Metabolite Biosensors Research Articles

Manipulating the molecular specificity of transcriptional biosensors for tryptophan metabolites and analogs

Manipulating the molecular specificity of transcriptional biosensors for tryptophan metabolites and analogs

A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution

Monitoring changes of signaling molecules and metabolites with high temporal resolution is key to understanding dynamic biological systems. Here, we use directed evolution to develop a genetically encoded ratiometric biosensor for c-di-GMP, a ubiquitous bacterial second messenger regulating important biological processes like motility, surface attachment, virulence and persistence. The resulting biosensor, cdGreen2, faithfully tracks c-di-GMP in single cells and with high temporal resolution over extended imaging times, making it possible to resolve regulatory networks driving bimodal developmental programs in different bacterial model organisms. We further adopt cdGreen2 as a simple tool for in vitro studies, facilitating high-throughput screens for compounds interfering with c-di-GMP signaling and biofilm formation. The sensitivity and versatility of cdGreen2 could help reveal c-di-GMP dynamics in a broad range of microorganisms with high temporal resolution. Its design principles could also serve as a blueprint for the development of similar, orthogonal biosensors for other signaling molecules, metabolites and antibiotics.

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.

Comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo using highly responsive biosensors.

As a key glycolytic metabolite, lactate has a central role in diverse physiological and pathological processes. However, comprehensive multiscale analysis of lactate metabolic dynamics in vitro and in vivo has remained an unsolved problem until now owing to the lack of a high-performance tool. We recently developed a series of genetically encoded fluorescent sensors for lactate, named FiLa, which illuminate lactate metabolism in cells, subcellular organelles, animals, and human serum and urine. In this protocol, we first describe the FiLa sensor-based strategies for real-time subcellular bioenergetic flux analysis by profiling the lactate metabolic response to different nutritional and pharmacological conditions, which provides a systematic-level view of cellular metabolic function at the subcellular scale for the first time. We also report detailed procedures for imaging lactate dynamics in live mice through a cell microcapsule system or recombinant adeno-associated virus and for the rapid and simple assay of lactate in human body fluids. This comprehensive multiscale metabolic analysis strategy may also be applied to other metabolite biosensors using various analytic platforms, further expanding its usability. The protocol is suited for users with expertise in biochemistry, molecular biology and cell biology. Typically, the preparation of FiLa-expressing cells or mice takes 2 days to 4 weeks, and live-cell and in vivo imaging can be performed within 1-2 hours. For the FiLa-based assay of body fluids, the whole measuring procedure generally takes ~1 min for one sample in a manual assay or ~3 min for 96 samples in an automatic microplate assay.

Encoding Genetic Circuits with DNA Barcodes Paves the Way for High-Throughput Profiling of Dose-Response Curves of Metabolite Biosensors.

Metabolite biosensors, through which the intracellular metabolite concentrations could be converted to changes in gene expression, are widely used in a variety of applications according to the different output signals. However, it remains challenging to fine-tune the dose-response relationships of biosensors to meet the needs of various scenarios. On the other hand, the short read length of next-generation sequencing (NGS) has greatly limited the design capability of sequence libraries. To address these issues, we describe a DNA trackable assembly method, coupled with fluorescence-activated cell sorting and NGS (Sort-Seq), to achieve the characterization of dose-response curves in a massively parallel manner. As a proof of the concept, we constructed a malonyl-CoA biosensor library containing 5184 combinations with six levels of transcription factor dosage, four different operator positions, and 216 possible upstream enhancer sequence (UAS) designs in Saccharomyces cerevisiae BY4700. By using Sort-Seq and machine learning approach, we obtained comprehensive dose-response relationships of the combinatorial sequence space. Therefore, our pipeline provides a platform for the design, tuning, and profiling of biosensor response curves and shows great potential to facilitate the rational design of genetic circuits.

Recent advancements of fluorescent biosensors using semisynthetic probes

Fluorescent biosensors are crucial experimental tools for live-cell imaging and the quantification of different biological analytes. Fluorescent protein (FP)-based biosensors are widely used for imaging applications in living systems. However, the use of FP-based biosensors is hindered by their large size, poor photostability, and laborious genetic manipulations required to improve their properties. Recently, semisynthetic fluorescent biosensors have been developed to address the limitations of FP-based biosensors using chemically modified fluorescent probes and self-labeling protein tag/peptide tags or DNA/RNA-based hybrid systems. Semisynthetic biosensors have unique advantages, as they can be easily modified using different probes. Moreover, the self-labeling protein tag, which labels synthetically developed ligands via covalent bonds, has immense potential for biosensor development. This review discusses the recent progress in different types of fluorescent biosensors for metabolites, protein aggregation and degradation, DNA methylation, endocytosis and exocytosis, membrane tension, and cellular viscosity. Here, we explain in detail the design strategy and working principle of these biosensors. The information presented will help the reader to create new biosensors using self-labeling protein tags for various applications.

Synthesis and characterization of metabolite-nanoparticle conjugates for use in metabolite biosensor systems

Detecting specific metabolites and measuring their changing levels in various biofluids has played a key role in understanding health and diagnosing disease. While most metabolite measurements are performed via spectroscopic (colorimetric, UV, mass spectrometry-MS, nuclear magnetic resonance) methods, it is also possible to perform metabolite detection and quantification via metabolite-antibody interactions, such as those used in competitive enzyme-linked immunosorbent assays (ELISAs), and/or impedance measurements. While metabolite-specific antibodies are available, conjugated-metabolites needed for metabolite-antibody detection are rare. Here we describe a general method that allows for the efficient conjugation of different classes of metabolites (acids, amines, and aromatic compounds) to gold nanoparticles and liposomes. We also describe a method for the efficient insertion of metabolite-lipid complexes into liposomes. We extensively characterized these conjugates, confirming their identity and composition, using a wide variety of analytical (thermal gravimetric analysis), spectroscopic (dynamic light scattering, MS) and microscopic (transmission electron microscopy-TEM, cryo-EM, high resolution EM) techniques. We also demonstrated that these metabolite conjugates can successfully bind to their metabolite-specific antibodies. We used scanning electron microscopy, atomic force microscopy, and ELISA to confirm the successful binding of these NPs-conjugates (N1-acetylspermine (AcSpm), hippuric acid and creatinine) to metabolite-specific antibodies attached to silicon wafers and electrodes. We believe the methods developed here are quite general and could be used in the development of a number of different types of metabolite biosensors and portable metabolite assays.

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.

Interactions of Catalytic Enzymes with n-Type Polymers for High-Performance Metabolite Sensors.

The tight regulation of the glucose concentration in the body is crucial for balanced physiological function. We developed an electrochemical transistor comprising an n-type conjugated polymer film in contact with a catalytic enzyme for sensitive and selective glucose detection in bodily fluids. Despite the promise of these sensors, the property of the polymer that led to such high performance has remained unknown, with charge transport being the only characteristic under focus. Here, we studied the impact of the polymer chemical structure on film surface properties and enzyme adsorption behavior using a combination of physiochemical characterization methods and correlated our findings with the resulting sensor performance. We developed five n-type polymers bearing the same backbone with side chains differing in polarity and charge. We found that the nature of the side chains modulated the film surface properties, dictating the extent of interactions between the enzyme and the polymer film. Quartz crystal microbalance with dissipation monitoring studies showed that hydrophobic surfaces retained more enzymes in a densely packed arrangement, while hydrophilic surfaces captured fewer enzymes in a flattened conformation. X-ray photoelectron spectroscopy analysis of the surfaces revealed strong interactions of the enzyme with the glycolated side chains of the polymers, which improved for linear side chains compared to those for branched ones. We probed the alterations in the enzyme structure upon adsorption using circular dichroism, which suggested protein denaturation on hydrophobic surfaces. Our study concludes that a negatively charged, smooth, and hydrophilic film surface provides the best environment for enzyme adsorption with desired mass and conformation, maximizing the sensor performance. This knowledge will guide synthetic work aiming to establish close interactions between proteins and electronic materials, which is crucial for developing high-performance enzymatic metabolite biosensors and biocatalytic charge-conversion devices.

Flexing frontiers: Pioneering advances in biosensors for instant health insights

Biosensors that incorporate a biological recognition element with a transducer offer a rapid, simple approach for selective detection of analytes. However, conventional rigid biosensors have limited capabilities for continuous on-body monitoring applications. The emergence of flexible electronics has opened new possibilities for developing conformal biosensing platforms that can intimately interface with soft, curvilinear surfaces like skin for non-invasive health tracking. This review covers the fundamentals of biosensor technologies and recent progress in flexible biosensors aimed at pointof- care diagnostics and medical monitoring. Key developments in materials, electrochemical sensing interfaces, microfluidics integration and wireless connectivity are highlighted. Examples of flexible biosensors for metabolites, electrolytes, proteins and nucleic acids are presented along with capabilities and limitations. Current challenges include improving biocompatibility, correlation with standard assays, wireless range, manufacturability, and regulatory requirements. Further research on translating flexible biosensor devices from initial proof-of-concepts to validated medical products is warranted. Overall, flexible biosensors hold immense promise to enable continuous personalized health monitoring.

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.

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

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

High‐Performance Organic Electrochemical Transistors and Neuromorphic Devices Comprising Naphthalenediimide‐Dialkoxybithiazole Copolymers Bearing Glycol Ether Pendant Groups

AbstractOrganic electrochemical transistors (OECTs) have emerged as building blocks for low power circuits, biosensors, and neuromorphic computing. While p‐type polymer materials for OECTs are well developed, the choice of high‐performance n‐type polymers is limited, despite being essential for cation and metabolite biosensors, and crucial for constructing complementary circuits. N‐type conjugated polymers that have efficient ion‐to‐electron transduction are highly desired for electrochemical applications. In this contribution, three non‐fused, planar naphthalenediimide (NDI)‐dialkoxybithiazole (2Tz) copolymers, which systematically increase the amount of polar tri(ethylene glycol) (TEG) side chains: PNDI2OD‐2Tz (0 TEG), PNDIODTEG‐2Tz (1 TEG), PNDI2TEG‐2Tz (2 TEG), are reported. It is demonstrated that the OECT performance increases with the number of TEG side chains resulting from the progressively higher hydrophilicity and larger electron affinities. Benefiting from the high electron mobility, excellent ion conduction capability, efficient ion‐to‐electron transduction, and low‐lying lowest unoccupied molecular orbital energy level, the 2 TEG polymer achieves close to 105 on‐off ratio, fast switching, 1000 stable operation cycles in aqueous electrolyte, and has a long shelf life. Moreover, the higher number TEG chain substituted polymer exhibits good conductance state retention over two orders of magnitudes in electrochemical resistive random‐access memory devices, highlighting its potential for neuromorphic computing.

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

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

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering.

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

Biosensors for animal health and meat safety monitoring: farm-to-slaughterhouse continuum

The meat supply chain needs to be managed for sufficient levels of consumer protection. Healthy animals are an essential precondition for a safe food supply, since zoonotic diseases, including meatborne pathogens, are a major threat to public health. Information about the livestock’s general health, animal welfare and prevalence of major meatborne hazards such as Salmonella, Campylobacter, STEC and Listeria monocytogenes is of utmost importance for effective biosecurity control on farm. Early detection of these hazards in faecal samples, monitoring blood levels of metabolites relevant for animal welfare (hormones) and animal health (acute phase proteins) can provide high-level control in the animal farming industry. Multiplex biosensors for pathogens and metabolites in the farm-to-slaughterhouse continuum constitute a practical and cost-efficient tool for early detection of signs related to meat safety. Point-of-care multiplex biosensors are an advantage versus commonly used methods ELISA and RT-PCR, since they provide possibilities for early detection and do not require expensive equipment, trained personnel or significant time for sample transfer and analyses. Biosensors can improve meat inspection and meat safety controls, and can serve as a primary tool for monitoring food safety parameters and contribute to the modernization of veterinary inspection and risk-based meat safety assurance system.

Progress in transcription factor-based metabolite biosensors

Transcription factor-based biosensors (TFBs) play an essential role in metabolic engineering and synthetic biology. TFBs sense the metabolite concentration signals and convert them into specific signal output. They hold high sensitivity, strong specificity, brief analysis speed, and are widely used in response to target metabolites. Here we reviewe the principles of TFBs, the application examples, and challenges faced in recent years in microbial cells, including detecting target metabolite concentrations, high-throughput screening, adaptive laboratory evolutionary selection, and dynamic control. Simultaneously, to overcome the challenges in the application, we also focus on reviewing the performance tuning strategies of TFBs, mainly including traditional and computer-aided tuning strategies. We also discuss the opportunities and challenges that TFBs may face in practical applications, and propose the future research trend.

Transportome-wide engineering of Saccharomyces cerevisiae

Synthetic biology enables the production of small molecules by recombinant microbes for pharma, food, and materials applications. The secretion of products reduces the cost of separation and purification, but it is challenging to engineer due to the limited understanding of the transporter proteins' functions. Here we describe a method for genome-wide transporter disruption that, in combination with a metabolite biosensor, enables the identification of transporters impacting the production of a given target metabolite in yeast Saccharomyces cerevisiae. We applied the method to study the transport of xenobiotic compounds, cis,cis-muconic acid (CCM), protocatechuic acid (PCA), and betaxanthins. We found 22 transporters that influenced the production of CCM or PCA. The transporter of the 12-spanner drug:H(+) antiporter (DHA1) family Tpo2p was further confirmed to import CCM and PCA in Xenopus expression assays. We also identified three transporter proteins (Qdr1p, Qdr2p, and Apl1p) involved in betaxanthins transport. In summary, the described method enables high-throughput transporter identification for small molecules in cell factories.

Does co-expression of Yarrowia lipolytica genes encoding Yas1p, Yas2p and Yas3p make a potential alkane-responsive biosensor in Saccharomyces cerevisiae?

Alkane-based biofuels are desirable to produce at a commercial scale as these have properties similar to current petroleum-derived transportation fuels. Rationally engineering microorganisms to produce a desirable compound, such as alkanes, is, however, challenging. Metabolic engineers are therefore increasingly implementing evolutionary engineering approaches combined with high-throughput screening tools, including metabolite biosensors, to identify productive cells. Engineering Saccharomyces cerevisiae to produce alkanes could be facilitated by using an alkane-responsive biosensor, which can potentially be developed from the native alkane-sensing system in Yarrowia lipolytica, a well-known alkane-assimilating yeast. This putative alkane-sensing system is, at least, based on three different transcription factors (TFs) named Yas1p, Yas2p and Yas3p. Although this system is not fully elucidated in Y. lipolytica, we were interested in evaluating the possibility of translating this system into an alkane-responsive biosensor in S. cerevisiae. We evaluated the alkane-sensing system in S. cerevisiae by developing one sensor based on the native Y. lipolytica ALK1 promoter and one sensor based on the native S. cerevisiae CYC1 promoter. In both systems, we found that the TFs Yas1p, Yas2p and Yas3p do not seem to act in the same way as these have been reported to do in their native host. Additional analysis of the TFs suggests that more knowledge regarding their mechanism is needed before a potential alkane-responsive sensor based on the Y. lipolytica system can be established in S. cerevisiae.

Mechanical Tolerance of Cascade Bioreactions via Adaptive Curvature Engineering for Epidermal Bioelectronics.

Epidermal bioelectronics that can monitor human health status non-invasively and in real time are core to wearable healthcare equipment. Achieving mechanically tolerant surface bioreactions that convert biochemical information to detectable signals is crucial for obtaining high sensing fidelity. In this work, by combining simulations and experiments, a typical epidermal biosensor system is investigated based on a redox enzyme cascade reaction (RECR) comprising glucose oxidase/lactate oxidase enzymes and Prussian blue nanoparticles. Simulations reveal that strain-induced change in surface reactant flux is the key to the performance drop in traditional flat bioelectrodes. In contrast, wavy bioelectrodes capable of curvature adaptation maintain the reactant flux under strain, which preserves sensing fidelity. This rationale is experimentally proven by bioelectrodes with flat/wavy geometry under both static strain and dynamic stretching. When exposed to 50% strain, the signal fluctuations for wavy bioelectrodes are only 7.0% (4.9%) in detecting glucose (lactate), which are significantly lower than the 40.3% (51.8%) in flat bioelectrodes. Based on this wavy bioelectrode, a stable human epidermal metabolite biosensor insensitive to human gestures is further demonstrated. This mechanically tolerant biosensor based on adaptive curvature engineering provides a reliable bio/chemical-information monitoring platform for soft healthcare bioelectronics.