A Two-Layer Gene Circuit for Decoupling Cell Growth from Metabolite Production

Molecular parts and genetic circuits for metabolic engineering of microorganisms

Microbial conversion of biomass into value-added biochemicals is a highly sustainable process compared to petroleum-based production. In this regard, microorganisms have been engineered via simple overexpression or deletion of metabolic genes to facilitate the production. However, the producer microorganisms require complex regulatory circuits to maximize productivity and performance. To address this issue, diverse genetic circuits have been developed that allow cells to minimize their metabolic burden, overcome metabolic imbalances and respond to a dynamically changing environment. In this review, we briefly explain the basic strategy for constructing genetic circuits by assembling molecular parts such as input, operation and output modules. Next, we describe recent applications of the circuits in the metabolic engineering of microorganisms to improve biochemical production. Beyond those achievements, genetic circuits will facilitate more innovative approaches to future strain development through mining and engineering new genetic elements and improving the complexity of genetic circuit design.

Mixed carbon substrates: a necessary nuisance or a missed opportunity?

Although fermentation with single carbon sources is the preferred mode of operation in current industrial biotechnology, the use of multiple substrates has been continuously investigated throughout the years. Generally, microbial metabolism varies significantly when cells are presented with mixed carbon substrates compared to a single carbon-energy source, as different nutrients interact in complex ways within the metabolic network. By exploiting these distinct modes of interaction, researchers have identified unique opportunities to optimize metabolism using mixed carbon sources. Here we review situations where process yield and productivity are markedly improved through the judicious introduction of substrate mixtures. Our goal is to illustrate that with proper design of the choice of substrates and the way they are introduced to cultures, metabolic optimization with mixed substrates can be a unique strategy that complements genetic engineering techniques to enhance cell performance beyond what is accomplished in single substrate fermentations.

Genetic Circuit-Assisted Smart Microbial Engineering

Rapid advances in DNA synthesis, genetic manipulation, and biosensors have greatly improved the ability to engineer microorganisms with complex functions. By accurately integrating quality biosensors and complex genetic circuits, recently emerged smart microorganisms have enabled exciting opportunities for dissecting complex signaling networks and making responses without artificial intervention. However, because of the lack of design principles, developing such smart microorganisms remains challenging. In this review, we propose the concept of smart microbial engineering (SME) and describe the general features of basic SME, including the circuit architecture, components, and design process. We also summarize the latest SME achievements, remaining challenges, and potential solutions in this growing field.

Engineering grass biomass for sustainable and enhanced bioethanol production

Bioethanol from lignocellulosic biomass is a promising step for the future energy requirements. Grass is a potential lignocellulosic biomass which can be utilised for biorefinery-based bioethanol production. Grass biomass is a suitable feedstock for bioethanol production due to its all the year around production, requirement of less fertile land and noninterference with food system. However, the processes involved, i.e. pretreatment, enzymatic hydrolysis and fermentation for bioethanol production from grass biomass, are both time consuming and costly. Developing the grass biomass in planta for enhanced bioethanol production is a promising step for maximum utilisation of this valuable feedstock and, thus, is the focus of the present review. Modern breeding techniques and transgenic processes are attractive methods which can be utilised for development of the feedstock. However, the outcomes are not always predictable and the time period required for obtaining a robust variety is generation dependent. Sophisticated genome editing technologies such as synthetic genetic circuits (SGC) or clustered regularly interspaced short palindromic repeats (CRISPR) systems are advantageous for induction of desired traits/heritable mutations in a foreseeable genome location in the 1st mutant generation. Although, its application in grass biomass for bioethanol is limited, these sophisticated techniques are anticipated to exhibit more flexibility in engineering the expression pattern for qualitative and qualitative traits. Nevertheless, the fundamentals rendered by the genetics of the transgenic crops will remain the basis of such developments for obtaining biorefinery-based bioethanol concepts from grass biomass. Grasses which are abundant and widespread in nature epitomise attractive lignocellulosic feedstocks for bioethanol production. The complexity offered by the grass cell wall in terms of lignin recalcitrance and its binding to polysaccharides forms a barricade for its commercialization as a biofuel feedstock. Inspired by the possibilities for rewiring the genetic makeup of grass biomass for reduced lignin and lignin-polysaccharide linkages along with increase in carbohydrates, innovative approaches for in planta modifications are forging ahead. In this review, we highlight the progress made in the field of transgenic grasses for bioethanol production and focus our understanding on improvements of simple breeding techniques and post-harvest techniques for development in shortening of lignin-carbohydrate and carbohydrate-carbohydrate linkages. Further, we discuss about the designer lignins which are aimed for qualitable lignins and also emphasise on remodelling of polysaccharides and mixed-linkage glucans for enhancing carbohydrate content and in planta saccharification efficiency. As a final point, we discuss the role of synthetic genetic circuits and CRISPR systems in targeted improvement of cell wall components without compromising the plant growth and health. It is anticipated that this review can provide a rational approach towards a better understanding of application of in planta genetic engineering aspects for designing synthetic genetic circuits which can promote grass feedstocks for biorefinery-based bioethanol concepts.

Synthetic genetic circuits for programmable biological functionalities

Living organisms evolve complex genetic networks to interact with the environment. Due to the rapid development of synthetic biology, various modularized genetic parts and units have been identified from these networks. They have been employed to construct synthetic genetic circuits, including toggle switches, oscillators, feedback loops and Boolean logic gates. Building on these circuits, complex genetic machines with capabilities in programmable decision-making could be created. Consequently, these accomplishments have led to novel applications, such as dynamic and autonomous modulation of metabolic networks, directed evolution of biological units, remote and targeted diagnostics and therapies, as well as biological containment methods to prevent release of engineered microorganisms and genetic materials. Herein, we outline the principles in genetic circuit design that have initiated a new chapter in transforming concepts to realistic applications. The features of modularized building blocks and circuit architecture that facilitate realization of circuits for a variety of novel applications are discussed. Furthermore, recent advances and challenges in employing genetic circuits to impart microorganisms with distinct and programmable functionalities are highlighted. We envision that this review gives new insights into the design of synthetic genetic circuits and offers a guideline for the implementation of different circuits in various aspects of biotechnology and bioengineering.

Mechanistic Models of Inducible Synthetic Circuits for Joint Description of DNA Copy Number, Regulatory Protein Level, and Cell Load

Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. However, such equations may be poorly predictive in practical situations that are typical in bottom-up design, including changes in promoter copy number, regulatory protein level, and cell load. In this work, we derived novel mechanistic steady-state models of the lux inducible system, used as case study, relying on different assumptions on regulatory protein (LuxR) and cognate promoter (Plux) concentrations, inducer-protein complex formation, and resource usage limitation. We demonstrated that a change in the considered model assumptions can significantly affect circuit output, and preliminary experimental data are in accordance with the simulated activation curves. We finally showed that the models are identifiable a priori (in the analytically tractable cases) and a posteriori, and we determined the specific experiments needed to parametrize them. Although a larger-scale experimental validation is required, in the future the reported models may support synthetic circuits output prediction in practical situations with unprecedented details.

Engineering Robust Production Microbes for Large-Scale Cultivation

Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.

A Synthetic Close-Loop Controller Circuit for the Regulation of an Extracellular Molecule by Engineered Bacteria

Feedback control is ubiquitous in biological systems. It can also play a crucial role in the design of synthetic circuits implementing novel functions in living systems, to achieve self-regulation of gene expression, noise reduction, rise time decrease, or adaptive pathway control. Despite in vitro, in vivo, and ex vivo implementations have been successfully reported, the design of biological close-loop systems with quantitatively predictable behavior is still a major challenge. In this work, we tested a model-based bottom-up design of a synthetic close-loop controller in engineered Escherichia coli, aimed to automatically regulate the concentration of an extracellular molecule, N-(3-oxohexanoyl)-L-homoserine lactone (HSL), by rewiring the elements of heterologous quorum sensing/quenching networks. The synthetic controller was successfully constructed and experimentally validated. Relying on mathematical model and experimental characterization of individual regulatory parts and enzymes, we evaluated the predictability of the interconnected system behavior in vivo. The culture was able to reach an HSL steady-state level of 72 nM, accurately predicted by the model, and showed superior capabilities in terms of robustness against cell density variation and disturbance rejection, compared with a corresponding open-loop circuit. This engineering-inspired design approach may be adopted for the implementation of other close-loop circuits for different applications and contribute to decreasing trial-and-error steps.

An Optimized Bistable Metabolic Switch To Decouple Phenotypic States during Anaerobic Fermentation

Metabolic engineers aim to genetically modify microorganisms to improve their ability to produce valuable compounds. Despite the prevalence of growth-coupled production processes, these strategies can significantly limit production rates. Instead, rates can be improved by decoupling and optimizing growth and production independently, and operating with a growth stage followed by a production stage. Here, we implement a bistable transcriptional controller to decouple and switch between these two states. We optimize the controller in anaerobic conditions, typical of industrial fermentations, to ensure stability and tight expression control, while improving switching dynamics. The stability of this controller can be maintained through a simulated seed train scale-up from 5 mL to 500 000 L, indicating industrial feasibility. Finally, we demonstrate a two-stage production process using our optimal construct to improve the instantaneous rate of lactate production by over 50%, motivating the use of these systems in broad metabolic engineering applications.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Optimal control of bacterial growth for the maximization of metabolite production

Microorganisms have evolved complex strategies for controlling the distribution of available resources over cellular functions. Biotechnology aims at interfering with these strategies, so as to optimize the production of metabolites and other compounds of interest, by (re)engineering the underlying regulatory networks of the cell. The resulting reallocation of resources can be described by simple so-called self-replicator models and the maximization of the synthesis of a product of interest formulated as a dynamic optimal control problem. Motivated by recent experimental work, we are specifically interested in the maximization of metabolite production in cases where growth can be switched off through an external control signal. We study various optimal control problems for the corresponding self-replicator models by means of a combination of analytical and computational techniques. We show that the optimal solutions for biomass maximization and product maximization are very similar in the case of unlimited nutrient supply, but diverge when nutrients are limited. Moreover, external growth control overrides natural feedback growth control and leads to an optimal scheme consisting of a first phase of growth maximization followed by a second phase of product maximization. This two-phase scheme agrees with strategies that have been proposed in metabolic engineering. More generally, our work shows the potential of optimal control theory for better understanding and improving biotechnological production processes.

Synthetic biology toolkits and applications in Saccharomyces cerevisiae

Synthetic biologists construct biological components and systems to look into biological phenomena and drive a myriad of practical applications that aim to tackle current global challenges in energy, healthcare and the environment. While most tools have been established in bacteria, particularly Escherichia coli, recent years have seen parallel developments in the model yeast strain Saccharomyces cerevisiae, one of the most well-understood eukaryotic biological system. Here, we outline the latest advances in yeast synthetic biology tools based on a framework of abstraction hierarchies of parts, circuits and genomes. In brief, the creation and characterization of biological parts are explored at the transcriptional, translational and post-translational levels. Using characterized parts as building block units, the designing of functional circuits is elaborated with examples. In addition, the status and potential applications of synthetic genomes as a genome level platform for biological system construction are also discussed. In addition to the development of a toolkit, we describe how those tools have been applied in the areas of drug production and screening, study of disease mechanisms, pollutant sensing and bioremediation. Finally, we provide a future outlook of yeast as a workhorse of eukaryotic genetics and a chosen chassis in this field.

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.

Glucose-Dependent Promoters for Dynamic Regulation of Metabolic Pathways

For an industrial fermentation process, it can be advantageous to decouple cell growth from product formation. This decoupling would allow for the rapid accumulation of biomass without inhibition from product formation, after which the fermentation can be switched to a mode where cells would grow minimally and primarily act as catalysts to convert substrate into desired product. The switch in fermentation mode should preferably be accomplished without the addition of expensive inducers. A common cell factory Saccharomyces cerevisiae is a Crabtree-positive yeast and is typically fermented at industrial scale under glucose-limited conditions to avoid the formation of ethanol. In this work, we aimed to identify and characterize promoters that depend on glucose concentration for use as dynamic control elements. Through analysis of mRNA data of S. cerevisiae grown in chemostats under glucose excess or limitation, we identified 34 candidate promoters that strongly responded to glucose presence or absence. These promoters were characterized in small-scale batch and fed-batch cultivations using a quickly maturing rapidly degrading green fluorescent protein yEGFP3-Cln2_PEST as a reporter. Expressing 3-hydroxypropionic acid (3HP) pathway from a set of selected regulated promoters allowed for suppression of 3HP production during glucose-excess phase of a batch cultivation with subsequent activation in glucose-limiting conditions. Regulating the 3HP pathway by the ICL1 promoter resulted in 70% improvement of 3HP titer in comparison to PGK1 promoter.

Synthetic addiction extends the productive life time of engineered Escherichia coli populations

Bioproduction of chemicals offers a sustainable alternative to petrochemical synthesis routes by using genetically engineered microorganisms to convert waste and simple substrates into higher-value products. However, efficient high-yield production commonly introduces a metabolic burden that selects for subpopulations of nonproducing cells in large fermentations. To postpone such detrimental evolution, we have synthetically addicted production cells to production by carefully linking signals of product presence to expression of nonconditionally essential genes. We addict Escherichia coli cells to their engineered biosynthesis of mevalonic acid by fine-tuned control of essential genes using a product-responsive transcription factor. Over the course of a long-term fermentation equivalent to industrial 200-m³ bioreactors such addicted cells remained productive, unlike the control, in which evolution fully terminated production.

Bio-production of chemicals is an important driver of the societal transition toward sustainability. However, fermentations with heavily engineered production organisms can be challenging to scale to industrial volumes. Such fermentations are subject to evolutionary pressures that select for a wide range of genetic variants that disrupt the biosynthetic capacity of the engineered organism. Synthetic product addiction that couples high-yield production of a desired metabolite to expression of nonconditionally essential genes could offer a solution to this problem by selectively favoring cells with biosynthetic capacity in the population without constraining the medium. We constructed such synthetic product addiction by controlling the expression of two nonconditionally essential genes with a mevalonic acid biosensor. The product-addicted production organism retained high-yield mevalonic acid production through 95 generations of cultivation, corresponding to the number of cell generations required for >200-m³ industrial-scale production, at which time the nonaddicted strain completely abolished production. Using deep DNA sequencing, we find that the product-addicted populations do not accumulate genetic variants that compromise biosynthetic capacity, highlighting how synthetic networks can be designed to control genetic population heterogeneity. Such synthetic redesign of evolutionary forces with endogenous processes may be a promising concept for realizing complex cellular designs required for sustainable bio-manufacturing.

Re-using biological devices: a model-aided analysis of interconnected transcriptional cascades designed from the bottom-up

Background

The study of simplified, ad-hoc constructed model systems can help to elucidate if quantitatively characterized biological parts can be effectively re-used in composite circuits to yield predictable functions. Synthetic systems designed from the bottom-up can enable the building of complex interconnected devices via rational approach, supported by mathematical modelling. However, such process is affected by different, usually non-modelled, unpredictability sources, like cell burden.

Methods

Here, we analyzed a set of synthetic transcriptional cascades in Escherichia coli. We aimed to test the predictive power of a simple Hill function activation/repression model (no-burden model, NBM) and of a recently proposed model, including Hill functions and the modulation of proteins expression by cell load (burden model, BM). To test the bottom-up approach, the circuit collection was divided into training and test sets, used to learn individual component functions and test the predicted output of interconnected circuits, respectively.

Results

Among the constructed configurations, two test set circuits showed unexpected logic behaviour. Both NBM and BM were able to predict the quantitative output of interconnected devices with expected behaviour, but only the BM was also able to predict the output of one circuit with unexpected behaviour. Moreover, considering training and test set data together, the BM captures circuits output with higher accuracy than the NBM, which is unable to capture the experimental output exhibited by some of the circuits even qualitatively. Finally, resource usage parameters, estimated via BM, guided the successful construction of new corrected variants of the two circuits showing unexpected behaviour.

Conclusions

Superior descriptive and predictive capabilities were achieved considering resource limitation modelling, but further efforts are needed to improve the accuracy of models for biological engineering.

Electronic supplementary material

The online version of this article (10.1186/s13036-017-0090-3) contains supplementary material, which is available to authorized users.

Dynamic Blue Light-Inducible T7 RNA Polymerases (Opto-T7RNAPs) for Precise Spatiotemporal Gene Expression Control

Light has emerged as a control input for biological systems due to its precise spatiotemporal resolution. The limited toolset for light control in bacteria motivated us to develop a light-inducible transcription system that is independent from cellular regulation through the use of an orthogonal RNA polymerase. Here, we present our engineered blue light-responsive T7 RNA polymerases (Opto-T7RNAPs) that show properties such as low leakiness of gene expression in the dark state, high expression strength when induced with blue light, and an inducible range of more than 300-fold. Following optimization of the system to reduce expression variability, we created a variant that returns to the inactive dark state within minutes once the blue light is turned off. This allows for precise dynamic control of gene expression, which is a key aspect for most applications using optogenetic regulation. The regulators, which only require blue light from ordinary light-emitting diodes for induction, were developed and tested in the bacterium Escherichia coli, which is a crucial cell factory for biotechnology due to its fast and inexpensive cultivation and well understood physiology and genetics. Opto-T7RNAP, with minor alterations, should be extendable to other bacterial species as well as eukaryotes such as mammalian cells and yeast in which the T7 RNA polymerase and the light-inducible Vivid regulator have been shown to be functional. We anticipate that our approach will expand the applicability of using light as an inducer for gene expression independent from cellular regulation and allow for a more reliable dynamic control of synthetic and natural gene networks.

Dynamic pathway regulation: recent advances and methods of construction

Microbial cell factories are a renewable source for the production of biofuels and valuable chemicals. Dynamic pathway regulation has proved successful in improving production of molecules by balancing flux between growth of cells and production of metabolites. Systems for autonomous induction of pathway regulation are increasingly being developed, which include metabolite responsive promoters, biosensors, and quorum sensing systems. Since engineering such systems are dependent on the available methods for controlling protein abundance in the desired host, we review recent tools used for gene repression at the transcriptional, post-transcriptional and post-translational levels in Escherichia coli and Saccharomyces cerevisiae. These approaches may facilitate pathway engineering for biofuel and biochemical production.

Engineering a riboswitch-based genetic platform for the self-directed evolution of acid-tolerant phenotypes

Environmental pH is a fundamental signal continuously directing the metabolism and behavior of living cells. Programming the precise cellular response toward environmental pH is, therefore, crucial for engineering cells for increasingly sophisticated functions. Herein, we engineer a set of riboswitch-based pH-sensing genetic devices to enable the control of gene expression according to differential environmental pH. We next develop a digital pH-sensing system to utilize the analogue-sensing behavior of these devices for high-resolution recording of host cell exposure to discrete external pH levels. The application of this digital pH-sensing system is demonstrated in a genetic program that autonomously regulated the evolutionary engineering of host cells for improved tolerance to a broad spectrum of organic acids, a valuable phenotype for metabolic engineering and bioremediation applications.

Cells are exposed to shifts in environmental pH, which direct their metabolism and behavior. Here the authors design pH-sensing riboswitches to create a gene expression program, digitalize the system to respond to a narrow pH range and apply it to evolve host cells with improved tolerance to a variety of organic acids.

Decoupling Resource-Coupled Gene Expression in Living Cells

Synthetic biology aspires to develop frameworks that enable the construction of complex and reliable gene networks with predictable functionalities. A key limitation is that increasing network complexity increases the demand for cellular resources, potentially causing resource-associated interference among noninteracting circuits. Although recent studies have shown the effects of resource competition on circuit behaviors, mechanisms that decouple such interference remain unclear. Here, we constructed three systems in Escherichia coli, each consisting of two independent circuit modules where the complexity of one module (Circuit 2) was systematically increased while the other (Circuit 1) remained identical. By varying the expression level of Circuit 1 and measuring its effect on the expression level of Circuit 2, we demonstrated computationally and experimentally that indirect coupling between these seemingly unconnected genetic circuits can occur in three different regulatory topologies. More importantly, we experimentally verified the computational prediction that negative feedback can significantly reduce resource-coupled interference in regulatory circuits. Our results reveal a design principle that enables cells to reliably multitask while tightly controlling cellular resources.