Significance of inoculum size in the lag time of Listeria monocytogenes

Application of BactTiter-Glo ATP bioluminescence assay for Mycobacterium tuberculosis detection

BACKGROUND

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a global health threat, necessitating faster and more accessible diagnostic methods. This study investigates critical parameters in the application of a commercial ATP bioluminescence assay for the detection of MTB.

METHOD

Our objective was to optimize the ATP bioluminescence protocol using BacTiter-Glo™ for MTB, investigating the impact of varying volumes of MTB suspension and reagent on assay sensitivity, evaluating ATP extraction methods, establishing calibration curves, and elucidating strain-specific responses to antimicrobial agents.

RESULTS

ATP extraction methods showed no significant improvement over controls. Calibration curves revealed a linear correlation between relative light units (RLU) and colony-forming units (CFU/mL), establishing low detection limits. Antimicrobial testing demonstrated strain-specific responses aligning with susceptibility and resistance patterns.

CONCLUSION

Our findings contribute to refining ATP bioluminescence protocols for enhanced MTB detection and susceptibility testing. Further refinements and validation efforts are warranted, holding promise for more efficient diagnostic platforms in the future.

Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and β-lactam antibiotics

The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. We previously found that growth productivity, which captures the relationship between [ATP] and growth, can account for the strength of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. We show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. Specifically, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for β-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect and paves the way for intervention strategies to reduce the inoculum effect in the clinic.

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Insight in lag phase of Listeria monocytogenes during enrichment through proteomic and transcriptomic responses

The dynamics of the enrichment-based detection procedure of the foodborne pathogen Listeria monocytogenes from food still remains poorly understood. This enrichment is crucial in the reliable detection of this pathogen and more insight into the recovery mechanism during this step is important to advance our understanding of lag phase behaviour during enrichment. In this study we combined transcriptomic and proteomic analyses to better understand the physiological processes within the lag phase of L. monocytogenes during enrichment. Upon transfer of BHI-cultured stationary phase L. monocytogenes cells to half-Fraser enrichment broth (HFB), motility-associated genes and proteins were downregulated, while expression of metal uptake transporters, resuscitation-promoting factors that stimulate growth from dormancy, antibiotic efflux pumps and oxidative stress proteins were upregulated. Next to this, when cells with a heat stress history were cultured in enrichment broth, proteins necessary for recovery were upregulated with functions in DNA-damage repair, protein refolding, cell-wall repair, and zinc transport. Proteomic results pointed to possible factors that support shortening the lag duration, including the addition of 10 µM zinc and the addition of spent HFB containing presumed concentrations of resuscitation-promoting factors. However, these interventions did not lead to biologically relevant reduction of lag phase. Also, when cells were enriched in spent HFB, final cell concentrations were similar to enrichments in fresh HFB, indicating that the enrichment broth seems not to lack critical substrates. Concludingly, this study gives insight into the proteomic changes in the lag phase during enrichment and shows that supplementation of HFB is not the best strategy to optimize the current enrichment method.

Accelerated Adaptive Laboratory Evolution by Automated Repeated Batch Processes in Parallelized Bioreactors

Adaptive laboratory evolution (ALE) is a valuable complementary tool for modern strain development. Insights from ALE experiments enable the improvement of microbial cell factories regarding the growth rate and substrate utilization, among others. Most ALE experiments are conducted by serial passaging, a method that involves large amounts of repetitive manual labor and comes with inherent experimental design flaws. The acquisition of meaningful and reliable process data is a burdensome task and is often undervalued and neglected, but also unfeasible in shake flask experiments due to technical limitations. Some of these limitations are alleviated by emerging automated ALE methods on the μL and mL scale. A novel approach to conducting ALE experiments is described that is faster and more efficient than previously used methods. The conventional shake flask approach was translated to a parallelized, L scale stirred-tank bioreactor system that runs controlled, automated, repeated batch processes. The method was validated with a growth optimization experiment of E. coli K-12 MG1655 grown with glycerol minimal media as a benchmark. Off-gas analysis enables the continuous estimation of the biomass concentration and growth rate using a black-box model based on first principles (soft sensor). The proposed method led to the same stable growth rates of E. coli with the non-native carbon source glycerol 9.4 times faster than the traditional manual approach with serial passaging in uncontrolled shake flasks and 3.6 times faster than an automated approach on the mL scale. Furthermore, it is shown that the cumulative number of cell divisions (CCD) alone is not a suitable timescale for measuring and comparing evolutionary progress.

Understanding cellular growth strategies via optimal control

Evolutionary prediction and control are increasingly interesting research topics that are expanding to new areas of application. Unravelling and anticipating successful adaptations to different selection pressures becomes crucial when steering rapidly evolving cancer or microbial populations towards a chosen target. Here we introduce and apply a rich theoretical framework of optimal control to understand adaptive use of traits, which in turn allows eco-evolutionarily informed population control. Using adaptive metabolism and microbial experimental evolution as a case study, we show how demographic stochasticity alone can lead to lag time evolution, which appears as an emergent property in our model. We further show that the cycle length used in serial transfer experiments has practical importance as it may cause unintentional selection for specific growth strategies and lag times. Finally, we show how frequency-dependent selection can be incorporated to the state-dependent optimal control framework allowing the modelling of complex eco-evolutionary dynamics. Our study demonstrates the utility of optimal control theory in elucidating organismal adaptations and the intrinsic decision making of cellular communities with high adaptive potential.

Growth productivity as a determinant of the inoculum effect for bactericidal antibiotics

Understanding the mechanisms by which populations of bacteria resist antibiotics has implications in evolution, microbial ecology, and public health. The inoculum effect (IE), where antibiotic efficacy declines as the density of a bacterial population increases, has been observed for multiple bacterial species and antibiotics. Several mechanisms to account for IE have been proposed, but most lack experimental evidence or cannot explain IE for multiple antibiotics. We show that growth productivity, the combined effect of growth and metabolism, can account for IE for multiple bactericidal antibiotics and bacterial species. Guided by flux balance analysis and whole-genome modeling, we show that the carbon source supplied in the growth medium determines growth productivity. If growth productivity is sufficiently high, IE is eliminated. Our results may lead to approaches to reduce IE in the clinic, help standardize the analysis of antibiotics, and further our understanding of how bacteria evolve resistance.

A leader cell triggers end of lag phase in populations of Pseudomonas fluorescens

The relationship between the number of cells colonizing a new environment and time for resumption of growth is a subject of long-standing interest. In microbiology this is known as the "inoculum effect." Its mechanistic basis is unclear with possible explanations ranging from the independent actions of individual cells, to collective actions of populations of cells. Here, we use a millifluidic droplet device in which the growth dynamics of hundreds of populations founded by controlled numbers of Pseudomonas fluorescens cells, ranging from a single cell, to one thousand cells, were followed in real time. Our data show that lag phase decreases with inoculum size. The decrease of average lag time and its variance across droplets, as well as lag time distribution shapes, follow predictions of extreme value theory, where the inoculum lag time is determined by the minimum value sampled from the single-cell distribution. Our experimental results show that exit from lag phase depends on strong interactions among cells, consistent with a "leader cell" triggering end of lag phase for the entire population.

Reparameterization of the Weibull model for practical uses in food science

The reparameterization of the Weibull cumulative distribution function and its survival function was performed to obtain meaningful parameters in food and biological sciences such as the lag phase (λ), the maximum rate ( μ max ${\mu _{{\rm{max}}}}$ ), and the maximum increase/decrease of the function (A). The application of the Lambert function was crucial in order to achieve an explicit mathematical solution. Since the reparameterized model is applicable only when the shape parameter (α) is greater than one, the Weibull model was modified with the introduction of a new parameter ( μ β ${\mu _\beta }$ ) that represents the model rate at time β (scale parameter). All models were applied to literature data on food technology and microbiology topics: Microbial growth, thermal microbial inactivation, thermal degradation kinetics, and particle size distributions. The Weibull model and the reparameterized versions showed identical fitting performance in terms of coefficient of determination, residual mean standard error, values of residuals, and estimated values of the parameters. Some faults in the datasets used in this study permitted to re-mark the criticality of a good experimental plan when data modeling is approached. The parameter μ β ${\mu _\beta }$ resulted in an interesting new rate parameter that is not correlated with the scale parameter ( | r ¯ | $| {\bar{r}} |$ = 0.64 ± 0.37) and highly correlated with the shape parameter ( | r ¯ | $| {\bar{r}} |$ = 0.90 ± 0.11). Also, the reparameterization of the Weibull probability density function was performed by using both the standard and new parameters and applied to experimental data and gave useful information from the distribution curve, such as the value of the mode ( μ max ${\mu _{{\rm{max}}}}$ ) and a measure of the curve skewness (λ).

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.

Investigation of the effects of oxidative stress-inducing factors on culturing and productivity of Bordetella pertussis

The stress response of Bordetella pertussis during fermentation was assessed by means of fluorescence-based techniques. During the manufacturing of vaccines, B. pertussis is subjected to stress during adaptation to a new environment and operating conditions in the bioreactor, which can have harmful consequences on growth and protein yield. In this study, stress was imposed by varying the percentage of dissolved oxygen (DO) and inoculum size, and by adding rotenone and hydrogen peroxide. In this study, fluorescence spectroscopy is used as a tool for measuring oxidative stress. High levels of DO during fed-batch operation had no detrimental effect on growth, but the specific productivity of pertactin (PRN) decreased. Cultures that were started with an inoculum size that was 10 times smaller than the control resulted in significantly less PRN as compared to controls where reduction was more significant in flasks as compared to bioreactors. A comparison of filtered to heat-sterilized media revealed that filtered media offered a protective effect against H₂ O₂ . Heat sterilization of the media might result in the destruction of components that offer protection against oxidative stress. Nonetheless, filter sterilization on its own would be insufficient for large-scale manufacturing. It should be emphasized that the effects of these stressors while investigating for other microorganisms have not been studied for B. pertussis.

Bacillus cereus cshA Is Expressed during the Lag Phase of Growth and Serves as a Potential Marker of Early Adaptation to Low Temperature and pH

The spore-forming bacterium B. cereus is a major cause of foodborne outbreaks in Europe. Some B. cereus strains can grow at low temperatures and low pH in many processed foods. Modeling of the bacterial lag time is hampered by a lack of knowledge of the timing of events occurring during this phase. In this context, the identification of lag phase markers, not currently available, could be a real advance for the better prediction of lag time duration. Currently, no molecular markers of this phase are available. By determining that cshA was always expressed early during the lag phase, we provide a molecular marker of the early adaptation process of B. cereus cells when exposed to low temperature and pH.

Bacterial adaptation is characterized by a lag phase during which cells do not multiply or modify their physiology to cope with the constraints of their environment. Our aim was to determine a sequence of events during the lag phase of growth at low temperature and pH for three Bacillus cereus strains. The onsets of expression of two genes, one of which is essential for stress adaptation (cshA, coding for a RNA helicase) and one of which is involved in the transition between lag phase and exponential phase (abrB, coding for a transition regulator), were determined using fluorescent transcriptional reporter systems. Regardless of the stressing conditions and the tested strains, the cshA promoter was active very early, while the biomass increased and always did so before the first cell division. At 12°C and pH 7.0, the onset of cshA promoter activity occurred at between 3 h and 7 h, while the bacterial counts started to increase at between 12 h and 13 h. At pH 5.0 and at 20°C or 30°C, the onset of cshA promoter activity occurred before 1 h and earlier than at pH 7.0. In contrast, the onset of abrB promoter activity depended on the strain and the stressing conditions. In the ATCC 14579 strain, the onset of abrB promoter activity always started at between 30 min and 3 h, before biomass increased and cell division occurred. For the other strains, it took place along with the first cell division at 12°C but did so much later during growth under the other tested conditions.

IMPORTANCE The spore-forming bacterium B. cereus is a major cause of foodborne outbreaks in Europe. Some B. cereus strains can grow at low temperatures and low pH in many processed foods. Modeling of the bacterial lag time is hampered by a lack of knowledge of the timing of events occurring during this phase. In this context, the identification of lag phase markers, not currently available, could be a real advance for the better prediction of lag time duration. Currently, no molecular markers of this phase are available. By determining that cshA was always expressed early during the lag phase, we provide a molecular marker of the early adaptation process of B. cereus cells when exposed to low temperature and pH.

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.

A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling

BACKGROUND

The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth.

OBJECTIVES

In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies.

MATERIALS AND METHODS

Sigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli. Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning (i.e.,'c' and 'σ') of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE).

RESULTS

Applying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method.

CONCLUSION

Application of the NSGA-II-LSSVM hybrid algorithm to predict precise values of 'c' and 'σ' parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.

Factors limiting sulfolane biodegradation in contaminated subarctic aquifer substrate

Sulfolane, a water-soluble organosulfur compound, is used industrially worldwide and is associated with one of the largest contaminated groundwater plumes in the state of Alaska. Despite being widely used, little is understood about the degradation of sulfolane in the environment, especially in cold regions. We conducted aerobic and anaerobic microcosm studies to assess the biological and abiotic sulfolane degradation potential of contaminated subarctic aquifer groundwater and sediment from Interior Alaska. We also investigated the impacts of nutrient limitations and hydrocarbon co-contamination on sulfolane degradation. We found that sulfolane underwent biodegradation aerobically but not anaerobically under nitrate, sulfate, or iron-reducing conditions. No abiotic degradation activity was detectable under either oxic or anoxic conditions. Nutrient addition stimulated sulfolane biodegradation in sediment slurries at high sulfolane concentrations (100 mg L^-1), but not at low sulfolane concentrations (500 μg L^-1), and nutrient amendments were necessary to stimulate sulfolane biodegradation in incubations containing groundwater only. Hydrocarbon co-contamination retarded aerobic sulfolane biodegradation rates by ~30%. Our study is the first to investigate the sulfolane biodegradation potential of subarctic aquifer substrate and identifies several important factors limiting biodegradation rates. We concluded that oxygen is an important factor limiting natural attenuation of this sulfolane plume, and that nutrient amendments are unlikely to accelerate biodegradation within in the plume, although they may biostimulate degradation in ex situ groundwater treatment applications. Future work should be directed at elucidating the identity of indigenous sulfolane-degrading microorganisms and determining their distribution and potential activity in the environment.

Modeling Lactic Fermentation of Gowé Using Lactobacillus Starter Culture

A global model of the lactic fermentation step of gowé was developed by assembling blocks hosting models for bacterial growth, lactic acid production, and the drop of pH during fermentation. Commercial strains of Lactobacillus brevis and of Lactobacillus plantarum were used; their growth was modeled using Rosso's primary model and the gamma concept as a secondary model. The optimum values of pH and temperature were 8.3 ± 0.3, 44.6 ± 1.2 °C and 8.3 ± 0.3, 3.2 ± 37.1 °C with μmax values of 1.8 ± 0.2 and 1.4 ± 0.1 for L. brevis and L. plantarum respectively. The minimum inhibitory concentration of undissociated lactic acid was 23.7 mM and 35.6 mM for L. brevis and L. plantarum, respectively. The yield of lactic acid was five times higher for L. plantarum than for L. brevis, with a yield of glucose conversion to lactic acid close to 2.0 for the former and 0.8 for the latter. A model was developed to predict the pH drop during gowé fermentation. The global model was partially validated during manufacturing of gowé. The global model could be a tool to aid in the choice of suitable starters and to determine the conditions for the use of the starter.

Comparative evaluation of direct plating and most probable number for enumeration of low levels of Listeria monocytogenes in naturally contaminated ice cream products

A precise and accurate method for enumeration of low level of Listeria monocytogenes in foods is critical to a variety of studies. In this study, paired comparison of most probable number (MPN) and direct plating enumeration of L. monocytogenes was conducted on a total of 1730 outbreak-associated ice cream samples that were naturally contaminated with low level of L. monocytogenes. MPN was performed on all 1730 samples. Direct plating was performed on all samples using the RAPID'L.mono (RLM) agar (1600 samples) and agar Listeria Ottaviani and Agosti (ALOA; 130 samples). Probabilistic analysis with Bayesian inference model was used to compare paired direct plating and MPN estimates of L. monocytogenes in ice cream samples because assumptions implicit in ordinary least squares (OLS) linear regression analyses were not met for such a comparison. The probabilistic analysis revealed good agreement between the MPN and direct plating estimates, and this agreement showed that the MPN schemes and direct plating schemes using ALOA or RLM evaluated in the present study were suitable for enumerating low levels of L. monocytogenes in these ice cream samples. The statistical analysis further revealed that OLS linear regression analyses of direct plating and MPN data did introduce bias that incorrectly characterized systematic differences between estimates from the two methods.

Comparison of Primary Models to Predict Microbial Growth by the Plate Count and Absorbance Methods

The selection of a primary model to describe microbial growth in predictive food microbiology often appears to be subjective. The objective of this research was to check the performance of different mathematical models in predicting growth parameters, both by absorbance and plate count methods. For this purpose, growth curves of three different microorganisms (Bacillus cereus, Listeria monocytogenes, and Escherichia coli) grown under the same conditions, but with different initial concentrations each, were analysed. When measuring the microbial growth of each microorganism by optical density, almost all models provided quite high goodness of fit (r² > 0.93) for all growth curves. The growth rate remained approximately constant for all growth curves of each microorganism, when considering one growth model, but differences were found among models. Three-phase linear model provided the lowest variation for growth rate values for all three microorganisms. Baranyi model gave a variation marginally higher, despite a much better overall fitting. When measuring the microbial growth by plate count, similar results were obtained. These results provide insight into predictive microbiology and will help food microbiologists and researchers to choose the proper primary growth predictive model.

Comparative study of the effects of citral on the growth and injury of Listeria innocua and Listeria monocytogenes cells

This study investigates the effect of citral on growth and on the occurrence of sublethal damage in Listeria innocua Serovar 6a (CECT 910) and Listeria monocytogenes Serovar 4b (CECT 4032) cells that were exposed to citral as a natural antimicrobial agent. Two initial inoculum concentrations were considered in this investigation: 10² and 10⁶ cfu/mL. Citral exhibited antilisterial activity against L. innocua and L. monocytogenes, and the observed effects were dependent on the concentration of citral present in the culture medium (0, 0.150 and 0.250 μL/mL) (p ≤ 0.05). L. innocua had a shorter lag phase than L. monocytogenes, and the two species had nearly identical maximum specific growth rates. These results indicate that L. innocua could be used as surrogate for L. monocytogenes when testing the effects of this antimicrobial. Significant differences in the lag phase and growth rate were observed between the small and large inoculum concentration (p ≤ 0.05). Citral-treated L. innocua and L. monocytogenes that were recovered on selective medium (i.e., TSA-YE-SC) had a shorter lag phase and a higher maximum specific growth rate than cells that were recovered on non-selective medium (i.e., TSA-YE) (p ≤ 0.05). This result suggests that damage occurs at sublethal concentrations of citral.

Microbial growth parameters obtained from the analysis of time to detection data using a novel rearrangement of the Baranyi-Roberts model

AIMS

To explore the predictions of a novel rearrangement of the Baranyi-Roberts model (BRM) with time to detection data obtained from optical density data of microbial growth.

METHODS AND RESULTS

Growth of Escherichia coli and Salmonella Typhimurium under mild conditions of temperature (25-37°C), salt (0·086, 0·51 and 1·03 mol l(-1)) and pH (6·85-4·5) was examined using optical density. Time to detection (TTD) data were fitted to a model based on a rearrangement of the BRM. Observations showed compatibility with standard viable count studies and produced highly accurate specific growth rates and lag phase durations. At high salt and low pH, however, there was a substantial dependency on the initial inoculum for the observation of visible growth. At 30 and 37°C, with 1·03 mol l(-1) salt, and at pH <5·75, no visible growth was recorded for E. coli at initial inoculum levels below 10(7) CFU ml(-1).

CONCLUSIONS

The rearranged BRM can be used directly with TTD data obtained from optical density measurements.

SIGNIFICANCE AND IMPACT OF THE STUDY

A distinct advantage of the rearranged model is that it allows for a very simple interpretation of easily obtainable data using standard nonlinear regression. The rearranged model gives to TTD data the same modelling capability that the BRM gives to plate count data.

Canthaxanthin biosynthesis by Dietzia natronolimnaea HS-1: effects of inoculation and aeration rate

The interest in production of natural colorants by microbial fermentation has been currently increased. The effects of D-glucose concentration (3.18-36.82 g/L), inoculum size (12.5 × 10(9)-49.5 × 10(9) cfu cells/mL) and air-flow rate (1.95-12.05 L/L min) on the biomass, total carotenoid and canthaxanthin (CTX) accumulation of Dietzia natronolimnaea HS-1 in a batch bioreactor was scrutinized using a response surface methodology-central composite rotatable design (RSM-CCRD). Second-order polynomial models with high R (2) values ranging from 0.978 to 0.990 were developed for the studied responses using multiple linear regression analysis. The models showed the maximum cumulative amounts of biomass (7.85 g/L), total carotenoid (5.48 mg/L) and CTX (4.99 mg/L) could be achieved at 23.38 g/L of D-glucose, 31.2 × 10(9) cfu cells/mL of inoculation intensity and air-flow rate of 7.85 L/L min. The predicted values for optimum conditions were in good agreement with experimental data.

Acclimation and activity of ammonia-oxidizing bacteria with respect to variations in zinc concentration, temperature, and microbial population

Activity of ammonia-oxidizing bacteria (AOB) to simultaneous variation in Zn(2+) concentration (0.01-3.5mg/L), temperature (23-33°C), and AOB concentration (3-30 × 10(6)gene copies/mL) in a steel industry wastewater treatment plant was evaluated. Two equations were developed to describe the lag period (i.e., AOB acclimation) and ammonia oxidation rate (i.e., growth of the AOB) depending on the variables. AOB concentration and temperature both had significant effects on lag period and the ammonia oxidation rate. Zn(2+) concentration only had a significant effect on ammonia oxidation rate at 5% α-level. There was a significant interaction between AOB concentration and temperature for both lag period and ammonia oxidation rate. The effects of the variables were not significant when AOB concentration was higher than 2.0 × 10(7)copies/mL. There was no visible shift or changes in AOB communities based on DGGE analysis with amoA gene primers.

Safety-based shelf life model for frankfurters based on time to detect Listeria monocytogenes with initial inoculum below detection limit

The growth of Listeria monocytogenes inoculated on frankfurters at four inoculum levels (0.1, 0.04, 0.01, and 0.007 CFU/g) was examined at 4, 8, and 12 degrees C until the time L. monocytogenes populations reached a detectable limit of at least 2 CFU/g. A scaled-down assumption was made to simulate a 25-g sample from a 100-lb batch size in a factory setting by using a 0.55-g sample from a 1,000-g batch size in a laboratory. Samples of 0.55 g were enriched in PDX-LIB selective medium, and presumptive results were confirmed on modified Oxford agar. Based on the time to detect (TTD) from each inoculum level and at each temperature, a shelf life model was constructed to predict the detection or risk levels reached by L. monocytogenes on frankfurters. The TTD increased with reductions in inoculum size and storage temperature. At 4 degrees C the TTDs (+/- standard error) observed were 42.0 +/- 1.0, 43.5 +/- 0.5, 50.7 +/- 1.5, and 55.0 +/- 3.0 days when the inoculum sizes were 0.1, 0.04, 0.01, and 0.007 CFU/g, respectively. From the same corresponding inoculum sizes, the TTDs at 8 degrees C were 4.5 +/- 0.5, 6.5 +/- 0.5, 7.0 +/- 1.0, and 8.5 +/- 0.5 days. Significant differences (P < 0.05) between TTDs were observed only when the inoculum sizes differed by at least 2 log. On a shelf life plot of 1n (TTD) versus temperature, the Q10 (increase in TTD for a 10 degrees C increase in temperature) values ranged from 24.5 to 44.7 and with no significant influence from the inoculum densities. When the observed TTDs were compared with the expected detection times based on the data obtained from a study with an inoculum size of 10 to 20 CFU/g, significant deviations were noted at lower inoculum levels. These results can be valuable in designing a safety-based shelf life model for frankfurters and in performing quantitative risk assessment of listeriosis at low and practical contamination levels.

Quantitative risk assessment of Listeria monocytogenes in French cold-smoked salmon: II. Risk characterization

A model for the assessment of exposure to Listeria monocytogenes from cold-smoked salmon consumption in France was presented in the first of this pair of articles (Pouillot et al., 2007, Risk Analysis, 27:683-700). In the present study, the exposure model output was combined with an internationally accepted hazard characterization model, adapted to the French situation, to assess the risk of invasive listeriosis from cold-smoked salmon consumption in France in a second-order Monte Carlo simulation framework. The annual number of cases of invasive listeriosis due to cold-smoked salmon consumption in France is estimated to be 307, with a very large credible interval ([10; 12,453]), reflecting data uncertainty. This uncertainty is mainly associated with the dose-response model. Despite the significant uncertainty associated with the predictions, this model provides a scientific base for risk managers and food business operators to manage the risk linked to cold-smoked salmon contaminated with L. monocytogenes. Under the modeling assumptions, risk would be efficiently reduced through a decrease in the prevalence of L. monocytogenes or better control of the last steps of the cold chain (shorter and/or colder storage during the consumer step), whereas reduction of the initial contamination levels of the contaminated products and improvement in the first steps of the cold chain do not seem to be promising strategies. An attempt to apply the recent risk-based concept of FSO (food safety objective) on this example underlines the ambiguity in practical implementation of the risk management metrics and the need for further elaboration on these concepts.

Influence of stress on single-cell lag time and growth probability for Listeria monocytogenes in half Fraser broth

The impacts of 12 common food industry stresses on the single-cell growth probability and single-cell lag time distribution of Listeria monocytogenes were determined in half Fraser broth, the primary enrichment broth of the International Organization for Standardization detection method. First, it was determined that the ability of a cell to multiply in half Fraser broth is conditioned by its history (the probability for a cell to multiply can be decreased to 0.05), meaning that, depending on the stress in question, the risk of false-negative samples can be very high. Second, it was established that when cells are injured, the single-cell lag times increase in mean and in variability and that this increase represents a true risk of not reaching the detection threshold of the method in the enrichment broth. No relationship was observed between the impact on single-cell lag times and that on growth probabilities. These results emphasize the importance of taking into account the physiological state of the cells when evaluating the performance of methods to detect pathogens in food.

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.

Validation of growth as measurand for bacterial adhesion to food and feed ingredients

AIMS

A miniaturized adhesion test was designed to study the binding capacity of food and feed ingredients for bacterial cells.

METHODS AND RESULTS

Bacteria were allowed to adhere to different fibrous materials supplied as well coatings in microtitration plates. The amount of bacteria retained on the materials was determined in an automated way as growth after addition of liquid medium. The test principle was based on an inverse relationship between initial cell densities and the appearance of growth: The higher adhering cell numbers are, the shorter are the detection times of growth. The growth curves obtained were fitted by nonlinear regression analysis employing a sigmoidal curve model. Growth parameters as (i) the time after incubation at which half of the maximum growth yield was reached; (ii) the time-coordinate of the point of inflection; (iii) the detection time calculated as x-axis intercept of the maximum specific growth rate in the point of inflection; and (iv) the time-coordinate of a growth detection threshold at OD = 0.05 were highly separating for the binding capacity of different food and feed ingredients for bacteria.

SIGNIFICANCE AND IMPACT OF THE STUDY

With growth as measurand for adhesion, a simple, high-throughput method was developed for the screening of huge numbers of different binding matrices and bacteria.

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.