Genomic epidemiology of third-generation cephalosporin-resistant Escherichia coli from Argentinian pig and dairy farms reveals animal-specific patterns of co-resistance and resistance mechanisms.

Control measures are being introduced globally to reduce the prevalence of antibiotic resistance (ABR) in bacteria on farms. However, little is known about the current prevalence and molecular ecology of ABR in bacterial species with the potential to be key opportunistic human pathogens, such as Escherichia coli, on South American farms. Working with 30 dairy cattle farms and 40 pig farms across two provinces in central-eastern Argentina, we report a comprehensive genomic analysis of third-generation cephalosporin-resistant (3GC-R) E. coli, which were recovered from 34.8% (cattle) and 47.8% (pigs) of samples from fecally contaminated sites. Phylogenetic analysis revealed substantial diversity suggestive of long-term horizontal and vertical transmission of 3GC-R mechanisms. CTX-M-15 and CTX-M-2 were more often produced by isolates from dairy farms, while CTX-M-8 and CMY-2 and co-carriage of amoxicillin/clavulanate resistance and florfenicol resistance were more common in isolates from pig farms. This suggests different selective pressures for antibiotic use in these two animal types. We identified the ß-lactamase gene blaROB, which has previously only been reported in the family Pasteurellaceae, in 3GC-R E. coli. blaROB was found alongside a novel florfenicol resistance gene, ydhC, also mobilized from a pig pathogen as part of a new composite transposon. As the first comprehensive genomic survey of 3GC-R E. coli in Argentina, these data set a baseline from which to measure the effects of interventions aimed at reducing on-farm ABR and provide an opportunity to investigate the zoonotic transmission of resistant bacteria in this region. IMPORTANCE: Little is known about the ecology of critically important antibiotic resistance among bacteria with the potential to be opportunistic human pathogens (e.g., Escherichia coli) on South American farms. By studying 70 pig and dairy cattle farms in central-eastern Argentina, we identified that third-generation cephalosporin resistance (3GC-R) in E. coli was mediated by mechanisms seen more often in certain species and that 3GC-R pig E. coli were more likely to be co-resistant to florfenicol and amoxicillin/clavulanate. This suggests that on-farm antibiotic usage is key to selecting the types of E. coli present on these farms. 3GC-R E. coli and 3GC-R plasmids were diverse, suggestive of long-term circulation in this region. We identified the de novo mobilization of the resistance gene blaROB from pig pathogens into E. coli on a novel mobile genetic element, which shows the importance of surveying poorly studied regions for antibiotic resistance that might impact human health.

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission.

OBJECTIVES: Antibacterial resistance (ABR) is a major global health security threat, with a disproportionate burden on lower-and middle-income countries (LMICs). It is not understood how 'One Health', where human health is co-dependent on animal health and the environment, might impact the burden of ABR in LMICs. Thailand's 2017 "National Strategic Plan on Antimicrobial Resistance" (NSP-AMR) aims to reduce AMR morbidity by 50% through 20% reductions in human and 30% in animal antibacterial use (ABU). There is a need to understand the implications of such a plan within a One Health perspective. METHODS: A model of ABU, gut colonisation with extended-spectrum beta-lactamase (ESBL)-producing bacteria and transmission was calibrated using estimates of the prevalence of ESBL-producing bacteria in Thailand. This model was used to project the reduction in human ABR over 20 years (2020-2040) for each One Health driver, including individual transmission rates between humans, animals and the environment, and to estimate the long-term impact of the NSP-AMR intervention. RESULTS: The model predicts that human ABU was the most important factor in reducing the colonisation of humans with resistant bacteria (maximum 65.7-99.7% reduction). The NSP-AMR is projected to reduce human colonisation by 6.0-18.8%, with more ambitious targets (30% reductions in human ABU) increasing this to 8.5-24.9%. CONCLUSIONS: Our model provides a simple framework to explain the mechanisms underpinning ABR, suggesting that future interventions targeting the simultaneous reduction of transmission and ABU would help to control ABR more effectively in Thailand.

Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework.

OBJECTIVES: To develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case. DESIGN: Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths. SETTING: SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making. PARTICIPANTS: Publicly available data on patients with COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES: The expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time. RESULTS: SW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7). CONCLUSIONS: The developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.

Reduced Antibacterial Drug Resistance and blaCTX-M ß-Lactamase Gene Carriage in Cattle-Associated Escherichia coli at Low Temperatures, at Sites Dominated by Older Animals, and on Pastureland: Implications for Surveillance.

Little is known about the drivers of critically important antibacterial resistance in species with zoonotic potential present on farms (e.g., CTX-M ß-lactamase-positive Escherichia coli). We collected samples monthly between January 2017 and December 2018 on 53 dairy farms in South West England, along with data for 610 variables concerning antibacterial usage, management practices, and meteorological factors. We detected E. coli resistant to amoxicillin, ciprofloxacin, streptomycin, and tetracycline in 2,754/4,145 (66%), 263/4,145 (6%), 1,475/4,145 (36%), and 2,874/4,145 (69%), respectively, of samples from fecally contaminated on-farm and near-farm sites. E. coli positive for blaCTX-M were detected in 224/4,145 (5.4%) of samples. Multilevel, multivariable logistic regression showed antibacterial dry cow therapeutic choice (including use of cefquinome or framycetin) to be associated with higher odds of blaCTX-M positivity. Low average monthly ambient temperature was associated with lower odds of blaCTX-ME. coli positivity in samples and with lower odds of finding E. coli resistant to each of the four test antibacterials. This was in addition to the effect of temperature on total E. coli density. Furthermore, samples collected close to calves had higher odds of having E. coli resistant to each antibacterial, as well as E. coli positive for blaCTX-M Samples collected on pastureland had lower odds of having E. coli resistant to amoxicillin or tetracycline, as well as lower odds of being positive for blaCTX-MIMPORTANCE Antibacterial resistance poses a significant threat to human and animal health and global food security. Surveillance for resistance on farms is important for many reasons, including tracking impacts of interventions aimed at reducing the prevalence of resistance. In this longitudinal survey of dairy farm antibacterial resistance, we showed that local temperature-as it changes over the course of a year-was associated with the prevalence of antibacterial-resistant E. coli We also showed that prevalence of resistant E. coli was lower on pastureland and higher in environments inhabited by young animals. These findings have profound implications for routine surveillance and for surveys carried out for research. They provide important evidence that sampling at a single time point and/or single location on a farm is unlikely to be adequate to accurately determine the status of the farm regarding the presence of samples containing resistant E. coli.

Flux sampling is a powerful tool to study metabolism under changing environmental conditions.

The development of high-throughput 'omic techniques has sparked a rising interest in genome-scale metabolic models, with applications ranging from disease diagnostics to crop adaptation. Efficient and accurate methods are required to analyze large metabolic networks. Flux sampling can be used to explore the feasible flux solutions in metabolic networks by generating probability distributions of steady-state reaction fluxes. Unlike other methods, flux sampling can be used without assuming a particular cellular objective. We have undertaken a rigorous comparison of several sampling algorithms and concluded that the coordinate hit-and-run with rounding (CHRR) algorithm is the most efficient based on both run-time and multiple convergence diagnostics. We demonstrate the power of CHRR by using it to study the metabolic changes that underlie photosynthetic acclimation to cold of Arabidopsis thaliana plant leaves. In combination with experimental measurements, we show how the regulated interplay between diurnal starch and organic acid accumulation defines the plant acclimation process. We confirm fumarate accumulation as a requirement for cold acclimation and further predict γ-aminobutyric acid to have a key role in metabolic signaling under cold conditions. These results demonstrate how flux sampling can be used to analyze the feasible flux solutions across changing environmental conditions, whereas eliminating the need to make assumptions which introduce observer bias.