Abstract
Nontyphoidal Salmonella (NTS) is a leading global cause of bacterial foodborne morbidity and mortality. Our ability to treat severe NTS infections has been impaired by increasing antimicrobial resistance (AMR). To understand and mitigate the global health crisis AMR represents, we need to link the observed resistance phenotypes with their underlying genomic mechanisms. Broiler chickens represent a key reservoir and vector for NTS infections, but isolates from this setting have been characterized in only very low numbers relative to clinical isolates. In this study, we sequenced and assembled 97 genomes encompassing 7 serotypes isolated from broiler chicken in farms in British Columbia between 2005 and 2008. Through application of machine learning (ML) models to predict the observed AMR phenotype from this genomic data, we were able to generate highly (0.92 to 0.99) precise logistic regression models using known AMR gene annotations as features for 7 antibiotics (amoxicillin-clavulanic acid, ampicillin, cefoxitin, ceftiofur, ceftriaxone, streptomycin, and tetracycline). Similarly, we also trained "reference-free" k-mer-based set-covering machine phenotypic prediction models (0.91 to 1.0 precision) for these antibiotics. By combining the inferred k-mers and logistic regression weights, we identified the primary drivers of AMR for the 7 studied antibiotics in these isolates. With our research representing one of the largest studies of a diverse set of NTS isolates from broiler chicken, we can thus confirm that the AmpC-like CMY-2 β-lactamase is a primary driver of β-lactam resistance and that the phosphotransferases APH(6)-Id and APH(3″-Ib) are the principal drivers of streptomycin resistance in this important ecosystem.IMPORTANCE Antimicrobial resistance (AMR) represents an existential threat to the function of modern medicine. Genomics and machine learning methods are being increasingly used to analyze and predict AMR. This type of surveillance is very important to try to reduce the impact of AMR. Machine learning models are typically trained using genomic data, but the aspects of the genomes that they use to make predictions are rarely analyzed. In this work, we showed how, by using different types of machine learning models and performing this analysis, it is possible to identify the key genes underlying AMR in nontyphoidal Salmonella (NTS). NTS is among the leading cause of foodborne illness globally; however, AMR in NTS has not been heavily studied within the food chain itself. Therefore, in this work we performed a broad-scale analysis of the AMR in NTS isolates from commercial chicken farms and identified some priority AMR genes for surveillance.
Highlights
Nontyphoidal Salmonella (NTS) is a leading global cause of bacterial foodborne morbidity and mortality
There have been several approaches used for predicting antibiotic susceptibility testing (AST) from genomic data sets; these can be divided into antimicrobial resistance (AMR) gene-centered and gene-free k-mer-based models
The simplest of the approaches in the first category is that of annotation of known AMR genes within the genome and the direct tallying of their associated resistances; for example, in cases in which the genome contained a broad-spectrum -lactamase such as New Delhi metallo--lactamase 1 (NDM-1), the isolate would be considered resistant to -lactam antibiotics [7, 9]
Summary
Nontyphoidal Salmonella (NTS) is a leading global cause of bacterial foodborne morbidity and mortality. Genomic methods are being increasingly established as key tools in rapid continuous surveillance, tracking, and control strategy development for infectious diseases [1] They are critical to our ability to study the evolution and spread of antimicrobial resistance (AMR), especially as we adopt a broader One Health [2] approach that integrates clinical, food production, and environmental settings. Despite the development of high-quality curated databases such as the Comprehensive Antibiotic Resistance Database (CARD) [6], we still observe a high level of variability in our ability to predict the phenotypic AMR profile from purely genomic data [7, 8] This disconnect can be attributed to fundamental limitations in the genomic methods used to describe phenotype (i.e., representing genetic capacity but not necessarily gene expression) as well as gaps in our knowledge of resistance determinants. Such approaches are likely to perform best when organisms are well studied and the AMR mechanisms are relatively well characterized
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