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

Transcriptional regulator-based biosensors for biomanufacturing in Corynebacterium glutamicum

Intracellular biosensors based on transcriptional regulators have become essential instruments in biomanufacturing, extensively employed for the semi-quantitative assessment of intracellular metabolites, high-throughput screening of production strains, and the directed evolution of enzymes. Corynebacterium glutamicum serves as an industrial chassis for the production of amino acids and a variety of high-value-added chemicals. This paper discusses the varieties and modes of action of transcriptional regulators employed in the construction of intracellular biosensors in C. glutamicum. It also reviews the design principles and progress in the application of transcriptional regulator-based biosensors. Furthermore, measures designed to improve the efficacy of these biosensors are delineated. The challenges and future prospects of biosensors based on transcriptional regulators in practical applications are analyzed. This review seeks to offer theoretical direction for the systematic design and development of transcriptional regulator-based biosensors and to aid researchers in enhancing the growth and productivity of microbial production strains.

Biosensor-based dual-color droplet microfluidic platform for precise high-throughput screening of erythromycin hyperproducers

Biosensor-based droplet microfluidic high-throughput screening is extensively utilized in engineering microbial cell factories for the efficient production of various natural products. Under ideal conditions, biosensors detect product concentrations in the environment and emit corresponding measurable signals. However, bacteria cell growth rates and gene expression are significantly regulated in response to fluctuating environments, leading to substantial heterogeneity in cell density and gene expression among different subpopulations. In droplet environments, where cell density measurement is impractical, this heterogeneity can cause inaccuracies and an increase in false positives during biosensor-based screening, resulting in a significant additional workload for rescreening and verification processes. In this study, we developed modified dual-color, whole-cell Escherichia coli biosensors that report normalized fluorescent outputs, taking into account cell heterogeneity against various environmental stimuli. These biosensors were integrated with a droplet-based microfluidic platform to facilitate dual-color screening of libraries, achieving a superior enrichment ratio and increased droplet uniformity compared to single-color screening in the proof-of-concept attempt. In practical applications, the dual-color biosensor-assisted screening demonstrated 24.2 % and 11.9 % higher positive rates for wild-type Saccharopolyspora erythraea NRRL 23338 and industrial S0-derived mutagenesis libraries, respectively, compared to the single-color method. Additionally, S0-derived erythromycin hyperproducers with up to 19.6 % production improvement were successfully identified. This dual-color biosensor-assisted method enhances screening accuracy and reduces false positives by mitigating the impact of whole-cell biosensor heterogeneity, providing a universal strategy for engineering genetically encoded whole-cell biosensors. This advancement significantly improves high-throughput screening performance for various natural products in biosensor-driven applications.

Polyester-derived monomers as microbial feedstocks: Navigating the landscape of polyester upcycling

Since their large-scale adoption in the early 20th century, plastics have become indispensable to modern life. However, inadequate disposal and recycling methods have led to severe environmental consequences. While traditional end-of-life plastics management had predominantly relied on landfilling, a paradigm shift towards recycling and valorization emerged in the 1970s, leading to the development of various, mostly mechanochemical, recycling strategies, together with the more recent approach of biological depolymerization and upcycling. Plastic upcycling, which converts plastic waste into higher-value products, is gaining attention as a sustainable strategy to reduce environmental impact and reliance on virgin materials. Microbial plastic upcycling relies on efficient depolymerization methods to generate monomeric substrates, which are subsequently metabolized by native or engineered microbial systems yielding valuable bioproducts. This review focuses on the second phase of microbial polyester upcycling, examining the intracellular metabolic pathways that enable the assimilation and bioconversion of polyester-derived monomers into industrially relevant compounds. Both biodegradable and non-biodegradable polyesters with commercial significance are considered, with emphasis on pure monomeric feedstocks to elucidate intracellular carbon assimilation pathways. Understanding these metabolic processes provides a foundation for future metabolic engineering efforts, aiming to optimize microbial systems for efficient bioconversion of mixed plastic hydrolysates into valuable bioproducts.

Reprogramming yeast metabolism for customized starch-rich micro-grain through low-carbon microbial manufacturing

Starch is a primary food ingredient and industrial feedstock. Low-carbon microbial manufacturing offers a carbon-neutral/negative arable land-independent strategy for starch production. Here, we reconfigure the oleaginous yeast as a starch-rich micro-grain producer by rewiring the starch biosynthesis and gluconeogenesis pathways and regulating cell morphology. With the CO2 electro-synthesized acetate as the substrate, the strain accumulates starch 47.18% of dry cell weight. The optimized system renders spatial-temporal starch productivity (243.7 g/m2/d) approximately 50-fold higher than crop cultivation and volumetric productivity (160.83 mg/L/h) over other microbial systems by an order of magnitude. We demonstrate tunable starch composition and starch-protein ratios via strain and process engineering. The engineered artificial strains adopt a cellular resources reallocation strategy to ensure high-level starch production in micro-grain and could facilitate a highly efficient straw/cellulose-to-starch conversion. This work elucidates starch biosynthesis machinery and establishes a superior-to-nature platform for customizable starch synthesis, advancing low-carbon nutritional manufacturing.

Synthetic biology strategies for sustainable bioplastic production by yeasts

The increasing environmental concerns regarding conventional plastics have led to a growing demand for sustainable alternatives, such as biodegradable plastics. Yeast cell factories, specifically Saccharomyces cerevisiae and Yarrowia lipolytica, have emerged as promising platforms for bioplastic production due to their scalability, robustness, and ease of manipulation. This review highlights synthetic biology approaches aimed at developing yeast cell factories to produce key biodegradable plastics, including polylactic acid (PLA), polyhydroxyalkanoates (PHAs), and poly (butylene adipate-co-terephthalate) (PBAT). We explore recent advancements in engineered yeast strains that utilize various synthetic biology strategies, such as the incorporation of new genetic elements at the gene, pathway, and cellular system levels. The combined efforts of metabolic engineering, protein engineering, and adaptive evolution have enhanced strain efficiency and maximized product yields. Additionally, this review addresses the importance of integrating computational tools and machine learning into the Design-Build-Test-Learn cycle for strain development. This integration aims to facilitate strain development while minimizing effort and maximizing performance. However, challenges remain in improving strain robustness and scaling up industrial production processes. By combining advanced synthetic biology techniques with computational approaches, yeast cell factories hold significant potential for the sustainable and scalable production of bioplastics, thus contributing to a greener bioeconomy.

Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins

Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.

Genetically encoded biosensors for the circular plastics bioeconomy

Current plastic production and consumption routes are unsustainable due to impact upon climate change and pollution, and therefore reform across the entire value chain is required. Biotechnology offers solutions for production from renewable feedstocks, and to aid end of life recycling/upcycling of plastics. Biology sequence/design space is complex requiring high-throughput analytical methods to facilitate the iterative optimisation, design-build, test-learn (DBTL), cycle of Synthetic Biology. Furthermore, genetic regulatory tools can enable harmonisation between biotechnological demands and the physiological constraints of the selected production host. Genetically encoded biosensors offer a solution for both requirements to facilitate the circular plastic bioeconomy. In this review we present a summary of biosensors developed to date reported to be responsive to plastic precursors/monomers. In addition, we provide a summary of the demonstrated and prospective applications of these biosensors for the construction and deconstruction of plastics. Collectively, this review provides a valuable resource of biosensor tools and enabled applications to support the development of the circular plastics bioeconomy.

Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors

Transcription factor (TF)-based biosensors (TFBs) have received considerable attention in various fields due to their capability of converting biosignals, such as molecule concentrations, into analyzable signals, thereby bypassing the dependence on time-consuming and laborious detection techniques. Natural TFs are evolutionarily optimized to maintain microbial survival and metabolic balance rather than for laboratory scenarios. As a result, native TFBs often exhibit poor performance, such as low specificity, narrow dynamic range, and limited sensitivity, hindering their application in laboratory and industrial settings. This work analyzes four types of regulatory mechanisms underlying TFBs and outlines strategies for constructing efficient sensing systems. Recent advances in TFBs across various usage scenarios are reviewed with a particular focus on the challenges of commercialization. The systematic improvement of TFB performance by modifying the constituent elements is thoroughly discussed. Additionally, we propose future directions of TFBs for developing rapid-responsive biosensors and addressing the challenge of application isolation. Furthermore, we look to the potential of artificial intelligence (AI) technologies and various models for programming TFB genetic circuits. This review sheds light on technical suggestions and fundamental instructions for constructing and engineering TFBs to promote their broader applications in Industry 4.0, including smart biomanufacturing, environmental and food contaminants detection, and medical science.

Metabolic Engineering and Process Intensification for Muconic Acid Production Using Saccharomyces cerevisiae

The efficient production of biobased organic acids is crucial to move to a more sustainable and eco-friendly economy, where muconic acid is gaining interest as a versatile platform chemical to produce industrial building blocks, including adipic acid and terephthalic acid. In this study, a Saccharomyces cerevisiae platform strain able to convert glucose and xylose into cis,cis-muconic acid was further engineered to eliminate C2 dependency, improve muconic acid tolerance, enhance production and growth performance, and substantially reduce the side production of the intermediate protocatechuic acid. This was achieved by reintroducing the PDC5 gene and overexpression of QDR3 genes. The improved strain was integrated in low-pH fed-batch fermentations at bioreactor scale with integrated in situ product recovery. By adding a biocompatible organic phase consisting of CYTOP 503 and canola oil to the process, a continuous extraction of muconic acid was achieved, resulting in significant alleviation of product inhibition. Through this, the muconic acid titer and peak productivity were improved by 300% and 185%, respectively, reaching 9.3 g/L and 0.100 g/L/h in the in situ product recovery process as compared to 3.1 g/L and 0.054 g/L/h in the control process without ISPR.

Combinatorial iterative method for metabolic engineering of Yarrowia lipolytica: Application for betanin biosynthesis

Combinatorial library-based metabolic engineering approaches allow lower cost and faster strain development. We developed a genetic toolbox EXPRESSYALI for combinatorial engineering of the oleaginous yeast Yarrowia lipolytica. The toolbox enables consecutive rounds of engineering, where up to three combinatorially assembled gene expression cassettes can be integrated into each yeast clone per round. The cassettes are integrated into distinct intergenic sites or an open reading frame of a target gene if a simultaneous gene knockout is desired. We demonstrate the application of the toolbox by optimizing the Y. lipolytica to produce the red beet color betanin via six consecutive rounds of genome editing and screening. The library size varied between 24 and 360. Library screening was facilitated by automated color-based colony picking. In the first round, betanin pathway genes were integrated, resulting in betanin titer of around 20 mg/L. Through the following five consecutive rounds, additional biosynthetic genes were integrated, and the precursor supply was optimized, resulting in a titer of 70 mg/L. Three beta-glucosidases were deleted to prevent betanin deglycosylation, which led to a betanin titer of 130 mg/L in a small scale and a titer of 1.4 g/L in fed-batch bioreactors. The EXPRESSYALI toolbox can facilitate metabolic engineering efforts in Y. lipolytica (available via AddGene Cat. Nr. 212682-212704, Addgene kit ID # 1000000245).

Sustainable biocatalytic synthesis of substituted muconic acids

The development of sustainable routes to organic building blocks is a critical endeavor for reducing the environmental impact of chemical synthesis. Biocatalysts are poised to play an important role in sustainable synthesis, as they perform highly selective reactions under mild conditions. The application of enzymes to organic synthesis requires an approach which is operationally simple, inexpensive to prepare, and reasonably scalable. In this work, we demonstrated the utility of a Type I ring-cleaving dioxygenase CatA (P. putida KT2440) for preparative-scale synthesis of muconic acid derivatives. Muconic acids are important precursors in the synthesis of polymers and commodity chemicals. In this work, we optimized the performance of CatA under millimolar substrate concentrations and characterized the activity of the enzyme with an array of catechol substrates. Furthermore, we developed a scalable platform using cellular lysates to produce diverse muconic acids, generating up to a gram of the desired product. A simple trituration procedure was utilized for the purification of these muconic acids that obviated the need for chromatographic purification and reduced overall solvent waste.

Application of modern synthetic biology technology in aromatic amino acids and derived compounds biosynthesis

Aromatic amino acids (AAA) and derived compounds have enormous commercial value with extensive applications in the food, chemical and pharmaceutical fields. Microbial production of AAA and derived compounds is a promising prospect for its environmental friendliness and sustainability. However, low yield and production efficiency remain major challenges for realizing industrial production. With the advancement of synthetic biology, microbial production of AAA and derived compounds has been significantly facilitated. In this review, a comprehensive overview on the current progresses, challenges and corresponding solutions for AAA and derived compounds biosynthesis is provided. The most cutting-edge developments of synthetic biology technology in AAA and derived compounds biosynthesis, including CRISPR-based system, genetically encoded biosensors and synthetic genetic circuits, were highlighted. Finally, future prospects of modern strategies conducive to the biosynthesis of AAA and derived compounds are discussed. This review offers guidance on constructing microbial cell factory for aromatic compound using synthetic biology technology.

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.

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

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

Genome-wide identification of resistance genes and response mechanism analysis of key gene knockout strain to catechol in Saccharomyces cerevisiae

Engineering Saccharomyces cerevisiae for biodegradation and transformation of industrial toxic substances such as catechol (CA) has received widespread attention, but the low tolerance of S. cerevisiae to CA has limited its development. The exploration and modification of genes or pathways related to CA tolerance in S. cerevisiae is an effective way to further improve the utilization efficiency of CA. This study identified 36 genes associated with CA tolerance in S. cerevisiae through genome-wide identification and bioinformatics analysis and the ERG6 knockout strain (ERG6Δ) is the most sensitive to CA. Based on the omics analysis of ERG6Δ under CA stress, it was found that ERG6 knockout affects pathways such as intrinsic component of membrane and pentose phosphate pathway. In addition, the study revealed that 29 genes related to the cell wall-membrane system were up-regulated by more than twice, NADPH and NADP+ were increased by 2.48 and 4.41 times respectively, and spermidine and spermine were increased by 2.85 and 2.14 times, respectively, in ERG6Δ. Overall, the response of cell wall-membrane system, the accumulation of spermidine and NADPH, as well as the increased levels of metabolites in pentose phosphate pathway are important findings in improving the CA resistance. This study provides a theoretical basis for improving the tolerance of strains to CA and reducing the damage caused by CA to the ecological environment and human health.

Development of an Haa1-Based Biosensor for Acetic Acid Sensing in Saccharomyces cerevisiae

Transcription factor (TF)-based biosensors are important tools in strain development and screening as they can allow accurate monitoring of intracellular concentrations of a molecule. Acetic acid is one of the main inhibitors in lignocellulosic biomass and a major challenge when using yeast cell factories for biorefinery applications. Thus, developing acetic acid tolerant strains is of great importance. The acetic acid sensing biosensor developed relies on the endogenous Saccharomyces cerevisiae TF Haa1 that upon binding of acetic acid translocates to the nucleus. The acetic acid biosensor can be used as a tool for strain development and evaluation, as well as for screening of acetic acid-producing strains and for dynamic monitoring of acetic acid accumulation. This chapter describes a methodology for developing a TF-based biosensor for acetic acid sensing. Protocols for design considerations, part construction, and characterization procedures are included. The approach can potentially be adapted to any molecule where a suitable TF can be identified.

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.

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.

Growth-coupled high throughput selection for directed enzyme evolution

Directed enzyme evolution has revolutionized the rapid development of enzymes with desired properties. However, the lack of a high-throughput method to identify the most suitable variants from a large pool of genetic diversity poses a major bottleneck. To overcome this challenge, growth-coupled in vivo high-throughput selection approaches (GCHTS) have emerged as a novel selection system for enzyme evolution. GCHTS links the survival of the host cell with the properties of the target protein, resulting in a screening system that is easily measurable and has a high throughput-scale limited only by transformation efficiency. This allows for the rapid identification of desired variants from a pool of >109 variants in each experiment. In recent years, GCHTS approaches have been extensively utilized in the directed evolution of multiple enzymes, demonstrating success in catalyzing non-native substrates, enhancing catalytic activity, and acquiring novel functions. This review introduces three main strategies employed to achieve GCHTS: the elimination of toxic compounds via desired variants, enabling host cells to thrive in hazardous conditions; the complementation of an auxotroph with desired variants, where essential genes for cell growth have been eliminated; and the control of the transcription or expression of a reporter gene related to host cell growth, regulated by the desired variants. Additionally, we highlighted the recent developments in the in vivo continuous evolution of enzyme technology, including phage-assisted continuous evolution (PACE) and orthogonal DNA Replication (OrthoRep). Furthermore, this review discusses the challenges and future prospects in the field of growth-coupled selection for protein engineering.

Screening non-conventional yeasts for acid tolerance and engineering Pichia occidentalis for production of muconic acid

Saccharomyces cerevisiae is a workhorse of industrial biotechnology owing to the organism's prominence in alcohol fermentation and the suite of sophisticated genetic tools available to manipulate its metabolism. However, S. cerevisiae is not suited to overproduce many bulk bioproducts, as toxicity constrains production at high titers. Here, we employ a high-throughput assay to screen 108 publicly accessible yeast strains for tolerance to 20 g L-1 adipic acid (AA), a nylon precursor. We identify 15 tolerant yeasts and select Pichia occidentalis for production of cis,cis-muconic acid (CCM), the precursor to AA. By developing a genome editing toolkit for P. occidentalis, we demonstrate fed-batch production of CCM with a maximum titer (38.8 g L-1), yield (0.134 g g-1 glucose) and productivity (0.511 g L-1 h-1) that surpasses all metrics achieved using S. cerevisiae. This work brings us closer to the industrial bioproduction of AA and underscores the importance of host selection in bioprocessing.

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.

Combinatorial metabolic engineering and process optimization enables highly efficient production of L-lactic acid by acid-tolerant Saccharomyces cerevisiae

L-lactic acid (L-LA) is widely used in the food, pharmaceutical, and cosmetic industries. In recent years, the production of L-LA using microbial fermentation has been favored. Herein, a Saccharomyces cerevisiae TAM strain tolerant to pH 2.4, was used as the starting strain. Exogenous L-lactate dehydrogenase expressing S. cerevisiae TAM strain with downregulated glycerol and ethanol synthesis pathways produced an L-LA titer of 29.8 g/L, and it increased to 50.5 g/L after carboxylic acid transport pathway modulation at the shake-flask level. Subsequently, increased energy supply and redox balancing increased the L-LA titer to 72.7 g/L in shake-flask fermentation without a neutralizer, with the yield of 0.66 g/g. Finally, optimization of the fermentation conditions, such as the seed quantity, oxygen level, and pH in a 15-L bioreactor, increased the L-LA titer to 192.3 g/L at pH 4.5, with a yield of 0.78 g/g. Overall, this study proposes an efficient L-LA bioproduction method.

Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip

Biomolecular imaging of intracellular structures of a single cell and subsequent screening of the cells are of high demand in metabolic engineering to develop strains with the desired phenotype. However, the capability of current methods is limited to population-scale identification of cell phenotyping. To address this challenge, we propose to utilize dispersive phase microscopy incorporated with a droplet-based microfluidic system that combines droplet volume-on-demand generation, biomolecular imaging, and droplet-on-demand sorting, to achieve high-throughput screening of cells with an identified phenotype. Particularly, cells are encapsulated in homogeneous environments with microfluidic droplet formation, and the biomolecule-induced dispersive phase can be investigated to indicate the biomass of a specific metabolite in a single cell. The retrieved biomass information consequently guides the on-chip droplet sorting unit to screen cells with the desired phenotype. To demonstrate the proof of concept, we showcase the method by promoting the evolution of the Haematococcus lacustris strain toward a high production of natural antioxidant astaxanthin. The validation of the proposed system with on-chip single-cell imaging and droplet manipulation functionalities reveals the high-throughput single-cell phenotyping and selection potential that applies to many other biofactory scenarios, such as biofuel production, critical quality attribute control in cell therapy, etc.

Inducible Synthetic Growth Regulation Using the ClpXP Proteasome Enhances cis,cis-Muconic Acid and Glycolic Acid Yields in Saccharomyces cerevisiae

Engineered microbial cells can produce sustainable chemistry, but the production competes for resources with growth. Inducible synthetic control over the resource use would enable fast accumulation of sufficient biomass and then divert the resources to production. We developed inducible synthetic resource-use control overSaccharomyces cerevisiae by expressing a bacterial ClpXP proteasome from an inducible promoter. By individually targeting growth-essential metabolic enzymes Aro1, Hom3, and Acc1 to the ClpXP proteasome, cell growth could be efficiently repressed during cultivation. The ClpXP proteasome was specific to the target proteins, and there was no reduction in the targets when ClpXP was not induced. The inducible growth repression improved product yields from glucose (cis,cis-muconic acid) and per biomass (cis,cis-muconic acid and glycolic acid). The inducible ClpXP proteasome tackles uncertainties in strain optimization by enabling model-guided repression of competing, growth-essential, and metabolic enzymes. Most importantly, it allows improving production without compromising biomass accumulation when uninduced; therefore, it is expected to mitigate strain stability and low productivity challenges.

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k_app) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

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.

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

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

Recent advances and perspectives on production of value-added organic acids through metabolic engineering

Organic acids are important consumable materials with a wide range of applications in the food, biopolymer and chemical industries. The global consumer organic acids market is estimated to increase to $36.86 billion by 2026. Conventionally, organic acids are produced from the chemical catalysis process with petrochemicals as raw materials, which posts severe environmental concerns and conflicts with our sustainable development goals. Most of the commonly used organic acids can be produced from various organisms. As a state-of-the-art technology, large-scale fermentative production of important organic acids with genetically-modified microbes has become an alternative to the chemical route to meet the market demand. Despite the fact that bio-based organic acid production from renewable cheap feedstock provides a viable solution, low productivity has impeded their industrial-scale application. With our deeper understanding of strain genetics, physiology and the availability of strain engineering tools, new technologies including synthetic biology, various metabolic engineering strategies, omics-based system biology tools, and high throughput screening methods are gradually established to bridge our knowledge gap. And they were further applied to modify the cellular reaction networks of potential microbial hosts and improve the strain performance, which facilitated the commercialization of consumable organic acids. Here we present the recent advances of metabolic engineering strategies to improve the production of important organic acids including fumaric acid, citric acid, itaconic acid, adipic acid, muconic acid, and we also discuss the current challenges and future perspectives on how we can develop a cost-efficient, green and sustainable process to produce these important chemicals from low-cost feedstocks.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

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.

Endophytes in Lignin Valorization: A Novel Approach

Lignin, one of the essential components of lignocellulosic biomass, comprises an abundant renewable aromatic resource on the planet earth. Although 15%––40% of lignocellulose pertains to lignin, its annual valorization rate is less than 2% which raises the concern to harness and/or develop effective technologies for its valorization. The basic hindrance lies in the structural heterogeneity, complexity, and stability of lignin that collectively makes it difficult to depolymerize and yield common products. Recently, microbial delignification, an eco-friendly and cheaper technique, has attracted the attention due to the diverse metabolisms of microbes that can channelize multiple lignin-based products into specific target compounds. Also, endophytes, a fascinating group of microbes residing asymptomatically within the plant tissues, exhibit marvellous lignin deconstruction potential. Apart from novel sources for potent and stable ligninases, endophytes share immense ability of depolymerizing lignin into desired valuable products. Despite their efficacy, ligninolytic studies on endophytes are meagre with incomplete understanding of the pathways involved at the molecular level. In the recent years, improvement of thermochemical methods has received much attention, however, we lagged in exploring the novel microbial groups for their delignification efficiency and optimization of this ability. This review summarizes the currently available knowledge about endophytic delignification potential with special emphasis on underlying mechanism of biological funnelling for the production of valuable products. It also highlights the recent advancements in developing the most intriguing methods to depolymerize lignin. Comparative account of thermochemical and biological techniques is accentuated with special emphasis on biological/microbial degradation. Exploring potent biological agents for delignification and focussing on the basic challenges in enhancing lignin valorization and overcoming them could make this renewable resource a promising tool to accomplish Sustainable Development Goals (SDG’s) which are supposed to be achieved by 2030.

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.

Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses With a Toolbox of Biosensors

Industrial fermentation processes strive for high robustness to ensure optimal and consistent performance. Medium components, fermentation products, and physical perturbations may cause stress and lower performance. Cellular stress elicits a range of responses, whose extracellular manifestations have been extensively studied; whereas intracellular aspects remain poorly known due to lack of tools for real-time monitoring. Genetically encoded biosensors have emerged as promising tools and have been used to improve microbial productivity and tolerance toward industrially relevant stresses. Here, fluorescent biosensors able to sense the yeast intracellular environment (pH, ATP levels, oxidative stress, glycolytic flux, and ribosome production) were implemented into a versatile and easy-to-use toolbox. Marker-free and efficient genome integration at a conserved site on chromosome X of Saccharomyces cerevisiae strains and a commercial Saccharomyces boulardii strain was developed. Moreover, multiple biosensors were used to simultaneously monitor different intracellular parameters in a single cell. Even when combined together, the biosensors did not significantly affect key physiological parameters, such as specific growth rate and product yields. Activation and response of each biosensor and their interconnection were assessed using an advanced micro-cultivation system. Finally, the toolbox was used to screen cell behavior in a synthetic lignocellulosic hydrolysate that mimicked harsh industrial substrates, revealing differences in the oxidative stress response between laboratory (CEN.PK113-7D) and industrial (Ethanol Red) S. cerevisiae strains. In summary, the toolbox will allow both the exploration of yeast diversity and physiological responses in natural and complex industrial conditions, as well as the possibility to monitor production processes.

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.

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.

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.

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.

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.

Transportome-wide engineering of Saccharomyces cerevisiae

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

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

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

Current Challenges and Opportunities in Non-native Chemical Production by Engineered Yeasts

Yeasts are promising industrial hosts for sustainable production of fuels and chemicals. Apart from efficient bioethanol production, yeasts have recently demonstrated their potential for biodiesel production from renewable resources. The fuel-oriented product profiles of yeasts are now expanding to include non-native chemicals with the advances in synthetic biology. In this review, current challenges and opportunities in yeast engineering for sustainable production of non-native chemicals will be discussed, with a focus on the comparative evaluation of a bioethanol-producing Saccharomyces cerevisiae strain and a biodiesel-producing Yarrowia lipolytica strain. Synthetic pathways diverging from the distinctive cellular metabolism of these yeasts guide future directions for product-specific engineering strategies for the sustainable production of non-native chemicals on an industrial scale.

Determination of phenol biodegradation pathways in three psychrotolerant yeasts, Candida subhashii A011, Candida oregonensis B021 and Schizoblastosporion starkeyi-henricii L012, isolated from Rucianka peatland

In this study, three psychrotolerant phenol-degrading yeast strains Candida subhashii (strain A01₁), Candida oregonenis (strain B02₁) and Schizoblastosporion starkeyi-henricii (strain L01₂) isolated from Rucianka peatland were examined to determine which alternative metabolic pathway for phenol biodegradation is used by these microorganisms. All yeast strains were cultivated in minimal salt medium supplemented with phenol at 500, 750 and 1000 mg l^-1 concentration with two ways of conducting phenol biodegradation experiments: with and without the starving step of yeast cells. For studied yeast strains, no catechol 2,3-dioxygenase activities were detected by enzymatic assay and no products of catechol meta-cleavage in yeast cultures supernatants (GC-MS analysis), were detected. The detection of catechol 1,2-dioxygenase activity and the presence of cis,cis-muconic acid in the analyzed samples revealed that all studied psychrotolerant yeast strains were able to metabolize phenol via the ortho-cleavage pathway. Therefore, they may be tested in terms of their use to develop biotechnology for the production of cis,cis-muconic acid, a substrate used in the production of plastics (PET) and other valuable goods.

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

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

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

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

•

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

•

High resveratrol production required increased activities of Aro4p and Aro7p.

•

Multiple integration of resveratrol biosynthetic genes improved production.

•

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