Abstract

The acquisition of antibiotic resistance (AR) by foodborne pathogens, such as Salmonella enterica, has emerged as a serious public health concern. The relationship between the two key survival mechanisms (i.e., antibiotic resistance and virulence) of bacterial pathogens is complex. However, it is unclear if the presence of certain virulence determinants (i.e., virulence genes) and AR have any association in Salmonella. In this study, we report the prevalence of selected virulence genes and their association with AR in a set of phenotypically tested antibiotic-resistant (n = 117) and antibiotic-susceptible (n = 94) clinical isolates of Salmonella collected from Tennessee, USA. Profiling of virulence genes (i.e., virulotyping) in Salmonella isolates (n = 211) was conducted by targeting 13 known virulence genes and a gene for class 1 integron. The association of the presence/absence of virulence genes in an isolate with their AR phenotypes was determined by the machine learning algorithm Random Forest. The analysis revealed that Salmonella virulotypes with gene clusters consisting of avrA, gipA, sodC1, and sopE1 were strongly associated with any resistant phenotypes. To conclude, the results of this exploratory study shed light on the association of specific virulence genes with drug-resistant phenotypes of Salmonella. The presence of certain virulence genes clusters in resistant isolates may become useful for the risk assessment and management of salmonellosis caused by drug-resistant Salmonella in humans.

Highlights

  • Salmonella causes about 1.2 million illnesses, 23,000 hospitalizations, and 450 deaths in the United States every year [1,2]

  • This study shows that the status of selected virulence genes did not significantly differ between drug-resistant and drug-susceptible Salmonella isolates that were tested, but it does differ between multidrug-resistant and drug-susceptible isolates

  • Our work presented here highlights the association between the distribution of bacterial virulence genes and their phenotypic drug-resistance pattern among Salmonella isolates from patients diagnosed with salmonellosis using statistical and computational methods

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Summary

Introduction

Salmonella causes about 1.2 million illnesses, 23,000 hospitalizations, and 450 deaths in the United States every year [1,2]. Almost 1 million of those illnesses occur from foods contaminated with nontyphoidal Salmonella [1,3]. According to a 2019 estimate by the Centers for Disease Control and Prevention (CDC), drug-resistant nontyphoidal Salmonella caused more than 200,000 cases of illnesses and 70 deaths annually in the US [4]. Due to the emergence of antibiotic resistance (AR), treating nontyphoidal salmonellosis has become increasingly more difficult and sometimes impossible. In the United States alone, two million people are infected with antibiotic-resistant bacteria annually, and 23,000 people die [5]. It is predicted that AR will cause a loss of up to $100 trillion to the global economy due to premature deaths [6]

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