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In S. cerevisiae and many other micro-organisms an increase in metabolic efficiency (i.e. ATP yield on carbon) is accompanied by a decrease in growth rate. From a fundamental point of view, studying these yield-rate trade-offs provides insight in for example microbial evolution and cellular regulation. From a biotechnological point of view, increasing the ATP yield on carbon might increase the yield of anabolic products. We here aimed to select S. cerevisiae mutants with an increased biomass yield. Serial propagation of individual cells in water-in-oil emulsions previously enabled the selection of lactococci with increased biomass yields, and adapting this protocol for yeast allowed us to enrich an engineered Crabtree-negative S. cerevisiae strain with a high biomass yield on glucose. When we started the selection with an S. cerevisiae deletion collection, serial propagation in emulsion enriched hxk2Δ and reg1Δ strains with an increased biomass yield on glucose. Surprisingly, a tps1Δ strain was highly abundant in both emulsion- and suspension-propagated populations. In a separate experiment we propagated a chemically mutagenized S. cerevisiae population in emulsion, which resulted in mutants with a higher cell number yield on glucose, but no significantly changed biomass yield. Genome analyses indicate that genes involved in glucose repression and cell cycle processes play a role in the selected phenotypes. The repeated identification of mutations in genes involved in glucose-repression indicates that serial propagation in emulsion is a valuable tool to study metabolic efficiency in S. cerevisiae.

In this study, we have investigated the cheese starter culture as a microbial community through a question: can the metabolic behaviour of a co‐culture be explained by the characterized individual organism that constituted the co‐culture? To address this question, the dairy‐origin lactic acid bacteria Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. lactis, Streptococcus thermophilus and Leuconostoc mesenteroides, commonly used in cheese starter cultures, were grown in pure and four different co‐cultures. We used a dynamic metabolic modelling approach based on the integration of the genome‐scale metabolic networks of the involved organisms to simulate the co‐cultures. The strain‐specific kinetic parameters of dynamic models were estimated using the pure culture experiments and they were subsequently applied to co‐culture models. Biomass, carbon source, lactic acid and most of the amino acid concentration profiles simulated by the co‐culture models fit closely to the experimental results and the co‐culture models explained the mechanisms behind the dynamic microbial abundance. We then applied the co‐culture models to estimate further information on the co‐cultures that could not be obtained by the experimental method used. This includes estimation of the profile of various metabolites in the co‐culture medium such as flavour compounds produced and the individual organism level metabolic exchange flux profiles, which revealed the potential metabolic interactions between organisms in the co‐cultures., Co‐cultures of lactic acid bacteria were investigated using dynamic genome‐scale metabolic modelling techniques. Özcan and co‐workers estimated the metabolic capacity, microbial abundance, potential of flavour compounds production and metabolic interactions of the co‐cultures using the features of individual organisms that constituted the co‐cultures.

Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories., This project has received funding from MCIU/AEI/FEDER, UE (grant references RTI2018-093744-B-C31, RTI2018-093744-B-C32, RTI2018-093744-B-C33, and PID2019-104113RB-I00) and Xunta de Galicia (IN607B 2020/03). R.M. was supported by an FPI grant from the Ministerio de Economía y Competitividad, Spain (reference BES-2016-078202). S.N.M. acknowledges funding from CONICYT Becas Chile grant 72180373. S.N.M. and B.T. acknowledge support from YogurtDesign, EraCoBioTech grant 053.80.733.

Leuconostoc mesenteroides subsp. cremoris is an obligate heterolactic fermentative lactic acid bacterium that is mostly used in industrial dairy fermentations. The phosphoketolase pathway (PKP) is a unique feature of the obligate heterolactic fermentation, which leads to the production of lactate, ethanol, and/or acetate, and the final product profile of PKP highly depends on the energetics and redox state of the organism. Another characteristic of the L. mesenteroides subsp. cremoris is the production of aroma compounds in dairy fermentation, such as in cheese production, through the utilization of citrate. Considering its importance in dairy fermentation, a detailed metabolic characterization of the organism is necessary for its more efficient use in the industry. To this aim, a genome-scale metabolic model of dairy-origin L. mesenteroides subsp. cremoris ATCC 19254 (iLM.c559) was reconstructed to explain the energetics and redox state mechanisms of the organism in full detail. The model includes 559 genes governing 1088 reactions between 1129 metabolites, and the reactions cover citrate utilization and citrate-related flavor metabolism. The model was validated by simulating co-metabolism of glucose and citrate and comparing the in silico results to our experimental results. Model simulations further showed that, in co-metabolism of citrate and glucose, no flavor compounds were produced when citrate could stimulate the formation of biomass. Significant amounts of flavor metabolites (e.g., diacetyl and acetoin) were only produced when citrate could not enhance growth, which suggests that flavor formation only occurs under carbon and ATP excess. The effects of aerobic conditions and different carbon sources on product profiles and growth were also investigated using the reconstructed model. The analyses provided further insights for the growth stimulation and flavor formation mechanisms of the organism. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature. FEN-C-DRP-091116-0498 2214-A Türkiye Bilimsel ve Teknolojik Araştirma Kurumu Funding information This work received financial support from The Scientific and Technological Research Council of Turkey through TUBITAK 2214-A program and the Marmara University Scientific Research Project Fund through Project No: FEN-C-DRP-091116-0498.

Overflow metabolism is ubiquitous in nature, and it is often considered inefficient because it leads to a relatively low biomass yield per consumed carbon. This metabolic strategy has been described as advantageous because it supports high growth rates during nutrient competition.Here we experimentally evolved bacteria without nutrient competition by repeatedly growing and mixing millions of parallel batch cultures of E. coli. Each culture originated from a water-in-oil emulsion droplet seeded with a single cell. Unexpectedly we found that overflow metabolism (acetate production) did not change. Instead the numerical cell yield during the consumption of the accumulated acetate increased as a consequence of a reduction in cell size. Our experiments and a mathematical model show that fast growth and overflow metabolism followed by the consumption of the overflow metabolite, leads to a higher numerical cell yield and therefore a higher fitness compared to full respiration of the substrate. This provides an evolutionary scenario where overflow metabolism can be favourable even in the absence of nutrient competition.

Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade‐offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient‐rich conditions, i.e., multiple available nitrogen sources, such as Lactococcus lactis. Here, we present a proteome‐constrained genome‐scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose‐limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model‐based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient‐rich environments and thus form targets of fitness improvement., A proteome‐constrained genome‐scale metabolic model of Lactococcus lactis (pcLactis) is presented. The model is used to interpret microbial behaviour in nutrient‐rich conditions and predicts testable targets for fitness improvement.

Yeasts constitute over 1500 species with great potential for biotechnology. Still, the yeastSaccharomyces cerevisiaedominates industrial applications and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here we propose a multi-phase multi-objective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. Besides, we proposed a multi-phase multi-objective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism ofS. cerevisiaeandS. uvarumstrains in wine fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed modeling captures the dynamics of metabolism throughout the batch and offers a systematic approach to prospect or engineer novel yeast cell factories.

Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in—and even dominate—the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.

The turbidity of milk prohibits the use of optical density measurements for strain characterizations. This often limits research to laboratory media. Here, we cleared milk through centrifugation to remove insoluble milk solids. This resulted in a clear liquid phase, termed milk serum, in which optical density measurements can be used to track microbial growth until a pH of 5.2 is reached. At pH 5.2 coagulation of the soluble protein occurs, making the medium opaque again. We found that behavior in milk serum was predictive of that in milk for 39 Lactococcus lactis (R2 = 0.81) and to a lesser extent for 42 Lactiplantibacillus plantarum (formerly Lactobacillus plantarum; R2 = 0.49) strains. Hence, milk serum can be used as an optically clear alternative to milk for comparison of microbial growth and metabolic characteristics. Characterization of the growth rate, specific acidification rate for optical density at a wavelength of 600 nm, and the amount of acid produced per unit of biomass for all these strains in milk serum, showed that almost all strains could grow in milk, with higher specific acidification and growth rates of Lc. lactis strains compared with Lb. plantarum strains. Nondairy Lc. lactis isolates had a lower growth and specific acidification rate than dairy isolates. The amount of acid produced per unit biomass was relatively high and similar for Lc. lactis dairy and nondairy isolates, as opposed to Lb. plantarum isolates. Lactococcus lactis ssp. lactis showed slightly lower growth and acidification rates when compared with ssp. cremoris. For Lc. lactis strains a doubling of the specific acidification rate occurred with a doubling of the maximum growth rate. This relation was not found for Lb. plantarum strains, where the acidification rate remained relatively constant across 39 strains with growth rates ranging from 0.2 h−1 to 0.3 h−1. We conclude that milk serum is a valuable alternative to milk for high-throughput strain characterization during milk fermentation.

Lactococcus lactis serves as a paradigm organism for the lactic acid bacteria (LAB). Extensive research into the molecular biology, metabolism and physiology of several model strains of this species has been fundamental for our understanding of the LAB. Genomic studies have provided new insights into the species L. lactis, including the resolution of the genetic basis of its subspecies division, as well as the control mechanisms involved in the fine-tuning of growth rate and energy metabolism. In addition, it has enabled novel approaches to study lactococcal lifestyle adaptations to the dairy application environment, including its adjustment to near-zero growth rates that are particularly relevant in the context of cheese ripening. This review highlights various insights in these areas and exemplifies the strength of combining experimental evolution with functional genomics and bacterial physiology research to expand our fundamental understanding of the L. lactis lifestyle under different environmental conditions.

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

Experimental evolution of microbes has gained lots of interest in recent years, mainly due to the ease of strain characterisation through next-generation sequencing. While evolutionary and systems biologists use experimental evolution to address fundamental questions in their respective fields, studies with lactic acid bacteria are often more directed by applied questions. Insight into population and genome dynamics are valuable for experimental design and data interpretation, and it is becoming increasingly apparent how different constraints limit and govern the outcome of microbial adaptation to a selective environment. Examples for such constraints are the finite membrane and cellular space which can lead to trade-offs between cellular strategies. A powerful perspective is that of resource allocation, which allows cells to maximise fitness. This impacts on metabolic strategies that have different protein/resource demands. This review focuses on parameters and forces that shape cellular optimisation processes and that are determining for the outcome of laboratory evolution experiments. Phenotypic changes of experimentally evolved lactic acid bacteria will be discussed in the light of the selection conditions and the prevailing constraints.

Acetobacter pasteurianus 386B is a candidate functional starter culture for the cocoa bean fermentation process. To allow in silico simulations of its related metabolism in response to different environmental conditions, a genome-scale metabolic model for A. pasteurianus 386B was reconstructed. This is the first genome-scale metabolic model reconstruction for a member of the genus Acetobacter. The metabolic network reconstruction process was based on extensive genome re-annotation and comparative genomics analyses. The information content related to the functional annotation of metabolic enzymes and transporters was placed in a metabolic context by exploring and curating a Pathway/Genome Database of A. pasteurianus 386B using the Pathway Tools software. Metabolic reactions and curated gene-protein-reaction associations were bundled into a genome-scale metabolic model of A. pasteurianus 386B, named iAp386B454, containing 454 genes, 322 reactions, and 296 metabolites embedded in two cellular compartments. The reconstructed model was validated by performing growth experiments in a defined medium, which revealed that lactic acid as the sole carbon source could sustain growth of this strain. Further, the reconstruction of the A. pasteurianus 386B genome-scale metabolic model revealed knowledge gaps concerning the metabolism of this strain, especially related to the biosynthesis of its cell envelope and the presence or absence of metabolite transporters., info:eu-repo/semantics/published

Microbial life usually takes place in a community where individuals interact, by competition for nutrients, cross-feeding, inhibition by end-products, but also by their spatial distribution. Lactic acid bacteria are prominent members of microbial communities responsible for food fermentations. Their niche in a community depends on their own properties as well as those of the other species. Here, we apply a computational approach, which uses only genomic and metagenomic information and functional annotation of genes, to find properties that distinguish a species from others in the community, as well as to follow individual species in a community. We analyzed isolated and sequenced strains from a kefir community, and metagenomes from wine fermentations. We demonstrate how the distinguishing properties of an organism lead to experimentally testable hypotheses concerning the niche and the interactions with other species. We observe, for example, that L. kefiranofaciens, a dominant organism in kefir, stands out among the Lactobacilli because it potentially has more amino acid auxotrophies. Using metagenomic analysis of industrial wine fermentations we investigate the role of an inoculated L. plantarum in malolactic fermentation. We observed that L. plantarum thrives better on white than on red wine fermentations and has the largest number of phosphotransferase system among the bacteria observed in the wine communities. Also, L. plantarum together with Pantoea, Erwinia, Asaia, Gluconobacter, and Komagataeibacter genera had the highest number of genes involved in biosynthesis of amino acids.

Background A central theme in (micro)biology is understanding the molecular basis of fitness i.e. which strategies are successful under which conditions; how do organisms implement such strategies at the molecular level; and which constraints shape the trade-offs between alternative strategies. Highly standardized microbial laboratory evolution experiments are ideally suited to approach these questions. For example, prolonged chemostats provide a constant environment in which the growth rate can be set, and the adaptive process of the organism to such environment can be subsequently characterized. Results We performed parallel laboratory evolution of Lactococcus lactis in chemostats varying the quantitative value of the selective pressure by imposing two different growth rates. A mutation in one specific amino acid residue of the global transcriptional regulator of carbon metabolism, CcpA, was selected in all of the evolution experiments performed. We subsequently showed that this mutation confers predictable fitness improvements at other glucose-limited growth rates as well. In silico protein structural analysis of wild type and evolved CcpA, as well as biochemical and phenotypic assays, provided the underpinning molecular mechanisms that resulted in the specific reprogramming favored in constant environments. Conclusion This study provides a comprehensive understanding of a case of microbial evolution and hints at the wide dynamic range that a single fitness-enhancing mutation may display. It demonstrates how the modulation of a pleiotropic regulator can be used by cells to improve one trait while simultaneously work around other limiting constraints, by fine-tuning the expression of a wide range of cellular processes. Electronic supplementary material The online version of this article (10.1186/s12862-018-1331-x) contains supplementary material, which is available to authorized users.

Fluorescent proteins (FPs) are widely used in many organisms, but are commonly characterised in vitro. However, the in vitro properties may poorly reflect in vivo performance. Therefore, we characterised 27 FPs in vivo using Saccharomyces cerevisiae as model organism. We linked the FPs via a T2A peptide to a control FP, producing equimolar expression of the 2 FPs from 1 plasmid. Using this strategy, we characterised the FPs for brightness, photostability, photochromicity and pH-sensitivity, achieving a comprehensive in vivo characterisation. Many FPs showed different in vivo properties compared to existing in vitro data. Additionally, various FPs were photochromic, which affects readouts due to complex bleaching kinetics. Finally, we codon optimized the best performing FPs for optimal expression in yeast, and found that codon-optimization alters FP characteristics. These FPs improve experimental signal readout, opening new experimental possibilities. Our results may guide future studies in yeast that employ fluorescent proteins.

Since the 1970s, galactose metabolism in Lactococcus lactis has been in debate. Different studies led to diverse outcomes making it difficult to conclude whether galactose uptake was PEP- or ATP- dependent and decide what the exact connection was between galactose and lactose uptake and metabolism. It was shown that some Lactococcus strains possess two galactose-specific systems - a permease and a PTS, even if they lack the lactose utilization plasmid, proving that a lactose-independent PTSGal exists. However, the PTSGal transporter was never identified. Here, with the help of transcriptome analyses and genetic knock-out mutants, we reveal the identities of two low-affinity galactose PTSs. A novel plant-niche-related PTS component Llmg_0963 forming a hybrid transporter Llmg_0963PtcBA and a glucose/mannose-specific PTS are shown to be involved in galactose transport in L. lactis MG1363.

Many organisms have several similar transporters with different affinities for the same substrate. Typically, high-affinity transporters are expressed when substrate is scarce and low-affinity ones when it is abundant. The benefit of using low instead of high-affinity transporters remains unclear, especially when additional nutrient sensors are present. Here, we investigate two hypotheses. It was previously hypothesized that there is a trade-off between the affinity and the catalytic efficiency of transporters, and we find some but no definitive support for it. Additionally, we propose that for uptake by facilitated diffusion, at saturating substrate concentrations, lowering the affinity enhances the net uptake rate by reducing substrate efflux. As a consequence, there exists an optimal, external-substrate-concentration dependent transporter affinity. A computational model of Saccharomyces cerevisiae glycolysis shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis with yeast strains expressing a single glucose transporter modified to have either a high or a low affinity. However, due to the intimate link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive. Still, our theoretical results provide a novel reason for the presence of low-affinity transport systems.

Protein investment costs are considered a major driver for the choice of alternative metabolic strategies. We tested this premise in Lactococcus lactis, a bacterium that exhibits a distinct, anaerobic version of the bacterial Crabtree/Warburg effect; with increasing growth rates it shifts from a high yield metabolic mode [mixed-acid fermentation; 3 adenosine triphosphate (ATP) per glucose] to a low yield metabolic mode (homolactic fermentation; 2 ATP per glucose). We studied growth rate-dependent relative transcription and protein ratios, enzyme activities, and fluxes of L. lactis in glucose-limited chemostats, providing a high-quality and comprehensive data set. A three- to fourfold higher growth rate rerouted metabolism from acetate to lactate as the main fermentation product. However, we observed hardly any changes in transcription, protein levels and enzyme activities. Even levels of ribosomal proteins, constituting a major investment in cellular machinery, changed only slightly. Thus, contrary to the original hypothesis, central metabolism in this organism appears to be hardly regulated at the level of gene expression, but rather at the metabolic level. We conclude that L. lactis is either poorly adapted to growth at low and constant glucose concentrations, or that protein costs play a less important role in fitness than hitherto assumed.

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l -glutamine and l -glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

Maximization of growth rate is an important fitness strategy for bacteria. Bacteria can achieve this by expressing proteins at optimal concentrations, such that resources are not wasted. This is exemplified for Escherichia coli by the increase of its ribosomal protein-fraction with growth rate, which precisely matches the increased protein synthesis demand. These findings and others have led to the hypothesis that E. coli aims to maximize its growth rate in environments that support growth. However, what kind of regulatory strategy is required for a robust, optimal adjustment of the ribosome concentration to the prevailing condition is still an open question. In the present study, we analyze the ppGpp-controlled mechanism of ribosome expression used by E. coli and show that this mechanism maintains the ribosomes saturated with its substrates. In this manner, overexpression of the highly abundant ribosomal proteins is prevented, and limited resources can be redirected to the synthesis of other growth-promoting enzymes. It turns out that the kinetic conditions for robust, optimal protein-partitioning, which are required for growth rate maximization across conditions, can be achieved with basic biochemical interactions. We show that inactive ribosomes are the most suitable 'signal' for tracking the intracellular nutritional state and for adjusting gene expression accordingly, as small deviations from optimal ribosome concentration cause a huge fractional change in ribosome inactivity. We expect to find this control logic implemented across fast-growing microbial species because growth rate maximization is a common selective pressure, ribosomes are typically highly abundant and thus costly, and the required control can be implemented by a small, simple network.

Cells require membrane-located transporter proteins to import nutrients from the environment. Many organisms have several similar transporters for the same nutrient, which differ in their affinity. Typically, high affinity transporters are expressed when substrate is scarce and low affinity ones when substrate is more abundant. The benefit of using low affinity transporters when high affinity ones are available has so far remained unclear. Here, we investigate two hypotheses. First, it was previously hypothesized that a trade-off between the affinity and the maximal catalytic rate explains this phenomenon. We find some theoretical and experimental support for this hypothesis, but no conclusive evidence. Secondly, we propose a new hypothesis: for uptake by facilitated diffusion, at saturating extracellular substrate concentrations, lowering the affinity enhances the net uptake rate by reducing the substrate efflux rate. As a consequence, there exists an optimal, external substrate concentration dependent transporter affinity. Anin silicoanalysis of glycolysis inSaccharomyces cerevisiaeshows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis using yeast strains expressing a single glucose transporter that was modified to have either a high or a low affinity. Due to the intimate and reciprocal link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive in our hands. Still, our theoretical results provide a novel reason for the presence of low affinity transport systems which might have more general implications for enzyme catalyzed conversions.

Whooping cough is a highly contagious respiratory disease caused by Bordetella pertussis. Despite widespread vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm, and experimental data to probe the full metabolic potential of this pathogen, using B. pertussis strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine, using inorganic sulfur sources, such as thiosulfate, and it can grow on organic acids, such as citrate or lactate, as sole carbon sources, providing in vivo demonstration that its tricarboxylic acid (TCA) cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three strains were shown to grow on substrate combinations requiring a functional TCA cycle, but only one strain could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with > 2-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology and highlights the potential, but also the limitations, of models based solely on metabolic gene content., SCOPUS: ar.j, info:eu-repo/semantics/published

In microorganisms such as bacteria or yeasts, metabolic rates are tightly coupled to growth rate, and therefore to fitness. Although the topology of central pathways are largely conserved across organisms, the enzyme kinetics and their parameters generally vary. This prevents us to understand and predict (changes in) metabolic dynamics. The analytical treatment of metabolic pathways is generally restricted to small models, containing maybe two to four equations. Since such small core models involve much coarse graining, their biological interpretation is often hampered. In this paper we aim to bridge the gap between analytical, more in-depth treatment of small core models and biologically more realistic and detailed models by developing new methods. We illustrate these methods for a model of glycolysis in Saccharo-myces cerevisiae yeast, arguably the best characterised metabolic pathway in the literature. The model is more involved than in previous studies, and involves both ATP/ADP and NADH/NAD householding.A detailed analysis of the steady state equations sheds new light on two recently studied biological phenomena in yeast glycolysis: whether it is to be expected that fructose-1,6-biphosphate (FBP) parameterises all steady states, and the occurrence of bistability between a regular steady state and imbalanced steady state in which glycolytic intermediates keep accumulating.This work shows that the special structure of metabolic pathways does allow for more in-depth bifurcation analyses than is currently the norm. We especially emphasise which of the techniques developed here scale to larger pathways, and which do not.

A central theme in biology is to understand the molecular basis of fitness: which strategies succeed under which conditions; how are they mechanistically implemented; and which constraints shape trade-offs between alternative strategies. We approached these questions with parallel bacterial evolution experiments in chemostats. Chemostats provide a constant environment with a defined resource limitation (glucose), in which the growth rate can be controlled. UsingLactococcus lactis, we found a single mutation in a global regulator of carbon metabolism, CcpA, to confer predictable fitness improvements across multiple growth rates.In silicoprotein structural analysis complemented with biochemical and phenotypic assays, show that the mutation reprograms the CcpA regulon, specifically targeting transporters. This supports that membrane occupancy, rather than biosynthetic capacity, is the dominant constraint for the observed fitness enhancement. It also demonstrates that cells can modulate a pleiotropic regulator to work around limiting constraints.

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets.

Protein expression is shaped by evolutionary processes that tune microbial fitness. The limited biosynthetic capacity of a cell constrains protein expression and forces the cell to carefully manage its protein economy. In a chemostat, the physiology of the cell feeds back on the growth conditions, hindering intuitive understanding of how changes in protein concentration affect fitness. Here, we aim to provide a theoretical framework that addresses the selective pressures and optimal evolutionary-strategies in the chemostat. We show that the optimal enzyme levels are the result of a trade-off between the cost of their production and the benefit of their catalytic function. We also show that deviations from optimal enzyme levels are directly related to selection coefficients. The maximal fitness strategy for an organism in the chemostat is to express a well-defined metabolic subsystem known as an elementary flux mode. Using a coarse-grained, kinetic model of Saccharomyces cerevisiae’s metabolism and growth, we illustrate that the dynamics and outcome of evolution in a chemostat can be very counter-intuitive: Strictly-respiring and strictly-fermenting strains can evolve from a common ancestor. This work provides a theoretical framework that relates a kinetic, mechanistic view on metabolism with cellular physiology and evolutionary dynamics in the chemostat.

Lactic acid-producing bacteria survive in distinct environments, but show common metabolic characteristics. Here we studied the dynamic interactions of the central metabolism in Lactococcus lactis, extensively used as a starter culture in the dairy industry, and Streptococcus pyogenes, a human pathogen. Glucose-pulse experiments and enzymatic measurements were performed to parameterize kinetic models of glycolysis. Significant improvements were made to existing kinetic models for L. lactis, which subsequently accelerated the development of the first kinetic model of S. pyogenes glycolysis. The models revealed an important role for extracellular phosphate in the regulation of central metabolism and the efficient use of glucose. Thus, phosphate, which is rarely taken into account as an independent species in models of central metabolism, should be considered more thoroughly in the analysis of metabolic systems in the future. Insufficient phosphate supply can lead to a strong inhibition of glycolysis at high glucose concentrations in both species, but this was more severe in S. pyogenes. S. pyogenes is more efficient at converting glucose to ATP, showing a higher tendency towards heterofermentative energy metabolism than L. lactis. Our comparative systems biology approach revealed that the glycolysis of L. lactis and S. pyogenes have similar characteristics, but are adapted to their individual natural habitats with respect to phosphate regulation. Database The mathematical models described here have been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/Levering/index.html free of charge.

The Lactococcus lactis laboratory strain MG1363 has been described to be unable to utilize lactose. However, in a rich medium supplemented with lactose as the sole carbon source, it starts to grow after prolonged incubation periods. Transcriptome analyses showed that L. lactis MG1363 Lac + cells expressed celB , encoding a putative cellobiose-specific phosphotransferase system (PTS) IIC component, which is normally silent in MG1363 Lac − cells. Nucleotide sequence analysis of the cel cluster of a Lac + isolate revealed a change from one of the guanines to adenine in the promoter region. We showed here that one particular mutation, taking place at increased frequency, accounts for the lactose-utilizing phenotype occurring in MG1363 cultures. The G-to-A transition creates a −10 element at an optimal distance from the −35 element. Thus, a fully active promoter is created, allowing transcription of the otherwise cryptic cluster. Nuclear magnetic resonance (NMR) spectroscopy results show that MG1363 Lac + uses a novel pathway of lactose utilization.

Aim: A medium with minimal requirements for the growth of Lactobacillus plantarum WCFS was developed. The composition of the minimal medium was compared to a genome-scale metabolic model of L. plantarum. Methods and Results: By repetitive single omission experiments, two minimal media were developed: PMM5 (true minimal medium) and PMM7 [a pseudominimal medium, supporting proper biomass formation of 350 mg l−1 dry weight (DW)]. The specific growth rate of L. plantarum on PMM7 was found to be 50% and 63% lower when compared to growth on established growth media (chemically defined medium and MRS, respectively). Using a genome-scale metabolic model of L. plantarum, it was predicted that PMM5 and PMM7 would not support the growth of L. plantarum. This is because the biosynthesis of para-aminobenzoic acid (pABA) was predicted to be essential for growth. The discrepancy in simulated growth and experimental growth on PMM7 was further investigated for pABA; a molecule which plays an important role in folate production. The growth performance and folate production were determined on PMM7 in the presence and absence of pABA. It was found that a 12 000-fold reduction in folate pools exerted no influence on formation of biomass or growth rate of L. plantarum cultures when grown in the absence of pABA. Conclusion: Largely reduced folate production pools do not have an effect on the growth of L. plantarum, showing that L. plantarum makes folate in a large excess. Significance and Impact of the study: These experiments illustrate the importance of combining genome-scale metabolic models with growth experiments on minimal media.

Background The lactic acid bacterium Lactobacillus rhamnosus GG is the most studied probiotic bacterium with proven health benefits upon oral intake, including the alleviation of diarrhea. The mission of the Yoba for Life foundation is to provide impoverished communities in Africa increased access to Lactobacillus rhamnosus GG under the name Lactobacillus rhamnosus yoba 2012, world’s first generic probiotic strain. We have been able to overcome the strain’s limitations to grow in food matrices like milk, by formulating a dried starter consortium with Streptococcus thermophilus that enables the propagation of both strains in milk and other food matrices. The affordable seed culture is used by people in resource-poor communities. Results We used S. thermophilus C106 as an adjuvant culture for the propagation of L. rhamnosus yoba 2012 in a variety of fermented foods up to concentrations, because of its endogenous proteolytic activity, ability to degrade lactose and other synergistic effects. Subsequently, L. rhamnosus could reach final titers of 1E+09 CFU ml−1, which is sufficient to comply with the recommended daily dose for probiotics. The specific metabolic interactions between the two strains were derived from the full genome sequences of L. rhamnosus GG and S. thermophilus C106. The piliation of the L. rhamnosus yoba 2012, required for epithelial adhesion and inflammatory signaling in the human host, was stable during growth in milk for two rounds of fermentation. Sachets prepared with the two strains, yoba 2012 and C106, retained viability for at least 2 years. Conclusions A stable dried seed culture has been developed which facilitates local and low-cost production of a wide range of fermented foods that subsequently act as delivery vehicles for beneficial bacteria to communities in east Africa. Electronic supplementary material The online version of this article (doi:10.1186/s12934-015-0370-x) contains supplementary material, which is available to authorized users.

Contains fulltext : 36088teusink.pdf (Publisher’s version ) (Open Access) A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.

On the basis of the annotated genome we reconstructed the metabolic pathways of the lactic acid bacterium Lactobacillus plantarum WCFS1. After automatic reconstruction by the Pathologic tool of Pathway Tools ( http://bioinformatics.ai.sri.com/ptools/ ), the resulting pathway-genome database, LacplantCyc, was manually curated extensively. The current database contains refinements to existing routes and new gram-positive bacterium-specific reactions that were not present in the MetaCyc database. These reactions include, for example, reactions related to cell wall biosynthesis, molybdopterin biosynthesis, and transport. At present, LacplantCyc includes 129 pathways and 704 predicted reactions involving some 670 chemical species and 710 enzymes. We tested vitamin and amino acid requirements of L. plantarum experimentally and compared the results with the pathways present in LacplantCyc. In the majority of cases (32 of 37 cases) the experimental results agreed with the final reconstruction. LacplantCyc is the most extensively curated pathway-genome database for gram-positive bacteria and is open to the microbiology community via the World Wide Web ( www.lacplantcyc.nl ). It can be used as a reference pathway-genome database for gram-positive microbes in general and lactic acid bacteria in particular.

Contains fulltext : 60473teusink.pdf (Publisher’s version ) (Closed access) Lactic acid bacteria (LAB) have been widely used in food fermentations and, more recently, as probiotics in health-promoting food products. Genome sequencing and functional genomics studies of a variety of LAB are now rapidly providing insights into their diversity and evolution and revealing the molecular basis for important traits such as flavor formation, sugar metabolism, stress response, adaptation and interactions. Bioinformatics plays a key role in handling, integrating and analyzing the flood of 'omics' data being generated. Reconstruction of metabolic potential using bioinformatics tools and databases, followed by targeted experimental verification and exploration of the metabolic and regulatory network properties, are the present challenges that should lead to improved exploitation of these versatile food bacteria.

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models. Copyright

In the model eukaryote Saccharomyces cerevisiae, it has long been known that a functional trehalose pathway is indispensable for transitions to high glucose conditions. Upon addition of glucose, cells with a defect in trehalose 6-phosphate synthase (Tps1), the first committed step in the trehalose pathway, display what we have termed an imbalanced glycolytic state; in this state the flux through the upper part of glycolysis outpaces that through the lower part of glycolysis. As a consequence, the intermediate fructose 1,6-bisphosphate (FBP) accumulates at low concentrations of ATP and inorganic phosphate (Pi). Despite significant research efforts, a satisfactory understanding of the regulatory role that trehalose metabolism plays during such transitions has remained infamously unresolved. In a recent study, we demonstrate that the startup of glycolysis exhibits two dynamic fates: a proper, functional, steady state or the imbalanced state described above. Both states are stable, attracting states, and the probability distribution of initial states determines the fate of a yeast cell exposed to glucose. Trehalose metabolism steers the dynamics of glycolysis towards the proper functional state through its ATP hydrolysis activity; a mechanism that ensures that the demand and supply of ATP is balanced with Pi availability under dynamic conditions. [van Heerden et al. Science (2014), DOI: 10.1126/science.1245114.]

It is becoming accepted that steady-state fluxes are not necessarily controlled by single rate-limiting steps. This leaves open the issue whether cellular dynamics are controlled by single pacemaker enzymes, as has often been proposed. This paper shows that yeast sugar transport has substantial but not complete control of the frequency of glycolytic oscillations. Addition of maltose, a competitive inhibitor of glucose transport, reduced both average glucose consumption flux and frequency of glycolytic oscillations. Assuming a single kinetic component and a symmetrical carrier, a frequency control coefficient of between 0.4 and 0.6 and an average-flux control coefficient of between 0.6 and 0.9 were calculated for hexose transport activity. In a second approach, mannose was used as the carbon and free-energy source, and the dependencies on the extracellular mannose concentration of the transport activity, of the frequency of oscillations, and of the average flux were compared. In this case the frequency control coefficient and the average-flux control coefficient of hexose transport activity amounted to 0.7 and 0.9, respectively. From these results, we conclude that 1) transport is highly important for the dynamics of glycolysis, 2) most but not all control resides in glucose transport, and 3) there should at least be one step other than transport with substantial control.

This paper examines whether the in vivo behavior of yeast glycolysis can be understood in terms of the in vitro kinetic properties of the constituent enzymes. In nongrowing, anaerobic, compressed Saccharomyces cerevisiae the values of the kinetic parameters of most glycolytic enzymes were determined. For the other enzymes appropriate literature values were collected. By inserting these values into a kinetic model for glycolysis, fluxes and metabolites were calculated. Under the same conditions fluxes and metabolite levels were measured. In our first model, branch reactions were ignored. This model failed to reach the stable steady state that was observed in the experimental flux measurements. Introduction of branches towards trehalose, glycogen, glycerol and succinate did allow such a steady state. The predictions of this branched model were compared with the empirical behavior. Half of the enzymes matched their predicted flux in vivo within a factor of 2. For the other enzymes it was calculated what deviation between in vivo and in vitro kinetic characteristics could explain the discrepancy between in vitro rate and in vivo flux.

Although control of fluxes and concentrations tends to be distributed rather than confined to a single rate-limiting enzyme, the extent of control can differ widely between enzymes in a metabolic network. In some cases, there are enzymes that lack control completely. This paper identifies one surprising origin of such lack of control: If, in a metabolic system, there is a metabolite that affects the catalytic rate of only one enzyme, the corresponding enzyme cannot control any metabolic variable other than the concentration of that metabolite. We call such enzymes 'slave enzymes', and the corresponding metabolites 'slave metabolites'. Implications of the existence of slave enzymes for the control properties of enzymes further down the metabolic pathway are discussed and examined for the glycolytic pathway of yeast. Inadvertent assumptions in metabolic models may cause the latter incorrectly to calculate absence of metabolic control. The phenomenon of slave enzymes may well be important in enhancing metabolic signal transduction.

The extent to which the transport of glucose across the plasma membrane of the yeast Saccharomyces bayanus controls the glycolytic flux was determined. The magnitude of control was quantified by measuring the effect of small changes in the activity of the glucose transport system on the rate of glucose consumption. Two effectors were used to modulate the activity of glucose transport: (i) maltose, a competitive inhibitor of the glucose transport system in S. bayanus (as well as in Saccharomyces cerevisiae) and (ii) extracellular glucose, the substrate of the glucose transport system. Two approaches were followed to derive from the experimental data the flux control coefficient of glucose transport on the glycolytic flux: (i) direct comparison of the steady-state glycolytic flux with the zero trans-influx of glucose and (ii) comparison of the change in glycolytic flux with the concomitant change in calculated glucose transport activity on variation of the extracellular glucose concentration. Both these approaches demonstrated that in cells of S. bayanus grown on glucose and harvested at the point of glucose exhaustion, a high proportion of the control of the glycolytic flux resides in the transport of glucose across the plasma membrane.

Many catabolic pathways begin with an ATP-requiring activation step, after which further metabolism yields a surplus of ATP. Such a 'turbo' principle is useful but also contains an inherent risk. This is illustrated by a detailed kinetic analysis of a paradoxical Saccharomyces cerevisiae mutant; the mutant fails to grow on glucose because of overactive initial enzymes of glycolysis, but is defective only in an enzyme (trehalose 6-phosphates synthase) that appears to have little relevance to glycolysis. The ubiquity of pathways that possess an initial activation step, suggests that there might be many more genes that, when deleted, cause rather paradoxical regulation phenotypes (i.e. growth defects caused by enhanced utilization of growth substrate).

In the presence of cyanide, populations of yeast cells can exhibit sustained oscillations in the concentration of glycolytic metabolites, NADH and ATP. This study attempts to answer the long-standing question of whether and how oscillations of individual cells are synchronized. It shows that mixing two cell populations that oscillate 180 degrees out of phase only transiently abolishes the macroscopic oscillation. After a few minutes, NADH fluorescence of the mixed population resumes oscillations up to the original amplitude. At low cell densities, addition of acetaldehyde causes transient oscillations. At higher cell densities, where the oscillations are autonomous, 70 microM acetaldehyde causes phase shifts. Extracellular acetaldehyde is shown to oscillate around the 70 microM level. We conclude that acetaldehyde synchronizes the oscillations of the individual cells.

Glycolytic oscillations can be induced by adding glucose to starved Saccharomyces cerevisiae cells and, after a steady state has been established, cyanide. Transient oscillations or limit-cycle oscillations can be induced depending on the growth phase in which the cells are harvested. To find what causes these differences in the dynamic behaviour, we analyzed glycolytic enzyme activities at different growth phases. The hexokinase activity increased by a factor of three after growth substrate transition from glucose to ethanol; the other measured activities remained constant. Cyanide was found not only to block respiration, but also to trap acetaldehyde. Both cyanide actions appear necessary for the occurrence of sustained glycolytic oscillations.

Knowledge of how the activity of enzymes is affected under in vivo conditions is essential for analyzing their regulation and constructing models that yield an integrated understanding of cell behavior. Current kinetic parameters for Lactococcus lactis are scattered through different studies and performed under different assay conditions. Furthermore, assay conditions often diverge from conditions prevailing in the intracellular environment. To establish uniform assay conditions that resemble intracellular conditions, we analyzed the intracellular composition of anaerobic glucose-limited chemostat cultures of L. lactis subsp. cremoris MG 1363. Based on this, we designed a new assay medium for enzyme activity measurements of growing cells of L. lactis , mimicking as closely as practically possible its intracellular environment. Procedures were optimized to be carried out in 96-well plates, and the reproducibility and dynamic range were checked for all enzyme activity measurements. The effects of freezing and the carryover of ammonium sulfate from the addition of coupling enzymes were also established. Activities of all 10 glycolytic and 4 fermentative enzymes were measured. Remarkably, most in vivo -like activities were lower than previously published data. Yet, the ratios of V max over measured in vivo fluxes were above 1. With this work, we have developed and extensively validated standard protocols for enzyme activity measurements for L. lactis .

BackgroundLactobacillus reuteri harbors the genes responsible for glycerol utilization and vitamin B12 synthesis within a genetic island phylogenetically related to gamma-Proteobacteria. Within this island, resides a gene (lreu_1750) that based on its genomic context has been suggested to encode the regulatory protein PocR and presumably control the expression of the neighboring loci. However, this functional assignment is not fully supported by sequence homology, and hitherto, completely lacks experimental confirmation.ResultsIn this contribution, we have overexpressed and inactivated the gene encoding the putative PocR in L. reuteri. The comparison of these strains provided metabolic and transcriptional evidence that this regulatory protein controls the expression of the operons encoding glycerol utilization and vitamin B12 synthesis.ConclusionsWe provide clear experimental evidence for assigning Lreu_1750 as PocR in Lactobacillus reuteri. Our genome-wide transcriptional analysis further identifies the loci contained in the PocR regulon. The findings reported here could be used to improve the production-yield of vitamin B12, 1,3-propanediol and reuterin, all industrially relevant compounds.

The genomes of four Lactobacillus reuteri strains isolated from human breast milk and the gastrointestinal tract have been recently sequenced as part of the Human Microbiome Project. Preliminary genome comparisons suggested that these strains belong to two different clades, previously shown to differ with respect to antimicrobial production, biofilm formation, and immunomodulation. To explain possible mechanisms of survival in the host and probiosis, we completed a detailed genomic comparison of two breast milk-derived isolates representative of each group: an established probiotic strain (L. reuteri ATCC 55730) and a strain with promising probiotic features (L. reuteri ATCC PTA 6475). Transcriptomes of L. reuteri strains in different growth phases were monitored using strain-specific microarrays, and compared using a pan-metabolic model representing all known metabolic reactions present in these strains. Both strains contained candidate genes involved in the survival and persistence in the gut such as mucus-binding proteins and enzymes scavenging reactive oxygen species. A large operon predicted to encode the synthesis of an exopolysaccharide was identified in strain 55730. Both strains were predicted to produce health-promoting factors, including antimicrobial agents and vitamins (folate, vitamin B(12)). Additionally, a complete pathway for thiamine biosynthesis was predicted in strain 55730 for the first time in this species. Candidate genes responsible for immunomodulatory properties of each strain were identified by transcriptomic comparisons. The production of bioactive metabolites by human-derived probiotics may be predicted using metabolic modeling and transcriptomics. Such strategies may facilitate selection and optimization of probiotics for health promotion, disease prevention and amelioration.

The physiology of Lactobacillus plantarum at extremely low growth rates, through cultivation in retentostats, is much closer to carbon-limited growth than to stationary phase, as evidenced from transcriptomics data, metabolic fluxes, and biomass composition and viability. Using a genome-scale metabolic model and constraint-based computational analyses, amino-acid fluxes—in particular, the rather paradoxical excretion of Asp, Arg, Met, and Ala—could be rationalized as a means to allow extensive metabolism of other amino acids, that is, that of branched-chain and aromatic amino acids. Catabolic products from aromatic amino acids are known to have putative plant-hormone action. The metabolism of amino acids, as well as transcription data, strongly suggested a plant environment-like response in slow-growing L. plantarum, which was confirmed by significant effects of fermented medium on plant root formation., Natural ecosystems are usually characterized by extremely low and fluctuating nutrient availability. Hence, microorganisms in these environments live a ‘feast-and-famine' existence, with famine the most habitual state. As a result, extremely slow or no growth is the most common state of bacteria, and maintenance processes dominate their life. In the present study, Lactobacillus plantarum was used as a model microorganism to investigate the physiology of slow growth. Besides fermented foods, this microorganism can be observed in a variety of environmental niches, including plants and lakes, in which nutrient supply is limited. To mimic these conditions, L. plantarum was grown in a glucose-limited chemostat with complete biomass retention (retentostat). During cultivation, biomass progressively accumulated, resulting in steadily decreasing specific substrate availability. Less energy was thus available for growth, and the specific growth rate decreased accordingly, with a final calculated doubling time greater than one year. Detailed measurements of metabolic fluxes were used as constraints in a genome-scale metabolic model to precisely calculate the amount of energy used for net biomass synthesis and for maintenance purposes: at the lowest growth rate investigated (μ=0.0002 h−1), maintenance accounted for 94% of all energy expenses. Genome-scale metabolic analysis was used in combination with transcriptomics to study the adaptation of L. plantarum to extremely slow growth under limited carbon and energy supply. Importantly, slow growth as investigated here was fundamentally different from the widely studied carbon starvation-induced stationary phase: non-growing cells in retentostat conditions were glucose limited rather than starved, and the transition from a growing to a non-growing state under retentostat conditions was progressive, in contrast with the abrupt transition in batch cultures. These differences were reflected in various aspects of the cell physiology. The metabolic behavior was remarkably stable during adaptation to slow growth. Although carbon catabolite repression was clearly relieved, as indicated by the upregulation of genes for the utilization of alternative carbohydrates, the metabolism remained largely based on the conversion of glucose to lactate. Stress resistance mechanisms were also not massively induced. In particular, analysis of the biomass composition—which remained similar to fast-growing cells even under virtually non-growing conditions—and of the gene expression profile, failed to reveal clear stringent or general stress responses, which are generally triggered in glucose-starved cells. The observation that genes involved in growth-associated processes were not downregulated suggested that active synthesis of biomass components (RNA, proteins, and membranes) was required to account for the observed stable biomass and that turnover of macromolecules was high in slow-growing cells. Biomass viability or morphology was also not affected, compared with faster growth conditions. The only typical stress response was the induction of an SOS response—in particular, the upregulation of the two error-prone DNA polymerases—suggesting an increased potential for genetic diversity under adverse conditions. Although diversity was not apparent under the conditions studied here, such mechanisms of increased rates of mutagenesis are likely to have an important role in the adaptation of L. plantarum to slow growth. A surprising response of L. plantarum during adaptation to slow growth was the production of several amino acids (Arg, Asp, Met, and Ala). A priori, this metabolic behavior seemed inefficient in a context of energy limitation. However, reduced cost analysis using the genome-scale metabolic model indicated that it had a positive effect on energy generation. In-depth analysis of metabolic flux distributions showed that biosynthesis of these amino acids was connected to the catabolism of branched-chain and aromatic amino acids (BCAAs and AAAs), under conditions of limited ammonium efflux. At a fixed ammonium efflux—fixed at the measured value—flux balance analysis indicated that BCAAs and AAAs were expensive to metabolize, because the regeneration of 2-ketoglutarate through glutamate dehydrogenase was limited by ammonium dissipation. Therefore, alternative pathways had to be active to supply the necessary pool of 2-ketoglutarate. At low growth rates, amino-acid production (Arg, Asp, Ala, and Met) accounted for most of the 2-ketoglutarate regeneration. Although it came at the expense of ATP, this metabolic alternative to glutamate dehydrogenase was less energy costly than other solutions such as purine biosynthesis. This is thus an excellent example in which precise, quantitative modeling results in new insights in physiology that intuition would never have achieved. It also shows that flux balance analysis can be used to accurately predict energetically inefficient metabolism, provided the appropriate fluxes are constrained (here, ammonium efflux). The observation that BCAAs and AAAs were catabolized at the expense of energy was intriguing. However, several end products of these catabolic pathways can serve as signaling molecules for interactions with other organisms. In particular, precursors of plant hormones were predicted as possible end products in the model simulations. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. The metabolic analysis thus suggested that slow-growing L. plantarum produced plant hormones—or precursors thereof—as a strategy to divert the plant metabolism towards its own interest. In support of this view, transcriptome analysis indicated the upregulation of genes involved in the catabolism of β-glucosides—typical sugars from plant cell wall—as well as a very high induction of six gene clusters encoding cell-surface protein complexes predicted to have a role in the utilization of plant polysaccharides (csc clusters). In such a plant context, limited ammonium production would also make sense, because of the well-documented toxicity of ammonium for plants: production of amino acids could represent an alternative to ammonium excretion while keeping both parties satisfied. In conclusion, the physiology of L. plantarum at extremely low growth rates, as studied by genome-scale metabolic modeling and transcriptomics, is fundamentally different from that of starvation-induced stationary phase cells. Excitingly, these conditions seem to trigger responses that favor interactions with the environment, more specifically with plants. The reported observations were made in the absence of any plant-derived material, suggesting that this response might constitute a hardwired behavior., Situations of extremely low substrate availability, resulting in slow growth, are common in natural environments. To mimic these conditions, Lactobacillus plantarum was grown in a carbon-limited retentostat with complete biomass retention. The physiology of extremely slow-growing L. plantarum—as studied by genome-scale modeling and transcriptomics—was fundamentally different from that of stationary-phase cells. Stress resistance mechanisms were not massively induced during transition to extremely slow growth. The energy-generating metabolism was remarkably stable and remained largely based on the conversion of glucose to lactate. The combination of metabolic and transcriptomic analyses revealed behaviors involved in interactions with the environment, more particularly with plants: production of plant hormones or precursors thereof, and preparedness for the utilization of plant-derived substrates. Accordingly, the production of compounds interfering with plant root development was demonstrated in slow-growing L. plantarum. Thus, conditions of slow growth and limited substrate availability seem to trigger a plant environment-like response, even in the absence of plant-derived material, suggesting that this might constitute an intrinsic behavior in L. plantarum.

Recent functional genomics and genome-scale modeling approaches indicated that B 12 production in Lactobacillus reuteri could be improved by optimization of the medium. Here we show that a series of systematic single-amino-acid omissions could significantly modulate the production of B 12 from nearly undetectable levels (with omission of isoleucine) to levels 20-fold higher than the levels previously reported (with omission of cysteine). Using cDNA microarray experiments, we analyzed the transcriptional response of L. reuteri to medium lacking cysteine. The results supported the observed high level of B 12 production and provided new avenues for future improvement of production of vitamin B 12 .

In the study of metabolic networks, optimization techniques are often used to predict flux distributions, and hence, metabolic phenotype. Flux balance analysis in particular has been successful in predicting metabolic phenotypes. However, an inherent limitation of a stoichiometric approach such as flux balance analysis is that it can predict only flux distributions that result in maximal yields. Hence, previous attempts to use FBA to predict metabolic fluxes in Lactobacillus plantarum failed, as this lactic acid bacterium produces lactate, even under glucose-limited chemostat conditions, where FBA predicted mixed acid fermentation as an alternative pathway leading to a higher yield. In this study we tested, however, whether long-term adaptation on an unusual and poor carbon source (for this bacterium) would select for mutants with optimal biomass yields. We have therefore adapted Lactobacillus plantarum to grow well on glycerol as its main growth substrate. After prolonged serial dilutions, the growth yield and corresponding fluxes were compared to in silico predictions. Surprisingly, the organism still produced mainly lactate, which was corroborated by FBA to indeed be optimal. To understand these results, constraint-based elementary flux mode analysis was developed that predicted 3 out of 2669 possible flux modes to be optimal under the experimental conditions. These optimal pathways corresponded very closely to the experimentally observed fluxes and explained lactate formation as the result of competition for oxygen by the other flux modes. Hence, these results provide thorough understanding of adaptive evolution, allowing in silico predictions of the resulting flux states, provided that the selective growth conditions favor yield optimization as the winning strategy., Author Summary Being able to predict the metabolic fluxes and growth rate of a microorganism is an important topic in microbial systems biology. One approach, constraint-based modeling, uses a reconstructed metabolic network and optimization techniques to make such predictions. Although widely used, the success of this approach depends on a number of important assumptions. First, it assumes that evolutionary forces have shaped the metabolism towards optimality of, in most cases, growth rate. Second, through the nature of the modeling approach, it assumes that microorganisms maximize the growth rate through optimizing the yield on the growth substrate. Despite successes of the approach in model organisms such as Saccharomyces cerevisiae and Escherichia coli, we have previously observed that the approach fails in Lactobacillus plantarum, a lactic acid bacterium that clearly does not optimize its yield on glucose but “wastes” glucose by producing lactic acid. In the current study we provide evidence that L. plantarum does optimize its yield when grown under a poor carbon condition, i.e., when grown on glycerol as its main carbon source. The study provides new insight in when the application of in silico optimization techniques can be expected to be predictive.

In this report, we describe the amino acid metabolism and amino acid dependency of the dairy bacterium Streptococcus thermophilus LMG18311 and compare them with those of two other characterized lactic acid bacteria, Lactococcus lactis and Lactobacillus plantarum . Through the construction of a genome-scale metabolic model of S. thermophilus , the metabolic differences between the three bacteria were visualized by direct projection on a metabolic map. The comparative analysis revealed the minimal amino acid auxotrophy (only histidine and methionine or cysteine) of S. thermophilus LMG18311 and the broad variety of volatiles produced from amino acids compared to the other two bacteria. It also revealed the limited number of pyruvate branches, forcing this strain to use the homofermentative metabolism for growth optimization. In addition, some industrially relevant features could be identified in S. thermophilus , such as the unique pathway for acetaldehyde (yogurt flavor) production and the absence of a complete pentose phosphate pathway.