Challenges in risk assessment and predictive microbiology of foodborne spore-forming bacteria

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Cereulide and Emetic Bacillus cereus: Characterizations, Impacts and Public Precautions

Cereulide, which can be produced by Bacillus cereus, is strongly associated with emetic-type food poisoning outbreaks. It is an extremely stable emetic toxin, which is unlikely to be inactivated by food processing. Considering the high toxicity of cereulide, its related hazards raise public concerns. A better understanding of the impact of B. cereus and cereulide is urgently needed to prevent contamination and toxin production, thereby protecting public health. Over the last decade, a wide range of research has been conducted regarding B. cereus and cereulide. Despite this, summarized information highlighting precautions at the public level involving the food industry, consumers and regulators is lacking. Therefore, the aim of the current review is to summarize the available data describing the characterizations and impacts of emetic B. cereus and cereulide; based on this information, precautions at the public level are proposed.

Microrisk Lab: An Online Freeware for Predictive Microbiology

Microrisk Lab is an R-based online modeling freeware designed to realize parameter estimation and model simulation in predictive microbiology. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/inactivation under static and nonisothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/inactivation under static and nonisothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate data set and/or parameter setting. In this study, six case studies were reproduced in Microrisk Lab and compared in parallel with DMFit, GInaFiT, IPMP 2013/GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling systems. Microrisk Lab allows for a friendly user experience when modeling microbial behaviors owing to its interactive interfaces, high integration, and interconnectivity. Users can freely access this application at https://microrisklab.shinyapps.io/english/ or https://microrisklab.shinyapps.io/chinese/.

High levels of antibiotic resistance genes and opportunistic pathogenic bacteria indicators in urban wild bird feces

This study analyzed fresh feces from three common bird species that live in urban environments and interact with human communities. Antibiotic resistance genes (ARGs) encoding resistance to three major classes of antibiotics (i.e., tetracyclines, β-lactams, and sulfonamides) and the mobile genetic element integrase gene (intI1) were abundant (up to 10⁹, 10⁸, 10⁹, and 10¹⁰ copies/g dry feces for tetW, bla_TEM, sul1, and intI1, respectively), with relative concentrations surprisingly comparable to that in poultry and livestock that are occasionally fed antibiotics. Biomarkers for opportunistic pathogens were also abundant (up to 10⁷ copies/g dry feces) and the dominant isolates (i.e., Enterococcus spp. and Pseudomonas aeruginosa) harbored both ARGs and virulence genes. ARGs in bird feces followed first-order attenuation with half-lives ranging from 1.3 to 11.1 days in impacted soil. Although residual antibiotics were detected in the feces, no significant correlation was observed between fecal antibiotic concentrations and ARG relative abundance. Thus, other unaccounted factors likely contributed selective pressure for ARG maintenance. These findings highlight the contribution of wild urban bird feces to the maintenance and dissemination of ARGs, and the associated health risks.

Main Groups of Microorganisms of Relevance for Food Safety and Stability

Microbiology is important to food safety, production, processing, preservation, and storage. Microbes such as bacteria, molds, and yeasts are employed for the foods production and food ingredients such as production of wine, beer, bakery, and dairy products. On the other hand, the growth and contamination of spoilage and pathogenic microorganisms is considered as one of the main causes to loss of foodstuff nowadays. Although technology, hygienic strategies, and traceability are important factors to prevent and delay microbial growth and contamination, food remains susceptible to spoilage and activity of pathogen microorganisms. Food loss by either spoilage or contaminated food affects food industry and consumers leading to economic losses and increased hospitalization costs. This chapter focuses on general aspects, characteristics, and importance of main microorganisms (bacteria, yeasts, molds, virus, and parasites) involved in food spoilage or contamination: known and recently discovered species; defects and alterations in foodstuff; most common food associated with each foodborne disease; resistance to thermal processing; occurrence in different countries; outbreaks; and associated symptoms.

SpoVT: From Fine-Tuning Regulator in Bacillus subtilis to Essential Sporulation Protein in Bacillus cereus

Sporulation is a highly sophisticated developmental process adopted by most Bacilli as a survival strategy to withstand extreme conditions that normally do not support microbial growth. A complicated regulatory cascade, divided into various stages and taking place in two different compartments of the cell, involves a number of primary and secondary regulator proteins that drive gene expression directed toward the formation and maturation of an endospore. Such regulator proteins are highly conserved among various spore formers. Despite this conservation, both regulatory and phenotypic differences are observed between different species of spore forming bacteria. In this study, we demonstrate that deletion of the regulatory sporulation protein SpoVT results in a severe sporulation defect in Bacillus cereus, whereas this is not observed in Bacillus subtilis. Although spores are initially formed, the process is stalled at a later stage in development, followed by lysis of the forespore and the mother cell. A transcriptomic investigation of B. cereus ΔspoVT shows upregulation of genes involved in germination, potentially leading to premature lysis of prespores formed. Additionally, extreme variation in the expression of species-specific genes of unknown function was observed. Introduction of the B. subtilis SpoVT protein could partly restore the sporulation defect in the B. cereus spoVT mutant strain. The difference in phenotype is thus more than likely explained by differences in promoter targets rather than differences in mode of action of the conserved SpoVT regulator protein. This study stresses that evolutionary variances in regulon members of sporulation regulators can have profound effects on the spore developmental process and that mere protein homology is not a foolproof predictor of similar phenotypes.

Pressure-Based Strategy for the Inactivation of Spores

Since the first application of high hydrostatic pressure (HHP) for food preservation more than 100 years ago, a wealth of knowledge has been gained on molecular mechanisms underlying the HHP-mediated destruction of microorganisms. However, one observation made back then is still valid, i.e. that HHP alone is not sufficient for the complete inactivation of bacterial endospores. To achieve "commercial sterility" of low-acid foods, i.e. inactivation of spores capable of growing in a specific product under typical storage conditions, a combination of HHP with other hurdles is required (most effectively with heat (HPT)). Although HPT processes are not yet industrially applied, continuous technical progress and increasing consumer demand for minimally processed, additive-free food with long shelf life, makes HPT sterilization a promising alternative to thermal processing.In recent years, considerable progress has been made in understanding the response of spores of the model organism B. subtilis to HPT treatments and detailed insights into some basic mechanisms in Clostridium species shed new light on differences in the HPT-mediated inactivation of Bacillus and Clostridium spores. In this chapter, current knowledge on sporulation and germination processes, which presents the basis for understanding development and loss of the extreme resistance properties of spores, is summarized highlighting commonalities and differences between Bacillus and Clostridium species. In this context, the effect of HPT treatments on spores, inactivation mechanism and kinetics, the role of population heterogeneity, and influence factors on the results of inactivation studies are discussed.

Modeling the recovery of heat-treated Bacillus licheniformis Ad978 and Bacillus weihenstephanensis KBAB4 spores at suboptimal temperature and pH using growth limits

The apparent heat resistance of spores of Bacillus weihenstephanensis and Bacillus licheniformis was measured and expressed as the time to first decimal reduction (δ value) at a given recovery temperature and pH. Spores of B. weihenstephanensis were produced at 30°C and 12°C, and spores of B. licheniformis were produced at 45°C and 20°C. B. weihenstephanensis spores were then heat treated at 85°C, 90°C, and 95°C, and B. licheniformis spores were heat treated at 95°C, 100°C, and 105°C. Heat-treated spores were grown on nutrient agar at a range of temperatures (4°C to 40°C for B. weihenstephanensis and 15°C to 60°C for B. licheniformis) or a range of pHs (between pH 4.5 and pH 9.5 for both strains). The recovery temperature had a slight effect on the apparent heat resistance, except very near recovery boundaries. In contrast, a decrease in the recovery pH had a progressive impact on apparent heat resistance. A model describing the heat resistance and the ability to recover according to the sporulation temperature, temperature of treatment, and recovery temperature and pH was proposed. This model derived from secondary mathematical models for growth prediction. Previously published cardinal temperature and pH values were used as input parameters. The fitting of the model with apparent heat resistance data obtained for a wide range of spore treatment and recovery conditions was highly satisfactory.

SporeWeb: an interactive journey through the complete sporulation cycle of Bacillus subtilis

Bacterial spores are a continuous problem for both food-based and health-related industries. Decades of scientific research dedicated towards understanding molecular and gene regulatory aspects of sporulation, spore germination and spore properties have resulted in a wealth of data and information. To facilitate obtaining a complete overview as well as new insights concerning this complex and tightly regulated process, we have developed a database-driven knowledge platform called SporeWeb (http://sporeweb.molgenrug.nl) that focuses on gene regulatory networks during sporulation in the Gram-positive bacterium Bacillus subtilis. Dynamic features allow the user to navigate through all stages of sporulation with review-like descriptions, schematic overviews on transcriptional regulation and detailed information on all regulators and the genes under their control. The Web site supports data acquisition on sporulation genes and their expression, regulon network interactions and direct links to other knowledge platforms or relevant literature. The information found on SporeWeb (including figures and tables) can and will be updated as new information becomes available in the literature. In this way, SporeWeb offers a novel, convenient and timely reference, an information source and a data acquisition tool that will aid in the general understanding of the dynamics of the complete sporulation cycle.

Development of a time-to-detect growth model for heat-treated Bacillus cereus spores

The microbiological safety and quality of Refrigerated Processed Foods of Extended Durability (REPFEDs) relies on a combination of mild heat treatment and refrigeration, sometimes in combination with other inhibitory agents that are ineffective when used alone. In this context, a predictive model describing the time-to-detect growth (measured by turbidimetry) of psychrotrophic Bacillus cereus spores submitted to various combinations of pH, water activity (aw), heat treatment and storage temperature was developed. As the inoculum was high, the time-to-detect growth was the sum of two times: for a large part of the spore lag time (time before germination and outgrowth) and to a lesser extent of the time to have subsequent vegetative cells growing up to a detectable level. A dataset of 434 combinations (of pH, aw, heat treatment, storage temperature and B. cereus strain), originally collected at Ghent University to build a growth/no-growth model for two Bacillus cereus strains, was re-interpreted as time-to-detect growth values. In the growth area (223 combinations) the time-to-detect growth was set as the longest time where none, or only one, of the 8 replicated wells showed growth. In the no-growth area (211 combinations) the time-to-detect growth was set as longer than the time where the experiment was stopped (60days or more) and analysed as a censored response. The factors of variation were heat-treatment intensity (85°C, 87°C and 90°C in a time range of 1 to 38min), storage temperature (8-30°C), pH (5.2-6.4) and aw (0.973-0.995). Two different strains were analysed. The model had a Gamma multiplicative structure; it was solved by Bayesian inference with informative prior distributions. To be implemented in a decision tool, for instance to calculate the process and formulation conditions required to achieve a given detection time, each Gamma term had some constraints: they had to be monotonous, continuous and algebraically simple mathematical functions (i.e. having analytical solution). Overall, the cumulative effect of various stressful conditions (pasteurisation process, low temperature, and low pH) enables to extend the time-to-detect growth up to 60days or more, whereas the heat-treatment on its own did not have a similar effect. For example, with the most heat resistant strain (strain 1, FF140), for a product at aw0.99, stored at 10°C, heat-treated at 90°C for 10min, a time-to-detect growth of 2days was expected when the pH equalled 6.5. Under the same conditions, if the pH was reduced to 5.8, the time-to-detect growth was predicted to be 11days (and 33days at pH5.5). After a pasteurisation at 90°C for 10min, for a product kept at 10°C, combinations of pH and aw such as pH6.0-aw0.97, pH5.7-aw0.98 or pH5.5-aw0.99 were predicted to extend the time-to-detect growth up to 30days. The developed model is a useful tool for REPFED producers to guarantee the safety of their products towards psychrotrophic B. cereus.