Evolution-guided engineering of small-molecule biosensors

Transcriptional regulator-based biosensors for biomanufacturing in Corynebacterium glutamicum

Intracellular biosensors based on transcriptional regulators have become essential instruments in biomanufacturing, extensively employed for the semi-quantitative assessment of intracellular metabolites, high-throughput screening of production strains, and the directed evolution of enzymes. Corynebacterium glutamicum serves as an industrial chassis for the production of amino acids and a variety of high-value-added chemicals. This paper discusses the varieties and modes of action of transcriptional regulators employed in the construction of intracellular biosensors in C. glutamicum. It also reviews the design principles and progress in the application of transcriptional regulator-based biosensors. Furthermore, measures designed to improve the efficacy of these biosensors are delineated. The challenges and future prospects of biosensors based on transcriptional regulators in practical applications are analyzed. This review seeks to offer theoretical direction for the systematic design and development of transcriptional regulator-based biosensors and to aid researchers in enhancing the growth and productivity of microbial production strains.

Engineering microbial metabolic homeostasis for chemicals production

Microbial-based bio-refining promotes the development of a biotechnology revolution to encounter and tackle the enormous challenges in petroleum-based chemical production by biomanufacturing, biocomputing, and biosensing. Nevertheless, microbial metabolic homeostasis is often incompatible with the efficient synthesis of bioproducts mainly due to: inefficient metabolic flow, robust central metabolism, sophisticated metabolic network, and inevitable environmental perturbation. Therefore, this review systematically summarizes how to optimize microbial metabolic homeostasis by strengthening metabolic flux for improving biotransformation turnover, redirecting metabolic direction for rewiring bypass pathway, and reprogramming metabolic network for boosting substrate utilization. Future directions are also proposed for providing constructive guidance on the development of industrial biotechnology.

Engineering of L-threonine and L-proline biosensors by directed evolution of transcriptional regulator SerR and application for high-throughput screening

Amino acids are important bio-based products with a multi-billion-dollar market. The development of efficient high-throughput screening technologies utilizing biosensors is essential for the rapid identification of high-performance amino acid producers. However, there remains a pressing need for biosensors that specifically target certain critical amino acids, such as L-threonine and L-proline. In this study, a novel transcriptional regulator-based biosensor for L-threonine and L-proline was successfully developed, inspired by our new finding that SerE can export L-proline in addition to the previously known L-threonine and L-serine. Through directed evolution of SerR (the corresponding transcriptional regulator of SerE), the mutant SerRF104I which can recognize both L-threonine and L-proline as effectors and effectively distinguish strains with varying production levels was identified. Subsequently, the SerRF104I-based biosensor was employed for high-throughput screening of the superior enzyme mutants of L-homoserine dehydrogenase and γ-glutamyl kinase, which are critical enzymes in the biosynthesis of L-threonine and L-proline, respectively. A total of 25 and 13 novel mutants that increased the titers of L-threonine and L-proline by over 10% were successfully identified. Notably, six of the newly identified mutants exhibited similarities to the most effective mutants reported to date, indicating the promising application potential of the SerRF104I-based biosensor. This study illustrates an effective strategy for the development of transcriptional regulator-based biosensors for amino acids and other chemical compounds.

Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors

Since the description of the lac operon in 1961 by Jacob and Monod, transcriptional regulation in prokaryotes has been studied extensively and has led to the development of transcription factor-based biosensors. Due to the broad variety of detectable small molecules and their various applications across biotechnology, biosensor research and development have increased exponentially over the past decades. Throughout this period, key milestones in fundamental knowledge, synthetic biology, analytical tools, and computational learning have led to an immense expansion of the biosensor repertoire and its application portfolio. Over the years, biosensor engineering became a more multidisciplinary discipline, combining high-throughput analytical tools, DNA randomization strategies, forward engineering, and advanced protein engineering workflows. Despite these advances, many obstacles remain to fully unlock the potential of biosensor technology. This review analyzes the timeline of key milestones on fundamental research (1960s to 2000s) and engineering strategies (2000s onward), on both the DNA and protein level of biosensors. Moreover, insights into the future perspectives, remaining hurdles, and unexplored opportunities of this promising field are discussed.

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.

Harnessing DNA computing and nanopore decoding for practical applications: from informatics to microRNA-targeting diagnostics

DNA computing represents a subfield of molecular computing with the potential to become a significant area of next-generation computation due to the high programmability inherent in the sequence-dependent molecular behaviour of DNA. Recent studies in DNA computing have extended from mathematical informatics to biomedical applications, with a particular focus on diagnostics that exploit the biocompatibility of DNA molecules. The output of DNA computing devices is encoded in nucleic acid molecules, which must then be decoded into human-recognizable signals for practical applications. Nanopore technology, which utilizes an electrical and label-free decoding approach, provides a unique platform to bridge DNA and electronic computing for practical use. In this tutorial review, we summarise the fundamental knowledge, technologies, and methodologies of DNA computing (logic gates, circuits, neural networks, and non-DNA input circuity). We then focus on nanopore-based decoding, and highlight recent advances in medical diagnostics targeting microRNAs as biomarkers. Finally, we conclude with the potential and challenges for the practical implementation of these techniques. We hope that this tutorial will provide a comprehensive insight and enable the general reader to grasp the fundamental principles and diverse applications of DNA computing and nanopore decoding, and will inspire a wide range of scientists to explore and push the boundaries of these technologies.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

Harnessing microbial heterogeneity for improved biosynthesis fueled by synthetic biology

Metabolic engineering-driven microbial cell factories have made great progress in the efficient bioproduction of biochemical and recombinant proteins. However, the low efficiency and robustness of microbial cell factories limit their industrial applications. Harnessing microbial heterogeneity contributes to solving this. In this review, the origins of microbial heterogeneity and its effects on biosynthesis are first summarized. Synthetic biology-driven tools and strategies that can be used to improve biosynthesis by increasing and reducing microbial heterogeneity are then systematically summarized. Next, novel single-cell technologies available for unraveling microbial heterogeneity and facilitating heterogeneity regulation are discussed. Furthermore, a combined workflow of increasing genetic heterogeneity in the strain-building step to help in screening highly productive strains - reducing heterogeneity in the production process to obtain highly robust strains (IHP-RHR) facilitated by single-cell technologies was proposed to obtain highly productive and robust strains by harnessing microbial heterogeneity. Finally, the prospects and future challenges are discussed.

Directed evolution of highly sensitive and stringent choline-induced gene expression controllers

Gene expression controllers are useful tools for microbial production of recombinant proteins and valued bio-based chemicals. Despite its usefulness, they have rarely been applied to the practical industrial bioprocess, due to the lack of systems that meets the three requirements: low cost, safety, and tight control, to the inducer molecules. Previously, we have developed the high-spec gene induction system controlled by safe and cheap inducer choline. However, the system requires relatively high concentration (~100 mM) of choline to fully induce the gene under control. In this work, we attempted to drastically improve the sensitivity of this induction system to further reduce the induction costs. To this end, we devised a simple circuit which couples gene induction system with positive-feedback loop (P-loop) of choline importer protein BetT. After the tuning of translation level of BetT (strength of the P-loop) and deletion of endogenous betI (noise sources), highly active yet stringent control of gene expression was achieved using about 100 times less amount of inducer molecules. The choline induction system developed in this study has the lowest basal expression, the lowest choline needed to be activated, and the highest amplitude of induction as the highest available promoter such as those known as PT5 system. With this system, one can tightly control the expression level of genes of interest with negligible cost for inducer molecule, which has been the bottleneck for the application to the large-scale industrial processes.

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.

Fundamentals and Exceptions of the LysR-type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) are emerging as a promising group of macromolecules for the field of biosensors. As the largest family of bacterial transcription factors, the LTTRs represent a vast and mostly untapped repertoire of sensor proteins. To fully harness these regulators for transcription factor-based biosensor development, it is crucial to understand their underlying mechanisms and functionalities. In the first part, this Review discusses the established model and features of LTTRs. As dual-function regulators, these inducible transcription factors exude precise control over their regulatory targets. In the second part of this Review, an overview is given of the exceptions to the "classic" LTTR model. While a general regulatory mechanism has helped elucidate the intricate regulation performed by LTTRs, it is essential to recognize the variations within the family. By combining this knowledge, characterization of new regulators can be done more efficiently and accurately, accelerating the expansion of transcriptional sensors for biosensor development. Unlocking the pool of LTTRs would significantly expand the currently limited range of detectable molecules and regulatory functions available for the implementation of novel synthetic genetic circuitry.

Engineering a PbrR-Based Biosensor for Cell-Free Detection of Lead at the Legal Limit

Industrialization and failing infrastructure have led to a growing number of irreversible health conditions resulting from chronic lead exposure. While state-of-the-art analytical chemistry methods provide accurate and sensitive detection of lead, they are too slow, expensive, and centralized to be accessible to many. Cell-free biosensors based on allosteric transcription factors (aTFs) can address the need for accessible, on-demand lead detection at the point of use. However, known aTFs, such as PbrR, are unable to detect lead at concentrations regulated by the Environmental Protection Agency (24-72 nM). Here, we develop a rapid cell-free platform for engineering aTF biosensors with improved sensitivity, selectivity, and dynamic range characteristics. We apply this platform to engineer PbrR mutants for a shift in limit of detection from 10 μM to 50 nM lead and demonstrate use of PbrR as a cell-free biosensor. We envision that our workflow could be applied to engineer any aTF.

In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing, and metabolic engineering. Yet, the analysis of the activity of such in vitro networks is very often restricted by time- and cost-intensive methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that is compatible with the complex conditions of in vitro metabolism, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a 27-component in vitro metabolic system that converts CO2 into glycolate. As proof of concept, we constructed a novel glycolate sensor module that is based on the transcriptional repressor GlcR from Paracoccus denitrificans and established an IVT biosensing workflow that allows us to quantify glycolate from CETCH samples in the micromolar to millimolar range. We investigate the influence of 13 (shared) cofactors between the two in vitro systems to show that Mg2+, adenosine triphosphate , and other phosphorylated metabolites are critical for robust signal output. Our optimized IVT biosensor correlates well with liquid chromatography-mass spectrometry-based glycolate quantification of CETCH samples, with one or multiple components varying (linear correlation 0.94-0.98), but notably at ∼10-fold lowered cost and ∼10 times faster turnover time. Our results demonstrate the potential and challenges of IVT-based systems to quantify and prototype the activity of complex reaction cascades and in vitro metabolic networks.

Expanding salivary biomarker detection by creating a synthetic neuraminic acid sensor via chimeragenesis

Accurate and timely diagnosis of oral squamous cell carcinoma (OSCC) is crucial in preventing its progression to advanced stages with a poor prognosis. As such, the construction of sensors capable of detecting previously established disease biomarkers for the early and non-invasive diagnosis of this and many other conditions has enormous therapeutic potential. In this work, we apply synthetic biology techniques for the development of a whole-cell biosensor (WCB) that leverages the physiology of engineered bacteria in vivo to promote the expression of an observable effector upon detection of a soluble molecule. To this end, we have constructed a bacterial strain expressing a novel chimeric transcription factor (Sphnx) for the detection of N-acetylneuraminic acid (Neu5Ac), a salivary biomolecule correlated with the onset of OSCC. This WCB serves as the proof-of-concept of a platform that can eventually be applied to clinical screening panels for a multitude of oral and systemic medical conditions whose biomarkers are present in saliva.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Biosensor-assisted CRISPRi high-throughput screening to identify genetic targets in Zymomonas mobilis for high d-lactate production

Lactate is an important monomer for the synthesis of poly-lactate (PLA), which is a substitute for the petrochemical plastics. To achieve the goal of high lactate titer, rate, and yield for commercial production, efficient lactate production pathway is needed as well as genetic targets that affect high lactate production and tolerance. In this study, an LldR-based d-lactate biosensor with a broad dynamic range was first applied into Zymomonas mobilis to select mutant strains with strong GFP fluorescence, which could be the mutant strains with increased d-lactate production. Then, LldR-based d-lactate biosensor was combined with a genome-wide CRISPR interference (CRISPRi) library targeting the entire genome to generate thousands of mutants with gRNA targeting different genetic targets across the whole genome. Specifically, two mutant libraries were selected containing 105 and 104 mutants with different interference sites from two rounds of fluorescence-activated cell sorting (FACS), respectively. Two genetic targets of ZMO1323 and ZMO1530 were characterized and confirmed to be associated with the increased d-lactate production, further knockout of ZMO1323 and ZMO1530 resulted in a 15% and 21% increase of d-lactate production, respectively. This work thus not only established a high-throughput approach that combines genome-scale CRISPRi and biosensor-assisted screening to identify genetic targets associated with d-lactate production in Z. mobilis, but also provided a feasible high-throughput screening approach for rapid identification of genetic targets associated with strain performance for other industrial microorganisms.

Metabolic Engineering of Corynebacterium glutamicum for the Production of Flavonoids and Stilbenoids

Flavonoids and stilbenoids, crucial secondary metabolites abundant in plants and fungi, display diverse biological and pharmaceutical activities, including potent antioxidant, anti-inflammatory, and antimicrobial effects. However, conventional production methods, such as chemical synthesis and plant extraction, face challenges in sustainability and yield. Hence, there is a notable shift towards biological production using microorganisms like Escherichia coli and yeast. Yet, the drawbacks of using E. coli and yeast as hosts for these compounds persist. For instance, yeast's complex glycosylation profile can lead to intricate protein production scenarios, including hyperglycosylation issues. Consequently, Corynebacterium glutamicum emerges as a promising alternative, given its adaptability and recent advances in metabolic engineering. Although extensively used in biotechnological applications, the potential production of flavonoid and stilbenoid in engineered C. glutamicum remains largely untapped compared to E. coli. This review explores the potential of metabolic engineering in C. glutamicum for biosynthesis, highlighting its versatility as a cell factory and assessing optimization strategies for these pathways. Additionally, various metabolic engineering methods, including genomic editing and biosensors, and cofactor regeneration are evaluated, with a focus on C. glutamicum. Through comprehensive discussion, the review offers insights into future perspectives in production, aiding researchers and industry professionals in the field.

Genome-wide host-pathway interactions affecting cis-cis-muconic acid production in yeast

The success of forward metabolic engineering depends on a thorough understanding of the behaviour of a heterologous metabolic pathway within its host. We have recently described CRI-SPA, a high-throughput gene editing method enabling the delivery of a metabolic pathway to all strains of the Saccharomyces cerevisiae knock-out library. CRI-SPA systematically quantifies the effect of each modified gene present in the library on product synthesis, providing a complete map of host:pathway interactions. In its first version, CRI-SPA relied on the colour of the product betaxanthins to quantify strains synthesis ability. However, only a few compounds produce a visible or fluorescent phenotype limiting the scope of our approach. Here, we adapt CRI-SPA to onboard a biosensor reporting the interactions between host genes and the synthesis of the colourless product cis-cis-muconic acid (CCM). We phenotype >9,000 genotypes, including both gene knock-out and overexpression, by quantifying the fluorescence of yeast colonies growing in high-density agar arrays. We identify novel metabolic targets belonging to a broad range of cellular functions and confirm their positive impact on CCM biosynthesis. In particular, our data suggests a new interplay between CCM biosynthesis and cytosolic redox through their common interaction with the oxidative pentose phosphate pathway. Our genome-wide exploration of host:pathway interaction opens novel strategies for improved production of CCM in yeast cell factories.

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.

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.

Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape

Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.

Advances in ligand-specific biosensing for structurally similar molecules

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

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

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

Design of synthetic bacterial biosensors

Novel whole-cell bacterial biosensor designs require an emphasis on moving toward field deployment. Many current sensors are characterized under specified laboratory conditions, which frequently do not represent actual deployment conditions. To this end, recent developments such as toolkits for probing new host chassis that are more robust to environments of interest, have paved the way for improved designs. Strategies for rational tuning of genetic components or tools such as genetic amplifiers or designs that allow post hoc tuning are essential in optimizing existing biosensors for practical application. Furthermore, recent work has seen a rise in directed evolution techniques, which can be immensely valuable in both tuning existing sensors and developing sensors for new analytes that lack characterized sensors. Combined with advancements in bioinformatics and capabilities in rewiring two-component systems, many new sensors can be established, broadening biosensor use cases. Last, recent work in CRISPR-based dynamic regulation and memory mechanisms, as well as kill-switches for biosafety and innovative output integration concepts, represents promising steps toward designing bacterial biosensors for deployment in dynamic and heterogeneous conditions.

Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology

Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

Versatility and Complexity: Common and Uncommon Facets of LysR-Type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) form one of the largest families of bacterial regulators. They are widely distributed and contribute to all aspects of metabolism and physiology. Most are homotetramers, with each subunit composed of an N-terminal DNA-binding domain followed by a long helix connecting to an effector-binding domain. LTTRs typically bind DNA in the presence or absence of a small-molecule ligand (effector). In response to cellular signals, conformational changes alter DNA interactions, contact with RNA polymerase, and sometimes contact with other proteins. Many are dual-function repressor-activators, although different modes of regulation may occur at multiple promoters. This review presents an update on the molecular basis of regulation, the complexity of regulatory schemes, and applications in biotechnology and medicine. The abundance of LTTRs reflects their versatility and importance. While a single regulatory model cannot describe all family members, a comparison of similarities and differences provides a framework for future study.

Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes

BACKGROUND

In contrast to modern rational metabolic engineering, classical strain development strongly relies on random mutagenesis and screening for the desired production phenotype. Nowadays, with the availability of biosensor-based FACS screening strategies, these random approaches are coming back into fashion. In this study, we employ this technology in combination with comparative genome analyses to identify novel mutations contributing to product formation in the genome of a Corynebacterium glutamicum L-histidine producer. Since all known genetic targets contributing to L-histidine production have been already rationally engineered in this strain, identification of novel beneficial mutations can be regarded as challenging, as they might not be intuitively linkable to L-histidine biosynthesis.

RESULTS

In order to identify 100 improved strain variants that had each arisen independently, we performed > 600 chemical mutagenesis experiments, > 200 biosensor-based FACS screenings, isolated > 50,000 variants with increased fluorescence, and characterized > 4500 variants with regard to biomass formation and L-histidine production. Based on comparative genome analyses of these 100 variants accumulating 10-80% more L-histidine, we discovered several beneficial mutations. Combination of selected genetic modifications allowed for the construction of a strain variant characterized by a doubled L-histidine titer (29 mM) and product yield (0.13 C-mol C-mol-1) in comparison to the starting variant.

CONCLUSIONS

This study may serve as a blueprint for the identification of novel beneficial mutations in microbial producers in a more systematic manner. This way, also previously unexplored genes or genes with previously unknown contribution to the respective production phenotype can be identified. We believe that this technology has a great potential to push industrial production strains towards maximum performance.

Divergent directed evolution of a TetR-type repressor towards aromatic molecules

Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.

Biosensor-based high-throughput screening enabled efficient adipic acid production

Adipic acid is an industrially important chemical, but the current approach to synthesize it can be of serious pollution to the environment. Rencently, bio-based production of adipic acid has significantly advanced with the development of metabolic engineering and synthetic biology. However, genetic heterogeneity-caused decrease of product titer has largely limited the industrialization of chemicals like adipic acid. Therefore, in the attempt to overcome this challenge, we constitutively expressed the reverse adipate degradation pathway, designed and optimized an adipic acid biosensor, and established a high-throughput screening platform to screen for high-performance strains based on the optimized biosensor. Using this platform, we successfully screened a strain with an adipic acid titer of 188.08 mg·L^-1. Coupling the screening platform with fermentation optimization, the titer of adipic acid reached 531.88 mg·L^-1 under shake flask fermentation, which achieved an 18.78-fold improvement comparing to the initial strain. Scale-up fermentation in a 5-L fermenter utilizing the screened high-performance strain was eventually conducted, in which the adipic acid titer reached 3.62 g·L^-1. Overall, strategies developed in this study proved to be a potentially efficient method in reducing the genetic heterogeneity and was expected to provide guidance in helping to build a more efficient industrial screening process. KEY POINTS: • Developed a fine-tuned adipic acid biosensor. • Established a high-throughput screening platform to screen high-performance strains. • The titer of adipic acid reached 3.62 g·L^-1 in a 5-L fermenter.

Quantification and mitigation of byproduct formation by low-glycerol-producing Saccharomyces cerevisiae strains containing Calvin-cycle enzymes

BACKGROUND

Anaerobic Saccharomyces cerevisiae cultures require glycerol formation to re-oxidize NADH formed in biosynthetic processes. Introduction of the Calvin-cycle enzymes phosphoribulokinase (PRK) and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) has been shown to couple re-oxidation of biosynthetic NADH to ethanol production and improve ethanol yield on sugar in fast-growing batch cultures. Since growth rates in industrial ethanol production processes are not constant, performance of engineered strains was studied in slow-growing cultures.

RESULTS

In slow-growing anaerobic chemostat cultures (D = 0.05 h-1), an engineered PRK/RuBisCO strain produced 80-fold more acetaldehyde and 30-fold more acetate than a reference strain. This observation suggested an imbalance between in vivo activities of PRK/RuBisCO and formation of NADH in biosynthesis. Lowering the copy number of the RuBisCO-encoding cbbm expression cassette from 15 to 2 reduced acetaldehyde and acetate production by 67% and 29%, respectively. Additional C-terminal fusion of a 19-amino-acid tag to PRK reduced its protein level by 13-fold while acetaldehyde and acetate production decreased by 94% and 61%, respectively, relative to the 15 × cbbm strain. These modifications did not affect glycerol production at 0.05 h-1 but caused a 4.6 fold higher glycerol production per amount of biomass in fast-growing (0.29 h-1) anaerobic batch cultures than observed for the 15 × cbbm strain. In another strategy, the promoter of ANB1, whose transcript level positively correlated with growth rate, was used to control PRK synthesis in a 2 × cbbm strain. At 0.05 h-1, this strategy reduced acetaldehyde and acetate production by 79% and 40%, respectively, relative to the 15 × cbbm strain, without affecting glycerol production. The maximum growth rate of the resulting strain equalled that of the reference strain, while its glycerol production was 72% lower.

CONCLUSIONS

Acetaldehyde and acetate formation by slow-growing cultures of engineered S. cerevisiae strains carrying a PRK/RuBisCO bypass of yeast glycolysis was attributed to an in vivo overcapacity of PRK and RuBisCO. Reducing the capacity of PRK and/or RuBisCO was shown to mitigate this undesirable byproduct formation. Use of a growth rate-dependent promoter for PRK expression highlighted the potential of modulating gene expression in engineered strains to respond to growth-rate dynamics in industrial batch processes.

Latent generative landscapes as maps of functional diversity in protein sequence space

Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Engineering Modular and Highly Sensitive Cell-Based Biosensors for Aromatic Contaminant Monitoring and High-Throughput Enzyme Screening

Although a variety of whole-cell-based biosensors have been developed for different applications in recent years, most cannot meet practical requirements due to insufficient sensing performance. Here, we constructed two sets of modular genetic circuits by serial and parallel modes capable of significantly amplifying the input/output signal in Escherichia coli. The biosensors are engineered using σ54-dependent phenol-responsive regulator DmpR as a sensor and enhanced green fluorescent protein as a reporter. Cells harboring serial and parallel genetic circuits displayed nearly 9- and 16-fold higher sensitivity than the general circuit. The genetic circuits enabled rapid detection of six phenolic contaminants in 12 h and showed the low limit of detection of 2.5 and 2.2 ppb for benzopyrene (BaP) and tetracycline (Tet), with a broad detection range of 0.01-1 and 0.005-5 μM, respectively. Furthermore, the positive rate was as high as 73% when the biosensor was applied to screen intracellular enzymes with ester-hydrolysis activity from soil metagenomic libraries using phenyl acetate as a phenolic substrate. Several novel enzymes were isolated, identified, and biochemically characterized, including serine peptidases and alkaline phosphatase family protein/metalloenzyme. Consequently, this study provides a new signal amplification method for cell-based biosensors that can be widely applied to environmental contaminant assessment and screening of intracellular enzymes.

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.

Strengthening microbial cell factories for efficient production of bioactive molecules

High demand of bioactive molecules (food additives, antibiotics, plant growth enhancers, cosmetics, pigments and other commercial products) is the prime need for the betterment of human life where the applicability of the synthetic chemical product is on the saturation due to associated toxicity and ornamentations. It has been noticed that the discovery and productivity of such molecules in natural scenarios are limited due to low cellular yields as well as less optimized conventional methods. In this respect, microbial cell factories timely fulfilling the requirement of synthesizing bioactive molecules by improving production yield and screening more promising structural homologues of the native molecule. Where the robustness of the microbial host can be potentially achieved by taking advantage of cell engineering approaches such as tuning functional and adjustable factors, metabolic balancing, adapting cellular transcription machinery, applying high throughput OMICs tools, stability of genotype/phenotype, organelle optimizations, genome editing (CRISPER/Cas mediated system) and also by developing accurate model systems via machine-learning tools. In this article, we provide an overview from traditional to recent trends and the application of newly developed technologies, for strengthening the systemic approaches and providing future directions for enhancing the robustness of microbial cell factories to speed up the production of biomolecules for commercial purposes.

Synthetic biology-based bioreactor and its application in biochemical analysis

In the past few years, synthetic biologists have established some biological elements and bioreactors composed of nucleotides under the guidance of engineering methods. Following the concept of engineering, the common bioreactor components in recent years are introduced and compared. At present, biosensors based on synthetic biology have been applied to water pollution monitoring, disease diagnosis, epidemiological monitoring, biochemical analysis and other detection fields. In this paper, the biosensor components based on synthetic bioreactors and reporters are reviewed. In addition, the applications of biosensors based on cell system and cell-free system in the detection of heavy metal ions, nucleic acid, antibiotics and other substances are presented. Finally, the bottlenecks faced by biosensors and the direction of optimization are also discussed.

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

Systematic Comparison and Rational Design of Theophylline Riboswitches for Effective Gene Repression

Riboswitches are promising regulatory tools in synthetic biology. To date, 25 theophylline riboswitches have been developed for regulation of gene expression in bacteria. However, no one has systematically evaluated their regulatory effects. To promote efficient selection and application of theophylline riboswitches, we examined 25 theophylline riboswitches in Escherichia coli MG1655 and found that they varied widely in terms of activation/repression ratios and expression levels in the absence of theophylline. Of the 20 riboswitches that activate gene expression, only one exhibited a high activation ratio (63.6-fold) and low expression level without theophylline. Furthermore, none of the five riboswitches that repress gene expression were more than 2.0-fold efficient. To obtain an effective repression system, we rationally designed a novel theophylline riboswitch to control a downstream gene or genes by premature transcription termination. This riboswitch allowed theophylline-dependent downregulation of the TurboRFP reporter in a dose- and time-dependent manner. Its performance profile exceeded those of previously described repressive theophylline riboswitches. We then introduced as the second part a RepA tag (protein degradation tag) coding sequence fused at the 5'-terminal end of the turborfp gene, which further reduced protein level, while not reducing the repressive effect of the riboswitch. By combining two tandem theophylline riboswitches with a RepA tag, we constructed a regulatory cassette that represses the expression of the gene(s) of interest at both the transcriptional and posttranslational levels. This regulatory cassette can be used to repress the expression of any gene of interest and represents a crucial step toward harnessing theophylline riboswitches and expanding the synthetic biology toolbox. IMPORTANCE A variety of gene expression regulation tools with significant regulatory effects are essential for the construction of complex gene circuits in synthetic biology. Riboswitches have received wide attention due to their unique biochemical, structural, and genetic properties. Here, we have not only systematically and precisely characterized the regulatory properties of previously developed theophylline riboswitches but also engineered a novel repressive theophylline riboswitch acting at the transcriptional level. By introducing coding sequences of a tandem riboswitch and a RepA protein degradation tag at the 5' end of the reporter gene, we successfully constructed a simple and effective regulatory cassette for gene regulation. Our work provides useful biological components for the construction of synthetic biology gene circuits.

Transcription factor-based biosensors for screening and dynamic regulation

Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.

Online SFE-SFC-MS/MS colony screening: A high-throughput approach for optimizing (-)-limonene production

Conventional analysis of microbial bioproducers requires the extraction of metabolites from liquid cultures, where the culturing steps are time consuming and greatly limit throughput. To break through this barrier, the current study aims to directly evaluate microbial bioproduction colonies by way of supercritical fluid extraction-supercritical fluid chromatography-triple quadrupole mass spectrometry (SFE-SFC-MS/MS). The online SFE-SFC-MS/MS system offers great potential for high-throughput analysis due to automated metabolite extraction without any need for pretreatment. This is the first report of SFE-SFC-MS/MS as a method for direct colony screening, as demonstrated in the high-throughput screening of (-)-limonene bioproducers. Compared with conventional analysis, the SFE-SFC-MS/MS system enables faster and more convenient screening of highly productive strains.

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

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.

Tackling the Catch-22 Situation of Optimizing a Sensor and a Transporter System in a Whole-Cell Microbial Biosensor Design for an Anthropogenic Small Molecule

Whole-cell biosensors provide a convenient detection tool for the high-throughput screening of genetically engineered biocatalytic activity. But establishing a biosensor for an anthropogenic molecule requires both a custom transporter and a transcription factor. This results in an unavoidable "Catch-22" situation in which transporter activity cannot be easily confirmed without a biosensor and a biosensor cannot be established without a functional transporter in a host organism. We overcame this type of circular problem while developing an adipic acid (ADA) sensor. First, leveraging an established cis,cis-muconic acid (ccMA) sensor, an annotated ccMA transporter MucK, which is expected to be broadly responsive to dicarboxylates, was stably expressed in the genome of Pseudomonas putida to function as a transporter for ADA, and then a PcaR transcription factor (endogenous to the strain and naturally induced by β-ketoadipic acid, BKA) was diversified and selected to detect the ADA molecule. While MucK expression is otherwise very unstable in P. putida under strong promoter expression, our optimized mucK expression was functional for over 70 generations without loss of function, and we selected an ADA sensor that showed a specificity switch of over 35-fold from BKA at low concentrations (typically <0.1 mM of inducers). Our ADA and BKA biosensors show high sensitivity (low detection concentration <10 μM) and dynamic range (∼50-fold) in an industrially relevant organism and will open new avenues for high throughput discovery and optimization of enzymes and metabolic pathways for the biomanufacture of these molecules. In particular, the novel ADA sensor will aid the discovery and evolution of efficient biocatalysts for the biological recycling of ADA from the degradation of nylon-6,6 waste.

Using fungible biosensors to evolve improved alkaloid biosyntheses

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors; using RamR as a starting point, we evolve highly specific (>100-fold preference) and sensitive (half-maximum effective concentration (EC50) < 30 μM) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine and noscapine. High-resolution structures reveal multiple evolutionary avenues for the malleable effector-binding site and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, a precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors enable the rapid engineering of pathways for therapeutic alkaloids.

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.

Plant Flavonoid Production in Bacteria and Yeasts

Flavonoids, a major group of secondary metabolites in plants, are promising for use as pharmaceuticals and food supplements due to their health-promoting biological activities. Industrial flavonoid production primarily depends on isolation from plants or organic synthesis, but neither is a cost-effective or sustainable process. In contrast, recombinant microorganisms have significant potential for the cost-effective, sustainable, environmentally friendly, and selective industrial production of flavonoids, making this an attractive alternative to plant-based production or chemical synthesis. Structurally and functionally diverse flavonoids are derived from flavanones such as naringenin, pinocembrin and eriodictyol, the major basic skeletons for flavonoids, by various modifications. The establishment of flavanone-producing microorganisms can therefore be used as a platform for producing various flavonoids. This review summarizes metabolic engineering and synthetic biology strategies for the microbial production of flavanones. In addition, we describe directed evolution strategies based on recently-developed high-throughput screening technologies for the further improvement of flavanone production. We also describe recent progress in the microbial production of structurally and functionally complicated flavonoids via the flavanone modifications. Strategies based on synthetic biology will aid more sophisticated and controlled microbial production of various flavonoids.