Quantal microbiology

Effect of NaCl and KCl on fate and growth/no growth interfaces of Listeria monocytogenes Scott A at different pH and nisin concentrations

AIMS

The fate of Listeria monocytogenes Scott A, was studied in broth, at different a(w)s (by adding NaCl or KCl from 0.0 to 1.4 mol l(-1)), pHs (from 4.0 to 7.3 by adding lactic acid), and nisin concentrations (from 0 to 100 IU ml(-1)).

METHODS AND RESULTS

Increasing salt and nisin concentrations and decreasing pH resulted in lower growth rates and extended lag phases. At pH 4.5 no growth was observed while in presence of nisin and/or 1 mol l(-1) salts of both kinds, L. monocytogenes Scott A was inactivated. Equal-molar concentrations of NaCl or KCl (similar a(w)), exerted similar effects against L. monocytogenes in terms of lag phase duration, growth or death rate. The growth boundaries of L. monocytogenes Scott A at 5 degrees C were also estimated by growth/no growth turbidity data, modeled by logistic polynomial regression. The concordance of logistic models, were 99.6 and 99.8% for NaCl and KCl, respectively.

CONCLUSIONS

The growth interfaces derived by both NaCl and KCl models were almost identical. Hence, NaCl can be replaced by KCl without risking the microbiological safety of the product. Increasing nisin concentrations markedly affected the interface resulting in a more inhibitory environment for L. monocytogenes Scott A. Low to medium salt concentrations (0.3-0.7 mol l(-1) of either NaCl or KCl) provided a protective effect against inhibition of L. monocytogenes Scott A by nisin.

SIGNIFICANCE AND IMPACT OF THE STUDY

Modelling the growth boundaries not only contributes to the development of safer food by providing useful data, but can also be used to study interactions between factors affecting initiation of growth of pathogenic micro-organisms.

Modeling individual cell lag time distributions for Listeria monocytogenes

The food industry faces two paradoxical demands: on the one hand, foods need to be microbiologically safe for consumption and on the other hand, consumers want fresh, minimally processed foods. To meet these demands, more insight into the mechanisms of microbial growth is needed, which includes, among others, the microbial lag phase. This is the time needed by bacterial cells to adapt to a new environment (for example, after food product contamination) before starting an exponential growth regime. Since food products are often contaminated with low amounts of pathogenic microorganisms, it is important to know the distribution of these individual cell lag times to make accurate predictions concerning food safety. More precisely, cells with the shortest lag times (i.e., appearing in the left tail of the distribution) are largely decisive for the outgrowth of the population. In this study, an integrated modeling approach is proposed and applied to an existing data set of individual cell lag time measurements of Listeria monocytogenes. In a first step, a logistic modeling approach is applied to predict the fraction of zero-lag cells (which start growing immediately) as a function of temperature, pH, and water activity. For the nonzero-lag cells, the mean and variance of the lag time distribution are modeled with a hyperbolic-type model structure. This mean and variance allow identification of the parameters of a two-parameter Weibull distribution, representing the nonzero-lag cell lag time distribution. The integration of the developed models allows prediction of a global distribution of individual cell lag times for any combination of environmental conditions in the interpolation domain of the original temperature, pH, and water activity settings. The global fitting quality of the model is quantified using several measures indicating that the model gives accurate predictions, erring slightly on the fail-safe side when predicting the shortest lag times.

A cytomic approach reveals population heterogeneity ofCupriavidus necator in response to harmful phenol concentrations

The understanding of functions of cells within microbial populations or communities is certainly needed for existing and novel cytomic approaches which grip the individual scale. Population behaviour results from single cell performances and is caused by the individual genetic pool, history, life cycle states and microenvironmental surroundings. Mimicking natural impaired environments, the paper shows that the Gram-negative Betaproteobacterium Cupriavidus necator dramatically altered its population heterogeneity in response to harmful phenol concentrations. Multiparametric flow cytometry was used to follow variations in structural cellular parameters like chromosome contents and storage materials. The functioning of these different cell types was resolved by ensuing proteomics after the cells' spatial separation by cell sorting, finding 11 proteins changed in their expression profile, among them elongation factor Tu and the trigger factor. At least one third of the individuals clearly underwent starving states; however, simultaneously these cells prepared themselves for entering the life cycle again. Using cytomics to recognise individual structure and function on the microbial scale represents an innovative technical design to describe the complexity of such systems, overcoming the disadvantage of small cell volumes and, thus, to resolve bacterial strategies to survive harmful environments by altering population heterogeneity.

Information systems in food safety management

Information systems are concerned with data capture, storage, analysis and retrieval. In the context of food safety management they are vital to assist decision making in a short time frame, potentially allowing decisions to be made and practices to be actioned in real time. Databases with information on microorganisms pertinent to the identification of foodborne pathogens, response of microbial populations to the environment and characteristics of foods and processing conditions are the cornerstone of food safety management systems. Such databases find application in: Identifying pathogens in food at the genus or species level using applied systematics in automated ways. Identifying pathogens below the species level by molecular subtyping, an approach successfully applied in epidemiological investigations of foodborne disease and the basis for national surveillance programs. Predictive modelling software, such as the Pathogen Modeling Program and Growth Predictor (that took over the main functions of Food Micromodel) the raw data of which were combined as the genesis of an international web based searchable database (ComBase). Expert systems combining databases on microbial characteristics, food composition and processing information with the resulting "pattern match" indicating problems that may arise from changes in product formulation or processing conditions. Computer software packages to aid the practical application of HACCP and risk assessment and decision trees to bring logical sequences to establishing and modifying food safety management practices. In addition there are many other uses of information systems that benefit food safety more globally, including: Rapid dissemination of information on foodborne disease outbreaks via websites or list servers carrying commentary from many sources, including the press and interest groups, on the reasons for and consequences of foodborne disease incidents. Active surveillance networks allowing rapid dissemination of molecular subtyping information between public health agencies to detect foodborne outbreaks and limit the spread of human disease. Traceability of individual animals or crops from (or before) conception or germination to the consumer as an integral part of food supply chain management. Provision of high quality, online educational packages to food industry personnel otherwise precluded from access to such courses.

Information from single-cell bacterial biosensors: what is it good for?

Bacterial reporter cells (i.e. strains engineered to produce easily measurable signals in response to one or more chemical targets) can principally be used to quantify chemical signals and analytes, physicochemical conditions and gradients on a microscale (i.e. micrometer to submillimeter distances), when the reporter signal is determined in individual cells. This makes sense, as bacterial life essentially thrives in microheterogenic environments and single-cell reporter information can help us to understand the microphysiology of bacterial cells and its importance for macroscale processes like pollutant biodegradation, beneficial bacteria-eukaryote interactions, and infection. Recent findings, however, showed that clonal bacterial populations are essentially always physiologically, phenotypically and genotypically heterogeneous, thus emphasizing the need for sound statistical approaches for the interpretation of reporter response in individual bacterial cells. Serious attempts have been made to measure and interpret single-cell reporter gene expression and to understand variability in reporter expression among individuals in a population.

Single-cell microbiology: tools, technologies, and applications

The field of microbiology has traditionally been concerned with and focused on studies at the population level. Information on how cells respond to their environment, interact with each other, or undergo complex processes such as cellular differentiation or gene expression has been obtained mostly by inference from population-level data. Individual microorganisms, even those in supposedly "clonal" populations, may differ widely from each other in terms of their genetic composition, physiology, biochemistry, or behavior. This genetic and phenotypic heterogeneity has important practical consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. New appreciation of the importance of cellular heterogeneity, coupled with recent advances in technology, has driven the development of new tools and techniques for the study of individual microbial cells. Because observations made at the single-cell level are not subject to the "averaging" effects characteristic of bulk-phase, population-level methods, they offer the unique capacity to observe discrete microbiological phenomena unavailable using traditional approaches. As a result, scientists have been able to characterize microorganisms, their activities, and their interactions at unprecedented levels of detail.

The effect of growth atmosphere on the ability of Listeria monocytogenes to survive exposure to acid, proteolytic enzymes and bile salts

Four isolates of Listeria monocytogenes from food, human and environmental sources were grown separately in broth (pH 6.0 at 8 degrees C) under atmospheres of air, 100% N(2), 40% CO(2):60% N(2) or 100% CO(2). Exponential and stationary phase cells were harvested to determine if growth atmosphere and growth phase influenced this pathogen's ability to survive exposure to an acid environment coupled with proteolytic enzymes, and the activity of bile salts. In general, isolates were more resistant to the acid environment than the bile salts environment and stationary phase cells were significantly more resistant to both environments than exponential phase cells. Irrespective of prior growth atmosphere, none of the isolates when in exponential phase remained detectable following full exposure to the acid environment (110 min at 37 degrees C) or the bile environment (3 h at 37 degrees C). With the exception of one isolate grown under the atmosphere of 40% CO(2):60% N(2), all isolates when in stationary phase were detectable following full exposure to the acid environment but death rates varied significantly. Stationary phase cells of all isolates grown under 40% CO(2):60% N(2) and 100% CO(2) were highly susceptible to the bile salts environment: cells were not detectable after a 2-min exposure whereas stationary phase cells grown under air or 100% N(2) were recovered following full exposure to the bile environment. Survival curves were characterised by a population decline of at least 3 log(10)/ml (from an initial level of 7 log(10) CFU/ml) in the first 15 min; thereafter a constant population number of approximately 4 log(10)/ml was maintained over the remaining exposure period. No survival was observed when stationary phase cells of L. monocytogenes FRRB 2538 grown in air and 100% N(2) were subjected to the acid environment followed by immediate exposure to the bile salts environment. The results showed that growth atmosphere and growth phase could influence survival of this pathogen against conditions that imitate the extremes of the most important nonspecific defence mechanisms against microbial infection: the acid environment of the stomach coupled with the activity of proteolytic enzymes, and the activity of bile salts in the small intestine.

Predictive microbiology: providing a knowledge-based framework for change management

This contribution considers predictive microbiology in the context of the Food Micro 2002 theme, "Microbial adaptation to changing environments". To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of predictive microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the "clean, green" aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. The challenge is to demonstrate the validity of such models and to develop applications of benefit to the food industry and consumers as was achieved with growth models to predict shelf life and the hygienic equivalence of food processing operations.

Proposal of a novel parameter to describe the influence of pH on the lag phase of Listeria monocytogenes

Predictive models for lag phase duration (lambda) have been less reliable than specific growth rate (mu) models due, in part, to the influence of the pre-growth environment on lambda. A discrete modelling approach was taken to more completely define the response of individual cells to new environments. Time to detection (td) data was obtained from serial twofold dilutions of Listeria monocytogenes growing in a Bioscreen at 30 degrees C. Comparison of the inoculum densities required to achieve maximum td at growth pH values from 7.2 to 4.7 revealed that, as the growth pH decreased, fewer cells were capable of making the transition to the exponential phase. The proportion of these cells (termed "adaptable cells") in the original inoculum was used to define a new parameter (r0) which, when combined with the constant mean individual cell physiological state parameter (p0), the variation in p0 (SDp0), the inital inoculum (N0) and the maximum population density (Nmax) was able to simulate a complete growth curve. Power transformations with rescaled explanatory variables provided suitable models for the influence of pH on mu, r0, and SDp0, (r2>0.70).