation was not associated with a higher expression of PD-1 and CTLA-4 in the periparturient period. However, the incidence of IMIs by major pathogens throughout the first month of lactation was significantly associated with higher expression of PD-1 at 14 days before calving (P = 0.03) and CTLA-4 at parturition (P = 0.03) by blood T-cells. Also, the expression of CTLA-4 at D0 (P = 0.012) by T-cells was associated with the occurrence of persistent IMIs during the first month of lactation. CONCLUSIONS: To our knowledge, this is the first report to investigate the expression of PD-1 and CTLA-4 by blood T-lymphocytes during the periparturient period in dairy cows and to explore their relationship with the incidence of new IMIs in the postpartum period. Thus, a comprehensive understanding of leukocyte biology during peripartum would appear to be a prerequisite for the identification of resilient dairy cows or targets innovative (immunological) non-antibiotic approaches in the transition period.

In-Line Detection of Clinical Mastitis by Identifying Clots in Milk Using Images and a Neural Network Approach.

Automated milking systems (AMSs) already incorporate a variety of milk monitoring and sensing equipment, but the sensitivity, specificity, and positive predictive value of clinical mastitis (CM) detection remain low. A typical symptom of CM is the presence of clots in the milk during fore-stripping. The objective of this study was the development and evaluation of a deep learning model with image recognition capabilities, specifically a convolutional neural network (NN), capable of detecting such clots on pictures of the milk filter socks of the milking system, after the phase in which the first streams of milk have been discarded. In total, 696 pictures were taken with clots and 586 pictures without. These were randomly divided into 60/20/20 training, validation, and testing datasets, respectively, for the training and validation of the NN. A convolutional NN with residual connections was trained, and the hyperparameters were optimized based on the validation dataset using a genetic algorithm. The integrated gradients were calculated to explain the interpretation of the NN. The accuracy of the NN on the testing dataset was 100%. The integrated gradients showed that the NN identified the clots. Further field validation through integration into AMS is necessary, but the proposed deep learning method is very promising for the inline detection of CM on AMS farms.