Transcription factor-based biosensors in biotechnology: current state and future prospects

Development of a highly sensitive PbrR-based biosensor via directed evolution and its application for lead detection

The Whole-cell biosensor (WCB) is a convenient and practical assay that can monitor bioavailable lead (Pb) contamination. However, existing Pb-responsive WCB struggle to meet practical detection needs due to the lack of sensitivity, specificity, and stability. In this study, we developed a Pb WCB using the Pb resistance transcriptional regulatory factor (PbrR) and green fluorescent protein (GFP), and improved its performance by directed evolution in conjunction with fluorescence-activated cell sorting (FACS). After 3 rounds of screening, we acquired a biosensor mutant (PbrR-E3). The evolved biosensor exhibited an approximately 11-fold increase in maximum fluorescence output signal compared to the non-evolved biosensor, resulting in an improvement of its sensitivity and specificity. This biosensor demonstrated a limit of detection (LOD) of 0.045 μg/L. Furthermore, the evolved biosensor showcased outstanding performance in the detection of Pb(II) in tea infusion and also demonstrated good stability in tests with spiked real water samples. These results highlight the potential of the evolved WCB as a viable approach for monitoring Pb.

Cell-free biosensor with automated acoustic liquid handling for rapid and scalable characterization of cellobiohydrolases on microcrystalline cellulose

Engineering enzymes to degrade solid substrates, such as crystalline cellulose from paper sludge or microplastics in sewage sludge, presents challenges for high-throughput screening (HTS), as solid substrates are not readily accessible in cell-based biosensor systems. To address this challenge, we developed a cell-free cellobiose-detectable biosensor (CB-biosensor) for rapid characterization of cellobiohydrolase (CBH) activity, enabling direct detection of hydrolysis products without cellular constraints. The CB-biosensor demonstrates higher sensitivity than conventional assays and distinguishes between CBH subtypes (CBHI and CBHII) based on their modes of action. Integration with the Echo 525 liquid handler enables precise and reproducible sample processing, with fluorescence signals from automated preparations comparable to manual experiments. Furthermore, assay volumes can be reduced to just a few microlitres-impractical with manual methods. This cell-free CB-biosensor with Echo 525 minimizes reagent consumption, accelerates testing, and facilitates reliable large-scale screening. These findings highlight its potential to overcome current HTS limitations, advancing enzyme screening and accelerating the Design-Build-Test-Learn cycle for sustainable biomanufacturing.

Microbial Transcription Factor-Based Biosensors: Innovations from Design to Applications in Synthetic Biology

Transcription factor-based biosensors (TFBs) are powerful tools in microbial biosensor applications, enabling dynamic control of metabolic pathways, real-time monitoring of intracellular metabolites, and high-throughput screening (HTS) for strain engineering. These systems use transcription factors (TFs) to convert metabolite concentrations into quantifiable outputs, enabling precise regulation of metabolic fluxes and biosynthetic efficiency in microbial cell factories. Recent advancements in TFB, including improved sensitivity, specificity, and dynamic range, have broadened their applications in synthetic biology and industrial biotechnology. Computational tools such as Cello have further revolutionized TFB design, enabling in silico optimization and construction of complex genetic circuits for integrating multiple signals and achieving precise gene regulation. This review explores innovations in TFB systems for microbial biosensors, their role in metabolic engineering and adaptive evolution, and their future integration with artificial intelligence and advanced screening technologies to overcome critical challenges in synthetic biology and industrial bioproduction.

Combining biosensor and metabolic network optimization strategies for enhanced L-threonine production in Escherichia coli

L-threonine is an integral nutrient for mammals, often used in animal feeds to enhance growth and reduce breeding costs. Developing L-threonine engineered strains that meet industrial production specifications has significant economic value. Here, we developed a biosensor that monitors L-threonine concentration to assist in high-throughput screening to capture high-yielding L-threonine mutants. Among them, the PcysK promoter and CysB protein were used to construct a primary L-threonine biosensor, and then the CysBT102A mutant was obtained through directed evolution resulting in a 5.6-fold increase in the fluorescence responsiveness of biosensor over the 0-4 g/L L-threonine concentration range. In addition, the metabolic network of mutant was further optimized through multi-omics analysis and in silico simulation. Ultimately, the THRM13 strain produced 163.2 g/L L-threonine, with a yield of 0.603 g/g glucose in a 5 L bioreactor. The biosensor constructed here could be employed for iterative upgrading of subsequent strains, and these engineering strategies described provide guidance for other chemical overproducers.

Biosensor characterization: formal methods from the perspective of proteome fractions

Many studies characterize transcription factors and other regulatory elements to control gene expression in recombinant systems. However, most lack a formal approach to analyse the inherent and context-specific variations of these regulatory components. This study addresses this gap by establishing a formal framework from which convenient methods are inferred to characterize regulatory circuits. We modelled the bacterial cell as a collection of proteome fractions. Deriving the time-dependent proteome fraction, we obtained a general theorem that describes its change as a function of its expression fraction, a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, the experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. For an Isopropyl β- d-1-thiogalactopyranoside (IPTG) biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced strikingly similar dose-response parameters when independently fit to a Hill function. Furthermore, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be applied effectively to various systems. Therefore, the concepts presented here provide convenient methods for obtaining dose-response parameters, clearly defining the time interval of their validity and offering a framework for interpreting typical biosensor outputs in terms of bacterial physiology. Graphical Abstract Nutrients, transformed by the action of the Nutrient Fixators (purple arrow), are used at a rate of ρ for Protein biosynthesis. The total rate ρ is multiplied by expression fractions fR, fC, fH, and fQ to obtain the biosynthesis rate (black arrows) of each proteome fraction ΦR, ΦC, ΦH, ΦQ, respectively. In a graph of Growth rate versus Proteome Fraction Production Rate, a linear function (green lines) can be observed, and its slope is equal to the expression fraction at each condition.

Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors

Since the description of the lac operon in 1961 by Jacob and Monod, transcriptional regulation in prokaryotes has been studied extensively and has led to the development of transcription factor-based biosensors. Due to the broad variety of detectable small molecules and their various applications across biotechnology, biosensor research and development have increased exponentially over the past decades. Throughout this period, key milestones in fundamental knowledge, synthetic biology, analytical tools, and computational learning have led to an immense expansion of the biosensor repertoire and its application portfolio. Over the years, biosensor engineering became a more multidisciplinary discipline, combining high-throughput analytical tools, DNA randomization strategies, forward engineering, and advanced protein engineering workflows. Despite these advances, many obstacles remain to fully unlock the potential of biosensor technology. This review analyzes the timeline of key milestones on fundamental research (1960s to 2000s) and engineering strategies (2000s onward), on both the DNA and protein level of biosensors. Moreover, insights into the future perspectives, remaining hurdles, and unexplored opportunities of this promising field are discussed.

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.

Harnessing DNA computing and nanopore decoding for practical applications: from informatics to microRNA-targeting diagnostics

DNA computing represents a subfield of molecular computing with the potential to become a significant area of next-generation computation due to the high programmability inherent in the sequence-dependent molecular behaviour of DNA. Recent studies in DNA computing have extended from mathematical informatics to biomedical applications, with a particular focus on diagnostics that exploit the biocompatibility of DNA molecules. The output of DNA computing devices is encoded in nucleic acid molecules, which must then be decoded into human-recognizable signals for practical applications. Nanopore technology, which utilizes an electrical and label-free decoding approach, provides a unique platform to bridge DNA and electronic computing for practical use. In this tutorial review, we summarise the fundamental knowledge, technologies, and methodologies of DNA computing (logic gates, circuits, neural networks, and non-DNA input circuity). We then focus on nanopore-based decoding, and highlight recent advances in medical diagnostics targeting microRNAs as biomarkers. Finally, we conclude with the potential and challenges for the practical implementation of these techniques. We hope that this tutorial will provide a comprehensive insight and enable the general reader to grasp the fundamental principles and diverse applications of DNA computing and nanopore decoding, and will inspire a wide range of scientists to explore and push the boundaries of these technologies.

Engineering conditional protein-protein interactions for dynamic cellular control

Conditional protein-protein interactions enable dynamic regulation of cellular activity and are an attractive approach to probe native protein interactions, improve metabolic engineering of microbial factories, and develop smart therapeutics. Conditional protein-protein interactions have been engineered to respond to various chemical, light, and nucleic acid-based stimuli. These interactions have been applied to assemble protein fragments, build protein scaffolds, and spatially organize proteins in many microbial and higher-order hosts. To foster the development of novel conditional protein-protein interactions that respond to new inputs or can be utilized in alternative settings, we provide an overview of the process of designing new engineered protein interactions while showcasing many recently developed computational tools that may accelerate protein engineering in this space.

Highly multiplexed design of an allosteric transcription factor to sense new ligands

Allosteric transcription factors (aTF) regulate gene expression through conformational changes induced by small molecule binding. Although widely used as biosensors, aTFs have proven challenging to design for detecting new molecules because mutation of ligand-binding residues often disrupts allostery. Here, we develop Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screen a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identifies biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design shows shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we develop cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid and scalable design of new biosensors, overcoming constraints of natural biosensors.

High Throughput Screening of Transcription Factor LysG for Constructing a Better Lysine Biosensor

The biosensors based on transcription factors (TFs) are widely used in high throughput screening of metabolic overproducers. The unsatisfactory performances (narrow detection and dynamic ranges) of biosensors limit their practical application and need more improvement. In this study, using the TF LysG (sensing lysine) as an example, a biosensor optimization method was constructed by growth-coupled screening of TF random mutant libraries. The better the performance of the biosensor, the faster the strain grows under screening pressure. A LysGE15D, A54D, and I164V-based biosensors were obtained, which were about 2-fold of the control in the detection and dynamic ranges. A lysine high-producer was screened effectively using the optimized biosensor with the production at 1.51 ± 0.30 g/L in flasks (2.22-fold of the original strain). This study provided a promising strategy for optimizing TF-based biosensors and was of high potential to be applied in the lysine high-producers screening process.

Ultrasensitive Electrochemical Biosensors Based on Allosteric Transcription Factors (aTFs) for Pb2+ Detection

Exposure to Pb2+ in the environment, especially in water, poses a significant threat to human health and urgently necessitates the development of highly sensitive Pb2+ detection methods. In this study, we have integrated the high sensitivity of electrochemical techniques with allosteric transcription factors (aTFs) to develop an innovative electrochemical biosensing platform. This biosensors leverage the specific binding and dissociation of DNA to the aTFs (PbrR) on electrode surfaces to detect Pb2+. Under the optimal conditions, the platform has a broad linear detection range from 1 pM to 10 nM and an exceptionally low detection threshold of 1 pM, coupled with excellent selectivity for Pb2+. Notably, the biosensor demonstrates regenerative capabilities, enabling up to five effective Pb2+ measurements. After one week of storage at 4 °C, effective lead ion detection was still possible, demonstrating the biosensor's excellent stability, this can effectively save the cost of detection. The biosensor also achieves a recovery rate of 93.3% to 106.6% in real water samples. The biosensor shows its potential as a robust tool for the ultrasensitive detection of Pb2+ in environmental monitoring. Moreover, this research provides new insights into the future applications of aTFs in electrochemical sensing.

Electrical monitoring of human-serum-albumin-templated molecularly imprinted polymer nanoparticles with high affinity based on molecular charges and their visualization

Human-serum-albumin (HSA)-templated molecularly imprinted polymer nanoparticles (nano-MIPs) were integrated with a solution-gated field-effect transistor-based biosensor. The real-time electrical analysis of nano-MIP-HSA binding showed a high affinity and specificity of nano-MIPs for HSA. Moreover, the binding behaviour was continuously visualised using a solution-gated complementary metal-oxide semiconductor array image biosensor.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Conducting polymer hydrogels for biomedical application: Current status and outstanding challenges

Conducting polymer hydrogels (CPHs) are composite polymeric materials with unique properties that combine the electrical capabilities of conducting polymers (CPs) with the excellent mechanical properties and biocompatibility of traditional hydrogels. This review aims to highlight how the unique properties CPHs have from combining their two constituent materials are utilized within the biomedical field. First, the synthesis approaches and applications of non-CPH conductive hydrogels are discussed briefly, contrasting CPH-based systems. The synthesis routes of hydrogels, CPs, and CPHs are then discussed. This review also provides a comprehensive overview of the recent advancements and applications of CPHs in the biomedical field, encompassing their applications as biosensors, drug delivery scaffolds (DDSs), and tissue engineering platforms. Regarding their applications within tissue engineering, a comprehensive discussion of the usage of CPHs for skeletal muscle prosthetics and regeneration, cardiac regeneration, epithelial regeneration and wound healing, bone and cartilage regeneration, and neural prosthetics and regeneration is provided. Finally, critical challenges and future perspectives are also addressed, emphasizing the need for continued research; however, this fascinating class of materials holds promise within the vastly evolving field of biomedicine.

Systems and Methods for Continuous Evolution of Enzymes

Directed evolution generates novel biomolecules with desired functions by iteratively diversifying the genetic sequence of wildtype biomolecules, relaying the genetic information to the molecule with function, and selecting the variants that progresses towards the properties of interest. While traditional directed evolution consumes significant labor and time for each step, continuous evolution seeks to automate all steps so directed evolution can proceed with minimum human intervention and dramatically shortened time. A major application of continuous evolution is the generation of novel enzymes, which catalyze reactions under conditions that are not favorable to their wildtype counterparts, or on altered substrates. The challenge to continuously evolve enzymes lies in automating sufficient, unbiased gene diversification, providing selection for a wide array of reaction types, and linking the genetic information to the phenotypic function. Over years of development, continuous evolution has accumulated versatile strategies to address these challenges, enabling its use as a general tool for enzyme engineering. As the capability of continuous evolution continues to expand, its impact will increase across various industries. In this review, we summarize the working mechanisms of recently developed continuous evolution strategies, discuss examples of their applications focusing on enzyme evolution, and point out their limitations and future directions.

Engineering of a Substrate Affinity Reduced S-Adenosyl-methionine Synthetase as a Novel Biosensor for Growth-Coupling Selection of L-Methionine Overproducers

Biosensors are powerful tools for monitoring specific metabolites or controlling metabolic flux towards the products in a single cell, which play important roles in microbial cell factory construction. Despite their potential role in metabolic flux monitoring, the development of biosensors for small molecules is still limited. Reported biosensors often exhibit bottlenecks of poor specificity and a narrow dynamic range. Moreover, fine-tuning the substrate binding affinity of a crucial enzyme can decrease its catalytic activity, which ultimately results in the repression of the corresponding essential metabolite biosynthesis and impairs cell growth. However, increasing intracellular substrate concentration can elevate the availability of the essential metabolite and may lead to restore cellular growth. Herein, a new strategy was proposed for constructing whole-cell biosensors based on enzyme encoded by essential gene that offer inherent specificity and universality. Specifically, S-adenosyl-methionine synthetase (MetK) in E. coli was chosen as the crucial enzyme, and a series of MetK variants were identified that were sensitive to L-methionine concentration. This occurrence enabled the engineered cell to sense L-methionine and exhibit L-methionine dose-dependent cell growth. To improve the biosensor's dynamic range, an S-adenosyl-methionine catabolic enzyme was overexpressed to reduce the intracellular availability of S-adenosyl-methionine. The resulting whole-cell biosensor effectively coupled the intracellular concentration of L-methionine with growth and was successfully applied to select strains with enhanced L-methionine biosynthesis from random mutagenesis libraries. Overall, our study presents a universal strategy for designing and constructing growth-coupled biosensors based on crucial enzyme, which can be applied to select strains overproducing high value-added metabolites in cellular metabolism.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Development of a whole-cell biosensor for ethylene oxide and ethylene

Ethylene and ethylene oxide are widely used in the chemical industry, and ethylene is also important for its role in fruit ripening. Better sensing systems would assist risk management of these chemicals. Here, we characterise the ethylene regulatory system in Mycobacterium strain NBB4 and use these genetic parts to create a biosensor. The regulatory genes etnR1 and etnR2 and cognate promoter Petn were combined with a fluorescent reporter gene (fuGFP) in a Mycobacterium shuttle vector to create plasmid pUS301-EtnR12P. Cultures of M. smegmatis mc2-155(pUS301-EtnR12P) gave a fluorescent signal in response to ethylene oxide with a detection limit of 0.2 μM (9 ppb). By combining the epoxide biosensor cells with another culture expressing the ethylene monooxygenase, the system was converted into an ethylene biosensor. The co-culture was capable of detecting ethylene emission from banana fruit. These are the first examples of whole-cell biosensors for epoxides or aliphatic alkenes. This work also resolves long-standing questions concerning the regulation of ethylene catabolism in bacteria.

Structure and function of bacterial transcription regulators of the SorC family

The SorC family is a large group of bacterial transcription regulators involved in controlling carbohydrate catabolism and quorum sensing. SorC proteins consist of a conserved C-terminal effector-binding domain and an N-terminal DNA-binding domain, whose type divides the family into two subfamilies: SorC/DeoR and SorC/CggR. Proteins of the SorC/CggR subfamily are known to regulate the key node of glycolysis-triose phosphate interconversion. On the other hand, SorC/DeoR proteins are involved in a variety of peripheral carbohydrate catabolic pathways and quorum sensing functions, including virulence. Despite the abundance and importance of this family, SorC proteins seem to be on the periphery of scientific interest, which might be caused by the fragmentary information about its representatives. This review aims to compile the existing knowledge and provide material to inspire future questions about the SorC protein family.

A sensitive, portable, and smartphone-based whole-cell biosensor device for salicylic acid monitoring

Considerable effort has been invested in developing salicylic acid (SA) biosensors for various application purposes. Here, by engineering the sensing modules and host cell chassis, we have gradually optimized the NahR-Psal/Pr-based SA biosensor, increasing the sensitivity and maximum output by 17.2-fold and 9.4-fold, respectively, and improving the detection limit by 800-fold, from 80 μM to 0.1 μM. A portable SA sensing device was constructed by embedding a gelatin-based hydrogel containing an optimized biosensor into the perforations of tape adhered to glass slide, which allowed good determination of SA in the range of 0.1 μM-10 μM. Then, we developed a customized smartphone App to measure the fluorescence intensity of each perforation and automatically calculate the corresponding SA concentration so that we could detect SA concentrations in real cosmetic samples. We anticipate that this smartphone-based imaging biosensor, with its compact size, higher sensitivity, cost-effectiveness, and easy data transfer, will be useful for long-term monitoring of SA.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

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.

A novel Escherichia coli cell-based bioreporter for quantification of salicylic acid in cosmetics

Transcription factor-based bioreporters have been extensively studied for monitoring and detecting environmental toxicants. In Escherichia coli, the multiple antibiotic resistance regulator (MarR) induces transcription upon binding to salicylic acid (SA). We generated SA-specific E. coli cell-based bioreporters utilizing the operator region of the mar operon and MarR as components of the reporter and sensing domains, respectively. Although bioreporters based on endogenous MarR and wild-type E. coli cells responded to SA, their sensitivity and selectivity were insufficient for practical sample monitoring. To improve these parameters, we genetically engineered host strains for optimal MarR expression, which enhanced the sensitivity of the biosensor to micromolar quantities of SA with increased selectivity. Under the optimized experimental conditions, the biosensor could quantify SA in environmental samples. For validation, the SA concentration in artificially contaminated SA-containing cosmetic samples was determined using the developed biosensor. Reliability assessment by comparing the concentrations determined using LC-MS/MS revealed > 90% accuracy of the bioreporters. Although bioreporters are not considered standard tools for environmental monitoring, bacterial cell-based bioreporters may serve as alternative tools owing to their affordability and simplicity. The SA biosensor developed in this study can potentially be a valuable tool for monitoring SA in environmental systems. KEY POINTS: • SA-responsive bioreporter is generated by employing mar operon system in E. coli • SA specificity and selectivity were enhanced by genetic/biochemical engineering • The novel bioreporter would be valuable for SA monitoring in environmental systems.

Recent Trends in Chemical Sensors for Detecting Toxic Materials

Industrial development has led to the widespread production of toxic materials, including carcinogenic, mutagenic, and toxic chemicals. Even with strict management and control measures, such materials still pose threats to human health. Therefore, convenient chemical sensors are required for toxic chemical monitoring, such as optical, electrochemical, nanomaterial-based, and biological-system-based sensors. Many existing and new chemical sensors have been developed, as well as new methods based on novel technologies for detecting toxic materials. The emergence of material sciences and advanced technologies for fabrication and signal-transducing processes has led to substantial improvements in the sensing elements for target recognition and signal-transducing elements for reporting interactions between targets and sensing elements. Many excellent reviews have effectively summarized the general principles and applications of different types of chemical sensors. Therefore, this review focuses on chemical sensor advancements in terms of the sensing and signal-transducing elements, as well as more recent achievements in chemical sensors for toxic material detection. We also discuss recent trends in biosensors for the detection of toxic materials.

Biosensors for the detection of chorismate and cis,cis-muconic acid in Corynebacterium glutamicum

Corynebacterium glutamicum ATCC 13032 is a promising microbial chassis for industrial production of valuable compounds, including aromatic amino acids derived from the shikimate pathway. In this work, we developed two whole-cell, transcription factor based fluorescent biosensors to track cis,cis-muconic acid (ccMA) and chorismate in C. glutamicum. Chorismate is a key intermediate in the shikimate pathway from which value-added chemicals can be produced, and a shunt from the shikimate pathway can divert carbon to ccMA, a high value chemical. We transferred a ccMA-inducible transcription factor, CatM, from Acinetobacter baylyi ADP1 into C. glutamicum and screened a promoter library to isolate variants with high sensitivity and dynamic range to ccMA by providing benzoate, which is converted to ccMA intracellularly. The biosensor also detected exogenously supplied ccMA, suggesting the presence of a putative ccMA transporter in C. glutamicum, though the external ccMA concentration threshold to elicit a response was 100-fold higher than the concentration of benzoate required to do so through intracellular ccMA production. We then developed a chorismate biosensor, in which a chorismate inducible promoter regulated by natively expressed QsuR was optimized to exhibit a dose-dependent response to exogenously supplemented quinate (a chorismate precursor). A chorismate-pyruvate lyase encoding gene, ubiC, was introduced into C. glutamicum to lower the intracellular chorismate pool, which resulted in loss of dose dependence to quinate. Further, a knockout strain that blocked the conversion of quinate to chorismate also resulted in absence of dose dependence to quinate, validating that the chorismate biosensor is specific to intracellular chorismate pool. The ccMA and chorismate biosensors were dually inserted into C. glutamicum to simultaneously detect intracellularly produced chorismate and ccMA. Biosensors, such as those developed in this study, can be applied in C. glutamicum for multiplex sensing to expedite pathway design and optimization through metabolic engineering in this promising chassis organism.

ONE-SENTENCE SUMMARY

High-throughput screening of promoter libraries in Corynebacterium glutamicum to establish transcription factor based biosensors for key metabolic intermediates in shikimate and β-ketoadipate pathways.

Advances in the dynamic control of metabolic pathways in Saccharomyces cerevisiae

The metabolic engineering of Saccharomyces cerevisiae has great potential for enhancing the production of high-value chemicals and recombinant proteins. Recent studies have demonstrated the effectiveness of dynamic regulation as a strategy for optimizing metabolic flux and improving production efficiency. In this review, we provide an overview of recent advancements in the dynamic regulation of S. cerevisiae metabolism. Here, we focused on the successful utilization of transcription factor (TF)-based biosensors within the dynamic regulatory network of S. cerevisiae. These biosensors are responsive to a wide range of endogenous and exogenous signals, including chemical inducers, light, temperature, cell density, intracellular metabolites, and stress. Additionally, we explored the potential of omics tools for the discovery of novel responsive promoters and their roles in fine-tuning metabolic networks. We also provide an outlook on the development trends in this field.

Dynamic Metabolic Control: From the Perspective of Regulation Logic

Establishing microbial cell factories has become a sustainable and increasingly promising approach for the synthesis of valuable chemicals. However, introducing heterologous pathways into these cell factories can disrupt the endogenous cellular metabolism, leading to suboptimal production performance. To address this challenge, dynamic pathway regulation has been developed and proven effective in improving microbial biosynthesis. In this review, we summarized typical dynamic regulation strategies based on their control logic. The applicable scenarios for each control logic were highlighted and perspectives for future research direction in this area were discussed.

Non-Native Site-Selective Enzyme Catalysis

The ability to site-selectively modify equivalent functional groups in a molecule has the potential to streamline syntheses and increase product yields by lowering step counts. Enzymes catalyze site-selective transformations throughout primary and secondary metabolism, but leveraging this capability for non-native substrates and reactions requires a detailed understanding of the potential and limitations of enzyme catalysis and how these bounds can be extended by protein engineering. In this review, we discuss representative examples of site-selective enzyme catalysis involving functional group manipulation and C-H bond functionalization. We include illustrative examples of native catalysis, but our focus is on cases involving non-native substrates and reactions often using engineered enzymes. We then discuss the use of these enzymes for chemoenzymatic transformations and target-oriented synthesis and conclude with a survey of tools and techniques that could expand the scope of non-native site-selective enzyme catalysis.

Divergent directed evolution of a TetR-type repressor towards aromatic molecules

Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.

Influence of Varying Pre-Culture Conditions on the Level of Population Heterogeneity in Batch Cultures with an Escherichia coli Triple Reporter Strain

When targeting robust, high-yielding bioprocesses, phenomena such as population heterogeneity have to be considered. Therefore, the influence of the conditions which the cells experience prior to the main culture should also be evaluated. Here, the influence of a pre-culture medium (complex vs. minimal medium), optical density for inoculation of the main culture (0.005, 0.02 and 0.0125) and harvest time points of the pre-culture in exponential growth phase (early, mid and late) on the level of population heterogeneity in batch cultures of the Escherichia coli triple reporter strain G7_BL21(DE3) in stirred-tank bioreactors was studied. This strain allows monitoring the growth (rrnB-EmGFP), general stress response (rpoS-mStrawberry) and oxygen limitation (nar-TagRFP657) of single cells through the expression of fluorescent proteins. Data from batch cultivations with varying pre-culture conditions were analysed with principal component analysis. According to fluorescence data, the pre-culture medium had the largest impact on population heterogeneities during the bioprocess. While a minimal medium as a pre-culture medium elevated the differences in cellular growth behaviour in the subsequent batch process, a complex medium increased the general stress response and led to a higher population heterogeneity. The latter was promoted by an early harvest of the cells with low inoculation density. Seemingly, nar-operon expression acted independently of the pre-culture conditions.

Precise and versatile microplate reader-based analyses of biosensor signals from arrayed microbial colonies

Genetically encoded fluorescent biosensors have emerged as a powerful tool to support phenotypic screenings of microbes. Optical analyses of fluorescent sensor signals from colonies grown on solid media can be challenging as imaging devices need to be equipped with appropriate filters matching the properties of fluorescent biosensors. Toward versatile fluorescence analyses of different types of biosensor signals derived from arrayed colonies, we investigate here the use of monochromator equipped microplate readers as an alternative to imaging approaches. Indeed, for analyses of the LacI-controlled expression of the reporter mCherry in Corynebacterium glutamicum, or promoter activity using GFP as reporter in Saccharomyces cerevisiae, an improved sensitivity and dynamic range was observed for a microplate reader-based analyses compared to their analyses via imaging. The microplate reader allowed us to capture signals of ratiometric fluorescent reporter proteins (FRPs) with a high sensitivity and thereby to further improve the analysis of internal pH via the pH-sensitive FRP mCherryEA in Escherichia coli colonies. Applicability of this novel technique was further demonstrated by assessing redox states in C. glutamicum colonies using the FRP Mrx1-roGFP2. By the use of a microplate reader, oxidative redox shifts were measured in a mutant strain lacking the non-enzymatic antioxidant mycothiol (MSH), indicating its major role for maintaining a reduced redox state also in colonies on agar plates. Taken together, analyses of biosensor signals from microbial colonies using a microplate reader allows comprehensive phenotypic screenings and thus facilitates further development of new strains for metabolic engineering and systems biology.

MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors

All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning.

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

Development of a 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.

Metabolically-targeted dCas9 expression in bacteria

The ability to restrict gene expression to a relevant bacterial species in a complex microbiome is an unsolved problem. In the context of the human microbiome, one desirable target metabolic activity are glucuronide-utilization enzymes (GUS) that are implicated in the toxic re-activation of glucuronidated compounds in the human gastrointestinal (GI) tract, including the chemotherapeutic drug irinotecan. Here, we take advantage of the variable distribution of GUS enzymes in bacteria as a means to distinguish between bacteria with GUS activity, and re-purpose the glucuronide-responsive GusR transcription factor as a biosensor to regulate dCas9 expression in response to glucuronide inducers. We fused the Escherichia coli gusA regulatory region to the dCas9 gene to create pGreg-dCas9, and showed that dCas9 expression is induced by glucuronides, but not other carbon sources. When conjugated from E. coli to Gammaproteobacteria derived from human stool, dCas9 expression from pGreg-dCas9 was restricted to GUS-positive bacteria. dCas9-sgRNAs targeted to gusA specifically down-regulated gus operon transcription in Gammaproteobacteria, with a resulting ∼100-fold decrease in GusA activity. Our data outline a general strategy to re-purpose bacterial transcription factors responsive to exogenous metabolites for precise ligand-dependent expression of genetic tools such as dCas9 in diverse bacterial species.

Synthetic biology tools for engineering Corynebacterium glutamicum

Corynebacterium glutamicum is a promising organism for the industrial production of amino acids, fuels, and various value-added chemicals. From the whole genome sequence release, C. glutamicum has been valuable in the field of industrial microbiology and biotechnology. Continuous discovery of genetic manipulations and regulation mechanisms has developed C. glutamicum as a synthetic biology platform chassis. This review summarized diverse genomic manipulation technologies and gene expression tools for static, dynamic, and multiplex control at transcription and translation levels. Moreover, we discussed the current challenges and applicable tools to C. glutamicum for future advancements.

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

Harnessing Interactional Sensory Genes for Rationally Reprogramming Chaotic Metabolism

Rationally controlling cellular metabolism is of great importance but challenging owing to its highly complex and chaotic nature. Natural existing sensory proteins like histidine kinases (HKs) are understood as "sensitive nodes" of biological networks that can trigger disruptive metabolic reprogramming (MRP) upon perceiving environmental fluctuation. Here, the "sensitive node" genes were adopted to devise a global MRP platform consisting of a CRISPR interference-mediated dual-gene combinational knockdown toolbox and survivorship-based metabolic interaction decoding algorithm. The platform allows users to decode the interfering effects of n × n gene pairs while only requiring the synthesis of n pairs of primers. A total of 35 HK genes and 24 glycine metabolic genes were selected as the targets to determine the effectiveness of our platform in a Vibrio sp. FA2. The platform was applied to decode the interfering impact of HKs on antibiotic resistance in strain FA2. A pattern of combined knockdown of HK genes (sasA_8 and 04288) was demonstrated to be capable of reducing antibiotic resistance of Vibrio by 108-fold. Patterns of combined knockdown of glycine pathway genes (e.g., gcvT and ltaE) and several HK genes (e.g., cpxA and btsS) were also revealed to increase glycine production. Our platform may enable an efficient and rational approach for global MRP based on the elucidation of high-order gene interactions. A web-based 1-stop service (https://smrp.sjtu.edu.cn) is also provided to simplify the implementation of this smart strategy in a broad range of cells.

Development of novel Escherichia coli cell-based biosensors to monitor Mn(II) in environmental systems

Escherichia coli uses manganese [Mn(II)] as an essential trace element; thus, it has a genetic system that regulates cellular Mn(II) levels. Several genes in the mnt-operon of E. coli respond to intercellular Mn(II) levels, and transcription is regulated by a transcription factor (MntR) that interacts with Mn(II). This study aimed to develop Mn(II)-sensing biosensors based on mnt-operon genetic systems. Additionally, the properties of biosensors developed based on the promoter regions of mntS, mntH, and mntP were investigated. MntR represses the transcription of MntS and MntH after binding with Mn(II), while it induces MntP transcription. Thus, Mn(II) biosensors that decrease and increase signals could be obtained by fusing the promoter regions of mntS/mntH and mntP, with egfp encoding an enhanced green fluorescent protein. However, only the biosensor-based mntS:egfp responded to Mn(II) exposure. Further, E. coli harboring P mntS :egfp showed a concentration-dependent decrease in fluorescence signals. To enhance the sensitivity of the biosensor toward Mn(II), E. coli containing a deleted MntP gene that encodes Mn(II) exporter, was used as a host cell for biosensor development. The sensitivity toward Mn(II) increased by two times on using E. coli-mntP, and the biosensor could quantify 0.01-10 μM of Mn(II). Further, the applicability of Mn(II) in artificially contaminated water samples was quantified and showed >95% accuracy. The newly developed Mn(II) biosensors could detect and quantify the residual Mn(II) from mancozeb in soil samples, with the quantification accuracy being approximately 90%. To the best of our knowledge, this is the first Mn (II)-specific bacterial cell-based biosensor that serves as a valuable tool for monitoring and assessing the risks of Mn(II) in environmental systems.

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.

Engineering Transcription Factor XylS for Sensing Phthalic Acid and Terephthalic Acid: An Application for Enzyme Evolution

Poly(ethylene terephthalate) (PET) and phthalate esters (PAEs) are used extensively as plastics and plasticizers. Enzymatic degradation of PET and PAEs has drawn great attention in recent years; however, evolution of PET- and PAE-degrading enzymes is still a big challenge, partly because of the lack of an effective screening method to detect phthalic acid (PA) and terephthalic acid (TPA), which are the main hydrolysis products of PAEs and PET. Here, by directed evolution of a promiscuous transcription factor, XylS from Pseudomonas putida, we created two novel variants, XylS-K38R-L224Q and XylS-W88C-L224Q, that are able to bind PA and TPA and activate the downstream expression of a fluorescent reporter protein. Based on these elements, whole-cell biosensors were constructed, which enabled the fluorimetric detection of as little as 10 μM PA or TPA. A PAE hydrolase, GoEst15, was preliminarily engineered using this new biosensor, yielding a mutant GoEst15-V3 whose activity toward dibutyl phthalate (DBP) and p-nitrophenyl butyrate was enhanced 2.0- and 2.5-fold, respectively. It was shown that 96.5% DBP (5 mM) was degraded by GoEst15-V3 in 60 min, while the wild-type enzyme degraded only 55% DBP. This study provides an effective screening tool for directed evolution of PAE-/PET-degrading enzymes.

Harnessing plasmid replication mechanism to enable dynamic control of gene copy in bacteria

Dynamic regulation has been proved efficient in controlling gene expression at transcriptional, translational, and post-translational level. However, the dynamic regulation at gene replication level has been rarely explored so far. In this study, we established dynamic regulation at gene copy level through engineering controllable plasmid replication to dynamically control the gene expression. Prototypic genetic circuits with different control logic were applied to enable diversified dynamic behaviors of gene copy. To explore the applicability of this strategy, the dynamic gene copy control was employed in regulating the biosynthesis of p-coumaric acid, which resulted in an up to 78% increase in p-coumaric acid titer to 1.69 g/L in shake flasks. These results indicated the great potential of applying dynamic gene copy control for engineering biosynthesis of valuable compounds in metabolic engineering.

A Recombinase-Based Genetic Circuit for Heavy Metal Monitoring

Rapid progress in the genetic circuit design enabled whole-cell biosensors (WCBs) to become prominent in detecting an extensive range of analytes with promise in many fields, from medical diagnostics to environmental toxicity assessment. However, several drawbacks, such as high background signal or low precision, limit WCBs to transfer from proof-of-concept studies to real-world applications, particularly for heavy metal toxicity monitoring. For an alternative WCB module design, we utilized Bxb1 recombinase that provides tight control as a switch to increase dose-response behavior concerning leakiness. The modularity of Bxb1 recombinase recognition elements allowed us to combine an engineered semi-specific heat shock response (HSR) promoter, sensitive to stress conditions including toxic ions such as cadmium, with cadmium resistance regulatory elements; a cadmium-responsive transcription factor and its cognitive promoter. We optimized the conditions for the recombinase-based cadmium biosensor to obtain increased fold change and shorter response time. This system can be expanded for various heavy metals to make an all-in-one type of WCB, even using semi-specific parts of a sensing system.

Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production

Branched-chain amino acid (BCAA) metabolism fulfills numerous physiological roles and can be harnessed to produce valuable chemicals. However, the lack of eukaryotic biosensors specific for BCAA-derived products has limited the ability to develop high-throughput screens for strain engineering and metabolic studies. Here, we harness the transcriptional regulator Leu3p from Saccharomyces cerevisiae to develop a genetically encoded biosensor for BCAA metabolism. In one configuration, we use the biosensor to monitor yeast production of isobutanol, an alcohol derived from valine degradation. Small modifications allow us to redeploy Leu3p in another biosensor configuration that monitors production of the leucine-derived alcohol, isopentanol. These biosensor configurations are effective at isolating high-producing strains and identifying enzymes with enhanced activity from screens for branched-chain higher alcohol (BCHA) biosynthesis in mitochondria as well as cytosol. Furthermore, this biosensor has the potential to assist in metabolic studies involving BCAA pathways, and offers a blueprint to develop biosensors for other products derived from BCAA metabolism.

There are a lack of eukaryotic biosensors specific for branched-chain amino acid (BCAA)-derived products. Here the authors report a genetically encoded biosensor for BCAA metabolism based on the Leu3p transcriptional regulator; they use this to monitor yeast production of isobutanol and isopentanol.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

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

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

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.