Measuring enzyme activities under standardized in vivo-like conditions for systems biology

Solutes unmask differences in clustering versus phase separation of FET proteins

Phase separation and percolation contribute to phase transitions of multivalent macromolecules. Contributions of percolation are evident through the viscoelasticity of condensates and through the formation of heterogeneous distributions of nano- and mesoscale pre-percolation clusters in sub-saturated solutions. Here, we show that clusters formed in sub-saturated solutions of FET (FUS-EWSR1-TAF15) proteins are affected differently by glutamate versus chloride. These differences on the nanoscale, gleaned using a suite of methods deployed across a wide range of protein concentrations, are prevalent and can be unmasked even though the driving forces for phase separation remain unchanged in glutamate versus chloride. Strikingly, differences in anion-mediated interactions that drive clustering saturate on the micron-scale. Beyond this length scale the system separates into coexisting phases. Overall, we find that sequence-encoded interactions, mediated by solution components, make synergistic and distinct contributions to the formation of pre-percolation clusters in sub-saturated solutions, and to the driving forces for phase separation.

Both Las17-binding sites on Arp2/3 complex are important for branching nucleation and assembly of functional endocytic actin networks in S. cerevisiae

Arp2/3 complex nucleates branched actin filaments that drive membrane invagination during endocytosis and leading-edge protrusion in lamellipodia. Arp2/3 complex is maximally activated in vitro by binding of a WASP family protein to two sites-one on the Arp3 subunit and one spanning Arp2 and ARPC1-but the importance of each site in the regulation of force-producing actin networks is unclear. Here, we identify mutations in budding yeast Arp2/3 complex that decrease or block engagement of Las17, the budding yeast WASP, at each site. As in the mammalian system, both sites are required for maximal activation in vitro. Dimerization of Las17 partially restores activity of mutations at both CA-binding sites. Arp2/3 complexes defective at either site assemble force-producing actin networks in a bead motility assay, but their reduced activity hinders motility by decreasing actin assembly near the bead surface and by failing to suppress actin filament bundling within the networks. While even the most defective Las17-binding site mutants assembled actin filaments at endocytic sites, they showed significant internalization defects, potentially because they lack the proper architecture to drive plasma membrane remodeling. Together, our data indicate that both Las17-binding sites are important to assemble functional endocytic actin networks in budding yeast, but Arp2/3 complex retains some activity in vitro and in vivo even with a severe defect at either Las17-binding site.

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration

Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

Inhibitor titrations reveal low control of glyceraldehyde 3-phosphate dehydrogenase and high control of hexokinase on glycolytic flux in an aggressive triple-negative breast cancer cell line

The glycolytic flux, and in particular lactate production, is strongly increased in cancer cells compared to normal cells, a characteristic often referred to as aerobic glycolysis or the Warburg effect. This makes the glycolytic pathway a potential drug target, in particular if the flux control distribution in the pathway has shifted due to the metabolic reprogramming in cancer cells. The flux response of a drug is dependent on both the sensitivity of the target to the drug and the flux control of the target, and both these characteristics can be exploited to obtain selectivity for cancer cells. Traditionally drug development programs have focused on selective sensitivity of the drug, not necessarily focussing on the flux control of the target. We determined the flux control of two steps that have been suggested to have high control in cancer cells, using two inhibitors, iodoacetic acid and 3-bromopyruvate, and measured a flux control of the glyceraldehyde 3-phosphate dehydrogenase close to zero, while the hexokinase holds 50% of all flux control in glycolysis in an invasive cancer cell line MDA-mb-231.

Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

The Molecular Basis for Zinc Bioavailability

Zinc is an essential micronutrient, and its deficiency is perhaps the most prevalent and least understood worldwide. Recent advances have expanded the understanding of zinc’s unique chemistry and molecular roles in a vast array of critical functions. However, beyond the concept of zinc absorption, few studies have explored the molecular basis of zinc bioavailability that determines the proportion of dietary zinc utilized in zinc-dependent processes in the body. The purpose of this review is to merge the concepts of zinc molecular biology and bioavailability with a focus on the molecular determinants of zinc luminal availability, absorption, transport, and utilization.

How a highly acidic SH3 domain folds in the absence of its charged peptide target

Charged residues on the surface of proteins are critical for both protein stability and interactions. However, many proteins contain binding regions with a high net-charge that may destabilize the protein but are useful for binding to oppositely charged targets. We hypothesized that these domains would be marginally stable, as electrostatic repulsion would compete with favorable hydrophobic collapse during folding. Furthermore, by increasing the salt concentration we predict that these protein folds would be stabilized by mimicking some of the favorable electrostatic interactions that take place during target binding. We varied the salt and urea concentrations to probe the contributions of electrostatic and hydrophobic interactions for the folding of the 60-residue yeast SH3 domain found in Abp1p. The SH3 domain was significantly stabilized with increased salt concentrations according to the Debye-Huckel limiting law. Molecular dynamics and NMR show that sodium ions interact with all 15 acidic residues but do little to change backbone dynamics or overall structure. Folding kinetics experiments show that the addition of urea or salt primarily affects the folding rate, indicating that almost all the hydrophobic collapse and electrostatic repulsion occurs in the transition state. After the transition state formation, modest yet favorable short-range salt-bridges are formed along with hydrogen bonds, as the native state fully folds. Thus, hydrophobic collapse offsets electrostatic repulsion to ensure this highly charged binding domain can still fold and be ready to bind to its charged peptide targets, a property that is likely evolutionarily conserved over one billion years.

Statement for broader audience

Some protein domains are highly charged because they are adapted to bind oppositely charged proteins and nucleic acids. However, it is unknown how these highly charged domains fold as during folding there will be significant repulsion between like-charges. We investigate how one of these highly charged domains folds in the presence of salt, which can screen the charge repulsion and make folding easier, allowing us to understand how folding occurs despite the protein’s high charge.

Supplementary material

Supplementary material document containing additional details on protein expression methods, thermodynamics and kinetics equations, and the effect of urea on electrostatic interactions, as well as 4 supplemental figures and 4 supplemental data tables. ( Supplementary_Material.docx ), 15 pages Supplemental excel file containing covariation data across AbpSH3 orthologs ( FileS1.xlsx ).

Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling

Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

Tuning Material States and Functionalities of G-Quadruplex-Modulated RNA-Peptide Condensates

RNA encodes sequence- and structure-dependent interactions to modulate the assembly and properties of biomolecular condensates. RNA G-quadruplexes (rG4s) formed by guanine-rich sequences can trigger the formation of liquid- or solid-like condensates that are involved in many aberrant phase transitions. However, exactly how rG4 motifs modulate different phase transitions and impart distinct material properties to condensates is unclear. Here, using RNA oligonucleotides and cationic peptides as model systems, we show that RNA-peptide condensates exhibit tunability in material properties over a wide spectrum via interactions arising from rG4 folding/unfolding kinetics. rG4-containing oligonucleotides formed strong pairwise attraction with peptides and tended to form solid-like condensates, while their less-structured non-G4 mutants formed liquid-like droplets. We find that the coupling between rG4 dissociation and RNA-peptide complex coacervation triggers solid-to-liquid transition of condensates prior to the complete unfolding of rG4s. This coupling points to a mechanism that material states of rG4-modulated condensates can be finely tuned from solid-like to liquid-like by the addition of less-structured RNA oligonucleotides, which have weak but dominant binding with peptides. We further show that the tunable material states of condensates can enhance RNA aptamer compartmentalization and RNA cleavage reactions. Our results suggest that condensates with complex properties can emerge from subtle changes in RNA oligonucleotides, contributing ways to treat dysfunctional condensates in diseases and insights into prebiotic compartmentalization.

Recommendations for performing measurements of apparent equilibrium constants of enzyme-catalyzed reactions and for reporting the results of these measurements

The measurement of values of apparent equilibrium constants K' for enzyme-catalyzed reactions involve a substantial number of critical details, neglect of which could lead to systematic errors. Here, interferences, impurities in the substances used, and failure to achieve equilibrium are matters of substantial consequence. Careful reporting of results is of great importance if the results are to have archival value. Thus, attention must be paid to the identification of the substances, specification of the reaction(s), the conditions of reaction, the definition of the equilibrium constant(s) and standard states, the use of standard nomenclature, symbols, and units, and uncertainties. This document contains a general discussion of various aspects of these equilibrium measurements as well as STRENDA (Standards for Reporting Enzymology Data) recommendations regarding the measurements and the reporting of results.

Structure-Guided Discovery of Potent Antifungals that Prevent Ras Signaling by Inhibiting Protein Farnesyltransferase

Infections by fungal pathogens are difficult to treat due to a paucity of antifungals and emerging resistances. Next-generation antifungals therefore are needed urgently. We have developed compounds that prevent farnesylation of Cryptoccoccus neoformans Ras protein by inhibiting protein farnesyltransferase with 3-4 nanomolar affinities. Farnesylation directs Ras to the cell membrane and is required for infectivity of this lethal pathogenic fungus. Our high-affinity compounds inhibit fungal growth with 3-6 micromolar minimum inhibitory concentrations (MICs), 4- to 8-fold better than Fluconazole, an antifungal commonly used in the clinic. Compounds bound with distinct inhibition mechanisms at two alternative, partially overlapping binding sites, accessed via different inhibitor conformations. We showed that antifungal potency depends critically on the selected inhibition mechanism because this determines the efficacy of an inhibitor at low in vivo levels of enzyme and farnesyl substrate. We elucidated how chemical modifications of the antifungals encode desired inhibitor conformation and concomitant inhibitory mechanism.

pH dependencies of glycolytic enzymes of yeast under in vivo-like assay conditions

Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S. cerevisiae under in vivo-like conditions at different pH. We demonstrate that glycolytic enzymes exhibit differential pH dependencies, and optima, in the pH range observed during carbon source transitions. The forward reaction of GAPDH shows the highest decrease in activity, 83%, during a simulated feast/famine regime upon glucose removal (cytosolic pH drop from 7.1 to 6.4). We complement our biochemical characterization of the glycolytic enzymes by fitting the V_max to the progression curves of product formation or decay over time. The fitting analysis shows that the observed changes in enzyme activities require changes in V_max , but changes in K_m cannot be excluded. Our study highlights the relevance of pH as a key player in metabolic regulation and provides a large set of quantitative data that can be explored to improve our understanding of metabolism in dynamic environments.

Manganese is a physiologically relevant TORC1 activator in yeast and mammals

The essential biometal manganese (Mn) serves as a cofactor for several enzymes that are crucial for the prevention of human diseases. Whether intracellular Mn levels may be sensed and modulate intracellular signaling events has so far remained largely unexplored. The highly conserved target of rapamycin complex 1 (TORC1, mTORC1 in mammals) protein kinase requires divalent metal cofactors such as magnesium (Mg²⁺) to phosphorylate effectors as part of a homeostatic process that coordinates cell growth and metabolism with nutrient and/or growth factor availability. Here, our genetic approaches reveal that TORC1 activity is stimulated in vivo by elevated cytoplasmic Mn levels, which can be induced by loss of the Golgi-resident Mn²⁺ transporter Pmr1 and which depend on the natural resistance-associated macrophage protein (NRAMP) metal ion transporters Smf1 and Smf2. Accordingly, genetic interventions that increase cytoplasmic Mn²⁺ levels antagonize the effects of rapamycin in triggering autophagy, mitophagy, and Rtg1-Rtg3-dependent mitochondrion-to-nucleus retrograde signaling. Surprisingly, our in vitro protein kinase assays uncovered that Mn²⁺ activates TORC1 substantially better than Mg²⁺, which is primarily due to its ability to lower the K_m for ATP, thereby allowing more efficient ATP coordination in the catalytic cleft of TORC1. These findings, therefore, provide both a mechanism to explain our genetic observations in yeast and a rationale for how fluctuations in trace amounts of Mn can become physiologically relevant. Supporting this notion, TORC1 is also wired to feedback control mechanisms that impinge on Smf1 and Smf2. Finally, we also show that Mn²⁺-mediated control of TORC1 is evolutionarily conserved in mammals, which may prove relevant for our understanding of the role of Mn in human diseases.

Single-molecule analysis of actin filament debranching by cofilin and GMF

Eukaryotic cells contain branched actin networks that are essential for endocytosis, motility, and other key cellular processes. These networks, which are formed by filamentous actin and the Arp2/3 complex, must subsequently be debranched to allow network remodeling and to recycle the Arp2/3 complex. Debranching appears to be catalyzed by two different members of the actin depolymerizing factor homology protein family: cofilin and glial maturation factor (GMF). However, their mechanisms of debranching are only partially understood. Here, we used single-molecule fluorescence imaging of Arp2/3 complex and actin filaments under physiological ionic conditions to observe debranching by GMF and cofilin. We demonstrate that cofilin, like GMF, is an authentic debrancher independent of its filament-severing activity and that the debranching activities of the two proteins are additive. While GMF binds directly to the Arp2/3 complex, cofilin selectively accumulates on branch-junction daughter filaments in tropomyosin-decorated networks just prior to debranching events. Quantitative comparison of debranching rates with the known kinetics of cofilin-actin binding suggests that cofilin occupancy of a particular single actin site at the branch junction is sufficient to trigger debranching. In rare cases in which the order of departure could be resolved during GMF- or cofilin-induced debranching, the Arp2/3 complex left the branch junction bound to the pointed end of the daughter filament, suggesting that both GMF and cofilin can work by destabilizing the mother filament-Arp2/3 complex interface. Taken together, these observations suggest that GMF and cofilin promote debranching by distinct yet complementary mechanisms.

Full humanization of the glycolytic pathway in Saccharomyces cerevisiae

Although transplantation of single genes in yeast plays a key role in elucidating gene functionality in metazoans, technical challenges hamper humanization of full pathways and processes. Empowered by advances in synthetic biology, this study demonstrates the feasibility and implementation of full humanization of glycolysis in yeast. Single gene and full pathway transplantation revealed the remarkable conservation of glycolytic and moonlighting functions and, combined with evolutionary strategies, brought to light context-dependent responses. Human hexokinase 1 and 2, but not 4, required mutations in their catalytic or allosteric sites for functionality in yeast, whereas hexokinase 3 was unable to complement its yeast ortholog. Comparison with human tissues cultures showed preservation of turnover numbers of human glycolytic enzymes in yeast and human cell cultures. This demonstration of transplantation of an entire essential pathway paves the way for establishment of species-, tissue-, and disease-specific metazoan models.

Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints

The concept of metabolic models with resource allocation constraints has been around for over a decade and has clear advantages even when implementation is relatively rudimentary. Nonetheless, the number of organisms for which such a model is reconstructed is low. Various approaches exist, from coarse-grained consideration of enzyme usage to fine-grained description of protein translation. These approaches are reviewed here, with a particular focus on user-friendly solutions that can introduce resource allocation constraints to metabolic models of any organism. The availability of k_cat data is a major hurdle, where recent advances might help to fill in the numerous gaps that exist for this data, especially for nonmodel organisms.

Genetically encoded tools for measuring and manipulating metabolism

Over the past few years, we have seen an explosion of novel genetically encoded tools for measuring and manipulating metabolism in live cells and animals. Here, we will review the genetically encoded tools that are available, describe how these tools can be used and outline areas where future development is needed in this fast-paced field. We will focus on tools for direct measurement and manipulation of metabolites. Metabolites are master regulators of metabolism and physiology through their action on metabolic enzymes, signaling enzymes, ion channels and transcription factors, among others. We hope that this Perspective will encourage more people to use these novel reagents or even join this exciting new field to develop novel tools for measuring and manipulating metabolism.

High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering

As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k_cat, termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the k_cat values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate kmaxvivo (k_app) for k_cat and developed a high throughput method to acquire kmaxvivo from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology.

A counter-enzyme complex regulates glutamate metabolism in Bacillus subtilis

Multi-enzyme assemblies composed of metabolic enzymes catalyzing sequential reactions are being increasingly studied. Here, we report the discovery of a 1.6 megadalton multi-enzyme complex from Bacillus subtilis composed of two enzymes catalyzing opposite ('counter-enzymes') rather than sequential reactions: glutamate synthase (GltAB) and glutamate dehydrogenase (GudB), which make and break glutamate, respectively. In vivo and in vitro studies show that the primary role of complex formation is to inhibit the activity of GudB. Using cryo-electron microscopy, we elucidated the structure of the complex and the molecular basis of inhibition of GudB by GltAB. The complex exhibits unusual oscillatory progress curves and is necessary for both planktonic growth, in glutamate-limiting conditions, and for biofilm growth, in glutamate-rich media. The regulation of a key metabolic enzyme by complexing with its counter enzyme may thus enable cell growth under fluctuating glutamate concentrations.

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Parameter Reliability and Understanding Enzyme Function

Knowledge of the Michaelis-Menten parameters and their meaning in different circumstances is an essential prerequisite to understanding enzyme function and behaviour. The published literature contains an abundance of values reported for many enzymes. The problem concerns assessing the appropriateness and validity of such material for the purpose to which it is to be applied. This review considers the evaluation of such data with particular emphasis on the assessment of its fitness for purpose.

Septin Assembly and Remodeling at the Cell Division Site During the Cell Cycle

The septin family of proteins can assemble into filaments that further organize into different higher order structures to perform a variety of different functions in different cell types and organisms. In the budding yeast Saccharomyces cerevisiae, the septins localize to the presumptive bud site as a cortical ring prior to bud emergence, expand into an hourglass at the bud neck (cell division site) during bud growth, and finally “split” into a double ring sandwiching the cell division machinery during cytokinesis. While much work has been done to understand the functions and molecular makeups of these structures, the mechanisms underlying the transitions from one structure to another have largely remained elusive. Recent studies involving advanced imaging and in vitro reconstitution have begun to reveal the vast complexity involved in the regulation of these structural transitions, which defines the focus of discussion in this mini-review.

Functional Roles of Chelated Magnesium Ions in RNA Folding and Function

RNA regulates myriad cellular events such as transcription, translation, and splicing. To perform these essential functions, RNA often folds into complex tertiary structures in which its negatively charged ribose-phosphate backbone interacts with metal ions. Magnesium, the most abundant divalent metal ion in cells, neutralizes the backbone, thereby playing essential roles in RNA folding and function. This has been known for more than 50 years, and there are now thousands of in vitro studies, most of which have used ≥10 mM free Mg²⁺ ions to achieve optimal RNA folding and function. In the cell, however, concentrations of free Mg²⁺ ions are much lower, with most Mg²⁺ ions chelated by metabolites. In this Perspective, we curate data from a number of sources to provide extensive summaries of cellular concentrations of metabolites that bind Mg²⁺ and to estimate cellular concentrations of metabolite-chelated Mg²⁺ species, in the representative prokaryotic and eukaryotic systems Escherichia coli, Saccharomyces cerevisiae, and iBMK cells. Recent research from our lab and others has uncovered the fact that such weakly chelated Mg²⁺ ions can enhance RNA function, including its thermodynamic stability, chemical stability, and catalysis. We also discuss how metabolite-chelated Mg²⁺ complexes may have played roles in the origins of life. It is clear from this analysis that bound Mg²⁺ should not be simply considered non-RNA-interacting and that future RNA research, as well as protein research, could benefit from considering chelated magnesium.

Engineering the thermotolerant industrial yeast Kluyveromyces marxianus for anaerobic growth

Current large-scale, anaerobic industrial processes for ethanol production from renewable carbohydrates predominantly rely on the mesophilic yeast Saccharomyces cerevisiae. Use of thermotolerant, facultatively fermentative yeasts such as Kluyveromyces marxianus could confer significant economic benefits. However, in contrast to S. cerevisiae, these yeasts cannot grow in the absence of oxygen. Responses of K. marxianus and S. cerevisiae to different oxygen-limitation regimes were analyzed in chemostats. Genome and transcriptome analysis, physiological responses to sterol supplementation and sterol-uptake measurements identified absence of a functional sterol-uptake mechanism as a key factor underlying the oxygen requirement of K. marxianus. Heterologous expression of a squalene-tetrahymanol cyclase enabled oxygen-independent synthesis of the sterol surrogate tetrahymanol in K. marxianus. After a brief adaptation under oxygen-limited conditions, tetrahymanol-expressing K. marxianus strains grew anaerobically on glucose at temperatures of up to 45 °C. These results open up new directions in the development of thermotolerant yeast strains for anaerobic industrial applications.

Critical parameters and procedures for anaerobic cultivation of yeasts in bioreactors and anaerobic chambers

All known facultatively fermentative yeasts require molecular oxygen for growth. Only in a small number of yeast species, these requirements can be circumvented by supplementation of known anaerobic growth factors such as nicotinate, sterols and unsaturated fatty acids. Biosynthetic oxygen requirements of yeasts are typically small and, unless extensive precautions are taken to minimize inadvertent entry of trace amounts of oxygen, easily go unnoticed in small-scale laboratory cultivation systems. This paper discusses critical points in the design of anaerobic yeast cultivation experiments in anaerobic chambers and laboratory bioreactors. Serial transfer or continuous cultivation to dilute growth factors present in anaerobically pre-grown inocula, systematic inclusion of control strains and minimizing the impact of oxygen diffusion through tubing are identified as key elements in experimental design. Basic protocols are presented for anaerobic-chamber and bioreactor experiments.

A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux

RNA-based sensors for intracellular metabolites are a promising solution to the emerging issue of metabolic heterogeneity. However, their development, i.e., the conversion of an aptamer into an in vivo-functional intracellular metabolite sensor, still harbors challenges. Here, we accomplished this for the glycolytic flux-signaling metabolite, fructose-1,6-bisphosphate (FBP). Starting from in vitro selection of an aptamer, we constructed device libraries with a hammerhead ribozyme as actuator. Using high-throughput screening in yeast with fluorescence-activated cell sorting (FACS), next-generation sequencing, and genetic-environmental perturbations to modulate the intracellular FBP levels, we identified a sensor that generates ratiometric fluorescent readout. An abrogated response in sensor mutants and occurrence of two sensor conformations-revealed by RNA structural probing-indicated in vivo riboswitching activity. Microscopy showed that the sensor can differentiate cells with different glycolytic fluxes within yeast populations, opening research avenues into metabolic heterogeneity. We demonstrate the possibility to generate RNA-based sensors for intracellular metabolites for which no natural metabolite-binding RNA element exits.

Modeling Microtubule Counterion Distributions and Conductivity Using the Poisson-Boltzmann Equation

Microtubules are highly negatively charged proteins which have been shown to behave as bio-nanowires capable of conducting ionic currents. The electrical characteristics of microtubules are highly complicated and have been the subject of previous work; however, the impact of the ionic concentration of the buffer solution on microtubule electrical properties has often been overlooked. In this work we use the non-linear Poisson Boltzmann equation, modified to account for a variable permittivity and a Stern Layer, to calculate counterion concentration profiles as a function of the ionic concentration of the buffer. We find that for low-concentration buffers ([KCl] from 10 μM to 10 mM) the counterion concentration is largely independent of the buffer's ionic concentration, but for physiological-concentration buffers ([KCl] from 100 to 500 mM) the counterion concentration varies dramatically with changes in the buffer's ionic concentration. We then calculate the conductivity of microtubule-counterion complexes, which are found to be more conductive than the buffer when the buffer's ionic concentrations is less than ≈100 mM and less conductive otherwise. These results demonstrate the importance of accounting for the ionic concentration of the buffer when analyzing microtubule electrical properties both under laboratory and physiological conditions. We conclude by calculating the basic electrical parameters of microtubules over a range of ionic buffer concentrations applicable to nanodevice and medical applications.

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neighbor regressor with the Reversible Differential Evolution and the Evolution of Distribution Algorithm, that speeds up convergence. We present the optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.

All Wired Up: An Exploration of the Electrical Properties of Microtubules and Tubulin

Microtubules are hollow, cylindrical polymers of the protein α, β tubulin, that interact mechanochemically with a variety of macromolecules. Due to their mechanically robust nature, microtubules have gained attention as tracks for precisely directed transport of nanomaterials within lab-on-a-chip devices. Primarily due to the unusually negative tail-like C-termini of tubulin, recent work demonstrates that these biopolymers are also involved in a broad spectrum of intracellular electrical signaling. Microtubules and their electrostatic properties are discussed in this Review, followed by an evaluation of how these biopolymers respond mechanically to electrical stimuli, through microtubule migration, electrorotation and C-termini conformation changes. Literature focusing on how microtubules act as nanowires capable of intracellular ionic transport, charge storage, and ionic signal amplification is reviewed, illustrating how these biopolymers attenuate ionic movement in response to electrical stimuli. The Review ends with a discussion on the important questions, challenges, and future opportunities for intracellular microtubule-based electrical signaling.

Macroscopic modeling of bioreactors for recombinant protein producing Pichia pastoris in defined medium

The methylotrophic yeast Pichia pastoris is widely used as a microbial host for recombinant protein production. Bioreactor models for P. pastoris can inform understanding of cellular metabolism and can be used to optimize bioreactor operation. This article constructs an extensive macroscopic bioreactor model for P. pastoris which describes substrates, biomass, total protein, other medium components, and off-gas components. Species and elemental balances are introduced to describe uptake and evolution rates for medium components and off-gas components. Additionally, a pH model is constructed using an overall charge balance, acid/base equilibria, and activity coefficients to describe production of recombinant protein and precipitation of medium components. The extent of run-to-run variability is modeled by distributions of a subset of the model parameters, which are estimated using the maximum likelihood method. Model prediction from the extensive macroscopic bioreactor model well describes experimental data with different operating conditions. The probability distributions of the model predictions quantified from the parameter distribution are quantifiably consistent with the run-to-run variability observed in the experimental data. The uncertainty description in this macroscopic bioreactor model identifies the model parameters that have large variability and provides guidance as to which aspects of cellular metabolism should be the focus of additional experimental studies. The model for medium components with pH and precipitation can be used for improving chemically defined medium by minimizing the amount of components needed while meeting cellular requirements.

Efficient DNA double-strand break formation at single or multiple defined sites in the Saccharomyces cerevisiae genome

DNA double-strand breaks (DSBs) are common genome lesions that threaten genome stability and cell survival. Cells use sophisticated repair machineries to detect and heal DSBs. To study DSB repair pathways and associated factors, inducible site-specific endonucleases have proven to be fundamental tools. In Saccharomyces cerevisiae, galactose-inducible rare-cutting endonucleases are commonly used to create a single DSB at a unique cleavage site. Galactose induction requires cell cultivation in suboptimal growth media, which is tedious especially when working with slow growing DSB repair mutants. Moreover, endonucleases that simultaneously create DSBs in multiple defined and unique loci of the yeast genome are not available, hindering studies of DSB repair in different genomic regions and chromatin contexts. Here, we present new tools to overcome these limitations. We employ a heterologous media-independent induction system to express the yeast HO endonuclease or bacterial restriction enzymes for single or multiple DSB formation, respectively. The systems facilitate tightly controlled and efficient DSB formation at defined genomic sites and will be valuable tools to study DSB repair at a local and genome-wide scale.

Simultaneous Induction of Glycolysis and Oxidative Phosphorylation during Activation of Hepatic Stellate Cells Reveals Novel Mitochondrial Targets to Treat Liver Fibrosis

Upon liver injury, hepatic stellate cells (HSCs) transdifferentiate to migratory, proliferative and extracellular matrix-producing myofibroblasts (e.g., activated HSCs; aHSCs) causing liver fibrosis. HSC activation is associated with increased glycolysis and glutaminolysis. Here, we compared the contribution of glycolysis, glutaminolysis and mitochondrial oxidative phosphorylation (OXPHOS) in rat and human HSC activation. Basal levels of glycolysis (extracellular acidification rate ~3-fold higher) and particularly mitochondrial respiration (oxygen consumption rate ~5-fold higher) were significantly increased in rat aHSCs, when compared to quiescent rat HSC. This was accompanied by extensive mitochondrial fusion in rat and human aHSCs, which occurred without increasing mitochondrial DNA content and electron transport chain (ETC) components. Inhibition of glycolysis (by 2-deoxy-D-glucose) and glutaminolysis (by CB-839) did not inhibit rat aHSC proliferation, but did reduce Acta2 (encoding α-SMA) expression slightly. In contrast, inhibiting mitochondrial OXPHOS (by rotenone) significantly suppressed rat aHSC proliferation, as well as Col1a1 and Acta2 expression. Other than that observed for rat aHSCs, human aHSC proliferation and expression of fibrosis markers were significantly suppressed by inhibiting either glycolysis, glutaminolysis or mitochondrial OXPHOS (by metformin). Activation of HSCs is marked by simultaneous induction of glycolysis and mitochondrial metabolism, extending the possibilities to suppress hepatic fibrogenesis by interfering with HSC metabolism.

In Vitro Synthesis and Self-Assembly of Cellulose II Nanofibrils Catalyzed by the Reverse Reaction of Clostridium thermocellum Cellodextrin Phosphorylase

In nature, various organisms produce cellulose as microfibrils, which are processed into their nano- and microfibrillar and/or crystalline components by humans in order to obtain desired material properties. Interestingly, the natural synthesis machinery can be circumvented by enzymatically synthesizing cellulose from precursor molecules in vitro. This approach is appealing for producing tailor-made cellulosic particles and materials because it enables optimization of the reaction conditions for cellulose synthesis in order to generate particles with a desired morphology in their pure form. Here, we present enzymatic cellulose synthesis catalyzed by the reverse reaction of Clostridium thermocellum cellodextrin phosphorylase in vitro. We were able to produce cellulose II nanofibril networks in all conditions tested, using varying concentrations of the glycosyl acceptors d-glucose or d-cellobiose (0.5, 5, and 50 mM). We show that shorter cellulose chains assemble into flat ribbon-like fibrils with greater diameter, while longer chains assemble into cylindrical fibrils with smaller diameter.

Investigation of the Electrical Properties of Microtubule Ensembles under Cell-Like Conditions

Microtubules are hollow cylindrical polymers composed of the highly negatively-charged (~23e), high dipole moment (1750 D) protein α, β- tubulin. While the roles of microtubules in chromosomal segregation, macromolecular transport, and cell migration are relatively well-understood, studies on the electrical properties of microtubules have only recently gained strong interest. Here, we show that while microtubules at physiological concentrations increase solution capacitance, free tubulin has no appreciable effect. Further, we observed a decrease in electrical resistance of solution, with charge transport peaking between 20-60 Hz in the presence of microtubules, consistent with recent findings that microtubules exhibit electric oscillations at such low frequencies. We were able to quantify the capacitance and resistance of the microtubules (MT) network at physiological tubulin concentrations to be 1.27 × 10^-5 F and 9.74 × 10⁴ Ω. Our results show that in addition to macromolecular transport, microtubules also act as charge storage devices through counterionic condensation across a broad frequency spectrum. We conclude with a hypothesis of an electrically tunable cytoskeleton where the dielectric properties of tubulin are polymerisation-state dependent.

Weak Acid Permeation in Synthetic Lipid Vesicles and Across the Yeast Plasma Membrane

We present a fluorescence-based approach for determination of the permeability of small molecules across the membranes of lipid vesicles and living cells. With properly designed experiments, the method allows us to assess the membrane physical properties both in vitro and in vivo. We find that the permeability of weak acids increases in the order of benzoic > acetic > formic > lactic, both in synthetic lipid vesicles and the plasma membrane of Saccharomyces cerevisiae, but the permeability is much lower in yeast (one to two orders of magnitude). We observe a relation between the molecule permeability and the saturation of the lipid acyl chain (i.e., lipid packing) in the synthetic lipid vesicles. By analyzing wild-type yeast and a manifold knockout strain lacking all putative lactic acid transporters, we conclude that the yeast plasma membrane is impermeable to lactic acid on timescales up to ∼2.5 h.

Highly thermostable carboxylic acid reductases generated by ancestral sequence reconstruction

Carboxylic acid reductases (CARs) are biocatalysts of industrial importance. Their properties, especially their poor stability, render them sub-optimal for use in a bioindustrial pipeline. Here, we employed ancestral sequence reconstruction (ASR) - a burgeoning engineering tool that can identify stabilizing but enzymatically neutral mutations throughout a protein. We used a three-algorithm approach to reconstruct functional ancestors of the Mycobacterial and Nocardial CAR1 orthologues. Ancestral CARs (AncCARs) were confirmed to be CAR enzymes with a preference for aromatic carboxylic acids. Ancestors also showed varied tolerances to solvents, pH and in vivo-like salt concentrations. Compared to well-studied extant CARs, AncCARs had a Tm up to 35 °C higher, with half-lives up to nine times longer than the greatest previously observed. Using ancestral reconstruction we have expanded the existing CAR toolbox with three new thermostable CAR enzymes, providing access to the high temperature biosynthesis of aldehydes to drive new applications in biocatalysis.

Keep talking: crosstalk between iron and sulfur networks fine-tunes growth and development to promote survival under iron limitation

Plants are capable of synthesizing all the molecules necessary to complete their life cycle from minerals, water, and light. This plasticity, however, comes at a high energetic cost and therefore plants need to regulate their economy and allocate resources accordingly. Iron-sulfur (Fe-S) clusters are at the center of photosynthesis, respiration, amino acid, and DNA metabolism. Fe-S clusters are extraordinary catalysts, but their main components (Fe2+ and S2-) are highly reactive and potentially toxic. To prevent toxicity, plants have evolved mechanisms to regulate the uptake, storage, and assimilation of Fe and S. Recent advances have been made in understanding the cellular economy of Fe and S metabolism individually, and growing evidence suggests that there is dynamic crosstalk between Fe and S networks. In this review, we summarize and discuss recent literature on Fe sensing, allocation, use efficiency, and, when pertinent, its relationship to S metabolism. Our future perspectives include a discussion about the open questions and challenges ahead and how the plant nutrition field can come together to approach these questions in a cohesive and more efficient way.

Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

Metabolic Models of Protein Allocation Call for the Kinetome

The flux of metabolites in the living cell depend on enzyme activities. Recently, many metabolic phenotypes have been explained by computer models that incorporate enzyme activity data. To move further, the scientific community needs to measure the kinetics of all enzymes in a systematic way.

In vitro reconstitution and characterisation of the oxidative D-xylose pathway for production of organic acids and alcohols

The oxidative d-xylose pathway, i.e. Dahms pathway, can be utilised to produce from cheap biomass raw material useful chemical intermediates. In vitro metabolic pathways offer a fast way to study the rate-limiting steps and find the most suitable enzymes for each reaction. We have constructed here in vitro multi-enzyme cascades leading from d-xylose or d-xylonolactone to ethylene glycol, glycolic acid and lactic acid, and use simple spectrophotometric assays for the read-out of the efficiency of these pathways. Based on our earlier results, we focussed particularly on the less studied xylonolactone ring opening (hydrolysis) reaction. The bacterial Caulobacter crescentus lactonase (Cc XylC), was shown to be a metal-dependent enzyme clearly improving the formation of d-xylonic acid at pH range from 6 to 8. The following dehydration reaction by the ILVD/EDD family d-xylonate dehydratase is a rate-limiting step in the pathway, and an effort was made to screen for novel enolase family d-xylonate dehydratases, however, no suitable replacing enzymes were found for this reaction. Concerning the oxidation of glycolaldehyde to glycolic acid, several enzyme candidates were also tested. Both Escherichia coli aldehyde dehydrogenase (Ec AldA) and Azospirillum brasilense α-ketoglutarate semialdehyde dehydrogenase (Ab AraE) proved to be suitable enzymes for this reaction.

Combined In-Cell NMR and Simulation Approach to Probe Redox-Dependent Pathway Control

Dynamic response of intracellular reaction cascades to changing environments is a hallmark of living systems. As metabolism is complex, mechanistic models have gained popularity for describing the dynamic response of cellular metabolism and for identifying target genes for engineering. At the same time, the detailed tracking of transient metabolism in living cells on the subminute time scale has become amenable using dynamic nuclear polarization-enhanced ¹³C NMR. Here, we suggest an approach combining in-cell NMR spectroscopy with perturbation experiments and modeling to obtain evidence that the bottlenecks of yeast glycolysis depend on intracellular redox state. In pre-steady-state glycolysis, pathway bottlenecks shift from downstream to upstream reactions within a few seconds, consistent with a rapid decline in the NAD⁺/NADH ratio. Simulations using mechanistic models reproduce the experimentally observed response and help identify unforeseen biochemical events. Remaining inaccuracies in the computational models can be identified experimentally. The combined use of rapid injection NMR spectroscopy and in silico simulations provides a promising method for characterizing cellular reactions with increasing mechanistic detail.

PI(3,5)P2 controls vacuole potassium transport to support cellular osmoregulation

Lysosomes are dynamic organelles with critical roles in cellular physiology. The lysosomal signaling lipid phosphatidylinositol 3,5-bisphosphate (PI(3,5)P₂) is a key regulator that has been implicated to control lysosome ion homeostasis, but the scope of ion transporters targeted by PI(3,5)P₂ and the purpose of this regulation is not well understood. Through an unbiased screen in Saccharomyces cerevisiae, we identified loss-of-function mutations in the vacuolar H⁺-ATPase (V-ATPase) and in Vnx1, a vacuolar monovalent cation/proton antiporter, as suppressor mutations that relieve the growth defects and osmotic swelling of vacuoles (lysosomes) in yeast lacking PI(3,5)P₂. We observed that depletion of PI(3,5)P₂ synthesis in yeast causes a robust accumulation of multiple cations, most notably an ∼85 mM increase in the cellular concentration of potassium, a critical ion used by cells to regulate osmolarity. The accumulation of potassium and other cations in PI(3,5)P₂-deficient yeast is relieved by mutations that inactivate Vnx1 or inactivate the V-ATPase and by mutations that increase the activity of a vacuolar cation export channel, Yvc1. Collectively, our data demonstrate that PI(3,5)P₂ signaling orchestrates vacuole/lysosome cation transport to aid cellular osmoregulation.

Picosecond orientational dynamics of water in living cells

Cells are extremely crowded, and a central question in biology is how this affects the intracellular water. Here, we use ultrafast vibrational spectroscopy and dielectric-relaxation spectroscopy to observe the random orientational motion of water molecules inside living cells of three prototypical organisms: Escherichia coli, Saccharomyces cerevisiae (yeast), and spores of Bacillus subtilis. In all three organisms, most of the intracellular water exhibits the same random orientational motion as neat water (characteristic time constants ~9 and ~2 ps for the first-order and second-order orientational correlation functions), whereas a smaller fraction exhibits slower orientational dynamics. The fraction of slow intracellular water varies between organisms, ranging from ~20% in E. coli to ~45% in B. subtilis spores. Comparison with the water dynamics observed in solutions mimicking the chemical composition of (parts of) the cytosol shows that the slow water is bound mostly to proteins, and to a lesser extent to other biomolecules and ions.

The cytoplasm’s crowdedness leads one to expect that cell water is different from bulk water. By measuring the rotational motion of water molecules in living cells, Tros et al. find that apart from a small fraction of water solvating biomolecules, cell water has the same dynamics as bulk water.

Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks

Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive capabilities. In this review, we present an overview of the relevant mathematical frameworks for kinetic formulation, construction and analysis. Starting with kinetic formalisms, we next review statistical methods for parameter inference, as well as recent computational frameworks applied to the construction and analysis of kinetic models. Finally, we discuss opportunities and limitations hindering the development of larger kinetic reconstructions.

Quantifying enzyme activity in living cells

For over a century, enzymatic activity has been studied in vitro, assuming similar activity in the crowded cellular milieu. Here, we determined in real time the catalytic activity of TEM1-β-lactamase inside living cells and compared the values to those obtained in vitro We found the apparent in vivo catalytic efficiency, k_cat/K_m , to be lower than in vitro, with significant cell-to-cell variability. Surprisingly, the results show that inside the cell the apparent catalytic efficiency decreases, and K_m increases with increasing enzyme concentration. To rationalize these findings, we measured enzyme and substrate diffusion rates in the cell and found the latter to be slower than expected. Simulations showed that for attenuated diffusion the substrate flux becomes rate-limiting, explaining why reaction rates in vivo can be independent on enzyme concentrations. The octanol/water partition of the substrate is 4.5, which is in the range of Food and Drug Administration-approved drugs. This suggests substrate-limited reaction rates to be common. These findings indicate that in vitro data cannot be simply extrapolated to the crowded in vivo environment.

The Peculiar Glycolytic Pathway in Hyperthermophylic Archaea: Understanding Its Whims by Experimentation In Silico

Mathematical models are key to systems biology where they typically describe the topology and dynamics of biological networks, listing biochemical entities and their relationships with one another. Some (hyper)thermophilic Archaea contain an enzyme, called non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN), which catalyzes the direct oxidation of glyceraldehyde-3-phosphate to 3-phosphoglycerate omitting adenosine 5′-triphosphate (ATP) formation by substrate-level-phosphorylation via phosphoglycerate kinase. In this study we formulate three hypotheses that could explain functionally why GAPN exists in these Archaea, and then construct and use mathematical models to test these three hypotheses. We used kinetic parameters of enzymes of Sulfolobus solfataricus (S. solfataricus) which is a thermo-acidophilic archaeon that grows optimally between 60 and 90 °C and between pH 2 and 4. For comparison, we used a model of Saccharomyces cerevisiae (S. cerevisiae), an organism that can live at moderate temperatures. We find that both the first hypothesis, i.e., that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plus phosphoglycerate kinase (PGK) route (the alternative to GAPN) is thermodynamically too much uphill and the third hypothesis, i.e., that GAPDH plus PGK are required to carry the flux in the gluconeogenic direction, are correct. The second hypothesis, i.e., that the GAPDH plus PGK route delivers less than the 1 ATP per pyruvate that is delivered by the GAPN route, is only correct when GAPDH reaction has a high rate and 1,3-bis-phosphoglycerate (BPG) spontaneously degrades to 3PG at a high rate.

Synthetic Riboswitches: From Plug and Pray toward Plug and Play

In synthetic biology, metabolic engineering, and gene therapy, there is a strong demand for orthogonal or externally controlled regulation of gene expression. Here, RNA-based regulatory devices represent a promising emerging alternative to proteins, allowing a fast and direct control of gene expression, as no synthesis of regulatory proteins is required. Besides programmable ribozyme elements controlling mRNA stability, regulatory RNA structures in untranslated regions are highly interesting for engineering approaches. Riboswitches are especially well suited, as they show a modular composition of sensor and response elements, allowing a free combination of different modules in a plug-and-play-like mode. The sensor or aptamer domain specifically interacts with a trigger molecule as a ligand, modulating the activity of the adjacent response domain that controls the expression of the genes located downstream, in most cases at the level of transcription or translation. In this review, we discuss the recent advances and strategies for designing such synthetic riboswitches based on natural or artificial components and readout systems, from trial-and-error approaches to rational design strategies. As the past several years have shown dramatic development in this fascinating field of research, we can give only a limited overview of the basic riboswitch design principles that is far from complete, and we apologize for not being able to consider every successful and interesting approach described in the literature.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as k_cat and K_M constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

“Enzyme cost”, the amount of protein needed for a given metabolic flux, is crucial for the metabolic choices cells have to make. However, due to the technical limitations of linear optimization methods, this cost has traditionally been ignored by constraint-based metabolic models such as Flux Balance Analysis. On the other hand, more detailed kinetic models which use ordinary differential equations to simulate fluxes for different choices of enzyme allocation, are computationally demanding and not scalable enough. In this work, we developed a method which utilizes the full kinetic model to predict steady-state enzyme costs, using a scalable and robust algorithm based on convex optimization. We show that the minimization of enzyme cost is a meaningful optimality principle by comparing our predictions to measured enzyme and metabolite levels in exponentially growing E. coli. This method could be used to quantify the enzyme cost of many other pathways and explain why evolution has selected some low-yield metabolic strategies, including aerobic fermentation in yeast and cancer cells. Furthermore, future metabolic engineering projects could benefit from our method by choosing pathways that reduce the total amount of enzyme required for the synthesis of a value-added product.