Association of Virulence and Antibiotic Resistance in Salmonella-Statistical and Computational Insights into a Selected Set of Clinical Isolates.
Daleniece HigginsNabanita MukherjeeChandan PalIrshad M SulaimanYu JiangSamir HannaJohn R DunnWilfried J J KarmausPratik BanerjeePublished in: Microorganisms (2020)
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.
Keyphrases
- escherichia coli
- antimicrobial resistance
- drug resistant
- pseudomonas aeruginosa
- biofilm formation
- staphylococcus aureus
- genome wide
- genome wide identification
- machine learning
- acinetobacter baumannii
- public health
- multidrug resistant
- risk assessment
- listeria monocytogenes
- bioinformatics analysis
- cystic fibrosis
- dna methylation
- gram negative
- deep learning
- single cell
- heavy metals