Assessing phenotypic virulence of Salmonella enterica across serovars and sources

Abstract

IntroductionWhole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica.MethodsTo test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed.Results and DiscussionP(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)].

The European Union One Health 2022 Zoonoses Report

“…To further complicate this situation, several studies have demonstrated that control strategies based on ‘serovars’ without consideration of the actual pathogenic potential of the single strain, as is promoted by the current EU legislation, might overlook highly pathogenic Salmonella strains, which do not necessarily belong to the target serovars. Such strains could spread as emerging clones, becoming potential causes of new outbreaks (Petrin et al., 2023 ). Unfortunately, so far, the identification of virulence patterns to unambiguously predict Salmonella pathogenicity in different hosts still remains very challenging.…”

Antimicrobial resistance and genomic investigation of Salmonella isolated from retail foods in Guizhou, China

“…However, all 102 strains were successfully sequenced. Consequently, all 102 strains were utilized for the further antimicrobial susceptibility testing and whole genomic sequencing ( Zhao et al, 2022 ; Petrin et al, 2023 ). Among these, 50 strains were from the northern region, and 52 were from the southern region.…”

Phenotypic and genotypic antimicrobial resistance correlation and plasmid characterization in Salmonella spp. isolates from Italy reveal high heterogeneity among serovars

“…High-throughput sequencing was performed with MiSeq Reagent kit v3, resulting in 251 bp long paired-end reads, or NextSeq High Output kit v2.5, resulting in 151 bp long paired end reads. Subsequent bioinformatics analyzes on raw reads were performed as previously described in Petrin et al ( 11 ).…”