Is the glycolytic flux in Lactococcus lactis primarily controlled by the redox charge? Kinetics of NAD(+) and NADH pools determined in vivo by 13C NMR

A label-free fluorescence DNA probe based on ligation reaction with quadruplex formation for highly sensitive and selective detection of nicotinamide adenine dinucleotide

A simple label-free fluorescent sensing scheme for sensitive and selective detection of nicotinamide adenine dinucleotide (NAD(+)) has been developed based on DNA ligation reaction with ligand-responsive quadruplex formation. This approach can detect 0.5 nM NAD(+) with high selectivity against other NAD(+) analogs.

From meadows to milk to mucosa - adaptation of Streptococcus and Lactococcus species to their nutritional environments

Lactic acid bacteria (LAB) are indigenous to food-related habitats as well as associated with the mucosal surfaces of animals. The LAB family Streptococcaceae consists of the genera Lactococcus and Streptococcus. Members of the family include the industrially important species Lactococcus lactis, which has a long history safe use in the fermentative food industry, and the disease-causing streptococci Streptococcus pneumoniae and Streptococcus pyogenes. The central metabolic pathways of the Streptococcaceae family have been extensively studied because of their relevance in the industrial use of some species, as well as their influence on virulence of others. Recent developments in high-throughput proteomic and DNA-microarray techniques, in in vivo NMR studies, and importantly in whole-genome sequencing have resulted in new insights into the metabolism of the Streptococcaceae family. The development of cost-effective high-throughput sequencing has resulted in the publication of numerous whole-genome sequences of lactococcal and streptococcal species. Comparative genomic analysis of these closely related but environmentally diverse species provides insight into the evolution of this family of LAB and shows that the relatively small genomes of members of the Streptococcaceae family have been largely shaped by the nutritionally rich environments they inhabit.

A graphical user interface for a method to infer kinetics and network architecture (MIKANA)

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis–Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

CcpA ensures optimal metabolic fitness of Streptococcus pneumoniae

In gram-positive bacteria, the transcriptional regulator CcpA is at the core of catabolite control mechanisms. In the human pathogen Streptococcus pneumoniae, links between CcpA and virulence have been established, but its role as a master regulator in different nutritional environments remains to be elucidated. Thus, we performed whole-transcriptome and metabolic analyses of S. pneumoniae D39 and its isogenic ccpA mutant during growth on glucose or galactose, rapidly and slowly metabolized carbohydrates presumably encountered by the bacterium in different host niches. CcpA affected the expression of up to 19% of the genome covering multiple cellular processes, including virulence, regulatory networks and central metabolism. Its prevalent function as a repressor was observed on glucose, but unexpectedly also on galactose. Carbohydrate-dependent CcpA regulation was also observed, as for the tagatose 6-phosphate pathway genes, which were activated by galactose and repressed by glucose. Metabolite analyses revealed that two pathways for galactose catabolism are functionally active, despite repression of the Leloir genes by CcpA. Surprisingly, galactose-induced mixed-acid fermentation apparently required CcpA, since genes involved in this type of metabolism were mostly under CcpA-repression. These findings indicate that the role of CcpA extends beyond transcriptional regulation, which seemingly is overlaid by other regulatory mechanisms. In agreement, CcpA influenced the level of many intracellular metabolites potentially involved in metabolic regulation. Our data strengthen the view that a true understanding of cell physiology demands thorough analyses at different cellular levels. Moreover, integration of transcriptional and metabolic data uncovered a link between CcpA and the association of surface molecules (e.g. capsule) to the cell wall. Hence, CcpA may play a key role in mediating the interaction of S. pneumoniae with its host. Overall, our results support the hypothesis that S. pneumoniae optimizes basic metabolic processes, likely enhancing in vivo fitness, in a CcpA-mediated manner.

High yields of 2,3-butanediol and mannitol in Lactococcus lactis through engineering of NAD⁺ cofactor recycling

Manipulation of NADH-dependent steps, and particularly disruption of the las-located lactate dehydrogenase (ldh) gene in Lactococcus lactis, is common to engineering strategies envisaging the accumulation of reduced end products other than lactate. Reverse transcription-PCR experiments revealed that three out of the four genes assigned to lactate dehydrogenase in the genome of L. lactis, i.e., the ldh, ldhB, and ldhX genes, were expressed in the parental strain MG1363. Given that genetic redundancy is often a major cause of metabolic instability in engineered strains, we set out to develop a genetically stable lactococcal host tuned for the production of reduced compounds. Therefore, the ldhB and ldhX genes were sequentially deleted in L. lactis FI10089, a strain with a deletion of the ldh gene. The single, double, and triple mutants, FI10089, FI10089ΔldhB, and FI10089ΔldhBΔldhX, showed similar growth profiles and displayed mixed-acid fermentation, ethanol being the main reduced end product. Hence, the alcohol dehydrogenase-encoding gene, the adhE gene, was inactivated in FI10089, but the resulting strain reverted to homolactic fermentation due to induction of the ldhB gene. The three lactate dehydrogenase-deficient mutants were selected as a background for the production of mannitol and 2,3-butanediol. Pathways for the biosynthesis of these compounds were overexpressed under the control of a nisin promoter, and the constructs were analyzed with respect to growth parameters and product yields under anaerobiosis. Glucose was efficiently channeled to mannitol (maximal yield, 42%) or to 2,3-butanediol (maximal yield, 67%). The theoretical yield for 2,3-butanediol was achieved. We show that FI10089ΔldhB is a valuable basis for engineering strategies aiming at the production of reduced compounds.

Response of Pseudomonas putida KT2440 to increased NADH and ATP demand

Adenosine phosphate and NAD cofactors play a vital role in the operation of cell metabolism, and their levels and ratios are carefully regulated in tight ranges. Perturbations of the consumption of these metabolites might have a great impact on cell metabolism and physiology. Here, we investigated the impact of increased ATP hydrolysis and NADH oxidation rates on the metabolism of Pseudomonas putida KT2440 by titration of 2,4-dinitrophenol (DNP) and overproduction of a water-forming NADH oxidase, respectively. Both perturbations resulted in a reduction of the biomass yield and, as a consequence of the uncoupling of catabolic and anabolic activities, in an amplification of the net NADH regeneration rate. However, a stimulation of the specific carbon uptake rate was observed only when P. putida was challenged with very high 2,4-dinitrophenol concentrations and was comparatively unaffected by recombinant NADH oxidase activity. This behavior contrasts with the comparably sensitive performance described, for example, for Escherichia coli or Saccharomyces cerevisiae. The apparent robustness of P. putida metabolism indicates that it possesses a certain buffering capacity and a high flexibility to adapt to and counteract different stresses without showing a distinct phenotype. These findings are important, e.g., for the development of whole-cell redox biocatalytic processes that impose equivalent burdens on the cell metabolism: stoichiometric consumption of (reduced) redox cofactors and increased energy expenditures, due to the toxicity of the biocatalytic compounds.

Engineering trehalose synthesis in Lactococcus lactis for improved stress tolerance

Trehalose accumulation is a common cell defense strategy against a variety of stressful conditions. In particular, our team detected high levels of trehalose in Propionibacterium freudenreichii in response to acid stress, a result that led to the idea that endowing Lactococcus lactis with the capacity to synthesize trehalose could improve the acid tolerance of this organism. To this end, we took advantage of the endogenous genes involved in the trehalose catabolic pathway of L. lactis, i.e., trePP and pgmB, encoding trehalose 6-phosphate phosphorylase and β-phosphoglucomutase, respectively, which enabled the synthesis of trehalose 6-phosphate. Given that L. lactis lacks trehalose 6-phosphate phosphatase, the respective gene, otsB, from the food-grade organism P. freudenreichii was used to provide the required activity. The trehalose yield was approximately 15% in resting cells and in mid-exponential-phase cells grown without pH control. The intracellular concentration of trehalose reached maximal values of approximately 170 mM, but at least 67% of the trehalose produced was found in the growth medium. The viability of mutant and control strains was examined after exposure to heat, cold or acid shock, and freeze-drying. The trehalose-producing strains showed improved tolerance (5- to 10-fold-higher survivability) to acid (pH 3) and cold shock (4°C); there was also a strong improvement in cell survival in response to heat shock (45°C), and no protection was rendered against dehydration. The insight provided by this work may help the design of food-grade strains optimized for the dairy industry as well as for oral drug delivery.

Subcellular metabolic organization in the context of dynamic energy budget and biochemical systems theories

The dynamic modelling of metabolic networks aims to describe the temporal evolution of metabolite concentrations in cells. This area has attracted increasing attention in recent years owing to the availability of high-throughput data and the general development of systems biology as a promising approach to study living organisms. Biochemical Systems Theory (BST) provides an accurate formalism to describe biological dynamic phenomena. However, knowledge about the molecular organization level, used in these models, is not enough to explain phenomena such as the driving forces of these metabolic networks. Dynamic Energy Budget (DEB) theory captures the quantitative aspects of the organization of metabolism at the organism level in a way that is non-species-specific. This imposes constraints on the sub-organismal organization that are not present in the bottom-up approach of systems biology. We use in vivo data of lactic acid bacteria under various conditions to compare some aspects of BST and DEB approaches. Due to the large number of parameters to be estimated in the BST model, we applied powerful parameter identification techniques. Both models fitted equally well, but the BST model employs more parameters. The DEB model uses similarities of processes under growth and no-growth conditions and under aerobic and anaerobic conditions, which reduce the number of parameters. This paper discusses some future directions for the integration of knowledge from these two rich and promising areas, working top-down and bottom-up simultaneously. This middle-out approach is expected to bring new ideas and insights to both areas in terms of describing how living organisms operate.

Towards enhanced galactose utilization by Lactococcus lactis

Accumulation of galactose in dairy products due to partial lactose fermentation by lactic acid bacteria yields poor-quality products and precludes their consumption by individuals suffering from galactosemia. This study aimed at extending our knowledge of galactose metabolism in Lactococcus lactis, with the final goal of tailoring strains for enhanced galactose consumption. We used directed genetically engineered strains to examine galactose utilization in strain NZ9000 via the chromosomal Leloir pathway (gal genes) or the plasmid-encoded tagatose 6-phosphate (Tag6P) pathway (lac genes). Galactokinase (GalK), but not galactose permease (GalP), is essential for growth on galactose. This finding led to the discovery of an alternative route, comprising a galactose phosphotransferase system (PTS) and a phosphatase, for galactose dissimilation in NZ9000. Introduction of the Tag6P pathway in a galPMK mutant restored the ability to metabolize galactose but did not sustain growth on this sugar. The latter strain was used to prove that lacFE, encoding the lactose PTS, is necessary for galactose metabolism, thus implicating this transporter in galactose uptake. Both PTS transporters have a low affinity for galactose, while GalP displays a high affinity for the sugar. Furthermore, the GalP/Leloir route supported the highest galactose consumption rate. To further increase this rate, we overexpressed galPMKT, but this led to a substantial accumulation of α-galactose 1-phosphate and α-glucose 1-phosphate, pointing to a bottleneck at the level of α-phosphoglucomutase. Overexpression of a gene encoding α-phosphoglucomutase alone or in combination with gal genes yielded strains with galactose consumption rates enhanced up to 50% relative to that of NZ9000. Approaches to further improve galactose metabolism are discussed.

Inhibition kinetics of catabolic dehydrogenases by elevated moieties of ATP and ADP--implication for a new regulation mechanism in Lactococcus lactis

ATP and ADP inhibit, in varying degrees, several dehydrogenases of the central carbon metabolism of Lactococcus lactis ATCC 19435 in vitro, i.e. glyceraldehyde-3-phosphate dehydrogenase (GAPDH), lactate dehydrogenase (LDH) and alcohol dehydrogenase (ADH). Here we demonstrate mixed inhibition for GAPDH and competitive inhibition for LDH and ADH by adenine nucleotides in single inhibition studies. The nonlinear negative co-operativity was best modelled with Hill-type kinetics, showing greater flexibility than the usual parabolic inhibition equation. Because these natural inhibitors are present simultaneously in the cytoplasm, multiple inhibition kinetics was determined for each dehydrogenase. For ADH and LDH, the inhibitor combinations ATP plus NAD and ADP plus NAD are indifferent to each other. Model discrimination suggested that the weak allosteric inhibition of GAPDH had no relevance when multiple inhibitors are present. Interestingly, with ADH and GAPDH the combination of ATP and ADP exhibits lower dissociation constants than with either inhibitor alone. Moreover, the concerted inhibition of ADH and GAPDH, but not of LDH, shows synergy between the two nucleotides. Similar kinetics, but without synergies, were found for horse liver and yeast ADHs, indicating that dehydrogenases can be modulated by these nucleotides in a nonlinear manner in many organisms. The action of an elevated pool of ATP and ADP may effectively inactivate lactococcal ADH, but not GAPDH and LDH, providing leverage for the observed metabolic shift to homolactic acid formation in lactococcal resting cells on maltose. Therefore, we interpret these results as a regulation mechanism contributing to readjusting the flux of ATP production in L. lactis.

Enzyme catalyzed reactions: from experiment to computational mechanism reconstruction

The traditional experimental practice in enzyme kinetics involves the measurement of substrate or product concentrations as a function of time. Advances in computing have produced novel approaches for modeling enzyme catalyzed reactions from time course data. One example of such an approach is the selection of appropriate chemical reactions that best fit the data. A common limitation of this approach resides in the number of chemical species considered. The number of possible chemical reactions grows exponentially with the number of chemical species, which makes difficult to select reactions that uniquely describe the data and diminishes the efficiency of the methods. In addition, a method's performance is also dependent on several quantitative and qualitative properties of the time course data, of which we know very little. This information is important to experimentalists as it could allow them to setup their experiments in ways that optimize the network reconstruction. We have previously described a method for inferring reaction mechanisms and kinetic rate parameters from time course data. Here, we address the limitations in the number of chemical reactions by allowing the introduction of information about chemical interactions. We also address the unknown properties of the input data by determining experimental data properties that maximize our method's performance. We investigate the following properties: initial substrate-enzyme concentration ratios; initial substrate-enzyme concentration variation ranges; number of data points; number of different experiments (time courses); and noise. We test the method using data generated in silico from the Michaelis-Menten and the Hartley-Kilby reaction mechanisms. Our results demonstrate the importance of experimental design for time course assays that has not been considered in experimental protocols. These considerations can have far reaching implications for the computational mechanism reconstruction process.

Metabolomics for biotransformations: Intracellular redox cofactor analysis and enzyme kinetics offer insight into whole cell processes

For redox reactions catalyzed by microbial cells the analysis of involved cofactors is of special interest since the availability of cofactors such as NADH or NADPH is often limiting and crucial for the biotransformation efficiency. The measurement of these cofactors has usually been carried out using spectrophotometric cycling assays. Today LC-MS/MS methods have become a valuable tool for the identification and quantification of intracellular metabolites. This technology has been adapted to measure all four nicotinamide cofactors (NAD, NADP, NADH, and NADPH) during a whole cell biotransformation process catalyzed by recombinant Escherichia coli cells. The cells overexpressing an alcohol dehydrogenase from Lactobacillus brevis were used for the reduction of methyl acetoacetate (MAA) with substrate-coupled cofactor regeneration by oxidation of 2-propanol. To test the reliability of the measurement the data were evaluated using a process model. This model was derived using the measured concentrations of reactants and cofactors for initiation as well as the kinetic constants from in vitro measurements of the isolated enzyme. This model proves to be highly effective in the process development for a whole cell redox biotransformation in predicting both the right concentrations of cofactors and reactants in a batch and in a CSTR process as well as the right in vivo expression level of the enzyme. Moreover, a sensitivity analysis identifies the cofactor regeneration reaction as the limiting step in case for the reduction of MAA to the corresponding product (R)-methyl 3-hydroxybutyrate. Using the combination of in vitro enzyme kinetic measurements, measurements of cofactors and reactants and an adequate model initiated by intracellular concentrations of all involved reactants and cofactors the whole cell biotransformation process can be understood quantitatively.

Pyruvate formate lyase is required for pneumococcal fermentative metabolism and virulence

Knowledge of the in vivo physiology and metabolism of Streptococcus pneumoniae is limited, even though pneumococci rely on efficient acquisition and metabolism of the host nutrients for growth and survival. Because the nutrient-limited, hypoxic host tissues favor mixed-acid fermentation, we studied the role of the pneumococcal pyruvate formate lyase (PFL), a key enzyme in mixed-acid fermentation, which is activated posttranslationally by PFL-activating enzyme (PFL-AE). Mutations were introduced to two putative pfl genes, SPD0235 and SPD0420, and two putative pflA genes, SPD0229 and SPD1774. End-product analysis showed that there was no formate, the main end product of the reaction catalyzed by PFL, produced by mutants defective in SPD0420 and SPD1774, indicating that SPD0420 codes for PFL and SPD1774 for putative PFL-AE. Expression of SPD0420 was elevated in galactose-containing medium in anaerobiosis compared to growth in glucose, and the mutation of SPD0420 resulted in the upregulation of fba and pyk, encoding, respectively, fructose 1,6-bisphosphate aldolase and pyruvate kinase, under the same conditions. In addition, an altered fatty acid composition was detected in SPD0420 and SPD1774 mutants. Mice infected intranasally with the SPD0420 and SPD1774 mutants survived significantly longer than the wild type-infected cohort, and bacteremia developed later in the mutant cohort than in the wild type-infected group. Furthermore, the numbers of CFU of the SPD0420 mutant were lower in the nasopharynx and the lungs after intranasal infection, and fewer numbers of mutant CFU than of wild-type CFU were recovered from blood specimens after intravenous infection. The results demonstrate that there is a direct link between pneumococcal fermentative metabolism and virulence.

Recent developments in parameter estimation and structure identification of biochemical and genomic systems

The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.

Efficient, sparse biological network determination

BACKGROUND

Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open.

RESULTS

We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections) while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have - something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters.

CONCLUSION

The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational) and uncertainties in the data are taken into account. It also produces a network structure that can explain the real experimental data of L. lactis and is similar to the one found in the literature. Numerical methods based on Linear Programming can therefore help determine efficiently the network structure of biological systems from large data sets. The overall objective of this work is to provide methods to increase our understanding of complex biochemical systems, particularly through their interconnection and their non-equilibrium behavior.

Multiple control of the acetate pathway in Lactococcus lactis under aeration by catabolite repression and metabolites

To explore the factors controlling metabolite formation under aeration in Lactococcus lactis, metabolic patterns, enzymatic activities, and transcriptional profiles of genes involved in the aerobic pathway for acetate anabolism were compared between a parental L. lactis strain and its NADH-oxidase-overproducer derivative. Deregulated catabolite repression mutans in the ccpA or pstH genes, encoding CcpA or its co-activator HPr, respectively, were compared with a parental strain, as well. Although the NADH-oxidase activity was derepressed in ccpA, but not in the pstH background, a mixed fermentation was displayed by either mutant, with a higher acetate production in the pstH variant. Moreover, transcription of genes encoding phosphotransacetylase and acetate kinase were derepressed, and the corresponding enzymatic activities increased, in both catabolite repression mutants. These results and the dependence on carbon source for acetate production in the NADH-oxidase-overproducer support the conclusion that catabolite repression, rather than NADH oxidation, plays a critical role to control acetate production. Furthermore, fructose 1,6-bisphosphate influenced the in vitro phosphotransacetylase and acetate kinase activities, while the former was sensitive to diacetyl. Our study strongly supports the model that, under aerobic conditions, the homolactic fermentation in L. lactis MG1363 is maintained by CcpA-mediated repression of mixed acid fermentation.

Characterization of the individual glucose uptake systems of Lactococcus lactis: mannose-PTS, cellobiose-PTS and the novel GlcU permease

According to previous reports, Lactococcus lactis imports glucose via two distinct phosphoenolpyruvate:phosphotransferase systems (mannose-PTS and cellobiose-PTS) and one or more unknown non-PTS permease(s). GlcU was identified as the sole non-PTS permease involved in the transport of glucose. Additionally, the biochemical properties of PTS(Man), PTS(Cel) and GlcU were characterized in double knockout mutants with glucose uptake restricted to a single system. Transport susceptibility to protonophores indicated that glucose uptake via GlcU is proton-motive force dependent. Competition assays revealed a high specificity of GlcU for glucose. Furthermore, the permease has low affinity for glucose and displays strong preference for the beta-anomer as shown by the profiles of consumption of the two glucose anomers studied by (13)C-NMR. Similar kinetic properties were found for PTS(Cel), while PTS(Man) is a high-affinity system recognizing equally well the two anomeric forms of glucose. Transcripts of the genes encoding the three transporters are present simultaneously in the parent strain NZ9000 as shown by reverse transcription-PCR. Investigation of the distribution of GlcU homologues among bacteria showed that these proteins are restricted to the low-GC Gram-positive Firmicutes. This work completes the identification of the glucose transport systems in L. lactis MG1363.

System estimation from metabolic time-series data

MOTIVATION

At the center of computational systems biology are mathematical models that capture the dynamics of biological systems and offer novel insights. The bottleneck in the construction of these models is presently the identification of model parameters that make the model consistent with observed data. Dynamic flux estimation (DFE) is a novel methodological framework for estimating parameters for models of metabolic systems from time-series data. DFE consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model-based mathematical characterization of process representations. The model-free phase reveals inconsistencies within the data, and between data and the alleged system topology, while the model-based phase allows quantitative diagnostics of whether--or to what degree--the assumed mathematical formulations are appropriate or in need of improvement. Hallmarks of DFE are the facility to: diagnose data and model consistency; circumvent undue compensation of errors; determine functional representations of fluxes uncontaminated by errors in other fluxes and pinpoint sources of remaining errors. Our results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data. Its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.

The metabolic pH response in Lactococcus lactis: an integrative experimental and modelling approach

Lactococcus lactis is characterised by its ability to convert sugar almost exclusively into lactic acid. This organic acid lowers extracellular pH, thus inhibiting growth of competing bacteria. Although L. lactis is able to survive at low pH, glycolysis is strongly affected at pH values below 5, showing reduced rate of glucose consumption. Therefore, in order to deepen our knowledge on central metabolism of L. lactis in natural or industrial environments, an existing full scale kinetic model of glucose metabolism was extended to simulate the impact of lowering extracellular pH in non-growing cells of L. lactis MG1363. Validation of the model was performed using (13)C NMR, (31)P NMR, and nicotinamide adenine dinucleotide hydride auto-fluorescence data of living cells metabolizing glucose at different pH values. The changes in the rate of glycolysis as well as in the dynamics of intracellular metabolites (NADH, nucleotide triphosphates and fructose-1,6-bisphosphate) observed during glucose pulse experiments were reproduced by model simulations. The model allowed investigation of key enzymes at sub-optimum extracellular pH, simulating their response to changing conditions in the complex network, as opposed to in vitro enzyme studies. The model predicts that a major cause of the decrease in the glycolytic rate, upon lowering the extracellular pH, is the lower pool of phosphoenolpyruvate available to fuel glucose uptake via the phosphoenolpyruvate-dependent transport system.

Non-equivalence of hydrogen transfer from glucose to the pro-R and pro-S methylene positions of ethanol during fermentation by Leuconostoc mesenteroides quantified by 2H NMR at natural abundance

The anaerobic fermentation of glucose by Leuconostoc mesenteroides via the reductive pentose phosphate pathway leads to the accumulation of lactic acid and ethanol. The isotope redistribution coefficients (a(ij)) that characterize the specific derivation of each hydrogen atom in ethanol in relation to the non-exchangeable hydrogen atoms in glucose and the medium water have been determined using quantitative (2)H NMR. First, it is confirmed that the hydrogens of the methylene group are related only to the 1 and 3 positions of glucose via the NAD(P)H pool and not to the 4 position, in contrast to ethanol produced by Saccharomyces cerevisiae. Second, it is found that the conversion factors (C(f)) for the transfer of hydrogen to the pro-S and pro-R positions of the methylene group are not equivalent: the C(f)-1-R:C(f)-1-S ratio is 2.1, whereas the C(f)-3-R:C(f)-3-S ratio is 0.8. It is shown that this non-equivalence is not determined by the stereochemistry of the terminal NADH- and NADPH-dependent alcohol dehydrogenases, but is dependent on the cofactor selectivities of the reductive and oxidative steps of the reduced nucleotide cycle.

The lactate dehydrogenases encoded by the ldh and ldhB genes in Lactococcus lactis exhibit distinct regulation and catalytic properties - comparative modeling to probe the molecular basis

Lactococcus lactis FI9078, a construct carrying a disruption of the ldh gene, converted approximately 90% of glucose into lactic acid, like the parental strain MG1363. This unexpected lactate dehydrogenase activity was purified, and ldhB was identified as the gene encoding this protein. The activation of ldhB was explained by the insertion of an IS905-like element that created a hybrid promoter in the intergenic region upstream of ldhB. The biochemical and kinetic properties of this alternative lactate dehydrogenase (LDHB) were compared to those of the ldh-encoded enzyme (LDH), purified from the parental strain. In contrast to LDH, the affinity of LDHB for NADH and the activation constant for fructose 1,6-bisphosphate were strongly dependent on pH. The activation constant increased 700-fold, whereas the K(m) for NADH increased more than 10-fold, in the pH range 5.5-7.2. The two enzymes also exhibited different pH profiles for maximal activity. Moreover, inorganic phosphate acted as a strong activator of LDHB. The impact of replacing LDH by LDHB on the physiology of L. lactis was assessed by monitoring the evolution of the pools of glycolytic intermediates and cofactors during the metabolism of glucose by in vivo NMR. Structural analysis by comparative modeling of the two proteins showed that LDH has a slightly larger negative charge than LDHB and a greater concentration of positive charges at the interface between monomers. The calculated pH titration curves of the catalytic histidine residues explain why LDH maintains its activity at low pH as compared to LDHB, the histidines in LDH showing larger pH titration ranges.

Reconstructing biochemical pathways from time course data

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Expression of plant flavor genes in Lactococcus lactis

Lactic acid bacteria, such as Lactococcus lactis, are attractive hosts for the production of plant-bioactive compounds because of their food grade status, efficient expression, and metabolic engineering tools. Two genes from strawberry (Fragaria x ananassa), encoding an alcohol acyltransferase (SAAT) and a linalool/nerolidol synthase (FaNES), were cloned in L. lactis and actively expressed using the nisin-induced expression system. The specific activity of SAAT could be improved threefold (up to 564 pmol octyl acetate h-1 mg protein-1) by increasing the concentration of tRNA1Arg, which is a rare tRNA molecule in L. lactis. Fermentation tests with GM17 medium and milk with recombinant L. lactis strains expressing SAAT or FaNES resulted in the production of octyl acetate (1.9 microM) and linalool (85 nM) to levels above their odor thresholds in water. The results illustrate the potential of the application of L. lactis as a food grade expression platform for the recombinant production of proteins and bioactive compounds from plants.

An automated procedure for the extraction of metabolic network information from time series data

Novel high-throughput measurement techniques in vivo are beginning to produce dense high-quality time series which can be used to investigate the structure and regulation of biochemical networks. We propose an automated information extraction procedure which takes advantage of the unique S-system structure and supports model building from time traces, curve fitting, model selection, and structure identification based on parameter estimation. The procedure comprises of three modules: model Generation, parameter estimation or model Fitting, and model Selection (GFS algorithm). The GFS algorithm has been implemented in MATLAB and returns a list of candidate S-systems which adequately explain the data and guides the search to the most plausible model for the time series under study. By combining two strategies (namely decoupling and limiting connectivity) with methods of data smoothing, the proposed algorithm is scalable up to realistic situations of moderate size. We illustrate the proposed methodology with a didactic example.

The α-Phosphoglucomutase of Lactococcus lactis Is Unrelated to the α-d-Phosphohexomutase Superfamily and Is Encoded by the Essential Gene pgmH

alpha-Phosphoglucomutase (alpha-PGM) plays an important role in carbohydrate metabolism by catalyzing the reversible conversion of alpha-glucose 1-phosphate to glucose 6-phosphate. Isolation of alpha-PGM activity from cell extracts of Lactococcus lactis strain MG1363 led to the conclusion that this activity is encoded by yfgH, herein renamed pgmH. Its gene product has no sequence homology to proteins in the alpha-d-phosphohexomutase superfamily and is instead related to the eukaryotic phosphomannomutases within the haloacid dehalogenase superfamily. In contrast to known bacterial alpha-PGMs, this 28-kDa enzyme is highly specific for alpha-glucose 1-phosphate and glucose 6-phosphate and showed no activity for mannose phosphate. To elucidate the function of pgmH, the metabolism of glucose and galactose was characterized in mutants overproducing or with a deficiency of alpha-PGM activity. Overproduction of alpha-PGM led to increased glycolytic flux and growth rate on galactose. Despite several attempts, we failed to obtain a deletion mutant of pgmH. The essentiality of this gene was proven by using a conditional knock-out strain in which a native copy of the gene was provided in trans under the control of the nisin promoter. Growth of this strain was severely impaired when alpha-PGM activity was below the control level. We show that the novel L. lactis alpha-PGM is the only enzyme that mediates the interconversion of alpha-glucose 1-phosphate to glucose 6-phosphate and is essential for growth.

Parameter estimation using Simulated Annealing for S-system models of biochemical networks

MOTIVATION

High-throughput technologies now allow the acquisition of biological data, such as comprehensive biochemical time-courses at unprecedented rates. These temporal profiles carry topological and kinetic information regarding the biochemical network from which they were drawn. Retrieving this information will require systematic application of both experimental and computational methods.

RESULTS

S-systems are non-linear mathematical approximative models based on the power-law formalism. They provide a general framework for the simulation of integrated biological systems exhibiting complex dynamics, such as genetic circuits, signal transduction and metabolic networks. We describe how the heuristic optimization technique simulated annealing (SA) can be effectively used for estimating the parameters of S-systems from time-course biochemical data. We demonstrate our methods using three artificial networks designed to simulate different network topologies and behavior. We then end with an application to a real biochemical network by creating a working model for the cadBA system in Escherichia coli.

AVAILABILITY

The source code written in C++ is available at http://www.engg.upd.edu.ph/~naval/bioinformcode.html. All the necessary programs including the required compiler are described in a document archived with the source code.

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Natural sweetening of food products by engineering Lactococcus lactis for glucose production

We show that sweetening of food products by natural fermentation can be achieved by a combined metabolic engineering and transcriptome analysis approach. A Lactococcus lactis ssp. cremoris strain was constructed in which glucose metabolism was completely disrupted by deletion of the genes coding for glucokinase (glk), EII(man/glc) (ptnABCD), and the newly discovered glucose-PTS EII(cel) (ptcBAC). After introducing the lactose metabolic genes, the deletion strain could solely ferment the galactose moiety of lactose, while the glucose moiety accumulated extracellularly. Additionally, less lactose remained in the medium after fermentation. The resulting strain can be used for in situ production of glucose, circumventing the need to add sweeteners as additional ingredients to dairy products. Moreover, the enhanced removal of lactose achieved by this strain could be very useful in the manufacture of products for lactose intolerant individuals.

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles

BACKGROUND

In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems.

RESULTS

The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations.

CONCLUSION

The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles.

Glyceraldehyde-3-phosphate dehydrogenase regulation in Lactococcus lactis ssp. cremoris MG1363 or relA mutant at low pH

AIMS

To analyse the phenotype of a relA acid-resistant mutant of Lactococcus lactis ssp. cremoris MG1363, and to compare the glyceraldehyde-3-phosphate dehydrogenase regulation in both strains.

METHODS AND RESULTS

Lactococcus lactis ssp. cremoris MG1363 and the relA mutant affected in the (p)ppGpp synthetase were grown in a series of batch-mode fermentation at different pH-regulated conditions with glucose as carbon substrate. All the determinants of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) regulation were quantified. In L. lactis MG1363, the GAPDH was strongly inhibited in vitro by decreased pH values, but this inhibition was totally compensated in vivo by the lower NADH/NAD+ ratio and more efficiently by the important increase in the intracellular amount of GAPDH. In contrast to the wild type, GAPDH activity of the relA strain was not increased when grown at low pH but the level of GAPDH remained constitutively high. However, pH homeostasis was not improved in the relA mutant and it grew slower and exhibited a lower glycolytic flux than the wild-type strain at low pH.

CONCLUSIONS

Despite a better resistance to acid stress, the increased survival in L. lactis relA mutant at low pH was not related with an improved pH homeostasis but was associated with a diminished capacity to maintain a high flux through glycolysis.

SIGNIFICANCE AND IMPACT OF THE STUDY

The phenotype of a strong acid-resistant L. lactis strain was established in acid conditions and some key metabolic parameters compared with the wild type. This analysis led to the conclusion that growth and survival seem to be antinomic parameters, since improving one of them leads to a decrease in the other one.

The simultaneous measurement of nicotinamide adenine dinucleotide and related compounds by liquid chromatography/electrospray ionization tandem mass spectrometry

We have developed a liquid chromatographic-tandem mass spectrometric method that is sensitive and specific and that simultaneously measures cellular NAD(+) and related compounds. Using this method, NAD(+), NAAD, NMN, NAMN, NAM, NA, ADPR, and 5'AMP were first separated over a reverse-phase high-performance liquid chromatography resin in a mobile ammonium formate-methanol linear gradient. Then each compound was ionized at an electrospray source and detected in the positive multiple reaction monitoring mode of a triple-quadrupole tandem mass spectrometer. We found a good linear response for each NAD(+)-related compound. The limits of quantification for NAD(+) and related compounds range from 0.1 to 1 pmol. The extraction efficiency of NAD(+) and related compounds from mouse erythrocytes is between 84 and 114%. The coefficients of variation for the analyses are all less than 6%. Using our method, we measured, in a single analysis, the amounts of NMN, NAMN, NAD(+), and 5'AMP present in mouse erythrocytes. Additionally, this is the first report of a direct determination of the amounts of NMN and NAMN present in any type of cell. These results indicate that our method sensitively, specifically, and simultaneously measures cellular NAD(+) and related compounds.

Cofactor engineering in Saccharomyces cerevisiae: Expression of a H2O-forming NADH oxidase and impact on redox metabolism

The pyridine nucleotides NAD(H) and NADP(H) play major roles in the formation of by-products. To analyse how Saccharomyces cerevisiae (S. cerevisiae) metabolism during growth on glucose might be altered when intracellular NADH pool is decreased, we expressed noxE encoding a water-forming NADH oxidase from Lactococcus lactis (L. lactis) in the S. cerevisiae strain V5. During batch fermentation under controlled microaeration conditions, expression of the NADH oxidase under the control of a yeast promoter lead to large decreases in the intracellular NADH concentration (five-fold) and NADH/NAD+ ratio (six-fold). This increased NADH consumption caused a large redistribution of metabolic fluxes. The ethanol, glycerol, succinate and hydroxyglutarate yields were significantly reduced as a result of the lower NADH availability, whereas the formation of more oxidized metabolites, acetaldehyde, acetate and acetoin was favoured. The biomass yield was low and consumption of glucose, at concentration above 10%, was impaired. The metabolic redistribution in cells expressing the NADH oxidase was a consequence of the maintenance of a redox balance and of the management of acetaldehyde formation, which accumulated at toxic levels early in the process.

Parameter estimation in modulated, unbranched reaction chains within biochemical systems

Modern biology is increasingly developing techniques for measuring time series of global gene expression and of many simultaneous proteins or metabolites. These data contain valuable information on the dynamics of cells, which has to be extracted with computational means. Given a suitable mathematical model, this extraction is in principle a straightforward regression task, but the complexity and nonlinearity of the differential equations that describe biological systems cause severe difficulties when the systems are of realistic size. We propose a method of stepwise regression that can be applied effectively to linear portions of pathways. The method may be combined with other estimation methods and either directly yields reasonable parameter estimates or at least provides appropriate start values for subsequent nonlinear search algorithms. We illustrate the method with the analysis of in vivo NMR data describing the dynamics of glycolytic metabolites in Lactococcus lactis.

Sorbitol synthesis by an engineered Lactobacillus casei strain expressing a sorbitol-6-phosphate dehydrogenase gene within the lactose operon

Sorbitol is claimed to have important health-promoting effects and Lactobacillus casei is a lactic acid bacterium relevant as probiotic and used as a cheese starter culture. A sorbitol-producing L. casei strain might therefore be of considerable interest in the food industry. A recombinant strain of L. casei was constructed by the integration of a d-sorbitol-6-phosphate dehydrogenase-encoding gene (gutF) in the chromosomal lactose operon (strain BL232). gutF expression in this strain followed the same regulation as that of the lac genes, that is, it was repressed by glucose and induced by lactose. (13)C-nuclear magnetic resonance analysis of supernatants of BL232 resting cells demonstrated that, when pre-grown on lactose, cells were able to synthesize sorbitol from glucose. Inactivation of the l-lactate dehydrogenase gene in BL232 led to an increase in sorbitol production, suggesting that the engineered route provides an alternative pathway for NAD(+) regeneration.

Quantitative interdependence of coeffectors, CcpA and cre in carbon catabolite regulation of Bacillus subtilis

The phosphoproteins HPrSerP and CrhP are the main effectors for CcpA-mediated carbon catabolite regulation (CCR) in Bacillus subtilis. Complexes of CcpA with HPrSerP or CrhP regulate genes by binding to the catabolite responsive elements (cre). We present a quantitative analysis of HPrSerP and CrhP interaction with CcpA by surface plasmon resonance (SPR) revealing small and similar equilibrium constants of 4.8 +/- 0.4 microm for HPrSerP-CcpA and 19.1 +/- 2.5 microm for CrhP-CcpA complex dissociation. Forty millimolar fructose-1,6-bisphosphate (FBP) or glucose-6-phosphate (Glc6-P) increases the affinity of HPrSerP to CcpA at least twofold, but have no effect on CrhP-CcpA binding. Saturation of binding of CcpA to cre as studied by fluorescence and SPR is dependent on 50 microm of HPrSerP or > 200 microm CrhP. The rate constants of HPrSerP-CcpA-cre complex formation are k(a) = 3 +/- 1 x 10(6) m(-1).s(-1) and k(d) = 2.0 +/- 0.4 x 10(-3).s(-1), resulting in a K(D) of 0.6 +/- 0.3 nm. FBP and Glc6-P stimulate CcpA-HPrSerP but not CcpA-CrhP binding to cre. Maximal HPrSerP-CcpA-cre complex formation in the presence of 10 mm FBP requires about 10-fold less HPrSerP. These data suggest a specific role for FBP and Glc6-P in enhancing only HPrSerP-mediated CCR.

On-line monitoring of CO2 production inLactococcus lactis during physiological pH decrease using membrane inlet mass spectrometry with dynamic pH calibration

Monitoring CO2 production in systems, where pH is changing with time is hampered by the chemical behavior and pH-dependent volatility of this compound. In this article, we present the first method where the concentration and production rate of dissolved CO2 can be monitored directly, continuously, and quantitatively under conditions where pH changes rapidly ( approximately 2 units in 15 min). The method corrects membrane inlet mass spectrometry (MIMS) measurements of CO2 for pH dependency using on-line pH analysis and an experimentally established calibration model. It is valid within the pH range of 3.5 to 7, despite pH-dependent calibration constants that vary in a non-linear fashion with more than a factor of 3 in this interval. The method made it possible to determine the carbon dioxide production during Lactococcus lactis fermentations, where pH drops up to 3 units during the fermentation. The accuracy was approximately 5%. We used the method to investigate the effect of initial extracellular pH on carbon dioxide production during anarobic glucose fermentation by non-growing Lactocoocus lactis and demonstrated that the carbon dioxide production rate increases considerably, when the initial pH was increased from 6 to 6.8.

Transcriptional analysis of biofilm formation processes in the anaerobic, hyperthermophilic bacterium Thermotoga maritima

Thermotoga maritima, a fermentative, anaerobic, hyperthermophilic bacterium, was found to attach to bioreactor glass walls, nylon mesh, and polycarbonate filters during chemostat cultivation on maltose-based media at 80 degrees C. A whole-genome cDNA microarray was used to examine differential expression patterns between biofilm and planktonic populations. Mixed-model statistical analysis revealed differential expression (twofold or more) of 114 open reading frames in sessile cells (6% of the genome), over a third of which were initially annotated as hypothetical proteins in the T. maritima genome. Among the previously annotated genes in the T. maritima genome, which showed expression changes during biofilm growth, were several that corresponded to biofilm formation genes identified in mesophilic bacteria (i.e., Pseudomonas species, Escherichia coli, and Staphylococcus epidermidis). Most notably, T. maritima biofilm-bound cells exhibited increased transcription of genes involved in iron and sulfur transport, as well as in biosynthesis of cysteine, thiamine, NAD, and isoprenoid side chains of quinones. These findings were all consistent with the up-regulation of iron-sulfur cluster assembly and repair functions in biofilm cells. Significant up-regulation of several beta-specific glycosidases was also noted in biofilm cells, despite the fact that maltose was the primary carbon source fed to the chemostat. The reasons for increased beta-glycosidase levels are unclear but are likely related to the processing of biofilm-based polysaccharides. In addition to revealing insights into the phenotype of sessile T. maritima communities, the methodology developed here can be extended to study other anaerobic biofilm formation processes as well as to examine aspects of microbial ecology in hydrothermal environments.

The pool of ADP and ATP regulates anaerobic product formation in resting cells of Lactococcus lactis

Lactococcus lactis grows homofermentatively on glucose, while its growth on maltose under anaerobic conditions results in mixed acid product formation in which formate, acetate, and ethanol are formed in addition to lactate. Maltose was used as a carbon source to study mixed acid product formation as a function of the growth rate. In batch and nitrogen-limited chemostat cultures mixed acid product formation was shown to be linked to the growth rate, and homolactic fermentation occurred only in resting cells. Two of the four lactococcal strains investigated with maltose, L. lactis 65.1 and MG1363, showed more pronounced mixed acid product formation during growth than L. lactis ATCC 19435 or IL-1403. In resting cell experiments all four strains exhibited homolactic fermentation. In resting cells the intracellular concentrations of ADP, ATP, and fructose 1,6-bisphosphate were increased and the concentration of P(i) was decreased compared with the concentrations in growing cells. Addition of an ionophore (monensin or valinomycin) to resting cultures of L. lactis 65.1 induced mixed acid product formation concomitant with decreases in the ADP, ATP, and fructose 1,6-bisphosphate concentrations. ADP and ATP were shown to inhibit glyceraldehyde-3-phosphate dehydrogenase, lactate dehydrogenase, and alcohol dehydrogenase in vitro. Alcohol dehydrogenase was the most sensitive enzyme and was totally inhibited at an adenine nucleotide concentration of 16 mM, which is close to the sum of the intracellular concentrations of ADP and ATP of resting cells. This inhibition of alcohol dehydrogenase might be partially responsible for the homolactic behavior of resting cells. A hypothesis regarding the level of the ATP-ADP pool as a regulating mechanism for the glycolytic flux and product formation in L. lactis is discussed.

Natural-abundance isotope ratio mass spectrometry as a means of evaluating carbon redistribution during glucose-citrate cofermentation by Lactococcus lactis

The cometabolism of citrate and glucose by growing Lactococcus lactis ssp. lactis bv. diacetylactis was studied using a natural-abundance stable isotope technique. By a judicious choice of substrates differing slightly in their 13C/12C ratios, the simultaneous metabolism of citrate and glucose to a range of compounds was analysed. These end-products include lactate, acetate, formate, diacetyl and acetoin. All these products have pyruvate as a common intermediate. With the objective of estimating the degree to which glucose and citrate metabolism through pyruvate may be differentially regulated, the delta13C values of the products accumulated over a wide range of concentrations of citrate and glucose were compared. It was found that, whereas the relative accumulation of different products responds to both the substrate concentration and the ratio between the substrates, the delta13C values of the products primarily reflect the availability of the two substrates over the entire range examined. It can be concluded that in actively growing L. lactis the maintenance of pyruvate homeostasis takes precedence over the redox status of the cells as a regulatory factor.

Analysis of dynamic labeling data

Comprehensive assessments of the organization and regulation of metabolic pathways cannot be limited to steady-state measurements alone but require dynamic time series data. One experimental means of generating such data consists of radioactively labeling precursors and measuring their fate over time. While labeling experiments belong to the standard repertoire of biological laboratory techniques, corresponding mathematical tools for analyzing the non-linear dynamics of tracers are scarce. The article addresses this issue, using Biochemical Systems Theory as the modeling framework. The description of the dynamics of labeled metabolites alone is difficult, but it is demonstrated that these difficulties are easily overcome by setting up dynamic models in two or three blocks, one for the kinetics of the total pools, the second just for the labeled portions, and the third, optional, block for the remaining unlabeled components. Since the dynamic model is not limited in complexity and can account for linear pathways, converging and diverging branches, cycles, and the various observed modes of regulation, the proposed method of non-linear tracer analysis is rather general and permits simulations of most standard labeling experiments, both at steady state and during transients.

Priming nonlinear searches for pathway identification

BACKGROUND

Dense time series of metabolite concentrations or of the expression patterns of proteins may be available in the near future as a result of the rapid development of novel, high-throughput experimental techniques. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying metabolic or proteomic networks. The extraction of this information is a challenging task because it usually requires nonlinear estimation methods that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can greatly accelerate the search process. In this article, we propose to obtain such guesses by preprocessing the temporal profile data and fitting them preliminarily by multivariate linear regression.

RESULTS

The results of a small-scale analysis indicate that the regression coefficients reflect the connectivity of the network quite well. Using the mathematical modeling framework of Biochemical Systems Theory (BST), we also show that the regression coefficients may be translated into constraints on the parameter values of the nonlinear BST model, thereby reducing the parameter search space considerably.

CONCLUSION

The proposed method provides a good approach for obtaining a preliminary network structure from dense time series. This will be more valuable as the systems become larger, because preprocessing and effective priming can significantly limit the search space of parameters defining the network connectivity, thereby facilitating the nonlinear estimation task.

Effect of pyruvate kinase overproduction on glucose metabolism of Lactococcus lactis

Lactococcus lactis strain NZ9000(pNZpyk), which overproduces pyruvate kinase (PK), was constructed. The pNZpyk plasmid carries the P(nisA)-pyk transcriptional fusion, and the overexpression of its pyk gene was accomplished by using the nisin-inducible expression system of the NZ9000 strain. In vivo (13)C- and (31)P-NMR spectroscopy was used to evaluate the effect of this modification on the metabolism of glucose in non-growing cells. A detailed description of the kinetics of glucose, end products, glycolytic intermediates, NAD(+) and NADH was obtained. A 15-fold increase in the level of PK did not increase the overall glycolytic flux, which, on the contrary, was slightly reduced. Significant differences were observed in (i) the level of 3-phosphoglycerate (3-PGA) and phosphoenolpyruvate (PEP), metabolites associated with starvation; (ii) the rate of fructose 1,6-bisphosphate (FBP) depletion upon glucose exhaustion; and (iii) the NAD(+)/NADH ratio during glucose catabolism. In the mutant, the rate of FBP consumption after glucose depletion was notably accelerated under anaerobic conditions, whereas 3-PGA and PEP decreased to undetectable levels. Furthermore, the level of NAD(+) decreased steadily during the utilization of glucose, probably due to the unanticipated reduction in the lactate dehydrogenase activity in comparison with the control strain, NZ9000(pNZ8020). The results show that PK is an important bottleneck to carbon flux only when glucose becomes limiting; in the overproducer this constriction was no longer present, as evidenced by the faster FBP consumption and lack of accumulation of 3-PGA and PEP in anaerobic as well as aerobic conditions. Despite these clear changes, the PK-overproducing strain showed typical homolactic metabolism under anaerobic conditions, as did the strain harbouring the vector plasmid without the pyk insert. However, under an oxygen atmosphere, there was increased channelling of carbon to the production of acetate and acetoin, to the detriment of lactate production.

Reappraisal of the regulation of lactococcal L-lactate dehydrogenase

Lactococcal lactate dehydrogenases (LDHs) are coregulated at the substrate level by at least two mechanisms: the fructose-1,6-biphosphate/phosphate ratio and the NADH/NAD ratio. Among the Lactococcus lactis species, there are strains that are predominantly regulated by the first mechanism (e.g., strain 65.1) or by the second mechanism (e.g., strain NCDO 2118). A more complete model of the kinetics of the regulation of lactococcal LDH is discussed.