Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Searching for the optimal microbial factory: high-throughput biosensors and analytical techniques for screening small molecules

High-throughput screening technologies have been lacking in comparison to the plethora of high-throughput genetic diversification techniques developed in biotechnology. This review explores the challenges and advancements in high-throughput screening for high-value natural products, focusing on the critical need to expand ligand targets for biosensors and increase the throughput of analytical techniques in screening microbial cell libraries for optimal strain performance. The engineering techniques to broaden the scope of ligands for biosensors, such as transcription factors, G protein-coupled receptors and riboswitches are discussed. On the other hand, integration of microfluidics with traditional analytical methods is explored, covering fluorescence-activated cell sorting, Raman-activated cell sorting and mass spectrometry, emphasising recent developments in maximising throughput.

Improvement of Chalcone Synthase Activity and High-Efficiency Fermentative Production of (2S)-Naringenin via In Vivo Biosensor-Guided Directed Evolution

Chalcone synthase (CHS) catalyzes the rate-limiting step of (2S)-naringenin (the essential flavonoid skeleton) biosynthesis. Improving the activity of the CHS by protein engineering enhances (2S)-naringenin production by microbial fermentation and can facilitate the production of valuable flavonoids. A (2S)-naringenin biosensor based on the TtgR operon was constructed in Escherichia coli and its detection range was expanded by promoter optimization to 0-300 mg/L, the widest range for (2S)-naringenin reported. The high-throughput screening scheme for CHS was established based on this biosensor. A mutant, SjCHS1S208N with a 2.34-fold increase in catalytic activity, was discovered by directed evolution and saturation mutagenesis. A pathway for de novo biosynthesis of (2S)-naringenin by SjCHS1S208N was constructed in Saccharomyces cerevisiae, combined with CHS precursor pathway optimization, increasing the (2S)-naringenin titer by 65.34% compared with the original strain. Fed-batch fermentation increased the titer of (2S)-naringenin to 2513 ± 105 mg/L, the highest reported so far. These findings will facilitate efficient flavonoid biosynthesis and further modification of the CHS in the future.

Genome-wide host-pathway interactions affecting cis-cis-muconic acid production in yeast

The success of forward metabolic engineering depends on a thorough understanding of the behaviour of a heterologous metabolic pathway within its host. We have recently described CRI-SPA, a high-throughput gene editing method enabling the delivery of a metabolic pathway to all strains of the Saccharomyces cerevisiae knock-out library. CRI-SPA systematically quantifies the effect of each modified gene present in the library on product synthesis, providing a complete map of host:pathway interactions. In its first version, CRI-SPA relied on the colour of the product betaxanthins to quantify strains synthesis ability. However, only a few compounds produce a visible or fluorescent phenotype limiting the scope of our approach. Here, we adapt CRI-SPA to onboard a biosensor reporting the interactions between host genes and the synthesis of the colourless product cis-cis-muconic acid (CCM). We phenotype >9,000 genotypes, including both gene knock-out and overexpression, by quantifying the fluorescence of yeast colonies growing in high-density agar arrays. We identify novel metabolic targets belonging to a broad range of cellular functions and confirm their positive impact on CCM biosynthesis. In particular, our data suggests a new interplay between CCM biosynthesis and cytosolic redox through their common interaction with the oxidative pentose phosphate pathway. Our genome-wide exploration of host:pathway interaction opens novel strategies for improved production of CCM in yeast cell factories.

Progress in Gene Editing and Metabolic Regulation of Saccharomyces cerevisiae with CRISPR/Cas9 Tools

The CRISPR/Cas9 systems have been developed as tools for genetic engineering and metabolic engineering in various organisms. In this review, various aspects of CRISPR/Cas9 in Saccharomyces cerevisiae, from basic principles to practical applications, have been summarized. First, a comprehensive review has been conducted on the history of CRISPR/Cas9, successful cases of gene disruptions, and efficiencies of multiple DNA fragment insertions. Such advanced systems have accelerated the development of microbial engineering by reducing time and labor, and have enhanced the understanding of molecular genetics. Furthermore, the research progress of the CRISPR/Cas9-based systems in the production of high-value-added chemicals and the improvement of stress tolerance in S. cerevisiae have been summarized, which should have an important reference value for genetic and synthetic biology studies based on S. cerevisiae.

Directed Evolution of 4-Hydroxyphenylpyruvate Biosensors Based on a Dual Selection System

Biosensors based on allosteric transcription factors have been widely used in synthetic biology. In this study, we utilized the Acinetobacter ADP1 transcription factor PobR to develop a biosensor activating the PpobA promoter when bound to its natural ligand, 4-hydroxybenzoic acid (4HB). To screen for PobR mutants responsive to 4-hydroxyphenylpyruvate(HPP), we developed a dual selection system in E. coli. The positive selection of this system was used to enrich PobR mutants that identified the required ligands. The following negative selection eliminated or weakened PobR mutants that still responded to 4HB. Directed evolution of the PobR library resulted in a variant where PobRW177R was 5.1 times more reactive to 4-hydroxyphenylpyruvate than PobRWT. Overall, we developed an efficient dual selection system for directed evolution of biosensors.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology

Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.

Living electrochemical biosensing: Engineered electroactive bacteria for biosensor development and the emerging trends

Bioelectrical interfaces made of living electroactive bacteria (EAB) provide a unique opportunity to bridge biotic and abiotic systems, enabling the reprogramming of electrochemical biosensing. To develop these biosensors, principles from synthetic biology and electrode materials are being combined to engineer EAB as dynamic and responsive transducers with emerging, programmable functionalities. This review discusses the bioengineering of EAB to design active sensing parts and electrically connective interfaces on electrodes, which can be applied to construct smart electrochemical biosensors. In detail, by revisiting the electron transfer mechanism of electroactive microorganisms, engineering strategies of EAB cells for biotargets recognition, sensing circuit construction, and electrical signal routing, engineered EAB have demonstrated impressive capabilities in designing active sensing elements and developing electrically conductive interfaces on electrodes. Thus, integration of engineered EAB into electrochemical biosensors presents a promising avenue for advancing bioelectronics research. These hybridized systems equipped with engineered EAB can promote the field of electrochemical biosensing, with applications in environmental monitoring, health monitoring, green manufacturing, and other analytical fields. Finally, this review considers the prospects and challenges of the development of EAB-based electrochemical biosensors, identifying potential future applications.

Versatility and Complexity: Common and Uncommon Facets of LysR-Type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) form one of the largest families of bacterial regulators. They are widely distributed and contribute to all aspects of metabolism and physiology. Most are homotetramers, with each subunit composed of an N-terminal DNA-binding domain followed by a long helix connecting to an effector-binding domain. LTTRs typically bind DNA in the presence or absence of a small-molecule ligand (effector). In response to cellular signals, conformational changes alter DNA interactions, contact with RNA polymerase, and sometimes contact with other proteins. Many are dual-function repressor-activators, although different modes of regulation may occur at multiple promoters. This review presents an update on the molecular basis of regulation, the complexity of regulatory schemes, and applications in biotechnology and medicine. The abundance of LTTRs reflects their versatility and importance. While a single regulatory model cannot describe all family members, a comparison of similarities and differences provides a framework for future study.

Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes

BACKGROUND

In contrast to modern rational metabolic engineering, classical strain development strongly relies on random mutagenesis and screening for the desired production phenotype. Nowadays, with the availability of biosensor-based FACS screening strategies, these random approaches are coming back into fashion. In this study, we employ this technology in combination with comparative genome analyses to identify novel mutations contributing to product formation in the genome of a Corynebacterium glutamicum L-histidine producer. Since all known genetic targets contributing to L-histidine production have been already rationally engineered in this strain, identification of novel beneficial mutations can be regarded as challenging, as they might not be intuitively linkable to L-histidine biosynthesis.

RESULTS

In order to identify 100 improved strain variants that had each arisen independently, we performed > 600 chemical mutagenesis experiments, > 200 biosensor-based FACS screenings, isolated > 50,000 variants with increased fluorescence, and characterized > 4500 variants with regard to biomass formation and L-histidine production. Based on comparative genome analyses of these 100 variants accumulating 10-80% more L-histidine, we discovered several beneficial mutations. Combination of selected genetic modifications allowed for the construction of a strain variant characterized by a doubled L-histidine titer (29 mM) and product yield (0.13 C-mol C-mol-1) in comparison to the starting variant.

CONCLUSIONS

This study may serve as a blueprint for the identification of novel beneficial mutations in microbial producers in a more systematic manner. This way, also previously unexplored genes or genes with previously unknown contribution to the respective production phenotype can be identified. We believe that this technology has a great potential to push industrial production strains towards maximum performance.

Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast

Dynamic metabolic engineering is a strategy to switch key metabolic pathways in microbial cell factories from biomass generation to accumulation of target products. Here, we demonstrate that optogenetic intervention in the cell cycle of budding yeast can be used to increase production of valuable chemicals, such as the terpenoid β-carotene or the nucleoside analog cordycepin. We achieved optogenetic cell-cycle arrest in the G2/M phase by controlling activity of the ubiquitin-proteasome system hub Cdc48. To analyze the metabolic capacities in the cell cycle arrested yeast strain, we studied their proteomes by timsTOF mass spectrometry. This revealed widespread, but highly distinct abundance changes of metabolic key enzymes. Integration of the proteomics data in protein-constrained metabolic models demonstrated modulation of fluxes directly associated with terpenoid production as well as metabolic subsystems involved in protein biosynthesis, cell wall synthesis, and cofactor biosynthesis. These results demonstrate that optogenetically triggered cell cycle intervention is an option to increase the yields of compounds synthesized in a cellular factory by reallocation of metabolic resources.

Divergent directed evolution of a TetR-type repressor towards aromatic molecules

Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.

Quantification and mitigation of byproduct formation by low-glycerol-producing Saccharomyces cerevisiae strains containing Calvin-cycle enzymes

BACKGROUND

Anaerobic Saccharomyces cerevisiae cultures require glycerol formation to re-oxidize NADH formed in biosynthetic processes. Introduction of the Calvin-cycle enzymes phosphoribulokinase (PRK) and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) has been shown to couple re-oxidation of biosynthetic NADH to ethanol production and improve ethanol yield on sugar in fast-growing batch cultures. Since growth rates in industrial ethanol production processes are not constant, performance of engineered strains was studied in slow-growing cultures.

RESULTS

In slow-growing anaerobic chemostat cultures (D = 0.05 h^-1), an engineered PRK/RuBisCO strain produced 80-fold more acetaldehyde and 30-fold more acetate than a reference strain. This observation suggested an imbalance between in vivo activities of PRK/RuBisCO and formation of NADH in biosynthesis. Lowering the copy number of the RuBisCO-encoding cbbm expression cassette from 15 to 2 reduced acetaldehyde and acetate production by 67% and 29%, respectively. Additional C-terminal fusion of a 19-amino-acid tag to PRK reduced its protein level by 13-fold while acetaldehyde and acetate production decreased by 94% and 61%, respectively, relative to the 15 × cbbm strain. These modifications did not affect glycerol production at 0.05 h^-1 but caused a 4.6 fold higher glycerol production per amount of biomass in fast-growing (0.29 h^-1) anaerobic batch cultures than observed for the 15 × cbbm strain. In another strategy, the promoter of ANB1, whose transcript level positively correlated with growth rate, was used to control PRK synthesis in a 2 × cbbm strain. At 0.05 h^-1, this strategy reduced acetaldehyde and acetate production by 79% and 40%, respectively, relative to the 15 × cbbm strain, without affecting glycerol production. The maximum growth rate of the resulting strain equalled that of the reference strain, while its glycerol production was 72% lower.

CONCLUSIONS

Acetaldehyde and acetate formation by slow-growing cultures of engineered S. cerevisiae strains carrying a PRK/RuBisCO bypass of yeast glycolysis was attributed to an in vivo overcapacity of PRK and RuBisCO. Reducing the capacity of PRK and/or RuBisCO was shown to mitigate this undesirable byproduct formation. Use of a growth rate-dependent promoter for PRK expression highlighted the potential of modulating gene expression in engineered strains to respond to growth-rate dynamics in industrial batch processes.

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

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

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

Engineering a two-gene system to operate as a highly sensitive biosensor or a sharp switch upon induction with β-estradiol

The human estrogen receptor has been used for about thirty years, in the yeast S. cerevisiae, as a component of chimeric transcription factors. Its ligand, β-estradiol, permits to control the protein translocation into the nucleus and, as a consequence, the expression of the gene(s) targeted by the synthetic transcription factor. Activators that are orthogonal to the yeast genome have been realized by fusing the human estrogen receptor to an activation and a DNA-binding domain from bacteria, viruses, or higher eukaryotes. In this work, we optimized the working of a β-estradiol-sensing device-in terms of detection range and maximal output signal-where the human estrogen receptor is flanked by the bacterial protein LexA and either the strong VP64 (from herpes simplex virus) or the weaker B42 (from E. coli) activation domain. We enhanced the biosensor performance by thoroughly engineering both the chimeric activator and the reporter protein expression cassette. In particular, we constructed a synthetic promoter-where transcription is induced by the chimeric activators-based on the core sequence of the yeast CYC1 promoter, by tuning parameters such as the length of the 5' UTR, the distance between adjacent LexA binding sites (operators), and the spacing between the whole operator region and the main promoter TATA box. We found a configuration that works both as a highly sensitive biosensor and a sharp switch depending on the concentration of the chimeric activator and the strength of its activation domain.

Identification of acetic acid sensitive strains through biosensor-based screening of a Saccharomyces cerevisiae CRISPRi library

Background

Acetic acid tolerance is crucial for the development of robust cell factories for conversion of lignocellulosic hydrolysates that typically contain high levels of acetic acid. Screening mutants for growth in medium with acetic acid is an attractive way to identify sensitive variants and can provide novel insights into the complex mechanisms regulating the acetic acid stress response.

Results

An acetic acid biosensor based on the Saccharomyces cerevisiae transcription factor Haa1, was used to screen a CRISPRi yeast strain library where dCas9-Mxi was set to individually repress each essential or respiratory growth essential gene. Fluorescence-activated cell sorting led to the enrichment of a population of cells with higher acetic acid retention. These cells with higher biosensor signal were demonstrated to be more sensitive to acetic acid. Biosensor-based screening of the CRISPRi library strains enabled identification of strains with increased acetic acid sensitivity: strains with gRNAs targeting TIF34, MSN5, PAP1, COX10 or TRA1.

Conclusions

This study demonstrated that biosensors are valuable tools for screening and monitoring acetic acid tolerance in yeast. Fine-tuning the expression of essential genes can lead to altered acetic acid tolerance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01938-7.

Enzyme directed evolution using genetically encodable biosensors

Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.

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.

Signal Amplification of araC pBAD Using a Standardised Translation Initiation Region

araC pBAD is a genetic fragment that regulates the expression of the araBAD operon in bacteria, which is required for the metabolism of L-arabinose. It is widely used in bioengineering applications because it can drive regulatable and titratable expression of genes and genetic pathways in microbial cell factories. A notable limitation of araC pBAD is that it generates a low signal when induced with high concentrations of L-arabinose (the maximum ON state). Herein we have amplified the maximum ON state of araC pBAD by coupling it to a synthetically evolved translation initiation region (TIR^EVOL). The coupling maintains regulatable and titratable expression from araC pBAD and yet increases the maximal ON state by >5-fold. The general principle demonstrated in the study can be applied to amplify the signal from similar genetic modules.

Graphical Abstract

Assessment of fluorescent protein candidates for multi-color flow cytometry analysis of Saccharomyces cerevisiae

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In vivo fluorescence of candidate fluorescent proteins was assessed in Saccharomyces cerevisiae.

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eEGFP, CyOFP1opt and mBeRFPopt were found to be suitable for multicolour flow cytometry.

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Successful protein selection and equipment configuration allowed potential tri-color flow cytometry with 488 nm single-laser excitation.

Transcription factor-based biosensors represent promising tools in the construction and evaluation of efficient cell factories for the sustainable production of fuels, chemicals and pharmaceuticals. They can notably be designed to follow the production of a target compound or to monitor key cellular properties, such as stress or starvation. In most cases, the biosensors are built with fluorescent protein (FP) genes as reporter genes because of the direct correlation between promoter activity and fluorescence level that can be measured using, for instance, flow cytometry or fluorometry. The expansion of available FPs offers the possibility of using several FPs - and biosensors – in parallel in one host, with simultaneous detection using multicolor flow cytometry. However, the technique is currently limited by the unavailability of combinations of FP whose genes can be successfully expressed in the host and whose fluorescence can be efficiently distinguished from each other.

In the present study, the broad collection of available FPs was explored and four different FPs were successfully expressed in the yeast Saccharomyces cerevisiae: yEGFP, mEGFP, CyOFP1opt and mBeRFPopt. After studying their fluorescence signals, population heterogeneity and possible interactions, we recommend two original combinations of FPs for bi-color flow cytometry: mEGFP together with either CyOFP1opt or mBeRFPopt, as well as the combination of all three FPs mEGFP, CyOFP1opt and mBeRFPopt for tri-color flow cytometry. These combinations will allow to perform different types of bi-color or possibly tri-color flow cytometry and FACS experiments with yeast, such as phenotype evaluation, screening or sorting, by single-laser excitation with a standard 488 nm blue laser.

Intracellular biosensor-based dynamic regulation to manipulate gene expression at the spatiotemporal level

The use of intracellular, biosensor-based dynamic regulation strategies to regulate and improve the production of useful compounds have progressed significantly over previous decades. By employing such an approach, it is possible to simultaneously realize high productivity and optimum growth states. However, industrial fermentation conditions contain a mixture of high- and low-performance non-genetic variants, as well as young and aged cells at all growth phases. Such significant individual variations would hinder the precise controlling of metabolic flux at the single-cell level to achieve high productivity at the macroscopic population level. Intracellular biosensors, as the regulatory centers of metabolic networks, can real-time sense intra- and extracellular conditions and, thus, could be synthetically adapted to balance the biomass formation and overproduction of compounds by individual cells. Herein, we highlight advances in the designing and engineering approaches to intracellular biosensors. Then, the spatiotemporal properties of biosensors associated with the distribution of inducers are compared. Also discussed is the use of such biosensors to dynamically control the cellular metabolic flux. Such biosensors could achieve single-cell regulation or collective regulation goals, depending on whether or not the inducer distribution is only intracellular.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

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

Synthetic Biology-Driven Microbial Production of Resveratrol: Advances and Perspectives

Resveratrol, a bioactive natural product found in many plants, is a secondary metabolite and has attracted much attention in the medicine and health care products fields due to its remarkable biological activities including anti-cancer, anti-oxidation, anti-aging, anti-inflammation, neuroprotection and anti-glycation. However, traditional chemical synthesis and plant extraction methods are impractical for industrial resveratrol production because of low yield, toxic chemical solvents and environmental pollution during the production process. Recently, the biosynthesis of resveratrol by constructing microbial cell factories has attracted much attention, because it provides a safe and efficient route for the resveratrol production. This review discusses the physiological functions and market applications of resveratrol. In addition, recent significant biotechnology advances in resveratrol biosynthesis are systematically summarized. Furthermore, we discuss the current challenges and future prospects for strain development for large-scale resveratrol production at an industrial level.

Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production

Branched-chain amino acid (BCAA) metabolism fulfills numerous physiological roles and can be harnessed to produce valuable chemicals. However, the lack of eukaryotic biosensors specific for BCAA-derived products has limited the ability to develop high-throughput screens for strain engineering and metabolic studies. Here, we harness the transcriptional regulator Leu3p from Saccharomyces cerevisiae to develop a genetically encoded biosensor for BCAA metabolism. In one configuration, we use the biosensor to monitor yeast production of isobutanol, an alcohol derived from valine degradation. Small modifications allow us to redeploy Leu3p in another biosensor configuration that monitors production of the leucine-derived alcohol, isopentanol. These biosensor configurations are effective at isolating high-producing strains and identifying enzymes with enhanced activity from screens for branched-chain higher alcohol (BCHA) biosynthesis in mitochondria as well as cytosol. Furthermore, this biosensor has the potential to assist in metabolic studies involving BCAA pathways, and offers a blueprint to develop biosensors for other products derived from BCAA metabolism.

There are a lack of eukaryotic biosensors specific for branched-chain amino acid (BCAA)-derived products. Here the authors report a genetically encoded biosensor for BCAA metabolism based on the Leu3p transcriptional regulator; they use this to monitor yeast production of isobutanol and isopentanol.

CasPER: A CRISPR/Cas9-Based Method for Directed Evolution in Genomic Loci in Saccharomyces cerevisiae

Here, in this chapter, we describe a detailed protocol for the method named Cas9-mediated protein evolution reaction or short CasPER. CasPER is based on the generation of large 300-600-bp mutagenized linear DNA fragments by error-prone PCR which are used as a donor for repair of double-strand break mediated by Cas9 and subsequently integrated to the genome. This method can be efficiently used for directed evolution of desired essential or nonessential genes in the genome and most importantly can be multiplexed. Altogether, the described method allows for heterogeneous DNA integration with successful transformation efficiencies of 98-100% for both single and multiplex targeting.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

Engineered l-Lactate Responding Promoter System Operating in Glucose-Rich and Anoxic Environments

Bacteria equipped with genetically encoded lactate biosensors are promising tools for biopharmaceutical production, diagnostics, and cellular therapies. However, many applications involve glucose-rich and anoxic environments, in which current whole-cell lactate biosensors show low performance. Here we engineer an optimized, synthetic lactate biosensor system by repurposing the natural LldPRD promoter regulated by the LldR transcriptional regulator. We removed glucose catabolite and anoxic repression by designing a hybrid promoter, containing LldR operators and tuned both regulator and reporter gene expressions to optimize biosensor signal-to-noise ratio. The resulting lactate biosensor, termed ALPaGA (A Lactate Promoter Operating in Glucose and Anoxia), can operate in glucose-rich, aerobic and anoxic conditions. We show that ALPaGA works reliably in the probiotic chassisEscherichia coliNissle 1917 and can detect endogenous l-lactate produced by 3D tumor spheroids with an improved dynamic range. In the future, the ALPaGA system could be used to monitor bioproduction processes and improve the specificity of engineered bacterial cancer therapies by restricting their activity to the lactate-rich microenvironment of solid tumors.

Development of a Highly Efficient Copper-Inducible GAL Regulation System (CuIGR) in Saccharomyces cerevisiae

Dynamic regulation of gene expression to decouple growth and production has been proven to be effective for improving the biosynthetic efficiency of microbial cell factories. However, the number of efficient regulatory systems available for regulation of Saccharomyces cerevisiae is limited. In the present study, a novel copper-inducible gene expression system (CuIGR) composed of the copper-induced transcriptional activator Gal4 and the copper-inhibited repressor Gal80 was constructed in S. cerevisiae. When Gal80 was fused with a N-degron tag (K15), the resulting CuIGR4 system exhibited the most stringent regulation of gene expression driven by GAL1/2/7/10 promoters. As compared to the native Cu²⁺-inducible CUP1 promoter, the CuIGR4 system amplified the response to copper by as much as 2.7 folds, resulting in 72-fold induction of EGFP expression and a 33-fold change in lycopene production (3-100 mg/L) with addition of 20 μM copper. This newly developed copper-inducible system provides a powerful tool for gene expression control in S. cerevisiae, which is expected to be widely applicable in the regulation of yeast cell factories for enhanced biosynthesis of valuable products.

Synthetic spatial patterning in bacteria: advances based on novel diffusible signals

Engineering multicellular patterning may help in the understanding of some fundamental laws of pattern formation and thus may contribute to the field of developmental biology. Furthermore, advanced spatial control over gene expression may revolutionize fields such as medicine, through organoid or tissue engineering. To date, foundational advances in spatial synthetic biology have often been made in prokaryotes, using artificial gene circuits. In this review, engineered patterns are classified into four levels of increasing complexity, ranging from spatial systems with no diffusible signals to systems with complex multi‐diffusor interactions. This classification highlights how the field was held back by a lack of diffusible components. Consequently, we provide a summary of both previously characterized and some new potential candidate small‐molecule signals that can regulate gene expression in Escherichia coli. These diffusive signals will help synthetic biologists to successfully engineer increasingly intricate, robust and tuneable spatial structures.

An integrated yeast-based process for cis,cis-muconic acid production

Cis,cis‐muconic acid (CCM) is a promising polymer building block. CCM can be made by whole‐cell bioconversion of lignin hydrolysates or de novo biosynthesis from sugar feedstocks using engineered microorganisms. At present, however, there is no established process for large‐scale CCM production. In this study, we developed an integrated process for manufacturing CCM from glucose by yeast fermentation. We systematically engineered the CCM‐producing Saccharomyces cerevisiae strain by rewiring the shikimate pathway flux and enhancing phosphoenolpyruvate supply. The engineered strain ST10209 accumulated less biomass but produced 1.4 g/L CCM (70 mg CCM per g glucose) in microplate assay, 71% more than the previously engineered strain ST8943. The strain ST10209 produced 22.5 g/L CCM in a 2 L fermenter with a productivity of 0.19 g/L/h, compared to 0.14 g/L/h achieved by ST8943 in our previous report under the same fermentation conditions. The fermentation process was demonstrated at pilot scale in 10 and 50 L steel tanks. In 10 L fermenter, ST10209 produced 20.8 g/L CCM with a CCM yield of 0.1 g/g glucose and a productivity of 0.21 g/L/h, representing the highest to‐date CCM yield and productivity. We developed a CCM recovery and purification process by treating the fermentation broth with activated carbon at low pH and low temperature, achieving an overall CCM recovery yield of 66.3% and 95.4% purity. In summary, we report an integrated CCM production process employing engineered S. cerevisiae yeast.

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Evolving Small-Molecule Biosensors with Improved Performance and Reprogrammed Ligand Preference Using OrthoRep

Genetically encoded biosensors are valuable for the optimization of small-molecule biosynthesis pathways, because they transduce the production of small-molecule ligands into a readout compatible with high-throughput screening or selection in vivo. However, engineering biosensors with appropriate response functions and ligand preferences remains challenging. Here, we show that the continuous hypermutation system, OrthoRep, can be effectively applied to evolve biosensors with a high dynamic range, reprogrammed activity toward desired noncognate ligands, and proper operational range for coupling to biosynthetic pathways. In particular, we encoded the allosteric transcriptional factor, BenM, on OrthoRep such that the propagation of host yeast cells resulted in BenM's rapid and continuous diversification. When these cells were subjected to cycles of culturing and sorting on BenM activity in the presence and absence of its cognate ligand, muconic acid, or the noncognate ligand, adipic acid, we obtained multiple BenM variants that respond to their corresponding ligands. These biosensors outperform previously engineered BenM-based biosensors by achieving a substantially greater dynamic range (up to ∼180-fold induction) and broadened operational range. The expression of select BenM variants in the presence of a muconic acid biosynthetic pathway demonstrated sensitive biosensor activation without saturating response, which should enable pathway and host engineering for higher production of muconic and adipic acids. Given the streamlined manner in which high-performance and versatile biosensors were evolved using OrthoRep, this study provides a template for generating custom biosensors for metabolic pathway engineering and other biotechnology goals.

Modular tuning engineering and versatile applications of genetically encoded biosensors

Genetically encoded biosensors have a diverse range of detectable signals and potential applications in many fields, including metabolism control and high-throughput screening. Their ability to be used in situ with minimal interference to the bioprocess of interest could revolutionize synthetic biology and microbial cell factories. The performance and functions of these biosensors have been extensively studied and have been rapidly improved. We review here current biosensor tuning strategies and attempt to unravel how to obtain ideal biosensor functions through experimental adjustments. Strategies for expanding the biosensor input signals that increases the number of detectable compounds have also been summarized. Finally, different output signals and their practical requirements for biotechnology and biomedical applications and environmental safety concerns have been analyzed. This in-depth review of the responses and regulation mechanisms of genetically encoded biosensors will assist to improve their design and optimization in various application scenarios.

Microbial Lipopeptide-Producing Strains and Their Metabolic Roles under Anaerobic Conditions

The lipopeptide produced by microorganisms is one of the representative biosurfactants and is characterized as a series of structural analogues of different families. Thirty-four families covering about 300 lipopeptide compounds have been reported in the last decades, and most of the reported lipopeptides produced by microorganisms were under aerobic conditions. The lipopeptide-producing strains under anaerobic conditions have attracted much attention from both the academic and industrial communities, due to the needs and the challenge of their applications in anaerobic environments, such as in oil reservoirs and in microbial enhanced oil recovery (MEOR). In this review, the fifty-eight reported bacterial strains, mostly isolated from oil reservoirs and dominated by the species Bacillus subtilis, producing lipopeptide biosurfactants, and the species Pseudomonas aeruginosa, producing glycolipid biosurfactants under anaerobic conditions were summarized. The metabolic pathway and the non-ribosomal peptide synthetases (NRPSs) of the strain Bacillus subtilis under anaerobic conditions were analyzed, which is expected to better understand the key mechanisms of the growth and production of lipopeptide biosurfactants of such kind of bacteria under anaerobic conditions, and to expand the industrial application of anaerobic biosurfactant-producing bacteria.

Development of an Haa1-based biosensor for acetic acid sensing in Saccharomyces cerevisiae

Acetic acid is one of the main inhibitors of lignocellulosic hydrolysates and acetic acid tolerance is crucial for the development of robust cell factories for conversion of biomass. As a precursor of acetyl-coenzyme A, it also plays an important role in central carbon metabolism. Thus, monitoring acetic acid levels is a crucial aspect when cultivating yeast. Transcription factor-based biosensors represent useful tools to follow metabolite concentrations. Here, we present the development of an acetic acid biosensor based on the Saccharomyces cerevisiae transcription factor Haa1 that upon binding to acetic acid relocates to the nucleus. In the biosensor, a synthetic transcription factor consisting of Haa1 and BM3R1 from Bacillus megaterium was used to control expression of a reporter gene under a promoter containing BM3R1 binding sites. The biosensor did not drive expression under a promoter containing Haa1 binding sites and responded to acetic acid over a linear range spanning from 10 to 60 mM. To validate its applicability, the biosensor was integrated into acetic acid-producing strains. A direct correlation between biosensor output and acetic acid production was detected. The developed biosensor enables high-throughput screening of strains producing acetic acid and could also be used to investigate acetic acid-tolerant strain libraries.

The Neurospora crassa Inducible Q System Enables Simultaneous Optogenetic Amplification and Inversion in Saccharomyces cerevisiae for Bidirectional Control of Gene Expression

Bidirectional optogenetic control of yeast gene expression has great potential for biotechnological applications. Our group has developed optogenetic inverter circuits that activate transcription using darkness, as well as amplifier circuits that reach high expression levels under limited light. However, because both types of circuits harness Gal4p and Gal80p from the galactose (GAL) regulon they cannot be used simultaneously. Here, we apply the Q System, a transcriptional activator/inhibitor system from Neurospora crassa, to build circuits in Saccharomyces cerevisiae that are inducible using quinic acid, darkness, or blue light. We develop light-repressed OptoQ-INVRT circuits that initiate darkness-triggered transcription within an hour of induction, as well as light-activated OptoQ-AMP circuits that achieve up to 39-fold induction. The Q System does not exhibit crosstalk with the GAL regulon, allowing coutilization of OptoQ-AMP circuits with previously developed OptoINVRT circuits. As a demonstration of practical applications in metabolic engineering, we show how simultaneous use of these circuits can be used to dynamically control both growth and production to improve acetoin production, as well as enable light-tunable co-production of geraniol and linalool, two terpenoids implicated in the hoppy flavor of beer. OptoQ-AMP and OptoQ-INVRT circuits enable simultaneous optogenetic signal amplification and inversion, providing powerful additions to the yeast optogenetic toolkit.

Optogenetic Control of Microbial Consortia Populations for Chemical Production

Microbial co-culture fermentations can improve chemical production from complex biosynthetic pathways over monocultures by distributing enzymes across multiple strains, thereby reducing metabolic burden, overcoming endogenous regulatory mechanisms, or exploiting natural traits of different microbial species. However, stabilizing and optimizing microbial subpopulations for maximal chemical production remains a major obstacle in the field. In this study, we demonstrate that optogenetics is an effective strategy to dynamically control populations in microbial co-cultures. Using a new optogenetic circuit we call OptoTA, we regulate an endogenous toxin-antitoxin system, enabling tunability of Escherichia coli growth using only blue light. With this system we can control the population composition of co-cultures of E. coli and Saccharomyces cerevisiae. When introducing in each strain different metabolic modules of biosynthetic pathways for isobutyl acetate or naringenin, we found that the productivity of co-cultures increases by adjusting the population ratios with specific light duty cycles. This study shows the feasibility of using optogenetics to control microbial consortia populations and the advantages of using light to control their chemical production.

In-situ muconic acid extraction reveals sugar consumption bottleneck in a xylose-utilizing Saccharomyces cerevisiae strain

Background

The current shift from a fossil-resource based economy to a more sustainable, bio-based economy requires development of alternative production routes based on utilization of biomass for the many chemicals that are currently produced from petroleum. Muconic acid is an attractive platform chemical for the bio-based economy because it can be converted in chemicals with wide industrial applicability, such as adipic and terephthalic acid, and because its two double bonds offer great versatility for chemical modification.

Results

We have constructed a yeast cell factory converting glucose and xylose into muconic acid without formation of ethanol. We consecutively eliminated feedback inhibition in the shikimate pathway, inserted the heterologous pathway for muconic acid biosynthesis from 3-dehydroshikimate (DHS) by co-expression of DHS dehydratase from P. anserina, protocatechuic acid (PCA) decarboxylase (PCAD) from K. pneumoniae and oxygen-consuming catechol 1,2-dioxygenase (CDO) from C. albicans, eliminated ethanol production by deletion of the three PDC genes and minimized PCA production by enhancing PCAD overexpression and production of its co-factor. The yeast pitching rate was increased to lower high biomass formation caused by the compulsory aerobic conditions. Maximal titers of 4 g/L, 4.5 g/L and 3.8 g/L muconic acid were reached with glucose, xylose, and a mixture, respectively. The use of an elevated initial sugar level, resulting in muconic acid titers above 2.5 g/L, caused stuck fermentations with incomplete utilization of the sugar. Application of polypropylene glycol 4000 (PPG) as solvent for in situ product removal during the fermentation shows that this is not due to toxicity by the muconic acid produced.

Conclusions

This work has developed an industrial yeast strain able to produce muconic acid from glucose and also with great efficiency from xylose, without any ethanol production, minimal production of PCA and reaching the highest titers in batch fermentation reported up to now. Utilization of higher sugar levels remained conspicuously incomplete. Since this was not due to product inhibition by muconic acid or to loss of viability, an unknown, possibly metabolic bottleneck apparently arises during muconic acid fermentation with high sugar levels and blocks further sugar utilization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-021-01594-3.

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.