Phenotypic Prediction: Linking in vitro Virulence to the Genomics of 59 Salmonella enterica Strains

The increased availability of whole-genome-sequencing techniques generates a wealth of DNA data on numerous organisms, including foodborne pathogens such as Salmonella. However, how these data can be used to improve microbial risk assessment and understanding of Salmonella epidemiology remains a challenge. The aim of this study was to assess variability in in vitro virulence and genetic characteristics between and within different serovars. The phenotypic behavior of 59 strains of 32 different Salmonella enterica serovars from animal, human and food origin was assessed in an in vitro gastro-intestinal tract (GIT) system and they were analyzed for the presence of 233 putative virulence genes as markers for phenotypic prediction. The probability of in vitro infection, P(inf), defined as the fraction of infectious cells passing from inoculation to host cell invasion at the last stage of the GIT system, was interpreted as the in vitro virulence. Results showed that the (average) P(inf) of Salmonella serovars ranged from 5.3E-05 (S. Kedougou) to 5.2E-01 (S. Typhimurium). In general, a higher P(inf) on serovar level corresponded to higher reported human incidence from epidemiological reporting data. Of the 233 virulence genes investigated, only 101 showed variability in presence/absence among the strains. In vitro P(inf) was found to be positively associated with the presence of specific plasmid related virulence genes (mig-5, pef, rck, and spv). However, not all serovars with a relatively high P(inf), > 1E-02, could be linked with these specific genes. Moreover, some outbreak related strains (S. Heidelberg and S. Thompson) did not reveal this association with P(inf). No clear association with in vitro virulence P(inf) was identified when grouping serovars with the same virulence gene profile (virulence plasmid, Typhoid toxin, peg operon and stk operon). This study shows that the in vitro P(inf) variation among individual strains from the same serovar is larger than that found between serovars. Therefore, ranking P(inf) of S. enterica on serovar level alone, or in combination with a serovar specific virulence gene profile, cannot be recommended. The attribution of single biological phenomena to individual strains or serovars is not sufficient to improve the hazard characterization for S. enterica. Future microbial risk assessments, including virulence gene profiles, require a systematic approach linked to epidemiological studies rather than revealing differences in characteristics on serovar level alone

Quantification of Salmonella Survival and Infection in an In vitro Model of the Human Intestinal Tract as Proxy for Foodborne Pathogens.

Different techniques are available for assessing differences in virulence of bacterial foodborne pathogens. The use of animal models or human volunteers is not expedient for various reasons; the use of epidemiological data is often hampered by lack of crucial data. In this paper, we describe a static, sequential gastrointestinal tract (GIT) model system in which foodborne pathogens are exposed to simulated gastric and intestinal contents of the human digestive tract, including the interaction of pathogens with the intestinal epithelium. The system can be employed with any foodborne bacterial pathogens. Five strains of Salmonella Heidelberg and one strain of Salmonella Typhimurium were used to assess the robustness of the system. Four S. Heidelberg strains originated from an outbreak, the fifth S. Heidelberg strain and the S. Typhimurium strain originated from routine meat inspections. Data from plate counts, collected for determining the numbers of surviving bacteria in each stage, were used to quantify both the experimental uncertainty and biological variability of pathogen survival throughout the system. For this, a hierarchical Bayesian framework using Markov chain Monte Carlo (MCMC) was employed. The model system is able to distinguish serovars/strains for in vitro infectivity when accounting for within strain biological variability and experimental uncertainty

Phenotypic Prediction: Linking Virulence to the Genomics of 59 Strains.

The increased availability of whole-genome-sequencing techniques generates a wealth of DNA data on numerous organisms, including foodborne pathogens such a

Assessing phenotypic virulence of Salmonella enterica across serovars and sources.

Whole 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