A Transcription Factor-Based Biosensor for Detection of Itaconic Acid

Enhancement of Sensitivity in Aggregation-Based Whole-Cell Arsenite Sensor Utilizing Arsenic Metabolism Regulation

Arsenite [As(III)] is a toxic substance widely present on Earth, and the development of low-cost and simple microbial-based As(III) sensors has been attracting attention. Recently, we discovered that the protein LuxR, which contains multiple cysteine residues with high affinity for As(III), forms an insoluble structure upon binding to As(III) and exhibits OFF-switching properties as a quorum sensing transcriptional activator. Based on this property, the LuxR sensor operates on a new principle distinct from conventional whole-cell As(III) sensors; however, its sensitivity remains a challenge. In this study, we aimed to improve the sensitivity of the whole-cell OFF-type As(III) sensor by increasing the frequency of intracellular interactions between the sensor protein and As(III). We utilized the super-repressor properties of ArsR, a transcriptional repressor of the As(III)-metabolizing ars operon, achieved by replacing C34 in its As(III)-binding domain with Y. By linking ArsRC34Y with the OFF-type As(III) sensor protein LuxR, we constructed a single plasmid to create a portable ArsRC34Y-LuxR sensor protein. By suppressing the expression of ArsB, an As(III) efflux transporter encoded in the ars operon, using ArsRC34Y, we successfully enhanced the sensitivity of the OFF-type As(III) response.

Dissecting S-itaconation at host-pathogen interactions with chemical proteomics tools

The molecular essence of the battle between host and pathogens lies in the protein-protein or protein-metabolite interactions. Itaconate is one of the most upregulated immunometabolites, regulating immune responses through either noncovalent binding or covalent modification in the host. We herein briefly review recent progresses in the discoveries of physiological and pathological roles of itaconate and applications of chemical proteomic technologies in exploring itaconate modifications on cysteines (S-itaconation) at the interface of host-pathogen interactions. Key challenges are also proposed as future outlook.

A cell-free biosensor signal amplification circuit with polymerase strand recycling

Cell-free systems are powerful synthetic biology technologies that can recapitulate gene expression and sensing without the complications of living cells. Cell-free systems can perform more advanced functions when genetic circuits are incorporated. Here we expand cell-free biosensing by engineering a highly specific isothermal amplification circuit called polymerase strand recycling (PSR), which leverages T7 RNA polymerase off-target transcription to recycle nucleic acid inputs within DNA strand displacement circuits. We first construct simple PSR circuits to detect different RNA targets with high specificity. We then interface PSR circuits to amplify signals from allosteric transcription factor-based biosensors for small molecule detection. A double equilibrium model of transcription factor-DNA/ligand binding predicts that PSR can improve biosensor sensitivity, which we confirm experimentally by improving the limits of detection by 10-fold to submicromolar levels for two biosensors. We believe this work expands the capabilities of cell-free circuits and demonstrates PSR's potential for diverse applications in biotechnology.

Challenges and opportunities in commercializing whole-cell bioreporters in environmental application

Since the initial introduction of whole-cell bioreporters (WCBs) nearly 30 years ago, their high sensitivity, selectivity, and suitability for on-site detection have rendered them highly promising for environmental monitoring, medical diagnosis, food safety, biomanufacturing, and other fields. Especially in the environmental field, the technology provides a fast and efficient way to assess the bioavailability of pollutants in the environment. Despite these advantages, the technology has not been commercialized. This lack of commercialization is confusing, given the broad application prospects of WCBs. Over the years, numerous research papers have focused primarily on enhancing the sensitivity and selectivity of WCBs, with little attention paid to their wider commercial applications. So far, there is no a critical review has been published yet on this topic. Therefore, in this article we critically reviewed the research progress of WCBs over the past three decades, assessing the performance and limitations of current systems to understand the barriers to commercial deployment. By identifying these obstacles, this article provided researchers and industry stakeholders with deeper insights into the challenges hindering market entry and inspire further research toward overcoming these barriers, thereby facilitating the commercialization of WCBs as a promising technology for environmental monitoring.

Genetically encoded biosensors for the circular plastics bioeconomy

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

Biosensor-Assisted Engineering for Diverse Microbial Cellular Physiologies

Recent advancements in biosensor technology have revolutionized the field of microbial engineering, enabling efficient and precise optimization of strains for the production of valuable chemicals. This review comprehensively explores the innovative integration of biosensors to enhance microbial cell factories, with a particular emphasis on the crucial role of high-throughput biosensor-assisted screening. Biosensor-assisted approaches have enabled the identification of novel transporters, the elucidation of underlying transport mechanisms, and the fine-tuning of metabolic pathways for enhanced production. Furthermore, this review illustrates the utilization of biosensors for manipulating cellular behaviors, including interactions with environmental factors, and the reduction of nongenetic cell-to-cell variations. This review highlights the indispensable role of biosensors in advancing the field of microbial engineering through the modulation and exploitation of diverse cellular physiological processes.

Structural and functional characterization of itaconyl-CoA hydratase and citramalyl-CoA lyase involved in itaconate metabolism of Pseudomonas aeruginosa

Itaconate is a key anti-inflammatory/antibacterial metabolite in pathogen-macrophage interactions that induces adaptive changes in Pseudomonas aeruginosa-exposed airways. However, the impact and mechanisms underlying itaconate metabolism remain unclear. Our study reveals that itaconate significantly upregulates the expression of pyoverdine in P. aeruginosa and enhances its tolerance to tobramycin. Notably, the enzymes responsible for efficient itaconate metabolism, PaIch and PaCcl, play crucial roles in both utilizing itaconate and clearing its toxic metabolic intermediates. By using protein crystallography and molecular dynamics simulations analyses, we have elucidated the unique catalytic center and substrate-binding pocket of PaIch, which contribute to its highly efficient catalysis. Meanwhile, analysis of PaCcl has revealed how interactions between domains regulate the conformational changes of the active sites and binding pockets, influencing the catalytic process. Overall, our research uncovers the significance and mechanisms of PaIch and PaCcl in the efficient metabolism of itaconate by P. aeruginosa.

Itaconate in host inflammation and defense

Immune cells undergo rapid and extensive metabolic changes during inflammation. In addition to contributing to energetic and biosynthetic demands, metabolites can also function as signaling molecules. Itaconate (ITA) rapidly accumulates to high levels in myeloid cells under infectious and sterile inflammatory conditions. This metabolite binds to and regulates the function of diverse proteins intracellularly to influence metabolism, oxidative response, epigenetic modification, and gene expression and to signal extracellularly through binding the G protein-coupled receptor (GPCR). Administration of ITA protects against inflammatory diseases and blockade of ITA production enhances antitumor immunity in preclinical models. In this article, we review ITA metabolism and its regulation, discuss its target proteins and mechanisms, and conjecture a rationale for developing ITA-based therapeutics to treat inflammatory diseases and cancer.

Directed Evolution of Protein-Based Sensors for Anaerobic Biological Activation of Methane

Microbial alkane degradation pathways provide biological routes for converting these hydrocarbons into higher-value products. We recently reported the functional expression of a methyl-alkylsuccinate synthase (Mas) system in Escherichia coli, allowing for the heterologous anaerobic activation of short-chain alkanes. However, the enzymatic activation of methane via natural or engineered alkylsuccinate synthases has yet to be reported. To address this, we employed high-throughput screening to engineer the itaconate (IA)-responsive regulatory protein ItcR (WT-ItcR) from Yersinia pseudotuberculosis to instead respond to methylsuccinate (MS, the product of methane addition to fumarate), resulting in genetically encoded biosensors for MS. Here, we describe ItcR variants that, when regulating fluorescent protein expression in E. coli, show increased sensitivity, improved overall response, and enhanced specificity toward exogenously added MS relative to the wild-type repressor. Structural modeling and analysis of the ItcR ligand binding pocket provide insights into the altered molecular recognition. In addition to serving as biosensors for screening alkylsuccinate synthases capable of methane activation, MS-responsive ItcR variants also establish a framework for the directed evolution of other molecular reporters, targeting longer-chain alkylsuccinate products or other succinate derivatives.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

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.

Expanding the synthetic biology toolbox of Cupriavidus necator for establishing fatty acid production

The Gram-negative betaproteobacterium Cupriavidus necator is a chemolithotroph that can convert carbon dioxide into biomass. Cupriavidus necator has been engineered to produce a variety of high-value chemicals in the past. However, there is still a lack of a well-characterized toolbox for gene expression and genome engineering. Development and optimization of biosynthetic pathways in metabolically engineered microorganisms necessitates control of gene expression via functional genetic elements such as promoters, ribosome binding sites (RBSs), and codon optimization. In this work, a set of inducible and constitutive promoters were validated and characterized in C. necator, and a library of RBSs was designed and tested to show a 50-fold range of expression for green fluorescent protein (gfp). The effect of codon optimization on gene expression in C. necator was studied by expressing gfp and mCherry genes with varied codon-adaptation indices and was validated by expressing codon-optimized variants of a C12-specific fatty acid thioesterase to produce dodecanoic acid. We discuss further hurdles that will need to be overcome for C. necator to be widely used for biosynthetic processes.

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.

AraC-Based Biosensor for the Detection of Isoprene in E. coli

Isoprene is a valuable platform chemical, which is produced by engineered microorganisms, albeit in low quantities. The amount of isoprene produced is usually measured by gas chromatography, which can be time-consuming and expensive. Alternatively, biosensors have evolved as a powerful tool for real-time high-throughput screening and monitoring of product synthesis. The AraC-pBAD-inducible system has been widely studied, evolved, and engineered to develop biosensors for small molecules. In our preliminary studies, the AraC-pBAD system was mildly induced at higher isoprene concentrations when arabinose was also available. Hence, in the present study, we designed and constructed a synthetic biosensor based on the AraC-pBAD system, wherein the ligand-binding domain of AraC was replaced with IsoA. On introducing this chimeric AraC-IsoA (AcIa) transcription factor with the native PBAD promoter system regulating rfp gene expression, fluorescence output was observed only when wild-type Escherichia coli cells were induced with both isoprene and arabinose. The biosensor sensitivity and dynamic range were further enhanced by removing operator sequences and by substituting the native promoter (PAraC) with the strong tac promoter (Ptac). The chimeric sensor did not work in AraC knockout strains; however, functionality was restored by reintroducing AraC. Hence, AraC is essential for the functioning of our biosensor, while AcIa provides enhanced sensitivity and specificity for isoprene. However, insights into how AraC-AcIa interacts and the possible working mechanism remain to be explored. This study provides a prototype for developing chimeric AraC-based biosensors with proteins devoid of known dimerizing domains and opens a new avenue for further study and exploration.

Microbial itaconic acid bioproduction towards sustainable development: Insights, challenges, and prospects

Microbial biomanufacturing is a promising approach to produce high-value compounds with low-carbon footprint and significant economic benefits. Among twelve "Top Value-Added Chemicals from Biomass", itaconic acid (IA) stands out as a versatile platform chemical with numerous applications. IA is naturally produced by Aspergillus and Ustilago species through a cascade enzymatic reaction between aconitase (EC 4.2.1.3) and cis-aconitic acid decarboxylase (EC 4.1.1.6). Recently, non-native hosts such as Escherichia coli, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Yarrowia lipolytica have been genetically engineered to produce IA through the introduction of key enzymes. This review provides an up-to-date summary of the progress made in IA bioproduction, from native to engineered hosts, covers in vivo and in vitro approaches, and highlights the prospects of combination tactics. Current challenges and recent endeavors are also addressed to envision comprehensive strategies for renewable IA production in the future towards sustainable development goals (SDGs).

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.

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.

Recent Advances on the Production of Itaconic Acid via the Fermentation and Metabolic Engineering

Itaconic acid (ITA) is one of the top 12 platform chemicals. The global ITA market is expanding due to the rising demand for bio-based unsaturated polyester resin and its non-toxic qualities. Although bioconversion using microbes is the main approach in the current industrial production of ITA, ecological production of bio-based ITA faces several issues due to: low production efficiency, the difficulty to employ inexpensive raw materials, and high manufacturing costs. As metabolic engineering advances, the engineering of microorganisms offers a novel strategy for the promotion of ITA bio-production. In this review, the most recent developments in the production of ITA through fermentation and metabolic engineering are compiled from a variety of perspectives, including the identification of the ITA synthesis pathway, the metabolic engineering of natural ITA producers, the design and construction of the ITA synthesis pathway in model chassis, and the creation, as well as application, of new metabolic engineering strategies in ITA production. The challenges encountered in the bio-production of ITA in microbial cell factories are discussed, and some suggestions for future study are also proposed, which it is hoped offers insightful views to promote the cost-efficient and sustainable industrial production of ITA.

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

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

Metabolic Engineering: Methodologies and Applications

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

A genetically encoded fluorescent biosensor for detecting itaconate with subcellular resolution in living macrophages

Itaconate is a newly discovered endogenous metabolite promoting an anti-inflammatory program during innate immune response, but the precise mechanisms underlying its effect remains poorly understood owing primarily to the limitations of available itaconate-monitoring techniques. Here, we develop and validate a genetically encoded fluorescent itaconate biosensor, BioITA, for directly monitoring itaconate dynamics in subcellular compartments of living macrophages. Utilizing BioITA, we monitor the itaconate dynamics in response to lipopolysaccharide (LPS) stimulation in the context of modulating itaconate transportation and metabolism. Moreover, we show that STING activation induces itaconate production, and injection of AAVs expressing cytosolic BioITA into mice allows directly reporting elevation of itaconate level in activated macrophages derived from LPS-injected mice. Thus, BioITA enables subcellular resolution imaging of itaconate in living macrophages.

Kinetic compartmentalization by unnatural reaction for itaconate production

Physical compartmentalization of metabolism using membranous organelles in eukaryotes is helpful for chemical biosynthesis to ensure the availability of substrates from competitive metabolic reactions. Bacterial hosts lack such a membranous system, which is one of the major limitations for efficient metabolic engineering. Here, we employ kinetic compartmentalization with the introduction of an unnatural enzymatic reaction by an engineered enzyme as an alternative strategy to enable substrate availability from competitive reactions through kinetic isolation of metabolic pathways. As a proof of concept, we kinetically isolate the itaconate synthetic pathway from the tricarboxylic acid cycle in Escherichia coli, which is natively separated by mitochondrial membranes in Aspergillus terreus. Specifically, 2-methylcitrate dehydratase is engineered to alternatively catalyze citrate and kinetically secure cis-aconitate for efficient production using a high-throughput screening system. Itaconate production can be significantly improved with kinetic compartmentalization and its strategy has the potential to be widely applicable.

A CRISPRi mediated self-inducible system for dynamic regulation of TCA cycle and improvement of itaconic acid production in Escherichia coli

Itaconic acid (ITA), an effective alternative fossil fuel, derives from the bypass pathway of the tricarboxylic acid (TCA) cycle. Therefore, the imbalance of metabolic flux between TCA cycle and ITA biosynthetic pathway seriously limits the production of ITA. The optimization of flux distribution between biomass and production has the potential to the productivity of ITA. Based on the previously constructed strain Escherichia coli MG1655 Δ1-SAS-3 (ITA titer: 1.87 g/L), a CRISPRi-mediated self-inducible system (CiMS), which contained a responsive module based on the ITA biosensor YpItcR/P ccl and a regulative CRISPRi-mediated interferential module, was developed to regulate the flux of the TCA cycle and to enhance the capacity of the strain to produce ITA. First, a higher ITA-yielding strain, Δ4-P rmd -SAS-3 (ITA titer: 3.20 g/L), derived from Δ1-SAS-3, was constructed by replacing the promoter P J23100 , for the expression of ITA synthesis genes, with P rmd and knocking out the three bypass genes poxB, pflB, and ldhA. Subsequently, the CiMS was used to inhibit the expression of key genes icd, pykA, and sucCD to dynamically balance the metabolic flux between TCA cycle and ITA biosynthetic pathway during the ITA production stage. The constructed strain Δ4-P rmd -SAS-3 under the dynamic regulation of the CiMS, showed a 23% increase in the ITA titer, which reached 3.93 g/L. This study indicated that CiMS was a practical strategy to dynamically and precisely regulated the metabolic flux in microbial cell factories.

Synthetic spatial patterning in bacteria: advances based on novel diffusible signals

Engineering multicellular patterning may help in the understanding of some fundamental laws of pattern formation and thus may contribute to the field of developmental biology. Furthermore, advanced spatial control over gene expression may revolutionize fields such as medicine, through organoid or tissue engineering. To date, foundational advances in spatial synthetic biology have often been made in prokaryotes, using artificial gene circuits. In this review, engineered patterns are classified into four levels of increasing complexity, ranging from spatial systems with no diffusible signals to systems with complex multi-diffusor interactions. This classification highlights how the field was held back by a lack of diffusible components. Consequently, we provide a summary of both previously characterized and some new potential candidate small-molecule signals that can regulate gene expression in Escherichia coli. These diffusive signals will help synthetic biologists to successfully engineer increasingly intricate, robust and tuneable spatial structures.

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

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

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.

Biosensor-informed engineering of Cupriavidus necator H16 for autotrophic D-mannitol production

Cupriavidus necator H16 is one of the most researched carbon dioxide (CO₂)-fixing bacteria. It can store carbon in form of the polymer polyhydroxybutyrate and generate energy by aerobic hydrogen oxidation under lithoautotrophic conditions, making C. necator an ideal chassis for the biological production of value-added compounds from waste gases. Despite its immense potential, however, the experimental evidence of C. necator utilisation for autotrophic biosynthesis of chemicals is limited. Here, we genetically engineered C. necator for the high-level de novo biosynthesis of the industrially relevant sugar alcohol mannitol directly from Calvin-Benson-Bassham (CBB) cycle intermediates. To identify optimal mannitol production conditions in C. necator, a mannitol-responsive biosensor was applied for screening of mono- and bifunctional mannitol 1-phosphate dehydrogenases (MtlDs) and mannitol 1-phosphate phosphatases (M1Ps). We found that MtlD/M1P from brown alga Ectocarpus siliculosus performed overall the best under heterotrophic growth conditions and was selected to be chromosomally integrated. Consequently, autotrophic fermentation of recombinant C. necator yielded up to 3.9 g/L mannitol, representing a substantial improvement over mannitol biosynthesis using recombinant cyanobacteria. Importantly, we demonstrate that at the onset of stationary growth phase nearly 100% of carbon can be directed from the CBB cycle into mannitol through the glyceraldehyde 3-phosphate and fructose 6-phosphate intermediates. This study highlights for the first time the potential of C. necator to generate sugar alcohols from CO₂ utilising precursors derived from the CBB cycle.

Recognition elements based on the molecular biological techniques for detecting pesticides in food: A review

Excessive use of pesticides can cause contamination of the environment and agricultural products that are directly threatening human life and health. Therefore, in the process of food safety supervision, it is crucial to conduct sensitive and rapid detection of pesticide residues. The recognition element is the vital component of sensors and methods for fast testing pesticide residues in food. Improper recognition elements may lead to defects of testing methods, such as poor stability, low sensitivity, high economic costs, and waste of time. We can use the molecular biological technique to address these challenges as a good strategy for recognition element production and modification. Herein, we review the molecular biological methods of five specific recognition elements, including aptamers, genetic engineering antibodies, DNAzymes, genetically engineered enzymes, and whole-cell-based biosensors. In addition, the application of these identification elements combined with biosensor and immunoassay methods in actual detection was also discussed. The purpose of this review was to provide a valuable reference for further development of rapid detection methods for pesticide residues.

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.

Synthetic biology toolkit for engineering Cupriviadus necator H16 as a platform for CO2 valorization

BACKGROUND

CO₂ valorization is one of the effective methods to solve current environmental and energy problems, in which microbial electrosynthesis (MES) system has proved feasible and efficient. Cupriviadus necator (Ralstonia eutropha) H16, a model chemolithoautotroph, is a microbe of choice for CO₂ conversion, especially with the ability to be employed in MES due to the presence of genes encoding [NiFe]-hydrogenases and all the Calvin-Benson-Basham cycle enzymes. The CO₂ valorization strategy will make sense because the required hydrogen can be produced from renewable electricity independently of fossil fuels.

MAIN BODY

In this review, synthetic biology toolkit for C. necator H16, including genetic engineering vectors, heterologous gene expression elements, platform strain and genome engineering, and transformation strategies, is firstly summarized. Then, the review discusses how to apply these tools to make C. necator H16 an efficient cell factory for converting CO₂ to value-added products, with the examples of alcohols, fatty acids, and terpenoids. The review is concluded with the limitation of current genetic tools and perspectives on the development of more efficient and convenient methods as well as the extensive applications of C. necator H16.

CONCLUSIONS

Great progress has been made on genetic engineering toolkit and synthetic biology applications of C. necator H16. Nevertheless, more efforts are expected in the near future to engineer C. necator H16 as efficient cell factories for the conversion of CO₂ to value-added products.

Pseudomonas aeruginosa transcriptome adaptations from colonization to biofilm infection of skin wounds

In burn patients Pseudomonas aeruginosa infection is a major cause of morbidity. Analysis of the pathogen's gene expression as it transitions from colonization to acute and then biofilm wound infection may provide strategies for infection control. Toward this goal, we seeded log-phase P. aeruginosa (PAO1) into 3-day-old, full-thickness excision wounds (rabbit ear) and harvested the bacteria during colonization (Hrs 2 and 6), acute infection (Hr 24), and biofilm infection (Days 5 and 9) for transcriptome analysis (RNA-Seq). After 2-6 h in the wound, genes for metabolism and cell replication were down-regulated while wound-adaptation genes were up-regulated (vs. expression in log-phase culture). As the infection progressed from acute to biofilm infection, more genes became up-regulated than down-regulated, but the down-regulated genes enriched in more pathways, likely because the genes and pathways that bacteria already colonizing wounds up-regulate to establish biofilm infection are less known. Across the stages of infection, carbon-utilization pathways shifted. During acute infection, itaconate produced by myeloid cells appears to have been a carbon source because myeloid cell infiltration and the expression of the host gene, ACOD1, for itaconate production peaked coincidently with the expression of the PAO1 genes for itaconate transport and catabolism. Additionally, branched-chain amino acids are suggested to be a carbon source in acute infection and in biofilm infection. In biofilm infection, fatty acid degradation was also up-regulated. These carbon sources feed into the glyoxylate cycle that was coincidently up-regulated, suggesting it provided the precursors for P. aeruginosa to synthesize macromolecules in establishing wound infection.

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.

Bioproduction process of natural products and biopharmaceuticals: Biotechnological aspects

Decades of research have been put in place for developing sustainable routes of bioproduction of high commercial value natural products (NPs) on the global market. In the last few years alone, we have witnessed significant advances in the biotechnological production of NPs. The development of new methodologies has resulted in a better understanding of the metabolic flux within the organisms, which have driven manipulations to improve production of the target product. This was further realised due to the recent advances in the omics technologies such as genomics, transcriptomics, proteomics, metabolomics and secretomics, as well as systems and synthetic biology. Additionally, the combined application of novel engineering strategies has made possible avenues for enhancing the yield of these products in an efficient and economical way. Invention of high-throughput technologies such as next generation sequencing (NGS) and toolkits for genome editing Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated 9 (CRISPR/Cas9) have been the game changers and provided unprecedented opportunities to generate rationally designed synthetic circuits which can produce complex molecules. This review covers recent advances in the engineering of various hosts for the production of bioactive NPs and biopharmaceuticals. It also highlights general approaches and strategies to improve their biosynthesis with higher yields in a perspective of plants and microbes (bacteria, yeast and filamentous fungi). Although there are numerous reviews covering this topic on a selected species at a time, our approach herein is to give a comprehensive understanding about state-of-art technologies in different platforms of organisms.

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

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

l-arginine production in Corynebacterium glutamicum: manipulation and optimization of the metabolic process

As an important semi-essential amino acid, l-arginine is extensively used in the food and pharmaceutical fields. At present, l-arginine production depends on cost-effective, green, and sustainable microbial fermentation by using a renewable carbon source. To enhance its fermentative production, various metabolic engineering strategies have been employed, which provide valid paths for reducing the cost of l-arginine production. This review summarizes recent advances in molecular biology strategies for the optimization of l-arginine-producing strains, including manipulating the principal metabolic pathway, modulating the carbon metabolic pathway, improving the intracellular biosynthesis of cofactors and energy usage, manipulating the assimilation of ammonia, improving the transportation and membrane permeability, and performing biosensor-assisted high throughput screening, providing useful insight into the current state of l-arginine production.

SPRINT: a Cas13a-based platform for detection of small molecules

Recent efforts in biological engineering have made detection of nucleic acids in samples more rapid, inexpensive and sensitive using CRISPR-based approaches. We expand one of these Cas13a-based methods to detect small molecules in a one-batch assay. Using SHERLOCK-based profiling of in vitrotranscription (SPRINT), in vitro transcribed RNA sequence-specifically triggers the RNase activity of Cas13a. This event activates its non-specific RNase activity, which enables cleavage of an RNA oligonucleotide labeled with a quencher/fluorophore pair and thereby de-quenches the fluorophore. This fluorogenic output can be measured to assess transcriptional output. The use of riboswitches or proteins to regulate transcription via specific effector molecules is leveraged as a coupled assay that transforms effector concentration into fluorescence intensity. In this way, we quantified eight different compounds, including cofactors, nucleotides, metabolites of amino acids, tetracycline and monatomic ions in samples. In this manner, hundreds of reactions can be easily quantified in a few hours. This increased throughput also enables detailed characterization of transcriptional regulators, synthetic compounds that inhibit transcription, or other coupled enzymatic reactions. These SPRINT reactions are easily adaptable to portable formats and could therefore be used for the detection of analytes in the field or at point-of-care situations.

Long-Term Rewritable Report and Recording of Environmental Stimuli in Engineered Bacterial Populations

DNA writing (living sensing recorders) based whole-cell biosensors can capture transient signals and then convert them into readable genomic DNA changes. The primitive signals can be easily obtained by sequencing technology or analysis of protein activity (such as fluorescent protein). However, the functions of the current living sensing recorders still need to be expanded, and the difficulty of rewriting in complex biological environments has further limited their applications. In this study, we designed a long-term rewritable recording system using a CRISPR base editor-based synthetic genetic circuit, named CRISPR-istop. This system can convert stimuli into changes in the fluorescence intensity (reporter) and single-base mutations in genomic DNA (recording). Furthermore, we updated the biological circuit through the strategy of coupling the single-base mutation (record site) and the loss-of-function of the targeted protein (translation stopped by stop codon introduction), and we can remove edited bacteria from a population through selective sweeps upon applying a selective pressure. It successfully conducted the rewritable reporter and recording of the nutrient arabinose and pollutant arsenite with two rounds of continuous operation (10 passages/round, 12 h/passage). These observations indicated that the CRISPR-istop system can report and record stimuli over time; moreover, the recording can be manually erased and rewritten as needed. This method has great potential to be extended to more complicated recording systems to execute sophisticated tasks in inaccessible environments for synthetic biology and biomedical applications, such as monitoring disease-relevant physiological markers or other molecules.

Engineered bacteria to report gut function: technologies and implementation

Advances in synthetic biology and microbiology have enabled the creation of engineered bacteria which can sense and report on intracellular and extracellular signals. When deployed in vivo these whole-cell bacterial biosensors can act as sentinels to monitor biomolecules of interest in human health and disease settings. This is particularly interesting in the context of the gut microbiota, which interacts extensively with the human host throughout time and transit of the gut and can be accessed from feces without requiring invasive collection. Leveraging rational engineering approaches for genetic circuits as well as an expanding catalog of disease-associated biomarkers, bacterial biosensors can act as non-invasive and easy-to-monitor reporters of the gut. Here, we summarize recent engineering approaches applied in vivo in animal models and then highlight promising technologies for designing the next generation of bacterial biosensors.

Itaconate is an effector of a Rab GTPase cell-autonomous host defense pathway against Salmonella

The guanosine triphosphatase (GTPase) Rab32 coordinates a cell-intrinsic host defense mechanism that restricts the replication of intravacuolar pathogens such as Salmonella Here, we show that this mechanism requires aconitate decarboxylase 1 (IRG1), which synthesizes itaconate, a metabolite with antimicrobial activity. We find that Rab32 interacts with IRG1 on Salmonella infection and facilitates the delivery of itaconate to the Salmonella-containing vacuole. Mice defective in IRG1 rescued the virulence defect of a S. enterica serovar Typhimurium mutant specifically defective in its ability to counter the Rab32 defense mechanism. These studies provide a link between a metabolite produced in the mitochondria after stimulation of innate immune receptors and a cell-autonomous defense mechanism that restricts the replication of an intracellular bacterial pathogen.

Effects on soil microbial community after exposure to neonicotinoid insecticides thiamethoxam and dinotefuran

The wide application of neonicotinoid insecticides in soil may affect soil microbial community, yet the information is limited. This study first reports the effects of thiamethoxam and dinotefuranon on soil microbial community. Soil from a forest land was collected and spiked with different nominal levels (0.02 mg kg^-1, 0.2 mg kg^-1 and 2.0 mg kg^-1) of thiamethoxam and dinotefuran, respectively, and cultivated for 112 days. During the study, concentrations of the two neonicotinoids and their potential degradation products were monitored by LC-MS/MS. At day 112, the soils were analyzed for genetic profile by high-throughput sequencing and carbon metabolic profile by Biolog-ECO plate. The results showed that thiamethoxam and dinotefuran were both attenuated during the study with rate constants being 0.008-0.017 d^-1 and 0.024-0.032 d^-1, respectively, and biodegradation played an important role. As compared to the blank control, the exposure to the studied two neonicotinoids changed the microbial community, and the changes were influenced by both the type of neonicotinoid and the level of exposure. As compared to the blank control, the relative abundances of phyla Gemmatimonadetes and OD1 decreased under most exposed conditions, while the relative abundances of Chloroflexi and Nitrospirae increased under most exposed conditions. The community transition changed the functional potential, particularly carbon metabolism (mostly decreased) and nitrogen metabolism (mostly increased). As compared to the blank control, the utilization of total 31 carbon sources (including six categories) was increased under low exposure to thiamethoxam, but was decreased under all other exposed conditions. Low exposure to dinotefuran stimulated the utilization of three categories of carbon sources (amines, carbohydrates and phenolic compounds). Low exposure to both neonicotinoids increased the community diversity, while middle and high exposure to both neonicotinoids decreased the community diversity. These findings provide new insights into the effects of neonicotinoids on microbial community in soil.

Biosensors design in yeast and applications in metabolic engineering

Engineering microbial cell factories is a potential approach of sustainable production of chemicals, fuels and pharmaceuticals. However, testing the production of molecules in high throughput is still a time-consuming and laborious process since product synthesis usually does not confer a clear phenotype. Therefore, it is necessary to develop new techniques for fast high-producer screening. Genetically encoded biosensors are considered to be promising devices for high-throughput analysis owing to their ability to sense metabolites and couple detection to an actuator, thereby facilitating the rapid detection of small molecules at single-cell level. Here, we review recent advances in the design and engineering of biosensors in Saccharomyces cerevisiae, and their applications in metabolic engineering. Three types of biosensor are introduced in this review: transcription factor based, RNA-based and enzyme-coupled biosensors. The studies to improve the features of biosensors are also described. Moreover, we summarized their metabolic engineering applications in dynamic regulation and high producer selection. Current challenges in biosensor design and future perspectives on sensor applications are also discussed.

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.

Development of a Novel Biosensor-Driven Mutation and Selection System via in situ Growth of Corynebacterium crenatum for the Production of L-Arginine

The high yield mutants require a high-throughput screening method to obtain them quickly. Here, we developed an L-arginine biosensor (ARG-Select) to obtain increased L-arginine producers among a large number of mutant strains. This biosensor was constructed by ArgR protein and argC promoter, and could provide the strain with the output of bacterial growth via the reporter gene sacB; strains with high L-arginine production could survive in 10% sucrose screening. To extend the screening limitation of 10% sucrose, the sensitivity of ArgR protein to L-arginine was decreased. Corynebacterium crenatum SYPA5-5 and its systems pathway engineered strain Cc6 were chosen as the original strains. This biosensor was employed, and L-arginine hyperproducing mutants were screened. Finally, the HArg1 and DArg36 mutants of C. crenatum SYPA5-5 and Cc6 could produce 56.7 and 95.5 g L^-1 of L-arginine, respectively, which represent increases of 35.0 and 13.5%. These results demonstrate that the transcription factor-based biosensor could be applied in high yield strains selection as an effective high-throughput screening method.

A genome-wide approach for identification and characterisation of metabolite-inducible systems

Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules.

Inducible gene expression tools have important applications as genetically encoded biosensors. Here the authors conduct a genome-wide approach to identify and utilise functional sensors.

Transcription Factor Engineering for High-Throughput Strain Evolution and Organic Acid Bioproduction: A Review

Metabolic regulation of gene expression for the microbial production of fine chemicals, such as organic acids, is an important research topic in post-genomic metabolic engineering. In particular, the ability of transcription factors (TFs) to respond precisely in time and space to various small molecules, signals and stimuli from the internal and external environment is essential for metabolic pathway engineering and strain development. As a key component, TFs are used to construct many biosensors in vivo using synthetic biology methods, which can be used to monitor the concentration of intracellular metabolites in organic acid production that would otherwise remain “invisible” within the intracellular environment. TF-based biosensors also provide a high-throughput screening method for rapid strain evolution. Furthermore, TFs are important global regulators that control the expression levels of key enzymes in organic acid biosynthesis pathways, therefore determining the outcome of metabolic networks. Here we review recent advances in TF identification, engineering, and applications for metabolic engineering, with an emphasis on metabolite monitoring and high-throughput strain evolution for the organic acid bioproduction.

Advanced Strategies for Production of Natural Products in Yeast

Natural products account for more than 50% of all small-molecule pharmaceutical agents currently in clinical use. However, low availability often becomes problematic when a bioactive natural product is promising to become a pharmaceutical or leading compound. Advances in synthetic biology and metabolic engineering provide a feasible solution for sustainable supply of these compounds. In this review, we have summarized current progress in engineering yeast cell factories for production of natural products, including terpenoids, alkaloids, and phenylpropanoids. We then discuss advanced strategies in metabolic engineering at three different dimensions, including point, line, and plane (corresponding to the individual enzymes and cofactors, metabolic pathways, and the global cellular network). In particular, we comprehensively discuss how to engineer cofactor biosynthesis for enhancing the biosynthesis efficiency, other than the enzyme activity. Finally, current challenges and perspective are also discussed for future engineering direction.

Development of a Highly Sensitive Whole-Cell Biosensor for Arsenite Detection through Engineered Promoter Modifications

Whole-cell biosensors have attracted considerable interests because they are robust, eco-friendly, and cost-effective. However, most of the biosensors harness the naturally occurring wild-type promoter, which often suffers from high background noise and low sensitivity. In this study, we demonstrate how to design the core elements (i.e., RNA polymerase binding site and transcription factor binding site) of the promoters to obtain a significant gain in the signal-to-noise output ratio of the whole-cell biosensor circuits. As a proof of concept, we modified the arsenite-regulated promoter from Escherichia coli K-12 genome, such that it has a lower background and higher expression. This was achieved by balancing the relationship between the number of ArsR binding sites (ABS) and the activity of the promoter and adjusting the location of the auxiliary ABS. A promoter variant ParsD-ABS-8 was obtained with an induction ratio of 179 (11-fold increase over the wild-type promoter) when induced with 1 μM arsenite. Importantly, the developed biosensor exhibited good dose-response in the range of 0.1 to 4 μM (R² = 0.9928) of arsenite with a detection limit of ca. 10 nM. These results indicated that the engineered promoter modification approach could be used to improve the performance of whole-cell biosensors, thereby facilitating their practical application.