Development of a gene-coded biosensor to establish a high-throughput screening platform for salidroside production.

Metabolic engineering reconfigures cellular networks to produce value-added compounds from renewable substrates efficiently. However, identifying strains with desired phenotypes from large libraries through rational or random mutagenesis remains challenging. To overcome this bottleneck, an effective high-throughput screening (HTS) method must be developed to detect and analyze target candidates rapidly. Salidroside is an aromatic compound with broad applications in food, healthcare, medicine, and daily chemicals. However, there currently needs to be HTS methods available to monitor salidroside levels or to screen enzyme variants and strains for high-yield salidroside biosynthesis, which severely limits the development of microbial cell factories capable of efficiently producing salidroside on an industrial scale. This study developed a gene-encoded whole-cell biosensor that is specifically responsive to salidroside. The biosensor was created by screening a site-saturated mutagenic library of uric acid response regulatory protein binding bags. This work demonstrates the feasibility of monitoring metabolic flux with whole-cell biosensors for critical metabolites. It provides a promising tool for building salidroside high-yielding strains for high-throughput screening and metabolic regulation to meet industrial needs.

A hybrid RNA-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis.

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. High-throughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a high-throughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites.

Chloroplast thiol redox dynamics through the lens of genetically encoded biosensors.

Chloroplasts fix carbon by using light energy and have evolved a complex redox network that supports plastid functions by protection against ROS as well as by metabolic regulation according to environmental conditions. In thioredoxin- and glutathione/glutaredoxin-dependent redox cascades, protein cysteinyl redox steady states are set by varying oxidation and reduction rates. The specificity and interplay of these different redox-active proteins are still under investigation, e.g. to understand how plants cope with adverse environmental conditions by acclimating. Genetically encoded biosensors with distinct specificity can be targeted to subcellular compartments such as the chloroplast stroma, enabling in vivo real-time measurements of physiological parameters at different scales. These data have provided unique insights into dynamic behaviours of physiological parameters and redox-responsive proteins at several levels of the known redox cascades. This review summarizes current applications of different biosensor types as well as the revealed dynamics of distinct protein cysteinyl redox steady states with an emphasis on light responses.

Contrasting cytosolic glutathione redox dynamics under abiotic and biotic stress in barley as revealed by the biosensor Grx1-roGFP2.

Barley is a staple crop of major global importance and relatively resilient to a wide range of stress factors in the field. Transgenic reporter lines to investigate physiological parameters during stress treatments remain scarce. We generated and characterized transgenic homozygous barley lines (cv. Golden Promise Fast) expressing the genetically encoded biosensor Grx1-roGFP2, which indicates the redox potential of the major antioxidant glutathione in the cytosol. Our results demonstrated functionality of the sensor in living barley plants. We determined the glutathione redox potential (EGSH) of the cytosol to be in the range of -308 mV to -320 mV. EGSH was robust against a combined NaCl (150 mM) and water deficit treatment (-0.8 MPa) but responded with oxidation to infiltration with the phytotoxic secretome of the necrotrophic fungus Botrytis cinerea. The generated reporter lines are a novel resource to study biotic and abiotic stress resilience in barley, pinpointing that even severe abiotic stress leading to a growth delay does not automatically induce cytosolic EGSH oxidation, while necrotrophic pathogens can undermine this robustness.

Chloroplasts lacking class I glutaredoxins are functional but show a delayed recovery of protein cysteinyl redox state after oxidative challenge.

Redox status of protein cysteinyl residues is mediated via glutathione (GSH)/glutaredoxin (GRX) and thioredoxin (TRX)-dependent redox cascades. An oxidative challenge can induce post-translational protein modifications on thiols, such as protein S-glutathionylation. Class I GRX are small thiol-disulfide oxidoreductases that reversibly catalyse S-glutathionylation and protein disulfide formation. TRX and GSH/GRX redox systems can provide partial backup for each other in several subcellular compartments, but not in the plastid stroma where TRX/light-dependent redox regulation of primary metabolism takes place. While the stromal TRX system has been studied at detail, the role of class I GRX on plastid redox processes is still unknown. We generate knockout lines of GRXC5 as the only chloroplast class I GRX of the moss Physcomitrium patens. While we find that PpGRXC5 has high activities in GSH-dependent oxidoreductase assays using hydroxyethyl disulfide or redox-sensitive GFP2 as substrates in vitro, Δgrxc5 plants show no detectable growth defect or stress sensitivity, in contrast to mutants with a less negative stromal EGSH (Δgr1). Using stroma-targeted roGFP2, we show increased protein Cys steady state oxidation and decreased reduction rates after oxidative challenge in Δgrxc5 plants in vivo, indicating kinetic uncoupling of the protein Cys redox state from EGSH. Compared to wildtype, protein Cys disulfide formation rates and S-glutathionylation levels after H2O2 treatment remained unchanged. Lack of class I GRX function in the stroma did not result in impaired carbon fixation. Our observations suggest specific roles for GRXC5 in the efficient transfer of electrons from GSH to target protein Cys as well as negligible cross-talk with metabolic regulation via the TRX system. We propose a model for stromal class I GRX function in efficient catalysis of protein dithiol/disulfide equilibria upon redox steady state alterations affecting stromal EGSH and highlight the importance of identifying in vivo target proteins of GRXC5.

Genetic Circuits for Feedback Control of Gamma-Aminobutyric Acid Biosynthesis in Probiotic Escherichia coli Nissle 1917.

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate the production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators.

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

Advances in ligand-specific biosensing for structurally similar molecules.

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium.

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

Fluorescent Biosensor Imaging of Nitrate in Arabidopsis thaliana.

Nitrate (NO3-) is an essential element and nutrient for plants and animals. Despite extensive studies on the regulation of nitrate uptake and downstream responses in various cells, our knowledge of the distribution of nitrogen forms in different root cell types and their cellular compartments is still limited. Previous physiological models have relied on in vitro biochemistry and metabolite level analysis, which limits the ability to differentiate between cell types and compartments. Here, to address this, we report a nuclear-localized, genetically encoded fluorescent biosensor, which we named nlsNitraMeter3.0, for the quantitative visualization of nitrate concentration and distribution at the cellular level in Arabidopsis thaliana. This biosensor was specifically designed for nitrate measurements, not nitrite. Through genetic engineering to create and select sensors using yeast, Xenopus oocyte, and Arabidopsis expression systems, we developed a reversible and highly specific nitrate sensor. This method, combined with fluorescence imaging systems such as confocal microscopy, allows for the understanding and monitoring of nitrate transporter activity in plant root cells in a minimally invasive manner. Furthermore, this approach enables the functional analysis of nitrate transporters and the measurement of nitrate distribution in plants, providing a valuable tool for plant biology research. In summary, we provide a protocol for sensor development and a biosensor that can be used to monitor nitrate levels in plants. Key features This protocol builds upon the concept of FRET biosensors for in vivo visualization of spatiotemporal nitrate levels at a cellular resolution. Nitrate levels can be quantified utilizing the biosensor in conjunction with either a plate reader or a fluorescence microscope.

Development of an Efficient FRET-Based Ratiometric Uranium Biosensor.

The dispersion of uranium in the environment can pose a problem for the health of humans and other living organisms. It is therefore important to monitor the bioavailable and hence toxic fraction of uranium in the environment, but no efficient measurement methods exist for this. Our study aims to fill this gap by developing a genetically encoded FRET-based ratiometric uranium biosensor. This biosensor was constructed by grafting two fluorescent proteins to both ends of calmodulin, a protein that binds four calcium ions. By modifying the metal-binding sites and the fluorescent proteins, several versions of the biosensor were generated and characterized in vitro. The best combination results in a biosensor that is affine and selective for uranium compared to metals such as calcium or other environmental compounds (sodium, magnesium, chlorine). It has a good dynamic range and should be robust to environmental conditions. In addition, its detection limit is below the uranium limit concentration in drinking water defined by the World Health Organization. This genetically encoded biosensor is a promising tool to develop a uranium whole-cell biosensor. This would make it possible to monitor the bioavailable fraction of uranium in the environment, even in calcium-rich waters.

Ratiometric NAD+ Sensors Reveal Subcellular NAD+ Modulators.

Mapping NAD+ dynamics in live cells and human is essential for translating NAD+ interventions into effective therapies. Yet, genetically encoded NAD+ sensors with better specificity and pH resistance are still needed for the cost-effective monitoring of NAD+ in both subcellular compartments and clinical samples. Here, we introduce multicolor, resonance energy transfer-based NAD+ sensors covering nano- to millimolar concentration ranges for clinical NAD+ measurement and subcellular NAD+ visualization. The sensors captured the blood NAD+ increase induced by NMN supplementation and revealed the distinct subcellular effects of NAD+ precursors and modulators. The sensors then enabled high-throughput screenings for mitochondrial and nuclear NAD+ modulators and identified α-GPC, a cognition-related metabolite that induces NAD+ redistribution from mitochondria to the nucleus relative to the total adenine nucleotides, which was further confirmed by NAD+ FRET microscopy.

In vivo calibration of genetically encoded metabolite biosensors must account for metabolite metabolism during calibration and cellular volume.

Isotopic assays of brain glucose utilization rates have been used for more than four decades to establish relationships between energetics, functional activity, and neurotransmitter cycling. Limitations of these methods include the relatively long time (1-60 min) for the determination of labeled metabolite levels and the lack of cellular resolution. Identification and quantification of fuels for neurons and astrocytes that support activation and higher brain functions are a major, unresolved issues. Glycolysis is preferentially up-regulated during activation even though oxygen level and supply are adequate, causing lactate concentrations to quickly rise during alerting, sensory processing, cognitive tasks, and memory consolidation. However, the fate of lactate (rapid release from brain or cell-cell shuttling coupled with local oxidation) is long disputed. Genetically encoded biosensors can determine intracellular metabolite concentrations and report real-time lactate level responses to sensory, behavioral, and biochemical challenges at the cellular level. Kinetics and time courses of cellular lactate concentration changes are informative, but accurate biosensor calibration is required for quantitative comparisons of lactate levels in astrocytes and neurons. An in vivo calibration procedure for the Laconic lactate biosensor involves intracellular lactate depletion by intravenous pyruvate-mediated trans-acceleration of lactate efflux followed by sensor saturation by intravenous infusion of high doses of lactate plus ammonium chloride. In the present paper, the validity of this procedure is questioned because rapid lactate-pyruvate interconversion in blood, preferential neuronal oxidation of both monocarboxylates, on-going glycolytic metabolism, and cellular volumes were not taken into account. Calibration pitfalls for the Laconic lactate biosensor also apply to other metabolite biosensors that are standardized in vivo by infusion of substrates that can be metabolized in peripheral tissues. We discuss how technical shortcomings negate the conclusion that Laconic sensor calibrations support the existence of an in vivo astrocyte-neuron lactate concentration gradient linked to lactate shuttling from astrocytes to neurons to fuel neuronal activity.

A Genetically Encoded Biosensor for the Detection of Levulinic Acid.

Levulinic acid (LA) is a valuable chemical used in fuel additives, fragrances, and polymers. In this study, we proposed possible biosynthetic pathways for LA production from lignin and poly(ethylene terephthalate). We also created a genetically encoded biosensor responsive to LA, which can be used for screening and evolving the LA biosynthesis pathway genes, by employing an LvaR transcriptional regulator of Pseudomonas putida KT2440 to express a fluorescent reporter gene. The LvaR regulator senses LA as a cognate ligand. The LA biosensor was first examined in an Escherichia coli strain and was found to be non-functional. When the host of the LA biosensor was switched from E. coli to P. putida KT2440, the LA biosensor showed a linear correlation between fluorescence intensity and LA concentration in the range of 0.156-10 mM LA. In addition, we determined that 0.156 mM LA was the limit of LA detection in P. putida KT2440 harboring an LA-responsive biosensor. The maximal fluorescence increase was 12.3-fold in the presence of 10 mM LA compared to that in the absence of LA. The individual cell responses to LA concentrations reflected the population-averaged responses, which enabled high-throughput screening of enzymes and metabolic pathways involved in LA biosynthesis and sustainable production of LA in engineered microbes.

GPCR Signaling Measurement and Drug Profiling with an Automated Live-Cell Microscopy System.

A major limitation of time-lapse microscopy combined with fluorescent biosensors, a powerful tool for quantifying spatiotemporal dynamics of signaling in single living cells, is low-experimental throughput. To overcome this limitation, we created a highly customizable, MATLAB-based platform: flexible automated liquid-handling combined microscope (FALCOscope) that coordinates an OpenTrons liquid handler and a fluorescence microscope to automate drug treatments, fluorescence imaging, and single-cell analysis. To test the feasibility of the FALCOscope, we quantified G protein-coupled receptor (GPCR)-stimulated Protein Kinase A activity and cAMP responses to GPCR agonists and antagonists. We also characterized cAMP dynamics induced by GPR68/OGR1, a proton-sensing GPCR, in response to variable extracellular pH values. GPR68-induced cAMP responses were more transient in acidic than neutral pH values, suggesting a pH-dependence for signal attenuation. Ogerin, a GPR68 positive allosteric modulator, enhanced cAMP response most strongly at pH 7.0 and sustained cAMP response for acidic pH values, thereby demonstrating the capability of the FALCOscope to capture allosteric modulation. At a high concentration, ogerin increased cAMP signaling independent of GPR68, likely via phosphodiesterase inhibition. The FALCOscope system thus enables enhanced throughput single-cell dynamic measurements and is a versatile system for interrogating spatiotemporal regulation of signaling molecules in living cells and for drug profiling and screening.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications.

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

Genetically encoded ATP and NAD(P)H biosensors: potential tools in metabolic engineering.

To date, many metabolic engineering tools and strategies have been developed, including tools for cofactor engineering, which is a common strategy for bioproduct synthesis. Cofactor engineering is used for the regulation of pyridine nucleotides, including NADH/NAD+ and NADPH/NADP+, and adenosine triphosphate/adenosine diphosphate (ATP/ADP), which is crucial for maintaining redox and energy balance. However, the intracellular levels of NADH/NAD+, NADPH/NADP+, and ATP/ADP cannot be monitored in real time using traditional methods. Recently, many biosensors for detecting, monitoring, and regulating the intracellular levels of NADH/NAD+, NADPH/NADP+, and ATP/ADP have been developed. Although cofactor biosensors have been mainly developed for use in mammalian cells, the potential application of cofactor biosensors in metabolic engineering in bacterial and yeast cells has received recent attention. Coupling cofactor biosensors with genetic circuits is a promising strategy in metabolic engineering for optimizing the production of biochemicals. In this review, we focus on the development of biosensors for NADH/NAD+, NADPH/NADP+, and ATP/ADP and the potential application of these biosensors in metabolic engineering. We also provide critical perspectives, identify current research challenges, and provide guidance for future research in this promising field.

Surveying the Genetic Design Space for Transcription Factor-Based Metabolite Biosensors: Synthetic Gamma-Aminobutyric Acid and Propionate Biosensors in E. coli Nissle 1917.

Engineered probiotic bacteria have been proposed as a next-generation strategy for noninvasively detecting biomarkers in the gastrointestinal tract and interrogating the gut-brain axis. A major challenge impeding the implementation of this strategy has been the difficulty to engineer the necessary whole-cell biosensors. Creation of transcription factor-based biosensors in a clinically-relevant strain often requires significant tuning of the genetic parts and gene expression to achieve the dynamic range and sensitivity required. Here, we propose an approach to efficiently engineer transcription-factor based metabolite biosensors that uses a design prototyping construct to quickly assay the gene expression design space and identify an optimal genetic design. We demonstrate this approach using the probiotic bacterium Escherichia coli Nissle 1917 (EcN) and two neuroactive gut metabolites: the neurotransmitter gamma-aminobutyric acid (GABA) and the short-chain fatty acid propionate. The EcN propionate sensor, utilizing the PrpR transcriptional activator from E. coli, has a large 59-fold dynamic range and >500-fold increased sensitivity that matches biologically-relevant concentrations. Our EcN GABA biosensor uses the GabR transcriptional repressor from Bacillus subtilis and a synthetic GabR-regulated promoter created in this study. This work reports the first known synthetic microbial whole-cell biosensor for GABA, which has an observed 138-fold activation in EcN at biologically-relevant concentrations. Using this rapid design prototyping approach, we engineer highly functional biosensors for specified in vivo metabolite concentrations that achieve a large dynamic range and high output promoter activity upon activation. This strategy may be broadly useful for accelerating the engineering of metabolite biosensors for living diagnostics and therapeutics.

Fluorescently labeled nuclear morphology is highly informative of neurotoxicity.

Neurotoxicity can be detected in live microscopy by morphological changes such as retraction of neurites, fragmentation, blebbing of the neuronal soma and ultimately the disappearance of fluorescently labeled neurons. However, quantification of these features is often difficult, low-throughput, and imprecise due to the overreliance on human curation. Recently, we showed that convolutional neural network (CNN) models can outperform human curators in the assessment of neuronal death from images of fluorescently labeled neurons, suggesting that there is information within the images that indicates toxicity but that is not apparent to the human eye. In particular, the CNN's decision strategy indicated that information within the nuclear region was essential for its superhuman performance. Here, we systematically tested this prediction by comparing images of fluorescent neuronal morphology from nuclear-localized fluorescent protein to those from freely diffused fluorescent protein for classifying neuronal death. We found that biomarker-optimized (BO-) CNNs could learn to classify neuronal death from fluorescent protein-localized nuclear morphology (mApple-NLS-CNN) alone, with super-human accuracy. Furthermore, leveraging methods from explainable artificial intelligence, we identified novel features within the nuclear-localized fluorescent protein signal that were indicative of neuronal death. Our findings suggest that the use of a nuclear morphology marker in live imaging combined with computational models such mApple-NLS-CNN can provide an optimal readout of neuronal death, a common result of neurotoxicity.

Design of a genetically encoded biosensor to establish a high-throughput screening platform for L-cysteine overproduction.

Metabolic engineering seeks to rewire the metabolic network of cells for the efficient production of value-added compounds from renewable substrates. However, it remains challenging to evaluate and identify strains with the desired phenotype from the vast rational or random mutagenesis library. One effective approach to resolve this bottleneck is to design an efficient high-throughput screening (HTS) method to rapidly detect and analyze target candidates. L-cysteine is an important sulfur-containing amino acid and has been widely used in agriculture, pharmaceuticals, cosmetics, and food additive industries. However, HTS methods that enable monitoring of L-cysteine levels and screening of the enzyme variants and strains to confer superior L-cysteine biosynthesis remain unavailable, greatly limiting the development of efficient microbial cell factories for L-cysteine production at the industrial scale. Here, we took advantage of the L-cysteine-responsive transcriptional regulator CcdR to develop a genetically encoded biosensor for engineering and screening the L-cysteine overproducer. The in vivo L-cysteine-responsive assays and in vitro electrophoretic mobility shift assay (EMSA) and DNase I footprint analysis indicated that CcdR is a transcriptional activator that specifically interacts with L-cysteine and binds to its regulatory region to induce the expression of target genes. To improve the response performance of the L-cysteine biosensor, multilevel optimization strategies were performed, including regulator engineering by semi-rational design and systematic optimization of the genetic elements by modulating the promoter and RBS combination. As a result, the dynamic range and sensitivity of the biosensor were significantly improved. Using this the excellent L-cysteine biosensor, a HTS platform was established by coupling with fluorescence-activated cell sorting (FACS) and was successfully applied to achieve direct evolution of the key enzyme in the L-cysteine biosynthetic pathway to increase its catalytic performance and to screen the high L-cysteine-producing strains from the random mutagenesis library. These results presented a paradigm of design and optimization of biosensors to dynamically detect metabolite concentrations and provided a promising tool enabling HTS and metabolic regulation to construct L-cysteine hyperproducing strains to satisfy industrial demand.