Identification of Primary Antimicrobial Resistance Drivers in Agricultural Nontyphoidal Salmonella enterica Serovars by Using Machine Learning

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.

Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming

“…Other studies attempted the analysis of antimicrobial resistance on E . coli with machine learning and larger sample sizes [ 20 , 26 , 27 , 86 ]. However, the downside of requiring larger sample sizes is limitations in data availability, often requiring reliance on public databases and thus compromise on the type of available data and possible studies.…”

Litter commensal bacteria can limit the horizontal gene transfer of antimicrobial resistance toSalmonellain chickens

“…Pathogen induced gut modulation can create an imbalance in the abundance of bacterial species i.e., some species outgrow known resident bacteria, leading to a dysbiosis of the gut microbiome and successful Salmonella colonization and antibiotic transfer (55,56). In this study, we observed that competition from commensal bacteria in reused litter significantly reduced S. Heidelberg population that acquired AR from E. coli strains harbored by neonatal chicks.…”

Genomic Characteristics of Colistin-Resistant Salmonella enterica subsp. enterica Serovar Infantis from Poultry Farms in the Republic of Serbia

“…[ 36 ], but in this paper it was detected in S. Infantis isolate 8418/2948. However, the vgaC , streptogramin A resistance gene was detected frequently in S. Kentucky isolates and in a single S. Typhimuirum isolate from broiler chicken farms in British Columbia, Canada, by Maguire et al [ 37 ]. Resistance to tetracycline is a frequent phenotype in S. Infantis and it was not surprising that four out of seven S. Infantis isolates expressed resistance to this antibiotic by utilizing the efflux mechanism encoded either by tetA gene (1371/1 and 1981/1) or by tetA and tetK genes (8418/2948 and 9520/2).…”