The Key Events Dose-Response Framework: its potential for application to foodborne pathogenic microorganisms

Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose-response modeling framework

Estimating microbial dose-response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of Salmonella genes that could be associated with illness, given exposure, in hosts. Here, an elastic net-based weighted Poisson regression method was proposed to identify Salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. The best-fit elastic net model was obtained by 10-fold cross-validation. The best-fit elastic net model identified 33 gene expression-dose interaction terms that added to the predictability of the model. Of these, nine genes associated with Salmonella metabolism and virulence were found to be significant by the best-fit Poisson regression model (p < 0.05). This method could improve or redefine dose-response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.

Listeria monocytogenes-How This Pathogen Uses Its Virulence Mechanisms to Infect the Hosts

Listeriosis is a serious food-borne illness, especially in susceptible populations, including children, pregnant women, and elderlies. The disease can occur in two forms: non-invasive febrile gastroenteritis and severe invasive listeriosis with septicemia, meningoencephalitis, perinatal infections, and abortion. Expression of each symptom depends on various bacterial virulence factors, immunological status of the infected person, and the number of ingested bacteria. Internalins, mainly InlA and InlB, invasins (invasin A, LAP), and other surface adhesion proteins (InlP1, InlP4) are responsible for epithelial cell binding, whereas internalin C (InlC) and actin assembly-inducing protein (ActA) are involved in cell-to-cell bacterial spread. L. monocytogenes is able to disseminate through the blood and invade diverse host organs. In persons with impaired immunity, the elderly, and pregnant women, the pathogen can also cross the blood-brain and placental barriers, which results in the invasion of the central nervous system and fetus infection, respectively. The aim of this comprehensive review is to summarize the current knowledge on the epidemiology of listeriosis and L. monocytogenes virulence mechanisms that are involved in host infection, with a special focus on their molecular and cellular aspects. We believe that all this information is crucial for a better understanding of the pathogenesis of L. monocytogenes infection.

Listeria monocytogenes in foods-From culture identification to whole-genome characteristics

Listeria monocytogenes is an important foodborne pathogen, which is able to persist in the food production environments. The presence of these bacteria in different niches makes them a potential threat for public health. In the present review, the current information on the classical and alternative methods used for isolation and identification of L. monocytogenes in food have been described. Although these techniques are usually simple, standardized, inexpensive, and are routinely used in many food testing laboratories, several alternative molecular-based approaches for the bacteria detection in food and food production environments have been developed. They are characterized by the high sample throughput, a short time of analysis, and cost-effectiveness. However, these methods are important for the routine testing toward the presence and number of L. monocytogenes, but are not suitable for characteristics and typing of the bacterial isolates, which are crucial in the study of listeriosis infections. For these purposes, novel approaches, with a high discriminatory power to genetically distinguish the strains during epidemiological studies, have been developed, e.g., whole-genome sequence-based techniques such as NGS which provide an opportunity to perform comparison between strains of the same species. In the present review, we have shown a short description of the principles of microbiological, alternative, and modern methods of detection of L. monocytogenes in foods and characterization of the isolates for epidemiological purposes. According to our knowledge, similar comprehensive papers on such subject have not been recently published, and we hope that the current review may be interesting for research communities.

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.

Experimentally observed Campylobacter jejuni survival kinetics in chicken meat products during model gastric digestion tended to be lower than model predictions

Campylobacter jejuni-related foodborne diseases are mainly attributed to the consumption of undercooked chicken meat and cross-contaminated produce. This study aimed to develop a survival kinetics model, based on the Weibull model, for predicting foodborne C. jejuni survival during gastric digestion in a model stomach. We previously confirmed that C. jejuni can survive temperatures up to 62 °C; therefore, certain types of grilled chicken skewers (yakitori) were examined for C. jejuni survival during simulated gastric digestion. C. jejuni survival on a chicken thigh following grilling was examined to confirm the foods for digestion experiments. Further, C. jejuni survival during model digestion was investigated through simultaneous digestion of raw chicken and cross-contaminated iceberg lettuce. The model stomach pH increased from 1.5 to 6.0 immediately after yakitori ingestion and did not decrease below 4.0 within 3 h of digestion. Gastric digestion did not significantly contribute to C. jejuni inactivation (<1.5 log reduction after 3 h digestion). Our model could predict C. jejuni survival kinetics in simulated gastric fluid under varying pH during model digestion. This approach can be used to predict C. jejuni survival rates following digestion to improve food safety and reduce Campylobacter-related disease outbreaks.

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.

Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling

This study was conducted to quantitatively evaluate the variability of stress resistance in different strains of Campylobacter jejuni and the uncertainty of such strain variability. We developed Bayesian statistical models with multilevel analysis to quantify variability within a strain, variability between different strains, and the uncertainty associated with these estimates. Furthermore, we measured the inactivation of 11 strains of C. jejuni in simulated gastric fluid with low pH, using the Weibullian survival model. The model was first developed for separate pH conditions and then analyzed over a range of pH levels. We found that the model parameters developed under separate pH conditions exhibited a clear dependence of survival on pH. In addition, the uncertainty of the variability between different strains could be described as the joint distribution of the model parameters. The latter model, including pH dependency, accurately predicted the number of surviving cells in individual as well as multiple strains. In conclusion, variabilities and uncertainties in inactivation could be simultaneously evaluated and interpreted via a probabilistic approach based on Bayesian theory. Such hierarchical Bayesian models could be useful for understanding individual-strain variability in quantitative microbial risk assessment. IMPORTANCE Since microbial strains vary in their growth and inactivation patterns in food materials, it is important to accurately predict these patterns for quantitative microbial risk assessment. However, most previous studies in this area have used highly resistant strains, which could lead to inaccurate predictions. Moreover, variability, including measurement errors and variability within a strain and between different strains, can contribute to predicted individual-level outcomes. Therefore, a multilevel framework is required to resolve these levels of variability and estimate their uncertainties. We developed a Bayesian predictive model for the survival of Campylobacter jejuni under simulated gastric conditions taking into account the variabilities and uncertainties. We demonstrated a high correspondence between predictions from the model and empirical measurements. The modeling procedure proposed in this study recommends a novel framework for predicting pathogen behavior, which can help improve quantitative microbial risk assessment during food production and distribution.

Modeling Invasion of Campylobacter jejuni into Human Small Intestinal Epithelial-Like Cells by Bayesian Inference

Current approaches used for dose-response modeling of low-dose exposures of pathogens rely on assumptions and extrapolations. These models are important for quantitative microbial risk assessment of food. A mechanistic framework has been advocated as an alternative approach for evaluating dose-response relationships. The objectives of this study were to investigate the invasion behavior of Campylobacter jejuni, which could arise as a foodborne illness even if there are low counts of pathogens, into Caco-2 cells as a model of intestinal cells and to develop a mathematical model for invading cell counts to reveal a part of the infection dose-response mechanism. Monolayer-cultured Caco-2 cells and various concentrations of C. jejuni in culture were cocultured for up to 12 h. The numbers of C. jejuni bacteria invading Caco-2 cells were determined after coculture for different time periods. There appeared to be a maximum limit to the invading bacterial counts, which showed an asymptotic exponential increase. The invading bacterial counts were higher with higher exposure concentrations (maximum, 5.0 log CFU/cm²) than with lower exposure concentrations (minimum, 0.6 log CFU/cm²). In contrast, the ratio of invading bacteria (number of invading bacteria divided by the total number of bacteria exposed) showed a similar trend regardless of the exposure concentration. Invasion of C. jejuni into intestinal cells was successfully demonstrated and described by the developed differential equation model with Bayesian inference. The model accuracy showed that the 99% prediction band covered more than 97% of the observed values. These findings provide important information on mechanistic pathogen dose-response relationships and an alternative approach for dose-response modeling.IMPORTANCE One of the infection processes of C. jejuni, the invasion behavior of the bacteria in intestinal epithelial cells, was revealed, and a mathematical model for prediction of the cell-invading pathogen counts was developed for the purpose of providing part of a dose-response model for C. jejuni based on the infection mechanism. The developed predictive model showed a high accuracy of more than 97% and successfully described the C. jejuni invading counts. The bacterial invasion predictive model of this study will be essential for the development of a dose-response model for C. jejuni based on the infection mechanism.

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.

Dose-response and transmission: the nexus between reservoir hosts, environment and recipient hosts

Dose is the nexus between exposure and all upstream processes that determine pathogen pressure, and is thereby an important element underlying disease dynamics. Understanding the relationship between dose and disease is particularly important in the context of spillover, where nonlinearities in the dose-response could determine the likelihood of transmission. There is a need to explore dose-response models for directly transmitted and zoonotic pathogens, and how these interactions integrate within-host factors to consider, for example, heterogeneity in host susceptibility and dose-dependent antagonism. Here, we review the dose-response literature and discuss the unique role dose-response models have to play in understanding and predicting spillover events. We present a re-analysis of dose-response experiments for two important zoonotic pathogens (Middle East respiratory syndrome coronavirus and Nipah virus), to exemplify potential difficulties in differentiating between appropriate models with small exposure experiment datasets. We also discuss the data requirements needed for robust selection between dose-response models. We then suggest how these processes could be modelled to gain more realistic predictions of zoonotic transmission outcomes and highlight the exciting opportunities that could arise with increased collaboration between the virology and epidemiology disciplines. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

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

The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose-response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose-response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose-response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen-host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose-response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen-immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose-response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined.

Diffusible substances from lactic acid bacterial cultures exert strong inhibitory effects on Listeria monocytogenes and Salmonella enterica serovar enteritidis in a co-culture model

Background

Food-borne infections cause huge economic and human life losses. Listeria monocytogenes and Salmonella enterica serovar Enteritidis are among the top ranking pathogens causing such losses. Control of such infections is hampered by persistent contamination of foods and food-processing environments, resistance of pathogens to sanitizing agents, existence of heterogeneous populations of pathogens (including culturable and viable but non-culturable cells) within the same food items, and inability to detect all such pathogens by culture-based methods. Modern methods such as flow cytometry allow analyses of cells at the single cell level within a short time and enable better and faster detection of such pathogens and distinctions between live and dead cells. Such methods should be complemented by control strategies including the use of beneficial bacteria that produce metabolites capable of inhibiting food-borne pathogens. In this study, broth cultures of lactic acid bacteria (LAB) isolated from fermented milk were tested for production of substances capable of inhibiting L. monocytogenes and S. Enteritidis in co-culture with LAB by assessment of colony-forming units (CFU) and live:dead cell populations by flow cytometry.

Results

The LAB isolates belonged to the species Lactococcus lactis, Enterococcus faecalis and Enterococcus faecium. Some LAB were effective in inhibition. Plating indicated up to 99% reduction in CFU from co-cultures compared to control cultures. Most of the bacteria in both cultures were in the viable but non-culturable state. The flow data showed that there were significantly higher dead cell numbers in co-cultures than in control cultures, indicating that such killing was caused by diffusible substances produced by the LAB cultures.

Conclusion

This study showed that metabolites from selected local LAB species can be used to significantly reduce pathogen load. However, conditions of use and application need to be further investigated and optimized for large-scale utilization.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-0944-3) contains supplementary material, which is available to authorized users.

Predictive Modeling for Estimation of Bacterial Behavior from Farm to Table

Microbial contamination is inevitable for raw and/or minimally processed ready-to-eat foods. As a consequence of the pathogenic bacterial contamination, the risk of food-borne illness increases during distribution and storage until consumption. Prediction of microbial growth and/or inactivation in/on those foods provides important information for ensuring the microbial food safety. Although numerous predictive models for bacterial growth have been proposed for various microorganisms, this review focuses on the modeling of pathogenic bacterial growth in raw and minimally processed ready-to-eat foods such as fresh-cut produce and raw minced-tuna, a common ingredient for sushi. The growth models described here take into account both the environment temperature and microbial competition in the food matrix. Microbial competition plays a key role in real food environments. Food-based predictive models enable not only to directly estimate the microbial growth on those foods, but also to apply to validation of culture-medium-based predictive models. Furthermore, toward a development of accurate and/or realistic bacterial dose-response models, a model for inactivation of pathogenic bacteria during simulated gastric fluid is also introduced.

Development of Dose-Response Models to Predict the Relationship for Human Toxoplasma gondii Infection Associated with Meat Consumption

Toxoplasma gondii is a protozoan parasite that is responsible for approximately 24% of deaths attributed to foodborne pathogens in the United States. It is thought that a substantial portion of human T. gondii infections is acquired through the consumption of meats. The dose-response relationship for human exposures to T. gondii-infected meat is unknown because no human data are available. The goal of this study was to develop and validate dose-response models based on animal studies, and to compute scaling factors so that animal-derived models can predict T. gondii infection in humans. Relevant studies in literature were collected and appropriate studies were selected based on animal species, stage, genotype of T. gondii, and route of infection. Data were pooled and fitted to four sigmoidal-shaped mathematical models, and model parameters were estimated using maximum likelihood estimation. Data from a mouse study were selected to develop the dose-response relationship. Exponential and beta-Poisson models, which predicted similar responses, were selected as reasonable dose-response models based on their simplicity, biological plausibility, and goodness fit. A confidence interval of the parameter was determined by constructing 10,000 bootstrap samples. Scaling factors were computed by matching the predicted infection cases with the epidemiological data. Mouse-derived models were validated against data for the dose-infection relationship in rats. A human dose-response model was developed as P (d) = 1-exp (-0.0015 × 0.005 × d) or P (d) = 1-(1 + d × 0.003 / 582.414)(-1.479) . Both models predict the human response after consuming T. gondii-infected meats, and provide an enhanced risk characterization in a quantitative microbial risk assessment model for this pathogen.

A proof-of-concept model for the identification of the key events in the infection process with specific reference to Pseudomonas aeruginosa in corneal infections

BACKGROUND

It is a common medical practice to characterise an infection based on the causative agent and to adopt therapeutic and prevention strategies targeting the agent itself. However, from an epidemiological perspective, exposure to a microbe can be harmless to a host as a result of low-level exposure or due to host immune response, with opportunistic infection only occurring as a result of changes in the host, pathogen, or surrounding environment.

METHODS

We have attempted to review systematically the key host, pathogen, and environmental factors that may significantly impact clinical outcomes of exposure to a pathogen, using Pseudomonas aeruginosa eye infection as a case study.

RESULTS AND DISCUSSION

Extended contact lens wearing and compromised hygiene may predispose users to microbial keratitis, which can be a severe and vision-threatening infection. P. aeruginosa has a wide array of virulence-associated genes and sensing systems to initiate and maintain cell populations at the corneal surface and beyond. We have adapted the well-known concept of the epidemiological triangle in combination with the classic risk assessment framework (hazard identification, characterisation, and exposure) to develop a conceptual pathway-based model that demonstrates the overlapping relationships between the host, the pathogen, and the environment; and to illustrate the key events in P. aeruginosa eye infection.

CONCLUSION

This strategy differs from traditional approaches that consider potential risk factors in isolation, and hopefully will aid the identification of data and models to inform preventive and therapeutic measures in addition to risk assessment. Furthermore, this may facilitate the identification of knowledge gaps to direct research in areas of greatest impact to avert or mitigate adverse outcomes of infection.

Modelling microbial health risk of wastewater reuse: A systems perspective

There is a widespread need for the use of quantitative microbial risk assessment (QMRA) to determine reclaimed water quality for specific uses, however neither faecal indicator levels nor pathogen concentrations alone are adequate for assessing exposure health risk. The aim of this study was to build a conceptual model representing factors contributing to the microbiological health risks of reusing water treated in maturation ponds. This paper describes the development of an unparameterised model that provides a visual representation of theoretical constructs and variables of interest. Information was collected from the peer-reviewed literature and through consultation with experts from regulatory authorities and academic disciplines. In this paper we explore how, considering microbial risk as a modular system, following the QMRA framework enables incorporation of the many factors influencing human exposure and dose response, to better characterise likely human health impacts. By using and expanding upon the QMRA framework we deliver new insights into this important field of environmental exposures. We present a conceptual model of health risk of microbial exposure which can be used for maturation ponds and, more importantly, as a generic tool to assess health risk in diverse wastewater reuse scenarios.

Making pathogens sociable: the [corrected] emergence of high relatedness through limited host invasibility

Cooperation depends upon high relatedness, the high genetic similarity of interacting partners relative to the wider population. For pathogenic bacteria, which show diverse cooperative traits, the population processes that determine relatedness are poorly understood. Here, we explore whether within-host dynamics can produce high relatedness in the insect pathogen Bacillus thuringiensis. We study the effects of host/pathogen interactions on relatedness via a model of host invasion and fit parameters to competition experiments with marked strains. We show that invasibility is a key parameter for determining relatedness and experimentally demonstrate the emergence of high relatedness from well-mixed inocula. We find that a single infection cycle results in a bottleneck with a similar level of relatedness to those previously reported in the field. The bottlenecks that are a product of widespread barriers to infection can therefore produce the population structure required for the evolution of cooperative virulence.

Bacterial Cooperation Causes Systematic Errors in Pathogen Risk Assessment due to the Failure of the Independent Action Hypothesis

The Independent Action Hypothesis (IAH) states that pathogenic individuals (cells, spores, virus particles etc.) behave independently of each other, so that each has an independent probability of causing systemic infection or death. The IAH is not just of basic scientific interest; it forms the basis of our current estimates of infectious disease risk in humans. Despite the important role of the IAH in managing disease interventions for food and water-borne pathogens, experimental support for the IAH in bacterial pathogens is indirect at best. Moreover since the IAH was first proposed, cooperative behaviors have been discovered in a wide range of microorganisms, including many pathogens. A fundamental principle of cooperation is that the fitness of individuals is affected by the presence and behaviors of others, which is contrary to the assumption of independent action. In this paper, we test the IAH in Bacillus thuringiensis (B.t), a widely occurring insect pathogen that releases toxins that benefit others in the inoculum, infecting the diamondback moth, Plutella xylostella. By experimentally separating B.t. spores from their toxins, we demonstrate that the IAH fails because there is an interaction between toxin and spore effects on mortality, where the toxin effect is synergistic and cannot be accommodated by independence assumptions. Finally, we show that applying recommended IAH dose-response models to high dose data leads to systematic overestimation of mortality risks at low doses, due to the presence of synergistic pathogen interactions. Our results show that cooperative secretions can easily invalidate the IAH, and that such mechanistic details should be incorporated into pathogen risk analysis.

Listeria monocytogenes dose response revisited--incorporating adjustments for variability in strain virulence and host susceptibility

Evaluations of Listeria monocytogenes dose-response relationships are crucially important for risk assessment and risk management, but are complicated by considerable variability across population subgroups and L. monocytogenes strains. Despite difficulties associated with the collection of adequate data from outbreak investigations or sporadic cases, the limitations of currently available animal models, and the inability to conduct human volunteer studies, some of the available data now allow refinements of the well-established exponential L. monocytogenes dose response to more adequately represent extremely susceptible population subgroups and highly virulent L. monocytogenes strains. Here, a model incorporating adjustments for variability in L. monocytogenes strain virulence and host susceptibility was derived for 11 population subgroups with similar underlying comorbidities using data from multiple sources, including human surveillance and food survey data. In light of the unique inherent properties of L. monocytogenes dose response, a lognormal-Poisson dose-response model was chosen, and proved able to reconcile dose-response relationships developed based on surveillance data with outbreak data. This model was compared to a classical beta-Poisson dose-response model, which was insufficiently flexible for modeling the specific case of L. monocytogenes dose-response relationships, especially in outbreak situations. Overall, the modeling results suggest that most listeriosis cases are linked to the ingestion of food contaminated with medium to high concentrations of L. monocytogenes. While additional data are needed to refine the derived model and to better characterize and quantify the variability in L. monocytogenes strain virulence and individual host susceptibility, the framework derived here represents a promising approach to more adequately characterize the risk of listeriosis in highly susceptible population subgroups.

Dose-response algorithms for water-borne Pseudomonas aeruginosa folliculitis

We developed two dose-response algorithms for P. aeruginosa pool folliculitis using bacterial and lesion density estimates, associated with undetectable, significant, and almost certain folliculitis. Literature data were fitted to Furumoto & Mickey's equations, developed for plant epidermis-invading pathogens: N l = A ln(1 + BC) (log-linear model); P inf = 1-e(-r c C) (exponential model), where A and B are 2.51644 × 107 lesions/m2 and 2.28011 × 10-11 c.f.u./ml P. aeruginosa, respectively; C = pathogen density (c.f.u./ml), N l = folliculitis lesions/m2, P inf = probability of infection, and r C = 4·3 × 10-7 c.f.u./ml P. aeruginosa. Outbreak data indicates these algorithms apply to exposure durations of 41 ± 25 min. Typical water quality benchmarks (≈10-2 c.f.u./ml) appear conservative but still useful as the literature indicated repeated detection likely implies unstable control barriers and bacterial bloom potential. In future, culture-based outbreak testing should be supplemented with quantitative polymerase chain reaction and organic carbon assays, and quantification of folliculitis aetiology to better understand P. aeruginosa risks.

Modeling inoculum dose dependent patterns of acute virus infections

Inoculum dose, i.e. the number of pathogens at the beginning of an infection, often affects key aspects of pathogen and immune response dynamics. These in turn determine clinically relevant outcomes, such as morbidity and mortality. Despite the general recognition that inoculum dose is an important component of infection outcomes, we currently do not understand its impact in much detail. This study is intended to start filling this knowledge gap by analyzing inoculum dependent patterns of viral load dynamics in acute infections. Using experimental data for adenovirus and infectious bronchitis virus infections as examples, we demonstrate inoculum dose dependent patterns of virus dynamics. We analyze the data with the help of mathematical models to investigate what mechanisms can reproduce the patterns observed in experimental data. We find that models including components of both the innate and adaptive immune response are needed to reproduce the patterns found in the data. We further analyze which types of innate or adaptive immune response models agree with observed data. One interesting finding is that only models for the adaptive immune response that contain growth terms partially independent of viral load can properly reproduce observed patterns. This agrees with the idea that an antigen-independent, programmed response is part of the adaptive response. Our analysis provides useful insights into the types of model structures that are required to properly reproduce observed virus dynamics for varying inoculum doses. We suggest that such models should be taken as basis for future models of acute viral infections.

New data, strategies, and insights for Listeria monocytogenes dose-response models: summary of an interagency workshop, 2011

Listeria monocytogenes is a leading cause of hospitalization, fetal loss, and death due to foodborne illnesses in the United States. A quantitative assessment of the relative risk of listeriosis associated with the consumption of 23 selected categories of ready-to-eat foods, published by the U.S. Department of Health and Human Services and the U.S. Department of Agriculture in 2003, has been instrumental in identifying the food products and practices that pose the greatest listeriosis risk and has guided the evaluation of potential intervention strategies. Dose-response models, which quantify the relationship between an exposure dose and the probability of adverse health outcomes, were essential components of the risk assessment. However, because of data gaps and limitations in the available data and modeling approaches, considerable uncertainty existed. Since publication of the risk assessment, new data have become available for modeling L. monocytogenes dose-response. At the same time, recent advances in the understanding of L. monocytogenes pathophysiology and strain diversity have warranted a critical reevaluation of the published dose-response models. To discuss strategies for modeling L. monocytogenes dose-response, the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) held a scientific workshop in 2011 (details available at http://foodrisk.org/irac/events/). The main findings of the workshop and the most current and relevant data identified during the workshop are summarized and presented in the context of L. monocytogenes dose-response. This article also discusses new insights on dose-response modeling for L. monocytogenes and research opportunities to meet future needs.

Application of the International Life Sciences Institute Key Events Dose-Response Framework to food contaminants

Contaminants are undesirable constituents in food. They may be formed during production of a processed food, present as a component in a source material, deliberately added to substitute for the proper substance, or the consequence of poor food-handling practices. Contaminants may be chemicals or pathogens. Chemicals generally degrade over time and become of less concern as a health threat. Pathogens have the ability to multiply, potentially resulting in an increased threat level. Formal structures have been lacking for systematically generating and evaluating hazard and exposure data for bioactive agents when problem situations arise. We need to know what the potential risk may be to determine whether intervention to reduce or eliminate contact with the contaminant is warranted. We need tools to aid us in assembling and assessing all available relevant information in an expeditious and scientifically sound manner. One such tool is the International Life Sciences Institute (ILSI) Key Events Dose-Response Framework (KEDRF). Developed as an extension of the WHO's International Program on Chemical Safety/ILSI mode of action/human relevance framework, it allows risk assessors to understand not only how a contaminant exerts its toxicity but also the dose response(s) for each key event and the ultimate outcome, including whether a threshold exists. This presentation will illustrate use of the KEDRF with case studies included in its development (chloroform and Listeriaonocytogenes) after its publication in the peer-reviewed scientific literature (chromium VI) and in a work in progress (3-monochloro-1, 2-propanediol).

State of the art in benefit-risk analysis: food microbiology

Over the past years benefit-risk analysis (BRA) in relation to foods and food ingredients has gained much attention; in Europe and worldwide. BRA relating to food microbiology is however a relatively new field of research. Microbiological risk assessment is well defined but assessment of microbial benefits and the weighing of benefits and risk has not been systematically addressed. In this paper the state of the art in benefit-risk analysis in food microbiology is presented, with a brief overview of microbiological food safety practices. The quality and safety of foods is commonly best preserved by delaying the growth of spoilage bacteria and contamination by bacterial pathogens. However, microorganisms in food can be both harmful and beneficial. Many microorganisms are integral to various food production processes e.g. the production of beer, wine and various dairy products. Moreover, the use of some microorganisms in the production of fermented foods are often claimed to have beneficial effects on food nutrition and consumer health. Furthermore, food safety interventions leading to reduced public exposure to foodborne pathogens can be regarded as benefits. The BRA approach integrates an independent assessment of both risks and benefits and weighs the two using a common currency. Recently, a number of initiatives have been launched in the field of food and nutrition to address the formulation of the benefit-risk assessment approach. BRA has recently been advocated by EFSA for the public health management of food and food ingredients; as beneficial and adverse chemicals can often be found within the same foods and even the same ingredients. These recent developments in the scoping of BRA could be very relevant for food microbiological issues. BRA could become a valuable methodology to support evaluations and decision making regarding microbiological food safety and public health, supplementing other presently available policy making and administrative tools for microbiological food safety management.

Construction of food and water borne pathogens' dose-response curves using the expanded Fermi Solution

Theoretically, the relationship between the number of pathogens that cause acute infection if settling in the gut, N, and that initially ingested, M, can be constructed from the survival probabilities at the different "stations" along the digestive tract. These probabilities are rarely known exactly, but their ranges can be estimated. If for a given N one generates estimates of M using random probabilities within these ranges, the estimates' distribution will be approximately lognormal and its cumulative (CDF) form will represent the pathogen's dose-response curve. The distribution's logarithmic mean and standard deviation can be calculated from the ranges with a formula and used to plot the curve. The method was used to generate dose-response curves of hypothetical food and waterborne pathogens and calculate their infective dose (ID) at 5%, 50%, and 95% probability. The curves were compatible with the Beta Poisson model and robust against minor perturbations in the underlying probabilities' ranges. The calculation and plotting procedure was automated and posted on the Internet as a freely downloadable interactive Wolfram Demonstration. It allows the user to generate, modify, examine, and compare dose-response curves, and to calculate their characteristics, by moving sliders on the screen.

Modeling of pathogen survival during simulated gastric digestion

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

Remediating office environments of spore-forming bacteria

This study examines decontamination processes that were developed on an emergency basis to eliminate Bacillus anthracis spores from deliberately contaminated buildings. The recommended steps include a survey with sampling, the removal of sensitive items, and HEPA vacuuming of all readily available surfaces, followed by biocide treatment and subsequent analyses for viable cells. There are several analytical challenges posed by this approach. These include the ability to discriminate the added strain from naturally occurring resident microbes, determining detection limits for anthrax spores in settled dusts, and detecting viable but nonculturable spores. There are also logistical issues relating to the various skill sets required from investigation to reconstruction. In the present study, a model office was constructed, and a strain of Bacillus pumilus was isolated from the carpet and reintroduced to the office in excess. The abundance of the B. pumilus strain was monitored in settled dust using a strain-specific, quantitative polymerase chain reaction (QPCR)-based detection method following repeated HEPA vacuum cleanings. The QPCR method had a limit of detection corresponding to < or = 10(2) colony forming units per gram of settled dust. QPCR results were compared with measures of dust recoveries and fungal glucan and endotoxin levels in the dust samples. The largest fraction (ca. 81%) of added spores was recovered during the first HEPA cleaning. Subsequent cleanings resulted in incrementally lower recoveries, with removal of 93% of the initial inoculum by the third HEPA vacuuming. HEPA vacuuming prior to removal of items such as office contents and furnishings would result in much less resuspension of dust and limiting the extent of contamination. This approach also ensures that residual contaminants are as low as can be reasonably achieved.

The Key Events Dose-Response Framework: a cross-disciplinary mode-of-action based approach to examining dose-response and thresholds

The ILSI Research Foundation convened a cross-disciplinary working group to examine current approaches for assessing dose-response and identifying safe levels of intake or exposure for four categories of bioactive agents—food allergens, nutrients, pathogenic microorganisms, and environmental chemicals. This effort generated a common analytical framework—the Key Events Dose-Response Framework (KEDRF)—for systematically examining key events that occur between the initial dose of a bioactive agent and the effect of concern. Individual key events are considered with regard to factors that influence the dose-response relationship and factors that underlie variability in that relationship. This approach illuminates the connection between the processes occurring at the level of fundamental biology and the outcomes observed at the individual and population levels. Thus, it promotes an evidence-based approach for using mechanistic data to reduce reliance on default assumptions, to quantify variability, and to better characterize biological thresholds. This paper provides an overview of the KEDRF and introduces a series of four companion papers that illustrate initial application of the approach to a range of bioactive agents.