Advancing risk assessment: mechanistic dose-response modelling of Listeria monocytogenes infection in human populations

Extension of probability models of the risk of infections by human enteric viruses

This study presents a novel approach for obtaining reliable models and coefficients to estimate the probability of infection caused by common human enteric viruses. The aim is to provide guidance for public health policies in disease prevention and control, by reducing uncertainty and management costs in health risk assessments. Conventional dose-response (DR) models, based on the theory elaborated by Furumoto and Mickey [1], exhibit limitations stemming from the heterogeneity of individual host susceptibilities to infection resulting from ingesting aggregate viruses. Moreover, the scarcity of well-designed viral challenge experiments contributes to significant uncertainty in these DR models. To address these issues, we conducted a review of infection models used in health risk analysis, focusing on Norovirus (NoV) GI.1, pooled Enterovirus group (EV), Poliovirus 1/SM, and Echo-12 virus via contaminated water or food. Using a mechanistic approach, we reevaluated the known DR models and coefficients for the probability of individual host infection in the mentioned viruses based on dose-infection challenge experiments. Specifically, we sought to establish a relationship between the minimum infectious dose (ID) and the ID having a 50% probability of initiating host infection in the same challenge experiment. Furthermore, we developed a new formula to estimate the degree of aggregation of GI.1 NoV at the mean infectious dose. The proposed models, based on "exact" beta-Poisson DR models, effectively predicted infection probabilities from ingestion of both disaggregated and aggregate NoV GI.1. Through a numerical evaluation, we compared the results with the maximum likelihood estimation (MLE) probability obtained from a controlled challenge trial with the NoV GI.1 virus described in the literature, demonstrating the accuracy of our approach. By addressing the indetermination of the unmeasured degree of NoV aggregation in each single infectious dose, our models reduce overestimations and uncertainties in microbial risk assessments. This improvement enhances the management of health risks associated with enteric virus infections.

Modeling the invasion of human small intestinal epithelial-like cells by Salmonella enterica Typhimurium and Listeria monocytogenes using Bayesian inference

In order to develop a mechanistic bacterial dose-response model, based on the concept of Key Events Dose-Response Framework (KEDRF), this study aimed to investigate the invasion of intestinal model cells (Caco-2) by Salmonella Typhimurium and Listeria monocytogenes and described the behavior of both pathogens as a mathematical model using Bayesian inference. Monolayer-cultured Caco-2 cells (approximately 10⁵ cells) were co-cultured with various concentrations (10³ - 10⁷ CFU·ml^-1 ) of S. Typhimurium and L. monocytogenes for up to 9 h to investigate the invasion of the pathogens into the Caco-2 cells. While an exposure of ≥ 10³ CFU·ml^-1 of S. Typhimurium initiated the invasion of Caco-2 cells within 3 h, much less exposure (10² CFU·ml^-1 ) of L. monocytogenes was sufficient for invasion within the same period. Furthermore, while the maximum number of invading S. Typhimurium cells reached by approximately 10³ CFU·cm^-2 for 6 h exposure , the invading maximum numbers of L. monocytogenes cells increased by approximately 10⁶ CFU·cm^-2 for the same exposure period. The invasion kinetics of both the pathogens was successfully described as an asymptotic exponential mathematical model using Bayesian inference. The developed pathogen invasion model allowed the estimation of probability of S. Typhimurium and L. monocytogenes infection, based on the physiological natures of digestion process, which was comparable to the published dose-response relationship. The invasion models developed in the present study will play a key role in the development of an alternative pathogen dose-response models based on KEDRF concept.

Different Shades of Listeria monocytogenes: Strain, Serotype, and Lineage-Based Variability in Virulence and Stress Tolerance Profiles

Listeria monocytogenes is a public health and food safety challenge due to its virulence and natural stress resistance phenotypes. The variable distribution of L. monocytogenes molecular subtypes with respect to food products and processing environments and among human and animal clinical listeriosis cases is observed. Sixty-two clinical and food-associated L. monocytogenes isolates were examined through phenome and genome analysis. Virulence assessed using a zebrafish infection model revealed serotype and genotype-specific differences in pathogenicity. Strains of genetic lineage I serotype 4b and multilocus sequence type clonal complexes CC1, CC2, CC4, and CC6 grew and survived better and were more virulent than serotype 1/2a and 1/2c lineage II, CC8, and CC9 strains. Hemolysis, phospholipase activity, and lysozyme tolerance profiles were associated with the differences observed in virulence. Osmotic stress resistance evaluation revealed serotype 4b lineage I CC2 and CC4 strains as more osmotolerant, whereas serotype 1/2c lineage II CC9 strains were more osmo-sensitive than others. Variable tolerance to the widely used quaternary ammonium compound benzalkonium chloride (BC) was observed. Some outbreak and sporadic clinical case associated strains demonstrated BC tolerance, which might have contributed to their survival and transition in the food-processing environment facilitating food product contamination and ultimately outbreaks or sporadic listeriosis cases. Genome comparison uncovered various moderate differences in virulence and stress associated genes between the strains indicating that these differences in addition to gene expression regulation variations might largely be responsible for the observed virulence and stress sensitivity phenotypic differences. Overall, our study uncovered strain and genotype-dependent variation in virulence and stress resilience among clinical and food-associated L. monocytogenes isolates with potential public health risk implications. The extensive genome and phenotypic data generated provide a basis for developing improved Listeria control strategies and policies.

Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157:H7 and Listeria monocytogenes with enteric microflora in a small-intestine model

Aims

The biological events occurring during human digestion help to understand the mechanisms underlying the dose–response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine.

Methods and Results

The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased.

Conclusions

Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived.

Significance and Impact of the Study

Competition experiments in a small‐intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose–response relationships.

Implementing a new dose-response model for estimating infection probability of Campylobacter jejuni based on the key events dose-response framework

Understanding the dose-response relationship between ingested pathogenic bacteria and infection probability is a key factor for appropriate risk assessment of foodborne pathogens. The objectives of this study were to develop and validate a novel mechanistic dose-response model for Campylobacter jejuni and simulate the underlying mechanism of foodborne illness during digestion. Bacterial behavior in the human gastrointestinal environment, including survival at low pH in the gastric environment after meals, transition to intestines, and invasion to intestinal tissues, was described using a Bayesian statistical model based on the reported experimental results of each process while considering physical food types (liquid or solid) and host age (young adult or elderly). Combining the models in each process, the relationship between pathogen intake and the infection probability of C. jejuni was estimated and compared with reported epidemiological dose-response relationships. Taking food types and host age into account, the prediction range of the infection probability of C. jejuni successfully covered the reported dose-response relationships from actual C. jejuni outbreaks. According to sensitivity analysis of predicted infection probabilities, the host age factor and the food type factor have relatively higher relevance than other factors. Thus, the developed Key Events Dose Response Framework can derive novel information for quantitative microbiological risk assessment in addition of dose-response relationship. The developed framework is potentially applicable to other pathogens to quantify the dose-response relationship from experimental data obtained from digestion. Importance Based on the mechanistic approach called Key Events Dose Response Framework alternative to previous non-mechanistic approach, the dose-response models for infection probability of C. jejuni were developed considering with age of people who take pathogen and food type. The developed predictive framework illustrated highly accurate prediction of dose (minimum difference 0.21 log CFU) for a certain infection probability compared with the previously reported dose-response relationship. In addition, the developed prediction procedure revealed that the dose-response relationship strongly depends on food type as well as host age. The implementation of Key Event Dose Response Framework will mechanistically and logically reveal the dose-response relationship and provide useful information with quantitative microbiological risk assessment of C. jejuni on foods.

An agent-based simulator for the gastrointestinal pathway of Listeria monocytogenes

We developed an agent-based gastric simulator for a human host to illustrate the within host survival mechanisms of Listeria monocytogenes. The simulator incorporates the gastric physiology and digestion processes that are critical for pathogen survival in the stomach. Mathematical formulations for the pH dynamics, stomach emptying time, and survival probability in the presence of gastric acid are integrated in the simulator to evaluate the portion of ingested bacteria that survives in the stomach and reaches the small intestine. The parameters are estimated using in vitro data relevant to the human stomach and L. monocytogenes. The simulator predicts that 5%-29% of ingested bacteria can survive a human stomach and reach the small intestine. In the absence of extensive scientific experiments, which are not feasible on the grounds of ethical and safety concerns, this simulator may provide a supplementary tool to evaluate pathogen survival and subsequent infection, especially with regards to the ingestion of small doses.

A dose response model for quantifying the infection risk of antibiotic-resistant bacteria

Quantifying the human health risk of microbial infection helps inform regulatory policies concerning pathogens, and the associated public health measures. Estimating the infection risk requires knowledge of the probability of a person being infected by a given quantity of pathogens, and this relationship is modeled using pathogen specific dose response models (DRMs). However, risk quantification for antibiotic-resistant bacteria (ARB) has been hindered by the absence of suitable DRMs for ARB. A new approach to DRMs is introduced to capture ARB and antibiotic-susceptible bacteria (ASB) dynamics as a stochastic simple death (SD) process. By bridging SD with data from bench experiments, we demonstrate methods to (1) account for the effect of antibiotic concentrations and horizontal gene transfer on risk; (2) compute total risk for samples containing multiple bacterial types (e.g., ASB, ARB); and (3) predict if illness is treatable with antibiotics. We present a case study of exposure to a mixed population of Gentamicin-susceptible and resistant Escherichia coli and predict the health outcomes for varying Gentamicin concentrations. Thus, this research establishes a new framework to quantify the risk posed by ARB and antibiotics.

Exploring Listeria monocytogenes Transcriptomes in Correlation with Divergence of Lineages and Virulence as Measured in Galleria mellonella

As for many opportunistic pathogens, the virulence potential of Listeria monocytogenes is highly heterogeneous between isolates and correlated, to some extent, with phylogeny and gene repertoires. In sharp contrast with copious data on intraspecies genome diversity, little is known about transcriptome diversity despite the role of complex genetic regulation in pathogenicity. The current study implemented RNA sequencing to characterize the transcriptome profiles of 33 isolates under optimal in vitro growth conditions. Transcript levels of conserved single-copy genes were comprehensively explored from several perspectives, including phylogeny, in silico-predicted virulence category based on epidemiological multilocus sequence typing (MLST) data, and in vivo virulence phenotype assessed in Galleria mellonella Comparing baseline transcriptomes between isolates was intrinsically more complex than standard genome comparison because of the inherent plasticity of gene expression in response to environmental conditions. We show that the relevance of correlation analyses and their statistical power can be enhanced by using principal-component analysis to remove the first level of irrelevant, highly coordinated changes linked to growth phase. Our results highlight the major contribution of transcription factors with key roles in virulence to the diversity of transcriptomes. Divergence in the basal transcript levels of a substantial fraction of the transcriptome was observed between lineages I and II, echoing previously reported epidemiological differences. Correlation analysis with in vivo virulence identified numerous sugar metabolism-related genes, suggesting that specific pathways might play roles in the onset of infection in G. mellonella IMPORTANCE Listeria monocytogenes is a multifaceted bacterium able to proliferate in a wide range of environments from soil to mammalian host cells. The accumulated genomic data underscore the contribution of intraspecies variations in gene repertoire to differential adaptation strategies between strains, including infection and stress resistance. It seems very likely that the fine-tuning of the transcriptional regulatory network is also a key component of the phenotypic diversity, albeit more difficult to investigate than genome content. Some studies reported incongruity in the basal transcriptome between isolates, suggesting a putative relationship with phenotypes, but small isolate numbers hampered proper correlation analyses with respect to their characteristics. The present study is the embodiment of the promising approach that consists of analyzing correlations between transcriptomes and various isolate characteristics. Statistically significant correlations were found with phylogenetic groups, epidemiological evidence of virulence potential, and virulence in Galleria mellonella larvae used as an in vivo model.

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of 'omics' techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.