Development of a Synthetic Malonyl-CoA Sensor in Saccharomyces cerevisiae for Intracellular Metabolite Monitoring and Genetic Screening

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Promoter engineering enables precise metabolic regulation towards efficient β-elemene production in Ogataea polymorpha

Precisely controlling gene expression is beneficial for optimizing biosynthetic pathways for improving the production. However, promoters in nonconventional yeasts such as Ogataea polymorpha are always limited, which results in incompatible gene modulation. Here, we expanded the promoter library in O. polymorpha based on transcriptional data, among which 13 constitutive promoters had the strengths ranging from 0-55% of PGAP, the commonly used strong constitutive promoter, and 2 were growth phase-dependent promoters. Subsequently, 2 hybrid growth phase-dependent promoters were constructed and characterized, which had 2-fold higher activities. Finally, promoter engineering was applied to precisely regulate cellular metabolism for efficient production of β-elemene. The glyceraldehyde-3-phosphate dehydrogenase gene GAP was downregulated to drive more flux into pentose phosphate pathway (PPP) and then to enhance the supply of acetyl-CoA by using phosphoketolase-phosphotransacetylase (PK-PTA) pathway. Coupled with the phase-dependent expression of synthase module (ERG20∼LsLTC2 fusion), the highest titer of 5.24 g/L with a yield of 0.037 g/(g glucose) was achieved in strain YY150U under fed-batch fermentation in shake flasks. This work characterized and engineered a series of promoters, that can be used to fine-tune genes for constructing efficient yeast cell factories.

Self-controlled in silico gene knockdown strategies to enhance the sustainable production of heterologous terpenoid by Saccharomyces cerevisiae

Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L-1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

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.

Increased CO2 fixation enables high carbon-yield production of 3-hydroxypropionic acid in yeast

CO2 fixation plays a key role to make biobased production cost competitive. Here, we use 3-hydroxypropionic acid (3-HP) to showcase how CO2 fixation enables approaching theoretical-yield production. Using genome-scale metabolic models to calculate the production envelope, we demonstrate that the provision of bicarbonate, formed from CO2, restricts previous attempts for high yield production of 3-HP. We thus develop multiple strategies for bicarbonate uptake, including the identification of Sul1 as a potential bicarbonate transporter, domain swapping of malonyl-CoA reductase, identification of Esbp6 as a potential 3-HP exporter, and deletion of Uga1 to prevent 3-HP degradation. The combined rational engineering increases 3-HP production from 0.14 g/L to 11.25 g/L in shake flask using 20 g/L glucose, approaching the maximum theoretical yield with concurrent biomass formation. The engineered yeast forms the basis for commercialization of bio-acrylic acid, while our CO2 fixation strategies pave the way for CO2 being used as the sole carbon source.

Engineering status of protein for improving microbial cell factories

With the development of metabolic engineering and synthetic biology, microbial cell factories (MCFs) have provided an efficient and sustainable method to synthesize a series of chemicals from renewable feedstocks. However, the efficiency of MCFs is usually limited by the inappropriate status of protein. Thus, engineering status of protein is essential to achieve efficient bioproduction with high titer, yield and productivity. In this review, we summarize the engineering strategies for metabolic protein status, including protein engineering for boosting microbial catalytic efficiency, protein modification for regulating microbial metabolic capacity, and protein assembly for enhancing microbial synthetic capacity. Finally, we highlight future challenges and prospects of improving microbial cell factories by engineering status of protein.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Large-scale pathway reconstruction and colorimetric screening accelerate cellular metabolism engineering

The capability to manipulate and analyze hard-wired metabolic pathways sets the pace at which we can engineer cellular metabolism. Here, we present a framework to extensively rewrite the central metabolic pathway for malonyl-CoA biosynthesis in yeast and readily assess malonyl-CoA output based on pathway-scale DNA reconstruction in combination with colorimetric screening (Pracs). We applied Pracs to generate and test millions of enzyme variants by introducing genetic mutations into the whole set of genes encoding the malonyl-CoA biosynthetic pathway and identified hundreds of beneficial enzyme mutants with increased malonyl-CoA output. Furthermore, the synthetic pathways reconstructed by randomly integrating these beneficial enzyme variants generated vast phenotypic diversity, with some displaying higher production of malonyl-CoA as well as other metabolites, such as carotenoids and betaxanthin, thus demonstrating the generic utility of Pracs to efficiently orchestrate central metabolism to optimize the production of different chemicals in various metabolic pathways. Pracs will be broadly useful to advance our ability to understand and engineer cellular metabolism.

Exploiting a heterologous construction of the 3-hydroxypropionic acid carbon fixation pathway with mesaconate as an indicator in Saccharomyces cerevisiae

The 3-Hydroxypropionic acid (3-HP) pathway is one of the six known natural carbon fixation pathways, in which the carbon species used is bicarbonate. It has been considered to be the most suitable pathway for aerobic CO2 fixation among the six natural carbon fixation pathways. Mesaconate is a high value-added derivative in the 3-HP pathway and can be used as a co-monomer to produce fire-retardant materials and hydrogels. In this study, we use mesaconate as a reporting compound to evaluate the construction and optimization of the sub-part of the 3-HP pathway in Saccharomyces cerevisiae. Combined with fine-tuning of the malonyl-CoA reductase (MCR-C and MCR-N) expression level and optimization of 3-Hydroxypropionyl-CoA synthase, the 3-HP sub-pathway was optimized using glucose or ethanol as the substrate, with the productions of mesaconate reaching 90.78 and 61.2 mg/L, respectively.

Engineering 3-Hydroxypropionic Acid Production from Glucose in Yarrowia lipolytica through Malonyl-CoA Pathway

3-Hydroxypropionic acid (3-HP) is an important intermediate compound in the chemical industry. Green and environmentally friendly microbial synthesis methods are becoming increasingly popular in a range of industries. Compared to other chassis cells, Yarrowia lipolytica possesses advantages, such as high tolerance to organic acid and a sufficient precursor required to synthesize 3-HP. In this study, gene manipulations, including the overexpression of genes MCR-NCa, MCR-CCa, GAPNSm, ACC1 and ACSSeL641P and knocking out bypass genes MLS1 and CIT2, leading to the glyoxylate cycle, were performed to construct a recombinant strain. Based on this, the degradation pathway of 3-HP in Y. lipolytica was discovered, and relevant genes MMSDH and HPDH were knocked out. To our knowledge, this study is the first to produce 3-HP in Y. lipolytica. The yield of 3-HP in recombinant strain Po1f-NC-14 in shake flask fermentation reached 1.128 g·L^-1, and the yield in fed-batch fermentation reached 16.23 g·L^-1. These results are highly competitive compared to other yeast chassis cells. This study creates the foundation for the production of 3-HP in Y. lipolytica and also provides a reference for further research in the future.

Natural promoters and promoter engineering strategies for metabolic regulation in Saccharomyces cerevisiae

Sharomyces cerevisiae is currently one of the most important foreign gene expression systems. S. cerevisiae is an excellent host for high-value metabolite cell factories due to its advantages of simplicity, safety, and nontoxicity. A promoter, as one of the basic elements of gene transcription, plays an important role in regulating gene expression and optimizing metabolic pathways. Promoters control the direction and intensity of transcription, and the application of promoters with different intensities and performances will largely determine the effect of gene expression and ultimately affect the experimental results. Due to its significant role, there have been many studies on promoters for decades. While some studies have explored and analyzed new promoters with different functions, more studies have focused on artificially modifying promoters to meet their own scientific needs. Thus, this article reviews current research on promoter engineering techniques and related natural promoters in S. cerevisiae. First, we introduce the basic structure of promoters and the classification of natural promoters. Then, the classification of various promoter strategies is reviewed. Finally, by grouping related articles together using various strategies, this review anticipates the future development direction of promoter engineering.

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.

Dynamic-tuning yeast storage carbohydrate improves the production of acetyl-CoA-derived chemicals

Acetyl-coenzyme A (Acetyl-CoA) and malonyl-coenzyme A (malonyl-CoA) are important precursors for producing various chemicals, and their availability affects the production of their downstream chemicals. Storage carbohydrates are considered important carbon and energy reservoirs. Herein, we find that regulating the storage carbohydrate synthesis improves metabolic fluxes toward malonyl-CoA. Interestingly, not only directly decreasing storage carbohydrate accumulation improved malonyl-CoA availability but also increasing the storage carbohydrate by UGP1 overexpression enables an even higher production of acetyl-CoA- and malonyl-CoA-derived chemicals. We find that Ugp1p overexpression dynamically regulates the carbon flux to storage carbohydrate synthesis. In early exponential phases, Ugp1 overexpression causes more storage carbohydrate accumulation, while the carbon flux is then redirected toward acetyl-CoA and malonyl-CoA in later phases, thereby contributing to the synthesis of their derived products. Our study demonstrates the importance of storage carbohydrates rearrangement for the availability of acetyl-CoA and malonyl-CoA and therefore will facilitate the synthesis of their derived chemicals.

Construction and Optimization of Malonyl-CoA Sensors in Saccharomyces cerevisiae by Combining Promoter Engineering Strategies

Biosensors can be used for high-throughput screening, real-time monitoring of metabolites, and dynamic regulation of metabolic processes, which have been a popular research direction in recent years. Here, five promoters from Saccharomyces cerevisiae were selected to construct Malonyl-CoA sensors with the fapO/fapR system derived from Bacillus subtilis, and pCCW12 was finally selected for further optimization. Based on pCCW12, a series of sensors with different response sensitivities were obtained by selecting different fapO insertion sites and combining the best two or three of them. Then, through a combination of promoter hybrid, intron insertion, and transcription factor modification strategies, we obtained sensors with different effects, one of which, the H-pCCW12(TFBS)-Cti6~fapR sensor, had the lowest background noise, doubled response range and higher response sensitivity compared to the original sensor. Sensors with different characteristics constructed in this study, can be applied to Malonyl-CoA related high-throughput screening and finer regulation of metabolism. It also proves that the combined application of different promoter engineering strategies is a feasible idea for the precise construction and regulation of biosensors.

A p-Coumaroyl-CoA Biosensor for Dynamic Regulation of Naringenin Biosynthesis in Saccharomyces cerevisiae

In vivo biosensors that can convert metabolite concentrations into measurable output signals are valuable tools for high-throughput screening and dynamic pathway control in the field of metabolic engineering. Here, we present a novel biosensor in Saccharomyces cerevisiae that is responsive to p-coumaroyl-CoA, a central precursor of many flavonoids. The sensor is based on the transcriptional repressor CouR from Rhodopseudomonas palustris and was applied in combination with a previously developed malonyl-CoA biosensor for dual regulation of p-coumaroyl-CoA synthesis within the naringenin production pathway. Using this approach, we obtained a naringenin titer of 47.3 mg/L upon external precursor feeding, representing a 15-fold increase over the nonregulated system.

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.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Recent Advances, Challenges and Metabolic Engineering Strategies in the Biosynthesis of 3-Hydroxypropionic Acid

As an attractive and valuable platform chemical, 3-hydroxypropionic acid (3-HP) can be used to produce a variety of industrially important commodity chemicals and biodegradable polymers. Moreover, the biosynthesis of 3-HP has drawn much attention in recent years due to its sustainability and environmental friendliness. Here, we focus on recent advances, challenges and metabolic engineering strategies in the biosynthesis of 3-HP. While glucose and glycerol are major carbon sources for its production of 3-HP via microbial fermentation, other carbon sources have also been explored. To increase yield and titer, synthetic biology and metabolic engineering strategies have been explored, including modifying pathway enzymes, eliminating flux blockages due to byproduct synthesis, eliminating toxic byproducts, and optimizing via genome-scale models. This review also provides insights on future directions for 3-HP biosynthesis. This article is protected by copyright. All rights reserved.

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.

Development and characterization of a glycine biosensor system for fine-tuned metabolic regulation in Escherichia coli

Background

In vivo biosensors have a wide range of applications, ranging from the detection of metabolites to the regulation of metabolic networks, providing versatile tools for synthetic biology and metabolic engineering. However, in view of the vast array of metabolite molecules, the existing number and performance of biosensors is far from sufficient, limiting their potential applications in metabolic engineering. Therefore, we developed the synthetic glycine-ON and -OFF riboswitches for metabolic regulation and directed evolution of enzyme in Escherichia coli.

Results

The results showed that a synthetic glycine-OFF riboswitch (glyOFF6) and an increased-detection-range synthetic glycine-ON riboswitch (glyON14) were successfully screened from a library based on the Bacillus subtilis glycine riboswitch using fluorescence-activated cell sorting (FACS) and tetA-based dual genetic selection. The two synthetic glycine riboswitches were successfully used in tunable regulation of lactate synthesis, dynamic regulation of serine synthesis and directed evolution of alanine-glyoxylate aminotransferase in Escherichia coli, respectively. Mutants AGXT22 and AGXT26 of alanine-glyoxylate aminotransferase with an increase of 58% and 73% enzyme activity were obtained by using a high-throughput screening platform based on the synthetic glycine-OFF riboswitch, and successfully used to increase the 5-aminolevulinic acid yield of engineered Escherichia coli.

Conclusions

A synthetic glycine-OFF riboswitch and an increased-detection-range synthetic glycine-ON riboswitch were successfully designed and screened. The developed riboswitches showed broad application in tunable regulation, dynamic regulation and directed evolution of enzyme in E. coli.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01779-4.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose-response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR-fapO-based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype-phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype-phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the recognition sequence insertion scheme and the choice of UAS have a significant impact on the dynamic range. Taken together, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Cheating the Cheater: Suppressing False-Positive Enrichment during Biosensor-Guided Biocatalyst Engineering

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false-positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection) suppresses cheater population enrichment. Furthermore, we show that, if we couple the biosensor-based screen with an orthogonal prescreen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting. Using the approach developed here, we were successfully able to isolate PAL variants with ∼70% higher k_cat after a single sort. These mutants have tremendous potential in phenylketonuria (PKU) treatment and flavonoid production.

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.

Interaction of a 1,3-Dicarbonyl Toxin with Ru(II)-Biimidazole Complexes for Luminescence Sensing: A Spectroscopic and Photochemical Experimental Study Rationalized by Time-Dependent Density Functional Theory Calculations

A family of ruthenium(II) complexes containing one 2,2'-biimidazole (bim) ligand and two polypyridyl (NN) ligands has been prepared and their photophysical and photochemical features have been tested in the presence of tenuazonic acid (TeA), a widespread food and feed mycotoxin of current concern. While not tested in in vivo studies, TeA and other secondary metabolites of Alternaria fungi are suspected to exert adverse effects on the human health, so sensors and rapid analytical procedures are required. It is well-known that 1,3-dicarbonyl compounds such as TeA are relatively easy to deprotonate (the pKa of TeA is 3.5), yielding an enolate anion stabilized by resonance. The chelating and hydrogen-donor features of bim allow simultaneous binding to the metal core and to the target β-diketonate delocalized anion. Such a binding induces changes in the blue absorption (40 nm bathochromic shift), red luminescence intensity (>75% quenching), and triplet lifetime (0.2 μs decrease) of the Ru(NN)2(bim)2+ luminophore. Moreover, we have computationally rationalized, by time-dependent density functional theory, the structure of the different adducts of Ru-bim complexes with TeA and the electronic nature of the spectral absorption bands and their change upon the addition of TeA.

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.

Effective use of biosensors for high-throughput library screening for metabolite production

The development of fast and affordable microbial production from recombinant pathways is a challenging endeavor, with targeted improvements difficult to predict due to the complex nature of living systems. To address the limitations in biosynthetic pathways, much work has been done to generate large libraries of various genetic parts (promoters, RBSs, enzymes, etc.) to discover library members that bring about significantly improved levels of metabolite production. To evaluate these large libraries, high throughput approaches are necessary, such as those that rely on biosensors. There are various modes of operation to apply biosensors to library screens that are available at different scales of throughput. The effectiveness of each biosensor-based method is dependent on the pathway or strain to which it is applied, and all approaches have strengths and weaknesses to be carefully considered for any high throughput library screen. In this review, we discuss the various approaches used in biosensor screening for improved metabolite production, focusing on transcription factor-based biosensors.

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.

Developing fungal heterologous expression platforms to explore and improve the production of natural products from fungal biodiversity

Natural products from fungi represent an important source of biologically active metabolites notably for therapeutic agent development. Genome sequencing revealed that the number of biosynthetic gene clusters (BGCs) in fungi is much larger than expected. Unfortunately, most of them are silent or barely expressed under laboratory culture conditions. Moreover, many fungi in nature are uncultivable or cannot be genetically manipulated, restricting the extraction and identification of bioactive metabolites from these species. Rapid exploration of the tremendous number of cryptic fungal BGCs necessitates the development of heterologous expression platforms, which will facilitate the efficient production of natural products in fungal cell factories. Host selection, BGC assembly methods, promoters used for heterologous gene expression, metabolic engineering strategies and compartmentalization of biosynthetic pathways are key aspects for consideration to develop such a microbial platform. In the present review, we summarize current progress on the above challenges to promote research effort in the relevant fields.

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.

LuxAB-Based Microbial Cell Factories for the Sensing, Manufacturing and Transformation of Industrial Aldehydes

The application of genetically encoded biosensors enables the detection of small molecules in living cells and has facilitated the characterization of enzymes, their directed evolution and the engineering of (natural) metabolic pathways. In this work, the LuxAB biosensor system from Photorhabdus luminescens was implemented in Escherichia coli to monitor the enzymatic production of aldehydes from primary alcohols and carboxylic acid substrates. A simple high-throughput assay utilized the bacterial luciferase—previously reported to only accept aliphatic long-chain aldehydes—to detect structurally diverse aldehydes, including aromatic and monoterpene aldehydes. LuxAB was used to screen the substrate scopes of three prokaryotic oxidoreductases: an alcohol dehydrogenase (Pseudomonas putida), a choline oxidase variant (Arthrobacter chlorophenolicus) and a carboxylic acid reductase (Mycobacterium marinum). Consequently, high-value aldehydes such as cinnamaldehyde, citral and citronellal could be produced in vivo in up to 80% yield. Furthermore, the dual role of LuxAB as sensor and monooxygenase, emitting bioluminescence through the oxidation of aldehydes to the corresponding carboxylates, promises implementation in artificial enzyme cascades for the synthesis of carboxylic acids. These findings advance the bio-based detection, preparation and transformation of industrially important aldehydes in living cells.

Advances in promoter engineering: novel applications and predefined transcriptional control

Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.

Harnessing the yeast Saccharomyces cerevisiae for the production of fungal secondary metabolites

Fungal secondary metabolites (FSMs) represent a remarkable array of bioactive compounds, with potential applications as pharmaceuticals, nutraceuticals, and agrochemicals. However, these molecules are typically produced only in limited amounts by their native hosts. The native organisms may also be difficult to cultivate and genetically engineer, and some can produce undesirable toxic side-products. Alternatively, recombinant production of fungal bioactives can be engineered into industrial cell factories, such as aspergilli or yeasts, which are well amenable for large-scale manufacturing in submerged fermentations. In this review, we summarize the development of baker's yeast Saccharomyces cerevisiae to produce compounds derived from filamentous fungi and mushrooms. These compounds mainly include polyketides, terpenoids, and amino acid derivatives. We also describe how native biosynthetic pathways can be combined or expanded to produce novel derivatives and new-to-nature compounds. We describe some new approaches for cell factory engineering, such as genome-scale engineering, biosensor-based high-throughput screening, and machine learning, and how these tools have been applied for S. cerevisiae strain improvement. Finally, we prospect the challenges and solutions in further development of yeast cell factories to more efficiently produce FSMs.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

The Role of Metabolic Engineering Technologies for the Production of Fatty Acids in Yeast

Simple Summary

Metabolic engineering involves the sustainable production of high-value products. E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques (i.e., CRISPR–Cas and synthetic biology) for developing FA-producing yeast cell factories.

Abstract

Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.

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.

Cell-based high-throughput screening of polysaccharide biosynthesis hosts

Valuable polysaccharides are usually produced using wild-type or metabolically-engineered host microbial strains through fermentation. These hosts act as cell factories that convert carbohydrates, such as monosaccharides or starch, into bioactive polysaccharides. It is desirable to develop effective in vivo high-throughput approaches to screen cells that display high-level synthesis of the desired polysaccharides. Uses of single or dual fluorophore labeling, fluorescence quenching, or biosensors are effective strategies for cell sorting of a library that can be applied during the domestication of industrial engineered strains and metabolic pathway optimization of polysaccharide synthesis in engineered cells. Meanwhile, high-throughput screening strategies using each individual whole cell as a sorting section are playing growing roles in the discovery and directed evolution of enzymes involved in polysaccharide biosynthesis, such as glycosyltransferases. These enzymes and their mutants are in high demand as tool catalysts for synthesis of saccharides in vitro and in vivo. This review provides an introduction to the methodologies of using cell-based high-throughput screening for desired polysaccharide-biosynthesizing cells, followed by a brief discussion of potential applications of these approaches in glycoengineering.

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.

Metabolic engineering of Saccharomyces cerevisiae for the production of top value chemicals from biorefinery carbohydrates

The implementation of biorefineries for a cost-effective and sustainable production of energy and chemicals from renewable carbon sources plays a fundamental role in the transition to a circular economy. The US Department of Energy identified a group of key target compounds that can be produced from biorefinery carbohydrates. In 2010, this list was revised and included organic acids (lactic, succinic, levulinic and 3-hydroxypropionic acids), sugar alcohols (xylitol and sorbitol), furans and derivatives (hydroxymethylfurfural, furfural and furandicarboxylic acid), biohydrocarbons (isoprene), and glycerol and its derivatives. The use of substrates like lignocellulosic biomass that impose harsh culture conditions drives the quest for the selection of suitable robust microorganisms. The yeast Saccharomyces cerevisiae, widely utilized in industrial processes, has been extensively engineered to produce high-value chemicals. For its robustness, ease of handling, genetic toolbox and fitness in an industrial context, S. cerevisiae is an ideal platform for the founding of sustainable bioprocesses. Taking these into account, this review focuses on metabolic engineering strategies that have been applied to S. cerevisiae for converting renewable resources into the previously identified chemical targets. The heterogeneity of each chemical and its manufacturing process leads to inevitable differences between the development stages of each process. Currently, 8 of 11 of these top value chemicals have been already reported to be produced by recombinant S. cerevisiae. While some of them are still in an early proof-of-concept stage, others, like xylitol or lactic acid, are already being produced from lignocellulosic biomass. Furthermore, the constant advances in genome-editing tools, e.g. CRISPR/Cas9, coupled with the application of innovative process concepts such as consolidated bioprocessing, will contribute for the establishment of S. cerevisiae-based biorefineries.

Rewiring Central Carbon Metabolism Ensures Increased Provision of Acetyl-CoA and NADPH Required for 3-OH-Propionic Acid Production

The central carbon metabolite acetyl-CoA and the cofactor NADPH are important for the synthesis of a wide array of biobased products. Here, we constructed a platform yeast strain for improved provision of acetyl-CoA and NADPH, and used the production of 3-hydroxypropionic acid (3-HP) as a case study. We first demonstrated that the integration of phosphoketolase and phosphotransacetylase improved 3-HP production by 41.9% and decreased glycerol production by 48.1% compared with that of the control strain. Then, to direct more carbon flux toward the pentose phosphate pathway, we reduced the expression of phosphoglucose isomerase by replacing its native promoter with a weaker promoter, and increased the expression of glucose-6-phosphate dehydrogenase and 6-phosphogluconate dehydrogenase by replacing their native promoters with stronger promoters. This further improved 3-HP production by 26.4%. Furthermore, to increase the NADPH supply we overexpressed cytosolic aldehyde dehydrogenase, and improved 3-HP production by another 10.5%. Together with optimizing enzyme expression of acetyl-CoA carboxylase and malonyl-CoA reductase, the final strain is able to produce 3-HP with a final titer of 864.5 mg/L, which is a more than 24-fold improvement compared with that of the starting strain. Our strategy combines the PK pathway with the oxidative pentose phosphate pathway for the efficient provision of acetyl-CoA and NADPH, which provides both a higher theoretical yield and overall yield than the reported yeast-based 3-HP production strategies via the malonyl-CoA reductase-dependent pathway and sheds light on the construction of efficient platform cell factories for other products.

Recent progress in metabolic engineering of Saccharomyces cerevisiae for the production of malonyl-CoA derivatives

To reduce dependence on petroleum, the biosynthesis of important chemicals from simple substrates using industrial microorganisms has attracted increased attention. Metabolic engineering of Saccharomyces cerevisiae offers a sustainable and flexible alternative for the production of various chemicals. As a key metabolic intermediate, malonyl-CoA is a precursor for many useful compounds. However, the productivity of malonyl-CoA derivatives is restricted by the low cellular level of malonyl-CoA and enzymatic performance. In this review, we focused on how to increase the intracellular malonyl-CoA level and summarize the recent advances in different metabolic engineering strategies for directing intracellular malonyl-CoA to the desired malonyl-CoA derivatives, including strengthening the malonyl-CoA supply, reducing malonyl-CoA consumption, and precisely controlling the intracellular malonyl-CoA level. These strategies provided new insights for further improving the synthesis of malonyl-CoA derivatives in microorganisms.

Microbes as Biosensors

The ability to detect disease early and deliver precision therapy would be transformative for the treatment of human illnesses. To achieve these goals, biosensors that can pinpoint when and where diseases emerge are needed. Rapid advances in synthetic biology are enabling us to exploit the information-processing abilities of living cells to diagnose disease and then treat it in a controlled fashion. For example, living sensors could be designed to precisely sense disease biomarkers, such as by-products of inflammation, and to respond by delivering targeted therapeutics in situ. Here, we provide an overview of ongoing efforts in microbial biosensor design, highlight translational opportunities, and discuss challenges for enabling sense-and-respond precision medicines.

Promoter Architecture and Promoter Engineering in Saccharomyces cerevisiae

Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs.

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.

Engineered dynamic distribution of malonyl-CoA flux for improving polyketide biosynthesis in Komagataella phaffii

Malonyl-CoA is a basic but limited precursor for the biosynthesis of various bioactive compounds and life-supporting fatty acids in cells. This study develops a biosynthetic system to dynamically redirect malonyl-CoA flux and improve production of malonyl-CoA derived polyketide (6-MSA) in Komagataella phaffii. A synthetic regulatory protein fusing a yeast activator Prm1 with a bacterial repressor FapR was proved to work with a hybrid promoter (-7)fapO-cP_AOX1 and activate gene expression. Expression mode by the Prm1-FapR/(-7)fapO-cP_AOX1 device was not affected by intracellular malonyl-CoA levels. Further, 9 promoter variants of P_GAP with insertion of fapO at various sites were tested with the Prm1-FapR. It generated a biosensor of Prm1-FapR/P_GAP-(+2)fapO with regulation behavior of malonyl-CoA-low-level repression/high-level derepression. Both devices were subsequently integrated into a single cell, for which fatty acid synthesis module was driven by Prm1-FapR/P_GAP-(+2)fapO but 6-MSA synthesis module was expressed by Prm1-FapR/(-7)fapO-cP_AOX1. The integrated system allowed continuous polyketide synthesis but malonyl-CoA-high-level "on"/low-level "off" fatty acid synthesis. This design finally increased 6-MSA production capacity by 260 %, proving the positive effects of dynamic malonyl-CoA distribution to the target compounds. It provides a new strategy for synthesis of malonyl-CoA derived compounds in eukaryotic chassis hosts.

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.

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

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

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.

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.