Indirect measurement of the lag time distribution of single cells of Listeria innocua in food

An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations

Variability in the behavior of microbial foodborne pathogens and spoilers causes difficulties in predicting the safety and quality of food products during their shelf life. Therefore, the quantification of the individual microbial lag phase distribution is of high relevance to the field of quantitative microbial risk assessment. To construct models that predict the effect of changes in environmental conditions on the individual lag, an accurate determination of these distributions is required. Therefore, the current research focuses on the development of an experimental and computational method for accurate determination of individual lag phase distribution. The experimental method is unique in the sense that full liquid volumes are sampled without using dilutions to detect the final population, thereby minimizing experimental errors. Moreover, the method does not aim at the isolation of single cells but at a low number of cells. The fact that several cells can be present in the initial samples instead of having a single cell is considered by the computational method. This method relies on Monte Carlo simulation to predict the individual lag phase distribution for a given set of distribution parameters and maximum likelihood estimation to find the parameters that describe the experimental data best. The method was validated both through simulation and experiments and was found to deliver a desired accuracy.

Inactivation and Subsequent Growth Kinetics of Listeria monocytogenes After Various Mild Bactericidal Treatments

This study was carried out to investigate the effects of mild heat, lactic acid, benzalkonium chloride and nisin treatments on the inactivation, sublethal injury, and subsequent growth of Listeria monocytogenes. Results showed that the Bigelow model successfully described the thermal inactivation kinetics, while the Log-linear model with tail consistently offered the most accurate fit to LA, BC, and nisin inactivation curves of cells. Differential plating indicated that percentage of sublethal injury for nisin treated cells was significantly higher than that for the other three treatments. Compared to non-treated cells, significant extension of lag time was observed for all treated cells. The longer exposures to heat treatment contributed to the extended lag time of the survivors. While for LA, BC and nisin treated cells, the longest lag time was not observed at the most severe treatment conditions. The correlation analysis of sublethal injury percentage on the duration of lag time revealed that only heat treatment showed the significant correlation. Overall, the lag time analysis could evaluate a wide range of bacterial injury. Lag time of treated cells was significantly influenced by stress treatments and temperatures of recovery, however, there were not any significant changes in the maximum specific growth rate between treated and non-treated cells under isothermal recovery conditions. The information generated from this study is valuable for utilizing intervention strategies in the elimination or growth inhibition of L. monocytogenes.

Lag Phase Is a Dynamic, Organized, Adaptive, and Evolvable Period That Prepares Bacteria for Cell Division

Lag is a temporary period of nonreplication seen in bacteria that are introduced to new media. Despite latency being described by Müller in 1895, only recently have we gained insights into the cellular processes characterizing lag phase. This review covers literature to date on the transcriptomic, proteomic, metabolomic, physiological, biochemical, and evolutionary features of prokaryotic lag. Though lag is commonly described as a preparative phase that allows bacteria to harvest nutrients and adapt to new environments, the implications of recent studies indicate that a refinement of this view is well deserved. As shown, lag is a dynamic, organized, adaptive, and evolvable process that protects bacteria from threats, promotes reproductive fitness, and is broadly relevant to the study of bacterial evolution, host-pathogen interactions, antibiotic tolerance, environmental biology, molecular microbiology, and food safety.

Competition for space during bacterial colonization of a surface

Competition for space is ubiquitous in the ecology of both microorganisms and macro-organisms. We introduce a bacterial model system in which the factors influencing competition for space during colonization of an initially empty habitat can be tracked directly. Using fluorescence microscopy, we follow the fate of individual Escherichia coli bacterial cell lineages as they undergo expansion competition (the race to be the first to colonize a previously empty territory), and as they later compete at boundaries between clonal territories. Our experiments are complemented by computer simulations of a lattice-based model. We find that both expansion competition, manifested as differences in individual cell lag times, and boundary competition, manifested as effects of neighbour cell geometry, can play a role in colonization success, particularly when lineages expand exponentially. This work provides a baseline for investigating how ecological interactions affect colonization of space by bacterial populations, and highlights the potential of bacterial model systems for the testing and development of ecological theory.

Promoter activity dynamics in the lag phase of Escherichia coli

Background

Lag phase is a period of time with no growth that occurs when stationary phase bacteria are transferred to a fresh medium. Bacteria in lag phase seem inert: their biomass does not increase. The low number of cells and low metabolic activity make it difficult to study this phase. As a consequence, it has not been studied as thoroughly as other bacterial growth phases. However, lag phase has important implications for bacterial infections and food safety. We asked which, if any, genes are expressed in the lag phase of Escherichia coli, and what is their dynamic expression pattern.

Results

We developed an assay based on imaging flow cytometry of fluorescent reporter cells that overcomes the challenges inherent in studying lag phase. We distinguish between lag1 phase- in which there is no biomass growth, and lag2 phase- in which there is biomass growth but no cell division. We find that in lag1 phase, most promoters are not active, except for the enzymes that utilize the specific carbon source in the medium. These genes show promoter activities that increase exponentially with time, despite the fact that the cells do not measurably increase in size. An oxidative stress promoter, katG, is also active. When cells enter lag2 and begin to grow in size, they switch to a full growth program of promoter activity including ribosomal and metabolic genes.

Conclusions

The observed exponential increase in enzymes for the specific carbon source followed by an abrupt switch to production of general growth genes is a solution of an optimal control model, known as bang-bang control. The present approach contributes to the understanding of lag phase, the least studied of bacterial growth phases.

Network analysis of the transcriptional pattern of young and old cells of Escherichia coli during lag phase

Background

The aging process of bacteria in stationary phase is halted if cells are subcultured and enter lag phase and it is then followed by cellular division. Network science has been applied to analyse the transcriptional response, during lag phase, of bacterial cells starved previously in stationary phase for 1 day (young cells) and 16 days (old cells).

Results

A genome scale network was constructed for E. coli K-12 by connecting genes with operons, transcription and sigma factors, metabolic pathways and cell functional categories. Most of the transcriptional changes were detected immediately upon entering lag phase and were maintained throughout this period. The lag period was longer for older cells and the analysis of the transcriptome revealed different intracellular activity in young and old cells. The number of genes differentially expressed was smaller in old cells (186) than in young cells (467). Relatively, few genes (62) were up- or down-regulated in both cultures. Transcription of genes related to osmotolerance, acid resistance, oxidative stress and adaptation to other stresses was down-regulated in both young and old cells. Regarding carbohydrate metabolism, genes related to the citrate cycle were up-regulated in young cells while old cells up-regulated the Entner Doudoroff and gluconate pathways and down-regulated the pentose phosphate pathway. In both old and young cells, anaerobic respiration and fermentation pathways were down-regulated, but only young cells up-regulated aerobic respiration while there was no evidence of aerobic respiration in old cells.

Numerous genes related to DNA maintenance and replication, translation, ribosomal biosynthesis and RNA processing as well as biosynthesis of the cell envelope and flagellum and several components of the chemotaxis signal transduction complex were up-regulated only in young cells. The genes for several transport proteins for iron compounds were up-regulated in both young and old cells. Numerous genes encoding transporters for carbohydrates and organic alcohols and acids were down-regulated in old cells only.

Conclusion

Network analysis revealed very different transcriptional activities during the lag period in old and young cells. Rejuvenation seems to take place during exponential growth by replicative dilution of old cellular components.

Analysis of the variability in the number of viable bacteria after mild heat treatment of food

Variability in the numbers of bacteria remaining in saline solution and whole milk following mild heat treatment has been studied with Listeria innocua, Enterococcus faecalis, Salmonella enterica serovar Enteritidis, and Pseudomonas fluorescens. As expected, the most heat-resistant bacterium was E. faecalis, while P. fluorescens was the least heat resistant, and all bacteria showed greater thermal resistance in whole milk than in saline solution. Despite the differences in the inactivation kinetics of these bacteria in different media, the variability in the final number of bacteria was affected neither by the species nor by the heating substrate, but it did depend on the intensity of the heat treatment. The more severe the heat treatment was, the lower the average number of surviving bacteria but the greater the variability. Our results indicated that the inactivation times for the cells within a population are not identically distributed random variables and that, therefore, the population includes subpopulations of cells with different distributions for the heat resistance parameters. A linear relationship between the variability of the log of the final bacterial concentration and the logarithmic reduction in the size of the bacterial population was found.

Stress- and growth rate-related differences between plate count and real-time PCR data during growth of Listeria monocytogenes

To assess the overestimation of bacterial cell counts in real-time PCR in relation to stress and growth phase, four different strains of L. monocytogenes were exposed to combinations of osmotic stress (0.5 to 8% [vol/vol] NaCl) and acid stress (pH 5 to 7) in a culture model at a growth temperature of 10 degrees C or were grown under optimal conditions. Growth curves obtained from real-time PCR, optical density, and viable count data were compared. As expected, optical density data revealed entirely different growth curves. Good to moderate growth conditions yielded good correlation of real-time PCR data and plate count data (r(2) = 0.96 and 0.99) with similar cell counts. When growth conditions became worse, the numbers of CFU decreased during the stationary phase, whereas real-time-PCR-derived bacterial cell equivalents differed in this regard; the correlation worsened (r(2) = 0.84). However, fitted growth curves revealed that maximum growth rates calculated from real-time PCR data were not significantly different from those derived from plate count data. The overestimation of bacterial cell counts by real-time PCR observed in the stationary phase under higher-stress conditions might be explained by the accumulation of viable but nonculturable bacteria or dead bacteria and extracellular DNA. Considering these results, real-time PCR data collected from naturally contaminated samples should be viewed with caution.

Modeling the variability of single-cell lag times for Listeria innocua populations after sublethal and lethal heat treatments

Optical density measurements were used to estimate the effect of heat treatments on the single-cell lag times of Listeria innocua fitted to a shifted gamma distribution. The single-cell lag time was subdivided into repair time (the shift of the distribution assumed to be uniform for all cells) and adjustment time (varying randomly from cell to cell). After heat treatments in which all of the cells recovered (sublethal), the repair time and the mean and the variance of the single-cell adjustment time increased with the severity of the treatment. When the heat treatments resulted in a loss of viability (lethal), the repair time of the survivors increased with the decimal reduction of the cell numbers independently of the temperature, while the mean and variance of the single-cell adjustment times remained the same irrespective of the heat treatment. Based on these observations and modeling of the effect of time and temperature of the heat treatment, we propose that the severity of a heat treatment can be characterized by the repair time of the cells whether the heat treatment is lethal or not, an extension of the F value concept for sublethal heat treatments. In addition, the repair time could be interpreted as the extent or degree of injury with a multiple-hit lethality model. Another implication of these results is that the distribution of the time for cells to reach unacceptable numbers in food is not affected by the time-temperature combination resulting in a given decimal reduction.

Single-cell and population lag times as a function of cell age

After inoculation, the times to the first divisions are longer and more widely distributed for those Escherichia coli single cells that spent more time in the stationary phase prior to inoculation. The second generation times are still longer than the typical generation times in the exponential phase, and this extended the apparent lag time of the cell population. The greater the variability of the single-cell interdivision intervals, the shorter are both the lag time and the doubling time of the population.

Use of optical density detection times to assess the effect of acetic acid on single-cell kinetics

The growth of Listeria innocua at different acetic acid concentrations (0 to 2,000 ppm) was monitored by optical density measurements in a Bioscreen (Labsystems, Vantaa, Finland). The generated populations came from low inocula that were obtained by serial dilution. A new method to estimate both the growth rate and the lag time of single cells from the detection times (time to reach an optical density of 0.11) was developed. It assumes that the single-cell lag times follow a gamma distribution and takes into account the randomness of the inoculation level. (The initial cell number per well was assumed to follow a Poisson distribution.) In this way, relatively small numbers of replicates are sufficient to obtain a robust estimation of the distribution of single-cell lag times. The results were validated with plate count experiments. It was found that logarithms of both the growth rates and of population lag times increased linearly with the acetic acid concentration. The logarithm of the scale parameter of the gamma distribution of the single-cell lag times also increased linearly with the acetic acid concentration irrespective of the phase of the inoculum.