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

Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors

Transcription factor (TF)-based biosensors (TFBs) have received considerable attention in various fields due to their capability of converting biosignals, such as molecule concentrations, into analyzable signals, thereby bypassing the dependence on time-consuming and laborious detection techniques. Natural TFs are evolutionarily optimized to maintain microbial survival and metabolic balance rather than for laboratory scenarios. As a result, native TFBs often exhibit poor performance, such as low specificity, narrow dynamic range, and limited sensitivity, hindering their application in laboratory and industrial settings. This work analyzes four types of regulatory mechanisms underlying TFBs and outlines strategies for constructing efficient sensing systems. Recent advances in TFBs across various usage scenarios are reviewed with a particular focus on the challenges of commercialization. The systematic improvement of TFB performance by modifying the constituent elements is thoroughly discussed. Additionally, we propose future directions of TFBs for developing rapid-responsive biosensors and addressing the challenge of application isolation. Furthermore, we look to the potential of artificial intelligence (AI) technologies and various models for programming TFB genetic circuits. This review sheds light on technical suggestions and fundamental instructions for constructing and engineering TFBs to promote their broader applications in Industry 4.0, including smart biomanufacturing, environmental and food contaminants detection, and medical science.

Comparative Study of Fluorescence Emission of Fisetin, Luteolin and Quercetin Powders and Solutions: Further Evidence of the ESIPT Process

Fisetin and Luteolin are important flavonoids produced in plants and known for their antioxidant, anti-inflammatory, neuroprotective, and analgesic properties. They are also good candidates for different types of biosensors. The model used to describe the fluorescence (FL) emission of these flavonoids involves an excited-state intermolecular proton transfer (ESIPT) process that causes a change in the molecule configuration and a corresponding decrease in the emission energy. Due to the different molecular structures of Fisetin and Luteolin, only one possible proton transfer within the molecule is allowed for each of them: transfer of the H3 proton for Fisetin and of the H5 for Luteolin. Here, we compare their calculated emission wavelengths, obtained using TDDFT/M06-2X/6-31++G(d,p), with their FL emission spectra measured on the corresponding powders and solutions and show that the experimental data are consistent with the presence of the ESIPT process. We also compare the emission wavelengths found for Fisetin and Luteolin with those calculated and measured for Quercetin, where, under photoexcitation, the transfers of both H3 and H5 protons are possible. We analyze the difference in the processes associated with the H3 and H5 proton transfers and discuss the reason for the predominance of the H5 proton transfer in Quercetin. Additionally, a new system of notation for flavonoid molecules is developed.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Sensbio: an online server for biosensor design

Allosteric transcription factor (aTF) based biosensors can be used to engineer genetic circuits for a wide range of applications. The literature and online databases contain hundreds of experimentally validated molecule-TF pairs; however, the knowledge is scattered and often incomplete. Additionally, compared to the number of compounds that can be produced in living systems, those with known associated TF-compound interactions are low. For these reasons, new tools that help researchers find new possible TF-ligand pairs are called for. In this work, we present Sensbio, a computational tool that through similarity comparison against a TF-ligand reference database, is able to identify putative transcription factors that can be activated by a given input molecule. In addition to the collection of algorithms, an online application has also been developed, together with a predictive model created to find new possible matches based on machine learning.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

De Novo Production of Plant 4'-Deoxyflavones Baicalein and Oroxylin A from Ethanol in Crabtree-Negative Yeast

Baicalein and oroxylin A are well-known medicinal 4'-deoxyflavones found mainly in the roots of traditional medicinal plant Scutellaria baicalensis Georgi. However, extraction from plants is time-consuming, environmentally unfriendly, and insufficient. Although microbial synthesis of flavonoids has been extensively reported, synthesis of downstream modified 4'-deoxyflavones has not, and their yields are extremely low. Here, we reassembled the S. baicalensis 4'-deoxyflavone biosynthetic pathway in a Crabtree-negative yeast, Pichia pastoris, with activity analysis and combinatorial expression of eight biosynthetic genes, allowing production of 4'-deoxyflavones like baicalein, oroxylin A, wogonin, norwogonin, 6-methoxywogonin, and the novel 6-methoxynorwogonin. De novo baicalein synthesis was then achieved by complete pathway assembly. Toxic intermediates highly impaired the cell production capacity; hence, we alleviated cinnamic acid growth inhibition by culturing the cells at near-neutral pH and using alcoholic carbon sources. To achieve pathway balance and improve baicalein and oroxylin A synthesis, we further divided the pathway into five modules. A series of ethanol-induced and constitutive transcriptional amplification devices were constructed to adapt to the modules. This fine-tuning pathway control considerably reduced byproduct and intermediate accumulation and achieved high-level de novo baicalein (401.9 mg/L with a total increase of 1182-fold, the highest titer reported) and oroxylin A (339.5 mg/L, for the first time) production from ethanol. This study provides new strategies for the microbial synthesis of 4'-deoxyflavones and other flavonoids.

Genetic regulatory element based whole-cell biosensors for the detection of metabolic disorders

Clinicians require simple, and cost-effective diagnostic tools for the quantitative determination of amino acids in physiological fluids for the detection of metabolic disorder diseases. Besides, amino acids also act as biological markers for different types of cancers and cardiovascular diseases. Herein, we applied an in-silico based approach to identify potential amino acid-responsive genetic regulatory elements for the detection of metabolic disorders in humans. Identified sequences were further transcriptionally fused with GFP, thus generating an optical readout in response to their cognate targets. Screening of genetic regulatory elements led us to discover two promoter elements (pmetE::GFP and ptrpL::GFP) that showed a significant change in the fluorescence response to homocysteine and tryptophan, respectively. The developed biosensors respond specifically and sensitively with a limit of detection of 3.8 μM and 3 μM for homocysteine and tryptophan, respectively. Furthermore, the clinical utility of this assay was demonstrated by employing it to identify homocystinuria and tryptophanuria diseases through the quantification of homocysteine and tryptophan in plasma and urine samples within 5 h. The precision and accuracy of the biosensors for disease diagnosis were well within an acceptable range. The general strategy used in this system can be expanded to screen different genetic regulatory elements present in other gram-negative and gram-positive bacteria for the detection of metabolic disorders.

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.

Substrate-activated expression of a biosynthetic pathway in Escherichia coli

Microbes can facilitate production of valuable chemicals more sustainably than traditional chemical processes in many cases: they utilize renewable feedstocks, require less energy intensive process conditions, and perform a variety of chemical reactions using endogenous or heterologous enzymes. In response to the metabolic burden imposed by production pathways, chemical inducers are frequently used to initiate gene expression after the cells have reached sufficient density. While chemically inducible promoters are a common research tool used for pathway expression, they introduce a compound extrinsic to the process along with the associated costs. We developed an expression control system for a biosynthetic pathway for the production of d-glyceric acid that utilizes galacturonate as both the inducer and the substrate, thereby eliminating the need for an extrinsic chemical inducer. Activation of expression in response to the feed is actuated by a galacturonate-responsive transcription factor biosensor. We constructed variants of the galacturonate biosensor with a heterologous transcription factor and cognate hybrid promoter, and selected for the best performer through fluorescence characterization. We showed that native E. coli regulatory systems do not interact with our biosensor and favorable biosensor response exists in the presence and absence of galacturonate consumption. We then employed the control circuit to regulate the expression of the heterologous genes of a biosynthetic pathway for the production d-glyceric acid that was previously developed in our lab. Productivity via substrate-induction with our control circuit was comparable to IPTG-controlled induction and significantly outperformed a constitutive expression control, producing 2.13 ± 0.03 g L^-1  d-glyceric acid within 6 h of galacturonate substrate addition. This work demonstrated feed-activated pathway expression to be an attractive control strategy for more readily scalable microbial biosynthesis.

Homogentisic Acid-Based Whole-Cell Biosensor for Detection of Alkaptonuria Disease

Clinicians require simple quantitative tools for the detection of homogentisic acid in alkaptonuria patients, a rare inherited disorder of amino acid metabolism. In this study, we report a whole-cell biosensor for homogentisic acid to detect alkaptonuria disease through the expression of green fluorescence protein. The assay system utilizes a promoter sequence (hmgA) isolated from the Pseudomonas aeruginosa genome. To increase the sensitivity, the sensor module harboring phmgA::GFP was further transformed into various transposon mutants debilitated in steps involved in the metabolism of phenylalanine and tyrosine via homogentisic acid as a central intermediate. The proposed biosensor was further checked for analytical features such as sensitivity, selectivity, linearity, and precision for the quantification of homogentisic acid in spiked urine samples. The limit of detection for the developed biosensor was calculated to be 3.9 μM, which is comparable to that of the various analytical techniques currently in use. The sensor construct showed no interference from all of the amino acids and its homolog molecules. The accuracy and precision of the proposed biosensor were validated using high-performance liquid chromatography (HPLC) with satisfactory results.

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.

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.

Tuning the Binding Affinity of Heme-Responsive Biosensor for Precise and Dynamic Pathway Regulation

Current challenge for dynamic pathway control in metabolic engineering is enabling the components of the artificial regulatory system to be tunable. Here, we designed and built a heme-responsive regulatory system containing a heme biosensor HrtR and CRISPRi to regulate chemicals production while maintaining the intracellular heme homeostasis. A series of engineered biosensors with varied sensitivity and threshold were obtained by semi-rational design with site saturated mutation of HrtR. The modified metabolite-binding affinity of HrtR was confirmed by heme titration and molecular dynamic simulation. Dynamic regulation pattern of the system was validated by the fluctuation of gene expression and intracellular heme concentration. The efficiency of this regulatory system was proved by improving the 5-aminolevulinic acid (ALA) production to 5.35g/L, the highest yield in batch fermentation of Escherichia coli. This system was also successfully used in improving porphobilinogen (PBG) and porphyrins biosynthesis and can be applied in many other biological processes.

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Designed and built a heme-responsive regulatory system employing HrtR and CRISPRi

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Turning the binding affinity of HrtR by site saturation mutations

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Optimizing the system to achieve dynamic regulation of target genes

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The system was applied to the ALA, PBG, and porphyrins production

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.

Biosensor-based enzyme engineering approach applied to psicose biosynthesis

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.

Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries

Synthetic biology utilizes the Design-Build-Test-Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure's low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.

Mechanistic Models of Inducible Synthetic Circuits for Joint Description of DNA Copy Number, Regulatory Protein Level, and Cell Load

Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. However, such equations may be poorly predictive in practical situations that are typical in bottom-up design, including changes in promoter copy number, regulatory protein level, and cell load. In this work, we derived novel mechanistic steady-state models of the lux inducible system, used as case study, relying on different assumptions on regulatory protein (LuxR) and cognate promoter (Plux) concentrations, inducer-protein complex formation, and resource usage limitation. We demonstrated that a change in the considered model assumptions can significantly affect circuit output, and preliminary experimental data are in accordance with the simulated activation curves. We finally showed that the models are identifiable a priori (in the analytically tractable cases) and a posteriori, and we determined the specific experiments needed to parametrize them. Although a larger-scale experimental validation is required, in the future the reported models may support synthetic circuits output prediction in practical situations with unprecedented details.

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.