Stochasticity in colonial growth dynamics of individual bacterial cells

Modelling the colony growth dynamics of Listeria monocytogenes single cells after exposure to peracetic acid and acidic conditions

Studies of classical microbiology rely on the average behaviour of large cell populations without considering that clonal bacterial populations may bifurcate into phenotypic distinct sub-populations by random switching mechanisms.Listeria monocytogenes exposure to sublethal stresses may induce different physiological states that co-exist (i.e., sublethal injury or dormancy) and present variable resuscitation capacity. Exposures to peracetic acid (PAA; 10-30 ppm; for 3 h), acetic acid and hydrochloric acid (AA and HCl; pH 3.0-2.5; for 5 h) at 20 °C were used to induce different physiological states in L. monocytogenes, Scott A strain. After stress exposure, colony growth of single cells was monitored, on Tryptic Soy Agar supplemented with 0.6 % Yeast Extract, using time-lapse microscopy, at 37 °C. Images were acquired every 5 min and were analyzed using BaSCA framework. Most of the obtained growth curves of the colonies were fitted to the model of Baranyi and Roberts for the estimation of lag time (λ) and maximum specific growth rate (μmax), except the ones obtained after exposure to AA pH 2.7 and 2.5 that were fitted to the Trilinear model. The data of λ and μmax that followed a multivariate normal distribution were used to predict growth variability using Monte Carlo simulations. Outgrowth kinetics after treatment with AA (pH 2.7 and 2.5; for 5 h at 20 °C), PAA (30 ppm; for 3 h at 20 °C) revealed that these stress conditions increase the skewness of the variability distributions to the right, meaning that the variability in lag times increases in favour of longer outgrowth. Exposures to AA pH 2.5 and 30 ppm PAA resulted in two distinct subpopulations per generation with different growth dynamics. This switching mechanism may have evolved as a survival strategy for L. monocytogenes cells, maximizing the chances of survival. Simulation of microbial growth showed that heterogeneity in growth dynamics is increased when cells are recovering from exposure to sublethal stresses (i.e. PAA and acidic conditions) that may induce injury or dormancy.

Resource limitation and population fluctuation drive spatiotemporal order in microbial communities

Microbial communities display complex spatiotemporal behaviors leading to spatially-structured and ordered organization driven by species interactions and environmental factors. Resource availability plays a pivotal role in shaping the dynamics of bacterial colonies. In this study, we delve into the intricate interplay between resource limitation and the emergent properties of a growing colony of two visually distinct bacterial strains having similar growth and mechanical properties. Employing an agent-based modeling and computer simulations, we analyze the resource-driven effect on segregation and sectoring, cell length regulation and nematic ordering within a growing colony. We introduce a dimensionless parameter referred to as the active layer thickness, derived from nutrient diffusion equations, indicating effective population participation due to local resource availability. Our results reveal that lower values of active layer thickness arising from decreased resource abundance lead to rougher colony fronts, fostering heightened population fluctuations within the colony and faster spatial genetic diversity loss. Our temporal analyses unveil the dynamics of mean cell length and fluctuations, showcasing how initial disturbances evolve as colonies are exposed to nutrients and subsequently settle. Furthermore, examining microscopic details, we find that lower resource levels yield diverse cell lengths and enhanced nematic ordering, driven by the increased prevalence of longer rod-shaped cells. Our investigation sheds light on the multifaceted relationship between resource constraints and bacterial colony dynamics, revealing insights into their spatiotemporal organization.

Impact of preculture temperature on peracetic acid-induced inactivation and sublethal injury of L. monocytogenes and subsequent growth potential of single cells

The disinfectant peracetic acid (PAA) that is used in the food industry can cause sublethal injury in L. monocytogenes. The effect of preculture temperature on the inactivation and sublethal injury of L. monocytogenes cells due to PAA was evaluated by plating on non-selective and selective agar medium supplemented with 5 % (w/v) NaCl. L. monocytogenes cells were precultured at 30 °C, 20 °C or 4 °C, and the former was used as reference temperature. Preculture of cells at 20 °C or 4 °C and subsequent exposure to PAA at the respective growth temperatures caused higher injury compared to cells grown at 30 °C and exposed to PAA 20 °C and PAA 4 °C, respectively. Survival was also affected by the preculture temperature; 20 °C-grown cultures resulted in lower survival at PAA 20 °C. Nevertheless, preculture at 4 °C resulted in a similar number of surviving cells when exposed to PAA 4 °C compared to cells precultured at 30 °C and exposed to PAA at 4 °C. Flow cytometry was subsequently used to quantify outgrowth capacity of stressed and sublethal damaged populations following sorting of single cells in nutrient rich medium (Tryptone soy broth supplemented with yeast extract [TSBY]). PAA treatment affected the outgrowth of L. monocytogenes at single-cell level resulting in increased outgrowth-times reflecting higher single cell heterogeneity. To conclude, the response of L. monocytogenes when exposed to PAA depended on the preculture conditions, and the highly heterogeneous outgrowth potential of PAA-injured cells may affect their detection accuracy and pose a food safety risk.

Cardinal models to describe the effect of temperature and pH on the growth of Anoxybacillus flavithermus & Bacillus licheniformis

Anoxybacillus flavithermus and Bacillus licheniformis are among the predominant spore-formers of heat-processed foods. To our knowledge, no systematic analysis of growth kinetic data of A. flavithermus or B. licheniformis is currently available. In the present study, the growth kinetics of A. flavithermus and B. licheniformis in broth at various temperature and pH conditions were studied. Cardinal models were used to model the effect of the above-mentioned factors on the growth rates. The estimated values for the cardinal parameters T_min,T_opt,T_max,pH_min and pH_1/2 for A. flavithermus were 28.70 ± 0.26, 61.23 ± 0.16 and 71.52 ± 0.32 °C, 5.52 ± 0.01 and 5.73 ± 0.01, respectively, while for B. licheniformis they were 11.68 ± 0.03, 48.05 ± 0.15, 57.14 ± 0.01 °C, 4.71 ± 0.01 and 5.670 ± 0.08, respectively. The growth behaviour of these spoilers was also investigated in a pea beverage at 62 and 49 °C, respectively, to adjust the models to this product. The adjusted models were further validated at static and dynamic conditions and demonstrated good performance with 85.7 and 97.4% of predicted populations for A. flavithermus and B. licheniformis, respectively, being within the -10%-10% relative error (RE) zone. The developed models can be useful tools in assessing the potential of spoilage of heat-processed foods including plant-based milk alternatives.

A compressed logistic equation bacteria growth: Inferring time-dependent growth rate

We propose a compressed logistic model for bacterial growth by invoking a time-dependent rate instead of the intrinsic growth rate (constant), which was adopted in traditional logistic models. The new model may have a better physiological basis than the traditional ones, and it replicates experimental observations, such as the case example for E. coli, Salmonella, and Staphylococcus aureus. Stochastic colonial growth at a different rate may have a fractal-like nature, which should be an origin of the time-dependent reaction rate. The present model, from a stochastic viewpoint, is approximated as a Gaussian time evolution of bacteria (error function).

Detection Time Distribution of Microcolonies Formed by Individual Heat-Injured Cells of Escherichia coli

The microcolony formation at 30℃ on an enriched minimal salts agar plates by individual Escherichia coli cells heated at 50℃ was monitored with a time-lapse shadow image analysis system, MicroBio μ3D^TM AutoScanner. While the time course of microcolony count detected every half an hour for the unheated cells seemingly demonstrated a normal distribution, that for the heated cell population demonstrated totally the growth delay probably resulting from cell injury and also interestingly distributed in its rather deformed pattern with a tailing. Those patterns of the cumulative counts of appearing microcolonies during the post-heating cultivation period were expressed in three different mathematical models. This approach may be proposed as a rapid cultivation method predictable for enumeration of viable and repairable injured cells in practical use.

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning

To predict bacterial population behavior in food, statistical models with specific function form have been applied in the field of predictive microbiology. Modelers need to consider the linear or non-linear relationship between the response and explanatory variables in the statistical modeling approach. In the present study, we focused on machine learning methods to skip definition of primary and secondary structure model. Support vector regression, extremely randomized trees regression, and Gaussian process regression were used to predict population growth of Escherichia coli O157 at 15 and 25 °C without defining the primary and secondary models. Furthermore, the support vector regression model was applied to predict small population of bacteria cells with probability theory. The model performance of the machine learning models were nearly equal to that of the current statistical models. Machine learning models have a potential for predicting bacterial population behavior.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data

Background

Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.

Results

We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.

Conclusions

ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04409-9.

Development and validation of a predictive model for the effect of temperature, pH and water activity on the growth kinetics of Bacillus coagulans in non-refrigerated ready-to-eat food products

A cardinal model (CM) for the effects of temperature (range: 32-59 °C), pH (range: 5.0-8.5) and water activity (a_w) (range: 0.980-0.995) on Bacillus coagulans DSM 1 growth rate was developed in brain heart infusion broth (BHI), using the Bioscreen C method and further validated in selected food products. The estimated values for the cardinal parameters T_min, T_opt, T_max, pH_min, pH_opt, pH_max, [Formula: see text] and [Formula: see text] were 23.77 ± 0.19 °C, 52.89 ± 0.01 °C, 59.37 ± 0.07 °C, 4.70 ± 0.02, 6.43 ± 0.02, 8.56 ± 0.01, 0.969 ± 0.0007 and 0.998 ± 0.0011, respectively. The growth behaviour of B. coagulans was studied in five commercial non-refrigerated ready-to-eat food products under static conditions at 53 °C in order to estimate the optimum specific growth rate for each tested food product. The developed models were validated in the five selected food products under four different dynamic temperature profiles by comparing predicted and observed growth behaviour of B. coagulans. The validation results indicated a good performance of the model for all tested products with the overall Bias factor (B_f) and Accuracy factor (A_f) estimated at 1.00 and 1.12, respectively. The developed model can be considered an effective tool in predicting B. coagulans growth and spoilage risks of non-refrigerated ready-to-eat food products during distribution and storage.

A facile and rapid route to self-digitization of samples into a high density microwell array for digital bioassays

Digital bioassays are powerful methods to detect rare analytes from complex mixtures and study the temporal processes of individual entities within biological systems. In digital bioassays, a crucial first step is the discretization of samples into a large number of identical independent partitions. Here, we developed a rapid and facile sample partitioning method for versatile digital bioassays. This method is based on a detachable self-digitization (DSD) chip which couples a reversible assembly configuration and a predegassing-based self-pumping mechanism to achieve an easy, fast, and large-scale sample partitioning. The DSD chip consists of a channel layer used for loading the sample and a microwell layer used for holding the sample partitions. Benefitting from its detachability, the chip avoids a lengthy oil flushing process used to remove the excess sample in loading channels and can compartmentalize a sample into more than 100,000 wells of picoliter volume with densities up to 14,000 wells/cm² in less than 30 s. We also demonstrated the utility of the proposed method by applying it to digital PCR and digital microbial assays.

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/.

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 10⁵ and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10⁴. The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 10¹, 10², and 10³ and growth with average of initial cell numbers of 10^–1, 10⁰, and 10¹. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10¹ were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens.

Development and validation of an extended predictive model for the effect of pH and water activity on the growth kinetics of Geobacillus stearothermophilus in plant-based milk alternatives

The cardinal model for the effect of temperature on Geobacillus stearothermophilus ATCC 7953 growth developed by Kakagianni, Gougouli, & Koutsoumanis, 2016 was expanded for the effect of pH and water activity (a_w). The effect of pH (range: 5.7-8.5) and a_w (range: 0.985-0.999) on G. stearothermophilus growth rate was studied in tryptone soy broth (TSB) using the Bioscreen C method and further modelled using a Cardinal Model (CM). The estimated values for the cardinal parameters [Formula: see text] , and [Formula: see text] were 5.65 ± 0.14, 6.74 ± 0.03, 8.71 ± 0.03, 0.984 ± 0.007 and 0.998 ± 0.001, respectively. The growth behaviour of G. stearothermophilus was investigated in 7 commercial non-refrigerated plant-based milk alternatives under static conditions (62 °C) and the estimated maximum specific growth rates were used to determine the optimum growth rate for each product. The developed model was validated against observed growth of G. stearothermophilus in the 7 products during storage at non-isothermal conditions (testing 4 different temperature profiles). The validation results showed a good performance of the model with overall Bias factor (B_f) = 1.06 and Accuracy factor (A_f) = 1.12. The developed model can be used as an effective tool by the food industry in predicting spoilage of plant-based milk alternatives during distribution and storage at retail and domestic levels.

Application of growth rate from kinetic model to calculate stochastic growth of a bacteria population at low contamination level

Traditional predictive microbiology is not suited for cell growth predictions for low-level contamination, where individual cell heterogeneity becomes apparent. Accordingly, we simulated a stochastic birth process of bacteria population using kinetic parameters. We predicted the variation in behavior of Salmonella enterica serovar Typhimurium cells at low inoculum density. The modeled cells were grown in tryptic soy broth at 25 °C. Kinetic growth parameters were first determined empirically for an initial cell number of 10⁴ cells. Monte Carlo simulation based on the growth kinetics and Poisson distribution for different initial cell numbers predicted the results of 50 replicate growth experiments with the initial cell number of 1, 10, and 64 cells. Indeed, measured behavior of 85% cells fell within the 95% prediction area of the simulation. The calculations link the kinetic and stochastic birth process with Poisson distribution. The developed model can be used to calculate the probability distribution of population size for exposure assessment and for the evaluation of a probability that a pathogen would exceed critical contamination level during food storage.

Initial cell density encodes proliferative potential in cancer cell populations

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.

Growth of Listeria monocytogenes in Partially Cooked Battered Chicken Nuggets as a Function of Storage Temperature

Battered poultry products may be wrongly regarded and treated by consumers as ready-to-eat and, as such, be implicated in foodborne disease outbreaks. This study aimed at the quantitative description of the growth behavior of Listeria monocytogenes in fresh, partially cooked (non-ready-to-eat) battered chicken nuggets as function of temperature. Commercially prepared chicken breast nuggets were inoculated with L. monocytogenes and stored at different isothermal conditions (4, 8, 12, and 16 °C). The pathogen’s growth behavior was characterized via a two-step predictive modelling approach: estimation of growth kinetic parameters using a primary model, and description of the effect of temperature on the estimated maximum specific growth rate (μ_max) using a secondary model. Model evaluation was undertaken using independent growth data under both constant and dynamic temperature conditions. According to the findings of this study, L. monocytogenes may proliferate in battered chicken nuggets in the course of their shelf life to levels potentially hazardous for susceptible population groups, even under well-controlled refrigerated storage conditions. Model evaluation demonstrated a satisfactory performance, where the estimated bias factor (B_f) was 0.92 and 1.08 under constant and dynamic temperature conditions, respectively, while the accuracy factor (A_f) value was 1.08, in both cases. The collected data should be useful in model development and quantitative microbiological risk assessment in battered poultry products.

A microscopy-based approach for determining growth probability and lag time of individual bacterial cells

The development of relevant predictive models for single-cell lag time and growth probability near growth limits is of critical importance for predicting pathogen behavior in foods. The classical methods for data acquisition in this field are based on turbidity measurements of culture media in microplate wells inoculated with approximately one bacterial cell per well. Yet, these methods are labour intensive and would benefit from higher throughput. In this study, we developed a quantitative experimental method using automated microscopy to determine the single-cell growth probability and lag time. The developed method consists of the use of direct cell observation with phase-contrast microscopy equipped with a 100× objective and a high-resolution device camera. The method is not a time-lapse method but is based on the observation of high numbers of colonies for a given time. Automation of image acquisition and image analysis was used to reach a high throughput. The single-cell growth probabilities and lag times of four strains of Listeria monocytogenes were determined at 4 °C. The microscopic method was shown to be a promising method for the determination of individual lag times and growth probability at the single-cell level.

Presence of Listeria monocytogenes in Ready-to-Eat Artisanal Chilean Foods

Ready-to-eat (RTE) artisanal foods are very popular, but they can be contaminated by Listeria monocytogenes. The aim was to determine the presence of L. monocytogenes in artisanal RTE foods and evaluate its food safety risk. We analyzed 400 RTE artisanal food samples requiring minimal (fresh products manufactured by a primary producer) or moderate processing (culinary products for sale from the home, restaurants such as small cafés, or on the street). Listeria monocytogenes was isolated according to the ISO 11290-1:2017 standard, detected with VIDAS equipment, and identified by real-time polymerase chain reaction (PCR). A small subset (n = 8) of the strains were further characterized for evaluation. The antibiotic resistance profile was determined by the CLSI methodology, and the virulence genes hlyA, prfA, and inlA were detected by PCR. Genotyping was performed by pulsed-field gel electrophoresis (PFGE). Listeria monocytogenes was detected in 7.5% of RTE artisanal foods. On the basis of food type, positivity in minimally processed artisanal foods was 11.6%, significantly different from moderately processed foods with 6.2% positivity (p > 0.05). All the L. monocytogenes strains (n = 8) amplified the three virulence genes, while six strains exhibited premature stop codons (PMSC) in the inlA gene; two strains were resistant to ampicillin and one strain was resistant to sulfamethoxazole-trimethoprim. Seven strains were 1/2a serotype and one was a 4b strain. The sampled RTE artisanal foods did not meet the microbiological criteria for L. monocytogenes according to the Chilean Food Sanitary Regulations. The presence of virulence factors and antibiotic-resistant strains make the consumption of RTE artisanal foods a risk for the hypersensitive population that consumes them.

Variability in microbial inactivation: From deterministic Bigelow model to probability distribution of single cell inactivation times

Phenotypic heterogeneity seems to be an important component leading to biological individuality and is of great importance in the case of microbial inactivation. Bacterial cells are characterized by their own resistance to stresses. This inherent stochasticity is reflected in microbial survival curve which, in this context, can be considered as cumulative probability distribution of lethal events. The objective of the present study was to present an overview on the assessment and quantification of variability in microbial inactivation originating from single cells and discuss this heterogeneity in the context of predicting microbial behavior and Risk assessment studies. The detailed knowledge of the distribution of the single cells' inactivation times can be the basis for stochastic inactivation models which, in turn, may be employed in a risk - based food safety approach.

Modeling the Effects of the Preculture Temperature on the Lag Phase of Listeria monocytogenes at 25°C

In predictive microbiology, the study of the microbial lag phase, i.e., the time needed for bacteria to adapt to a new environment before multiplying, has received a great deal of attention in the research literature. The microbial lag phase is more difficult to estimate than the specific growth rate because the lag phase is impacted by the previous and actual growth environments. In this study, the growth of Listeria monocytogenes preincubated at 0, 5, 10, and 15°C and subsequently grown at 25°C was investigated at the single-cell and population levels. The population lag phase of L. monocytogenes was obtained by fitting the Baranyi model, and the single-cell lag time was estimated by the time to detection method. The lag phase at the single-cell and population levels of L. monocytogenes presented a downward trend as the preculture temperature ranged from 0 to 15°C. The population lag phase of L. monocytogenes was lower than the single-cell lag time at the same preculture temperature. In addition, except for the zero-lag distribution at a preculture temperature of 15°C, all the single-cell lag time distributions of L. monocytogenes followed a Weibull distribution under all preculture temperatures. The preculture temperature had a significant impact on the rapid variation in the single-cell lag time distribution. Thus, the influence of preculture temperature on the lag phase needs to be quantitatively analyzed for better assessment of microbiological risk.

The Combined Effect of pH and Temperature on the Survival of Salmonella enterica Serovar Typhimurium and Implications for the Preparation of Raw Egg Mayonnaise

Raw egg products are often associated with salmonellosis. The Australian guidelines recommend raw egg mayonnaise to be prepared and stored under 5 °C and adjusted to a pH less than 4.6 or 4.2. Despite these guidelines, a significant amount of salmonellosis outbreaks are recorded annually in Australia. The aim of this study was to investigate the effect of pH and temperature on the survival of Salmonella Typhimurium (ST) in peptone water (PW) and mayonnaise. The pH of PW and mayonnaise was adjusted to 4.2, 4.4 and 4.6 using acetic acid and vinegar, respectively. The PW and mayonnaise were inoculated with ST and incubated at 37 °C, 23 °C, and 4 °C. The survival of Salmonella was determined using the drop plate method. Survival was significantly (p < 0.05) improved at 4 °C. In both mayonnaise and PW, following 24 h, there was no ST growth at pH 4.2. Resuscitation of ST was rapidly observed at 4 °C while complete inactivation was observed at 37 °C at pH 4.2, 4.4, and 4.6 in both PW and mayonnaise. Lower temperatures protected ST from the bactericidal effect of low pH. “The preparation of mayonnaise at pH 4.2 or less and incubating it at room temperature for at least 24 h could reduce the incidence of salmonellosis”.

Growing from a few cells: combined effects of initial stochasticity and cell-to-cell variability

The growth of a cell population from a large inoculum appears deterministic, although the division process is stochastic at the single-cell level. Microfluidic observations, however, display wide variations in the growth of small populations. Here we combine theory, simulations and experiments to explore the link between single-cell stochasticity and the growth of a population starting from a small number of individuals. The study yields descriptors of the probability distribution function (PDF) of the population size under three sources of stochasticity: cell-to-cell variability, uncertainty in the number of initial cells and generation-dependent division times. The PDF, rescaled to account for the exponential growth of the population, is found to converge to a stationary distribution. All moments of the PDF grow exponentially with the same growth rate, which depends solely on cell-to-cell variability. The shape of the PDF, however, contains the signature of all sources of stochasticity, and is dominated by the early stages of growth, and not by the cell-to-cell variability. Thus, probabilistic predictions of the growth of bacterial populations can be obtained with implications for both naturally occurring conditions and technological applications of single-cell microfluidics.

Tail or artefact? Illustration of the impact that uncertainty of the serial dilution and cell enumeration methods has on microbial inactivation

The estimation of the concentration of microorganisms in a sample is crucial for food microbiology. For instance, it is essential for prevalence studies, challenge tests (growth and/or inactivation studies) or microbial risk assessment. The application of serial dilutions followed by viable counts in Petri dishes is probably the most extended experimental methodology for this purpose. However, this enumeration technique is also a source of uncertainty. In this article, the uncertainty of the serial dilution and viable count methodology related to the sampling error is analyzed, as well as the approximation of the microbial concentration by the number of colonies in a Petri dish. We analyze from a theoretical point of view (statistical analysis) the application of the binomial and Poisson models, demonstrating that the Poisson distribution increases the variance when used to model individual serial dilutions. On the other hand, the binomial model produces unbiased results. Therefore, the Poisson distribution is only applicable when it is a good approximation of the binomial distribution, so the use of the latter is recommended. The relevance of this uncertainty is demonstrated by Monte Carlo simulations of a generic microbial inactivation experiment, where the only source of uncertainty/variability considered is the one generated by serial plating and viable cell enumeration. Due to both the uncertainty of the methodology and the omission of zero-count plates because of the log-transformation, the simulated survival curve can have a tail. Therefore, this phenomenon, which is usually attributed to biological variability, can be to some extent an artefact of the experimental design and/or methodology.

Heterogeneity of single cell inactivation: Assessment of the individual cell time to death and implications in population behavior

A direct microscopic time-lapse method, using appropriate staining for cell viability in a confocal scanning laser microscope, was used for the direct assessment of Salmonella Agona individual cell inactivation in small two-dimensional colonies exposed to osmotic stress. Individual cell inactivation times were fitted to a variety of continuous distributions using @Risk software. The best fitted distribution (LogLogistic) was further used to predict the inactivation of Salmonella populations of various initial levels using Monte Carlo simulation. The simulation results showed that the variability in inactivation kinetics is negligible for concentrations down to 100 cells and the population behavior can be described with a deterministic model. As the concentration decreases below 100 cells, however, the variability increases significantly indicating that the traditional D-value used in deterministic first order kinetic models is not valid. At a second stage, single cell behavior was monitored in larger three dimensional colonies. The results showed that colony size can affect the inactivation pattern. The effect of colony size on microbial inactivation was confirmed with validation experiments which showed a higher inactivation rate for populations consisting of single cells or small colonies compared to those consisting of cells organized in larger colonies.

Insights from genome-wide approaches to identify variants associated to phenotypes at pan-genome scale: Application to L. monocytogenes' ability to grow in cold conditions

Intraspecific variability of the behavior of most foodborne pathogens is well described and taken into account in Quantitative Microbial Risk Assessment (QMRA), but factors (strain origin, serotype, …) explaining these differences are scarce or contradictory between studies. Nowadays, Whole Genome Sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens, based on various recently published bioinformatics tools. The objective of this study is to get a better insight into different existing bioinformatics approaches to associate bacterial phenotype(s) and genotype(s). Therefore, a dataset of 51 L. monocytogenes strains, isolated from multiple sources (i.e. different food matrices and environments) and belonging to 17 clonal complexes (CC), were selected to represent large population diversity. Furthermore, the phenotypic variability of growth at low temperature was determined (i.e. qualitative phenotype), and the whole genomes of selected strains were sequenced. The almost exhaustive gene content, as well as the core genome SNPs based phylogenetic reconstruction, were derived from the whole sequenced genomes. A Bayesian inference method was applied to identify the branches on which the phenotype distribution evolves within sub-lineages. Two different Genome Wide Association Studies (i.e. gene- and SNP-based GWAS) were independently performed in order to link genetic mutations to the phenotype of interest. The genomic analyses presented in this study were successfully applied on the selected dataset. The Bayesian phylogenetic approach emphasized an association with "slow" growth ability at 2 °C of the lineage I, as well as CC9 of the lineage II. Moreover, both gene- and SNP-GWAS approaches displayed significant statistical associations with the tested phenotype. A list of 114 significantly associated genes, including genes already known to be involved in the cold adaption mechanism of L. monocytogenes and genes associated to mobile genetic elements (MGE), resulted from the gene-GWAS. On the other hand, a group of 184 highly associated SNPs were highlighted by SNP-GWAS, including SNPs detected in genes which were already likely involved in cold adaption; hypothetical proteins; and intergenic regions where for example promotors and regulators can be located. The successful application of combined bioinformatics approaches associating WGS-genotypes and specific phenotypes, could contribute to improve prediction of microbial behaviors in food. The implementation of this information in hazard identification and exposure assessment processes will open new possibilities to feed QMRA-models.

Simultaneous growth, survival and death: The trimodal behavior of Salmonella cells under osmotic stress giving rise to "Phoenix phenomenon"

Time-lapse microscopy methods were used to monitor growth, survival and death of Salmonella enterica serotype Agona individual cells on solid laboratory medium (tryptone soy agar) in the presence of various salt concentrations (0.5%, 3.5%, 4.5% and 5.7% NaCl). The results showed a highly heterogeneous behavior. As NaCl concentration increased, the distribution of the first division time was shifted to higher values and became wider. The mean first division time increased from 1.8 h at 0.5% NaCl to 5.48 h, 16.2 h, and 35.9 h at 3.5%, 4.5% and 5.7% NaCl, respectively. The concentration of NaCl in the growth medium also affected the ability of the cells to divide. The percentage of cells able to grow decreased from 88.9% at 0.5% NaCl to 66.5%, 32.8%, and 6.9% at 3.5%, 4.5% and 5.7% NaCl, respectively. In the latter case (5.7% NaCl), 74 cells out of 406 cells tested (18%) died with mean time to death 5.03 h and standard deviation 6.70 h. To investigate the effect of the behavior of individual cells on the dynamics of the whole population, simulation analysis was used. The simulation results showed that the simultaneous growth, survival and death of cells observed under osmotic stress can lead to a total population behavior known as the "Phoenix" phenomenon. The simulation findings were confirmed by validation experiments using both viable counts and time lapse microscopy. The results of the present study show the high heterogeneity of individual cell responses and the complexity in the behavior of microbial populations at conditions approaching the boundaries of growth.

Characterization of damage on Listeria innocua surviving to pulsed light: Effect on growth, DNA and proteome

The effect of pulsed light treatment on the lag phase and the maximum specific growth rate of Listeria innocua was determined in culture media at 7 °C. Fluences of 0.175, 0.350 and 0.525 J/cm² were tested. The lag phase of the survivors increased as fluence did, showing significant differences for all the doses; an 8.7-fold increase was observed at 0.525 J/cm². Pulsed light decreased the maximum specific growth rate by 38% at the same fluence. Both parameters were also determined by time-lapse microscopy at 25 °C in survivors to 0.525 J/cm², with an increase of 13-fold of the lag phase and a 45% decrease of the maximum specific growth rate. The higher the fluence, the higher the variability of both parameters was. To characterize pulsed light damage on L. innocua, the formation of dimers on DNA was assessed, and a proteomic study was undertaken. In cells treated with 0.525 J/cm², cyclobutane pyrimidine dimers and pyrimidine (6-4) pyrimidone photoproducts were detected at 5:1 ratio. Pulsed light induced the expression of three proteins, among them the general stress protein Ctc. Furthermore, treated cells showed an up-regulation of proteins related to metabolism of nucleotides and fatty acids, as well as with translation processes, whereas flagellin and some glucose metabolism proteins were down-regulated. Differences in the proteome of the survivors could contribute to explain the mechanisms of adaptation of L. innocua after pulsed light treatment.

Listeria monocytogenes contamination of ready-to-eat foods and the risk for human health in the EU

Food safety criteria for Listeria monocytogenes in ready-to-eat (RTE) foods have been applied from 2006 onwards (Commission Regulation (EC) 2073/2005). Still, human invasive listeriosis was reported to increase over the period 2009-2013 in the European Union and European Economic Area (EU/EEA). Time series analysis for the 2008-2015 period in the EU/EEA indicated an increasing trend of the monthly notified incidence rate of confirmed human invasive listeriosis of the over 75 age groups and female age group between 25 and 44 years old (probably related to pregnancies). A conceptual model was used to identify factors in the food chain as potential drivers for L. monocytogenes contamination of RTE foods and listeriosis. Factors were related to the host (i. population size of the elderly and/or susceptible people; ii. underlying condition rate), the food (iii. L. monocytogenes prevalence in RTE food at retail; iv. L. monocytogenes concentration in RTE food at retail; v. storage conditions after retail; vi. consumption), the national surveillance systems (vii. improved surveillance), and/or the bacterium (viii. virulence). Factors considered likely to be responsible for the increasing trend in cases are the increased population size of the elderly and susceptible population except for the 25-44 female age group. For the increased incidence rates and cases, the likely factor is the increased proportion of susceptible persons in the age groups over 45 years old for both genders. Quantitative modelling suggests that more than 90% of invasive listeriosis is caused by ingestion of RTE food containing > 2,000 colony forming units (CFU)/g, and that one-third of cases are due to growth in the consumer phase. Awareness should be increased among stakeholders, especially in relation to susceptible risk groups. Innovative methodologies including whole genome sequencing (WGS) for strain identification and monitoring of trends are recommended.

Assessing Antibiotic Permeability of Gram-Negative Bacteria via Nanofluidics

While antibiotic resistance is increasing rapidly, drug discovery has proven to be extremely difficult. Antibiotic resistance transforms some bacterial infections into deadly medical conditions. A significant challenge in antibiotic discovery is designing potent molecules that enter Gram-negative bacteria and also avoid active efflux mechanisms. Critical analysis in rational drug design has been hindered by the lack of effective analytical tools to analyze the bacterial membrane permeability of small molecules. We design, fabricate, and characterize the nanofluidic device that actively loads more than 200 single bacterial cells in a nanochannel array. We demonstrate a gigaohm seal between the nanochannel walls and the loaded bacteria, restricting small molecule transport to only occur through the bacterial cell envelope. Quantitation of clindamycin translocation through wild-type and efflux-deficient (ΔtolC) Escherichia coli strains via nanofluidic-interfaced liquid chromatography mass spectrometry shows higher levels of translocation for wild-type E. coli than for an efflux-deficient strain. We believe that the assessment of compound permeability in Gram-negative bacteria via the nanofluidic analysis platform will be an impactful tool for compound permeation and efflux studies in bacteria to assist rational antibiotic design.

Effect of storage temperature on the lag time of Geobacillus stearothermophilus individual spores

The lag times (λ) of Geobacillus stearothermophilus single spores were studied at different storage temperatures ranging from 45 to 59 °C using the Bioscreen C method. A significant variability of λ was observed among individual spores at all temperatures tested. The storage temperature affected both the position and the spread of the λ distributions. The minimum mean value of λ (i.e. 10.87 h) was observed at 55 °C, while moving away from this temperature resulted in an increase for both the mean and standard deviation of λ. A Cardinal Model with Inflection (CMI) was fitted to the reverse mean λ, and the estimated values for the cardinal parameters T_min, T_max, T_opt and the optimum mean λ of G. stearothermophilus were found to be 38.1, 64.2, 53.6 °C and 10.3 h, respectively. To interpret the observations, a probabilistic growth model for G. stearothermophilus individual spores, taking into account λ variability, was developed. The model describes the growth of a population, initially consisting of N₀ spores, over time as the sum of cells in each of the N₀ imminent subpopulations originating from a single spore. Growth simulations for different initial contamination levels showed that for low N₀ the number of cells in the population at any time is highly variable. An increase in N₀ to levels exceeding 100 spores results in a significant decrease of the above variability and a shorter λ of the population. Considering that the number of G. stearothermophilus surviving spores in the final product is usually very low, the data provided in this work can be used to evaluate the probability distribution of the time-to-spoilage and enable decision-making based on the "acceptable level of risk".

Subcellular Chemical Imaging of Antibiotics in Single Bacteria Using C60-Secondary Ion Mass Spectrometry

The inherent difficulty of discovering new and effective antibacterials and the rapid development of resistance particularly in Gram-negative bacteria, illustrates the urgent need for new methods that enable rational drug design. Here we report the development of 3D imaging cluster Time-of-Flight secondary ion mass spectrometry (ToF-SIMS) as a label-free approach to chemically map small molecules in aggregated and single Escherichia coli cells, with ∼300 nm spatial resolution and high chemical sensitivity. The feasibility of quantitative analysis was explored, and a nonlinear relationship between treatment dose and signal for tetracycline and ampicillin, two clinically used antibacterials, was observed. The methodology was further validated by the observation of reduction in tetracycline accumulation in an E. coli strain expressing the tetracycline-specific efflux pump (TetA) compared to the isogenic control. This study serves as a proof-of-concept for a new strategy for chemical imaging at the nanoscale and has the potential to aid discovery of new antibacterials.

Image analysis driven single-cell analytics for systems microbiology

BACKGROUND

Time-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology.

RESULTS

BaSCA can segment and track multiple bacterial colonies and single-cells, as they grow and divide over time (cell segmentation and lineage tree construction) to give rise to dense communities with thousands of interacting cells in the field of view. It combines advanced image processing and machine learning methods to deliver very accurate bacterial cell segmentation and tracking (F-measure over 95%) even when processing images of imperfect quality with several overcrowded colonies in the field of view. In addition, BaSCA extracts on the fly a plethora of single-cell properties, which get organized into a database summarizing the analysis of the cell movie. We present alternative ways to analyze and visually explore the spatiotemporal evolution of single-cell properties in order to understand trends and epigenetic effects across cell generations. The robustness of BaSCA is demonstrated across different imaging modalities and microscopy types.

CONCLUSIONS

BaSCA can be used to analyze accurately and efficiently cell movies both at a high resolution (single-cell level) and at a large scale (communities with many dense colonies) as needed to shed light on e.g. how bacterial community effects and epigenetic information transfer play a role on important phenomena for human health, such as biofilm formation, persisters' emergence etc. Moreover, it enables studying the role of single-cell stochasticity without losing sight of community effects that may drive it.

Modeling Stochastic Variability in the Numbers of Surviving Salmonella enterica, Enterohemorrhagic Escherichia coli, and Listeria monocytogenes Cells at the Single-Cell Level in a Desiccated Environment

Despite effective inactivation procedures, small numbers of bacterial cells may still remain in food samples. The risk that bacteria will survive these procedures has not been estimated precisely because deterministic models cannot be used to describe the uncertain behavior of bacterial populations. We used the Poisson distribution as a representative probability distribution to estimate the variability in bacterial numbers during the inactivation process. Strains of four serotypes of Salmonella enterica, three serotypes of enterohemorrhagic Escherichia coli, and one serotype of Listeria monocytogenes were evaluated for survival. We prepared bacterial cell numbers following a Poisson distribution (indicated by the parameter λ, which was equal to 2) and plated the cells in 96-well microplates, which were stored in a desiccated environment at 10% to 20% relative humidity and at 5, 15, and 25°C. The survival or death of the bacterial cells in each well was confirmed by adding tryptic soy broth as an enrichment culture. Changes in the Poisson distribution parameter during the inactivation process, which represent the variability in the numbers of surviving bacteria, were described by nonlinear regression with an exponential function based on a Weibull distribution. We also examined random changes in the number of surviving bacteria using a random number generator and computer simulations to determine whether the number of surviving bacteria followed a Poisson distribution during the bacterial death process by use of the Poisson process. For small initial cell numbers, more than 80% of the simulated distributions (λ = 2 or 10) followed a Poisson distribution. The results demonstrate that variability in the number of surviving bacteria can be described as a Poisson distribution by use of the model developed by use of the Poisson process.

IMPORTANCE We developed a model to enable the quantitative assessment of bacterial survivors of inactivation procedures because the presence of even one bacterium can cause foodborne disease. The results demonstrate that the variability in the numbers of surviving bacteria was described as a Poisson distribution by use of the model developed by use of the Poisson process. Description of the number of surviving bacteria as a probability distribution rather than as the point estimates used in a deterministic approach can provide a more realistic estimation of risk. The probability model should be useful for estimating the quantitative risk of bacterial survival during inactivation.

Do bacterial cell numbers follow a theoretical Poisson distribution? Comparison of experimentally obtained numbers of single cells with random number generation via computer simulation

We investigated a bacterial sample preparation procedure for single-cell studies. In the present study, we examined whether single bacterial cells obtained via 10-fold dilution followed a theoretical Poisson distribution. Four serotypes of Salmonella enterica, three serotypes of enterohaemorrhagic Escherichia coli and one serotype of Listeria monocytogenes were used as sample bacteria. An inoculum of each serotype was prepared via a 10-fold dilution series to obtain bacterial cell counts with mean values of one or two. To determine whether the experimentally obtained bacterial cell counts follow a theoretical Poisson distribution, a likelihood ratio test between the experimentally obtained cell counts and Poisson distribution which parameter estimated by maximum likelihood estimation (MLE) was conducted. The bacterial cell counts of each serotype sufficiently followed a Poisson distribution. Furthermore, to examine the validity of the parameters of Poisson distribution from experimentally obtained bacterial cell counts, we compared these with the parameters of a Poisson distribution that were estimated using random number generation via computer simulation. The Poisson distribution parameters experimentally obtained from bacterial cell counts were within the range of the parameters estimated using a computer simulation. These results demonstrate that the bacterial cell counts of each serotype obtained via 10-fold dilution followed a Poisson distribution. The fact that the frequency of bacterial cell counts follows a Poisson distribution at low number would be applied to some single-cell studies with a few bacterial cells. In particular, the procedure presented in this study enables us to develop an inactivation model at the single-cell level that can estimate the variability of survival bacterial numbers during the bacterial death process.