Engineering tunable biosensors for monitoring putrescine inEscherichia coli

Development of a Type I-E CRISPR-Based Programmable Repression System for Fine-Tuning Metabolic Flux toward D-Pantothenic Acid in Bacillus subtilis

The CRISPR-based regulation tools enable fine-tuning of gene transcription, showing potential in areas of biomanufacturing and live therapeutics. However, the cell toxicity and PAM specificity of existing CRISPR-based regulation systems limit their broad application. The development of new and less-toxic CRISPR-controlled expression systems remains highly desirable for expanding the application scope of CRISPR-based tools. Here, we reconstituted the type I CRISPR-Cas system from Escherichia coli to finely tune gene expression in Bacillus subtilis. Through engineering the 5' untranslated region (UTR) of mRNAs of cas genes, we remarkably improved the efficacy of the type I CRISPRi system. The improved type I CRISPRi system was applied in engineering the D-pantothenic acid (DPA)-producing B. subtilis, which was generated by strengthening the metabolic flux toward β-alanine and (R)-pantoate via enhancing expression of key enzymes at both transcriptional and translational levels. Through controlling the expression of pdhA with the CRISPRi system for fine-tuning the metabolic flux toward DPA and the TCA cycle, we elevated the DPA titer to 0.88 g/L in shake flasks and 12.81 g/L in fed-batch fermentations without the addition of the precursor β-alanine. The type I CRISPRi system and the strategy for fine-tuning metabolic flux reported here not only enrich the CRISPR toolbox in B. subtilis and facilitate DPA production through microbial fermentation but also provide a paradigm for programming important organisms to produce value-added chemicals with cheap raw materials.

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

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

An Intelligent Synthetic Bacterium for Chronological Toxicant Detection, Biodegradation, and Its Subsequent Suicide

Modules, toolboxes, and synthetic biology systems may be designed to address environmental bioremediation. However, weak and decentralized functional modules require complex control. To address this issue, an integrated system for toxicant detection and biodegradation, and subsequent suicide in chronological order without exogenous inducers is constructed. Salicylic acid, a typical pollutant in industrial wastewater, is selected as an example to demonstrate this design. Biosensors are optimized by regulating the expression of receptors and reporters to get 2-fold sensitivity and 6-fold maximum output. Several stationary phase promoters are compared, and promoter Pfic is chosen to express the degradation enzyme. Two concepts for suicide circuits are developed, with the toxin/antitoxin circuit showing potent lethality. The three modules are coupled in a stepwise manner. Detection and biodegradation, and suicide are sequentially completed with partial attenuation compared to pre-integration, except for biodegradation, being improved by the replacements of ribosome binding site. Finally, a long-term stability test reveals that the engineered strain maintained its function for ten generations. The study provides a novel concept for integrating and controlling functional modules that can accelerate the transition of synthetic biology from conceptual to practical applications.

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.

Application of Metabolite-Responsive Biosensors for Plant Natural Products Biosynthesis

Plant natural products (PNPs) have shown various pharmaceutical activities, possessing great potential in global markets. Microbial cell factories (MCFs) provide an economical and sustainable alternative for the synthesis of valuable PNPs compared with traditional approaches. However, the heterologous synthetic pathways always lack native regulatory systems, bringing extra burden to PNPs production. To overcome the challenges, biosensors have been exploited and engineered as powerful tools for establishing artificial regulatory networks to control enzyme expression in response to environments. Here, we reviewed the recent progress involved in the application of biosensors that are responsive to PNPs and their precursors. Specifically, the key roles these biosensors played in PNP synthesis pathways, including isoprenoids, flavonoids, stilbenoids and alkaloids, were discussed in detail.

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.

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.

A Synthetic Biosensor for Detecting Putrescine in Beef Samples

Biogenic amines (BAs) are toxicological risks present in many food products. Putrescine is the most common foodborne BA and is frequently used as a quality control marker. Currently, there is a lack of regulation concerning safe putrescine limits in food as well as outdated food handling practices leading to unnecessary putrescine intake. Conventional methods used to evaluate BAs in food are generally time-consuming and resource-heavy with few options for on-site analysis. In response to this challenge, we have developed a transcription factor-based biosensor for the quantification of putrescine in beef samples. In this work, we use a naturally occurring putrescine responsive repressor-operator pair (PuuR-puuO) native to Escherichia coli. Moreover, we demonstrate the use of the cell-free putrescine biosensor on a paper-based device that enables rapid low-cost detection of putrescine in beef samples stored at different temperatures. The results presented demonstrate the potential role of using paper-based biosensors for on-site testing, particularly as an index for determining meat product stability and quality.

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.

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 acetoacetate-inducible whole-cell biosensors based on the AtoSC two-component system

Whole-cell biosensors hold potential in a variety of industrial, medical, and environmental applications. These biosensors can be constructed through the repurposing of bacterial sensing mechanisms, including the common two-component system (TCS). Here we report on the construction of a range of novel biosensors that are sensitive to acetoacetate, a molecule that plays a number of roles in human health and biology. These biosensors are based on the AtoSC TCS. An ordinary differential equation model to describe the action of the AtoSC TCS was developed and sensitivity analysis of this model used to help inform biosensor design. The final collection of biosensors constructed displayed a range of switching behaviours at physiologically relevant acetoacetate concentrations and can operate in several Escherichia coli host strains. It is envisaged that these biosensor strains will offer an alternative to currently available commercial strip tests and, in future, may be adopted for more complex in vivo or industrial monitoring applications.

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.

SynCells: A 60 × 60 μm2 Electronic Platform with Remote Actuation for Sensing Applications in Constrained Environments

Autonomous electronic microsystems smaller than the diameter of a human hair (<100 μm) are promising for sensing in confined spaces such as microfluidic channels or the human body. However, they are difficult to implement due to fabrication challenges and limited power budget. Here we present a 60 × 60 μm electronic microsystem platform, or SynCell, that overcomes these issues by leveraging the integration capabilities of two-dimensional material circuits and the low power consumption of passive germanium timers, memory-like chemical sensors, and magnetic pads. In a proof-of-concept experiment, we magnetically positioned SynCells in a microfluidic channel to detect putrescine. After we extracted them from the channel, we successfully read out the timer and sensor signal, the latter of which can be amplified by an onboard transistor circuit. The concepts developed here will be applicable to microsystems targeting a variety of applications from microfluidic sensing to biomedical research.

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.

Engineering the Reductive TCA Pathway to Dynamically Regulate the Biosynthesis of Adipic Acid in Escherichia coli

Adipic acid is a versatile aliphatic dicarboxylic acid. It is applied mainly in the polymerization of nylon-6,6, which accounts for 50.8% of the global consumption market of adipic acid. The microbial production of adipic acid avoids the usage of petroleum resources and the emission of harmful nitrogen oxides that are generated by traditional chemical synthetic approaches. However, in the fermentation process, the low theoretical yield and the usage of expensive inducers hinders the large-scale industrial production of adipic acid. To overcome these challenges, we established an oxygen-dependent dynamic regulation (ODDR) system to control the expression of key genes (sucD, pyc, mdh, and frdABCD) that could be induced to enhance the metabolic flux of the reductive TCA pathway under anaerobic conditions. Coupling of the constitutively expressed adipic acid synthetic pathway not only avoids the use of inducers but also increases the theoretical yield by nearly 50%. After the gene combination and operon structure were optimized, the reaction catalyzed by frdABCD was found to be the rate-limiting step. Further optimizing the relative expression levels of sucD, pyc, and frdABCD improved the titer of adipic acid 41.62-fold compared to the control strain Mad1415, demonstrating the superior performance of our ODDR system.

CRISPRi-Mediated NIMPLY Logic Gate for Fine-Tuning the Whole-Cell Sensing toward Simple Urine Glucose Detection

Whole-cell biosensors have been regarded as a prominent alternative to chemical and physical biosensors due to their renewability, environmental friendliness, and biocompatibility. However, there is still a lack of noninvasive measurements of urine glucose, which plays a vital role in monitoring the risk of diabetes in the healthcare system, via whole-cell biosensors. In this study, we characterized a glucose-inducible promoter and further enhanced the sensing performance using three genetic effectors, which encompassed ribozyme regulator (RiboJ), clustered regularly interspaced short palindromic repeat interference (CRISPRi), and plasmid-based T7RNA polymerase (PDT7), to develop the noninvasive glucose biosensor by fluorescent signal. As a result, RiboJ increased dynamic range to 2989 au, but declined signal-to-noise (S/N) to 1.59, while CRISPRi-mediated NIMPLY gate intensified both dynamic range to 5720 au and S/N to 4.58. The use of single PDT7 orthogonal with T7 promoter in cells (i.e., P strain) achieved a 44 180 au of dynamic range with S/N at 3.08. By coupling the PDT7 and NIMPLY-mediated CRISPRi, we constructed an optimum PIGAS strain with the highest S/N value of 4.95. Finally, we adopted the synthetic bacteria into a microdevice to afford an integrative and portable system for daily urine glucose inspection, which would be an alternative approach for medical diagnosis in the future.

Diamine Biosynthesis: Research Progress and Application Prospects

Diamines are important monomers for polyamide plastics; they include 1,3-diaminopropane, 1,4-diaminobutane, 1,5-diaminopentane, and 1,6-diaminohexane, among others. With increasing attention on environmental problems and green sustainable development, utilizing renewable raw materials for the synthesis of diamines is crucial for the establishment of a sustainable plastics industry. Recently, high-performance microbial factories, such as Escherichia coli and Corynebacterium glutamicum, have been widely used in the production of diamines. In particular, several synthetic pathways of 1,6-diaminohexane have been proposed based on glutamate or adipic acid. Here, we reviewed approaches for the biosynthesis of diamines, including metabolic engineering and biocatalysis, and the application of bio-based diamines in nylon materials. The related challenges and opportunities in the development of renewable bio-based diamines and nylon materials are also discussed.

Development of a Biosensor for Crotonobetaine-CoA Ligase Screening Based on the Elucidation of Escherichia coli Carnitine Metabolism

l-Carnitine is essential in the intermediary metabolism of eukaryotes and is involved in the β-oxidation of medium- and long-chain fatty acids; thus, it has applications for medicinal purposes and as a dietary supplement. In addition, l-carnitine plays roles in bacterial physiology and metabolism, which have been exploited by the industry to develop biotechnological carnitine production processes. Here, on the basis of studies of l-carnitine metabolism in Escherichia coli and its activation by the transcriptional activator CaiF, a biosensor was developed. It expresses a fluorescent reporter gene that responds in a dose-dependent manner to crotonobetainyl-CoA, which is an intermediate of l-carnitine metabolism in E. coli and is proposed to be a coactivator of CaiF. Moreover, a dual-input biosensor for l-carnitine and crotonobetaine was developed. As an application of the biosensor, potential homologues of the betaine:CoA ligase CaiC from Citrobacter freundii, Proteus mirabilis, and Arcobacter marinus were screened and shown to be functionally active CaiC variants. These variants and the developed biosensor may be valuable for improving l-carnitine production processes.

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.

Improvement of l-Valine Production by Atmospheric and Room Temperature Plasma Mutagenesis and High-Throughput Screening in Corynebacterium glutamicum

As one of the branched-chain amino acids, l-valine is an essential nutrient for most mammalian species. In this study, the l-valine producer Corynebacterium glutamicum ΔppcΔaceEΔalatΔpqo was first constructed. Additionally, an improved biosensor based on the Lrp-type transcriptional regulator and temperature-sensitive replication was built. Then, the C. glutamicum strain was mutagenized by atmospheric and room temperature plasma. A sequential three-step procedure was carried out to screen l-valine-producing strains, including the fluorescence-activated cell sorting (FACS), 96-well plate screening, and flask fermentation. The final mutant HL2-7 obtained by screening produced 3.20 g/L of l-valine, which was 21.47% higher than the titer produced by the starting strain. This study demonstrates that the l-valine-producing mutants can be successfully isolated based on the Lrp sensor system in combination with FACS screening after random mutagenesis.

Biosensor-based enzyme engineering approach applied to psicose biosynthesis

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.

Genetic Selection for Small Molecule Production in Competitive Microfluidic Droplets

Biosensors can be used to screen or select for small molecule production in engineered microbes. However, mutations to the biosensor that interfere with accurate signal transduction are common, producing an excess of false positives. Strategies have been developed to avoid this limitation by physically separating the production pathway and biosensor, but these approaches have only been applied to screens, not selections. We have developed a novel biosensor-mediated selection strategy using competition between cocultured bacteria. When applied to the biosynthesis of cis,cis-muconate, we show that this strategy yields a selective advantage to producer strains that outweighs the costs of production. By encapsulating the competitive cocultures into microfluidic droplets, we successfully enriched the muconate-producing strains from a large population of control nonproducers. Facile selections for small molecule production will increase testing throughput for engineered microbes and allow for the rapid optimization of novel metabolic pathways.

Building a minimal and generalizable model of transcription factor-based biosensors: Showcasing flavonoids

Progress in synthetic biology tools has transformed the way we engineer living cells. Applications of circuit design have reached a new level, offering solutions for metabolic engineering challenges that include developing screening approaches for libraries of pathway variants. The use of transcription-factor-based biosensors for screening has shown promising results, but the quantitative relationship between the sensors and the sensed molecules still needs more rational understanding. Herein, we have successfully developed a novel biosensor to detect pinocembrin based on a transcriptional regulator. The FdeR transcription factor (TF), known to respond to naringenin, was combined with a fluorescent reporter protein. By varying the copy number of its plasmid and the concentration of the biosensor TF through a combinatorial library, different responses have been recorded and modeled. The fitted model provides a tool to understand the impact of these parameters on the biosensor behavior in terms of dose-response and time curves and offers guidelines to build constructs oriented to increased sensitivity and or ability of linear detection at higher titers. Our model, the first to explicitly take into account the impact of plasmid copy number on biosensor sensitivity using Hill-based formalism, is able to explain uncharacterized systems without extensive knowledge of the properties of the TF. Moreover, it can be used to model the response of the biosensor to different compounds (here naringenin and pinocembrin) with minimal parameter refitting.

Blueprints for Biosensors: Design, Limitations, and Applications

Biosensors are enabling major advances in the field of analytics that are both facilitating and being facilitated by advances in synthetic biology. The ability of biosensors to rapidly and specifically detect a wide range of molecules makes them highly relevant to a range of industrial, medical, ecological, and scientific applications. Approaches to biosensor design are as diverse as their applications, with major biosensor classes including nucleic acids, proteins, and transcription factors. Each of these biosensor types has advantages and limitations based on the intended application, and the parameters that are required for optimal performance. Specifically, the choice of biosensor design must consider factors such as the ligand specificity, sensitivity, dynamic range, functional range, mode of output, time of activation, ease of use, and ease of engineering. This review discusses the rationale for designing the major classes of biosensor in the context of their limitations and assesses their suitability to different areas of biotechnological application.