Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast

Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era

Simple Summary

Promoters are DNA sequences where the process of transcription starts. They can work constitutively or be controlled by environmental signals of different types. The quantity of proteins and RNA present in yeast genetic circuits highly depends on promoter strength. Hence, they have been deeply studied and modified over, at least, the last forty years, especially since the year 2000 when Synthetic Biology was born. Here, we present how promoter engineering changed over these four decades and discuss its possible future directions due to novel computational methods and technology.

Abstract

Synthetic gene circuits are made of DNA sequences, referred to as transcription units, that communicate by exchanging proteins or RNA molecules. Proteins are, mostly, transcription factors that bind promoter sequences to modulate the expression of other molecules. Promoters are, therefore, key components in genetic circuits. In this review, we focus our attention on the construction of artificial promoters for the yeast S. cerevisiae, a popular chassis for gene circuits. We describe the initial techniques and achievements in promoter engineering that predated the start of the Synthetic Biology epoch of about 20 years. We present the main applications of synthetic promoters built via different methods and discuss the latest innovations in the wet-lab engineering of novel promoter sequences.

Optogenetic Amplification Circuits for Light-Induced Metabolic Control

Dynamic control of microbial metabolism is an effective strategy to improve chemical production in fermentations. While dynamic control is most often implemented using chemical inducers, optogenetics offers an attractive alternative due to the high tunability and reversibility afforded by light. However, a major concern of applying optogenetics in metabolic engineering is the risk of insufficient light penetration at high cell densities, especially in large bioreactors. Here, we present a new series of optogenetic circuits we call OptoAMP, which amplify the transcriptional response to blue light by as much as 23-fold compared to the basal circuit (OptoEXP). These circuits show as much as a 41-fold induction between dark and light conditions, efficient activation at light duty cycles as low as ∼1%, and strong homogeneous light-induction in bioreactors of at least 5 L, with limited illumination at cell densities above 40 OD₆₀₀. We demonstrate the ability of OptoAMP circuits to control engineered metabolic pathways in novel three-phase fermentations using different light schedules to control enzyme expression and improve production of lactic acid, isobutanol, and naringenin. These circuits expand the applicability of optogenetics to metabolic engineering.

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.

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

Improving the design of an oxidative stress sensing biosensor in yeast

Transcription factor (TF)-based biosensors have proven useful for increasing biomanufacturing yields, large-scale functional screening, and in environmental monitoring. Most yeast TF-based biosensors are built from natural promoters, resulting in large DNA parts retaining considerable homology to the host genome, which can complicate biological engineering efforts. There is a need to explore smaller, synthetic biosensors to expand the options for regulating gene expression in yeast. Here, we present a systematic approach to improving the design of an existing oxidative stress sensing biosensor in Saccharomyces cerevisiae based on the Yap1 transcription factor. Starting from a synthetic core promoter, we optimized the activity of a Yap1-dependent promoter through rational modification of a minimalist Yap1 upstream activating sequence. Our novel promoter achieves dynamic ranges of activation surpassing those of the previously engineered Yap1-dependent promoter, while reducing it to only 171 base pairs. We demonstrate that coupling the promoter to a positive-feedback-regulated TF further improves the biosensor by increasing its dynamic range of activation and reducing its limit of detection. We have illustrated the robustness and transferability of the biosensor by reproducing its activity in an unconventional probiotic yeast strain, Saccharomyces boulardii. Our findings can provide guidance in the general process of TF-based biosensor design.

Engineering G protein-coupled receptor signalling in yeast for biotechnological and medical purposes

G protein-coupled receptors (GPCRs) comprise the largest class of membrane proteins in the human genome, with a common denominator of seven-transmembrane domains largely conserved among eukaryotes. Yeast is naturally armoured with three different GPCRs for pheromone and sugar sensing, with the pheromone pathway being extensively hijacked for characterising heterologous GPCR signalling in a model eukaryote. This review focusses on functional GPCR studies performed in yeast and on the elucidated hotspots for engineering, and discusses both endogenous and heterologous GPCR signalling. Key emphasis will be devoted to studies describing important engineering parameters to consider for successful coupling of GPCRs to the yeast mating pathway. We also review the various means of applying yeast for studying GPCRs, including the use of yeast armed with heterologous GPCRs as a platform for (i) deorphanisation of orphan receptors, (ii) metabolic engineering of yeast for production of bioactive products and (iii) medical applications related to pathogen detection and drug discovery. Finally, this review summarises the current challenges related to expression of functional membrane-bound GPCRs in yeast and discusses the opportunities to continue capitalising on yeast as a model chassis for functional GPCR signalling studies.

Robust and flexible platform for directed evolution of yeast genetic switches

A wide repertoire of genetic switches has accelerated prokaryotic synthetic biology, while eukaryotic synthetic biology has lagged in the model organism Saccharomyces cerevisiae. Eukaryotic genetic switches are larger and more complex than prokaryotic ones, complicating the rational design and evolution of them. Here, we present a robust workflow for the creation and evolution of yeast genetic switches. The selector system was designed so that both ON- and OFF-state selection of genetic switches is completed solely by liquid handling, and it enabled parallel screen/selection of different motifs with different selection conditions. Because selection threshold of both ON- and OFF-state selection can be flexibly tuned, the desired selection conditions can be rapidly pinned down for individual directed evolution experiments without a prior knowledge either on the library population. The system's utility was demonstrated using 20 independent directed evolution experiments, yielding genetic switches with elevated inducer sensitivities, inverted switching behaviours, sensory functions, and improved signal-to-noise ratio (>100-fold induction). The resulting yeast genetic switches were readily integrated, in a plug-and-play manner, into an AND-gated carotenoid biosynthesis pathway.

Transportome-wide engineering of Saccharomyces cerevisiae

Synthetic biology enables the production of small molecules by recombinant microbes for pharma, food, and materials applications. The secretion of products reduces the cost of separation and purification, but it is challenging to engineer due to the limited understanding of the transporter proteins' functions. Here we describe a method for genome-wide transporter disruption that, in combination with a metabolite biosensor, enables the identification of transporters impacting the production of a given target metabolite in yeast Saccharomyces cerevisiae. We applied the method to study the transport of xenobiotic compounds, cis,cis-muconic acid (CCM), protocatechuic acid (PCA), and betaxanthins. We found 22 transporters that influenced the production of CCM or PCA. The transporter of the 12-spanner drug:H(+) antiporter (DHA1) family Tpo2p was further confirmed to import CCM and PCA in Xenopus expression assays. We also identified three transporter proteins (Qdr1p, Qdr2p, and Apl1p) involved in betaxanthins transport. In summary, the described method enables high-throughput transporter identification for small molecules in cell factories.

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.

Sensing the future of bio-informational engineering

The practices of synthetic biology are being integrated into 'multiscale' designs enabling two-way communication across organic and inorganic information substrates in biological, digital and cyber-physical system integrations. Novel applications of 'bio-informational' engineering will arise in environmental monitoring, precision agriculture, precision medicine and next-generation biomanufacturing. Potential developments include sentinel plants for environmental monitoring and autonomous bioreactors that respond to biosensor signaling. As bio-informational understanding progresses, both natural and engineered biological systems will need to be reimagined as cyber-physical architectures. We propose that a multiple length scale taxonomy will assist in rationalizing and enabling this transformative development in engineering biology.

Does co-expression of Yarrowia lipolytica genes encoding Yas1p, Yas2p and Yas3p make a potential alkane-responsive biosensor in Saccharomyces cerevisiae?

Alkane-based biofuels are desirable to produce at a commercial scale as these have properties similar to current petroleum-derived transportation fuels. Rationally engineering microorganisms to produce a desirable compound, such as alkanes, is, however, challenging. Metabolic engineers are therefore increasingly implementing evolutionary engineering approaches combined with high-throughput screening tools, including metabolite biosensors, to identify productive cells. Engineering Saccharomyces cerevisiae to produce alkanes could be facilitated by using an alkane-responsive biosensor, which can potentially be developed from the native alkane-sensing system in Yarrowia lipolytica, a well-known alkane-assimilating yeast. This putative alkane-sensing system is, at least, based on three different transcription factors (TFs) named Yas1p, Yas2p and Yas3p. Although this system is not fully elucidated in Y. lipolytica, we were interested in evaluating the possibility of translating this system into an alkane-responsive biosensor in S. cerevisiae. We evaluated the alkane-sensing system in S. cerevisiae by developing one sensor based on the native Y. lipolytica ALK1 promoter and one sensor based on the native S. cerevisiae CYC1 promoter. In both systems, we found that the TFs Yas1p, Yas2p and Yas3p do not seem to act in the same way as these have been reported to do in their native host. Additional analysis of the TFs suggests that more knowledge regarding their mechanism is needed before a potential alkane-responsive sensor based on the Y. lipolytica system can be established in S. cerevisiae.

Cell-based and cell-free biocatalysis for the production of D-glucaric acid

D-Glucaric acid (GA) is a value-added chemical produced from biomass, and has potential applications as a versatile platform chemical, food additive, metal sequestering agent, and therapeutic agent. Marketed GA is currently produced chemically, but increasing demand is driving the search for eco-friendlier and more efficient production approaches. Cell-based production of GA represents an alternative strategy for GA production. A series of synthetic pathways for GA have been ported into Escherichia coli, Saccharomyces cerevisiae and Pichia pastoris, respectively, and these engineered cells show the ability to synthesize GA de novo. Optimization of the GA metabolic pathways in host cells has leapt forward, and the titer and yield have increased rapidly. Meanwhile, cell-free multi-enzyme catalysis, in which the desired pathway is constructed in vitro from enzymes and cofactors involved in GA biosynthesis, has also realized efficient GA bioconversion. This review presents an overview of studies of the development of cell-based GA production, followed by a brief discussion of potential applications of biosensors that respond to GA in these biosynthesis routes.

An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions remains a significant challenge. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce model capacity, a new information theoretic metric for correct cross-data-set comparisons. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We then applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

Artificial ER-Derived Vesicles as Synthetic Organelles for in Vivo Compartmentalization of Biochemical Pathways

Compartmentalization in membrane-surrounded organelles has the potential to overcome obstacles associated with the engineering of metabolic pathways, such as unwanted side reactions, accumulation of toxic intermediates, drain of intermediates out of the cell, and long diffusion distances. Strategies utilizing natural organelles suffer from the presence of endogenous pathways. In our approach, we make use of endoplasmic reticulum-derived vesicles loaded with enzymes of a metabolic pathway ("metabolic vesicles"). They are generated by fusion of synthetic peptides containing the N-terminal proline-rich and self-assembling region of the maize storage protein gamma-Zein ("Zera") to the pathway enzymes. We have applied a strategy to integrate three enzymes of a cis,cis-muconic acid production pathway into those vesicles in yeast. Using fluorescence microscopy and cell fractionation techniques, we have proven the formation of metabolic vesicles and the incorporation of enzymes. Activities of the enzymes and functionality of the compartmentalized pathway were demonstrated in fermentation experiments.

A unified design allows fine-tuning of biosensor parameters and application across bacterial species

In recent years, transcriptional biosensors have become valuable tools in metabolic engineering as they allow semiquantitative determination of metabolites in single cells. Although being perfectly suitable tools for high-throughput screenings, application of transcriptional biosensors is often limited by the intrinsic characteristics of the individual sensor components and their interplay. In addition, biosensors often fail to work properly in heterologous host systems due to signal saturation at low intracellular metabolite concentrations, which typically limits their use in high-level producer strains at advanced engineering stages.

We here introduce a biosensor design, which allows fine-tuning of important sensor parameters and restores the sensor response in a heterologous expression host. As a key feature of our design, the regulator activity is controlled through the expression level of the respective gene by different (synthetic) constitutive promoters selected for the used expression host. In this context, we constructed biosensors responding to basic amino acids or ring-hydroxylated phenylpropanoids for applications in Corynebacterium glutamicum and Escherichia coli. Detailed characterization of these biosensors in liquid cultures and during single-cell analysis using flow cytometry showed that the presented sensor design enables customization of important biosensor parameters as well as application of these sensors in relevant heterologous hosts.

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Development of a unified biosensor design for C. glutamicum and E. coli.

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Gradual expression of the regulator gene allows for biosensor fine-tuning.

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Biosensor response in a heterologous host can be restored.

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Biosensor characterization on the single-cell level prior to FACS is mandatory.

Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor

Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

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.

Engineering and application of a biosensor with focused ligand specificity

Cell factories converting bio-based precursors to chemicals present an attractive avenue to a sustainable economy, yet screening of genetically diverse strain libraries to identify the best-performing whole-cell biocatalysts is a low-throughput endeavor. For this reason, transcriptional biosensors attract attention as they allow the screening of vast libraries when used in combination with fluorescence-activated cell sorting (FACS). However, broad ligand specificity of transcriptional regulators (TRs) often prohibits the development of such ultra-high-throughput screens. Here, we solve the structure of the TR LysG of Corynebacterium glutamicum, which detects all three basic amino acids. Based on this information, we follow a semi-rational engineering approach using a FACS-based screening/counterscreening strategy to generate an l-lysine insensitive LysG-based biosensor. This biosensor can be used to isolate l-histidine-producing strains by FACS, showing that TR engineering towards a more focused ligand spectrum can expand the scope of application of such metabolite sensors.

Transcriptional biosensors represent powerful tools for the screening of vast strain libraries, but the broad ligand specificity of some transcriptional regulators (TRs) can prohibit such applications. Here authors present the engineering of a LysG-based biosensor with a focused ligand specificity to isolate L-histidine-producing strains.

A synthetic biosensor to detect peroxisomal acetyl-CoA concentration for compartmentalized metabolic engineering

Background

Sub-cellular compartmentalization is used by cells to create favorable microenvironments for various metabolic reactions. These compartments concentrate enzymes, separate competing metabolic reactions, and isolate toxic intermediates. Such advantages have been recently harnessed by metabolic engineers to improve the production of various high-value chemicals via compartmentalized metabolic engineering. However, measuring sub-cellular concentrations of key metabolites represents a grand challenge for compartmentalized metabolic engineering.

Methods

To this end, we developed a synthetic biosensor to measure a key metabolite, acetyl-CoA, in a representative compartment of yeast, the peroxisome. This synthetic biosensor uses enzyme re-localization via PTS1 signal peptides to construct a metabolic pathway in the peroxisome which converts acetyl-CoA to polyhydroxybutyrate (PHB) via three enzymes. The PHB is then quantified by HPLC.

Results

The biosensor demonstrated the difference in relative peroxisomal acetyl-CoA availability under various culture conditions and was also applied to screening a library of single knockout yeast mutants. The screening identified several mutants with drastically reduced peroxisomal acetyl-CoA and one with potentially increased levels. We expect our synthetic biosensors can be widely used to investigate sub-cellular metabolism and facilitate the “design-build-test” cycle of compartmentalized metabolic engineering.

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.

Recent Advances in Microbial Production of cis,cis-Muconic Acid

cis,cis-Muconic acid (MA) is a valuable C6 dicarboxylic acid platform chemical that is used as a starting material for the production of various valuable polymers and drugs, including adipic acid and terephthalic acid. As an alternative to traditional chemical processes, bio-based MA production has progressed to the establishment of de novo MA pathways in several microorganisms, such as Escherichia coli, Corynebacterium glutamicum, Pseudomonas putida, and Saccharomyces cerevisiae. Redesign of the metabolic pathway, intermediate flux control, and culture process optimization were all pursued to maximize the microbial MA production yield. Recently, MA production from biomass, such as the aromatic polymer lignin, has also attracted attention from researchers focusing on microbes that are tolerant to aromatic compounds. This paper summarizes recent microbial MA production strategies that involve engineering the metabolic pathway genes as well as the heterologous expression of some foreign genes involved in MA biosynthesis. Microbial MA production will continue to play a vital role in the field of bio-refineries and a feasible way to complement various petrochemical-based chemical processes.

Engineering the oleaginous yeast Yarrowia lipolytica for high-level resveratrol production

Resveratrol is a plant secondary metabolite with multiple health-beneficial properties. Microbial production of resveratrol in model microorganisms requires extensive engineering to reach commercially viable levels. Here, we explored the potential of the non-conventional yeast Yarrowia lipolytica to produce resveratrol and several other shikimate pathway-derived metabolites (p-coumaric acid, cis,cis-muconic acid, and salicylic acid). The Y. lipolytica strain expressing a heterologous pathway produced 52.1 ± 1.2 mg/L resveratrol in a small-scale cultivation. The titer increased to 409.0 ± 1.2 mg/L when the strain was further engineered with feedback-insensitive alleles of the key genes in the shikimate pathway and with five additional copies of the heterologous biosynthetic genes. In controlled fed-batch bioreactor, the strain produced 12.4 ± 0.3 g/L resveratrol, the highest reported titer to date for de novo resveratrol production, with a yield on glucose of 54.4 ± 1.6 mg/g and a productivity of 0.14 ± 0.01 g/L/h. The study showed that Y. lipolytica is an attractive host organism for the production of resveratrol and possibly other shikimate-pathway derived metabolites.

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Oleaginous yeast Y. lipolytica was engineered for production of aromatic compounds.

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High resveratrol production required increased activities of Aro4p and Aro7p.

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Multiple integration of resveratrol biosynthetic genes improved production.

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Fed-batch fermentation enabled de novo production of 12.4 g/L resveratrol.

Engineering transcription factor-based biosensors for repressive regulation through transcriptional deactivation design in Saccharomyces cerevisiae

Background

With the development of engineering the microbial cell factories, biosensors have been used widely for regulation of cellular metabolism and high-throughput screening. However, most of the biosensors constructed in Saccharomyces cerevisiae are designed for transcriptional activation. Very few studies have dedicated to the development of genetic circuit for repressive regulation, which is also indispensable for the dynamic control of metabolism.

Results

In this study, through transcriptional deactivation design, we developed transcription-factor-based biosensors to allow repressive regulation in response to ligand. Using a malonyl-CoA sensing system as an example, the biosensor was constructed and systematically engineered to optimize the dynamic range by comparing transcriptional activity of the activators, evaluating the positions and numbers of the operators in the promoter and comparing the effects of different promoters. A biosensor with 82% repression ratio was obtained. Based on this design principle, another two biosensors, which sense acyl-CoA or xylose and downregulate gene expression, were also successfully constructed.

Conclusions

Our work systematically optimized the biosensors for repressive regulation in yeast for the first time. It provided useful framework to construct similar biosensors. Combining the widely reported biosensors for transcriptional activation with the biosensors developed here, it is now possible to construct biosensors with opposing transcriptional activities in yeast.

Protein Engineering for Improving and Diversifying Natural Product Biosynthesis

Proteins found in nature have traditionally been the most frequently used biocatalysts to produce numerous natural products ranging from commodity chemicals to pharmaceuticals. Protein engineering has emerged as a powerful biotechnological toolbox in the development of metabolic engineering, particularly for the biosynthesis of natural products. Recently, protein engineering has become a favored method to improve enzymatic activity, increase enzyme stability, and expand product spectra in natural product biosynthesis. This review summarizes recent advances and typical strategies in protein engineering, highlighting the paramount role of protein engineering in improving and diversifying the biosynthesis of natural products. Future prospects and research directions are also discussed.

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.

Biosensors design in yeast and applications in metabolic engineering

Engineering microbial cell factories is a potential approach of sustainable production of chemicals, fuels and pharmaceuticals. However, testing the production of molecules in high throughput is still a time-consuming and laborious process since product synthesis usually does not confer a clear phenotype. Therefore, it is necessary to develop new techniques for fast high-producer screening. Genetically encoded biosensors are considered to be promising devices for high-throughput analysis owing to their ability to sense metabolites and couple detection to an actuator, thereby facilitating the rapid detection of small molecules at single-cell level. Here, we review recent advances in the design and engineering of biosensors in Saccharomyces cerevisiae, and their applications in metabolic engineering. Three types of biosensor are introduced in this review: transcription factor based, RNA-based and enzyme-coupled biosensors. The studies to improve the features of biosensors are also described. Moreover, we summarized their metabolic engineering applications in dynamic regulation and high producer selection. Current challenges in biosensor design and future perspectives on sensor applications are also discussed.

Obtaining a series of native gradient promoter-5'-UTR sequences in Corynebacterium glutamicum ATCC 13032

BACKGROUND

Corynebacterium glutamicum is an important industrial microorganism used for the production of many valuable compounds, especially amino acids and their derivatives. For fine-tuning of metabolic pathways, synthetic biological tools are largely based on the rational application of promoters. However, the limited number of promoters make it difficult.

RESULTS

In this study, according to the analysis of RNA-Seq data, 90 DNA fragments with lengths of 200-500 bp that may contain promoter-5'-UTR (PUTR) sequences were amplified and linked to a fluorescent protein gene. When compared with the common strong PUTR P_sodUTR, 17 strong PUTRs were obtained, which maintained stable expression strengths from the early to post stationary phase. Among them, P_NCgl1676UTR was the strongest and its fluorescent protein expression level was more than five times higher than that of P_sodUTR. Furthermore, nine typical chemicals related to the biosynthesis of sulfur-containing amino acids (such as L-methionine, L-cysteine) were selected as stress substances to preliminarily explore the stress on these PUTRs. The results showed that the expression of P_brnFUTR was activated by L-methionine, while that of P_NCgl1202UTR was severely inhibited by L-lysine.

CONCLUSIONS

These findings demonstrated that the selected PUTRs can stably express different genes, such as the red fluorescence protein gene, and can be useful for fine-tuning regulation of metabolic networks in C. glutamicum or for establishing high-throughput screening strategies through biosensor for the production of useful compounds.

A Synthetic Malonyl-CoA Metabolic Oscillator in Komagataella phaffii

Malonyl-CoA is a key metabolic molecule that participates in a diverse range of physiological responses and can act as a building block for a variety of value-added pharmaceuticals and chemicals. The cytosolic malonyl-CoA concentration is usually very low, and thus dynamic metabolic control of malonyl-CoA variation will aid its stable formation and efficient consumption. We developed a synthetic malonyl-CoA metabolic oscillator in yeast. A synthetic regulatory protein, Prm1-FapR, was constructed by fusing a yeast transcriptional activator, Prm1, with a bacterial malonyl-CoA-sensitive transcription repressor, FapR. Two oppositely regulated biosensors were then engineered. A total of 18 hybrid promoter variants were designed, each carrying the operator sequence (fapO) of FapR and the core promoter of P_AOX1 (cP_AOX1), which is naturally regulated by Prm1. The promoter activities of these variants, regulated by Prm1-FapR, were tested. Through this process, a sensor for Prm1-FapR/(-52)fapO-P_AOX1 carrying an activation/deactivation regulation module was built. Meanwhile, 24 promoter variants of P_GAP with fapO inserted were designed and tested using the fusion regulator, giving a sensor for Prm1-FapR/P_GAP-(+22) fapO that contained a repression/derepression regulation module. Both sensors were subsequently integrated into a single cell, which exhibited correct metabolic switching of eGFP and mCherry reporters following manipulation of cytosolic malonyl-CoA levels. The Prm1-FapR/(-52)fapO-P_AOX1 and the Prm1-FapR/P_GAP-(+22)fapO were also used to control the malonyl-CoA source and sink pathways, respectively, for the synthesis of 6-methylsalicylic acid. This finally led to an oscillatory metabolic mode of cytosolic malonyl-CoA. Such a metabolator is useful in exploring potential industrial and biomedical applications not limited by natural cellular behavior.

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.

Engineering Prokaryotic Transcriptional Activator XylR as a Xylose-Inducible Biosensor for Transcription Activation in Yeast

Biosensors regulated by specific substrates are needed to develop genetic tools to meet the needs of engineering microbial cell factories. Here, a xylose-inducible biosensor (xylbiosensor), comprising the Escherichia coli activation factor XylR, fusion activation domain (AD) VPRH, and a hybrid promoter with operator xylO, was established in Yarrowia lipolytica. The addition of xylose to an engineered Y. lipolytica strain harboring the xylbiosensor could trigger significant transcriptional activation of target genes, such as mcherry and the xylose utilization gene. Furthermore, a novel promoter Pleu-Pxo-Ptef was developed to construct a bidirectional expression system. The xylbiosensor showed good portability in Saccharomyces cerevisiae, suggesting its potential value in other eukaryotic cells. This study is the first to construct a "turn-on" xylbiosensor induced by xylose addition based on a prokaryotic activator XylR and eukaryotic universal AD. The xylbiosensor exhibits potential in pathway engineering for xylose utilization and xylose-derived product biosynthesis in yeast.

Yeast-Based Biosensors: Current Applications and New Developments

Biosensors are regarded as a powerful tool to detect and monitor environmental contaminants, toxins, and, more generally, organic or chemical markers of potential threats to human health. They are basically composed of a sensor part made up of either live cells or biological active molecules coupled to a transducer/reporter technological element. Whole-cells biosensors may be based on animal tissues, bacteria, or eukaryotic microorganisms such as yeasts and microalgae. Although very resistant to adverse environmental conditions, yeasts can sense and respond to a wide variety of stimuli. As eukaryotes, they also constitute excellent cellular models to detect chemicals and organic contaminants that are harmful to animals. For these reasons, combined with their ease of culture and genetic modification, yeasts have been commonly used as biological elements of biosensors since the 1970s. This review aims first at giving a survey on the different types of yeast-based biosensors developed for the environmental and medical domains. We then present the technological developments currently undertaken by academic and corporate scientists to further drive yeasts biosensors into a new era where the biological element is optimized in a tailor-made fashion by in silico design and where the output signals can be recorded or followed on a smartphone.

Light-powered Escherichia coli cell division for chemical production

Cell division can perturb the metabolic performance of industrial microbes. The C period of cell division starts from the initiation to the termination of DNA replication, whereas the D period is the bacterial division process. Here, we first shorten the C and D periods of E. coli by controlling the expression of the ribonucleotide reductase NrdAB and division proteins FtsZA through blue light and near-infrared light activation, respectively. It increases the specific surface area to 3.7 μm⁻¹ and acetoin titer to 67.2 g·L⁻¹. Next, we prolong the C and D periods of E. coli by regulating the expression of the ribonucleotide reductase NrdA and division protein inhibitor SulA through blue light activation-repression and near-infrared (NIR) light activation, respectively. It improves the cell volume to 52.6 μm³ and poly(lactate-co-3-hydroxybutyrate) titer to 14.31 g·L⁻¹. Thus, the optogenetic-based cell division regulation strategy can improve the efficiency of microbial cell factories.

Manipulation of genes controlling microbial shapes can affect bio-production. Here, the authors employ an optogenetic method to realize dynamic morphological engineering of E. coli replication and division and show the increased production of acetoin and poly(lactate-co-3-hydroxybutyrate).

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.

Discovery and identification of medium-chain fatty acid responsive promoters in Saccharomyces cerevisiae

Medium-chain fatty acids (MCFAs) and their derivatives are important chemicals that can be used in lubricants, detergents, and cosmetics. MCFAs can be produced in several microbes, although production is not high. Dynamic regulation by synthetic biology is a good method of improving production of chemicals that avoids toxic intermediates, but chemical-responsive promoters are required. Several MCFA sensors or promoters have been reported in Saccharomyces cerevisiae. In this study, by using transcriptomic analysis of S. cerevisiae exposed to fatty acids with 6-, 12-, and 16-carbon chains, we identified 58 candidate genes that may be responsive to MCFAs. Using a fluorescence-based screening method, we identified MCFA-responsive promoters, four that upregulated gene expression, and three that downregulated gene expression. Dose-response analysis revealed that some of the promoters were sensitive to fatty acid concentrations as low as 0.02-0.06 mM. The MCFA-responsive promoters reported in this study could be used in dynamic regulation of fatty acids and fatty acid-derived products in S. cerevisiae.

Developing a highly efficient hydroxytyrosol whole-cell catalyst by de-bottlenecking rate-limiting steps

Hydroxytyrosol is an antioxidant free radical scavenger that is biosynthesized from tyrosine. In metabolic engineering efforts, the use of the mouse tyrosine hydroxylase limits its production. Here, we design an efficient whole-cell catalyst of hydroxytyrosol in Escherichia coli by de-bottlenecking two rate-limiting enzymatic steps. First, we replace the mouse tyrosine hydroxylase by an engineered two-component flavin-dependent monooxygenase HpaBC of E. coli through structure-guided modeling and directed evolution. Next, we elucidate the structure of the Corynebacterium glutamicum VanR regulatory protein complexed with its inducer vanillic acid. By switching its induction specificity from vanillic acid to hydroxytyrosol, VanR is engineered into a hydroxytyrosol biosensor. Then, with this biosensor, we use in vivo-directed evolution to optimize the activity of tyramine oxidase (TYO), the second rate-limiting enzyme in hydroxytyrosol biosynthesis. The final strain reaches a 95% conversion rate of tyrosine. This study demonstrates the effectiveness of sequentially de-bottlenecking rate-limiting steps for whole-cell catalyst development.

Whole-cell catalyst-based hydroxytyrosol production is low. Here, the authors increase the efficiency of its production in E. coli by de-bottlenecking two enzymatic steps catalyzed by monooxygenase and tyramine oxidase using structure-based enzyme redesign or in vivo-directed evolution with the aid of a newly developed biosensor.

Improvement of cis,cis-Muconic Acid Production in Saccharomyces cerevisiae through Biosensor-Aided Genome Engineering

Muconic acid is a potential platform chemical for the production of nylon, polyurethanes, and terephthalic acid. It is also an attractive functional copolymer in plastics due to its two double bonds. At this time, no economically viable process for the production of muconic acid exists. To harness novel genetic targets for improved production of cis,cis-muconic acid (CCM) in the yeast Saccharomyces cerevisiae, we employed a CCM-biosensor coupled to GFP expression with a broad dynamic response to screen UV-mutagenesis libraries of CCM-producing yeast. Via fluorescence activated cell sorting we identified a clone Mut131 with a 49.7% higher CCM titer and 164% higher titer of biosynthetic intermediate-protocatechuic acid (PCA). Genome resequencing of the Mut131 and reverse engineering identified seven causal missense mutations of the native genes (PWP2, EST2, ATG1, DIT1, CDC15, CTS2, and MNE1) and a duplication of two CCM biosynthetic genes, encoding dehydroshikimate dehydratase and catechol 1,2-dioxygenase, which were not recognized as flux controlling before. The Mut131 strain was further rationally engineered by overexpression of the genes encoding for PCA decarboxylase and AROM protein without shikimate dehydrogenase domain (Aro1p^ΔE), and by restoring URA3 prototrophy. The resulting engineered strain produced 20.8 g/L CCM in controlled fed-batch fermentation, with a yield of 66.2 mg/g glucose and a productivity of 139 mg/L/h, representing the highest reported performance metrics in a yeast for de novo CCM production to date and the highest production of an aromatic compound in yeast. The study illustrates the benefit of biosensor-based selection and brings closer the prospect of biobased muconic acid.

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

High-Resolution Scanning of Optimal Biosensor Reporter Promoters in Yeast

Small-molecule binding allosteric transcription factors (aTFs) derived from bacteria enable real-time monitoring of metabolite abundances, high-throughput screening of genetic designs, and dynamic control of metabolism. Yet, engineering of reporter promoter designs of prokaryotic aTF biosensors in eukaryotic cells is complex. Here we investigate the impact of aTF binding site positions at single-nucleotide resolution in >300 reporter promoter designs in Saccharomyces cerevisiae. From this we identify biosensor output landscapes with transient and distinct aTF binding site position effects for aTF repressors and activators, respectively. Next, we present positions for tunable reporter promoter outputs enabling metabolite-responsive designs for a total of four repressor-type and three activator-type aTF biosensors with dynamic output ranges up to 8- and 26-fold, respectively. This study highlights aTF binding site positions in reporter promoters as key for successful biosensor engineering and that repressor-type aTF biosensors allows for more flexibility in terms of choice of binding site positioning compared to activator-type aTF biosensors.

Engineering glucose metabolism for enhanced muconic acid production in Pseudomonas putida KT2440

Pseudomonas putida KT2440 has received increasing attention as an important biocatalyst for the conversion of diverse carbon sources to multiple products, including the olefinic diacid, cis,cis-muconic acid (muconate). P. putida has been previously engineered to produce muconate from glucose; however, periplasmic oxidation of glucose causes substantial 2-ketogluconate accumulation, reducing product yield and selectivity. Deletion of the glucose dehydrogenase gene (gcd) prevents 2-ketogluconate accumulation, but dramatically slows growth and muconate production. In this work, we employed adaptive laboratory evolution to improve muconate production in strains incapable of producing 2-ketogluconate. Growth-based selection improved growth, but reduced muconate titer. A new muconate-responsive biosensor was therefore developed to enable muconate-based screening using fluorescence activated cell sorting. Sorted clones demonstrated both improved growth and muconate production. Mutations identified by whole genome resequencing of these isolates indicated that glucose metabolism may be dysregulated in strains lacking gcd. Using this information, we used targeted engineering to recapitulate improvements achieved by evolution. Deletion of the transcriptional repressor gene hexR improved strain growth and increased the muconate production rate, and the impact of this deletion was investigated using transcriptomics. The genes gntZ and gacS were also disrupted in several evolved clones, and deletion of these genes further improved strain growth and muconate production. Together, these targets provide a suite of modifications that improve glucose conversion to muconate by P. putida in the context of gcd deletion. Prior to this work, our engineered strain lacking gcd generated 7.0 g/L muconate at a productivity of 0.07 g/L/h and a 38% yield (mol/mol) in a fed-batch bioreactor. Here, the resulting strain with the deletion of hexR, gntZ, and gacS achieved 22.0 g/L at 0.21 g/L/h and a 35.6% yield (mol/mol) from glucose in similar conditions. These strategies enabled enhanced muconic acid production and may also improve production of other target molecules from glucose in P. putida.

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

Design and Characterization of Biosensors for the Screening of Modular Assembled Naringenin Biosynthetic Library in Saccharomyces cerevisiae

A common challenge in the assembly and optimization of plant natural product biosynthetic pathways in recombinant hosts is the identification of gene orthologues that will result in best production titers. Here, we describe the modular assembly of a naringenin biosynthetic pathway in Saccharomyces cerevisiae that was facilitated by optimized naringenin-inducible prokaryotic transcription activators used as biosensors. The biosensors were designed and developed in S. cerevisiae by a multiparametric engineering strategy, which further was applied for the in vivo, high-throughput screening of the established yeast library. The workflow for assembling naringenin biosynthetic pathways involved Golden gate-directed combinatorial assembly of genes and promoters, resulting in a strain library ideally covering 972 combinations in S. cerevisiae. For improving the performance of our screening biosensor, a series of fundamental components was optimized, affecting the efficiency of the biosensor such as nuclear localization signal (NLS), the detector module and the effector module. One biosensor (pTDH3_NLS_FdeR-N_tPGK1-pGPM1-fdeO_mcherry_tTDH1-MV2) showed better performance, defined as better dynamic range and sensitivity than others established in this study as well as other previously reported naringenin biosensors. Using this biosensor, we were able to identify a recombinant S. cerevisiae strain as the most efficient candidate for the production of naringenin from the established naringenin biosynthetic library. This approach can be exploited for the optimization of other metabolites derived from the flavonoid biosynthetic pathways and more importantly employed in the characterization of putative flavonoid biosynthetic genes.

Recent advancement of engineering microbial hosts for the biotechnological production of flavonoids

Flavonoids are polyphenols that are important organic chemicals in plants. The health benefits of flavonoids that result in high commercial values make them attractive targets for large-scale production through bioengineering. Strategies such as engineering a flavonoid biosynthetic pathway in microbial hosts provide an alternative way to produce these beneficial compounds. Escherichia coli, Saccharomyces cerevisiae and Streptomyces sp. are among the expression systems used to produce recombinant products, as well as for the production of flavonoid compounds through various bioengineering approaches including clustered regularly interspaced short palindromic repeats (CRISPR)-based genome engineering and genetically encoded biosensors to detect flavonoid biosynthesis. In this study, we review the recent advances in engineering model microbial hosts as being the factory to produce targeted flavonoid compounds.

Designing cell function: assembly of synthetic gene circuits for cell biology applications

Synthetic biology is the discipline of engineering application-driven biological functionalities that were not evolved by nature. Early breakthroughs of cell engineering, which were based on ectopic (over)expression of single sets of transgenes, have already had a revolutionary impact on the biotechnology industry, regenerative medicine and blood transfusion therapies. Now, we require larger-scale, rationally assembled genetic circuits engineered to programme and control various human cell functions with high spatiotemporal precision in order to solve more complex problems in applied life sciences, biomedicine and environmental sciences. This will open new possibilities for employing synthetic biology to advance personalized medicine by converting cells into living therapeutics to combat hitherto intractable diseases.