Enabling tools for high-throughput detection of metabolites: Metabolic engineering and directed evolution applications

Bone and Joint-on-Chip Platforms: Construction Strategies and Applications

Organ-on-a-chip, also known as "tissue chip," is an advanced platform based on microfluidic systems for constructing miniature organ models in vitro. They can replicate the complex physiological and pathological responses of human organs. In recent years, the development of bone and joint-on-chip platforms aims to simulate the complex physiological and pathological processes occurring in human bones and joints, including cell-cell interactions, the interplay of various biochemical factors, the effects of mechanical stimuli, and the intricate connections between multiple organs. In the future, bone and joint-on-chip platforms will integrate the advantages of multiple disciplines, bringing more possibilities for exploring disease mechanisms, drug screening, and personalized medicine. This review explores the construction and application of Organ-on-a-chip technology in bone and joint disease research, proposes a modular construction concept, and discusses the new opportunities and future challenges in the construction and application of bone and joint-on-chip platforms.

Engineering of the Lrp/AsnC-type transcriptional regulator DecR as a genetically encoded biosensor for multilevel optimization of L-cysteine biosynthesis pathway in Escherichia coli

L-cysteine is an important sulfur-containing amino acid being difficult to produce by microbial fermentation. Due to the lack of high-throughput screening methods, existing genetically engineered bacteria have been developed by simply optimizing the expression of L-cysteine-related genes one by one. To overcome this limitation, in this study, a biosensor-based approach for multilevel biosynthetic pathway optimization of L-cysteine from the DecR regulator variant of Escherichia coli was applied. Through protein engineering, we obtained the DecRN29Y/C81E/M90Q/M99E variant-based biosensor with improved specificity and an 8.71-fold increase in dynamic range. Using the developed biosensor, we performed high-throughput screening of the constructed promoter and RBS combination library, and successfully obtained the optimized strain, which resulted in a 6.29-fold increase in L-cysteine production. Molecular dynamics (MD) simulations and electrophoretic mobility shift analysis (EMSA) showed that the N29Y/C81E/M90Q/M99E variant had enhanced induction activity. This enhancement may be due to the increased binding of the variant to DNA in the presence of L-cysteine, which enhances transcriptional activation. Overall, our biosensor-based strategy provides a promising approach for optimizing biosynthetic pathways at multiple levels. The successful implementation of this strategy demonstrates its potential for screening improved recombinant strains.

Fluorescence-Based Screens for Engineering Enzymes Linked to Halogenated Tryptophan

Directed evolution is often limited by the throughput of accurate screening methods. Here we demonstrate the feasibility of utilizing a singular transcription factor (TF)-system that can be refactored in two ways (both as an activator and repressor). Specifically, we showcase the use of previously evolved 5-halo- or 6-halo-tryptophan-specific TF biosensors suitable for the detection of a halogenated tryptophan molecule in vivo. We subsequently validate the biosensor's utility for two halogenase-specific halo-tryptophan accumulation screens. First, we isolated 5-tryptophan-halogenase, XsHal, from a mixed pool of halogenases with 100% efficiency. Thereafter, we generated a targeted library of the catalytic residue of 6-tryptophan halogenase, Th-Hal, and isolated functioning halogenases with 100% efficiency. Lastly, we refactor the TF circuit to respond to the depletion of halogenated tryptophan and prototype a high-throughput biosensor-directed evolution scheme to screen for downstream enzyme variants capable of promiscuously converting halogenated tryptophan. Altogether, this work takes a significant step toward the rapid and higher throughput screening of halogenases and halo-tryptophan converting enzymes to further reinforce efforts to enable high-level bioproduction of halogenated chemicals.

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.

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

Molecular Complementarity of Proteomimetic Materials for Target-Specific Recognition and Recognition-Mediated Complex Functions

As biomolecules essential for sustaining life, proteins are generated from long chains of 20 different α-amino acids that are folded into unique 3D structures. In particular, many proteins have molecular recognition functions owing to their binding pockets, which have complementary shapes, charges, and polarities for specific targets, making these biopolymers unique and highly valuable for biomedical and biocatalytic applications. Based on the understanding of protein structures and microenvironments, molecular complementarity can be exhibited by synthesizable and modifiable materials. This has prompted researchers to explore the proteomimetic potentials of a diverse range of materials, including biologically available peptides and oligonucleotides, synthetic supramolecules, inorganic molecules, and related coordination networks. To fully resemble a protein, proteomimetic materials perform the molecular recognition to mediate complex molecular functions, such as allosteric regulation, signal transduction, enzymatic reactions, and stimuli-responsive motions; this can also expand the landscape of their potential bio-applications. This review focuses on the recognitive aspects of proteomimetic designs derived for individual materials and their conformations. Recent progress provides insights to help guide the development of advanced protein mimicry with material heterogeneity, design modularity, and tailored functionality. The perspectives and challenges of current proteomimetic designs and tools are also discussed in relation to future applications.

Temporal sorting of microdroplets can identify productivity differences of itaconic acid from libraries of Yarrowia lipolytica

Microdroplet screening of microorganisms can improve the rate of strain selection and characterization within the canonical design-build-test paradigm. However, a full analysis of the microdroplet environment and how well these conditions translate to culturing conditions and techniques is lacking in the field. Quantification of three different biosensor/analyte combinations at 12 hour timepoints reveals the potential for extended dose-response ranges as compared to traditional in vitro conditions. Using these dynamics, we present an application and analysis of microfluidic droplet screening utilizing whole-cell biosensors, ultimately identifying an altered productivity profile of itaconic acid in a Yarrowia lipolytica-based piggyBac transposon library. Specifically, we demonstrate that the timepoint for microdroplet selection can influence the outcome of the selection and thus shift the identified strain productivity and final titer. In this case, strains selected at earlier timepoints showed increased early productivity in flask scale, with the converse true as well. Differences in response indicate microdroplet assays require tailored development to more accurately sort for phenotypes that are scalable to larger incubation volumes. Likewise, these results further highlight that screening conditions are critical parameters for success in high-throughput applications.

Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor

A cross-ribosome binding site (cRBS) adjusts the dynamic range of transcription factor-based biosensors (TFBs) by controlling protein expression and folding. The rational design of a cRBS with desired TFB dynamic range remains an important issue in TFB forward and reverse engineering. Here, we report a novel artificial intelligence (AI)-based forward-reverse engineering platform for TFB dynamic range prediction and de novo cRBS design with selected TFB dynamic ranges. The platform demonstrated superior in processing unbalanced minority-class datasets and was guided by sequence characteristics from trained cRBSs. The platform identified correlations between cRBSs and dynamic ranges to mimic bidirectional design between these factors based on Wasserstein generative adversarial network (GAN) with a gradient penalty (GP) (WGAN-GP) and balancing GAN with GP (BAGAN-GP). For forward and reverse engineering, the predictive accuracy was up to 98% and 82%, respectively. Collectively, we generated an AI-based method for the rational design of TFBs with desired dynamic ranges.

Multi-Level Optimization and Strategies in Microbial Biotransformation of Nature Products

Continuously growing demand for natural products with pharmacological activities has promoted the development of microbial transformation techniques, thereby facilitating the efficient production of natural products and the mining of new active compounds. Furthermore, due to the shortcomings and defects of microbial transformation, it is an important scientific issue of social and economic value to improve and optimize microbial transformation technology in increasing the yield and activity of transformed products. In this review, the aspects regarding the optimization of fermentation and the cross-disciplinary strategy, leading to the microbial transformation of increased levels of the high-efficiency process from natural products of a plant or microbial origin, were discussed. Additionally, due to the increasing craving for targeted and efficient methods for detecting transformed metabolites, analytical methods based on multiomics were also discussed. Such strategies can be well exploited and applied to the production of more efficient and more natural products from microbial resources.

Recent advances in screening amino acid overproducers

Microbial fermentation has contributed to 80% of global amino acid production. The key to microbial fermentation is to obtain fermentation strains with high performance to produce target amino acids with a high yield. These strains are primarily derived from screening enormous mutant libraries. Therefore, a high-throughput, rapid, accurate, and universal screening strategy for amino acid overproducers has become a guarantee for obtaining optional amino acid overproducers. In recent years, the rapid development of various novel screening strategies has been witnessed. However, proper analysis and discussion of these innovative technologies are lacking. Here we systematically reviewed recent advances in screening strategies: the auxotrophic-based strategy, the biosensor-based strategy, and the latest translation-based screening strategy. The design principle, application scope, working efficiency, screening accuracy, and universality of these strategies were discussed in detail. The potential for screening nonstandard amino acid overproducers was also analyzed. Guidance for the improvement of future screening strategies is provided in this review, which could expedite the reconstruction of amino acid overproducers and help promote the fermentation industry to reduce cost, increase yield, and improve quality.

Substantial Improvement of an Epimerase for the Synthesis of D-Allulose by Biosensor-Based High-Throughput Microdroplet Screening

Biosynthesis of D-allulose has been achieved using ketose 3-epimerases (KEases), but its application is limited by poor catalytic performance. In this study, we redesigned a genetically encoded biosensor based on a D-allulose-responsive transcriptional regulator for real-time monitoring of D-allulose. An ultrahigh-throughput droplet-based microfluidic screening platform was further constructed by coupling with this D-allulose-detecting biosensor for the directed evolution of the KEases. Structural analysis of Sinorhizobium fredii D-allulose 3-epimerase (SfDAE) revealed that a highly flexible helix/loop region exposes or occludes the catalytic center as an essential lid conformation regulating substrate recognition. We reprogrammed SfDAE using structure-guided rational design and directed evolution, in which a mutant M3-2 was identified with 17-fold enhanced catalytic efficiency. Our research offers a paradigm for the design and optimization of a biosensor-based microdroplet screening platform.

CRISPRi-microfluidics screening enables genome-scale target identification for high-titer protein production and secretion

Genome-scale target identification promises to guide microbial cell factory engineering for higher-titer production of biomolecules such as recombinant proteins (r-protein), but challenges remain due to the need not only for comprehensive genotypic perturbation but also in conjunction with high-throughput phenotypic screening strategies. Here, we developed a CRISPRi-microfluidics screening platform to systematically identify crucial gene targets that can be engineered to enhance r-protein secretion in Corynebacterium glutamicum. We created a CRISPR interference (CRISPRi) library containing 46,549 single-guide RNAs, where we aimed to unbiasedly target all genes for repression. Meanwhile, we developed a highly efficient droplet-based microfluidics system integrating the FlAsH-tetracysteine assay that enables screening of millions of strains to identify potential knockdowns conducive to nanobody VHH secretion. Among our highest-ranking candidates are a slew of previously unknown targets involved in transmembrane transport, amino-acid metabolism and redox regulation. Guided by these findings, we eventually constructed a hyperproducer for multiple proteins via combinatorial engineering of redox-response transcription factors. As the near-universal applicability of CRISPRi technology and the FlAsH-based screening platform, this procedure might be expanded to include a varied variety of microbial species and recombinant proteins.

Esterase Sequence Composition Patterns for the Identification of Catalytic Triad Microenvironment Motifs

Ester hydrolysis is of wide biomedical interest, spanning from the green synthesis of pharmaceuticals to biomaterials' development. Existing peptide-based catalysts exhibit low catalytic efficiency compared to natural enzymes, due to the conformational heterogeneity of peptides. Moreover, there is lack of understanding of the correlation between the primary sequence and catalytic function. For this purpose, we statistically analyzed 22 EC 3.1 hydrolases with known catalytic triads, characterized by unique and well-defined mechanisms. The aim was to identify patterns at the sequence level that will better inform the creation of short peptides containing important information for catalysis, based on the catalytic triad, oxyanion holes and the triad residues microenvironments. Moreover, fragmentation schemes of the primary sequence of selected enzymes alongside the study of their amino acid frequencies, composition, and physicochemical properties are proposed. The results showed highly conserved catalytic sites with distinct positional patterns and chemical microenvironments that favor catalysis and revealed variations in catalytic site composition that could be useful for the design of minimalistic catalysts.

Enzyme directed evolution using genetically encodable biosensors

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

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 in high-throughput metabolic engineering: Generation of oligonucleotide-mediated genetic libraries

The preparation of genetic libraries is an essential step to evolve microorganisms and study genotype-phenotype relationships by high-throughput screening/selection. As the large-scale synthesis of oligonucleotides becomes easy, cheap, and high-throughput, numerous novel strategies have been developed in recent years to construct high-quality oligo-mediated libraries, leveraging state-of-art molecular biology tools for genome editing and gene regulation. This review presents an overview of recent advances in creating and characterizing in vitro and in vivo genetic libraries, based on CRISPR/Cas, regulatory RNAs, and recombineering, primarily for Escherichia coli and Saccharomyces cerevisiae. These libraries' applications in high-throughput metabolic engineering, strain evolution and protein engineering are also discussed.

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

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

Elucidation of Sequence-Function Relationships for an Improved Biobutanol In Vivo Biosensor in E. coli

Transcription factor (TF)–promoter pairs have been repurposed from native hosts to provide tools to measure intracellular biochemical production titer and dynamically control gene expression. Most often, native TF–promoter systems require rigorous screening to obtain desirable characteristics optimized for biotechnological applications. High-throughput techniques may provide a rational and less labor-intensive strategy to engineer user-defined TF–promoter pairs using fluorescence-activated cell sorting and deep sequencing methods (sort-seq). Based on the designed promoter library’s distribution characteristics, we elucidate sequence–function interactions between the TF and DNA. In this work, we use the sort-seq method to study the sequence–function relationship of a σ⁵⁴-dependent, butanol-responsive TF–promoter pair, BmoR-P_BMO derived from Thauera butanivorans, at the nucleotide level to improve biosensor characteristics, specifically an improved dynamic range. Activities of promoters from a mutagenized P_BMO library were sorted based on gfp expression and subsequently deep sequenced to correlate site-specific sequences with changes in dynamic range. We identified site-specific mutations that increase the sensor output. Double mutant and a single mutant, CA(129,130)TC and G(205)A, in P_BMO promoter increased dynamic ranges of 4-fold and 1.65-fold compared with the native system, respectively. In addition, sort-seq identified essential sites required for the proper function of the σ⁵⁴-dependent promoter biosensor in the context of the host. This work can enable high-throughput screening methods for strain development.

Refactoring transcription factors for metabolic engineering

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

Evolving a Generalist Biosensor for Bicyclic Monoterpenes

Prokaryotic transcription factors can be repurposed as analytical and synthetic tools for precise chemical measurement and regulation. Monoterpenes encompass a broad chemical family that are commercially valuable as flavors, cosmetics, and fragrances, but have proven difficult to measure, especially in cells. Herein, we develop genetically encoded, generalist monoterpene biosensors by using directed evolution to expand the effector specificity of the camphor-responsive TetR-family regulator CamR from Pseudomonas putida. Using a novel negative selection coupled with a high-throughput positive screen (Seamless Enrichment of Ligand-Inducible Sensors, SELIS), we evolve CamR biosensors that can recognize four distinct monoterpenes: borneol, fenchol, eucalyptol, and camphene. Different evolutionary trajectories surprisingly yielded common mutations, emphasizing the utility of CamR as a platform for creating generalist biosensors. Systematic promoter optimization driving the reporter increased the system's signal-to-noise ratio to 150-fold. These sensors can serve as a starting point for the high-throughput screening and dynamic regulation of bicyclic monoterpene production strains.

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.

Biosensor-based isolation of amino acid-producing Vibrio natriegens strains

The marine bacterium Vibrio natriegens has recently been demonstrated to be a promising new host for molecular biology and next generation bioprocesses. V. natriegens is a Gram-negative, non-pathogenic slight-halophilic bacterium, with a high nutrient versatility and a reported doubling time of under 10 min. However, V. natriegens is not an established model organism yet, and further research is required to promote its transformation into a microbial workhorse. In this work, the potential of V. natriegens as an amino acid producer was investigated. First, the transcription factor-based biosensor LysG, from Corynebacterium glutamicum, was adapted for expression in V. natriegens to facilitate the detection of positively charged amino acids. A set of different biosensor variants were constructed and characterized, using the expression of a fluorescent protein as sensor output. After random mutagenesis, one of the LysG-based sensors was used to screen for amino acid producer strains. Here, fluorescence-activated cell sorting enabled the selective sorting of highly fluorescent cells, i.e. potential producer cells. Using this approach, individual L-lysine, L-arginine and L-histidine producers could be obtained producing up to 1 mM of the effector amino acid, extracellularly. Genome sequencing of the producer strains provided insight into the amino acid production metabolism of V. natriegens. This work demonstrates the successful expression and application of transcription factor-based biosensors in V. natriegens and provides insight into the underlying physiology, forming a solid basis for further development of this promising microbe.

Intelligent host engineering for metabolic flux optimisation in biotechnology

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

Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum

Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.

Bioproduction of l- and d-lactic acids: advances and trends in microbial strain application and engineering

Lactic acid is an important platform chemical used in the food, agriculture, cosmetic, pharmaceutical, and chemical industries. It serves as a building block for the production of polylactic acid (PLA), a biodegradable polymer, which can replace traditional petroleum-based plastics and help to reduce environmental pollution. Cost-effective production of optically pure l- and d-lactic acids is necessary to achieve a quality and thermostable PLA product. This paper evaluates research advances in the bioproduction of l- and d-lactic acids using microbial fermentation. Special emphasis is given to the development of metabolically engineered microbial strains and processes tailored to alternative and flexible feedstock concepts such as: lignocellulose, glycerol, C1-gases, and agricultural-food industry byproducts. Alternative fermentation concepts that can improve lactic acid production are discussed. The potential use of inducible gene expression systems for the development of biosensors to facilitate the screening and engineering of lactic acid-producing microorganisms is discussed.

Biotechnological applications of S-adenosyl-methionine-dependent methyltransferases for natural products biosynthesis and diversification

In the biosynthesis of natural products, methylation is a common and essential transformation to alter molecules' bioavailability and bioactivity. The main methylation reaction is performed by S-adenosylmethionine (SAM)-dependent methyltransferases (MTs). With advancements in genomic and chemical profiling technologies, novel MTs have been discovered to accept complex substrates and synthesize industrially valuable natural products. However, to achieve a high yield of small molecules in microbial hosts, many methyltransferase activities have been reported to be insufficient. Moreover, inadequate co-factor supplies and feedback inhibition of the by-product, S-adenosylhomocysteine (SAH), further limit MTs' activities. Here, we review recent advances in SAM-dependent MTs to produce and diversify natural products. First, we surveyed recently identified novel methyltransferases in natural product biosynthesis. Second, we summarized enzyme engineering strategies to improve methyltransferase activity, with a particular focus on high-throughput assay design and application. Finally, we reviewed innovations in co-factor regeneration and diversification, both in vitro and in vivo. Noteworthily, many MTs are able to accept multiple structurally similar substrates. Such promiscuous methyltransferases are versatile and can be tailored to design de novo pathways to produce molecules whose biosynthetic pathway is unknown or non-existent in nature, thus broadening the scope of biosynthesized functional molecules.

Rational engineering strategies for achieving high-yield, high-quality and high-stability of natural product production in actinomycetes

Actinomycetes are recognized as excellent producers of microbial natural products, which have a wide range of applications, especially in medicine, agriculture and stockbreeding. The three main indexes of industrialization (titer, purity and stability) must be taken into overall consideration in the manufacturing process of natural products. Over the past decades, synthetic biology techniques have expedited the development of industrially competitive strains with excellent performances. Here, we summarize various rational engineering strategies for upgrading the performance of industrial actinomycetes, which include enhancing the yield of natural products, eliminating the by-products and improving the genetic stability of engineered strains. Furthermore, the current challenges and future perspectives for optimizing the industrial strains more systematically through combinatorial engineering strategies are also discussed.

Methylotrophic bacterium-based molecular sensor for the detection of low concentrations of methanol

Methylotrophic bacterium Methylorubrum extorquens is a promising microorganism for the production of value-added compounds from methanol. This study focused on the development of a single-cell level biosensor system that detects methanol by using the intrinsic regulatory machinery which responds to the presence of methanol in this bacterium. A green fluorescent protein (GFP) gene located downstream of the promoter region of the serine glyoxylate aminotransferase gene (P_sga) or the methanol dehydrogenase subunit 1 precursor gene (P_mxaF) was inserted into the chromosome of M. extorquens wild-type strain AM1. The expression of GFP upon methanol exposure was measured by spectrofluorometer and fluorescence-activated cell sorting (FACS). The strain harboring P_sga-gfp emitted fluorescence only when methanol was supplied to the culture medium, while the other strain harboring P_mxaF-gfp showed high basal fluorescence even in the absence of methanol. The fluorescence intensity of the P_sga-gfp strain depended on a methanol concentration higher than 25 μM, and the sensitivity and dose-dependency of this strain were much higher than previous systems using Escherichia coli. The methanol-sensing properties of the engineered M. extorquens strain were comparable to those of a methylotrophic yeast-based biosensor, suggesting the usefulness of methylotrophic microorganisms as platforms for single-cell sensing of C₁ compounds. The constructed methanol sensor strain, coupled with flow cytometry techniques, provides a high-throughput and highly sensitive screening method for the selection of functional methanol-producing enzymes.

High-Throughput Screening of an Octanoic Acid Producer Strain Library Enables Detection of New Targets for Increasing Titers in Saccharomyces cerevisiae

Octanoic acid is an industrially relevant compound with applications in antimicrobials or as a precursor for biofuels. Microbial biosynthesis through yeast is a promising alternative to current unsustainable production methods. To increase octanoic acid titers in Saccharomyces cerevisiae, we use a previously developed biosensor that is based on the octanoic acid responsive pPDR12 promotor coupled to GFP. We establish a biosensor strain amenable for high-throughput screening of an octanoic acid producer strain library. Through development, optimization, and execution of a high-throughput screening approach, we were able to detect two new genetic targets, KCS1 and FSH2, which increased octanoic acid titers through combined overexpression by about 55% compared to the parental strain. Neither target has yet been reported to be involved in fatty acid biosynthesis. The presented methodology can be employed to screen any genetic library and thereby more genes involved in improving octanoic acid production can be detected in the future.

Engineering Alternate Ligand Recognition in the PurR Topology: A System of Novel Caffeine Biosensing Transcriptional Antirepressors

Recent advances in synthetic biology and protein engineering have increased the number of allosteric transcription factors used to regulate independent promoters. These developments represent an important increase in our biological computing capacity, which will enable us to construct more sophisticated genetic programs for a broad range of biological technologies. However, the majority of these transcription factors are represented by the repressor phenotype (BUFFER), and require layered inversion to confer the antithetical logical function (NOT), requiring additional biological resources. Moreover, these engineered transcription factors typically utilize native ligand binding functions paired with alternate DNA binding functions. In this study, we have advanced the state-of-the-art by engineering and redesigning the PurR topology (a native antirepressor) to be responsive to caffeine, while mitigating responsiveness to the native ligand hypoxanthine-i.e., a deamination product of the input molecule adenine. Importantly, the resulting caffeine responsive transcription factors are not antagonized by the native ligand hypoxanthine. In addition, we conferred alternate DNA binding to the caffeine antirepressors, and to the PurR scaffold, creating 38 new transcription factors that are congruent with our current transcriptional programming structure. Finally, we leveraged this system of transcription factors to create integrated NOR logic and related feedback operations. This study represents the first example of a system of transcription factors (antirepressors) in which both the ligand binding site and the DNA binding functions were successfully engineered in tandem.

Recent advances in droplet microfluidics for enzyme and cell factory engineering

Enzymes and cell factories play essential roles in industrial biotechnology for the production of chemicals and fuels. The properties of natural enzymes and cells often cannot meet the requirements of different industrial processes in terms of cost-effectiveness and high durability. To rapidly improve their properties and performances, laboratory evolution equipped with high-throughput screening methods and facilities is commonly used to tailor the desired properties of enzymes and cell factories, addressing the challenges of achieving high titer and the yield of the target products at high/low temperatures or extreme pH, in unnatural environments or in the presence of unconventional media. Droplet microfluidic screening (DMFS) systems have demonstrated great potential for exploring vast genetic diversity in a high-throughput manner (>10⁶/h) for laboratory evolution and have been increasingly used in recent years, contributing to the identification of extraordinary mutants. This review highlights the recent advances in concepts and methods of DMFS for library screening, including the key factors in droplet generation and manipulation, signal sources for sensitive detection and sorting, and a comprehensive summary of success stories of DMFS implementation for engineering enzymes and cell factories during the past decade.

Fluorescence-activated droplet sorting for enhanced pyruvic acid accumulation by Candida glabrata

One of the goals of metabolic engineering is to engineer strains that can optimally produce target metabolites. However, the current workflow for rational engineering of the metabolic pathway is sometimes time-consuming and labor-intensive. Here, we have established a cost-effective approach for screening for variants secreting metabolites. Different surface display systems were adopted and verified, which anchored pHluorin to the Candida glabrata cell surface to associate pyruvic acid detection with the read out of this reporter. A generalizable simulation approach based on computational fluid dynamics and regularity of generated droplet dimension was presented, which was found to be an efficient design tool to explore microfluidic characteristics or optimization. Finally, a microfluidic platform based on simulation coupled with surface display system was constructed. A mutant exhibiting a 73.6% increase in pyruvic acid production was identified. This ultrahigh-throughput screening pattern offers a practical guide for identifying microbial strains with many traits of interest.

High-Throughput Functional Genomics for Energy Production

Functional genomics remains a foundational field for establishing genotype-phenotype relationships that enable strain engineering. High-throughput (HTP) methods accelerate the Design-Build-Test-Learn cycle that currently drives synthetic biology towards a forward engineering future. Trackable mutagenesis techniques including transposon insertion sequencing and CRISPR-Cas-mediated genome editing allow for rapid fitness profiling of a collection, or library, of mutants to discover beneficial mutations. Due to the relative speed of these experiments compared to adaptive evolution experiments, iterative rounds of mutagenesis can be implemented for next-generation metabolic engineering efforts to design complex production and tolerance phenotypes. Additionally, the expansion of these mutagenesis techniques to novel bacteria are opening up industrial microbes that show promise for establishing a bio-based economy.

Design, Evolution, and Characterization of a Xylose Biosensor in Escherichia coli Using the XylR/xylO System with an Expanded Operating Range

Genetically encoded biosensors are extensively utilized in synthetic biology and metabolic engineering. However, reported xylose biosensors are far too sensitive with a limited operating range to be useful for most sensing applications. In this study, we describe directed evolution of Escherichia coli XylR, and construction of biosensors based on XylR and the corresponding operator xylO. The operating range of biosensors containing the mutant XylR was increased by nearly 10-fold comparing with the control. Two individual amino acid mutations (either L73P or N220T) in XylR were sufficient to extend the linear response range to upward of 10 g/L xylose. The evolved biosensors described here are well suited for developing whole-cell biosensors for detecting varying xylose concentrations across an expanded range. As an alternative use of this system, we also demonstrate the utility of XylR and xylO as a xylose inducible system to enable graded gene expression through testing with β-galactosidase gene and the lycopene synthetic pathway. This evolution strategy identified a less-sensitive biosensor for real applications, thus providing new insights into strategies for expanding operating ranges of other biosensors for synthetic biology applications.

High-throughput screening for high-efficiency small-molecule biosynthesis

Systems metabolic engineering faces the formidable task of rewiring microbial metabolism to cost-effectively generate high-value molecules from a variety of inexpensive feedstocks for many different applications. Because these cellular systems are still too complex to model accurately, vast collections of engineered organism variants must be systematically created and evaluated through an enormous trial-and-error process in order to identify a manufacturing-ready strain. The high-throughput screening of strains to optimize their scalable manufacturing potential requires execution of many carefully controlled, parallel, miniature fermentations, followed by high-precision analysis of the resulting complex mixtures. This review discusses strategies for the design of high-throughput, small-scale fermentation models to predict improved strain performance at large commercial scale. Established and promising approaches from industrial and academic groups are presented for both cell culture and analysis, with primary focus on microplate- and microfluidics-based screening systems.

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

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

Ultra-high throughput screening for novel protease specificities

The screening of large libraries of enzyme variants remains an essential tool in evolving biocatalysts toward improved properties for applications in medicine, chemistry, and a broad variety of other fields. Over the last decades, the technology for conducting systematic screens of arrayed members of a library of enzyme variants has made great strides in terms of increasing throughput and reducing assay volume. Here, we describe in detail an alternative to arrayed analysis, which is a screen based on density shifts in result of changed enzyme function, which allows highly parallelized screening. Specifically, we link changes in protease substrate specificity in vivo to the production of an alternative reporter protein, catalase. Depending on the catalase expression level, microcolonies of library bacteria with active protease variants contained in polymeric droplets generate an oxygen bubble, which causes a density shift in the droplet and enables it to float.

Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories

Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.

Consolidated bioprocessing of raw starch to ethanol by Saccharomyces cerevisiae: Achievements and challenges

Recent advances in amylolytic strain engineering for starch-to-ethanol conversion have provided a platform for the development of raw starch consolidated bioprocessing (CBP) technologies. Several proof-of-concept studies identified improved enzyme combinations, alternative feedstocks and novel host strains for evaluation and application under fermentation conditions. However, further research efforts are required before this technology can be scaled up to an industrial level. In this review, different CBP approaches are defined and discussed, also highlighting the role of auxiliary enzymes for a supplemented CBP process. Various achievements in the development of amylolytic Saccharomyces cerevisiae strains for CBP of raw starch and the remaining challenges that need to be tackled/pursued to bring yeast raw starch CBP to industrial realization, are described. Looking towards the future, it provides potential solutions to develop more cost-effective processes that include cheaper substrates, integration of the 1G and 2G economies and implementing a biorefinery concept where high-value products are also derived from starchy substrates.

Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms

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

A New Biosensor for Stilbenes and a Cannabinoid Enabled by Genome Mining of a Transcriptional Regulator

In vivo biosensors are powerful tools for metabolic engineering and synthetic biology applications. However, the development of biosensors is hindered by the limited number of characterized transcriptional regulators. The versatile sensing abilities of microbes and genome sequences available hold great potential for developing novel biosensors via genome mining for new transcriptional regulators. Here we report the development and engineering of a new stilbene-responsive biosensor discovered by mining the Novosphingobium aromaticivorans DSM 12444 genome. The biosensor can distinguish resveratrol from its precursors, p-coumaric acid and trans-cinnamic acid. Remarkably, it can detect other biologically active stilbenes with resorcinol groups, and cannabidiolic acid with a β-resorcylic acid functional group. When coupled to resveratrol biosynthesis enzymes, the biosensor can sense altered resveratrol production in cells, demonstrating a 667-fold enrichment in one round of fluorescence-activated cell sorting. Our biosensor will be potentially applicable to metabolic engineering of microbial cell factories for production of stilbenes and cannabinoids.

Evolution-guided engineering of small-molecule biosensors

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