Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks.

Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528 Mycobacterium tuberculosis, 1,694 Escherichia coli, 658 Salmonella enterica, and 1,236 Staphylococcus aureus isolates were included. We developed machine learning models trained by the features of the PointFinder and ResFinder programs detected to predict binary (susceptible/resistant) AMR profiles. We tested four feature representation methods to determine the most efficient way for introducing features into the models. When training the model only on the Mycobacterium tuberculosis isolates, high prediction performances were obtained for the six AMR profiles included. By adding information on ciprofloxacin from the additional 3,588 isolates, there was no reduction in performance for the other antimicrobials but an increased performance for ciprofloxacin AMR profile prediction for Mycobacterium tuberculosis and Escherichia coli In conclusion, the species-independent models can predict multi-AMR profiles for multiple species without losing any robustness.IMPORTANCE Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.

Predicting effects of changed antimicrobial usage on the abundance of antimicrobial resistance genes in finisher' gut microbiomes.

It is accepted that usage of antimicrobials (AMs) in food animals causes the emergence and spread of antimicrobial resistance (AMR) in this sector, while also contributing to the burden of AMR in humans. Curbing the increasing occurrence of AMR in food animals requires in-depth knowledge of the quantitative relationship between antimicrobial usage (AMU) and AMR to achieve desired resistance reductions from interventions targeting AMU. In the observational study, the relationships between lifetime AMU in 83 finisher batches from Danish farms and the AMR gene abundances of seven antimicrobial classes in their gut microbiomes were quantified using multi-variable linear regression models. These relationships and the national lifetime AMU in pigs were included in the predictive modelling that allowed for testing of scenarios with changed lifetime AMU for finishers produced in Denmark in 2014. A total of 50 farms from the observational study were included in validating the observational study and the predictive modelling. The results from the observational study showed that the relationship was linear, and that the parenteral usage of AMs had a high effect on specific AM-classes of resistance, whereas the peroral usage had a lower but broader effect on several classes. Three different scenarios of changed lifetime AMU were simulated in the predictive modelling. When all tetracycline usage ceased, the predicted interval reductions of aminoglycoside, lincosamide and tetracycline resistance were 4-42 %, 0-8 % and 9-18 %, respectively. When the peroral tetracycline usage of the 10 % highest users was replaced with peroral macrolide usage, the tetracycline resistance fell by 1-2 % and the macrolide and MLSb resistance increased by 5-8 %. When all extended-spectrum penicillin usage was replaced with parenteral lincosamide usage, the beta-lactam resistance fell by 2-7 %, but the lincosamide usage and resistance increased by 194 % and 10-45 %, respectively. The external validation provided results within the 95 % CI of the predictive modelling outcome at national level, while the external validation at farm level was less accurate. In conclusion, interventions targeting AMU will reduce AMR abundance, though differently depending on the targeted AM-class and provided the reduction of one AM-class usage is not replaced with usage of another AM-class. Predicting several classes of AMR gene abundance simultaneously will support stakeholders when deciding on interventions targeting AMU in the finisher production to avoid adverse and unforeseen effects on the AMR abundance. This study provides a sound predictive modelling framework for further development, including the dynamics of AMU on AMR in finishers at national level.

Human health risks associated with antimicrobial-resistant enterococci and Staphylococcus aureus on poultry meat.

Enterococci and staphylococci are frequent contaminants on poultry meat. Enterococcus faecalis, Enterococcus faecium and Staphylococcus aureus are also well-known aetiological agents of a wide variety of infections resulting in major healthcare costs. This review provides an overview of the human health risks associated with the occurrence of these opportunistic human pathogens on poultry meat with particular focus on the risk of food-borne transmission of antimicrobial resistance. In the absence of conclusive evidence of transmission, this risk was inferred using data from scientific articles and national reports on prevalence, bacterial load, antimicrobial resistance and clonal distribution of these three species on poultry meat. The risks associated with ingestion of antimicrobial-resistant enterococci of poultry origin comprise horizontal transfer of resistance genes and transmission of multidrug-resistant E. faecalis lineages such as sequence type ST16. Enterococcus faecium lineages occurring in poultry meat products are distantly related to those causing hospital-acquired infections but may act as donors of quinupristin/dalfopristin resistance and other resistance determinants of clinical interest to the human gut microbiota. Ingestion of poultry meat contaminated with S. aureus may lead to food poisoning. However, antimicrobial resistance in the toxin-producing strains does not have clinical implications because food poisoning is not managed by antimicrobial therapy. Recently methicillin-resistant S. aureus of livestock origin has been reported on poultry meat. In theory handling or ingestion of contaminated meat is a potential risk factor for colonization by methicillin-resistant S. aureus. However, this risk is presently regarded as negligible by public health authorities.

The first attempt of an active integrated laboratory-based Salmonella surveillance programme in the north-eastern region of Nigeria.

AIM: To identify the sources of Salmonella contamination, distribution, prevalence and antimicrobial susceptibility patterns, which have significant impact on public and animal health, and international trade. METHODS AND RESULTS: A total of 1888 samples were collected by stratified random sampling from 2009 to 2011 from cattle, camels, poultry, fish, vegetables and humans. All identified Salmonella isolates were serotyped and tested for antimicrobial susceptibility by MIC determinations. A total of 149 Salmonella isolates comprising 17 different serovars were obtained (7·9% prevalence). Salmonella Hadar (37%), S. Eko (17%), S. Enteritidis (10%), S. Kentucky (7%) and S. Uganda (7%) were isolated from different sources. The occurrence of antimicrobial resistance was generally low, but S. Enteritidis and S. Eko showed variable antimicrobial resistance patterns, while all S. Kentucky isolates were resistant to seven of 17 tested antimicrobials, including ciprofloxacin and nalidixic acid. Three S. Hadar isolates revealed reduced susceptibility to ciprofloxacin and susceptibility to nalidixic acid and harboured the plasmid-mediated quinolone resistance gene qnrS1. CONCLUSIONS: Salmonella serovars Hadar, Enteritidis and the previously very rarely reported Eko were the major serovars associated with human infections, animal and environmental contamination in the north-eastern region of Nigeria. SIGNIFICANCE AND IMPACT OF THE STUDY: These serovars constitute a health risk to poultry, environment and human population in the region.