A Genetically Encoded Biosensor for Monitoring Isoprene Production in Engineered Escherichia coli

Innovations, Challenges and Future Directions of T7RNA Polymerase in Microbial Cell Factories

The study of "resource allocator" bacteriophage T7 RNA polymerase (T7RNAP) has garnered significant interest, particularly for optimizing transcriptional systems in microbial cell factories (MCFs). Most previous reviews have primarily focused on T7RNAP by dissecting specific aspects of its molecular structure and functional dynamics; this critical review seeks to broaden the scope. We emphasize a comprehensive guide in utilizing the versatile T7RNAP variants, covering both fundamental principles and fine-tuned circuit designs for synthetic biology applications. Recent advancements in engineered T7RNAP with enhanced specificity and controllability are also highlighted. Furthermore, we discuss the host compatibility considerations for implementing T7RNAP systems in sustainable bioproduction. Finally, key challenges of regulatory complexities and emerging opportunities for next-generation T7RNAP technology are discussed, reinforcing future directions for improving MCF performance.

A sensitive PnpR-based biosensor for p-nitrophenol detection

A common aromatic and phenolic pollutant, p-nitrophenol (PNP), is widely used in various industry and has serious risk to the environmental health. Biosensors have been extensively employed as an alternative technology for pollutants monitoring. By mining the new sensing elements, more specific biosensors could be characterized for highly sensitive detection. Herein, the PnpR transcription factor was identified to activate the transcription of pnpC1 by binding to PpnpC1 promoter region in P. putida DLL-E4, and PNP was recognized as its specific inducer. The PnpR-based biosensor for detection of PNP was developed, demonstrating adequate sensitivity in a liquid solution with satisfactory specificity. The biosensor was optimized by adopting a transcriptional amplifier, which increased the maximum output by 149-fold, and improved the detection limit by 100-fold, from 1 mg/L to 10 μg/L. These biosensors had a linear range of 5-80 mg/L and 0.01-1.0 mg/L for PNP determination, respectively. Then, the agarose gel entrapment-based biosensor was constructed and allowed a good of PNP detection in the range of 5-60 mg/L in M9 solid agar within 70 min, and a detection sensitive of 16.8 mg/kg in soil. The good performance of the biosensor suggested its potential application of high-efficient and on-site detection in environmental matrices.

Utilizing a high-throughput visualization screening technology to develop a genetically encoded biosensor for monitoring 5-aminolevulinic acid production in engineered Escherichia coli

5-Aminolevulinic acid (5-ALA) is a non-protein amino acid widely used in agriculture, animal husbandry and medicine. Currently, microbial cell factories are a promising production pathway, but the lack of high-throughput fermentation strain screening tools often hinders the exploration of engineering strategies to increase cell factory yields. Here, mutant AC103-3H was screened from libraries of saturating mutants after response-specific engineering of the transcription factor AsnC of L-asparagine (Asn). Based on mutant AC103-3H, a whole-cell biosensor EAC103-3H with a specific response to 5-ALA was constructed, which has a linear dynamic detection range of 1-12 mM and a detection limit of 0.094 mM, and can be used for in situ screening of potential high-producing 5-ALA strains. With its support, overexpression of the C5 pathway genes using promoter engineering assistance resulted in a 4.78-fold enhancement of 5-ALA production in the engineered E. coli. This study provides an efficient strain screening tool for exploring approaches to improve the 5-ALA productivity of engineered strains.

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.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

Engineered Methylococcus capsulatus Bath for efficient methane conversion to isoprene

Isoprene has numerous industrial applications, including rubber polymer and potential biofuel. Microbial methane-based isoprene production could be a cost-effective and environmentally benign process, owing to a reduced carbon footprint and economical utilization of methane. In this study, Methylococcus capsulatus Bath was engineered to produce isoprene from methane by introducing the exogenous mevalonate (MVA) pathway. Overexpression of MVA pathway enzymes and isoprene synthase from Populus trichocarpa under the control of a phenol-inducible promoter substantially improved isoprene production. M. capsulatus Bath was further engineered using a CRISPR-base editor to disrupt the expression of soluble methane monooxygenase (sMMO), which oxidizes isoprene to cause toxicity. Additionally, optimization of the metabolic flux in the MVA pathway and culture conditions increased isoprene production to 228.1 mg/L, the highest known titer for methanotroph-based isoprene production. The developed methanotroph could facilitate the efficient conversion of methane to isoprene, resulting in the sustainable production of value-added chemicals.

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.

Decoding Catalysis by Terpene Synthases

The review by Christianson, published in 2017 on the twentieth anniversary of the emergence of the field, summarizes the foundational discoveries and key advances in terpene synthase/cyclase (TS) biocatalysis (Christianson, D. W. Chem Rev2017, 117 (17), 11570-11648. DOI: 10.1021/acs.chemrev.7b00287). Here, we review the TS literature published since then, bringing the field up to date and looking forward to what could be the near future of TS rational design. Many revealing discoveries have been made in recent years, building on the knowledge and fundamental principles uncovered during those initial two decades of study. We use these to explore TS reaction chemistry and see how a combined experimental and computational approach helps to decipher the complexities of TS catalysis. Revealed are a suite of catalytic motifs which control product outcome in TSs, some obvious, some more subtle. We examine each in detail, using the most recent papers and insights to illustrate how exactly this fascinating class of enzymes takes a single acyclic substrate and turns it into the many thousands of complex terpenoids found in Nature. We then explore some of the recent strategies for TS engineering, including machine learning and other data-driven approaches. From this, rational and predictive engineering of TSs, "designer terpene synthases", will begin to emerge as a realistic goal.

AraC-Based Biosensor for the Detection of Isoprene in E. coli

Isoprene is a valuable platform chemical, which is produced by engineered microorganisms, albeit in low quantities. The amount of isoprene produced is usually measured by gas chromatography, which can be time-consuming and expensive. Alternatively, biosensors have evolved as a powerful tool for real-time high-throughput screening and monitoring of product synthesis. The AraC-pBAD-inducible system has been widely studied, evolved, and engineered to develop biosensors for small molecules. In our preliminary studies, the AraC-pBAD system was mildly induced at higher isoprene concentrations when arabinose was also available. Hence, in the present study, we designed and constructed a synthetic biosensor based on the AraC-pBAD system, wherein the ligand-binding domain of AraC was replaced with IsoA. On introducing this chimeric AraC-IsoA (AcIa) transcription factor with the native PBAD promoter system regulating rfp gene expression, fluorescence output was observed only when wild-type Escherichia coli cells were induced with both isoprene and arabinose. The biosensor sensitivity and dynamic range were further enhanced by removing operator sequences and by substituting the native promoter (PAraC) with the strong tac promoter (Ptac). The chimeric sensor did not work in AraC knockout strains; however, functionality was restored by reintroducing AraC. Hence, AraC is essential for the functioning of our biosensor, while AcIa provides enhanced sensitivity and specificity for isoprene. However, insights into how AraC-AcIa interacts and the possible working mechanism remain to be explored. This study provides a prototype for developing chimeric AraC-based biosensors with proteins devoid of known dimerizing domains and opens a new avenue for further study and exploration.

Application of Metabolite-Responsive Biosensors for Plant Natural Products Biosynthesis

Plant natural products (PNPs) have shown various pharmaceutical activities, possessing great potential in global markets. Microbial cell factories (MCFs) provide an economical and sustainable alternative for the synthesis of valuable PNPs compared with traditional approaches. However, the heterologous synthetic pathways always lack native regulatory systems, bringing extra burden to PNPs production. To overcome the challenges, biosensors have been exploited and engineered as powerful tools for establishing artificial regulatory networks to control enzyme expression in response to environments. Here, we reviewed the recent progress involved in the application of biosensors that are responsive to PNPs and their precursors. Specifically, the key roles these biosensors played in PNP synthesis pathways, including isoprenoids, flavonoids, stilbenoids and alkaloids, were discussed in detail.

Advances in Synthetic-Biology-Based Whole-Cell Biosensors: Principles, Genetic Modules, and Applications in Food Safety

A whole-cell biosensor based on synthetic biology provides a promising new method for the on-site detection of food contaminants. The basic components of whole-cell biosensors include the sensing elements, such as transcription factors and riboswitches, and reporting elements, such as fluorescence, gas, etc. The sensing and reporting elements are coupled through gene expression regulation to form a simple gene circuit for the detection of target substances. Additionally, a more complex gene circuit can involve other functional elements or modules such as signal amplification, multiple detection, and delay reporting. With the help of synthetic biology, whole-cell biosensors are becoming more versatile and integrated, that is, integrating pre-detection sample processing, detection processes, and post-detection signal calculation and storage processes into cells. Due to the relative stability of the intracellular environment, whole-cell biosensors are highly resistant to interference without the need of complex sample preprocessing. Due to the reproduction of chassis cells, whole-cell biosensors replicate all elements automatically without the need for purification processing. Therefore, whole-cell biosensors are easy to operate and simple to produce. Based on the above advantages, whole-cell biosensors are more suitable for on-site detection than other rapid detection methods. Whole-cell biosensors have been applied in various forms such as test strips and kits, with the latest reported forms being wearable devices such as masks, hand rings, and clothing. This paper examines the composition, construction methods, and types of the fundamental components of synthetic biological whole-cell biosensors. We also introduce the prospect and development trend of whole-cell biosensors in commercial applications.

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.

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.

Enzyme directed evolution using genetically encodable biosensors

Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.

Engineering whole-cell microbial biosensors: Design principles and applications in monitoring and treatment of heavy metals and organic pollutants

Biosensors have been widely used as cost-effective, rapid, in situ, and real-time analytical tools for monitoring environments. The development of synthetic biology has enabled emergence of genetically engineered whole-cell microbial biosensors. This review updates the design and optimization principles for a diverse array of whole-cell biosensors based on transcription factors (TF) including activators or repressors derived from heavy metal resistance systems, alkanes, and aromatics metabolic pathways of bacteria. By designing genetic circuits, the whole-cell biosensors could be engineered to intelligently sense heavy metals (Hg²⁺, Zn²⁺, Pb²⁺, Au³⁺, Cd²⁺, As³⁺, Ni²⁺, Cu²⁺, and UO₂²⁺) or organic compounds (alcohols, alkanes, phenols, and benzenes) through one-component or two-component system-based TFs, transduce signals through genetic amplifiers, and response as various outputs such as cell fluorescence and bioelectricity for monitoring heavy metals and organic pollutants in real conditions, synthetic curli and surface metal-binding peptides for in situ bio-sorption of heavy metals. We further review strategies that have been implemented to optimize the selectivity and correlation between ligand concentration and output signal of the TF-based biosensors, so as to meet requirements of practical applications. The optimization strategies include protein engineering to change specificities, promoter engineering to improve sensitivities, and genetic circuit-based amplification to enhance dynamic ranges via designing transcriptional amplifiers, logic gates, and feedback loops. At last, we outlook future trends in developing novel forms of biosensors.

Recent advances in metabolic engineering of microorganisms for advancing lignocellulose-derived biofuels

Combating climate change and ensuring energy supply to a rapidly growing global population has highlighted the need to replace petroleum fuels with clean, and sustainable renewable fuels. Biofuels offer a solution to safeguard energy security with reduced ecological footprint and process economics. Over the past years, lignocellulosic biomass has become the most preferred raw material for the production of biofuels, such as fuel, alcohol, biodiesel, and biohydrogen. However, the cost-effective conversion of lignocellulose into biofuels remains an unsolved challenge at the industrial scale. Recently, intensive efforts have been made in lignocellulose feedstock and microbial engineering to address this problem. By improving the biological pathways leading to the polysaccharide, lignin, and lipid biosynthesis, limited success has been achieved, and still needs to improve sustainable biofuel production. Impressive success is being achieved by the retouring metabolic pathways of different microbial hosts. Several robust phenotypes, mostly from bacteria and yeast domains, have been successfully constructed with improved substrate spectrum, product yield and sturdiness against hydrolysate toxins. Cyanobacteria is also being explored for metabolic advancement in recent years, however, it also remained underdeveloped to generate commercialized biofuels. The bacterium Escherichia coli and yeast Saccharomyces cerevisiae strains are also being engineered to have cell surfaces displaying hydrolytic enzymes, which holds much promise for near-term scale-up and biorefinery use. Looking forward, future advances to achieve economically feasible production of lignocellulosic-based biofuels with special focus on designing more efficient metabolic pathways coupled with screening, and engineering of novel enzymes.

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

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

Predictive Engineering of Class I Terpene Synthases Using Experimental and Computational Approaches

Terpenoids are a highly diverse group of natural products with considerable industrial interest. Increasingly, engineered microbes are used for the production of terpenoids to replace natural extracts and chemical synthesis. Terpene synthases (TSs) show a high level of functional plasticity and are responsible for the vast structural diversity observed in natural terpenoids. Their relatively inert active sites guide intrinsically reactive linear carbocation intermediates along one of many cyclisation paths via exertion of subtle steric and electrostatic control. Due to the absence of a strong protein interaction with these intermediates, there is a remarkable lack of sequence-function relationship within the TS family, making product-outcome predictions from sequences alone challenging. This, in combination with the fact that many TSs produce multiple products from a single substrate hampers the design and use of TSs in the biomanufacturing of terpenoids. This review highlights recent advances in genome mining, computational modelling, high-throughput screening, and machine-learning that will allow more predictive engineering of these fascinating enzymes in the near future.

Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories

Terpenoids form the most diversified class of natural products, which have gained application in the pharmaceutical, food, transportation, and fine and bulk chemical industries. Extraction from naturally occurring sources does not meet industrial demands, whereas chemical synthesis is often associated with poor enantio-selectivity, harsh working conditions, and environmental pollutions. Microbial cell factories come as a suitable replacement. However, designing efficient microbial platforms for isoprenoid synthesis is often a challenging task. This has to do with the cytotoxic effects of pathway intermediates and some end products, instability of expressed pathways, as well as high enzyme promiscuity. Also, the low enzymatic activity of some terpene synthases and prenyltransferases, and the lack of an efficient throughput system to screen improved high-performing strains are bottlenecks in strain development. Metabolic engineering and synthetic biology seek to overcome these issues through the provision of effective synthetic tools. This review sought to provide an in-depth description of novel strategies for improving cell factory performance. We focused on improving transcriptional and translational efficiencies through static and dynamic regulatory elements, enzyme engineering and high-throughput screening strategies, cellular function enhancement through chromosomal integration, metabolite tolerance, and modularization of pathways.

CRISPR-Based Genetic Switches and Other Complex Circuits: Research and Application

CRISPR-based enzymes have offered a unique capability to the design of genetic switches, with advantages in designability, modularity and orthogonality. CRISPR-based genetic switches operate on multiple levels of life, including transcription and translation. In both prokaryotic and eukaryotic cells, deactivated CRISPR endonuclease and endoribonuclease have served in genetic switches for activating or repressing gene expression, at both transcriptional and translational levels. With these genetic switches, more complex circuits have been assembled to achieve sophisticated functions including inducible switches, non-linear response and logical biocomputation. As more CRISPR enzymes continue to be excavated, CRISPR-based genetic switches will be used in a much wider range of applications.

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.

Engineering of a Biosensor in Response to Malate in Bacillus licheniformis

Malate is an essential intermediate in the tricarboxylic acid (TCA) cycle; it also has valuable uses in medicine and food. The production of malate with a microbial synthesis method is still in its early stages. One of the key problems in metabolic engineering is that the dynamic and subtle changes in malate are difficult to detect. It remains critical to develop techniques with direct and precise detection of malate in microbial metabolism, which facilitates high-throughput screening of the engineered strains. In this study, a genetically encoded biosensor in response to malate was constructed in B. licheniformis. Key regulator MalR and the action site of the biosensor were first identified. Then, the output of the reporter gene expression was amplified by introducing a strong constitutive promoter and iteratively tuning the action sites. The engineered biosensor can respond to malate from 5 to 15 g/L; within this range, it shows a linear correlation between eGFP fluorescence and malate concentration. This biosensor enrich our toolbox of synthetic biology in pathway engineering for malate production in microorganisms.

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.

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.

Genetically Encoded Biosensor-Based Screening for Directed Bacteriophage T4 Lysozyme Evolution

Lysozyme is widely used as a model protein in studies of structure–function relationships. Recently, lysozyme has gained attention for use in accelerating the degradation of secondary sludge, which mainly consists of bacteria. However, a high-throughput screening system for lysozyme engineering has not been reported. Here, we present a lysozyme screening system using a genetically encoded biosensor. We first cloned bacteriophage T4 lysozyme (T4L) into a plasmid under control of the araBAD promoter. The plasmid was expressed in Escherichia coli with no toxic effects on growth. Next, we observed that increased soluble T4L expression decreased the fluorescence produced by the genetic enzyme screening system. To investigate T4L evolution based on this finding, we generated a T4L random mutation library, which was screened using the genetic enzyme screening system. Finally, we identified two T4L variants showing 1.4-fold enhanced lytic activity compared to native T4L. To our knowledge, this is the first report describing the use of a genetically encoded biosensor to investigate bacteriophage T4L evolution. Our approach can be used to investigate the evolution of other lysozymes, which will expand the applications of lysozyme.

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.

Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor

Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

CRISPR-based gene expression control for synthetic gene circuits

Synthetic gene circuits allow us to govern cell behavior in a programmable manner, which is central to almost any application aiming to harness engineered living cells for user-defined tasks. Transcription factors (TFs) constitute the 'classic' tool for synthetic circuit construction but some of their inherent constraints, such as insufficient modularity, orthogonality and programmability, limit progress in such forward-engineering endeavors. Here we review how CRISPR (clustered regularly interspaced short palindromic repeats) technology offers new and powerful possibilities for synthetic circuit design. CRISPR systems offer superior characteristics over TFs in many aspects relevant to a modular, predictable and standardized circuit design. Thus, the choice of CRISPR technology as a framework for synthetic circuit design constitutes a valid alternative to complement or replace TFs in synthetic circuits and promises the realization of more ambitious designs.

Development of a Biosensor for Crotonobetaine-CoA Ligase Screening Based on the Elucidation of Escherichia coli Carnitine Metabolism

l-Carnitine is essential in the intermediary metabolism of eukaryotes and is involved in the β-oxidation of medium- and long-chain fatty acids; thus, it has applications for medicinal purposes and as a dietary supplement. In addition, l-carnitine plays roles in bacterial physiology and metabolism, which have been exploited by the industry to develop biotechnological carnitine production processes. Here, on the basis of studies of l-carnitine metabolism in Escherichia coli and its activation by the transcriptional activator CaiF, a biosensor was developed. It expresses a fluorescent reporter gene that responds in a dose-dependent manner to crotonobetainyl-CoA, which is an intermediate of l-carnitine metabolism in E. coli and is proposed to be a coactivator of CaiF. Moreover, a dual-input biosensor for l-carnitine and crotonobetaine was developed. As an application of the biosensor, potential homologues of the betaine:CoA ligase CaiC from Citrobacter freundii, Proteus mirabilis, and Arcobacter marinus were screened and shown to be functionally active CaiC variants. These variants and the developed biosensor may be valuable for improving l-carnitine production processes.

Sensitive and Rapid Phenotyping of Microbes With Soluble Methane Monooxygenase Using a Droplet-Based Assay

Methanotrophs with soluble methane monooxygenase (sMMO) show high potential for various ecological and biotechnological applications. Here, we developed a high throughput method to identify sMMO-producing microbes by integrating droplet microfluidics and a genetic circuit-based biosensor system. sMMO-producers and sensor cells were encapsulated in monodispersed droplets with benzene as the substrate and incubated for 5 h. The sensor cells were analyzed as the reporter for phenol-sensitive transcription activation of fluorescence. Various combinations of methanotrophs and biosensor cells were investigated to optimize the performance of our droplet-integrated transcriptional factor biosensor system. As a result, the conditions to ensure sMMO activity to convert the starting material, benzene, into phenol, were determined. The biosensor signals were sensitive and quantitative under optimal conditions, showing that phenol is metabolically stable within both cell species and accumulates in picoliter-sized droplets, and the biosensor cells are healthy enough to respond quantitatively to the phenol produced. These results show that our system would be useful for rapid evaluation of phenotypes of methanotrophs showing sMMO activity, while minimizing the necessity of time-consuming cultivation and enzyme preparation, which are required for conventional analysis of sMMO activity.

Acclimation of bacterial cell state for high-throughput enzyme engineering using a DmpR-dependent transcriptional activation system

Genetic circuit-based biosensors have emerged as an effective analytical tool in synthetic biology; these biosensors can be applied to high-throughput screening of new biocatalysts and metabolic pathways. Sigma 54 (σ⁵⁴)-dependent transcription factor (TF) can be a valuable component of these biosensors owing to its intrinsic silent property compared to most of the housekeeping sigma 70 (σ⁷⁰) TFs. Here, we show that these unique characteristics of σ⁵⁴-dependent TFs can be used to control the host cell state to be more appropriate for high-throughput screening. The acclimation of cell state was achieved by using guanosine (penta)tetraphosphate ((p)ppGpp)-related genes (relA, spoT) and nutrient conditions, to link the σ⁵⁴ TF-based reporter expression with the target enzyme activity. By controlling stringent programmed responses and optimizing assay conditions, catalytically improved tyrosine phenol lyase (TPL) enzymes were successfully obtained using a σ⁵⁴-dependent DmpR as the TF component, demonstrating the practical feasibility of this biosensor. This combinatorial strategy of biosensors using σ factor-dependent TFs will allow for more effective high-throughput enzyme engineering with broad applicability.

New Insight into Plasmid-Driven T7 RNA Polymerase in Escherichia coli and Use as a Genetic Amplifier for a Biosensor

T7 RNA polymerase (T7RNAP) and T7 promoter are powerful genetic components, thus a plasmid-driven T7 (PDT7) genetic circuit could be broadly applied for synthetic biology. However, the limited knowledge of the toxicity and instability of such a system still restricts its application. Herein, we constructed 16 constitutive genetic circuts of PDT7 and investigated the orthogonal effects in toxicity and instability. The T7 toxicity was elucidated from the construction processes and cell growth characterization, showing the importance of optimal orthogonality for PDT7. Besides, a protein analysis was performed to validate how the T7 system affected cell metabolism and led to the instability. The application of constitutive PDT7 in functional protein expressions, including carbonic anhydrase, lysine decarboxylase, and 5-ALA synthetase was demonstrated. Furthermore, PDT7 working as a genetic amplifier had been designed for E. coli cell-based biosensors, which illustrated the opportunities in the future of PDT7 used in synthetic biology.

Design of a programmable biosensor-CRISPRi genetic circuits for dynamic and autonomous dual-control of metabolic flux in Bacillus subtilis

Dynamic regulation is an effective strategy for fine-tuning metabolic pathways in order to maximize target product synthesis. However, achieving dynamic and autonomous up- and down-regulation of the metabolic modules of interest simultaneously, still remains a great challenge. In this work, we created an autonomous dual-control (ADC) system, by combining CRISPRi-based NOT gates with novel biosensors of a key metabolite in the pathway of interest. By sensing the levels of the intermediate glucosamine-6-phosphate (GlcN6P) and self-adjusting the expression levels of the target genes accordingly with the GlcN6P biosensor and ADC system enabled feedback circuits, the metabolic flux towards the production of the high value nutraceutical N-acetylglucosamine (GlcNAc) could be balanced and optimized in Bacillus subtilis. As a result, the GlcNAc titer in a 15-l fed-batch bioreactor increased from 59.9 g/l to 97.1 g/l with acetoin production and 81.7 g/l to 131.6 g/l without acetoin production, indicating the robustness and stability of the synthetic circuits in a large bioreactor system. Remarkably, this self-regulatory methodology does not require any external level of control such as the use of inducer molecules or switching fermentation/environmental conditions. Moreover, the proposed programmable genetic circuits may be expanded to engineer other microbial cells and metabolic pathways.

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.

Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes

Background

Transcription factor-based biosensors are useful tools for the detection of metabolites and industrially valuable molecules, and present many potential applications in biotechnology and biomedicine. However, the most common approach to develop biosensors relies on employing a limited set of naturally occurring allosteric transcription factors (aTFs). Therefore, altering the ligand specificity of aTFs towards the detection of new effectors is an important goal.

Results

Here, the PcaV repressor, a member of the MarR aTF family, was used to develop a biosensor for the detection of hydroxyl-substituted benzoic acids, including protocatechuic acid (PCA). The PCA biosensor was further subjected to directed evolution to alter its ligand specificity towards vanillin and other closely related aromatic aldehydes, to generate the Van2 biosensor. Ligand recognition of Van2 was explored in vitro using a range of biochemical and biophysical analyses, and extensive in vivo genetic-phenotypic analysis was performed to determine the role of each amino acid change upon biosensor performance.

Conclusions

This is the first study to report directed evolution of a member of the MarR aTF family, and demonstrates the plasticity of the PCA biosensor by altering its ligand specificity to generate a biosensor for aromatic aldehydes.

Engineering transcription factor BmoR for screening butanol overproducers

The wild-type transcription factors are sensitive to their corresponding signal molecules. Using wild-type transcription factors as biosensors to screen industrial overproducers are generally impractical because of their narrow detection ranges. This study took transcription factor BmoR as an example and aimed to expand the detection range of BmoR for screening alcohols overproducers. Firstly, a BmoR mutation library was established, and the mutations distributed randomly in all predicted functional domains of BmoR. Structure of BmoR-isobutanol complex were modelled, and isobutanol binding sites were confirmed by site-directed mutagenesis. Subsequently, the effects of the mutations on the detection range or output were confirmed in the BmoR mutants. Four combinatorial mutants containing one increased-detection-range mutation and one enhanced-output mutation were constructed. Compared with wild-type BmoR, F276A/E627N BmoR and D333N/E627N BmoR have wider detection ranges (0-100 mM) and relatively high outputs to the isobutanol added quantitatively or produced intracellularly, demonstrating they have potential for screening isobutanol overproduction strains. This work presented an example of engineering the wild-type transcription factors with physiological significance for industrial utilization.

An automated pipeline for the screening of diverse monoterpene synthase libraries

Monoterpenoids are a structurally diverse group of natural products with applications as pharmaceuticals, flavourings, fragrances, pesticides, and biofuels. Recent advances in synthetic biology offer new routes to this chemical diversity through the introduction of heterologous isoprenoid production pathways into engineered microorganisms. Due to the nature of the branched reaction mechanism, monoterpene synthases often produce multiple products when expressed in monoterpenoid production platforms. Rational engineering of terpene synthases is challenging due to a lack of correlation between protein sequence and cyclisation reaction catalysed. Directed evolution offers an attractive alternative protein engineering strategy as limited prior sequence-function knowledge is required. However, directed evolution of terpene synthases is hampered by the lack of a convenient high-throughput screening assay for the detection of multiple volatile terpene products. Here we applied an automated pipeline for the screening of diverse monoterpene synthase libraries, employing robotic liquid handling platforms coupled to GC-MS, and automated data extraction. We used the pipeline to screen pinene synthase variant libraries, with mutations in three areas of plasticity, capable of producing multiple monoterpene products. We successfully identified variants with altered product profiles and demonstrated good agreement between the results of the automated screen and traditional shake-flask cultures. In addition, useful insights into the cyclisation reaction catalysed by pinene synthase were obtained, including the identification of positions with the highest level of plasticity, and the significance of region 2 in carbocation cyclisation. The results obtained will aid the prediction and design of novel terpene synthase activities towards clean monoterpenoid products.

Enhanced (−)-α-Bisabolol Productivity by Efficient Conversion of Mevalonate in Escherichia coli

(−)-α-Bisabolol, a naturally occurring sesquiterpene alcohol, has been used in pharmaceuticals and cosmetics owing to its beneficial effects on inflammation and skin healing. Previously, we reported the high production of (−)-α-bisabolol by fed-batch fermentation using engineered Escherichia coli (E. coli) expressing the exogenous mevalonate (MVA) pathway genes. The productivity of (−)-α-bisabolol must be improved before industrial application. Here, we report enhancement of initial (−)-α-bisabolol productivity to 3-fold higher than that observed in our previous study. We first harnessed a farnesyl pyrophosphate (FPP)-resistant mevalonate kinase 1 (MvaK1) from an archaeon Methanosarcina mazei (M. mazei) to create a more efficient heterologous MVA pathway that produces (−)-α-bisabolol in the engineered E. coli. The resulting strain produced 1.7-fold higher (−)-α-bisabolol relative to the strain expressing a feedback-inhibitory MvaK1 from Staphylococcus aureus (S. aureus). Next, to efficiently convert accumulated MVA to (−)-α-bisabolol, we additionally overexpressed genes involved in the lower MVA mevalonate pathway in E. coli containing the entire MVA pathway genes. (−)-α-Bisabolol production increased by 1.8-fold with reduction of MVA accumulation, relative to the control strain. Finally, we optimized the fermentation conditions including inducer concentration, aeration and enzymatic cofactor. The strain was able to produce 8.5 g/L of (−)-α-bisabolol with an initial productivity of 0.12 g/L h in the optimal fed-batch fermentation. Thus, the microbial production of (−)-α-bisabolol would be an economically viable bioprocess for its industrial application.