Modeling of Growth and Organic Acid Kinetics and Evolution of the Protein Profile and Amino Acid Content during Lactiplantibacillus plantarum ITM21B Fermentation in Liquid Sourdough.

The application of mathematical modeling to study and characterize lactic acid bacterial strains with pro-technological and functional features has gained attention in recent years to solve the problems relevant to the variabilities of the fermentation processes of sourdough. Since the key factors contributing to the sourdough quality are relevant to the starter strain growth and its metabolic activity, in this study, the cardinal growth parameters for pH, temperature (T), water activity (aw), and undissociated lactic acid of the sourdough strain Lactiplantibacillus plantarum ITM21B, were determined. The strain growth, pH, organic acids (lactic, acetic, phenyllactic, and hydroxy-phenyllactic), total free amino acids, and proteins were monitored during fermentation of a liquid sourdough based on wheat flour and gluten (Bio21B) after changing the starting T, pH, and inoculum load. Results demonstrated that the different fermentation conditions affected the strain growth and metabolite pattern. The organic acid production and growth performance were modeled in Bio21B, and the resulting predictive model allowed us to simulate in silico the strain performances in liquid sourdough under different scenarios. This mathematical predictive approach can be useful to optimize the fermentation conditions needed to obtain the suitable nutritional and technological characteristics of the L. plantarum ITM21B liquid sourdough.

A Predictive Growth Model for Pro-technological and Probiotic Lacticaseibacillus paracasei Strains Fermenting White Cabbage.

Bacterial strains belonging to Lacticaseibacillus paracasei species are generally used as starters in food fermentations and/or as probiotics. In the current study, the growth cardinal parameters of four L. paracasei strains (IMPC2.1, IMPC4.1, P40 and P101), isolated from table olives or human source, were determined. Strains were grown in liquid medium and incubated at several temperatures (10 values from 5.5°C-40°C) and pH (15 values from 3.2 to 9.1) along the growth range. The cardinal temperature model was used to describe temperature effects on the maximum specific growth rate of L. paracasei whereas new equations were developed for the effect of pH. The estimated Tmin values ranged between -0.97°C and 1.95°C and were lower than 0°C for strains IMPC4.1 and P101. Strain P40 was able to grow in the most restricted range of temperature (from 1.95°C to 37.46°C), while strain IMPC4.1 was estimated to survive at extreme conditions showing the lowest pHmin . Maximum specific growth rates of L. paracasei IMPC2.1 in white cabbage (Brassica oleracea var. capitata) were used to calculate the correction factor (Cf ) defined as the bias between the bacterial maximum specific growth rate in broth and in the food matrix. A simple bi-linear model was also developed for the effect of temperature on the maximum population density reached in white cabbage. This information was further used to simulate the growth of L. paracasei strains in cabbage and predict the time to reach the targeted probiotic level (7 log10 CFU/g) using in silico simulations. This study demonstrates the potential of the predictive microbiology to predict the growth of beneficial and pro-technological strains in foods in order to optimize the fermentative process.

Modelling the thermal inactivation of spores from different phylogenetic groups of Bacillus cereus.

The objective of this work is to match available phylogenetic information for Bacillus cereus strains with published thermal resistance parameters (D90°C, z) and to use this information to develop refined inactivation models for B. cereus sensu lato. To do so, the thermal resistance parameters were retrieved for 57 strains of B. cereus that could be assigned to a phylogenetic group. This information was used to build specific distributions for D90°C and z for the different phylogenetic groups of B. cereus to build refined thermal inactivation models for B. cereus. For validation purposes, thermal parameters were also retrieved for additional strains of unknown groups, but which had been classified as psychrotrophic or mesophilic. Monte Carlo simulations were first performed assuming that the model parameters D90°C and z are independent. However, based on the observation that combinations of very high D90°C and high z-values were not reported, an alternative Monte Carlo simulation set was explored for the phylogenetic Groups with very high z-values (i.e.i.e. Groups IV and VI). With both simulation sets, the predicted lower and upper limits of the D-values are close to the lowest and highest D-values reported in two previous meta-analysis studies. However, a better correspondence between the predicted and observed limits is obtained when using the alternative simulation set.

The effect of pH on the growth rate of Bacillus cereus sensu lato: Quantifying strain variability and modelling the combined effects of temperature and pH.

In this study, the effect of pH, alone or in combination with temperature, on the maximum growth rate (µmax) of B. cereus sensu lato was investigated. In phase 1, the effect of pH at 30 °C was studied for 16 mesophilic strains and 2 psychrotrophic strains of Bacillus cereus sensu lato. The µmax vs. pH relationship was found to show a similar pattern for all the strains. Several pH models from literature were evaluated and the best performing 'growth rate vs. pH' model selected. A stochastic model was then developed to predict the maximum specific growth rate of mesophilic B. cereus at 30 °C as a function of pH, the intra-species variability being incorporated via considering the model parameters (e.g. pHmin) randomly distributed. The predicted maximum specific growth rates were acceptably close to independent published data. In phase 2, the combined effects of temperature and pH were studied. Growth rates were also generated at 15, 20 and 40 °C for a selection of strains and the pH model was fitted at each temperature. Interestingly, the results showed that the estimates for the pHmin parameter for mesophilic strains were lower at 20-30 °C than near the optimum temperature (40 °C), suggesting that experiments for the determination of this parameter should be conducted at lower-than-optimum temperatures. New equations were proposed for the relationship between temperature and the minimum pH-values, which were also consistent with the experimental growth boundaries. The parameters defining this equation quantify the minimum temperature for growth observed experimentally, the temperature of maximum enzyme stability and the maximum temperature for growth. Deviations from the Gamma hypothesis (multiplicative effects of environmental factors on the maximum specific growth rate) were observed near the growth limits, especially at 40 °C. To improve model performance, two approaches, one based on a minimum pH-term (doi: https://doi.org/10.3389/fmicb.2019.01510) and one based on an interaction term (doi: http://dx.doi.org/10.1016/S0168-1605(01)00640-7) were evaluated.

A stochastic approach for modelling the effects of temperature on the growth rate of Bacillus cereus sensu lato.

A stochastic model that predicts the maximum specific growth rate (µmax) of Bacillus cereus sensu lato as a function of temperature was developed. The model integrates the intra-species variability by incorporating distributions of cardinal parameters (Tmin, Topt, Tmax) in the model. Growth rate data were generated for 22 strains, covering 5 major phylogenetic groups of B. cereus, and their cardinal temperatures identified. Published growth rate data were also incorporated in the model fitting, resulting in a set of 33 strains. Based on their cardinal temperatures, we identified clusters of Bacillus cereus strains that show similar response to temperature and these clusters were considered separately in the stochastic model. Interestingly, the µopt values for psychrotrophic strains were found to be significantly lower than those obtained for mesophilic strains. The model developed within this work takes into account some correlations existing between parameters (µopt, Tmin, Topt, Tmax). In particular, the relationship highlighted between the b-slope of the Ratkowsky model and Tmin (doi: https://doi.org/10.3389/fmicb.2017.01890) was adapted to the case of the popular Cardinal Temperature Model. This resulted in a reduced model in which µopt is replaced by a function of Tmin, Topt and 2 strain-independent parameters. A correlation between the Tmin parameter and the experimental minimal growth temperature was also highlighted and integrated in the model for improved predictions near the temperature growth limits. Compared to the classical approach, the model developed in this study leads to improved predictions for temperatures around Tmin and more realistic tails for the predicted distributions of µmax. It can be useful for describing the variability of the Bacillus cereus Group in Quantitative Microbial Risk Assessment (QMRA). An example of application of the stochastic model to Reconstituted Infant Formulae (RIF) was proposed.

Bacterial spores in spices and dried herbs: The risks for processed food.

Production and world consumption of spices are constantly increasing. Although the antimicrobial properties of some spices are well documented, their use in the agri-food industry is also responsible for microbial contamination and spoilage. Bacterial spores introduced by spices can withstand different preparation processes, particularly thermal treatments, leading to food alterations during storage. This review brings together data from the literature about the prevalence and concentrations of spore-forming bacteria in all commercially available spices. The sporeformers found in spices belong mainly to the genera Bacillus and Clostridium. Such contaminations are very common and sometimes reach high levels, as in pepper and turmeric. Bacillus licheniformis and Bacillus cereus are the most frequently detected species. Studying the harvesting, processing, and storage procedures for spices provides elements to explain why high prevalence and concentrations are observed. Spices are mostly produced in developing countries on small farms using traditional production methods. Spices become contaminated by bacterial spores in two main ways: by contact with soil during harvesting or drying, as for pepper, or by cross-contamination during the water-cooking step, as for turmeric. From these observations, we propose some recommendations. Different methods that can be used to eliminate bacterial spores from spices are presented indicating their efficiency and the limitations of their use.

Knowledge of the physiology of spore-forming bacteria can explain the origin of spores in the food environment.

Spore-forming bacteria are able to grow under a wide range of environmental conditions, to form biofilms and to differentiate into resistant forms: spores. This resistant form allows their dissemination in the environment; consequently, they may contaminate raw materials. Sporulation can occur all along the food chain, in raw materials, but also in food processes, leading to an increase in food contamination. However, the problem of sporulation during food processing is poorly addressed and sporulation niches are difficult to identify from the farm to the fork. Sporulation is a survival strategy. Some environmental factors are required to trigger this differentiation process and others act by modulating it. The efficiency of sporulation is the result of the combined effects of these two types of factors on vegetative cell metabolism. This paper aims to explain and help identify sporulation niches in the food chain, based on features of spore-former physiology.

mRNA biomarkers selection based on Partial Least Square algorithm in order to further predict Bacillus weihenstephanensis acid resistance.

In order to integrate omics data to quantitative microbiological risk assessment in foods, gene expressions may serve as bacterial behaviour biomarkers. In this study an integrative approach encompassing predictive modelling and mRNAs quantifications, was followed to select molecular biomarkers to further predict the acid resistance of Bacillus weihenstephanensis. A multivariate analysis was performed to correlate the acid bacterial resistance and the gene expression of vegetative cells with or without exposure to stressing conditions. This mathematical method provides the advantage to take gene expressions and their interactions into account. The use of the Partial Least Squares algorithm allowed the selection of nine genes as acid resistance biomarkers among thirty targeted genes. According to their involvement in the general acid stress response of Bacillus, these genes were assigned to three different biological modules namely, metabolic rearrangements, general stress response and oxidative stress response. The oxidative stress response appeared as the major activated biological module in B. weihenstephanensis cells submitted to acid stress conditions. Furthermore, as a firstly described model, the developed concept showed promising results to further be used to predict bacterial resistance using gene expression. Thus, this study underlines the possibility to integrate the bacterial physiology state, using omics biomarkers, into bacterial behaviour modelling and provide mechanistic understanding in acid bacterial resistance mechanisms.

Evolution of microbiological analytical methods for dairy industry needs.

Traditionally, culture-based methods have been used to enumerate microbial populations in dairy products. Recent developments in molecular methods now enable faster and more sensitive analyses than classical microbiology procedures. These molecular tools allow a detailed characterization of cell physiological states and bacterial fitness and thus, offer new perspectives to integration of microbial physiology monitoring to improve industrial processes. This review summarizes the methods described to enumerate and characterize physiological states of technological microbiota in dairy products, and discusses the current deficiencies in relation to the industry's needs. Recent studies show that Polymerase chain reaction-based methods can successfully be applied to quantify fermenting microbes and probiotics in dairy products. Flow cytometry and omics technologies also show interesting analytical potentialities. However, they still suffer from a lack of validation and standardization for quality control analyses, as reflected by the absence of performance studies and official international standards.

Bacillus cereus cell response upon exposure to acid environment: toward the identification of potential biomarkers.

Microorganisms are able to adapt to different environments and evolve rapidly, allowing them to cope with their new environments. Such adaptive response and associated protections toward other lethal stresses, is a crucial survival strategy for a wide spectrum of microorganisms, including food spoilage bacteria, pathogens, and organisms used in functional food applications. The growing demand for minimal processed food yields to an increasing use of combination of hurdles or mild preservation factors in the food industry. A commonly used hurdle is low pH which allows the decrease in bacterial growth rate but also the inactivation of pathogens or spoilage microorganisms. Bacillus cereus is a well-known food-borne pathogen leading to economical and safety issues in food industry. Because survival mechanisms implemented will allow bacteria to cope with environmental changes, it is important to provide understanding of B. cereus stress response. Thus this review deals with the adaptive traits of B. cereus cells facing to acid stress conditions. The acid stress response of B. cereus could be divided into four groups (i) general stress response (ii) pH homeostasis, (iii) metabolic modifications and alkali production and (iv) secondary oxidative stress response. This current knowledge may be useful to understand how B. cereus cells may cope to acid environment such as encountered in food products and thus to find some molecular biomarkers of the bacterial behavior. These biomarkers could be furthermore used to develop new microbial behavior prediction tools which can provide insights into underlying molecular physiological states which govern the behavior of microorganisms and thus opening the avenue toward the detection of stress adaptive behavior at an early stage and the control of stress-induced resistance throughout the food chain.

Sporulation boundaries and spore formation kinetics of Bacillus spp. as a function of temperature, pH and a(w).

Sporulation niches in the food chain are considered as a source of hazard and are not clearly identified. Determining the sporulation environmental boundaries could contribute to identify potential sporulation niches. Spore formation was determined in a Sporulation Mineral Buffer. The effect of incubation temperature, pH and water activity on time to one spore per mL, maximum sporulation rate and final spore concentration was investigated for a Bacillus weihenstephanensis and a Bacillus licheniformis strain. Sporulation boundaries of B. weihenstephanensis and of B. licheniformis were similar to, or included within, the range of temperatures, pH and water activities supporting growth. For instance, sporulation boundaries of B. weihenstephanensis were evaluated at 5°C, 35°C, pH 5.2 and a(w) 0.960 while growth boundaries were observed at 5°C, 37°C, pH 4.9 and a(w) 0.950. Optimum spore formation was determined at 30°C pH 7.2 for B. weihenstephanensis and at 45°C pH 7.2 for B. licheniformis. Lower temperatures and pH delayed the sporulation process. For instance, the time to one spore per mL was tenfold longer when sporulation occurred at 10°C and 20°C, for each strain respectively, than at optimum sporulation temperature. The relative effect of temperature and pH on sporulation rates and on growth rates is similar. This work suggests that the influence of environmental factors on the quantitative changes in sporulation boundaries and rates was similar to their influence on changes in growth rate.

An integrative approach to identify Bacillus weihenstephanensis resistance biomarkers using gene expression quantification throughout acid inactivation.

The aim of this study was to define an integrative approach to identify resistance biomarkers using gene expression quantification and mathematical modelling. Mid-exponentially growing cells were transferred into acid conditions (BHI, pH 4.6) to obtain inactivation kinetics, performed in triplicate. The inactivation curve was fitted with a mixed Weibull model. This model allowed to differentiate two subpopulations with various acid resistances among the initial population. In parallel, differential gene expression was quantified by RT-qPCR. While narL was down-regulated throughout acid inactivation, sigB and katA were up-regulated. sigB expression up-regulation peak was correlated to the less resistant subpopulation when katA up-regulation, was correlated to the more resistant subpopulation. Moreover, differences in population structure were highlighted between each replicate. The higher proportion of the more resistant subpopulation was linked to a higher katA gene expression. These results suggest that sigB and katA might be used as different types of biomarkers, for instance to track moderate and high acid-resistance, respectively. The use of this approach combining RT-qPCR and predictive modelling to track cellular biomarker variations appears as an interesting tool to take into account physiological cell responses into mathematical modelling, allowing an accurate prediction of microbial behaviour.

Tracking spore-forming bacteria in food: from natural biodiversity to selection by processes.

Sporeforming bacteria are ubiquitous in the environment and exhibit a wide range of diversity leading to their natural prevalence in foodstuff. The state of the art of sporeformer prevalence in ingredients and food was investigated using a multiparametric PCR-based tool that enables simultaneous detection and identification of various genera and species mostly encountered in food, i.e., Alicyclobacillus, Anoxybacillus flavithermus, Bacillus, B. cereus group, B. licheniformis, B. pumilus, B. sporothermodurans, B. subtilis, Brevibacillus laterosporus, Clostridium, Geobacillus stearothermophilus, Moorella and Paenibacillus species. In addition, 16S rDNA sequencing was used to extend identification to other possibly present contaminants. A total of 90 food products, with or without visible trace of spoilage were analysed, i.e., 30 egg-based products, 30 milk and dairy products and 30 canned food and ingredients. Results indicated that most samples contained one or several of the targeted genera and species. For all three tested food categories, 30 to 40% of products were contaminated with both Bacillus and Clostridium. The percentage of contaminations associated with Clostridium or Bacillus represented 100% in raw materials, 72% in dehydrated ingredients and 80% in processed foods. In the last two product types, additional thermophilic contaminants were identified (A. flavithermus, Geobacillus spp., Thermoanaerobacterium spp. and Moorella spp.). These results suggest that selection, and therefore the observed (re)-emergence of unexpected sporeforming contaminants in food might be favoured by the use of given food ingredients and food processing technologies.

Polyphasic approach for quantitative analysis of obligately heterofermentative Lactobacillus species in cheese.

Obligately heterofermentative lactobacilli (OHL) present in cheese during ripening can influence the flavour and texture of the final product. In order to better evaluate, follow and control this population, there is a current need for easy-to-use tools. In this study, a culture-dependent quantitative method (ABEV medium) was set up for direct and selective enumeration of total OHL from cheese, and a culture-independent method based on specific real time PCR (qPCR) assays was developed to target Lactobacillus fermentum and Lactobacillus parabuchneri individual species. These tools were applied for OHL quantification in manufactured Emmental and Tomme cheeses. The ABEV medium was well adapted for specific enumeration and isolation of OHL species present in milk-derived samples, even in the presence of background microbiota. qPCR assays showed 100% specificity and could accurately quantify the targeted species in various types of cheese. Culture-dependent and -independent techniques evaluated in manufactured cheese samples generated similar bacterial counts. The behaviour of L. fermentum and L. parabuchneri was characterized from milk samples to the end of ripening. In addition, PCR-TTGE was used to confirm the presence of inoculated species and to globally analyze the composition of naturally present species. This polyphasic approach illustrates the complementarity of the different methods.

Modeling heat resistance of Bacillus weihenstephanensis and Bacillus licheniformis spores as function of sporulation temperature and pH.

Although sporulation environmental factors are known to impact on Bacillus spore heat resistance, they are not integrated into predictive models used to calculate the efficiency of heating processes. This work reports the influence of temperature and pH encountered during sporulation on heat resistance of Bacillus weihenstephanensis KBAB4 and Bacillus licheniformis AD978 spores. A decrease in heat resistance (δ) was observed for spores produced either at low temperature, at high temperature or at acidic pH. Sporulation temperature and pH maximizing the spore heat resistance were identified. Heat sensitivity (z) was not modified whatever the sporulation environmental factors were. A resistance secondary model inspired by the Rosso model was proposed. Sporulation temperatures and pHs minimizing or maximizing the spore heat resistance (T(min(R)), T(opt(R)), T(max(R)), pH(min(R)) and pH(opt(R))) were estimated. The goodness of the model fit was assessed for both studied strains and literature data. The estimation of the sporulation temperature and pH maximizing the spore heat resistance is of great interest to produce spores assessing the spore inactivation in the heating processes applied by the food industry.

Reverse transcription quantitative PCR revealed persistency of thermophilic lactic acid bacteria metabolic activity until the end of the ripening of Emmental cheese.

For Emmental manufacture two kinds of adjunct culture are added: (i) thermophilic lactic acid bacteria (starters) such as Lactobacillus helveticus (LH), and Streptococcus thermophilus (ST) growing the first day of the manufacture and (ii) ripening culture. ST and LH have a key role in curd acidification and proteolysis at the beginning of the manufacture but are considered to be lyzed for a great part of them at the ripening step. The aim of this work was to assess the metabolic activity of these bacteria throughout manufacture and ripening. During Emmental cheesemaking, LH and ST were subjected to i) population quantification by numerations and by quantitative PCR (qPCR) ii) reverse transcription (RT) Temporal Temperature Gel Electrophoresis (TTGE) iii) transcript quantification by RT-qPCR targeting 16S rRNA, tuf and groL mRNAs to evaluate bacterial metabolic activity. During ripening, ST and LH numerations showed a 2.5 log(10) loss of culturability whereas qPCR on pelleted cells revealed only one log(10) of decrease for both of these species. 10(9) ST and 10(8) LH cells/g of cheese still remained. They contained a stable number of 16S transcript and at least 10(6) copies of mRNAs per 10(9) cells until the end of ripening. These results prove the unexpected persistency of thermophilic lactic acid bacteria starters (ST and LH) metabolic activity until the end of ripening and open new perspectives in term of their involvement in the quality of cheeses during ripening.

Recent advances in quantitative PCR (qPCR) applications in food microbiology.

Molecular methods are being increasingly applied to detect, quantify and study microbial populations in food or during food processes. Among these methods, PCR-based techniques have been the subject of considerable focus and ISO guidelines have been established for the detection of food-borne pathogens. More particularly, real-time quantitative PCR (qPCR) is considered as a method of choice for the detection and quantification of microorganisms. One of its major advantages is to be faster than conventional culture-based methods. It is also highly sensitive, specific and enables simultaneous detection of different microorganisms. Application of reverse-transcription-qPCR (RT-qPCR) to study population dynamics and activities through quantification of gene expression in food, by contrast with the use of qPCR, is just beginning. Provided that appropriate controls are included in the analyses, qPCR and RT-qPCR appear to be highly accurate and reliable for quantification of genes and gene expression. This review addresses some important technical aspects to be considered when using these techniques. Recent applications of qPCR and RT-qPCR in food microbiology are given. Some interesting applications such as risk analysis or studying the influence of industrial processes on gene expression and microbial activity are reported.

Specific metabolic activity of ripening bacteria quantified by real-time reverse transcription PCR throughout Emmental cheese manufacture.

Bacterial communities of fermented foods are usually investigated by culture-dependent methods. Real-time quantitative PCR (qPCR) and reverse transcription (RT)-qPCR offer new possibilities to quantify the populations present and their metabolic activity. The aim of this work was to develop qPCR and RT-qPCR methods to assess the metabolic activity and the stress level of the two species used as ripening cultures in Emmental cheese manufacture, Propionibacterium freudenreichii and Lactobacillus paracasei. Three small scale (1/100) microbiologically controlled Emmental cheeses batches were manufactured and inoculated with Lactobacillus helveticus, Streptococcus thermophilus, P. freudenreichii and L. paracasei. At 12 steps of cheese manufacture and ripening, the populations of P. freudenreichii and L. paracasei were quantified by numerations on agar media and by qPCR. 16S, tuf and groL transcript levels were quantified by RT-qPCR. Sampling was carried out in triplicate. qPCR and RT-qPCR assessments were specific, efficient and linear. The quantification limit was 10(3) copies of cells or cDNA/g of cheese. Cell quantifications obtained by qPCR gave similar results than plate count for P. freudenreichii growth and 0.5 to 1 log lower in the stationary phase. Bacterial counts and qPCR quantifications showed that L. paracasei began to grow during the pressing step while P. freudenreichii began to grow from the beginning of ripening (in the cold room). Tuf cDNA quantification results suggested that metabolic activity of L. paracasei reached a maximum during the first part of the ripening (in cold room) and decreased progressively during ripening (in the warm room). Metabolic activity of P. freudenreichii was maximum at the end of cold ripening room and was stable during the first two weeks in warm room. After lactate exhaustion (after two weeks of warm room), the number of tuf cDNA decreased reflecting reduced metabolic activity. For L. paracasei, groL cDNA were stable during ripening. For P. freudenreichii, groL1 gene was highly-expressed during acidification, while groL2 gene highly expression was only observed at the end of the ripening stage after lactate (carbon substrate of P. freudenreichii) exhaustion. The potential use of 16S and tuf genes for the normalization of cDNA quantification throughout an Emmental cheese manufacture is discussed. For the first time, specific gene expression was performed by RT-qPCR yielding metabolic activity and stress response evaluation for L. paracasei and P. freudenreichii in cheese.