Optimization and Testing of a Commercial Viability PCR Protocol to Detect <i>Escherichia coli</i> in Whole Blood

Bacteremia, specifically if progressed to sepsis, poses a time-sensitive threat to human and animal health. Escherichia coli is a main causative agent of sepsis in humans. The objective was to evaluate a propidium monoazide (PMA)-based viability PCR (vPCR) protocol to detect and quantify live E. coli from whole blood. We optimized the protocol by adding a eukaryotic-specific lysis step prior to PMA exposure, then used spiking experiments to determine the lower limit of detection (LOD) and linear range of quantification. We also compared the vPCR quantification method to standard colony count of spiked inoculum. Lastly, we calculated percent viability in spiked samples containing 50% live cells or 0% live cells. The LOD was 102 CFU/mL for samples containing live cells only and samples with mixed live and heat-killed cells. The linear range of quantification was 102 CFU/mL to 108 CFU/mL (R2 of 0.997) in samples containing only live cells and 103 CFU/mL to 108 CFU/mL (R2 of 0.998) in samples containing live plus heat-killed cells. A Bland–Altman analysis showed that vPCR quantification overestimates compared to standard plate count of the spiked inoculum, with an average bias of 1.85 Log10 CFU/mL across the linear range when only live cells were present in the sample and 1.98 Log10 CFU/mL when live plus heat-killed cells were present. Lastly, percent viability calculations showed an average 89.5% viable cells for samples containing 50% live cells and an average 19.3% for samples containing 0% live cells. In summary, this optimized protocol can detect and quantify viable E. coli in blood in the presence of heat-killed cells. Additionally, the data presented here provide the groundwork for further development of vPCR to detect and quantify live bacteria in blood in clinical settings

Establishing Galleria mellonella as an invertebrate model for the emerging multi-host pathogen Helcococcus ovis

ABSTRACTHelcococcus ovis (H. ovis) can cause disease in a broad range of animal hosts, including humans, and has been described as an emerging bacterial pathogen in bovine metritis, mastitis, and endocarditis. In this study, we developed an infection model that showed H. ovis can proliferate in the hemolymph and induce dose-dependent mortality in the invertebrate model organism Galleria mellonella (G. mellonella). We applied the model and identified H. ovis isolates with attenuated virulence originating from the uterus of a healthy post-partum dairy cow (KG38) and hypervirulent isolates (KG37, KG106) originating from the uterus of cows with metritis. Medium virulence isolates were also isolated (KG36, KG104) from the uterus of cows with metritis. A major advantage of this model is that a clear differentiation in induced mortality between H. ovis isolates was detected in just 48 h, resulting in an effective infection model able to identify virulence differences between H. ovis isolates with a short turnaround time. Histopathology showed G. mellonella employs hemocyte-mediated immune responses to H. ovis infection, which are analogous to the innate immune response in cows. In summary, G. mellonella can be used as an invertebrate infection model for the emerging multi-host pathogen Helcococcus ovis

Application of behavior data to predictive exploratory models of metritis self-cure and treatment failure in dairy cows

ABSTRACT: The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated-health monitoring device (AHMD) from −21 ± 3 to 60 ± 3 d relative to calving to monitor rumination time and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 d in milk (DIM). Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD), were randomly allocated to 1 of 2 treatments: control (CON; n = 62), no treatment at the time of metritis diagnosis (d 0); or ceftiofur (CEF; n = 70), subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC, and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and DIM at metritis diagnosis) and behavior data (i.e., daily averages of rumination time, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with P-values ≤0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, with sensitivity (Se) of 89% and specificity (Sp) of 77%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, with Se of 93% and Sp of 87%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions