Development of a Transcription Factor-Based Lactam Biosensor

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.

Genetically encoded biosensors for the circular plastics bioeconomy

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

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

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

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.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

In Vivo Detection of Low Molecular Weight Platform Chemicals and Environmental Contaminants by Genetically Encoded Biosensors

Genetically encoded biosensor systems operating in living cells are versatile, cheap, and transferable tools for the detection and quantification of a broad range of small molecules. This review presents state-of-the-art biosensor designs and assemblies, featuring transcription factor-, riboswitch-, and enzyme-coupled devices, highly engineered fluorescent probes, and emerging two-component systems. Importantly, (bioinformatic-assisted) strategies to resolve contextual issues, which cause biosensors to miss performance criteria in vivo, are highlighted. The optimized biosensing circuits can be used to monitor chemicals of low molecular mass (<200 g mol-1) and physicochemical properties that challenge conventional chromatographical methods with high sensitivity. Examples herein include but are not limited to formaldehyde, formate, and pyruvate as immediate products from (synthetic) pathways for the fixation of carbon dioxide (CO2), industrially important derivatives like small- and medium-chain fatty acids and biofuels, as well as environmental toxins such as heavy metals or reactive oxygen and nitrogen species. Lastly, this review showcases biosensors capable of assessing the biosynthesis of platform chemicals from renewable resources, the enzymatic degradation of plastic waste, or the bioadsorption of highly toxic chemicals from the environment. These applications offer new biosensor-based manufacturing, recycling, and remediation strategies to tackle current and future environmental and socioeconomic challenges including the wastage of fossil fuels, the emission of greenhouse gases like CO2, and the pollution imposed on ecosystems and human health.

Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor

A cross-ribosome binding site (cRBS) adjusts the dynamic range of transcription factor-based biosensors (TFBs) by controlling protein expression and folding. The rational design of a cRBS with desired TFB dynamic range remains an important issue in TFB forward and reverse engineering. Here, we report a novel artificial intelligence (AI)-based forward-reverse engineering platform for TFB dynamic range prediction and de novo cRBS design with selected TFB dynamic ranges. The platform demonstrated superior in processing unbalanced minority-class datasets and was guided by sequence characteristics from trained cRBSs. The platform identified correlations between cRBSs and dynamic ranges to mimic bidirectional design between these factors based on Wasserstein generative adversarial network (GAN) with a gradient penalty (GP) (WGAN-GP) and balancing GAN with GP (BAGAN-GP). For forward and reverse engineering, the predictive accuracy was up to 98% and 82%, respectively. Collectively, we generated an AI-based method for the rational design of TFBs with desired dynamic ranges.

TFBMiner: A User-Friendly Command Line Tool for the Rapid Mining of Transcription Factor-Based Biosensors

Transcription factors responsive to small molecules are essential elements in synthetic biology designs. They are often used as genetically encoded biosensors with applications ranging from the detection of environmental contaminants and biomarkers to microbial strain engineering. Despite our efforts to expand the space of compounds that can be detected using biosensors, the identification and characterization of transcription factors and their corresponding inducer molecules remain labor- and time-intensive tasks. Here, we introduce TFBMiner, a new data mining and analysis pipeline that enables the automated and rapid identification of putative metabolite-responsive transcription factor-based biosensors (TFBs). This user-friendly command line tool harnesses a heuristic rule-based model of gene organization to identify both gene clusters involved in the catabolism of user-defined molecules and their associated transcriptional regulators. Ultimately, biosensors are scored based on how well they fit the model, providing wet-lab scientists with a ranked list of candidates that can be experimentally tested. We validated the pipeline using a set of molecules for which TFBs have been reported previously, including sensors responding to sugars, amino acids, and aromatic compounds, among others. We further demonstrated the utility of TFBMiner by identifying a biosensor for S-mandelic acid, an aromatic compound for which a responsive transcription factor had not been found previously. Using a combinatorial library of mandelate-producing microbial strains, the newly identified biosensor was able to distinguish between low- and high-producing strain candidates. This work will aid in the unraveling of metabolite-responsive microbial gene regulatory networks and expand the synthetic biology toolbox to allow for the construction of more sophisticated self-regulating biosynthetic pathways.

Dynamic upregulation of the rate-limiting enzyme for valerolactam biosynthesis in Corynebacterium glutamicum

Valerolactam is a monomer used to manufacture high-value nylon-5 and nylon-6,5. However, the biological production of valerolactam has been limited by the inadequate efficiency of enzymes to cyclize 5-aminovaleric acid to produce valerolactam. In this study, we engineered Corynebacterium glutamicum with a valerolactam biosynthetic pathway consisting of DavAB from Pseudomonas putida to convert L-lysine to 5-aminovaleric acid and β-alanine CoA transferase (Act) from Clostridium propionicum to produce valerolactam from 5-aminovaleric acid. Most of the L-lysine was converted into 5-aminovaleric acid, but promoter optimization and increasing the copy number of Act were insufficient to significantly improve the titer of valerolactam. To eliminate the bottleneck at Act, we designed a dynamic upregulation system (a positive feedback loop based on the valerolactam biosensor ChnR/Pb). We used laboratory evolution to engineer ChnR/Pb to have higher sensitivity and a higher dynamic output range, and the engineered ChnR-B1/Pb-E1 system was used to overexpress the rate-limiting enzymes (Act/ORF26/CaiC) that cyclize 5-aminovaleric acid into valerolactam. In glucose fed-batch culture, we obtained 12.33 g/L valerolactam from the dynamic upregulation of Act, 11.88 g/L using ORF26, and 12.15 g/L using CaiC. Our engineered biosensor (ChnR-B1/Pb-E1 system) was also sensitive to 0.01-100 mM caprolactam, which suggests that this dynamic upregulation system can be used to enhance caprolactam biosynthesis in the future.

The Next Frontier of 3D Bioprinting: Bioactive Materials Functionalized by Bacteria

3D bioprinting has become a flexible technical means used in many fields. Currently, research on 3D bioprinting is mainly focused on the use of mammalian cells to print organ and tissue models, which has greatly promoted progress in the fields of tissue engineering, regenerative medicine, and pharmaceuticals. In recent years, bacterial bioprinting has gradually become a rapidly developing research fields, with a wide range of potential applications in basic research, biomedicine, bioremediation, and other field. Here, this works reviews new research on bacterial bioprinting, and discuss its future research direction.

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.

Permeability-Engineered Compartmentalization Enables In Vitro Reconstitution of Sustained Synthetic Biology Systems

In nature, biological compartments such as cells rely on dynamically controlled permeability for matter exchange and complex cellular activities. Likewise, the ability to engineer compartment permeability is crucial for in vitro systems to gain sustainability, robustness, and complexity. However, rendering in vitro compartments such a capability is challenging. Here, a facile strategy is presented to build permeability-configurable compartments, and marked advantages of such compartmentalization are shown in reconstituting sustained synthetic biology systems in vitro. Through microfluidics, the strategy produces micrometer-sized layered microgels whose shell layer serves as a sieving structure for biomolecules and particles. In this configuration, the transport of DNAs, proteins, and bacteriophages across the compartments can be controlled an guided by a physical model. Through permeability engineering, a compartmentalized cell-free protein synthesis system sustains multicycle protein production; ≈100 000 compartments are repeatedly used in a five-cycle synthesis, featuring a yield of 2.2 mg mL-1 . Further, the engineered bacteria-enclosing compartments possess near-perfect phage resistance and enhanced environmental fitness. In a complex river silt environment, compartmentalized whole-cell biosensors show maintained activity throughout the 32 h pollutant monitoring. It is anticipated that permeability-engineered compartmentalization should pave the way for practical synthetic biology applications such as green bioproduction, environmental sensing, and bacteria-based therapeutics.

A Versatile Transcription Factor Biosensor System Responsive to Multiple Aromatic and Indole Inducers

Allosteric transcription factor (aTF) biosensors are valuable tools for engineering microbes toward a multitude of applications in metabolic engineering, biotechnology, and synthetic biology. One of the challenges toward constructing functional and diverse biosensors in engineered microbes is the limited toolbox of identified and characterized aTFs. To overcome this, extensive bioprospecting of aTFs from sequencing databases, as well as aTF ligand-specificity engineering are essential in order to realize their full potential as biosensors for novel applications. In this work, using the TetR-family repressor CmeR from Campylobacter jejuni, we construct aTF genetic circuits that function as salicylate biosensors in the model organisms Escherichia coli and Saccharomyces cerevisiae. In addition to salicylate, we demonstrate the responsiveness of CmeR-regulated promoters to multiple aromatic and indole inducers. This relaxed ligand specificity of CmeR makes it a useful tool for detecting molecules in many metabolic engineering applications, as well as a good target for directed evolution to engineer proteins that are able to detect new and diverse chemistries.

Cheating the Cheater: Suppressing False-Positive Enrichment during Biosensor-Guided Biocatalyst Engineering

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false-positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection) suppresses cheater population enrichment. Furthermore, we show that, if we couple the biosensor-based screen with an orthogonal prescreen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting. Using the approach developed here, we were successfully able to isolate PAL variants with ∼70% higher k_cat after a single sort. These mutants have tremendous potential in phenylketonuria (PKU) treatment and flavonoid production.

Effective use of biosensors for high-throughput library screening for metabolite production

The development of fast and affordable microbial production from recombinant pathways is a challenging endeavor, with targeted improvements difficult to predict due to the complex nature of living systems. To address the limitations in biosynthetic pathways, much work has been done to generate large libraries of various genetic parts (promoters, RBSs, enzymes, etc.) to discover library members that bring about significantly improved levels of metabolite production. To evaluate these large libraries, high throughput approaches are necessary, such as those that rely on biosensors. There are various modes of operation to apply biosensors to library screens that are available at different scales of throughput. The effectiveness of each biosensor-based method is dependent on the pathway or strain to which it is applied, and all approaches have strengths and weaknesses to be carefully considered for any high throughput library screen. In this review, we discuss the various approaches used in biosensor screening for improved metabolite production, focusing on transcription factor-based biosensors.

Modular tuning engineering and versatile applications of genetically encoded biosensors

Genetically encoded biosensors have a diverse range of detectable signals and potential applications in many fields, including metabolism control and high-throughput screening. Their ability to be used in situ with minimal interference to the bioprocess of interest could revolutionize synthetic biology and microbial cell factories. The performance and functions of these biosensors have been extensively studied and have been rapidly improved. We review here current biosensor tuning strategies and attempt to unravel how to obtain ideal biosensor functions through experimental adjustments. Strategies for expanding the biosensor input signals that increases the number of detectable compounds have also been summarized. Finally, different output signals and their practical requirements for biotechnology and biomedical applications and environmental safety concerns have been analyzed. This in-depth review of the responses and regulation mechanisms of genetically encoded biosensors will assist to improve their design and optimization in various application scenarios.

Coproduction of 5-Aminovalerate and δ-Valerolactam for the Synthesis of Nylon 5 From L-Lysine in Escherichia coli

The compounds 5-aminovalerate and δ-valerolactam are important building blocks that can be used to synthesize bioplastics. The production of 5-aminovalerate and δ-valerolactam in microorganisms provides an ideal source that reduces the cost. To achieve efficient biobased coproduction of 5-aminovalerate and δ-valerolactam in Escherichia coli, a single biotransformation step from L-lysine was constructed. First, an equilibrium mixture was formed by L-lysine α-oxidase RaiP from Scomber japonicus. In addition, by adjusting the pH and H₂O₂ concentration, the titers of 5-aminovalerate and δ-valerolactam reached 10.24 and 1.82 g/L from 40 g/L L-lysine HCl at pH 5.0 and 10 mM H₂O₂, respectively. With the optimized pH value, the δ-valerolactam titer was improved to 6.88 g/L at pH 9.0 with a molar yield of 0.35 mol/mol lysine. The ratio of 5AVA and δ-valerolactam was obviously affected by pH value. The ratio of 5AVA and δ-valerolactam could be obtained in the range of 5.63:1-0.58:1 at pH 5.0-9.0 from the equilibrium mixture. As a result, the simultaneous synthesis of 5-aminovalerate and δ-valerolactam from L-lysine in Escherichia coli is highly promising. To our knowledge, this result constitutes the highest δ-valerolactam titer reported by biological methods. In summary, a commercially implied bioprocess developed for the coproduction of 5-aminovalerate and δ-valerolactam using engineered Escherichia coli.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

Transcription-Factor-based Biosensor Engineering for Applications in Synthetic Biology

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

Stimuli-responsive engineered living materials

Stimuli-responsive materials are able to undergo controllable changes in materials properties in response to external cues. Increasing efforts have been directed towards building materials that mimic the responsive nature of biological systems. Nevertheless, limitations remain surrounding the way these synthetic materials interact and respond to their environment. In particular, it is difficult to synthesize synthetic materials that respond with specificity to poorly differentiated (bio)chemical and weak physical stimuli. The emerging area of engineered living materials (ELMs) includes composites that combine living cells and synthetic materials. ELMs have yielded promising advances in the creation of stimuli-responsive materials that respond with diverse outputs in response to a broad array of biochemical and physical stimuli. This review describes advances made in the genetic engineering of the living component and the processing-property relationships of stimuli-responsive ELMs. Finally, the implementation of stimuli-responsive ELMs as environmental sensors, biomedical sensors, drug delivery vehicles, and soft robots is discussed.

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

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

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

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

A unified design allows fine-tuning of biosensor parameters and application across bacterial species

In recent years, transcriptional biosensors have become valuable tools in metabolic engineering as they allow semiquantitative determination of metabolites in single cells. Although being perfectly suitable tools for high-throughput screenings, application of transcriptional biosensors is often limited by the intrinsic characteristics of the individual sensor components and their interplay. In addition, biosensors often fail to work properly in heterologous host systems due to signal saturation at low intracellular metabolite concentrations, which typically limits their use in high-level producer strains at advanced engineering stages.

We here introduce a biosensor design, which allows fine-tuning of important sensor parameters and restores the sensor response in a heterologous expression host. As a key feature of our design, the regulator activity is controlled through the expression level of the respective gene by different (synthetic) constitutive promoters selected for the used expression host. In this context, we constructed biosensors responding to basic amino acids or ring-hydroxylated phenylpropanoids for applications in Corynebacterium glutamicum and Escherichia coli. Detailed characterization of these biosensors in liquid cultures and during single-cell analysis using flow cytometry showed that the presented sensor design enables customization of important biosensor parameters as well as application of these sensors in relevant heterologous hosts.

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Development of a unified biosensor design for C. glutamicum and E. coli.

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Gradual expression of the regulator gene allows for biosensor fine-tuning.

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Biosensor response in a heterologous host can be restored.

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Biosensor characterization on the single-cell level prior to FACS is mandatory.

Hydrogel-Embedded Quantum Dot-Transcription Factor Sensors for Quantitative Progesterone Detection

Immobilization of biosensors in or on a functional material is critical for subsequent device development and translation to wearable technology. Here, we present the development and assessment of an immobilized quantum dot-transcription factor-nucleic acid complex for progesterone detection as a first step toward such device integration. The sensor, composed of a polyhistidine-tagged transcription factor linked to a quantum dot and a fluorophore-modified cognate DNA, is embedded within a hydrogel as an immobilization matrix. The hydrogel is optically transparent, soft, and flexible as well as traps the quantum dot-transcription factor DNA assembly but allows free passage of the analyte, progesterone. Upon progesterone exposure, DNA dissociates from the quantum dot-transcription factor DNA assembly resulting in an attenuated ratiometric fluorescence output via Förster resonance energy transfer. The sensor performs in a dose-dependent manner with a limit of detection of 55 nM. Repeated analyte measurements are similarly successful. Our approach combines a systematically characterized hydrogel as an immobilization matrix and a transcription factor-DNA assembly as a recognition/transduction element, offering a promising framework for future biosensor devices.

Microbes as Biosensors

The ability to detect disease early and deliver precision therapy would be transformative for the treatment of human illnesses. To achieve these goals, biosensors that can pinpoint when and where diseases emerge are needed. Rapid advances in synthetic biology are enabling us to exploit the information-processing abilities of living cells to diagnose disease and then treat it in a controlled fashion. For example, living sensors could be designed to precisely sense disease biomarkers, such as by-products of inflammation, and to respond by delivering targeted therapeutics in situ. Here, we provide an overview of ongoing efforts in microbial biosensor design, highlight translational opportunities, and discuss challenges for enabling sense-and-respond precision medicines.

Engineering Prokaryotic Transcriptional Activator XylR as a Xylose-Inducible Biosensor for Transcription Activation in Yeast

Biosensors regulated by specific substrates are needed to develop genetic tools to meet the needs of engineering microbial cell factories. Here, a xylose-inducible biosensor (xylbiosensor), comprising the Escherichia coli activation factor XylR, fusion activation domain (AD) VPRH, and a hybrid promoter with operator xylO, was established in Yarrowia lipolytica. The addition of xylose to an engineered Y. lipolytica strain harboring the xylbiosensor could trigger significant transcriptional activation of target genes, such as mcherry and the xylose utilization gene. Furthermore, a novel promoter Pleu-Pxo-Ptef was developed to construct a bidirectional expression system. The xylbiosensor showed good portability in Saccharomyces cerevisiae, suggesting its potential value in other eukaryotic cells. This study is the first to construct a "turn-on" xylbiosensor induced by xylose addition based on a prokaryotic activator XylR and eukaryotic universal AD. The xylbiosensor exhibits potential in pathway engineering for xylose utilization and xylose-derived product biosynthesis in yeast.

Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms

Low yield and low titer of natural products are common issues in natural product biosynthesis through microbial cell factories. One effective way to resolve such bottlenecks is to design genetic biosensors to monitor and regulate the biosynthesis of target natural products. In this review, we evaluate the most recent advances in the design of genetic biosensors for natural product biosynthesis in microorganisms. In particular, we examine strategies for selection of genetic parts and construction principles for the design and evaluation of genetic biosensors. We also review the latest applications of transcription factor- and riboswitch-based genetic biosensors in natural product biosynthesis. Lastly, we discuss challenges and solutions in designing genetic biosensors for the biosynthesis of natural products in microorganisms.

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

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

Identification, Characterization, and Application of a Highly Sensitive Lactam Biosensor from Pseudomonas putida

Caprolactam is an important polymer precursor to nylon traditionally derived from petroleum and produced on a scale of 5 million tons per year. Current biological pathways for the production of caprolactam are inefficient with titers not exceeding 2 mg/L, necessitating novel pathways for its production. As development of novel metabolic routes often require thousands of designs and result in low product titers, a highly sensitive biosensor for the final product has the potential to rapidly speed up development times. Here we report a highly sensitive biosensor for valerolactam and caprolactam from Pseudomonas putida KT2440 which is >1000× more sensitive to an exogenous ligand than previously reported sensors. Manipulating the expression of the sensor oplR (PP_3516) substantially altered the sensing parameters, with various vectors showing K_d values ranging from 700 nM (79.1 μg/L) to 1.2 mM (135.6 mg/L). Our most sensitive construct was able to detect in vivo production of caprolactam above background at ∼6 μg/L. The high sensitivity and range of OplR is a powerful tool toward the development of novel routes to the biological synthesis of caprolactam.

Hydrogel Microparticles Functionalized with Engineered Escherichia coli as Living Lactam Biosensors

Recently there has been an increasing need for synthesizing valued chemicals through biorefineries. Lactams are an essential family of commodity chemicals widely used in the nylon industry with annual production of millions of tons. The bio-production of lactams can substantially benefit from high-throughput lactam sensing strategies for lactam producer screening. We present here a robust and living lactam biosensor that is directly compatible with high-throughput analytical means. The biosensor is a hydrogel microparticle encapsulating living microcolonies of engineered lactam-responsive Escherichia coli. The microparticles feature facile and ultra-high throughput manufacturing of up to 10,000,000 per hour through droplet microfluidics. We show that the biosensors can specifically detect major lactam species in a dose-dependent manner, which can be quantified using flow cytometry. The biosensor could potentially be used for high-throughput metabolic engineering of lactam biosynthesis.

Robust Characterization of Two Distinct Glutarate Sensing Transcription Factors of Pseudomonas putida l-Lysine Metabolism

A significant bottleneck in synthetic biology involves screening large genetically encoded libraries for desirable phenotypes such as chemical production. However, transcription factor-based biosensors can be leveraged to screen thousands of genetic designs for optimal chemical production in engineered microbes. In this study we characterize two glutarate sensing transcription factors (CsiR and GcdR) from Pseudomonas putida. The genomic contexts of csiR homologues were analyzed, and their DNA binding sites were bioinformatically predicted. Both CsiR and GcdR were purified and shown to bind upstream of their coding sequencing in vitro. CsiR was shown to dissociate from DNA in vitro when exogenous glutarate was added, confirming that it acts as a genetic repressor. Both transcription factors and cognate promoters were then cloned into broad host range vectors to create two glutarate biosensors. Their respective sensing performance features were characterized, and more sensitive derivatives of the GcdR biosensor were created by manipulating the expression of the transcription factor. Sensor vectors were then reintroduced into P. putida and evaluated for their ability to respond to glutarate and various lysine metabolites. Additionally, we developed a novel mathematical approach to describe the usable range of detection for genetically encoded biosensors, which may be broadly useful in future efforts to better characterize biosensor performance.

Genetic Selection for Small Molecule Production in Competitive Microfluidic Droplets

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

Artificial Caprolactam-Specific Riboswitch as an Intracellular Metabolite Sensor

Caprolactam is a monomer used for the synthesis of nylon-6, and a recombinant microbial strain for biobased production of nylon-6 was recently developed. An intracellular biosensor for caprolactam can facilitate high-throughput metabolic engineering of recombinant microbial strains. Because of the mixed production of caprolactam and valerolactam in the recombinant strain, a caprolactam biosensor should be highly specific for caprolactam. However, a highly specific caprolactam sensor has not been reported. Here, we developed an artificial riboswitch that specifically responds to caprolactam. This riboswitch was prepared using a coupled in vitro- in vivo selection strategy with a heterogeneous pool of RNA aptamers obtained from in vitro selection to construct a riboswitch library used in in vivo selection. The caprolactam riboswitch successfully discriminated caprolactam from valerolactam. Moreover, the riboswitch was activated by 3.36-fold in the presence of 50 mM caprolactam. This riboswitch enabled caprolactam-dependent control of cell growth, which will be useful for improving caprolactam production and is a valuable tool for metabolic engineering.

Metabolic engineering advances and prospects for amino acid production

Amino acid fermentation is one of the major pillars of industrial biotechnology. The multi-billion USD amino acid market is rising steadily and is diversifying. Metabolic engineering is no longer focused solely on strain development for the bulk amino acids L-glutamate and L-lysine that are produced at the million-ton scale, but targets specialty amino acids. These demands are met by the development and application of new metabolic engineering tools including CRISPR and biosensor technologies as well as production processes by enabling a flexible feedstock concept, co-production and co-cultivation schemes. Metabolic engineering advances are exemplified for specialty proteinogenic amino acids, cyclic amino acids, omega-amino acids, and amino acids functionalized by hydroxylation, halogenation and N-methylation.

Development of a Synthetic 3-Dehydroshikimate Biosensor in Escherichia coli for Metabolite Monitoring and Genetic Screening

Biosensors for target metabolites provide powerful high-throughput screening tools to obtain high-performing strains. However, well-characterized metabolite-sensing modules are often unavailable and limit rapid access to the robust biosensors with successful applications. In this study, we developed a strategy of transcriptome-assisted metabolite-sensing (TAMES) to identify the target metabolite-sensing module based on selectively comparative transcriptome analysis between the target metabolite producing and nonproducing strains and a subsequent quantative reverse transcription (RT-qPCR) evaluation. The strategy was applied to identify the sensing module cusR that responds positively to the metabolite 3-dehydroshikimate (DHS) and proved it was effective to narrow down the candidates. We further constructed the cusR-based synthetic biosensor and established the DHS biosensor-based high-throughput screening (HTS) platform to screen higher DHS-producing strains and successfully increased DHS production by more than 90%. This study demonstrated that the TAMES strategy was effective at exploiting the metabolite-sensing transcriptional regulator, and this could likely be extended to develop the biosensor-based HTS platforms for other molecules.

Establishment of BmoR-based biosensor to screen isobutanol overproducer

Background

Isobutanol, a C4 branched-chain higher alcohol, is regarded as an attractive next-generation transport fuel. Metabolic engineering for efficient isobutanol production has been achieved in many studies. BmoR, an alcohol-regulated transcription factor, mediates a σ⁵⁴-dependent promoter P_bmo of alkane monooxygenase in n-alkane metabolism of Thauera butanivorans and displays high sensitivity to C4–C6 linear alcohols and C3–C5 branched-chain alcohols. In this study, to achieve the high-level production of isobutanol, we established a screening system which relied on the combination of BmoR-based biosensor and isobutanol biosynthetic pathway and then employed it to screen isobutanol overproduction strains from an ARTP mutagenesis library.

Results

Firstly, we constructed and verified a GFP-based BmoR-P_bmo device responding to the isobutanol produced by the host. Then, this screening system was employed to select three mutants which exhibited higher GFP/OD₆₀₀ values than that of wild type. Significantly, GFP/OD₆₀₀ of mutant 10 was 190.7 ± 4.8, a 1.4-fold higher value than that of wild type. Correspondingly, the isobutanol titer of that strain was 1597.6 ± 129.6 mg/L, 2.0-fold higher than the wild type. With the overexpression of upstream pathway genes, the isobutanol production from mutant 10 reached 14.0 ± 1.0 g/L after medium optimization in shake flask. The isobutanol titer reached 56.5 ± 1.8 g/L in a fed-batch production experiment.

Conclusions

This work screened out isobutanol overproduction strains from a mutagenesis library by using a screening system which depended on the combination of BmoR-based biosensor and isobutanol biosynthetic pathway. Optimizing fermentation condition and reinforcing upstream pathway could realize the increase of isobutanol production from the overproducer. Lastly, fed-batch fermentation of the mutant enhanced the isobutanol production to 56.5 ± 1.8 g/L.

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

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

Expanding lysine industry: industrial biomanufacturing of lysine and its derivatives

l-Lysine is widely used as a nutrition supplement in feed, food, and beverage industries as well as a chemical intermediate. At present, great efforts are made to further decrease the cost of lysine to make it more competitive in the markets. Furthermore, lysine also shows potential as a feedstock to produce other high-value chemicals for active pharmaceutical ingredients, drugs, or materials. In this review, the current biomanufacturing of lysine is first presented. Second, the production of novel derivatives from lysine is discussed. Some chemicals like l-pipecolic acid, cadaverine, and 5-aminovalerate already have been obtained at a lab scale. Others like 6-aminocaproic acid, valerolactam, and caprolactam could be produced through a biological and chemical coupling pathway or be synthesized by a hypothetical pathway. This review demonstrates an active and expansive lysine industry, and these green biomanufacturing strategies could also be applied to enhance the competitiveness of other amino acid industry.

In vivo biosensors: mechanisms, development, and applications

In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

Isolation and characterization of novel mutations in the pSC101 origin that increase copy number

pSC101 is a narrow host range, low-copy plasmid commonly used for genetically manipulating Escherichia coli. As a byproduct of a genetic screen for a more sensitive lactam biosensor, we identified multiple novel mutations that increase the copy number of plasmids with the pSC101 origin. All mutations identified in this study occurred on plasmids which also contained at least one mutation localized to the RepA protein encoded within the origin. Homology modelling predicts that many of these mutations occur within the dimerization interface of RepA. Mutant RepA resulted in plasmid copy numbers between ~31 and ~113 copies/cell, relative to ~5 copies/cell in wild-type pSC101 plasmids. Combining the mutations that were predicted to disrupt multiple contacts on the dimerization interface resulted in copy numbers of ~500 copies/cell, while also attenuating growth in host strains. Fluorescent protein production expressed from an arabinose-inducible promoter on mutant origin derived plasmids did correlate with copy number. Plasmids harboring RepA with one of two mutations, E83K and N99D, resulted in fluorescent protein production similar to that from p15a- (~20 copies/cell) and ColE1- (~31 copies/cell) based plasmids, respectively. The mutant copy number variants retained compatibility with p15a, pBBR, and ColE1 origins of replication. These pSC101 variants may be useful in future metabolic engineering efforts that require medium or high-copy vectors compatible with p15a- and ColE1-based plasmids.

Development of a formaldehyde biosensor with application to synthetic methylotrophy

Formaldehyde is a prevalent environmental toxin and a key intermediate in single carbon metabolism. The ability to monitor formaldehyde concentration is, therefore, of interest for both environmental monitoring and for metabolic engineering of native and synthetic methylotrophs, but current methods suffer from low sensitivity, complex workflows, or require expensive analytical equipment. Here we develop a formaldehyde biosensor based on the FrmR repressor protein and cognate promoter of Escherichia coli. Optimization of the native repressor binding site and regulatory architecture enabled detection at levels as low as 1 µM. We then used the sensor to benchmark the in vivo activity of several NAD-dependent methanol dehydrogenase (Mdh) variants, the rate-limiting enzyme that catalyzes the first step of methanol assimilation. In order to use this biosensor to distinguish individuals in a mixed population of Mdh variants, we developed a strategy to prevent cross-talk by using glutathione as a formaldehyde sink to minimize intercellular formaldehyde diffusion. Finally, we applied this biosensor to balance expression of mdh and the formaldehyde assimilation enzymes hps and phi in an engineered E. coli strain to minimize formaldehyde build-up while also reducing the burden of heterologous expression. This biosensor offers a quick and simple method for sensitively detecting formaldehyde, and has the potential to be used as the basis for directed evolution of Mdh and dynamic formaldehyde control strategies for establishing synthetic methylotrophy.

Design, Optimization and Application of Small Molecule Biosensor in Metabolic Engineering

The development of synthetic biology and metabolic engineering has painted a great future for the bio-based economy, including fuels, chemicals, and drugs produced from renewable feedstocks. With the rapid advance of genome-scale modeling, pathway assembling and genome engineering/editing, our ability to design and generate microbial cell factories with various phenotype becomes almost limitless. However, our lack of ability to measure and exert precise control over metabolite concentration related phenotypes becomes a bottleneck in metabolic engineering. Genetically encoded small molecule biosensors, which provide the means to couple metabolite concentration to measurable or actionable outputs, are highly promising solutions to the bottleneck. Here we review recent advances in the design, optimization and application of small molecule biosensor in metabolic engineering, with particular focus on optimization strategies for transcription factor (TF) based biosensors.

Enabling tools for high-throughput detection of metabolites: Metabolic engineering and directed evolution applications

Within the Design-Build-Test Cycle for strain engineering, rapid product detection and selection strategies remain challenging and limit overall throughput. Here we summarize a wide variety of modalities that transduce chemical concentrations into easily measured absorbance, luminescence, and fluorescence signals. Specifically, we cover protein-based biosensors (including transcription factors), nucleic acid-based biosensors, coupled enzyme reactions, bioorthogonal chemistry, and fluorescent and chromogenic dyes and substrates as modalities for detection. We focus on the use of these methods for strain engineering and enzyme discovery and conclude with remarks on the current and future state of biosensor development for application in the metabolic engineering field.