Engineering metabolite-responsive transcriptional factors to sense small molecules in eukaryotes: current state and perspectives

Toxicity of nuclear-localized GFP in reporter mice

Various techniques using fluorescent reporter probes have been developed, such as GFP transgenic mouse lines that are used to detect spatial-temporal expression levels of genes. Although GFP expression is largely considered non-toxic, recent reports have indicated that under certain conditions GFP can display cellular toxicity. We hereby report the nuclear toxicity of H2B-GFP using a K14 specific Tet-on reporter mouse system. Using this system, GFP accumulates in the nucleus of all K14 expressing cells, such as the ocular surface epithelia and ocular adnexa. Expression of high levels of nuclear GFP during embryonic stages led to an eye open-at-birth (EOB) phenotype and abnormal ocular adnexa development and during adult and aging stages showed notable toxicity to ocular tissues. Other tissues, such as skin, also presented multiple defects associated with H2B-GFP expression. This toxicity was found to be concentration dependent, with homozygous mice presenting extremely high toxicity, while heterozygous mice presented limited toxicity. Upon induction, the accumulation of H2B-GFP in the nucleus of homozygous mice led to apoptosis within 2 weeks. This study therefore shows that although the use of nuclear GFP reporter mice is a valuable tool, at high levels, nuclear GFP can be toxic, leading to cell death and affecting tissue function.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Microbial engineering for shikimate biosynthesis

Shikimate, a precursor to the antiviral drug oseltamivir (Tamiflu®), can influence aromatic metabolites and finds extensive use in antimicrobial, antitumor, and cardiovascular applications. Consequently, various strategies have been developed for chemical synthesis and plant extraction to enhance shikimate biosynthesis, potentially impacting environmental conditions, economic sustainability, and separation and purification processes. Microbial engineering has been developed as an environmentally friendly approach for shikimate biosynthesis. In this review, we provide a comprehensive summary of microbial strategies for shikimate biosynthesis. These strategies primarily include chassis construction, biochemical optimization, pathway remodelling, and global regulation. Furthermore, we discuss future perspectives on shikimate biosynthesis and emphasize the importance of utilizing advanced metabolic engineering tools to regulate microbial networks for constructing robust microbial cell factories.

Metabolic engineering in hairy roots: An outlook on production of plant secondary metabolites

Plants are one of the vital sources of secondary metabolites. These secondary metabolites have diverse roles in human welfare, including therapeutic implication. Nevertheless, secondary metabolite yields obtained through the exploitation of natural plant populations is insufficient to meet the commercial demand due to their accumulation in low volumes. Besides, in-planta synthesis of these important metabolites is directly linked with the age and growing conditions of the plant. Such limitations have paved the way for the exploration of alternative production methodologies. Hairy root cultures, induced after the interaction of plants with Rhizobium rhizogenes (Agrobacterium rhizogenes), are a practical solution for producing valuable secondary metabolite at low cost and without the influence of seasonal, geographic or climatic variations. Hairy root cultures also offer the opportunity to get combined with other yield enhancements strategies (precursor feeding, elicitation and metabolic engineering) to further stimulate and/or enhance their production potential. Applications of metabolic engineering in exploiting hairy root cultures attracted the interest of several research groups as a means of yield enhancement. Currently, several engineering approaches like overexpression and silencing of pathway genes, and transcription factor overexpression are used to boost metabolite production, along with the contextual success of genome editing. This review attempts to cover metabolic engineering in hairy roots for the production of secondary metabolites, with a primary emphasis on alkaloids, and discusses prospects for taking this research forward to meet desired production demands.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Development of a synthetic transcription factor-based S-adenosylmethionine biosensor in Saccharomyces cerevisiae

S-Adenosylmethionine (SAM) is a crucial small-molecule metabolite widely used in food and medicine. The development of high-throughput biosensors for SAM biosynthesis can significantly improve the titer of SAM. This paper constructed a synthetic transcription factor (TF)-based biosensor for SAM detecting in Saccharomyces cerevisiae. The synthetic TF, named MetJ-hER-VP16, consists of an Escherichia coli-derived DNA-binding domain MetJ, GS linker, the human estrogen receptor binding domain hER, and the viral activation domain VP16. The synthetic biosensor is capable of sensing SAM in a dose-dependent manner with fluorescence as the output. Additionally, it is tightly regulated by the inducer SAM and β-estradiol, which means that the fluorescence output is only available when both are present together. The synthetic SAM biosensor could potentially be applied for high-throughput metabolic engineering and is expected to improve SAM production.

Rhizopine biosensors for plant-dependent control of bacterial gene expression

Engineering signalling between plants and microbes could be exploited to establish host-specificity between plant-growth-promoting bacteria and target crops in the environment. We previously engineered rhizopine-signalling circuitry facilitating exclusive signalling between rhizopine-producing (RhiP) plants and model bacterial strains. Here, we conduct an in-depth analysis of rhizopine-inducible expression in bacteria. We characterize two rhizopine-inducible promoters and explore the bacterial host-range of rhizopine biosensor plasmids. By tuning the expression of rhizopine uptake genes, we also construct a new biosensor plasmid pSIR05 that has minimal impact on host cell growth in vitro and exhibits markedly improved stability of expression in situ on RhiP barley roots compared to the previously described biosensor plasmid pSIR02. We demonstrate that a sub-population of Azorhizobium caulinodans cells carrying pSIR05 can sense rhizopine and activate gene expression when colonizing RhiP barley roots. However, these bacteria were mildly defective for colonization of RhiP barley roots compared to the wild-type parent strain. This work provides advancement towards establishing more robust plant-dependent control of bacterial gene expression and highlights the key challenges remaining to achieve this goal.

The bright frontiers of microbial metabolic optogenetics

In recent years, light-responsive systems from the field of optogenetics have been applied to several areas of metabolic engineering with remarkable success. By taking advantage of light's high tunability, reversibility, and orthogonality to host endogenous processes, optogenetic systems have enabled unprecedented dynamical controls of microbial fermentations for chemical production, metabolic flux analysis, and population compositions in co-cultures. In this article, we share our opinions on the current state of this new field of metabolic optogenetics.We make the case that it will continue to impact metabolic engineering in increasingly new directions, with the potential to challenge existing paradigms for metabolic pathway and strain optimization as well as bioreactor operation.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

An Allosteric Transcription Factor DNA-Binding Electrochemical Biosensor for Progesterone

We describe an electrochemical strategy to transduce allosteric transcription factor (aTF) binding affinity to sense steroid hormones. Our approach utilizes square wave voltammetry to monitor changes in current output as a progesterone (PRG)-specific aTF (SRTF1) unbinds from the cognate DNA sequence in the presence of PRG. The sensor detects PRG in artificial urine samples with sufficient sensitivity suitable for clinical applications. Our results highlight the capability of using aTFs as the biorecognition elements to develop electrochemical point-of-care biosensors for the detection of small-molecule biomarkers and analytes.

Refactoring transcription factors for metabolic engineering

Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review introduced the categories, action principles, prediction strategies, and related databases of transcription factors. Then, the application of global transcription machinery engineering technology and the transcription factor-based biosensors and quorum sensing systems are overviewed. In addition, strategies for optimizing the transcriptional regulatory tools' performance by refactoring transcription factors are summarized. Finally, the current limitations and prospects of constructing various regulatory tools based on transcription factors are discussed. This review will provide theoretical guidance for the rational design and construction of transcription factor-based metabolic regulation systems.

High-throughput screening of cell-free riboswitches by fluorescence-activated droplet sorting

Cell-free systems that display complex functions without using living cells are emerging as new platforms to test our understanding of biological systems as well as for practical applications such as biosensors and biomanufacturing. Those that use cell-free protein synthesis (CFPS) systems to enable genetically programmed protein synthesis have relied on genetic regulatory components found or engineered in living cells. However, biological constraints such as cell permeability, metabolic stability, and toxicity of signaling molecules prevent development of cell-free devices using living cells even if cell-free systems are not subject to such constraints. Efforts to engineer regulatory components directly in CFPS systems thus far have been based on low-throughput experimental approaches, limiting the availability of basic components to build cell-free systems with diverse functions. Here, we report a high-throughput screening method to engineer cell-free riboswitches that respond to small molecules. Droplet-sorting of riboswitch variants in a CFPS system rapidly identified cell-free riboswitches that respond to compounds that are not amenable to bacterial screening methods. Finally, we used a histamine riboswitch to demonstrate chemical communication between cell-sized droplets.

Engineering robust microorganisms for organic acid production

Organic acids are an important class of compounds that can be produced by microbial conversion of renewable feedstocks and have huge demands and broad applications in food, chemical, and pharmaceutical industries. An economically viable fermentation process for production of organic acids requires robust microbial cell factories with excellent tolerance to low pH conditions, high concentrations of organic acids, and lignocellulosic inhibitors. In this review, we summarize various strategies to engineer robust microorganisms for organic acid production and highlight their applications in a few recent examples.

Modular optimization in metabolic engineering

There is an increasing demand for bioproducts produced by metabolically engineered microbes, such as pharmaceuticals, biofuels, biochemicals and other high value compounds. In order to meet this demand, modular optimization, the optimizing of subsections instead of the whole system, has been adopted to engineer cells to overproduce products. Research into modularity has focused on traditional approaches such as DNA, RNA, and protein-level modularity of intercellular machinery, by optimizing metabolic pathways for enhanced production. While research into these traditional approaches continues, limitations such as scale-up and time cost hold them back from wider use, while at the same time there is a shift to more novel methods, such as moving from episomal expression to chromosomal integration. Recently, nontraditional approaches such as co-culture systems and cell-free metabolic engineering (CFME) are being investigated for modular optimization. Co-culture modularity looks to optimally divide the metabolic burden between different hosts. CFME seeks to modularly optimize metabolic pathways in vitro, both speeding up the design of such systems and eliminating the issues associated with live hosts. In this review we will examine both traditional and nontraditional approaches for modular optimization, examining recent developments and discussing issues and emerging solutions for future research in metabolic engineering.

Site-directed mutagenesis of the quorum-sensing transcriptional regulator SinR affects the biosynthesis of menaquinone in Bacillus subtilis

Background

Menaquinone (MK-7) is a highly valuable vitamin K₂ produced by Bacillus subtilis. Common static metabolic engineering approaches for promoting the production of MK-7 have been studied previously. However, these approaches caused an accumulation of toxic substances and reduced product yield. Hence, dynamic regulation by the quorum sensing (QS) system is a promising method for achieving a balance between product synthesis and cell growth.

Results

In this study, the QS transcriptional regulator SinR, which plays a significant role in biofilm formation and MK production simultaneously, was selected, and its site-directed mutants were constructed. Among these mutants, sinR knock out strain (KO-SinR) increased the biofilm biomass by 2.8-fold compared to the wild-type. SinR^quad maximized the yield of MK-7 (102.56 ± 2.84 mg/L). To decipher the mechanism of how this mutant regulates MK-7 synthesis and to find additional potential regulators that enhance MK-7 synthesis, RNA-seq was used to analyze expression changes in the QS system, biofilm formation, and MK-7 synthesis pathway. The results showed that the expressions of tapA, tasA and epsE were up-regulated 9.79-, 0.95-, and 4.42-fold, respectively. Therefore, SinR^quad formed more wrinkly and smoother biofilms than BS168. The upregulated expressions of glpF, glpk, and glpD in this biofilm morphology facilitated the flow of glycerol through the biofilm. In addition, NADH dehydrogenases especially sdhA, sdhB, sdhC and glpD, increased 1.01-, 3.93-, 1.87-, and 1.11-fold, respectively. The increased expression levels of NADH dehydrogenases indicated that more electrons were produced for the electron transport system. Electrical hyperpolarization stimulated the synthesis of the electron transport chain components, such as cytochrome c and MK, to ensure the efficiency of electron transfer. Wrinkly and smooth biofilms formed a network of interconnected channels with a low resistance to liquid flow, which was beneficial for the uptake of glycerol, and facilitated the metabolic flux of four modules of the MK-7 synthesis pathway.

Conclusions

In this study, we report for the first time that SinR^quad has significant effects on MK-7 synthesis by forming wrinkly and smooth biofilms, upregulating the expression level of most NADH dehydrogenases, and providing higher membrane potential to stimulate the accumulation of the components in the electron transport system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01603-5.

Transcription-Factor-based Biosensor Engineering for Applications in Synthetic Biology

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

Transcription Factor-Based Biosensor for Dynamic Control in Yeast for Natural Product Synthesis

The synthesis of natural products in yeast has gained remarkable achievements with intensive metabolic engineering efforts. In particular, transcription factor (TF)-based biosensors for dynamic control of gene circuits could facilitate strain evaluation, high-throughput screening (HTS), and adaptive laboratory evolution (ALE) for natural product synthesis. In this review, we summarized recent developments of several TF-based biosensors for core intermediates in natural product synthesis through three important pathways, i.e., fatty acid synthesis pathway, shikimate pathway, and methylerythritol-4-phosphate (MEP)/mevalonate (MVA) pathway. Moreover, we have shown how these biosensors are implemented in synthetic circuits for dynamic control of natural product synthesis and also discussed the design/evaluation principles for improved biosensor performance.

Spatiotemporal Patterning of Living Cells with Extracellular DNA Programs

Reactive extracellular media focus on engineering reaction networks outside the cell to control intracellular chemical composition across time and space. However, current implementations lack the feedback loops and out-of-equilibrium molecular dynamics for encoding spatiotemporal control. Here, we demonstrate that enzyme-DNA molecular programs combining these qualities are functional in an extracellular medium where human cells can grow. With this approach, we construct an internalization program that delivers fluorescent DNA inside living cells and remains functional for at least 48 h. Its nonequilibrium dynamics allows us to control both the time and position of cell internalization. In particular, a spatially inhomogeneous version of this program generates a tunable reaction-diffusion two-band pattern of cell internalization. This demonstrates that a synthetic extracellular program can provide temporal and positional information to living cells, emulating archetypal mechanisms observed during embryo development. We foresee that nonequilibrium reactive extracellular media could be advantageously applied to in vitro biomolecular tracking, tissue engineering, or smart bandages.

Transcription factor-based biosensors: a molecular-guided approach for natural product engineering

Natural products and their derivatives offer a rich source of chemical and biological diversity; however, traditional engineering of their biosynthetic pathways to improve yields and access to unnatural derivatives requires a precise understanding of their enzymatic processes. High-throughput screening platforms based on allosteric transcription-factor based biosensors can be leveraged to overcome the screening bottleneck to enable searching through large libraries of pathway/strain variants. Herein, the development and application of engineered allosteric transcription factor-based biosensors is described that enable optimization of precursor availability, product titers, and downstream product tailoring for advancing the natural product bioeconomy. We discuss recent successes for tailoring biosensor design, including computationally-based approaches, and present our future outlook with the integration of cell-free technologies and de novo protein design for rapidly generating biosensor tools.

A roadmap to engineering antiviral natural products synthesis in microbes

Natural products continue to be the inspirations for us to discover and acquire new drugs. The seemingly unstoppable viruses have kept records high to threaten human health and well-being. The diversity and complexity of natural products (NPs) offer remarkable efficacy and specificity to target viral infection steps and serve as excellent source for antiviral agents. The discovery and production of antiviral NPs remain challenging due to low abundance in their native hosts. Reconstruction of NP biosynthetic pathways in microbes is a promising solution to overcome this limitation. In this review, we surveyed 23 most prominent NPs (from more than 200 antiviral NP candidates) with distinct antiviral mode of actions and summarized the recent metabolic engineering effort to produce these compounds in various microbial hosts. We envision that the scalable and low-cost production of novel antiviral NPs, enabled by metabolic engineering, may light the hope to control and eradicate the deadliest viruses that plague our society and humanity.

Hydrogel-Embedded Quantum Dot-Transcription Factor Sensors for Quantitative Progesterone Detection

Immobilization of biosensors in or on a functional material is critical for subsequent device development and translation to wearable technology. Here, we present the development and assessment of an immobilized quantum dot-transcription factor-nucleic acid complex for progesterone detection as a first step toward such device integration. The sensor, composed of a polyhistidine-tagged transcription factor linked to a quantum dot and a fluorophore-modified cognate DNA, is embedded within a hydrogel as an immobilization matrix. The hydrogel is optically transparent, soft, and flexible as well as traps the quantum dot-transcription factor DNA assembly but allows free passage of the analyte, progesterone. Upon progesterone exposure, DNA dissociates from the quantum dot-transcription factor DNA assembly resulting in an attenuated ratiometric fluorescence output via Förster resonance energy transfer. The sensor performs in a dose-dependent manner with a limit of detection of 55 nM. Repeated analyte measurements are similarly successful. Our approach combines a systematically characterized hydrogel as an immobilization matrix and a transcription factor-DNA assembly as a recognition/transduction element, offering a promising framework for future biosensor devices.

Innovative Tools and Strategies for Optimizing Yeast Cell Factories

Metabolic engineering (ME) aims to develop efficient microbial cell factories that can produce a wide variety of valuable compounds, ideally at the highest yield and from various feedstocks. We summarize recent developments in ME methods for tailoring different yeast cell factories (YCFs). In particular, we highlight the most timely and cutting-edge molecular tools and strategies for biosynthetic pathway optimization (including genome-editing tools), combinatorial transcriptional and post-transcriptional engineering (cis/trans regulators), dynamic control of metabolic fluxes (e.g., rewiring of primary metabolism), and spatial reconfiguration of metabolic pathways. Finally, we discuss challenges and perspectives for adaptive laboratory evolution (ALE) of yeast to advance ME of microbial cell factories.

Coupling metabolic addiction with negative autoregulation to improve strain stability and pathway yield

Metabolic addiction, an organism that is metabolically addicted with a compound to maintain its growth fitness, is an underexplored area in metabolic engineering. Microbes with heavily engineered pathways or genetic circuits tend to experience metabolic burden leading to degenerated or abortive production phenotype during long-term cultivation or scale-up. A promising solution to combat metabolic instability is to tie up the end-product with an intermediary metabolite that is essential to the growth of the producing host. Here we present a simple strategy to improve both metabolic stability and pathway yield by coupling chemical addiction with negative autoregulatory genetic circuits. Naringenin and lipids compete for the same precursor malonyl-CoA with inversed pathway yield in oleaginous yeast. Negative autoregulation of the lipogenic pathways, enabled by CRISPRi and fatty acid-inducible promoters, repartitions malonyl-CoA to favor flavonoid synthesis and increased naringenin production by 74.8%. With flavonoid-sensing transcriptional activator FdeR and yeast hybrid promoters to control leucine synthesis and cell grwoth fitness, this amino acid feedforward metabolic circuit confers a flavonoid addiction phenotype that selectively enrich the naringenin-producing pupulation in the leucine auxotrophic yeast. The engineered yeast persisted 90.9% of naringenin titer up to 324 generations. Cells without flavonoid addiction regained growth fitness but lost 94.5% of the naringenin titer after cell passage beyond 300 generations. Metabolic addiction and negative autoregulation may be generalized as basic tools to eliminate metabolic heterogeneity, improve strain stability and pathway yield in long-term and large-scale bioproduction.

Unstructured kinetic models to simulate an arabinose switch that decouples cell growth from metabolite production

Modeling synthetic gene circuits to implement dynamic flux balancing is crucial in teaching and exploring metabolic engineering strategies to repartition metabolic precursors and construct efficient microbial cell factories. Microbial fitness and production rates are often complex phenotypes that are governed by highly non-linear, multivariable functions which are intrinsically linked through carbon metabolism. The solution of such dynamic system can be difficult for synthetic biologists to visualize or conceptualize. Recently, researchers (Santala et al., Metab. Eng. Comm., 2018) have implemented an arabinose based genetic switch to dynamically partition the central carbon flux between cell growth and product formation. The autonomous switch allowed dynamic shift from arabinose-associated cell growth to acetate-associated product (wax ester) formation. This system clearly demonstrates the effectiveness of using a genetic switch to decouple cell growth from product formation in a one-pot bioreactor to minimize operational cost. Coupled with Michaelis-Menten kinetics, and Luedeking-Piret equations, we were able to reconstruct and analyze this metabolic switch in silica and achieved graphical solutions that qualitatively match with the experimental data. By assessing physiologically-accessible parameter space, we observed a wide range of dynamic behavior and examined the different limiting cases. Graphical solutions for this dynamic system can be viewed simultaneously and resolved in real time via buttons on the graphical user interface (GUI). Metabolic bottlenecks in the system can be accurately predicted by varying the respective rate constants. The GUI serves as a diagnosis toolkit to troubleshoot genetic circuits design constraints and as an interactive workflow of using this arabinose based genetic switch to dynamically control carbon flux, which may provide a valuable computational toolbox for metabolic engineers and synthetic biologists to simulate and understand complex genetic-metabolic system.

Synthetic biology, systems biology, and metabolic engineering of Yarrowia lipolytica toward a sustainable biorefinery platform

Yarrowia lipolytica is an oleaginous yeast that has been substantially engineered for production of oleochemicals and drop-in transportation fuels. The unique acetyl-CoA/malonyl-CoA supply mode along with the versatile carbon-utilization pathways makes this yeast a superior host to upgrade low-value carbons into high-value secondary metabolites and fatty acid-based chemicals. The expanded synthetic biology toolkits enabled us to explore a large portfolio of specialized metabolism beyond fatty acids and lipid-based chemicals. In this review, we will summarize the recent advances in genetic, omics, and computational tool development that enables us to streamline the genetic or genomic modification for Y. lipolytica. We will also summarize various metabolic engineering strategies to harness the endogenous acetyl-CoA/malonyl-CoA/HMG-CoA pathway for production of complex oleochemicals, polyols, terpenes, polyketides, and commodity chemicals. We envision that Y. lipolytica will be an excellent microbial chassis to expand nature's biosynthetic capacity to produce plant secondary metabolites, industrially relevant oleochemicals, agrochemicals, commodity, and specialty chemicals and empower us to build a sustainable biorefinery platform that contributes to the prosperity of a bio-based economy in the future.

Combinatorial Control of Plant Specialized Metabolism: Mechanisms, Functions, and Consequences

Plants constantly perceive internal and external cues, many of which they need to address to safeguard their proper development and survival. They respond to these cues by selective activation of specific metabolic pathways involving a plethora of molecular players that act and interact in complex networks. In this review, we illustrate and discuss the complexity in the combinatorial control of plant specialized metabolism. We hereby go beyond the intuitive concept of combinatorial control as exerted by modular-acting complexes of transcription factors that govern expression of specialized metabolism genes. To extend this discussion, we also consider all known hierarchical levels of regulation of plant specialized metabolism and their interfaces by referring to reported regulatory concepts from the plant field. Finally, we speculate on possible yet-to-be-discovered regulatory principles of plant specialized metabolism that are inspired by knowledge from other kingdoms of life and areas of biological research.

Genetically-encoded biosensors for analyzing and controlling cellular process in yeast

Yeast has been a robust platform to manufacture a broad range of biofuels, commodity chemicals, natural products and pharmaceuticals. The membrane-bound organelles in yeast provide us the means to access the specialized metabolism for various biosynthetic applications. The separation and compartmentalization of genetic and metabolic events presents us the opportunity to precisely control and program gene expression for higher order biological functions. To further advance yeast synthetic biology platform, genetically encoded biosensors and actuators haven been engineered for in vivo monitoring and controlling cellular processes with spatiotemporal resolutions. The dynamic response, sensitivity and operational range of these genetically encoded sensors are determined by the regulatory architecture, dynamic assemly and interactions of the related proteins and genetic elements. This review provides an update of the basic design principles underlying the allosteric transcription factors, GPCR and optogenetics-based sensors, aiming to precisely analyze and control yeast cellular processes for various biotechnological applications.

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Living organism is an intelligent system coded by hierarchically-organized information to perform precisely-controlled biological functions. Biophysical models are important tools to uncover the design rules underlying complex genetic-metabolic circuit interactions. Based on a previously engineered synthetic malonyl-CoA switch (Xu et al., PNAS, 2014), we have formulated nine differential equations to unravel the design principles underlying an ideal metabolic switch to improve fatty acids production in E. coli. By interrogating the physiologically accessible parameter space, we have determined the optimal controller architecture to configure both the metabolic source pathway and metabolic sink pathway. We determined that low protein degradation rate, medium strength of metabolic inhibitory constant, high metabolic source pathway induction rate, strong binding affinity of the transcriptional activator toward the metabolic source pathway, weak binding affinity of the transcriptional repressor toward the metabolic sink pathway, and a strong cooperative interaction of transcriptional repressor toward metabolic sink pathway benefit the accumulation of the target molecule (fatty acids). The target molecule (fatty acid) production is increased from 50% to 10-folds upon application of the autonomous metabolic switch. With strong metabolic inhibitory constant, the system displays multiple steady states. Stable oscillation of metabolic intermediate is the driving force to allow the system deviate from its equilibrium state and permits bidirectional ON-OFF gene expression control, which autonomously compensates enzyme level for both the metabolic source and metabolic sink pathways. The computational framework may facilitate us to design and engineer predictable genetic-metabolic switches, quest for the optimal controller architecture of the metabolic source/sink pathways, as well as leverage autonomous oscillation as a powerful tool to engineer cell function.

Application of combinatorial optimization strategies in synthetic biology

In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.

A tight cold-inducible switch built by coupling thermosensitive transcriptional and proteolytic regulatory parts

Natural organisms have evolved intricate regulatory mechanisms that sense and respond to fluctuating environmental temperatures in a heat- or cold-inducible fashion. Unlike dominant heat-inducible switches, very few cold-inducible genetic switches are available in either natural or engineered systems. Moreover, the available cold-inducible switches still have many shortcomings, including high leaky gene expression, small dynamic range (<10-fold) or broad transition temperature (>10°C). To address these problems, a high-performance cold-inducible switch that can tightly control target gene expression is highly desired. Here, we introduce a tight and fast cold-inducible switch that couples two evolved thermosensitive variants, TF_ts and TEV_ts, as well as an additional Mycoplasma florum Lon protease (mf-Lon) to effectively turn-off target gene expression via transcriptional and proteolytic mechanisms. We validated the function of the switch in different culture media and various Escherichia coli strains and demonstrated its tightness by regulating two morphogenetic bacterial genes and expressing three heat-unstable recombinant proteins, respectively. Moreover, the additional protease module enabled the cold-inducible switch to actively remove the pre-existing proteins in slow-growing cells. This work establishes a high-performance cold-inducible system for tight and fast control of gene expression which has great potential for basic research, as well as industrial and biomedical applications.

Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms

Low yield and low titer of natural products are common issues in natural product biosynthesis through microbial cell factories. One effective way to resolve such bottlenecks is to design genetic biosensors to monitor and regulate the biosynthesis of target natural products. In this review, we evaluate the most recent advances in the design of genetic biosensors for natural product biosynthesis in microorganisms. In particular, we examine strategies for selection of genetic parts and construction principles for the design and evaluation of genetic biosensors. We also review the latest applications of transcription factor- and riboswitch-based genetic biosensors in natural product biosynthesis. Lastly, we discuss challenges and solutions in designing genetic biosensors for the biosynthesis of natural products in microorganisms.

Combining genetically-encoded biosensors with high throughput strain screening to maximize erythritol production in Yarrowia lipolytica

Erythritol is an important sweetener ingredient and chemical precursor for synthesizing materials with phase transition behavior. Commercial erythritol is primarily produced by industrial fermentation. Further strain engineering necessitates the development of high throughput screening method for rapid detection and screening of mutant strain libraries. In this work, we took advantage of the erythritol-responsive transcription factor EryD, and constructed a sensor-regulator system for rapid screening and characterization of erythritol overproducers. We configured the optimal architecture of the EryD sensor-regulator construct with improved sensitivity, specificity and dynamic response range. Coupled with mutagenesis and strain screening based on biosensors, we rapidly screened and characterized a strain library containing 1152 mutants derived from combined UV and ARTP mutagenesis, in a relatively short period of time (1 week). The optimal strain produced more than 148 g/L erythritol in bench-top reactors. This work provides a reference for other metabolic engineering researchers to develop industrially-relevant strains. The reported framework enables us to rapidly improve strain performance and engineer efficient microbial cell factories for industrial applications.

A genome-wide approach for identification and characterisation of metabolite-inducible systems

Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules.

Inducible gene expression tools have important applications as genetically encoded biosensors. Here the authors conduct a genome-wide approach to identify and utilise functional sensors.

Engineering Plant Cytochrome P450s for Enhanced Synthesis of Natural Products: Past Achievements and Future Perspectives

Cytochrome P450s (P450s) are the most versatile catalysts and are widely used by plants to synthesize a vast array of structurally diverse specialized metabolites that not only play essential ecological roles but also constitute a valuable resource for the development of new drugs. To accelerate the metabolic engineering of these high-value metabolites, it is imperative to identify and characterize pathway P450s, and to further improve their activities through protein engineering. In this review, we focus on P450 engineering and summarize the major strategies for enhancing the stability and activity of P450s and successful cases of P450 engineering. Studies in which the functions of P450s were altered to create de novo metabolic pathways or novel compounds are discussed as well. We also overview emerging tools, specifically DNA synthesis, machine learning, and de novo protein design, as well as the evolutionary patterns of P450s unveiled from a massive number of DNA sequences that could be integrated to accelerate the engineering of these enzymes. These approaches would greatly aid in the exploitation of plant-specialized metabolites or derivatives for various uses including medical applications.

Engineering a Bifunctional Phr60-Rap60-Spo0A Quorum-Sensing Molecular Switch for Dynamic Fine-Tuning of Menaquinone-7 Synthesis in Bacillus subtilis

Quorum sensing (QS)-based dynamic regulation has been widely used as basic tool for fine-tuning gene expression in response to cell density changes without adding expensive inducers. However, most reported QS systems primarily relied on down-regulation rather than up-regulation of gene expression, significantly limiting its potential as a molecular switch to control metabolic flux. To solve this challenge, we developed a bifunctional and modular Phr60-Rap60-Spo0A QS system, based on two native promoters, P_abrB (down-regulation by Spo0A-P) and P_spoiiA (up-regulation by Spo0A-P). We constructed a library of promoters with different capacities to implement down-regulation and up-regulation by changing the location, number, and sequences of the binding sites for Spo0A-P. The QS system can dynamically balance the relationship between efficient synthesis of the target product and cell growth. Finally, we validated the usefulness of this strategy by dynamic control of menaquinone-7 (MK-7) synthesis in Bacillus subtilis 168, a model Gram-positive bacterium, with the bifunctional Phr60-Rap60-Spo0A quorum sensing system. Our dynamic pathway regulation led to a 40-fold improvement of MK-7 production from 9 to 360 mg/L in shake flasks and 200 mg/L in 15-L bioreactor. Taken together, our bilayer QS system has been successfully integrated with biocatalytic functions to achieve dynamic pathway regulation in B. subtilis 168, which may be extended for use in other microbes to fine-tune gene expression and improve metabolites production.