Dataset Information

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 ?-lactamase is a primary driver of ?-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.

SUBMITTER: Maguire F

PROVIDER: S-EPMC6687941 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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

Maguire Finlay F Rehman Muhammad Attiq MA Carrillo Catherine C Diarra Moussa S MS Beiko Robert G RG

mSystems 20190806 4

Nontyphoidal <i>Salmonella</i> (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 charac ...[more]

PMID: 31387929

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Project description:UNLABELLED:Nontyphoidal Salmonella enterica (NTS) infections are a major burden to global public health, as they lead to diseases ranging from gastroenteritis to systemic infections and there is currently no vaccine available. Here, we describe a highly effective component vaccine against S. enterica serovar Typhimurium in both gastroenteritis and systemic murine infection models. We devised an approach to generate supernatants of S. enterica serovar Typhimurium, an organism that is highly abundant in virulence factors. Immunization of mice with this supernatant resulted in dramatic protection against a challenge with serovar Typhimurium, showing increased survival in the systemic model and decreased intestinal pathology in the gastrointestinal model. Protection correlated with specific IgA and IgG levels in the serum and specific secretory IgA levels in the feces of immunized mice. Initial characterization of the protective antigens in the bacterial culture supernatants revealed a subset of antigens that exhibited remarkable stability, a highly desirable characteristic of an effective vaccine to be used under suboptimal environmental conditions in developing countries. We were able to purify a subset of the peptides present in the supernatants and show their potential for immunization of mice against serovar Typhimurium resulting in a decreased level of colonization. This component vaccine shows promise with regard to protecting against NTS, and further work should significantly help to establish vaccines against these prevalent infections. IMPORTANCE:Salmonella enterica infections other than typhoid and paratyphoid fever are a major global health burden, as they cause high morbidity and mortality worldwide. Strategies that prevent Salmonella-related diseases are greatly needed, and there is a significant push for the development of vaccines against nontyphoidal Salmonella enterica serovars. In this work, we describe an S. Typhimurium supernatant-derived vaccine that is effective in reducing bacterial colonization in mouse models of gastroenteritis as well as invasive disease. This is a component vaccine that shows high stability to heat, a feature that is important for use under suboptimal conditions, such as those found in sub-Saharan Africa.

Dataset Information

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

Publications

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

Similar Datasets

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