Kristel Bernaerts

Protective effect of calcium on inactivation of Escherichia coli by high hydrostatic pressure

The effect of divalent cations on the inactivation of Escherichia coli by high hydrostatic pressure was investigated. The presence of 0·5 mmol l−1 of CaCl2, MgCl2, MnCl2 & FeCl2 reduced pressure inactivation of E. coli MG1655, while 0·5 mmol l−1 of ZnCl2, NiCl2, CuCl2 & CoCl2 increased inactivation. Baroprotection by Ca2+ was found to be dose‐dependent up to at least 80 mmol l−1 & was studied in more detail in terms of inactivation kinetics. Logarithmic survivor plots against time deviated from first order kinetics, suggesting that MG1655 cultures were heterogeneous with regard to pressure resistance. All cultures were shown to contain a small proportion of cells that were only slowly inactivated. Addition of Ca2+ increased the proportion of these tolerant cells in the cultures up to 1000‐fold at 80 mmol l−1, but did not affect their inactivation rate. The addition of EDTA resulted in the opposite effect, lowering the proportion of pressure‐tolerant cells in the cultures. Three pressure‐resistant mutants of E. coli MG1655 were found to be more resistant to EDTA under pressure compared with MG1655 & were unaffected by Ca2+ under pressure. In addition, these mutants had a 30–40% lower Ca2+ content than MG1655. Based on these results, it is postulated that pressure killing of E. coli MG1655 is mediated primarily by the destabilization of Ca2+‐binding components & that the mutations underlying pressure resistance have resulted in pressure‐stable targets with reduced Ca2+‐binding affinity.

Optimal temperature input design for estimation of the Square Root model parameters: parameter accuracy and model validity restrictions

As part of the model building process, parameter estimation is of great importance in view of accurate prediction making. Confidence limits on the predicted model output are largely determined by the parameter estimation accuracy that is reflected by its parameter estimation covariance matrix. In view of the accurate estimation of the Square Root model parameters, Bernaerts et al. have successfully applied the techniques of optimal experiment design for parameter estimation [Int. J. Food Microbiol. 54 (1-2) (2000) 27]. Simulation-based results have proved that dynamic (i.e., time-varying) temperature conditions characterised by a large abrupt temperature increase yield highly informative cell density data enabling precise estimation of the Square Root model parameters. In this study, it is shown by bioreactor experiments with detailed and precise sampling that extreme temperature shifts disturb the exponential growth of Escherichia coli K12. A too large shift results in an intermediate lag phase. Because common growth models lack the ability to model this intermediate lag phase, temperature conditions should be designed such that exponential growth persist even though the temperature may be changing. The current publication presents (i) the design of an optimal temperature input guaranteeing model validity yet yielding accurate Square Root model parameters, and (ii) the experimental implementation of the optimal input in a computer-controlled bioreactor. Starting values for the experiment design are generated by a traditional two-step procedure based on static experiments. Opposed to the single step temperature profile, the novel temperature input comprises a sequence of smaller temperature increments. The structural development of the temperature input is extensively explained. High quality data of E. coli K12 under optimally varying temperature conditions realised in a computer-controlled bioreactor yield accurate estimates for the Square Root model parameters. The latter is illustrated by means of the individual confidence intervals and the joint confidence region.

Introducing optimal experimental design in predictive modeling : A motivating example

Predictive microbiology emerges more and more as a rational quantitative framework for predicting and understanding microbial evolution in food products. During the mathematical modeling of microbial growth and/or inactivation, great, but not always efficient, effort is spent on the determination of the model parameters from experimental data. In order to optimize experimental conditions with respect to parameter estimation, experimental design has been extensively studied since the 1980s in the field of bioreactor engineering. The so-called methodology of optimal experimental design established in this research area enabled the reliable estimation of model parameters from data collected in well-designed fed-batch reactor experiments. In this paper, we introduce the optimal experimental design methodology for parameter estimation in the field of predictive microbiology. This study points out that optimal design of dynamic input signals is necessary to maximize the information content contained within the resulting experimental data. It is shown that from few dynamic experiments, more pertinent information can be extracted than from the classical static experiments. By introducing optimal experimental design into the field of predictive microbiology, a new promising frame for maximization of the information content of experimental data with respect to parameter estimation is provided. As a case study, the design of an optimal temperature profile for estimation of the parameters D(ref) and z of an Arrhenius-type model for the maximum inactivation rate kmax as a function of the temperature, T, was considered. Microbial inactivation by heating is described using the model of Geeraerd et al. (1999). The need for dynamic temperature profiles in experiments aimed at the simultaneous estimation of the model parameters from measurements of the microbial population density is clearly illustrated by analytical elaboration of the mathematical expressions involved on the one hand, and by numerical simulations on the other.

Recombinant protein production and streptomycetes.

The biopharmaceutical market has come a long way since 1982, when the first biopharmaceutical product, recombinant human insulin, was launched. Just over 200 biopharma products have already gained approval. The global market for biopharmaceuticals which is currently valued at over US

On the design of optimal dynamic experiments for parameter estimation of a Ratkowsky-type growth kinetics at suboptimal temperatures

99 billion has been growing at an impressive compound annual growth rate over the previous years. To produce these biopharmaceuticals and other industrially important heterologous proteins, different prokaryotic and eukaryotic expression systems are used. All expression systems have some advantages as well as some disadvantages that should be considered in selecting which one to use. Choosing the best one requires evaluating the options--from yield to glycosylation, to proper folding, to economics of scale-up. No host cell from which all the proteins can be universally expressed in large quantities has been found so far. Therefore, it is important to provide a variety of host-vector expression systems in order to increase the opportunities to screen for the most suitable expression conditions or host cell. In this overview, we focus on Streptomyces lividans, a Gram-positive bacterium with a proven excellence in secretion capacity, as host for heterologous protein production. We will discuss its advantages and disadvantages, and how with systems biology approaches strains can be developed to better producing cell factories.

Accurate estimation of cardinal growth temperatures of Escherichia coli from optimal dynamic experiments

It is generally known that accurate model building, i.e., proper model structure selection and reliable parameter estimation, constitutes an essential matter in the field of predictive microbiology, in particular, when integrating these predictive models in food safety systems. In this context, Versyck et al. (1999) have introduced the methodology of optimal experimental design techniques for parameter estimation within the field. Optimal experimental design focuses on the development of optimal input profiles such that the resulting rich (i.e., highly informative) experimental data enable unique model parameter estimation. As a case study, Versyck et al. (1999) [Versyck, K., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., 1999. Introducing optimal experimental design in predictive modeling: a motivating example. Int. J. Food Microbiol., 51(1), 39-51] have elaborated the estimation of Bigelow inactivation kinetics parameters (in a numerical way). Opposed to the classic (static) experimental approach in predictive modelling, an optimal dynamic experimental setup is presented. In this paper, the methodology of optimal experimental design or parameter estimation is applied to obtain uncorrelated estimates of the square root model parameters [Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A., 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol. 149, 1-5] describing the effect of suboptimal growth temperatures on the maximum specific growth rate of microorganisms. These estimates are the direct result of fitting a primary growth model to cell density measurements as a function of time. Apart from the design of an optimal time-varying temperature profile based on a sensitivity study of the model output, an important contribution of this publication is a first experimental validation of this innovative dynamic experimental approach for uncorrelated parameter identification. An optimal step temperature profile, within the range of model validity and practical feasibility, is developed for Escherichia coli K12 and successfully applied in practice. The presented experimental validation result illustrates the large potential of the dynamic experimental approach in the context of uncorrelated parameter estimation. Based on the experimental validation result, additional remarks are formulated related to future research in the field of optimal experimental design.

Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

Prediction of the microbial growth rate as a response to changing temperatures is an important aspect in the control of food safety and food spoilage. Accurate model predictions of the microbial evolution ask for correct model structures and reliable parameter values with good statistical quality. Given the widely accepted validity of the Cardinal Temperature Model with Inflection (CTMI) [Rosso, L., Lobry, J. R., Bajard, S. and Flandrois, J. P., 1995. Convenient model to describe the combined effects of temperature and pH on microbial growth, Applied and Environmental Microbiology, 61: 610-616], this paper focuses on the accurate estimation of its four parameters (T(min), T(opt), T(max) and micro(opt)) by applying the technique of optimal experiment design for parameter estimation (OED/PE). This secondary model describes the influence of temperature on the microbial specific growth rate from the minimum to the maximum temperature for growth. Dynamic temperature profiles are optimized within two temperature regions ([15 degrees C, 43 degrees C] and [15 degrees C, 45 degrees C]), focusing on the minimization of the parameter estimation (co)variance (D-optimal design). The optimal temperature profiles are implemented in a computer controlled bioreactor, and the CTMI parameters are identified from the resulting experimental data. Approximately equal CTMI parameter values were derived irrespective of the temperature region, except for T(max). The latter could only be estimated accurately from the optimal experiments within [15 degrees C, 45 degrees C]. This observation underlines the importance of selecting the upper temperature constraint for OED/PE as close as possible to the true T(max). Cardinal temperature estimates resulting from designs within [15 degrees C, 45 degrees C] correspond with values found in literature, are characterized by a small uncertainty error and yield a good result during validation. As compared to estimates from non-optimized dynamic experiments, more reliable CTMI parameter values were obtained from the optimal experiments within [15 degrees C, 45 degrees C].

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature

Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly when using glutamate and aspartate. Depletion of glutamate and aspartate causes a distinct shift in fluxes of the central carbon and nitrogen metabolism. In the current work, hurdles encountered in flux analysis at a genome-scale level are addressed using hierarchical flux balance analysis and uniform sampling of the constrained solution space. This general framework can now be adopted in further studies of S. lividans, e.g., as a host for heterologous protein production.

Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans.

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the systems dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable.

Concepts and tools for predictive modeling of microbial dynamics

Proteins secreted by Gram-positive bacteria are released into the culture medium with the obvious benefit that they usually retain their native conformation. This property makes these host cells potentially interesting for the production of recombinant proteins, as one can take full profit of established protocols for the purification of active proteins. Several state-of-the-art strategies to increase the yield of the secreted proteins will be discussed, using Streptomyces lividans as an example and compared with approaches used in some other host cells. It will be shown that approaches such as increasing expression and translation levels, choice of secretion pathway and modulation of proteins thereof, avoiding stress responses by changing expression levels of specific (stress) proteins, can be helpful to boost production yield. In addition, the potential of multi-omics approaches as a tool to understand the genetic background and metabolic fluxes in the host cell and to seek for new targets for strain and protein secretion improvement is discussed. It will be shown that S. lividans, along with other Gram-positive host cells, certainly plays a role as a production host for recombinant proteins in an economically viable way. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.