Genetically encoded sensors enable real-time observation of metabolite production

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

Enhancing glucaric acid production from myo-inositol in Escherichia coli by eliminating cell-to-cell variation

Glucaric acid (GA) is a value-added chemical and can be used to manufacture food additives, anticancer drugs, and polymers. The non-genetic cell-to-cell variations in GA biosynthesis are naturally inherent, indicating the presence of both high- and low-performance cells in culture. Low-performance cells can lead to nutrient waste and inefficient production. Furthermore, myo-inositol oxygenase (MIOX) is a key rate-limiting enzyme with the problem of low stability and activity in GA production. Therefore, eliminating cell-to-cell variations and increasing MIOX stability can select high-performance cells and improve GA production. In this study, an in vivo GA bioselector was constructed based on GA biosensor and tetracycline efflux pump protein TetA to continuously select GA-efficient production strains. Additionally, the upper limit of the GA biosensor was improved to 40 g/L based on ribosome-binding site optimization, achieving efficient enrichment of GA high-performance cells. A small ubiquitin-like modifier (SUMO) enhanced MIOX stability and activity. Overall, we used the GA bioselector and SUMO-MIOX fusion in fed-batch GA production and achieved a 5.52-g/L titer in Escherichia coli, which was 17-fold higher than that of the original strain.IMPORTANCEGlucaric acid is a non-toxic valuable product that was mainly synthesized by chemical methods. Due to the problems of non-selectivity, inefficiency, and environmental pollution, GA biosynthesis has attracted significant attention. The non-genetic cell-to-cell variations and MIOX stability were both critical factors for GA production. In addition, the high detection limit of the GA biosensor was a key condition for performing high-throughput screening of GA-efficient production strains. To increase GA titer, this work eliminated the cell-to-cell variations by GA bioselector constructed based on GA biosensor and TetA, and improved the stability and activity of MIOX in the GA biosynthetic pathway through fusing the SUMO to MIOX. Finally, these approaches improved the GA production by 17-fold to 5.52 g/L at 65 h. This study represents a significant step toward the industrial application of GA biosynthetic pathways in E. coli.

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.

Streptomyces-based whole-cell biosensors for detecting diverse cell envelope-targeting antibiotics

Cell envelope-targeting antibiotics are potent therapeutic agents against various bacterial infections. The emergence of multiple antibiotic-resistant strains underscores the significance of identifying potent antimicrobials specifically targeting the cell envelope. However, current drug screening approaches are tedious and lack sufficient specificity and sensitivity, warranting the development of more efficient methods. Genetic circuit-based whole-cell biosensors hold great promise for targeted drug discovery from natural products. Here, we performed comparative transcriptomic analysis of Streptomyces coelicolor M1146 exposed to diverse cell envelope-targeting antibiotics, aiming to identify regulatory elements involved in perceiving and responding to these compounds. Differential gene expression analysis revealed significant activation of VanS/R two-component system in response to the glycopeptide class of cell envelope-acting antibiotics. Therefore, we engineered a pair of VanS/R-based biosensors that exhibit functional complementarity and possess exceptional sensitivity and specificity for glycopeptides detection. Additionally, through promoter screening and characterization, we expanded the biosensor's detection range to include various cell envelope-acting antibiotics beyond glycopeptides. Our genetically engineered biosensor exhibits superior performance, including a dynamic range of up to 887-fold for detecting subtle antibiotic concentration changes in a rapid 2-h response time, enabling high-throughput screening of natural product libraries for antimicrobial agents targeting the bacterial cell envelope.

Strategies for the biological synthesis of D-glucuronic acid and its derivatives

D-glucuronic acid is a kind of glucose derivative, which has excellent properties such as anti-oxidation, treatment of liver disease and hyperlipidemia, and has been widely used in medicine, cosmetics, food and other fields. The traditional production methods of D-glucuronic acid mainly include natural extraction and chemical synthesis, which can no longer meet the growing market demand. The production of D-glucuronic acid by biocatalysis has become a promising alternative method because of its high efficiency and environmental friendliness. This review describes different production methods of D-glucuronic acid, including single enzyme catalysis, multi-enzyme cascade, whole cell catalysis and co-culture, as well as the intervention of some special catalysts. In addition, some feasible enzyme engineering strategies are provided, including the application of enzyme immobilized scaffold, enzyme mutation and high-throughput screening, which provide good ideas for the research of D-glucuronic acid biocatalysis.

Production of D-glucaric acid with phosphoglucose isomerase-deficient Saccharomyces cerevisiae

D-Glucaric acid is a potential biobased platform chemical. Previously mainly Escherichia coli, but also the yeast Saccharomyces cerevisiae, and Pichia pastoris, have been engineered for conversion of D-glucose to D-glucaric acid via myo-inositol. One reason for low yields from the yeast strains is the strong flux towards glycolysis. Thus, to decrease the flux of D-glucose to biomass, and to increase D-glucaric acid yield, the four step D-glucaric acid pathway was introduced into a phosphoglucose isomerase deficient (Pgi1p-deficient) Saccharomyces cerevisiae strain. High D-glucose concentrations are toxic to the Pgi1p-deficient strains, so various feeding strategies and use of polymeric substrates were studied. Uniformly labelled 13C-glucose confirmed conversion of D-glucose to D-glucaric acid. In batch bioreactor cultures with pulsed D-fructose and ethanol provision 1.3 g D-glucaric acid L-1 was produced. The D-glucaric acid titer (0.71 g D-glucaric acid L-1) was lower in nitrogen limited conditions, but the yield, 0.23 g D-glucaric acid [g D-glucose consumed]-1, was among the highest that has so far been reported from yeast. Accumulation of myo-inositol indicated that myo-inositol oxygenase activity was limiting, and that there would be potential to even higher yield. The Pgi1p-deficiency in S. cerevisiae provides an approach that in combination with other reported modifications and bioprocess strategies would promote the development of high yield D-glucaric acid yeast strains.

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.

Bacterial glycobiotechnology: A biosynthetic route for the production of biopharmaceutical glycans

The recent advancement in the human glycome and progress in the development of an inclusive network of glycosylation pathways allow the incorporation of suitable machinery for protein modification in non-natural hosts and explore novel opportunities for constructing next-generation tailored glycans and glycoconjugates. Fortunately, the emerging field of bacterial metabolic engineering has enabled the production of tailored biopolymers by harnessing living microbial factories (prokaryotes) as whole-cell biocatalysts. Microbial catalysts offer sophisticated means to develop a variety of valuable polysaccharides in bulk quantities for practical clinical applications. Glycans production through this technique is highly efficient and cost-effective, as it does not involve expensive initial materials. Metabolic glycoengineering primarily focuses on utilizing small metabolite molecules to alter biosynthetic pathways, optimization of cellular processes for glycan and glycoconjugate production, characteristic to a specific organism to produce interest tailored glycans in microbes, using preferably cheap and simple substrate. However, metabolic engineering faces one of the unique challenges, such as the need for an enzyme to catalyze desired substrate conversion when natural native substrates are already present. So, in metabolic engineering, such challenges are evaluated, and different strategies have been developed to overcome them. The generation of glycans and glycoconjugates via metabolic intermediate pathways can still be supported by glycol modeling achieved through metabolic engineering. It is evident that modern glycans engineering requires adoption of improved strain engineering strategies for creating competent glycoprotein expression platforms in bacterial hosts, in the future. These strategies include logically designing and introducing orthogonal glycosylation pathways, identifying metabolic engineering targets at the genome level, and strategically improving pathway performance (for example, through genetic modification of pathway enzymes). Here, we highlight current strategies, applications, and recent progress in metabolic engineering for producing high-value tailored glycans and their applications in biotherapeutics and diagnostics.

Identifying, Characterizing, and Engineering a Phenolic Acid-Responsive Transcriptional Factor from Bacillus amyloliquefaciens

Transcriptional factors-based biosensors are commonly used in metabolic engineering for inducible control of gene expression and related applications such as high-throughput screening and dynamic pathway regulations. Mining for novel transcriptional factors is essential for expanding the usability of these toolsets. Here, we report the identification, characterization, and engineering of a phenolic acid responsive regulator PadR from Bacillus amyloliquefaciens (BaPadR). This BaPadR-based biosensor system showed a unique ligand preference and exhibited a high output strength comparable to that of commonly used inducible expression systems. Through engineering the DNA binding region of BaPadR, we further enhanced the dynamic range of the biosensor system. The DNA sequences that are responsible for BaPadR recognition were located by promoter truncation and hybrid promoter building. To further explore the tunability of the sensor system, base substitutions were performed on the BaPadR binding region of the phenolic acid decarboxylase promoter (PpadC) and the hybrid promoter. This novel biosensor system can serve as a valuable tool in future synthetic biology applications.

Generalized Strategy for Engineering Mammalian Cell-Compatible RNA-Based Biosensors from Random Sequence Libraries

Fluorescent RNA-based biosensors are useful tools for real-time detection of molecules in living cells. These biosensors typically consist of a chromophore-binding aptamer and a target-binding aptamer, whereby the chromophore-binding aptamer is destabilized until a target is captured, which causes a conformational change to permit chromophore binding and an increase in fluorescence. The target-binding region is typically fabricated using known riboswitch motifs, which are already known to have target specificity and undergo structural changes upon binding. However, known riboswitches only exist for a limited number of molecules, significantly constraining biosensor design. To overcome this challenge, we designed a framework for producing mammalian cell-compatible biosensors using aptamers selected from a large random library by Capture-SELEX. As a proof-of-concept, we generated and characterized a fluorescent RNA biosensor against L-dopa, the precursor of several neurotransmitters. Overall, we suggest that this approach will have utility for generating RNA biosensors that can reliably detect custom targets in mammalian cells.

TFBMiner: A User-Friendly Command Line Tool for the Rapid Mining of Transcription Factor-Based Biosensors

Transcription factors responsive to small molecules are essential elements in synthetic biology designs. They are often used as genetically encoded biosensors with applications ranging from the detection of environmental contaminants and biomarkers to microbial strain engineering. Despite our efforts to expand the space of compounds that can be detected using biosensors, the identification and characterization of transcription factors and their corresponding inducer molecules remain labor- and time-intensive tasks. Here, we introduce TFBMiner, a new data mining and analysis pipeline that enables the automated and rapid identification of putative metabolite-responsive transcription factor-based biosensors (TFBs). This user-friendly command line tool harnesses a heuristic rule-based model of gene organization to identify both gene clusters involved in the catabolism of user-defined molecules and their associated transcriptional regulators. Ultimately, biosensors are scored based on how well they fit the model, providing wet-lab scientists with a ranked list of candidates that can be experimentally tested. We validated the pipeline using a set of molecules for which TFBs have been reported previously, including sensors responding to sugars, amino acids, and aromatic compounds, among others. We further demonstrated the utility of TFBMiner by identifying a biosensor for S-mandelic acid, an aromatic compound for which a responsive transcription factor had not been found previously. Using a combinatorial library of mandelate-producing microbial strains, the newly identified biosensor was able to distinguish between low- and high-producing strain candidates. This work will aid in the unraveling of metabolite-responsive microbial gene regulatory networks and expand the synthetic biology toolbox to allow for the construction of more sophisticated self-regulating biosynthetic pathways.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Directly Evolved AlkS-Based Biosensor Platform for Monitoring and High-Throughput Screening of Alkane Production

Biosynthetic alkane using acyl-ACP aldehyde reductase (AAR) and aldehyde-deformylating oxygenase (ADO) from cyanobacteria is considered a promising alternative for the production of biofuels and chemical feedstocks. However, the lack of suitable screening methods to improve the catalytic efficiency of AAR and ADO has hindered further improvements in alkane production. Herein, a novel alkane biosensor was developed based on transcriptional factor AlkS by directed evolution, which shows sensitive dynamic response curves for exogenous long-chain alkanes as well as in situ monitoring of endogenously produced alkanes. The evolved biosensor enables high-throughput screening of alkane-producing strains from the AAR and ADO mutant library, which led to a 13-fold increase in the production of long-chain alkanes, including a 22-fold increase of C15. This study is the first to improve the alkane production through biosensors, which provides a good reference for the establishment of microbial cell factories for alkane production.

Development and Application of Whole-Cell Biosensors for the Detection of Gallic Acid

Gallic acid is a prevalent secondary plant metabolite distinguished as one of the most effective free-radical scavengers among phenolic acids. This compound is also known for its cytotoxic, anti-inflammatory, and antimicrobial activities. Bulk quantities of gallic acid are conventionally produced by acid hydrolysis of tannins, a costly and environmentally hazardous process. With the aim to develop more sustainable approaches, microbial bioproduction strategies have been attempted recently. To advance synthetic biology and metabolic engineering of microorganisms for gallic acid production, we characterize here a transcription factor-based inducible system PpGalR/P_{PP_RS13150} that responds to the extracellular gallic acid in a dose-dependent manner in Pseudomonas putida KT2440. Surprisingly, this compound does not mediate induction when PpGalR/P_{PP_RS13150} is used in non-native host background. We show that the activation of the inducible system requires gallate dioxygenase activity encoded by galA gene. The 4-oxalomesaconic acid, an intermediate of gallic acid-metabolism, is identified as the effector molecule that interacts with the transcription factor GalR mediating activation of gene expression. Introduction of galA gene along galR enables development of biosensors suitable for detection and monitoring of gallic acid extracellularly using non-native hosts such as E. coli and C. necator. Moreover, the P. putida-based biosensor's applicability is demonstrated by detecting and measuring gallic acid in extracts of Camellia sinensis leaves. This study reports the strategy, which can be applied for developing gallic acid biosensors using bacterial species outside Pseudomonas genus.

Development of a Transcriptional Factor PuuR-Based Putrescine-Specific Biosensor in Corynebacterium glutamicum

Corynebacterium glutamicum is regarded as an industrially important microbial cell factory and is widely used to produce various value-added chemicals. Because of the importance of C. glutamicum applications, current research is increasingly focusing on developing C. glutamicum synthetic biology platforms. Because of its ability to condense with adipic acid to synthesize the industrial plastic nylon-46, putrescine is an important platform compound of industrial interest. Developing a high-throughput putrescine biosensor can aid in accelerating the design-build-test cycle of cell factories (production strains) to achieve high putrescine-generating strain production in C. glutamicum. This study developed a putrescine-specific biosensor (pSenPuuR) in C. glutamicum using Escherichia coli-derived transcriptional factor PuuR. The response characteristics of the biosensor to putrescine were further improved by optimizing the genetic components of pSenPuuR, such as the response promoter, reporter protein, and promoter for controlling PuuR expression. According to the findings of the study, pSenPuuR has the potential to be used to assess putrescine production in C. glutamicum and is suitable for high-throughput genetic variant screening.

Shikimic acid biosynthesis in microorganisms: Current status and future direction

Shikimic acid (SA), a hydroaromatic natural product, is used as a chiral precursor for organic synthesis of oseltamivir (Tamiflu®, an antiviral drug). The process of microbial production of SA has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Corynebacterium glutamicum (141.2 g/L) and Escherichia coli (87 g/L) laid a solid foundation for the microbial fermentation production of SA. However, its industrial application is restricted by limitations such as the lack of fermentation tests for industrial-scale and the requirement of growth-limiting factors, antibiotics, and inducers. Therefore, the development of SA biosensors and dynamic molecular switches, as well as genetic modification strategies and optimization of the fermentation process based on omics technology could improve the performance of SA-producing strains. In this review, recent advances in the development of SA-producing strains, including genetic modification strategies, metabolic pathway construction, and biosensor-assisted evolution, are discussed and critically reviewed. Finally, future challenges and perspectives for further reinforcing the development of robust SA-producing strains are predicted, providing theoretical guidance for the industrial production of SA.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.

The bright frontiers of microbial metabolic optogenetics

In recent years, light-responsive systems from the field of optogenetics have been applied to several areas of metabolic engineering with remarkable success. By taking advantage of light's high tunability, reversibility, and orthogonality to host endogenous processes, optogenetic systems have enabled unprecedented dynamical controls of microbial fermentations for chemical production, metabolic flux analysis, and population compositions in co-cultures. In this article, we share our opinions on the current state of this new field of metabolic optogenetics.We make the case that it will continue to impact metabolic engineering in increasingly new directions, with the potential to challenge existing paradigms for metabolic pathway and strain optimization as well as bioreactor operation.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

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

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

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

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

Insights on gastrointestinal motility through the use of optogenetic sensors and actuators

The muscularis of the gastrointestinal (GI) tract consists of smooth muscle cells (SMCs) and various populations of interstitial cells of Cajal (ICC), platelet-derived growth factor receptor α⁺ (PDGFRα⁺ ) cells, as well as excitatory and inhibitory enteric motor nerves. SMCs, ICC and PDGFRα⁺ cells form an electrically coupled syncytium, which together with inputs from the enteric nervous system (ENS) regulate GI motility. Early studies evaluating Ca²⁺ signalling behaviours in the GI tract relied upon indiscriminate loading of tissues with Ca²⁺ dyes. These methods lacked the means to study activity in specific cells of interest without encountering contamination from other cells within the preparation. Development of mice expressing optogenetic sensors (GCaMP, RCaMP) has allowed visualization of Ca²⁺ signalling behaviours in a cell specific manner. Additionally, availability of mice expressing optogenetic modulators (channelrhodopsins or halorhodospins) has allowed manipulation of specific signalling pathways using light. GCaMP expressing animals have been used to characterize Ca²⁺ signalling behaviours of distinct classes of ICC and SMCs throughout the GI musculature. These findings illustrate how Ca²⁺ signalling in ICC is fundamental in GI muscles, contributing to tone in sphincters, pacemaker activity in rhythmic muscles and relaying enteric signals to SMCs. Animals that express channelrhodopsin in specific neuronal populations have been used to map neural circuitry and to examine post junctional neural effects on GI motility. Thus, optogenetic approaches provide a novel means to examine the contribution of specific cell types to the regulation of motility patterns within complex multi-cellular systems. Abstract Figure Legends Optogenetic activators and sensors can be used to investigate the complex multi-cellular nature of the gastrointestinal (GI tract). Optogenetic activators that are activated by light such as channelrhodopsins (ChR2), OptoXR and halorhodopsinss (HR) proteins can be genetically encoded into specific cell types. This can be used to directly activate or silence specific GI cells such as various classes of enteric neurons, smooth muscle cells (SMC) or interstitial cells, such as interstitial cells of Cajal (ICC). Optogenetic sensors that are activated by different wavelengths of light such as green calmodulin fusion protein (GCaMP) and red CaMP (RCaMP) make high resolution of sub-cellular Ca²⁺ signalling possible within intact tissues of specific cell types. These tools can provide unparalleled insight into mechanisms underlying GI motility and innervation. This article is protected by copyright. All rights reserved.

Development and Characterization of Indole-Responsive Whole-Cell Biosensor Based on the Inducible Gene Expression System from Pseudomonas putida KT2440

Indole is a biologically active compound naturally occurring in plants and some bacteria. It is an important specialty chemical that is used as a precursor by the pharmaceutical and chemical industries, as well as in agriculture. Recently, indole has been identified as an important signaling molecule for bacteria in the mammalian gut. The regulation of indole biosynthesis has been studied in several bacterial species. However, this has been limited by the lack of in vivo tools suitable for indole-producing species identification and monitoring. The genetically encoded biosensors have been shown to be useful for real-time quantitative metabolite analysis. This paper describes the identification and characterization of the indole-inducible system PpTrpI/P_{PP_RS00425} from Pseudomonas putida KT2440. Indole whole-cell biosensors based on Escherichia coli and Cupriavidus necator strains are developed and validated. The specificity and dynamics of biosensors in response to indole and its structurally similar derivatives are investigated. The gene expression system PpTrpI/P_{PP_RS00425} is shown to be specifically induced up to 639.6-fold by indole, exhibiting a linear response in the concentration range from approximately 0.4 to 5 mM. The results of this study form the basis for the use of whole-cell biosensors in indole metabolism-relevant bacterial species screening and characterization.

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.

Identification and characterization of L- and D-lactate-inducible systems from Escherichia coli MG1655, Cupriavidus necator H16 and Pseudomonas species

Lactic acid is an important platform chemical used for the production of various compounds including polylactic acid (PLA). Optically pure L- and D-lactic acids are required to obtain high quality PLA. To advance the development and selection of microbial strains for improved production of lactic acid enantiomers, a high-throughput screening, dynamic pathway control, or real-time monitoring are often applied. Inducible gene expression systems and their application in the genetically encoded biosensors contribute to the development of these techniques and are important devices for the advancement of lactic acid biotechnology. Here, we identify and characterize eleven lactate-inducible systems from Escherichia coli, Cupriavidus necator, and Pseudomonas spp. The specificity and dynamics of these systems in response to L- and D-lactate, or structurally similar compounds are investigated. We demonstrate that the inducible systems EcLldR/P_lldP and CnGntR/P_{H16_RS19190} respond only to the L-lactate, exhibiting approximately 19- and 24-fold induction, respectively. Despite neither of the examined bacteria possess the D-lactate-specific inducible system, the PaPdhR/P_lldP and PfPdhR/P_lldP are induced approximately 37- and 366-fold, respectively, by D-lactate and can be used for developing biosensor with improved specificity. The findings of this study provide an insight into understanding of L- and D-lactate-inducible systems that can be employed as sensing and tuneable devices in synthetic biology.

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.

Chromogenic Escherichia coli reporter strain for screening DNA damaging agents

The presence of pollutants in soil and water has given rise to diverse analytical and biological approaches to detect and measure contaminants in the environment. Using bacterial cells as reporter strains represents an advantage for detecting pollutants present in soil or water samples. Here, an Escherichia coli reporter strain expressing a chromoprotein capable of interacting with soil or water samples and responding to DNA damaging compounds is validated. The reporter strain generates a qualitative signal and is based on the expression of the coral chromoprotein AmilCP under the control of the recA promoter. This strain can be used simply by applying soil or water samples directly and rendering activation upon DNA damage. This reporter strain responds to agents that damage DNA (with an apparent detection limit of 1 µg of mitomycin C) without observable response to membrane integrity damage, protein folding or oxidative stress generating agents, in the latter case, DNA damage was observed. The developed reporter strain reported here is effective for the detection of DNA damaging agents present in soils samples. In a proof-of-concept analysis using soil containing chromium, showing activation at 15.56 mg/L of Cr(VI) present in soil and leached samples and is consistent with Cr(III) toxicity at high concentrations (130 µg). Our findings suggest that chromogenic reporter strains can be applied for simple screening, thus reducing the number of samples requiring analytical techniques.

Development of Synthetic Riboswitches to Guide the Evolution of Metabolite Production in Microorganisms

The untranslated region (UTR) of prokaryotic mRNA contains riboswitches, which are gene regulating modules. Riboswitches can be used as biosensors to regulate the expression of a gene or an operon depending on the intracellular level of a target molecule and consequently modulate the cellular responses. In evolutionary engineering, riboswitch-based biosensors have been widely applied for high-throughput screening or selection of target phenotypes. Evolutionary approaches can overcome the limitations of rational approaches in metabolic engineering. Previous studies have reported synthetic riboswitches equipped with novel aptamers and marker genes based on a deep understanding of the operation mechanism of the riboswitch. Here, we introduce the development process of novel synthetic riboswitches for applications in metabolic engineering.

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.

Measurement of lactose concentration in milk by using engineered bacteria producing lycopene

Lycopene is an orange-red carotenoid, which confers a visual phenotype to assess genetic transformation of microorganisms. In this study, the lycopene synthesis pathway was constructed in engineered Escherichia coli BL21 (DE3) by transforming plasmid pET-15b-crtBEI, wherein crtB, crtE, and crtI could be expressed under the control of T7 promoter and LacO operator gene, and lycopene could be accumulated in the engineered bacteria upon induction of lactose. A good linear relationship was observed between the lycopene content in engineered bacterial culture and lactose concentration in the range of 4-52 g/L; using this relation, the lactose concentration in milk could be determined. This method could be used to overcome several limitations of the high-performance liquid chromatography (HPLC) method for lactose detection, such as cumbersome sample preparation and expensive detection equipment. Moreover, this method required only a clean bench, shaker, and spectrophotometer for lactose analysis. Additionally, no significant difference was observed between this method and HPLC in terms of lactose measurement in milk, indicating that this method is reasonable and simple.

Development of a Transcription Factor-Based Diamine Biosensor in Corynebacterium glutamicum

Diamines serve as major platform chemicals that can be employed to a variety of industrial scenarios, particularly as monomers for polymer synthesis. High-throughput sensors for diamine biosynthesis can greatly improve the biological production of diamines. Here, we identified and characterized a transcription factor-driven biosensor for putrescine and cadaverine in Corynebacterium glutamicum. The transcriptional TetR-family regulatory protein CgmR (CGL2612) is used for the specific detection of diamine compounds. This study also improved the dynamic range and the sensitivity to putrescine by systematically optimizing genetic components of pSenPut. By a single cell-based screening strategy for a library of CgmR with random mutations, this study obtained the most sensitive variant CgmR_I152T, which possessed an experimentally determined limit of detection (LoD) of ≤0.2 mM, a K of 11.4 mM, and a utility of 720. Using this highly sensitive putrescine biosensor pSenPut_I152T, we demonstrated that CgmRI_152T can be used as a sensor to detect putrescine produced biologically in a C. glutamicum system. This high sensitivity and the range of CgmR will be an influential tool for rewiring metabolic circuits and facilitating the directed evolution of recombinant strains toward the biological synthesis of diamine compounds.

Engineered microbial consortia: strategies and applications

Many applications of microbial synthetic biology, such as metabolic engineering and biocomputing, are increasing in design complexity. Implementing complex tasks in single populations can be a challenge because large genetic circuits can be burdensome and difficult to optimize. To overcome these limitations, microbial consortia can be engineered to distribute complex tasks among multiple populations. Recent studies have made substantial progress in programming microbial consortia for both basic understanding and potential applications. Microbial consortia have been designed through diverse strategies, including programming mutualistic interactions, using programmed population control to prevent overgrowth of individual populations, and spatial segregation to reduce competition. Here, we highlight the role of microbial consortia in the advances of metabolic engineering, biofilm production for engineered living materials, biocomputing, and biosensing. Additionally, we discuss the challenges for future research in microbial consortia.

Dynamic regulation of gut Clostridium-derived short-chain fatty acids

Short-chain fatty acids (SCFAs) are major products of intestinal microbial fermentation with beneficial effects for human health. The dynamic balance and real-time monitoring of endogenous SCFA biosynthesis are important for understanding their physiological functions. We discuss the promising future of applying CRISPRi genetic systems and biosensors for targeted SCFA improvement.

Modular tuning engineering and versatile applications of genetically encoded biosensors

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

Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis

Ligand-dependent biosensors are valuable tools for coupling the intracellular concentrations of small molecules to easily detectable readouts such as absorbance, fluorescence, or cell growth. While ligand-dependent biosensors are widely used for monitoring the production of small molecules in engineered cells and for controlling or optimizing biosynthetic pathways, their application to directed evolution for biocatalysts remains underexplored. As a consequence, emerging continuous evolution technologies are rarely applied to biocatalyst evolution. Here, we develop a panel of ligand-dependent biosensors that can detect a range of small molecules. We demonstrate that these biosensors can link enzymatic activity to the production of an essential phage protein to enable biocatalyst-dependent phage-assisted continuous evolution (PACE) and phage-assisted continuous selection (PACS). By combining these phage-based evolution and library selection technologies, we demonstrate that we can evolve enzyme variants with improved and expanded catalytic properties. Finally, we show that the genetic diversity resulting from a highly mutated PACS library is enriched for active enzyme variants with altered substrate scope. These results lay the foundation for using phage-based continuous evolution and selection technologies to engineer biocatalysts with novel substrate scope and reactivity.

Recent trends in biocatalysis

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

Biosynthesis pathways and strategies for improving 3-hydroxypropionic acid production in bacteria

3-Hydroxypropionic acid (3-HP) represents an economically important platform compound from which a panel of bulk chemicals can be derived. Compared with petroleum-dependent chemical synthesis, bioproduction of 3-HP has attracted more attention due to utilization of renewable biomass. This review outlines bacterial production of 3-HP, covering aspects of host strains (e.g., Escherichia coli and Klebsiella pneumoniae), metabolic pathways, key enzymes, and hurdles hindering high-level production. Inspired by the state-of-the-art advances in metabolic engineering and synthetic biology, we come up with protocols to overcome the hurdles constraining 3-HP production. The protocols range from rewiring of metabolic networks, alleviation of metabolite toxicity, to dynamic control of cell size and density. Especially, this review highlights the substantial contribution of microbial growth to 3-HP production, as we recognize the synchronization between cell growth and 3-HP formation. Accordingly, we summarize the following growth-promoting strategies: (i) optimization of fermentation conditions; (ii) construction of gene circuits to alleviate feedback inhibition; (iii) recruitment of RNA polymerases to overexpress key enzymes which in turn boost cell growth and 3-HP production. Lastly, we propose metabolic engineering approaches to simplify downstream separation and purification. Overall, this review aims to portray a picture of bacterial production of 3-HP.

Development of a growth coupled and multi-layered dynamic regulation network balancing malonyl-CoA node to enhance (2S)-naringenin biosynthesis in Escherichia coli

Metabolic heterogeneity and dynamic changes in metabolic fluxes are two inherent characteristics of microbial fermentation that limit the precise control of metabolisms, often leading to impaired cell growth and low productivity. Dynamic metabolic engineering addresses these challenges through the design of multi-layered and multi-genetic dynamic regulation network (DRN) that allow a single cell to autonomously adjust metabolic flux in response to its growth and metabolite accumulation conditions. Here, we developed a growth coupled NCOMB (Naringenin-Coumaric acid-Malonyl-CoA-Balanced) DRN with systematic optimization of (2S)-naringenin and p-coumaric acid-responsive regulation pathways for real-time control of intracellular supply of malonyl-CoA. In this scenario, the acyl carrier protein was used as a novel critical node for fine-tuning malonyl-CoA consumption instead of direct repression of fatty acid synthase commonly employed in previous studies. To do so, we first engineered a multi-layered DRN enabling single cells to concurrently regulate acpH, acpS, acpT, acs, and ACC in malonyl-CoA catabolic and anabolic pathways. Next, the NCOMB DRN was optimized to enhance the synergies between different dynamic regulation layers via a biosensor-based directed evolution strategy. Finally, a high producer obtained from NCOMB DRN approach yielded a 8.7-fold improvement in (2S)-naringenin production (523.7 ± 51.8 mg/L) with a concomitant 20% increase in cell growth compared to the base strain using static strain engineering approach, thus demonstrating the high efficiency of this system for improving pathway production.

Transcription-Factor-based Biosensor Engineering for Applications in Synthetic Biology

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

Synthetic protein quality control to enhance full-length translation in bacteria

Coupled transcription and translation processes in bacteria cause indiscriminate translation of intact and truncated messenger RNAs, inevitably generating nonfunctional polypeptides. Here, we devised a synthetic protein quality control (ProQC) system that enables translation only when both ends of mRNAs are present and followed by circularization based on sequence-specific RNA-RNA hybridization. We demonstrate that the ProQC system dramatically improved the fraction of full-length proteins among all synthesized polypeptides by selectively translating intact mRNA and reducing abortive translation. As a result, full-length protein synthesis increased up to 2.5-fold without changing the transcription or translation efficiency. Furthermore, we applied the ProQC system for 3-hydroxypropionic acid, violacein and lycopene production by ensuring full-length expression of enzymes in biosynthetic pathways, resulting in 1.6- to 2.3-fold greater biochemical production. We believe that our ProQC system can be universally applied to improve not only the quality of recombinant protein production but also efficiencies of metabolic pathways.

Analyzing the genetic characteristics of a tryptophan-overproducing Escherichia coli

L-tryptophan (L-trp) production in Escherichia coli has been developed by employing random mutagenesis and selection for a long time, but this approach produces an unclear genetic background. Here, we generated the L-trp overproducer TPD5 by combining an intracellular L-trp biosensor and fluorescence-activated cell sorting (FACS) in E. coli, and succeeded in elucidating the genetic basis for L-trp overproduction. The most significant identified positive mutations affected TnaA (deletion), AroG (S211F), TrpE (A63V), and RpoS (nonsense mutation Q33*). The underlying structure-function relationships of the feedback-resistant AroG (S211F) and TrpE (A63V) mutants were uncovered based on protein structure modeling and molecular dynamics simulations, respectively. According to transcriptomic analysis, the global regulator RpoS not only has a great influence on cell growth and morphology, but also on carbon utilization and the direction of carbon flow. Finally, by balancing the concentrations of the L-trp precursors' serine and glutamine based on the above analysis, we further increased the titer of L-trp to 3.18 g/L with a yield of 0.18 g/g. The analysis of the genetic characteristics of an L-trp overproducing E. coli provides valuable information on L-trp synthesis and elucidates the phenotype and complex cellular properties in a high-yielding strain, which opens the possibility to transfer beneficial mutations and reconstruct an overproducer with a clean genetic background.

Switching metabolic flux by engineering tryptophan operon-assisted CRISPR interference system in Klebsiella pneumoniae

One grand challenge for bioproduction of desired metabolites is how to coordinate cell growth and product synthesis. Here we report that a tryptophan operon-assisted CRISPR interference (CRISPRi) system can switch glycerol oxidation and reduction pathways in Klebsiella pneumoniae, whereby the oxidation pathway provides energy to sustain growth, and the reduction pathway generates 1,3-propanediol and 3-hydroxypropionic acid (3-HP), two economically important chemicals. Reverse transcription and quantitative PCR (RT-qPCR) showed that this CRISPRi-dependent switch affected the expression of glycerol metabolism-related genes and in turn improved 3-HP production. In shake-flask cultivation, the strain coexpressing dCas9-sgRNA and PuuC (an aldehyde dehydrogenase native to K. pneumoniae for 3-HP biosynthesis) produced 3.6 g/L 3-HP, which was 1.62 times that of the strain only overexpressing PuuC. In a 5 L bioreactor, this CRISPRi strain produced 58.9 g/L 3-HP. When circulation feeding was implemented to alleviate metabolic stress, biomass was substantially improved and 88.8 g/L 3-HP was produced. These results indicated that this CRISPRi-dependent switch can efficiently reconcile biomass formation and 3-HP biosynthesis. Furthermore, this is the first report of coupling CRISPRi system with trp operon, and this architecture holds huge potential in regulating gene expression and allocating metabolic flux.