Lost in transition: start-up of glycolysis yields subpopulations of nongrowing cells

Ongoing resolution of duplicate gene functions shapes the diversification of a metabolic network

The evolutionary mechanisms leading to duplicate gene retention are well understood, but the long-term impacts of paralog differentiation on the regulation of metabolism remain underappreciated. Here we experimentally dissect the functions of two pairs of ancient paralogs of the GALactose sugar utilization network in two yeast species. We show that the Saccharomyces uvarum network is more active, even as over-induction is prevented by a second co-repressor that the model yeast Saccharomyces cerevisiae lacks. Surprisingly, removal of this repression system leads to a strong growth arrest, likely due to overly rapid galactose catabolism and metabolic overload. Alternative sugars, such as fructose, circumvent metabolic control systems and exacerbate this phenotype. We further show that S. cerevisiae experiences homologous metabolic constraints that are subtler due to how the paralogs have diversified. These results show how the functional differentiation of paralogs continues to shape regulatory network architectures and metabolic strategies long after initial preservation.

Design of a bistable switch to control cellular uptake

Bistable switches are widely used in synthetic biology to trigger cellular functions in response to environmental signals. All bistable switches developed so far, however, control the expression of target genes without access to other layers of the cellular machinery. Here, we propose a bistable switch to control the rate at which cells take up a metabolite from the environment. An uptake switch provides a new interface to command metabolic activity from the extracellular space and has great potential as a building block in more complex circuits that coordinate pathway activity across cell cultures, allocate metabolic tasks among different strains or require cell-to-cell communication with metabolic signals. Inspired by uptake systems found in nature, we propose to couple metabolite import and utilization with a genetic circuit under feedback regulation. Using mathematical models and analysis, we determined the circuit architectures that produce bistability and obtained their design space for bistability in terms of experimentally tuneable parameters. We found an activation-repression architecture to be the most robust switch because it displays bistability for the largest range of design parameters and requires little fine-tuning of the promoters' response curves. Our analytic results are based on on-off approximations of promoter activity and are in excellent qualitative agreement with simulations of more realistic models. With further analysis and simulation, we established conditions to maximize the parameter design space and to produce bimodal phenotypes via hysteresis and cell-to-cell variability. Our results highlight how mathematical analysis can drive the discovery of new circuits for synthetic biology, as the proposed circuit has all the hallmarks of a toggle switch and stands as a promising design to control metabolic phenotypes across cell cultures.

High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays

Methods to rapidly assess cell growth would be useful for many applications, including drug susceptibility testing, but current technologies have limited sensitivity or throughput. Here we present an approach to precisely and rapidly measure growth rates of many individual cells simultaneously. We flow cells in suspension through a microfluidic channel with 10–12 resonant mass sensors distributed along its length, weighing each cell repeatedly over the 4–20 min it spends in the channel. Because multiple cells traverse the channel at the same time, we obtain growth rates for >60 cells/h with a resolution of 0.2 pg/h for mammalian cells and 0.02 pg/h for bacteria. We measure the growth of single lymphocytic cells, mouse and human T cells, primary human leukemia cells, yeast, Escherichia coli and Enterococcus faecalis. Our system reveals subpopulations of cells with divergent growth kinetics and enables assessment of cellular responses to antibiotics and antimicrobial peptides within minutes.

RETRACTED:A new function for the yeast trehalose-6P synthase (Tps1) protein, as key pro-survival factor during growth, chronological ageing, and apoptotic stress

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of Marie-Ange Teste, Isabelle Léger-Silvestre, Jean M François and Jean-Luc Parrou. Marjorie Petitjean could not be reached. The corresponding author identified major issues and brought them to the attention of the Journal. These issues span from significant errors in the Material and Methods section of the article and major flaws in cytometry data analysis to data fabrication on the part of one of the authors. Given these errors, the retracting authors state that the only responsible course of action would be to retract the article, to respect scientific integrity and maintain the standards and rigor of literature from the retracting authors' group as well as the Journal. The retracting authors sincerely apologize to the readers and editors.

Evolutionary pressures on microbial metabolic strategies in the chemostat

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.

Cell-to-cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community

Cells that grow together respond heterogeneously to stress even when they are genetically similar. Metabolism, a key determinant of cellular stress tolerance, may be one source of this phenotypic heterogeneity, however, this relationship is largely unclear. We used self‐establishing metabolically cooperating (SeMeCo) yeast communities, in which metabolic cooperation can be followed on the basis of genotype, as a model to dissect the role of metabolic cooperation in single‐cell heterogeneity. Cells within SeMeCo communities showed to be highly heterogeneous in their stress tolerance, while the survival of each cell under heat or oxidative stress, was strongly determined by its metabolic specialization. This heterogeneity emerged for all metabolite exchange interactions studied (histidine, leucine, uracil, and methionine) as well as oxidant (H₂O₂, diamide) and heat stress treatments. In contrast, the SeMeCo community collectively showed to be similarly tolerant to stress as wild‐type populations. Moreover, stress heterogeneity did not establish as sole consequence of metabolic genotype (auxotrophic background) of the single cell, but was observed only for cells that cooperated according to their metabolic capacity. We therefore conclude that phenotypic heterogeneity and cell to cell differences in stress tolerance are emergent properties when cells cooperate in metabolism.

Bypassing the Pentose Phosphate Pathway: Towards Modular Utilization of Xylose

The efficient use of hemicellulose in the plant cell wall is critical for the economic conversion of plant biomass to renewable fuels and chemicals. Previously, the yeast Saccharomyces cerevisiae has been engineered to convert the hemicellulose-derived pentose sugars xylose and arabinose to d-xylulose-5-phosphate for conversion via the pentose phosphate pathway (PPP). However, efficient pentose utilization requires PPP optimization and may interfere with its roles in NADPH and pentose production. Here, we developed an alternative xylose utilization pathway that largely bypasses the PPP. In the new pathway, d-xylulose is converted to d-xylulose-1-phosphate, a novel metabolite to S. cerevisiae, which is then cleaved to glycolaldehyde and dihydroxyacetone phosphate. This synthetic pathway served as a platform for the biosynthesis of ethanol and ethylene glycol. The use of d-xylulose-1-phosphate as an entry point for xylose metabolism opens the way for optimizing chemical conversion of pentose sugars in S. cerevisiae in a modular fashion.

Respiratory metabolism and calorie restriction relieve persistent endoplasmic reticulum stress induced by calcium shortage in yeast

Calcium homeostasis is crucial to eukaryotic cell survival. By acting as an enzyme cofactor and a second messenger in several signal transduction pathways, the calcium ion controls many essential biological processes. Inside the endoplasmic reticulum (ER) calcium concentration is carefully regulated to safeguard the correct folding and processing of secretory proteins. By using the model organism Saccharomyces cerevisiae we show that calcium shortage leads to a slowdown of cell growth and metabolism. Accumulation of unfolded proteins within the calcium-depleted lumen of the endoplasmic reticulum (ER stress) triggers the unfolded protein response (UPR) and generates a state of oxidative stress that decreases cell viability. These effects are severe during growth on rapidly fermentable carbon sources and can be mitigated by decreasing the protein synthesis rate or by inducing cellular respiration. Calcium homeostasis, protein biosynthesis and the unfolded protein response are tightly intertwined and the consequences of facing calcium starvation are determined by whether cellular energy production is balanced with demands for anabolic functions. Our findings confirm that the connections linking disturbance of ER calcium equilibrium to ER stress and UPR signaling are evolutionary conserved and highlight the crucial role of metabolism in modulating the effects induced by calcium shortage.

Structures of trehalose-6-phosphate phosphatase from pathogenic fungi reveal the mechanisms of substrate recognition and catalysis

Trehalose is a disaccharide essential for the survival and virulence of pathogenic fungi. The biosynthesis of trehalose requires trehalose-6-phosphate synthase, Tps1, and trehalose-6-phosphate phosphatase, Tps2. Here, we report the structures of the N-terminal domain of Tps2 (Tps2NTD) from Candida albicans, a transition-state complex of the Tps2 C-terminal trehalose-6-phosphate phosphatase domain (Tps2PD) bound to BeF3 and trehalose, and catalytically dead Tps2PD(D24N) from Cryptococcus neoformans bound to trehalose-6-phosphate (T6P). The Tps2NTD closely resembles the structure of Tps1 but lacks any catalytic activity. The Tps2PD-BeF3-trehalose and Tps2PD(D24N)-T6P complex structures reveal a "closed" conformation that is effected by extensive interactions between each trehalose hydroxyl group and residues of the cap and core domains of the protein, thereby providing exquisite substrate specificity. Disruption of any of the direct substrate-protein residue interactions leads to significant or complete loss of phosphatase activity. Notably, the Tps2PD-BeF3-trehalose complex structure captures an aspartyl-BeF3 covalent adduct, which closely mimics the proposed aspartyl-phosphate intermediate of the phosphatase catalytic cycle. Structures of substrate-free Tps2PD reveal an "open" conformation whereby the cap and core domains separate and visualize the striking conformational changes effected by substrate binding and product release and the role of two hinge regions centered at approximately residues 102-103 and 184-188. Significantly, tps2Δ, tps2NTDΔ, and tps2D705N strains are unable to grow at elevated temperatures. Combined, these studies provide a deeper understanding of the substrate recognition and catalytic mechanism of Tps2 and provide a structural basis for the future design of novel antifungal compounds against a target found in three major fungal pathogens.

Type 2 Diabetes Dysregulates Glucose Metabolism in Cardiac Progenitor Cells

Type 2 diabetes is associated with increased mortality and progression to heart failure. Recent studies suggest that diabetes also impairs reparative responses after cell therapy. In this study, we examined potential mechanisms by which diabetes affects cardiac progenitor cells (CPCs). CPCs isolated from the diabetic heart showed diminished proliferation, a propensity for cell death, and a pro-adipogenic phenotype. The diabetic CPCs were insulin-resistant, and they showed higher energetic reliance on glycolysis, which was associated with up-regulation of the pro-glycolytic enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3). In WT CPCs, expression of a mutant form of PFKFB, which mimics PFKFB3 activity and increases glycolytic rate, was sufficient to phenocopy the mitochondrial and proliferative deficiencies found in diabetic cells. Consistent with activation of phosphofructokinase in diabetic cells, stable isotope carbon tracing in diabetic CPCs showed dysregulation of the pentose phosphate and glycero(phospho)lipid synthesis pathways. We describe diabetes-induced dysregulation of carbon partitioning using stable isotope metabolomics-based coupling quotients, which relate relative flux values between metabolic pathways. These findings suggest that diabetes causes an imbalance in glucose carbon allocation by uncoupling biosynthetic pathway activity, which could diminish the efficacy of CPCs for myocardial repair.

Dynamic metabolic models in context: biomass backtracking

Mathematical modeling has proven to be a powerful tool to understand and predict functional and regulatory properties of metabolic processes. High accuracy dynamic modeling of individual pathways is thereby opposed by simplified but genome scale constraint based approaches. A method that links these two powerful techniques would greatly enhance predictive power but is so far lacking. We present biomass backtracking, a workflow that integrates the cellular context in existing dynamic metabolic models via stoichiometrically exact drain reactions based on a genome scale metabolic model. With comprehensive examples, for different species and environmental contexts, we show the importance and scope of applications and highlight the improvement compared to common boundary formulations in existing metabolic models. Our method allows for the contextualization of dynamic metabolic models based on all available information. We anticipate this to greatly increase their accuracy and predictive power for basic research and also for drug development and industrial applications.

An entropy-like index of bifurcational robustness for metabolic systems

Natural and synthetic metabolic pathways need to retain stability when faced against random changes in gene expression levels and kinetic parameters. In the presence of large parameter changes, a robust system should specifically avoid moving to an unstable region, an event that would dramatically change system behavior. Here we present an entropy-like index, denoted as S, for quantifying the bifurcational robustness of metabolic systems against loss of stability. We show that S enables the optimization of a metabolic model with respect to both bifurcational robustness and experimental data. We then demonstrate how the coupling of ensemble modeling and S enables us to discriminate alternative designs of a synthetic pathway according to bifurcational robustness. Finally, we show that S enables the identification of a key enzyme contributing to the bifurcational robustness of yeast glycolysis. The different applications of S demonstrated illustrate the versatile role it can play in constructing better metabolic models and designing functional non-native pathways.

A Toxoplasma gondii Gluconeogenic Enzyme Contributes to Robust Central Carbon Metabolism and Is Essential for Replication and Virulence

The expression of gluconeogenic enzymes is typically repressed when glucose is available. The protozoan parasite Toxoplasma gondii utilizes host glucose to sustain high rates of intracellular replication. However, despite their preferential utilization of glucose, intracellular parasites constitutively express two isoforms of the gluconeogenic enzyme fructose 1,6-bisphosphatase (TgFBP1 and TgFBP2). The rationale for constitutive expression of FBPases in T. gondii remains unclear. We find that conditional knockdown of TgFBP2 results in complete loss of intracellular growth in vitro under glucose-replete conditions and loss of acute virulence in mice. TgFBP2 deficiency was rescued by expression of catalytically active FBPase and was associated with altered glycolytic and mitochondrial TCA cycle fluxes, as well as dysregulation of glycolipid, amylopectin, and fatty acid biosynthesis. Futile cycling between gluconeogenic and glycolytic enzymes may constitute a regulatory mechanism that allows T. gondii to rapidly adapt to changes in nutrient availability in different host cells.

The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate

Bi-phasic or diauxic growth is often observed when microbes are grown in a chemically defined medium containing two sugars (for example glucose and lactose). Typically, the two growth stages are separated by an often lengthy phase of arrested growth, the so-called lag-phase. Diauxic growth is usually interpreted as an adaptation to maximise population growth in multi-nutrient environments. However, the lag-phase implies a substantial loss of growth during the switch-over. It therefore remains unexplained why the lag-phase is adaptive. Here we show by means of a stochastic simulation model based on the bacterial PTS system that it is not possible to shorten the lag-phase without incurring a permanent growth-penalty. Mechanistically, this is due to the inherent and well established limitations of biological sensors to operate efficiently at a given resource cost. Hence, there is a trade-off between lost growth during the diauxic switch and the long-term growth potential of the cell. Using simulated evolution we predict that the lag-phase will evolve depending on the distribution of conditions experienced during adaptation. In environments where switching is less frequently required, the lag-phase will evolve to be longer whereas, in frequently changing environments, the lag-phase will evolve to be shorter.

Trehalose-6-phosphate synthesis controls yeast gluconeogenesis downstream and independent of SNF1

Trehalose-6-P (T6P), an intermediate of trehalose biosynthesis, was identified as an important regulator of yeast sugar metabolism and signaling. tps1Δ mutants, deficient in T6P synthesis (TPS), are unable to grow on rapidly fermentable medium with uncontrolled influx in glycolysis, depletion of ATP and accumulation of sugar phosphates. However, the exact molecular mechanisms involved are not fully understood. We show that SNF1 deletion restores the tps1Δ growth defect on glucose, suggesting that lack of TPS hampers inactivation of SNF1 or SNF1-regulated processes. In addition to alternative, non-fermentable carbon metabolism, SNF1 controls two major processes: respiration and gluconeogenesis. The tps1Δ defect appears to be specifically associated with deficient inhibition of gluconeogenesis, indicating more downstream effects. Consistently, Snf1 dephosphorylation and inactivation on glucose medium are not affected, as confirmed with an in vivo Snf1 activity reporter. Detailed analysis shows that gluconeogenic Pck1 and Fbp1 expression, protein levels and activity are not repressed upon glucose addition to tps1Δ cells, suggesting a link between the metabolic defect and persistent gluconeogenesis. While SNF1 is essential for induction of gluconeogenesis, T6P/TPS is required for inactivation of gluconeogenesis in the presence of glucose, downstream and independent of SNF1 activity and the Cat8 and Sip4 transcription factors.

Cellular Growth Arrest and Persistence from Enzyme Saturation

Metabolic efficiency depends on the balance between supply and demand of metabolites, which is sensitive to environmental and physiological fluctuations, or noise, causing shortages or surpluses in the metabolic pipeline. How cells can reliably optimize biomass production in the presence of metabolic fluctuations is a fundamental question that has not been fully answered. Here we use mathematical models to predict that enzyme saturation creates distinct regimes of cellular growth, including a phase of growth arrest resulting from toxicity of the metabolic process. Noise can drive entry of single cells into growth arrest while a fast-growing majority sustains the population. We confirmed these predictions by measuring the growth dynamics of Escherichia coli utilizing lactose as a sole carbon source. The predicted heterogeneous growth emerged at high lactose concentrations, and was associated with cell death and production of antibiotic-tolerant persister cells. These results suggest how metabolic networks may balance costs and benefits, with important implications for drug tolerance.

In bacteria, changes in gene expression, with resulting changes in protein concentration, can drastically change how fast cells and cellular populations grow. This fact has big implications for how we treat infectious disease, which types of organisms make up our microbiomes, and what patterns of gene regulation have undergone evolutionary selection. Here, we show how, in principle, the expression level of a single enzyme can affect bacterial population growth by creating a threshold where cells grow optimally fast just below it, but rapidly reach a state of no growth just above it because metabolic byproducts build up and halt growth. The narrow margin between these two states makes entering either of them possible for the same bacterium because of intrinsic uncertainty, or "noise", in gene expression. The predicted result is a variety of growth rates in a single population of genetically identical cells, manifested as a mix of fast- and slow-growing cells. We created laboratory conditions that reproduce the effect in the model organism E. coli, and showed that there may be a benefit to having slower growing cells, because they can survive antibiotic exposure for longer.

Identifying Metabolic Subpopulations from Population Level Mass Spectrometry

Metabolism underlies many important cellular decisions, such as the decisions to proliferate and differentiate, and defects in metabolic signaling can lead to disease and aging. In addition, metabolic heterogeneity can have biological consequences, such as differences in outcomes and drug susceptibilities in cancer and antibiotic treatments. Many approaches exist for characterizing the metabolic state of a population of cells, but technologies for measuring metabolism at the single cell level are in the preliminary stages and are limited. Here, we describe novel analysis methodologies that can be applied to established experimental methods to measure metabolic variability within a population. We use mass spectrometry to analyze amino acid composition in cells grown in a mixture of (12)C- and (13)C-labeled sugars; these measurements allow us to quantify the variability in sugar usage and thereby infer information about the behavior of cells within the population. The methodologies described here can be applied to a large range of metabolites and macromolecules and therefore have the potential for broad applications.

Stability of Ensemble Models Predicts Productivity of Enzymatic Systems

Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantly in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. The EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.

Generation and filtering of gene expression noise by the bacterial cell cycle

Background

Gene expression within cells is known to fluctuate stochastically in time. However, the origins of gene expression noise remain incompletely understood. The bacterial cell cycle has been suggested as one source, involving chromosome replication, exponential volume growth, and various other changes in cellular composition. Elucidating how these factors give rise to expression variations is important to models of cellular homeostasis, fidelity of signal transmission, and cell-fate decisions.

Results

Using single-cell time-lapse microscopy, we measured cellular growth as well as fluctuations in the expression rate of a fluorescent protein and its concentration. We found that, within the population, the mean expression rate doubles throughout the cell cycle with a characteristic cell cycle phase dependent shape which is different for slow and fast growth rates. At low growth rate, we find the mean expression rate was initially flat, and then rose approximately linearly by a factor two until the end of the cell cycle. The mean concentration fluctuated at low amplitude with sinusoidal-like dependence on cell cycle phase. Traces of individual cells were consistent with a sudden two-fold increase in expression rate, together with other non-cell cycle noise. A model was used to relate the findings and to explain the cell cycle-induced variations for different chromosomal positions.

Conclusions

We found that the bacterial cell cycle contribution to expression noise consists of two parts: a deterministic oscillation in synchrony with the cell cycle and a stochastic component caused by variable timing of gene replication. Together, they cause half of the expression rate noise. Concentration fluctuations are partially suppressed by a noise cancelling mechanism that involves the exponential growth of cellular volume. A model explains how the functional form of the concentration oscillations depends on chromosome position.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-016-0231-z) contains supplementary material, which is available to authorized users.

Central Role of Pyruvate Kinase in Carbon Co-catabolism of Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) displays a high degree of metabolic plasticity to adapt to challenging host environments. Genetic evidence suggests thatMtbrelies mainly on fatty acid catabolism in the host. However,Mtbalso maintains a functional glycolytic pathway and its role in the cellular metabolism ofMtbhas yet to be understood. Pyruvate kinase catalyzes the last and rate-limiting step in glycolysis and theMtbgenome harbors one putative pyruvate kinase (pykA, Rv1617). Here we show thatpykAencodes an active pyruvate kinase that is allosterically activated by glucose 6-phosphate (Glc-6-P) and adenosine monophosphate (AMP). Deletion ofpykApreventsMtbgrowth in the presence of fermentable carbon sources and has a cidal effect in the presence of glucose that correlates with elevated levels of the toxic catabolite methylglyoxal. Growth attenuation was also observed in media containing a combination of short chain fatty acids and glucose and surprisingly, in media containing odd and even chain fatty acids alone. Untargeted high sensitivity metabolomics revealed that inactivation of pyruvate kinase leads to accumulation of phosphoenolpyruvate (P-enolpyruvate), citrate, and aconitate, which was consistent with allosteric inhibition of isocitrate dehydrogenase by P-enolpyruvate. This metabolic block could be relieved by addition of the α-ketoglutarate precursor glutamate. Taken together, our study identifies an essential role of pyruvate kinase in preventing metabolic block during carbon co-catabolism inMtb.

A new laboratory evolution approach to select for constitutive acetic acid tolerance in Saccharomyces cerevisiae and identification of causal mutations

BACKGROUND

Acetic acid, released during hydrolysis of lignocellulosic feedstocks for second generation bioethanol production, inhibits yeast growth and alcoholic fermentation. Yeast biomass generated in a propagation step that precedes ethanol production should therefore express a high and constitutive level of acetic acid tolerance before introduction into lignocellulosic hydrolysates. However, earlier laboratory evolution strategies for increasing acetic acid tolerance of Saccharomyces cerevisiae, based on prolonged cultivation in the presence of acetic acid, selected for inducible rather than constitutive tolerance to this inhibitor.

RESULTS

Preadaptation in the presence of acetic acid was shown to strongly increase the fraction of yeast cells that could initiate growth in the presence of this inhibitor. Serial microaerobic batch cultivation, with alternating transfers to fresh medium with and without acetic acid, yielded evolved S. cerevisiae cultures with constitutive acetic acid tolerance. Single-cell lines isolated from five such evolution experiments after 50-55 transfers were selected for further study. An additional constitutively acetic acid tolerant mutant was selected after UV-mutagenesis. All six mutants showed an increased fraction of growing cells upon a transfer from a non-stressed condition to a medium containing acetic acid. Whole-genome sequencing identified six genes that contained (different) mutations in multiple acetic acid-tolerant mutants. Haploid segregation studies and expression of the mutant alleles in the unevolved ancestor strain identified causal mutations for the acquired acetic acid tolerance in four genes (ASG1, ADH3, SKS1 and GIS4). Effects of the mutations in ASG1, ADH3 and SKS1 on acetic acid tolerance were additive.

CONCLUSIONS

A novel laboratory evolution strategy based on alternating cultivation cycles in the presence and absence of acetic acid conferred a selective advantage to constitutively acetic acid-tolerant mutants and may be applicable for selection of constitutive tolerance to other stressors. Mutations in four genes (ASG1, ADH3, SKS1 and GIS4) were identified as causative for acetic acid tolerance. The laboratory evolution strategy as well as the identified mutations can contribute to improving acetic acid tolerance in industrial yeast strains.

Conditional iron and pH-dependent activity of a non-enzymatic glycolysis and pentose phosphate pathway

Little is known about the evolutionary origins of metabolism. However, key biochemical reactions of glycolysis and the pentose phosphate pathway (PPP), ancient metabolic pathways central to the metabolic network, have non-enzymatic pendants that occur in a prebiotically plausible reaction milieu reconstituted to contain Archean sediment metal components. These non-enzymatic reactions could have given rise to the origin of glycolysis and the PPP during early evolution. Using nuclear magnetic resonance spectroscopy and high-content metabolomics that allowed us to measure several thousand reaction mixtures, we experimentally address the chemical logic of a metabolism-like network constituted from these non-enzymatic reactions. Fe(II), the dominant transition metal component of Archean oceanic sediments, has binding affinity toward metabolic sugar phosphates and drives metabolism-like reactivity acting as both catalyst and cosubstrate. Iron and pH dependencies determine a metabolism-like network topology and comediate reaction rates over several orders of magnitude so that the network adopts conditional activity. Alkaline pH triggered the activity of the non-enzymatic PPP pendant, whereas gentle acidic or neutral conditions favored non-enzymatic glycolytic reactions. Fe(II)-sensitive glycolytic and PPP-like reactions thus form a chemical network mimicking structural features of extant carbon metabolism, including topology, pH dependency, and conditional reactivity. Chemical networks that obtain structure and catalysis on the basis of transition metals found in Archean sediments are hence plausible direct precursors of cellular metabolic networks.

Effective Estimation of Dynamic Metabolic Fluxes Using (13)C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability

The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and ¹³C labeling to identify piecewise-affine (PWA) flux functions has been described recently. Obtaining flux estimates nevertheless still required frequent manual reinitalization to obtain a good reproduction of the experimental data and, moreover, did not optimize on all observables simultaneously (metabolites and isotopomer concentrations). In our contribution we focus on measures to achieve faster and robust dynamic flux estimation which leads to a high dimensional parameter estimation problem. Specifically, we address the following challenges within the PWA problem formulation: (1) Fast selection of sufficient domains for the PWA flux functions, (2) Control of over-fitting in the concentration space using shape-prescriptive modeling and (3) robust and efficient implementation of the parameter estimation using the hybrid implicit filtering algorithm. With the improvements we significantly speed up the convergence by efficiently exploiting that the optimization problem is partly linear. This allows application to larger-scale metabolic networks and demonstrates that the proposed approach is not purely theoretical, but also applicable in practice.

A synthetic growth switch based on controlled expression of RNA polymerase

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium-independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild-type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology.

Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-‘omics’ steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population.

Fluorescent Reporter Libraries as Useful Tools for Optimizing Microbial Cell Factories: A Review of the Current Methods and Applications

The use of genetically encoded fluorescent reporters allows speeding up the initial optimization steps of microbial bioprocesses. These reporters can be used for determining the expression level of a particular promoter, not only the synthesis of a specific protein but also the content of intracellular metabolites. The level of protein/metabolite is thus proportional to a fluorescence signal. By this way, mean expression profiles of protein/metabolites can be determined non-invasively at a high-throughput rate, allowing the rapid identification of the best producers. Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal. Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

T cell metabolism drives immunity

Buck et al. discuss the role of lymphocyte metabolism on immune cell development and function.

Lymphocytes must adapt to a wide array of environmental stressors as part of their normal development, during which they undergo a dramatic metabolic remodeling process. Research in this area has yielded surprising findings on the roles of diverse metabolic pathways and metabolites, which have been found to regulate lymphocyte signaling and influence differentiation, function and fate. In this review, we integrate the latest findings in the field to provide an up-to-date resource on lymphocyte metabolism.

Acetate Exposure Determines the Diauxic Behavior of Escherichia coli during the Glucose-Acetate Transition

Growth of Escherichia coli on glucose in batch culture is accompanied by the excretion of acetate, which is consumed by the cells when glucose is exhausted. This glucose-acetate transition is classically described as a diauxie (two successive growth stages). Here, we investigated the physiological and metabolic properties of cells after glucose exhaustion through the analysis of growth parameters and gene expression. We found that E. coli cells grown on glucose in batch culture produce acetate and consume it after glucose exhaustion but do not grow on acetate. Acetate is catabolized, but key anabolic genes--such as the genes encoding enzymes of the glyoxylate shunt--are not upregulated, hence preventing growth. Both the induction of the latter anabolic genes and growth were observed only after prolonged exposure to low concentrations of acetate and could be accelerated by high acetate concentrations. We postulate that such decoupling between acetate catabolism and acetate anabolism might be an advantage for the survival of E. coli in the ever-changing environment of the intestine.

IMPORTANCE

The glucose-acetate transition is a valuable experimental model for comprehensive investigations of metabolic adaptation and a current paradigm for developing modeling approaches in systems microbiology. Yet, the work reported in our paper demonstrates that the metabolic behavior of Escherichia coli during the glucose-acetate transition is much more complex than what has been reported so far. A decoupling between acetate catabolism and acetate anabolism was observed after glucose exhaustion, which has not been reported previously. This phenomenon could represent a strategy for optimal utilization of carbon resources during colonization and persistence of E. coli in the gut and is also of significant interest for biotechnological applications.

Connecting growth with gene expression: of noise and numbers

Growth is a dynamic process whereby cells accumulate mass. Growth rates of single cells are connected to RNA and protein synthesis rates, and therefore with biomolecule numbers. Noise in gene expression depends on these numbers, and is thus linked with cellular growth. Whether these global attributes of the cell participate in gene regulation is still largely unexplored. New experimental and modelling studies suggest that systemic variations in biomolecule numbers can coordinate cellular processes, including growth itself, through global regulatory feedback that acts in addition to genetic regulatory networks. Here, we review these findings and speculate on possible implications of this less appreciated layer of gene regulation for cellular physiology and adaptation to changing environments.

Posttranslational regulation of microbial metabolism

Fluxes in microbial metabolism are controlled by various regulatory layers that alter abundance or activity of metabolic enzymes. Recent studies suggest a division of labor between these layers: transcriptional regulation mostly controls the allocation of protein resources, passive flux regulation by enzyme saturation and thermodynamics allows rapid responses at the expense of higher protein cost, and posttranslational regulation is utilized by cells to directly take control of metabolic decisions. We present recent advances in elucidating the role of these regulatory layers, focusing on posttranslational modifications and allosteric interactions. As the systematic mapping of posttranslational regulatory events has now become possible, the next challenge is to identify those regulatory events that are functionally relevant under a given condition.

Characterizing the in vivo role of trehalose in Saccharomyces cerevisiae using the AGT1 transporter

Trehalose is a highly stable, nonreducing disaccharide of glucose. A large body of research exists implicating trehalose in a variety of cellular phenomena, notably response to stresses of various kinds. However, in very few cases has the role of trehalose been examined directly in vivo. Here, we describe the development and characterization of a system in Saccharomyces cerevisiae that allows us to manipulate intracellular trehalose concentrations independently of the biosynthetic enzymes and independently of any applied stress. We found that many physiological roles heretofore ascribed to intracellular trehalose, including heat resistance, are not due to the presence of trehalose per se. We also found that many of the metabolic and growth defects associated with mutations in the trehalose biosynthesis pathway are not abolished by providing abundant intracellular trehalose. Instead, we made the observation that intracellular accumulation of trehalose or maltose (another disaccharide of glucose) is growth-inhibitory in a carbon source-specific manner. We conclude that the physiological role of the trehalose pathway is fundamentally metabolic: i.e., more complex than simply the consequence of increased concentrations of the sugar and its attendant physical properties (with the exception of the companion paper where Tapia et al. [Tapia H, et al. (2015) Proc Natl Acad Sci USA, 10.1073/pnas.1506415112] demonstrate a direct role for trehalose in protecting cells against desiccation).

The glycerol-dependent metabolic persistence of Pseudomonas putida KT2440 reflects the regulatory logic of the GlpR repressor

The growth of the soil bacterium Pseudomonas putida KT2440 on glycerol as the sole carbon source is characterized by a prolonged lag phase, not observed with other carbon substrates. We examined the bacterial growth in glycerol cultures while monitoring the metabolic activity of individual cells. Fluorescence microscopy and flow cytometry, as well as the analysis of the temporal start of growth in single-cell cultures, revealed that adoption of a glycerol-metabolizing regime was not the result of a gradual change in the whole population but rather reflected a time-dependent bimodal switch between metabolically inactive (i.e., nongrowing) and fully active (i.e., growing) bacteria. A transcriptional Φ(glpD-gfp) fusion (a proxy of the glycerol-3-phosphate [G3P] dehydrogenase activity) linked the macroscopic phenotype to the expression of the glp genes. Either deleting glpR (encoding the G3P-responsive transcriptional repressor that controls the expression of the glpFKRD gene cluster) or altering G3P formation (by overexpressing glpK, encoding glycerol kinase) abolished the bimodal glpD expression. These manipulations eliminated the stochastic growth start by shortening the otherwise long lag phase. Provision of glpR in trans restored the phenotypes lost in the ΔglpR mutant. The prolonged nongrowth regime of P. putida on glycerol could thus be traced to the regulatory device controlling the transcription of the glp genes. Since the physiological agonist of GlpR is G3P, the arrangement of metabolic and regulatory components at this checkpoint merges a positive feedback loop with a nonlinear transcriptional response, a layout fostering the observed time-dependent shift between two alternative physiological states.

Phenotypic variation is a widespread attribute of prokaryotes that leads, inter alia, to the emergence of persistent bacteria, i.e., live but nongrowing members within a genetically clonal population. Persistence allows a fraction of cells to avoid the killing caused by conditions or agents that destroy most growing bacteria (e.g., some antibiotics). Known molecular mechanisms underlying the phenomenon include genetic changes, epigenetic variations, and feedback-based multistability. We show that a prolonged nongrowing state of the bacterial population can be brought about by a distinct regulatory architecture of metabolic genes when cells face specific nutrients (e.g., glycerol). Pseudomonas putida may have adopted the resulting carbon source-dependent metabolic bet hedging as an advantageous trait for exploring new chemical and nutritional landscapes. Defeating such naturally occurring adaptive features of environmental bacteria is instrumental in improving the performance of these microorganisms as whole-cell catalysts in a bioreactor setup.

Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.

Single-cell screening of photosynthetic growth and lactate production by cyanobacteria

BACKGROUND

Photosynthetic cyanobacteria are attractive for a range of biotechnological applications including biofuel production. However, due to slow growth, screening of mutant libraries using microtiter plates is not feasible.

RESULTS

We present a method for high-throughput, single-cell analysis and sorting of genetically engineered l-lactate-producing strains of Synechocystis sp. PCC6803. A microfluidic device is used to encapsulate single cells in picoliter droplets, assay the droplets for l-lactate production, and sort strains with high productivity. We demonstrate the separation of low- and high-producing reference strains, as well as enrichment of a more productive l-lactate-synthesizing population after UV-induced mutagenesis. The droplet platform also revealed population heterogeneity in photosynthetic growth and lactate production, as well as the presence of metabolically stalled cells.

CONCLUSIONS

The workflow will facilitate metabolic engineering and directed evolution studies and will be useful in studies of cyanobacteria biochemistry and physiology.

Review of methods to probe single cell metabolism and bioenergetics

Single cell investigations have enabled unexpected discoveries, such as the existence of biological noise and phenotypic switching in infection, metabolism and treatment. Herein, we review methods that enable such single cell investigations specific to metabolism and bioenergetics. Firstly, we discuss how to isolate and immobilize individuals from a cell suspension, including both permanent and reversible approaches. We also highlight specific advances in microbiology for its implications in metabolic engineering. Methods for probing single cell physiology and metabolism are subsequently reviewed. The primary focus therein is on dynamic and high-content profiling strategies based on label-free and fluorescence microspectroscopy and microscopy. Non-dynamic approaches, such as mass spectrometry and nuclear magnetic resonance, are also briefly discussed.

How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics

Metabolic reaction rates (fluxes) contribute fundamentally to our understanding of metabolic phenotypes and mechanisms of cellular regulation. Stable isotope-based fluxomics integrates experimental data with biochemical networks and mathematical modeling to 'measure' the in vivo fluxes within an organism that are not directly observable. In recent years, (13)C fluxomics has evolved into a technology with great experimental, analytical, and mathematical diversity. This review aims at establishing a unified taxonomy by means of which the various fluxomics methods can be compared to each other. By linking the developed modeling approaches to recent studies, their challenges and opportunities are put into perspective. The proposed classification serves as a guide for scientific 'travelers' who are striving to resolve research questions with the currently available (13)C fluxomics toolset.

Deletion of PHO13, encoding haloacid dehalogenase type IIA phosphatase, results in upregulation of the pentose phosphate pathway in Saccharomyces cerevisiae

The haloacid dehalogenase (HAD) superfamily is one of the largest enzyme families, consisting mainly of phosphatases. Although intracellular phosphate plays important roles in many cellular activities, the biological functions of HAD enzymes are largely unknown. Pho13 is 1 of 16 putative HAD enzymes in Saccharomyces cerevisiae. Pho13 has not been studied extensively, but previous studies have identified PHO13 to be a deletion target for the generation of industrially attractive phenotypes, namely, efficient xylose fermentation and high tolerance to fermentation inhibitors. In order to understand the molecular mechanisms underlying the improved xylose-fermenting phenotype produced by deletion of PHO13 (pho13Δ), we investigated the response of S. cerevisiae to pho13Δ at the transcriptomic level when cells were grown on glucose or xylose. Transcriptome sequencing analysis revealed that pho13Δ resulted in upregulation of the pentose phosphate (PP) pathway and NADPH-producing enzymes when cells were grown on glucose or xylose. We also found that the transcriptional changes induced by pho13Δ required the transcription factor Stb5, which is activated specifically under NADPH-limiting conditions. Thus, pho13Δ resulted in the upregulation of the PP pathway and NADPH-producing enzymes as a part of an oxidative stress response mediated by activation of Stb5. Because the PP pathway is the primary pathway for xylose, its upregulation by pho13Δ might explain the improved xylose metabolism. These findings will be useful for understanding the biological function of S. cerevisiae Pho13 and the HAD superfamily enzymes and for developing S. cerevisiae strains with industrially attractive phenotypes.

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.

Multi-tasking of biosynthetic and energetic functions of glycolysis explained by supply and demand logic

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi-layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the coherence of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free-energy carriers, and precursors for biosynthesis and stress-related compounds. Here we will review the regulation of multi-tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components.