Regulatory architecture determines optimal regulation of gene expression in metabolic pathways

The modular biochemical reaction network structure of cellular translation

Translation is an essential attribute of all living cells. At the heart of cellular operation, it is a chemical information decoding process that begins with an input string of nucleotides and ends with the synthesis of a specific output string of peptides. The translation process is interconnected with gene expression, physiological regulation, transcription, and responses to signaling molecules, among other cellular functions. Foundational efforts have uncovered a wealth of knowledge about the mechanistic functions of the components of translation and their many interactions between them, but the broader biochemical connections between translation, metabolism and polymer biosynthesis that enable translation to occur have not been comprehensively mapped. Here we present a multilayer graph of biochemical reactions describing the translation, polymer biosynthesis and metabolism networks of an Escherichia coli cell. Intriguingly, the compounds that compose these three layers are distinctly aggregated into three modes regardless of their layer categorization. Multimodal mass distributions are well-known in ecosystems, but this is the first such distribution reported at the biochemical level. The degree distributions of the translation and metabolic networks are each likely to be heavy-tailed, but the polymer biosynthesis network is not. A multimodal mass-degree distribution indicates that the translation and metabolism networks are each distinct, adaptive biochemical modules, and that the gaps between the modes reflect evolved responses to the functional use of metabolite, polypeptide and polynucleotide compounds. The chemical reaction network of cellular translation opens new avenues for exploring complex adaptive phenomena such as percolation and phase changes in biochemical contexts.

Glucose Starvation Stimulates the Promoting Strength of a Novel Evolved Suc2 Promoter

Promoters are essential for designing Saccharomyces cerevisiae cell factories. Identifying novel promoters tuned to produce specific metabolites under increasingly diverse industrial stresses is required to improve the economic feasibility of whole fermentation processes. In this study, a positively evolved Suc2 promoter (SUC 2p) with promoter activity stronger than that of the wild-type Suc2 promoter (SUC 2wtp) was obtained. Quantitative real-time PCR, fluorescence analysis, Western blotting, and a β-galactosidase activity assay revealed that SUC 2p is a medium-strength promoter compared with eight reported promoters at a medium glucose concentration (2% (w/v)). Different glucose concentrations modulated the strength of SUC 2p. Low glucose concentrations (0.05 and 0.5% (w/v)) enhanced the promoter strength of SUC 2p dramatically, with promoter activity higher than that of reported strong promoters. Glucose starvation resulted in the formation of a new Msn2/4 binding site on SUC 2p. Our work should facilitate the development of promoters with novel fine-tuning properties and the construction of S. cerevisiae cell factories suitable for the industrial production of essential chemicals under glucose-deprived conditions.

The impact of metabolism on the adaptation of organisms to environmental change

Since Jacob and Monod's discovery of the lac operon ∼1960, the explanations offered for most metabolic adaptations have been genetic. The focus has been on the adaptive changes in gene expression that occur, which are often referred to as "metabolic reprogramming." The contributions metabolism makes to adaptation have been largely ignored. Here we point out that metabolic adaptations, including the associated changes in gene expression, are highly dependent on the metabolic state of an organism prior to the environmental change to which it is adapting, and on the plasticity of that state. In support of this hypothesis, we examine the paradigmatic example of a genetically driven adaptation, the adaptation of E. coli to growth on lactose, and the paradigmatic example of a metabolic driven adaptation, the Crabtree effect in yeast. Using a framework based on metabolic control analysis, we have reevaluated what is known about both adaptations, and conclude that knowledge of the metabolic properties of these organisms prior to environmental change is critical for understanding not only how they survive long enough to adapt, but also how the ensuing changes in gene expression occur, and their phenotypes post-adaptation. It would be useful if future explanations for metabolic adaptations acknowledged the contributions made to them by metabolism, and described the complex interplay between metabolic systems and genetic systems that make these adaptations possible.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures

Cellular responses to the presence of toxic compounds in their environment require prompt expression of the correct levels of the appropriate enzymes, which are typically regulated by transcription factors that control gene expression for the duration of the response. The characteristics of each response dictate the choice of regulatory parameters such as the affinity of the transcription factor to its binding sites and the strength of the promoters it regulates. Although much is known about the dynamics of cellular responses, we still lack a framework to understand how different regulatory strategies evolved in natural systems relate to the selective pressures acting in each particular case. Here, we analyze a dynamical model of a typical antibiotic response in bacteria, where a transcriptionally repressed enzyme is induced by a sudden exposure to the drug that it processes. We identify strategies of gene regulation that optimize this response for different types of selective pressures, which we define as a set of costs associated with the drug, enzyme, and repressor concentrations during the response. We find that regulation happens in a limited region of the regulatory parameter space. While responses to more costly (toxic) drugs favor the usage of strongly self-regulated repressors, responses where expression of enzyme is more costly favor the usage of constitutively expressed repressors. Only a very narrow range of selective pressures favor weakly self-regulated repressors. We use this framework to determine which costs and benefits are most critical for the evolution of a variety of natural cellular responses that satisfy the approximations in our model and to analyze how regulation is optimized in new environments with different demands.

Pls1 Is a Peroxisomal Matrix Protein with a Role in Regulating Lysine Biosynthesis

Peroxisomes host essential metabolic enzymes and are crucial for human health and survival. Although peroxisomes were first described over 60 years ago, their entire proteome has not yet been identified. As a basis for understanding the variety of peroxisomal functions, we used a high-throughput screen to discover peroxisomal proteins in yeast. To visualize low abundance proteins, we utilized a collection of strains containing a peroxisomal marker in which each protein is expressed from the constitutive and strong TEF2 promoter. Using this approach, we uncovered 18 proteins that were not observed in peroxisomes before and could show their metabolic and targeting factor dependence for peroxisomal localization. We focus on one newly identified and uncharacterized matrix protein, Ynl097c-b, and show that it localizes to peroxisomes upon lysine deprivation and that its localization to peroxisomes depends on the lysine biosynthesis enzyme, Lys1. We demonstrate that Ynl097c-b affects the abundance of Lys1 and the lysine biosynthesis pathway. We have therefore renamed this protein Pls1 for Peroxisomal Lys1 Stabilizing 1. Our work uncovers an additional layer of regulation on the central lysine biosynthesis pathway. More generally it highlights how the discovery of peroxisomal proteins can expand our understanding of cellular metabolism.

Dichotomous feedback: a signal sequestration-based feedback mechanism for biocontroller design

We introduce a new design framework for implementing negative feedback regulation in synthetic biology, which we term ‘dichotomous feedback’. Our approach is different from current methods, in that it sequesters existing fluxes in the process to be controlled, and in this way takes advantage of the process’s architecture to design the control law. This signal sequestration mechanism appears in many natural biological systems and can potentially be easier to realize than ‘molecular sequestration’ and other comparison motifs that are nowadays common in biomolecular feedback control design. The loop is closed by linking the strength of signal sequestration to the process output. Our feedback regulation mechanism is motivated by two-component signalling systems, where a second response regulator could be competing with the natural response regulator thus sequestering kinase activity. Here, dichotomous feedback is established by increasing the concentration of the second response regulator as the level of the output of the natural process increases. Extensive analysis demonstrates how this type of feedback shapes the signal response, attenuates intrinsic noise while increasing robustness and reducing crosstalk.

Use of the volatile trichodiene to reduce Fusarium head blight and trichothecene contamination in wheat

Fusarium graminearum is the primary cause of Fusarium head blight (FHB), one of the most economically important diseases of wheat worldwide. FHB reduces yield and contaminates grain with the trichothecene mycotoxin deoxynivalenol (DON), which poses a risk to plant, human and animal health. The first committed step in trichothecene biosynthesis is formation of trichodiene (TD). The volatile nature of TD suggests that it could be a useful intra or interspecies signalling molecule, but little is known about the potential signalling role of TD during F. graminearum-wheat interactions. Previous work using a transgenic Trichoderma harzianum strain engineered to emit TD (Th + TRI5) indicated that TD can function as a signal that can modulate pathogen virulence and host plant resistance. Herein, we demonstrate that Th + TRI5 has enhanced biocontrol activity against F. graminearum and reduced DON contamination by 66% and 70% in a moderately resistant and a susceptible cultivar, respectively. While Th + TRI5 volatiles significantly influenced the expression of the pathogenesis-related 1 (PR1) gene, the effect was dependent on cultivar. Th + TRI5 volatiles strongly reduced DON production in F. graminearum plate cultures and downregulated the expression of TRI genes. Finally, we confirm that TD fumigation reduced DON accumulation in a detached wheat head assay.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

Metabolome and proteome analyses reveal transcriptional misregulation in glycolysis of engineered E. coli

Synthetic metabolic pathways are a burden for engineered bacteria, but the underlying mechanisms often remain elusive. Here we show that the misregulated activity of the transcription factor Cra is responsible for the growth burden of glycerol overproducing E. coli. Glycerol production decreases the concentration of fructose-1,6-bisphoshate (FBP), which then activates Cra resulting in the downregulation of glycolytic enzymes and upregulation of gluconeogenesis enzymes. Because cells grow on glucose, the improper activation of gluconeogenesis and the concomitant inhibition of glycolysis likely impairs growth at higher induction of the glycerol pathway. We solve this misregulation by engineering a Cra-binding site in the promoter controlling the expression of the rate limiting enzyme of the glycerol pathway to maintain FBP levels sufficiently high. We show the broad applicability of this approach by engineering Cra-dependent regulation into a set of constitutive and inducible promoters, and use one of them to overproduce carotenoids in E. coli.

Synthetic pathways represent a metabolic burden on host cells. Here the authors engineer Cra-binding sites to prevent misregulation in glycerol and carotenoid overproducing E. coli strains.

Recent advances in tuning the expression and regulation of genes for constructing microbial cell factories

To overcome environmental problems caused by the use of fossil resources, microbial cell factories have become a promising technique for the sustainable and eco-friendly development of valuable products from renewable resources. Constructing microbial cell factories with high titers, yields, and productivity requires a balance between growth and production; to this end, tuning gene expression and regulation is necessary to optimise and precisely control complicated metabolic fluxes. In this article, we review the current trends and advances in tuning gene expression and regulation and consider their engineering at each of the three stages of gene regulation: genomic, mRNA, and protein. In particular, the technological approaches utilised in a diverse range of genetic-engineering-based tools for the construction of microbial cell factories are reviewed and representative applications of these strategies are presented. Finally, the prospects for strategies and systems for tuning gene expression and regulation are discussed.

Computation of Single-Cell Metabolite Distributions Using Mixture Models

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

Design Principle for Decoding Calcium Signals to Generate Specific Gene Expression Via Transcription

The second messenger calcium plays a key role in conveying specificity of signaling pathways in plant cells. Specific calcium signatures are decoded to generate correct gene expression responses and amplification of calcium signatures is vital to this process. (1) It is not known if this amplification is an intrinsic property of all calcium-regulated gene expression responses and whether all calcium signatures have the potential to be amplified, or (2) how a given calcium signature maintains specificity in cells containing a great number of transcription factors (TFs) and other proteins with the potential to be calcium-regulated. The work presented here uncovers the design principle by which it is possible to decode calcium signals into specific changes in gene transcription in plant cells. Regarding the first question, we found that the binding mechanism between protein components possesses an intrinsic property that will nonlinearly amplify any calcium signal. This nonlinear amplification allows plant cells to effectively distinguish the kinetics of different calcium signatures to produce specific and appropriate changes in gene expression. Regarding the second question, we found that the large number of calmodulin (CaM)-binding TFs or proteins in plant cells form a buffering system such that the concentration of an active CaM-binding TF is insensitive to the concentration of any other CaM-binding protein, thus maintaining specificity. The design principle revealed by this work can be used to explain how any CaM-binding TF decodes calcium signals to generate specific gene expression responses in plant cells via transcription.

Metabolite Sequestration Enables Rapid Recovery from Fatty Acid Depletion in Escherichia coli

Microbes adapt their metabolism to take advantage of nutrients in their environment. Such adaptations control specific metabolic pathways to match energetic demands with nutrient availability. Upon depletion of nutrients, rapid pathway recovery is key to release cellular resources required for survival under the new nutritional conditions. Yet, little is known about the regulatory strategies that microbes employ to accelerate pathway recovery in response to nutrient depletion. Using the fatty acid catabolic pathway in Escherichia coli, here, we show that fast recovery can be achieved by rapid release of a transcriptional regulator from a metabolite-sequestered complex. With a combination of mathematical modeling and experiments, we show that recovery dynamics depend critically on the rate of metabolite consumption and the exposure time to nutrients. We constructed strains with rewired transcriptional regulatory architectures that highlight the metabolic benefits of negative autoregulation over constitutive and positive autoregulation. Our results have wide-ranging implications for our understanding of metabolic adaptations, as well as for guiding the design of gene circuitry for synthetic biology and metabolic engineering.IMPORTANCE Rapid metabolic recovery during nutrient shift is critical to microbial survival, cell fitness, and competition among microbiota, yet little is known about the regulatory mechanisms of rapid metabolic recovery. This work demonstrates a previously unknown mechanism where rapid release of a transcriptional regulator from a metabolite-sequestered complex enables fast recovery to nutrient depletion. The work identified key regulatory architectures and parameters that control the speed of recovery, with wide-ranging implications for the understanding of metabolic adaptations as well as synthetic biology and metabolic engineering.

Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism

Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs.

Aberrant gene expression in cancer coincides with drastic changes in metabolism. Here, the authors combined metabolome, transcriptome and proteome data in 54 cancer cell lines to uncover a genome-scale network of associations between transcriptional regulators and metabolites.

Extreme pathway analysis reveals the organizing rules of metabolic regulation

Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment.

Allosteric Feedback Inhibition Enables Robust Amino Acid Biosynthesis in E. coli by Enforcing Enzyme Overabundance

Microbes must ensure robust amino acid metabolism in the face of external and internal perturbations. This robustness is thought to emerge from regulatory interactions in metabolic and genetic networks. Here, we explored the consequences of removing allosteric feedback inhibition in seven amino acid biosynthesis pathways in Escherichia coli (arginine, histidine, tryptophan, leucine, isoleucine, threonine, and proline). Proteome data revealed that enzyme levels decreased in five of the seven dysregulated pathways. Despite that, flux through the dysregulated pathways was not limited, indicating that enzyme levels are higher than absolutely needed in wild-type cells. We showed that such enzyme overabundance renders the arginine, histidine, and tryptophan pathways robust against perturbations of gene expression, using a metabolic model and CRISPR interference experiments. The results suggested a sensitive interaction between allosteric feedback inhibition and enzyme-level regulation that ensures robust yet efficient biosynthesis of histidine, arginine, and tryptophan in E. coli.

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Amino acid biosynthesis enzymes do not normally operate at maximum capacity

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Allosteric feedback inhibition ensures that enzymes are overabundant

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Enzyme overabundance provides robustness against decreases in gene expression

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

Metabolic Feedback Circuits Provide Rapid Control of Metabolite Dynamics

Metabolism constitutes the basis of life, and the dynamics of metabolism dictate various cellular processes. However, exactly how metabolite dynamics are controlled remains poorly understood. By studying an engineered fatty acid-producing pathway as a model, we found that upon transcription activation a metabolic product from an unregulated pathway required seven cell cycles to reach to its steady state level, with the speed mostly limited by enzyme expression dynamics. To overcome this limit, we designed metabolic feedback circuits (MeFCs) with three different architectures, and experimentally measured and modeled their metabolite dynamics. Our engineered MeFCs could dramatically shorten the rise-time of metabolites, decreasing it by as much as 12-fold. The findings of this study provide a systematic understanding of metabolite dynamics in different architectures of MeFCs and have potentially immense applications in designing synthetic circuits to improve the productivities of engineered metabolic pathways.

Genome-Wide Mapping of Transcriptional Regulation and Metabolism Describes Information-Processing Units in Escherichia coli

In the face of changes in their environment, bacteria adjust gene expression levels and produce appropriate responses. The individual layers of this process have been widely studied: the transcriptional regulatory network describes the regulatory interactions that produce changes in the metabolic network, both of which are coordinated by the signaling network, but the interplay between them has never been described in a systematic fashion. Here, we formalize the process of detection and processing of environmental information mediated by individual transcription factors (TFs), utilizing a concept termed genetic sensory response units (GENSOR units), which are composed of four components: (1) a signal, (2) signal transduction, (3) genetic switch, and (4) a response. We used experimentally validated data sets from two databases to assemble a GENSOR unit for each of the 189 local TFs of Escherichia coli K-12 contained in the RegulonDB database. Further analysis suggested that feedback is a common occurrence in signal processing, and there is a gradient of functional complexity in the response mediated by each TF, as opposed to a one regulator/one pathway rule. Finally, we provide examples of other GENSOR unit applications, such as hypothesis generation, detailed description of cellular decision making, and elucidation of indirect regulatory mechanisms.

Adaptive Benefits of Storage Strategy and Dual AMPK/TOR Signaling in Metabolic Stress Response

Cellular metabolism must ensure that supply of nutrient meets the biosynthetic and bioenergetic needs. Cells have therefore developed sophisticated signaling and regulatory pathways in order to cope with dynamic fluctuations of both resource and demand and to regulate accordingly diverse anabolic and catabolic processes. Intriguingly, these pathways are organized around a relatively small number of regulatory hubs, such as the highly conserved AMPK and TOR kinase families in eukaryotic cells. Here, the global metabolic adaptations upon dynamic environment are investigated using a prototypical model of regulated metabolism. In this model, the optimal enzyme profiles as well as the underlying regulatory architecture are identified by combining perturbation and evolutionary methods. The results reveal the existence of distinct classes of adaptive strategies, which differ in the management of storage reserve depending on the intensity of the stress and in the regulation of ATP-producing reaction depending on the nature of the stress. The regulatory architecture that optimally implements these adaptive features is characterized by a crosstalk between two specialized signaling pathways, which bears close similarities with the sensing and regulatory properties of AMPK and TOR pathways.

Glucose becomes one of the worst carbon sources for E.coli on poor nitrogen sources due to suboptimal levels of cAMP

In most conditions, glucose is the best carbon source for E. coli: it provides faster growth than other sugars, and is consumed first in sugar mixtures. Here we identify conditions in which E. coli strains grow slower on glucose than on other sugars, namely when a single amino acid (arginine, glutamate, or proline) is the sole nitrogen source. In sugar mixtures with these nitrogen sources, E. coli still consumes glucose first, but grows faster rather than slower after exhausting glucose, generating a reversed diauxic shift. We trace this counterintuitive behavior to a metabolic imbalance: levels of TCA-cycle metabolites including α-ketoglutarate are high, and levels of the key regulatory molecule cAMP are low. Growth rates were increased by experimentally increasing cAMP levels, either by adding external cAMP, by genetically perturbing the cAMP circuit or by inhibition of glucose uptake. Thus, the cAMP control circuitry seems to have a ‘bug’ that leads to slow growth under what may be an environmentally rare condition.

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.

Regulating ehrlich and demethiolation pathways for alcohols production by the expression of ubiquitin-protein ligase gene HUWE1

Ehrlich and demethiolation pathways as two competing branches converted amino acid into alcohols. Controlling both pathways offers considerable potential for industrial applications including alcohols overproduction, flavor-quality control and developing new flavors. While how to regulate ehrlich and demethiolation pathways is still not applicable. Taking the conversion of methionine into methionol and methanethiol for example, we constructed two suppression subtractive cDNA libraries of Clonostachys rosea by using suppression subtractive hybridization (SSH) technology for screening regulators controlling the conversion. E3 ubiquitin-protein ligase gene HUWE1 screened from forward SSH library was validated to be related with the biosynthesis of end products. Overexpressing HUWE1 in C. rosea and S. cerevisiae significantly increased the biosynthesis of methanethiol and its derivatives in demethiolation pathway, while suppressed the biosynthesis of methional and methionol in ehrlich pathway. These results attained the directional regulation of both pathways by overexpressing HUWE1. Thus, HUWE1 has potential to be a key target for controlling and enhancing alcohols production by metabolic engineering.

A synthetic gene circuit for measuring autoregulatory feedback control

Autoregulatory feedback loops occur in the regulation of molecules ranging from ATP to MAP kinases to zinc. Negative feedback loops can increase a system's robustness, while positive feedback loops can mediate transitions between cell states. Recent genome-wide experimental and computational studies predict hundreds of novel feedback loops. However, not all physical interactions are regulatory, and many experimental methods cannot detect self-interactions. Our understanding of regulatory feedback loops is therefore hampered by the lack of high-throughput methods to experimentally quantify the presence, strength and temporal dynamics of autoregulatory feedback loops. Here we present a mathematical and experimental framework for high-throughput quantification of feedback regulation and apply it to RNA binding proteins (RBPs) in yeast. Our method is able to determine the existence of both direct and indirect positive and negative feedback loops, and to quantify the strength of these loops. We experimentally validate our model using two RBPs which lack native feedback loops and by the introduction of synthetic feedback loops. We find that RBP Puf3 does not natively participate in any direct or indirect feedback regulation, but that replacing the native 3'UTR with that of COX17 generates an auto-regulatory negative feedback loop which reduces gene expression noise. Likewise, RBP Pub1 does not natively participate in any feedback loops, but a synthetic positive feedback loop involving Pub1 results in increased expression noise. Our results demonstrate a synthetic experimental system for quantifying the existence and strength of feedback loops using a combination of high-throughput experiments and mathematical modeling. This system will be of great use in measuring auto-regulatory feedback by RNA binding proteins, a regulatory motif that is difficult to quantify using existing high-throughput methods.

Sequential metabolic phases as a means to optimize cellular output in a constant environment

Temporal changes of gene expression are a well-known regulatory feature of all cells, which is commonly perceived as a strategy to adapt the proteome to varying external conditions. However, temporal (rhythmic and non-rhythmic) changes of gene expression are also observed under virtually constant external conditions. Here we hypothesize that such changes are a means to render the synthesis of the metabolic output more efficient than under conditions of constant gene activities. In order to substantiate this hypothesis, we used a flux-balance model of the cellular metabolism. The total time span spent on the production of a given set of target metabolites was split into a series of shorter time intervals (metabolic phases) during which only selected groups of metabolic genes are active. The related flux distributions were calculated under the constraint that genes can be either active or inactive whereby the amount of protein related to an active gene is only controlled by the number of active genes: the lower the number of active genes the more protein can be allocated to the enzymes carrying non-zero fluxes. This concept of a predominantly protein-limited efficiency of gene expression clearly differs from other concepts resting on the assumption of an optimal gene regulation capable of allocating to all enzymes and transporters just that fraction of protein necessary to prevent rate limitation. Applying this concept to a simplified metabolic network of the central carbon metabolism with glucose or lactate as alternative substrates, we demonstrate that switching between optimally chosen stationary flux modes comprising different sets of active genes allows producing a demanded amount of target metabolites in a significantly shorter time than by a single optimal flux mode at fixed gene activities. Our model-based findings suggest that temporal expression of metabolic genes can be advantageous even under conditions of constant external substrate supply.

Negative feedback regulation of fatty acid production based on a malonyl-CoA sensor-actuator

Engineering metabolic biosynthetic pathways has enabled the microbial production of many useful chemicals. However, pathway productivities and yields are often limited by metabolic imbalances. Synthetic regulatory circuits have been shown to be able to balance engineered pathways, improving titers and productivities. Here we developed a negative feedback regulatory circuit based on a malonyl-CoA-based sensor-actuator. Malonyl-CoA is biosynthesized from acetyl-CoA by the acetyl-CoA carboxylase, which is the rate-limiting step for fatty acid biosynthesis. Overexpression of acetyl-CoA carboxylase improves fatty acid production, but slows down cell growth. We have devised a malonyl-CoA sensor-actuator that controls gene expression levels based on intracellular malonyl-CoA concentrations. This sensor-actuator is used to construct a negative feedback circuit to regulate the expression of acetyl-CoA carboxylase. The negative feedback circuit is able to up-regulate acetyl-CoA carboxylase expression when the malonyl-CoA concentration is low and down-regulate acetyl-CoA carboxylase expression when excess amounts of malonyl-CoA have accumulated. We show that the regulatory circuit effectively alleviates the toxicity associated with acetyl-CoA carboxylase overexpression. When used to regulate the fatty acid pathway, the feedback circuit increases fatty acid titer and productivity by 34% and 33%, respectively.

A genetically encoded Förster resonance energy transfer sensor for monitoring in vivo trehalose-6-phosphate dynamics

Trehalose-6-phosphate is a pivotal regulator of sugar metabolism, growth, and osmotic equilibrium in bacteria, yeasts, and plants. To directly visualize the intracellular levels of intracellular trehalose-6-phosphate, we developed a series of specific Förster resonance energy transfer (FRET) sensors for in vivo microscopy. We demonstrated real-time monitoring of regulation in the trehalose pathway of Escherichia coli. In Saccharomyces cerevisiae, we could show that the concentration of free trehalose-6-phosphate during growth on glucose is in a range sufficient for inhibition of hexokinase. These findings support the hypothesis of trehalose-6-phosphate as the effector of a negative feedback system, similar to the inhibition of hexokinase by glucose-6-phosphate in mammalian cells and controlling glycolytic flux.

Computing the functional proteome: recent progress and future prospects for genome-scale models

Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M-Models). Recent expansions in network content to encompass proteome synthesis have resulted in models of metabolism and protein expression (ME-Models). ME-Models advance the predictions possible with constraint-based models from network flux states to the spatially resolved molecular composition of a cell. Specifically, ME-Models enable the prediction of transcriptome and proteome allocation and limitations, and basal expression states and regulatory needs. Continued expansion in reconstruction content and constraints will result in an increasingly refined representation of cellular composition and behavior.

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.

Tuning transcription of nutrient utilization genes to catabolic rate promotes growth in a gut bacterium

Cells respond to nutrient availability by expressing nutrient catabolic genes. We report that the regulator controlling utilization of chondroitin sulphate (CS) in the mammalian gut symbiont Bacteroides thetaiotaomicron is activated by an intermediate in CS breakdown rather than CS itself. We determine that the rate-determining enzyme in CS breakdown is responsible for degrading this intermediate and establish that the levels of the enzyme increase 100-fold, whereas those of the regulator remain constant upon exposure to CS. Because enzyme and regulator compete for the intermediate, B. thetaiotaomicron tunes transcription of CS utilization genes to CS catabolic rate. This tuning results in a transient increase in CS utilization transcripts upon exposure to excess CS. Constitutive expression of the rate-determining enzyme hindered activation of CS utilization genes and growth on CS. An analogous mechanism regulates heparin utilization genes, suggesting that the identified strategy aids B. thetaiotaomicron in the competitive gut environment.

Improving fatty acids production by engineering dynamic pathway regulation and metabolic control

Global energy demand and environmental concerns have stimulated increasing efforts to produce carbon-neutral fuels directly from renewable resources. Microbially derived aliphatic hydrocarbons, the petroleum-replica fuels, have emerged as promising alternatives to meet this goal. However, engineering metabolic pathways with high productivity and yield requires dynamic redistribution of cellular resources and optimal control of pathway expression. Here we report a genetically encoded metabolic switch that enables dynamic regulation of fatty acids (FA) biosynthesis in Escherichia coli. The engineered strains were able to dynamically compensate the critical enzymes involved in the supply and consumption of malonyl-CoA and efficiently redirect carbon flux toward FA biosynthesis. Implementation of this metabolic control resulted in an oscillatory malonyl-CoA pattern and a balanced metabolism between cell growth and product formation, yielding 15.7- and 2.1-fold improvement in FA titer compared with the wild-type strain and the strain carrying the uncontrolled metabolic pathway. This study provides a new paradigm in metabolic engineering to control and optimize metabolic pathways facilitating the high-yield production of other malonyl-CoA-derived compounds.

Coordination of microbial metabolism

Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

Advancing metabolic models with kinetic information

Kinetic models are crucial to quantitatively understand and predict how functional behavior emerges from dynamic concentration changes of cellular components. The current challenge is on resolving uncertainties about parameter values of reaction kinetics. Additionally, there are also major structural uncertainties due to unknown molecular interactions and only putatively assigned regulatory functions. What if one or few key regulators of biochemical reactions are missing in a metabolic model? By reviewing current advances in building kinetic models of metabolism, we found that such models experience a paradigm shift away from fitting parameters towards identifying key regulatory interactions.

Noise minimisation in gene expression switches

Gene expression is subject to stochastic variation which leads to fluctuations in the rate of protein production. Recently, a study in yeast at a genomic scale showed that, in some cases, gene expression variability alters phenotypes while, in other cases, these remain unchanged despite fluctuations in the expression of other genes. These studies suggested that noise in gene expression is a physiologically relevant trait and, to prevent harmful stochastic variation in the expression levels of some genes, it can be subject to minimisation. However, the mechanisms for noise minimisation are still unclear. In the present work, we analysed how noise expression depends on the architecture of the cis-regulatory system, in particular on the number of regulatory binding sites. Using analytical calculations and stochastic simulations, we found that the fluctuation level in noise expression decreased with the number of regulatory sites when regulatory transcription factors interacted with only one other bound transcription factor. In contrast, we observed that there was an optimal number of binding sites when transcription factors interacted with many bound transcription factors. This finding suggested a new mechanism for preventing large fluctuations in the expression of genes which are sensitive to the concentration of regulators.

Optimal performance of the tryptophan operon of E. coli: a stochastic, dynamical, mathematical-modeling approach

In this work, we develop a detailed, stochastic, dynamical model for the tryptophan operon of E. coli, and estimate all of the model parameters from reported experimental data. We further employ the model to study the system performance, considering the amount of biochemical noise in the trp level, the system rise time after a nutritional shift, and the amount of repressor molecules necessary to maintain an adequate level of repression, as indicators of the system performance regime. We demonstrate that the level of cooperativity between repressor molecules bound to the first two operators in the trp promoter affects all of the above enlisted performance characteristics. Moreover, the cooperativity level found in the wild-type bacterial strain optimizes a cost-benefit function involving low biochemical noise in the tryptophan level, short rise time after a nutritional shift, and low number of regulatory molecules.

Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis

Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

Most enzymes are indeed highly expressed in conditions where they are more active.

Quantitatively, however, the observed changes in expression between conditions do not match the changes in activity for most enzymes.

A good quantitative match is only observed for enzymes involved in the TCA cycle.

Metabolomics reveals that increased substrate availability explains only a few instances of changes in activity.

One of the key ways in which microbes are thought to regulate their metabolism is by modulating the availability of enzymes through transcriptional regulation. However, the limited success of efforts to manipulate metabolic fluxes by rewiring the transcriptional network has cast doubt on the idea that transcript abundance controls metabolic fluxes. In this study, we investigate control of metabolic flux in the model bacterium Bacillus subtilis by quantifying fluxes, transcripts, and metabolites in eight metabolic states enforced by different environmental conditions. We find that most enzymes whose flux switches between on and off states, such as those involved in substrate uptake, exhibit large corresponding transcriptional changes. However, for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes—only for a number of reactions in the tricarboxylic acid cycle were enzyme changes approximately proportional to flux changes. Surprisingly, substrate changes revealed by metabolomics were also insufficient to explain observed fluxes, leaving a large role for allosteric regulation and enzyme modification in the control of metabolic fluxes.

Dissecting specific and global transcriptional regulation of bacterial gene expression

Gene expression is regulated by specific transcriptional circuits but also by the global expression machinery as a function of growth. Simultaneous specific and global regulation thus constitutes an additional--but often neglected--layer of complexity in gene expression. Here, we develop an experimental-computational approach to dissect specific and global regulation in the bacterium Escherichia coli. By using fluorescent promoter reporters, we show that global regulation is growth rate dependent not only during steady state but also during dynamic changes in growth rate and can be quantified through two promoter-specific parameters. By applying our approach to arginine biosynthesis, we obtain a quantitative understanding of both specific and global regulation that allows accurate prediction of the temporal response to simultaneous perturbations in arginine availability and growth rate. We thereby uncover two principles of joint regulation: (i) specific regulation by repression dominates the transcriptional response during metabolic steady states, largely repressing the biosynthesis genes even when biosynthesis is required and (ii) global regulation sets the maximum promoter activity that is exploited during the transition between steady states.

Indirect and suboptimal control of gene expression is widespread in bacteria

Gene regulation in bacteria is usually described as an adaptive response to an environmental change so that genes are expressed when they are required. We instead propose that most genes are under indirect control: their expression responds to signal(s) that are not directly related to the genes' function. Indirect control should perform poorly in artificial conditions, and we show that gene regulation is often maladaptive in the laboratory. In Shewanella oneidensis MR-1, 24% of genes are detrimental to fitness in some conditions, and detrimental genes tend to be highly expressed instead of being repressed when not needed. In diverse bacteria, there is little correlation between when genes are important for optimal growth or fitness and when those genes are upregulated. Two common types of indirect control are constitutive expression and regulation by growth rate; these occur for genes with diverse functions and often seem to be suboptimal. Because genes that have closely related functions can have dissimilar expression patterns, regulation may be suboptimal in the wild as well as in the laboratory.

Somewhat in control--the role of transcription in regulating microbial metabolic fluxes

The most common way for microbes to control their metabolism is by controlling enzyme levels through transcriptional regulation. Yet recent studies have shown that in many cases, perturbations to the transcriptional regulatory network do not result in altered metabolic phenotypes on the level of the flux distribution. We suggest that this may be a consequence of cells protecting their metabolism against stochastic fluctuations in expression as well as enabling a fast response for those fluxes that may need to be changed quickly. Furthermore, it is impossible for a regulatory program to guarantee optimal expression levels in all conditions. Several studies have found examples of demonstrably suboptimal regulation of gene expression, and improvements to the regulatory network have been investigated in laboratory evolution experiments.

Synthetic gene circuits for metabolic control: design trade-offs and constraints

A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations.

Natural variation in enzyme activity of the African cichlid Pseudocrenilabrus multicolor victoriae

This study describes the metabolic capacities of the African cichlid Pseudocrenilabrus multicolor victoriae from four sites in Uganda, East Africa. Fish were captured during the dry season, from two aquatic systems in different regions (Lake Nabugabo and Mpanga River). Within the Lake Nabugabo region, individuals were sampled from Lake Kayanja (normoxic) and Lwamunda Swamp (hypoxic); within the Mpanga River system, individuals were sampled from Bunoga and Kahunge (characterized by seasonal variation in dissolved oxygen (D.O.)). Enzyme activity levels of pyruvate kinase, lactate dehydrogenase, citrate synthase, and cytochrome C oxidase were measured in four tissues: white skeletal muscle, heart, brain, and liver. Two additional enzymes were measured in the liver, malate dehydrogenase and fructose 1,6-bisphosphatase. Regional differences between enzyme activities in most tissues were evident; however, little variation was observed between two sites within a region despite differences in D.O. In general, P. multicolor from the Mpanga River system displayed greater anaerobic enzyme activity in white skeletal muscle, lower gluconeogenic enzyme activity in the liver, and an overall higher enzyme activity in the heart and brain tissues than fish from the Nabugabo region. The latter may reflect a long-term adaptation to low-oxygen conditions at the metapopulation level in the Nabugabo region.

Engineering plant metabolism into microbes: from systems biology to synthetic biology

Plant metabolism represents an enormous repository of compounds that are of pharmaceutical and biotechnological importance. Engineering plant metabolism into microbes will provide sustainable solutions to produce pharmaceutical and fuel molecules that could one day replace substantial portions of the current fossil-fuel based economy. Metabolic engineering entails targeted manipulation of biosynthetic pathways to maximize yields of desired products. Recent advances in Systems Biology and the emergence of Synthetic Biology have accelerated our ability to design, construct and optimize cell factories for metabolic engineering applications. Progress in predicting and modeling genome-scale metabolic networks, versatile gene assembly platforms and delicate synthetic pathway optimization strategies has provided us exciting opportunities to exploit the full potential of cell metabolism. In this review, we will discuss how systems and synthetic biology tools can be integrated to create tailor-made cell factories for efficient production of natural products and fuel molecules in microorganisms.

Multi-criteria optimization of regulation in metabolic networks

Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different "environments" (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks.