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

Controllable detection threshold achieved through the toehold switch system in a mercury ion whole-cell biosensor

Due to the toxicity of mercury and its harmful effects on human health, it is essential to establish a low-cost, highly sensitive and highly specific monitoring method with a wide detection range, ideally with a simple visual readout. In this study, a whole-cell biosensor with adjustable detection limits was developed for the detection of mercury ions in water samples, allowing controllable threshold detection with an expanded detection range. Gene circuits were constructed by combining the toehold switch system with lactose operon, mercury-ion-specific operon, and inducible red fluorescent protein gene. Using MATLAB for design and selection, a total of eleven dual-input single-output sensing logic circuits were obtained based on the basic logic of gene circuit construction. Then, biosensor DTS-3 was selected based on its fluorescence response at different isopropyl β-D-Thiogalactoside (IPTG) concentrations, exhibiting the controllable detection threshold. At 5-20 μM IPTG, DTS-3 can achieve variable threshold detection in the range of 0.005-0.0075, 0.06-0.08, 1-2, and 4-6 μM mercury ion concentrations, respectively. Specificity experiments demonstrated that DTS-3 exhibits good specificity, not showing fluorescence response changes compared with other metal ions. Furthermore spiked sample experiments demonstrated its good resistance to interference, allowing it to distinguish mercury ion concentrations as low as 7.5 nM by the naked eye and 5 nM using a microplate reader. This study confirms the feasibility and performance of biosensor with controllable detection threshold, providing a new detection method and new ideas for expanding the detection range of biosensors while ensuring rapid and convenient measurements without compromising sensitivity.

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.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural 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.

Directed Evolution of 4-Hydroxyphenylpyruvate Biosensors Based on a Dual Selection System

Biosensors based on allosteric transcription factors have been widely used in synthetic biology. In this study, we utilized the Acinetobacter ADP1 transcription factor PobR to develop a biosensor activating the PpobA promoter when bound to its natural ligand, 4-hydroxybenzoic acid (4HB). To screen for PobR mutants responsive to 4-hydroxyphenylpyruvate(HPP), we developed a dual selection system in E. coli. The positive selection of this system was used to enrich PobR mutants that identified the required ligands. The following negative selection eliminated or weakened PobR mutants that still responded to 4HB. Directed evolution of the PobR library resulted in a variant where PobRW177R was 5.1 times more reactive to 4-hydroxyphenylpyruvate than PobRWT. Overall, we developed an efficient dual selection system for directed evolution of biosensors.

Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing

In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For AraC family regulators (AFRs) represented in these data, we posited AFR/promoter/inducer groupings. Twelve promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in Escherichia coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled the further diversification of these inducible plasmids.

Building Yarrowia lipolytica Cell Factories for Advanced Biomanufacturing: Challenges and Solutions

Microbial cell factories have shown great potential for industrial production with the benefit of being environmentally friendly and sustainable. Yarrowia lipolytica is a promising and superior non-model host for biomanufacturing due to its cumulated advantages compared to model microorganisms, such as high fluxes of metabolic precursors (acetyl-CoA and malonyl-CoA) and its naturally hydrophobic microenvironment. However, although diverse compounds have been synthesized in Y. lipolytica cell factories, most of the relevant studies have not reached the level of industrialization and commercialization due to a number of remaining challenges, including unbalanced metabolic flux, conflict between cell growth and product synthesis, and cytotoxic effects. Here, various metabolic engineering strategies for solving the challenges are summarized, which is developing fast and extremely conducive to rational design and reconstruction of robust Y. lipolytica cell factories for advanced biomanufacturing. Finally, future engineering efforts for enhancing the production efficiency of this platform strain are highlighted.

Enzyme Reaction-Assisted Programmable Transcriptional Switches for Bioactive Molecule Detection

Bioactive molecules are highly worthwhile to recognize and explore the latent pathogenic mechanism. Conventional methods for bioactive molecule detection, including mass spectrometry and fluorescent probe imaging, are limited due to the complex processing and signal interference. Here, we designed enzyme-reaction-assisted programmable transcriptional switches for the detection of bioactive molecules. The approach is based on the use of programmable enzyme site-specific cleavage-assisted DNA triplex-based conformational switches that, upon responding to bioactive molecules, can trigger the transcription of fluorescent light-up aptamers. Thanks to the programmable nature of the sensing platform, the method can be adapted to different bioactive molecules, and we demonstrated the enzyme-small molecule catalytic reaction combination of myeloperoxidase (MPO)-hydrogen peroxide (H2O2) as a model that transcriptional switches was capable of detecting H2O2 and possessed the specificity and anti-interference ability in vitro. Furthermore, we successfully applied the switches into cells to observe the detection feasibility in vivo, and dynamically monitored changes of H2O2 in cellular oxidative stress levels. Therefore, we attempt to amalgamate the advantages of enzyme reaction with the pluripotency of programmable transcriptional switches, which can take both fields a step further, which may promote the research of biostimuli and the construction of DNA molecular devices.

Transcription factor-based biosensors for detection of naturally occurring phenolic acids

Phenolic acids including hydroxybenzoic and hydroxycinnamic acids are secondary plant and fungal metabolites involved in many physiological processes offering health and dietary benefits. They are often utilised as precursors for production of value-added compounds. The limited availability of synthetic biology tools, such as whole-cell biosensors suitable for monitoring the dynamics of phenolic acids intracellularly and extracellularly, hinders the capabilities to develop high-throughput screens to study their metabolism and forward engineering. Here, by applying a multi-genome approach, we have identified phenolic acid-inducible gene expression systems composed of transcription factor-inducible promoter pairs responding to eleven different phenolic acids. Subsequently, they were used for the development of whole-cell biosensors based on model bacterial hosts, such as Escherichia coli, Cupriavidus necator and Pseudomonas putida. The dynamics and range of the biosensors were evaluated by establishing their response and sensitivity landscapes. The specificity and previously uncharacterised interactions between transcription factor and its effector(s) were identified by a screen of twenty major phenolic acids. To exemplify applicability, we utilise a protocatechuic acid-biosensor to identify enzymes with enhanced activity for conversion of p-hydroxybenzoate to protocatechuate. Transcription factor-based biosensors developed in this study will advance the analytics of phenolic acids and expedite research into their metabolism.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

Application of whole-cell biosensors for analysis and improvement of L- and D-lactic acid fermentation by Lactobacillus spp. from the waste of glucose syrup production

BACKGROUND

Lactic acid is one of the most important organic acids, with various applications in the food, beverage, pharmaceutical, cosmetic, and chemical industries. Optically pure forms of L- and D-lactic acid produced via microbial fermentation play an important role in the synthesis of biodegradable polylactic acid. Alternative substrates, including by-products and residues from the agro-food industry, provide a cost-effective solution for lactic acid production and are a promising avenue for the circular economy.

RESULTS

In this study, the transcription factor (TF)-based whole-cell biosensor strategy was developed for the L- and D-lactic acid determination. It was cross validated with commonly used high-performance liquid chromatography and enzymatic methods. The utility of biosensors as an efficient analytical tool was demonstrated by their application for the lactic acid determination and fermentation improvement. We explored the ability of Lacticaseibacillus paracasei subsp. paracasei, Lactobacillus delbrueckii subsp. lactis, and Lactobacillus amylovorus to biosynthesize optically pure L-lactic acid, D-lactic acid or mixture of both from organic-rich residual fraction (ORRF), a waste of glucose syrup production from wheat starch. The fermentation of this complex industrial waste allowed the production of lactic acid without additional pretreatment obtaining yields from 0.5 to 0.9 Cmol/Cmol glucose.

CONCLUSIONS

This study highlights the utility of whole cell biosensors for the determination of L- and D-forms of lactic acid. The fermentation of L-lactic acid, D-lactic acid and mixture of both by L. paracasei, L. lactis, and L. amylovorus, respectively, was demonstrated using waste of glucose syrup production, the ORRF.

A versatile regulatory toolkit of arabinose-inducible artificial transcription factors for Enterobacteriaceae

The Gram-negative bacteria Salmonella enterica and Escherichia coli are important model organisms, powerful prokaryotic expression platforms for biotechnological applications, and pathogenic strains constitute major public health threats. To facilitate new approaches for research and biotechnological applications, we here develop a set of arabinose-inducible artificial transcription factors (ATFs) using CRISPR/dCas9 and Arabidopsis-derived DNA-binding proteins to control gene expression in E. coli and Salmonella over a wide inducer concentration range. The transcriptional output of the different ATFs, in particular when expressed in Salmonella rewired for arabinose catabolism, varies over a wide spectrum (up to 35-fold gene activation). As a proof-of-concept, we use the developed ATFs to engineer a Salmonella two-input biosensor strain, SALSOR 0.2 (SALmonella biosenSOR 0.2), which detects and quantifies alkaloid drugs through a measurable fluorescent output. Moreover, we use plant-derived ATFs to regulate β-carotene biosynthesis in E. coli, resulting in ~2.1-fold higher β-carotene production compared to expression of the biosynthesis pathway using a strong constitutive promoter.

Identifying, Characterizing, and Engineering a Phenolic Acid-Responsive Transcriptional Factor from Bacillus amyloliquefaciens

Transcriptional factors-based biosensors are commonly used in metabolic engineering for inducible control of gene expression and related applications such as high-throughput screening and dynamic pathway regulations. Mining for novel transcriptional factors is essential for expanding the usability of these toolsets. Here, we report the identification, characterization, and engineering of a phenolic acid responsive regulator PadR from Bacillus amyloliquefaciens (BaPadR). This BaPadR-based biosensor system showed a unique ligand preference and exhibited a high output strength comparable to that of commonly used inducible expression systems. Through engineering the DNA binding region of BaPadR, we further enhanced the dynamic range of the biosensor system. The DNA sequences that are responsible for BaPadR recognition were located by promoter truncation and hybrid promoter building. To further explore the tunability of the sensor system, base substitutions were performed on the BaPadR binding region of the phenolic acid decarboxylase promoter (PpadC) and the hybrid promoter. This novel biosensor system can serve as a valuable tool in future synthetic biology applications.

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.

MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors

All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning.

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

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

Application of Adaptive Laboratory Evolution in Lipid and Terpenoid Production in Yeast and Microalgae

Due to the complexity of metabolic and regulatory networks in microorganisms, it is difficult to obtain robust phenotypes through artificial rational design and genetic perturbation. Adaptive laboratory evolution (ALE) engineering plays an important role in the construction of stable microbial cell factories by simulating the natural evolution process and rapidly obtaining strains with stable traits through screening. This review summarizes the application of ALE technology in microbial breeding, describes the commonly used methods for ALE, and highlights the important applications of ALE technology in the production of lipids and terpenoids in yeast and microalgae. Overall, ALE technology provides a powerful tool for the construction of microbial cell factories, and it has been widely used in improving the level of target product synthesis, expanding the range of substrate utilization, and enhancing the tolerance of chassis cells. In addition, in order to improve the production of target compounds, ALE also employs environmental or nutritional stress strategies corresponding to the characteristics of different terpenoids, lipids, and strains.

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.

Development and Application of Whole-Cell Biosensors for the Detection of Gallic Acid

Gallic acid is a prevalent secondary plant metabolite distinguished as one of the most effective free-radical scavengers among phenolic acids. This compound is also known for its cytotoxic, anti-inflammatory, and antimicrobial activities. Bulk quantities of gallic acid are conventionally produced by acid hydrolysis of tannins, a costly and environmentally hazardous process. With the aim to develop more sustainable approaches, microbial bioproduction strategies have been attempted recently. To advance synthetic biology and metabolic engineering of microorganisms for gallic acid production, we characterize here a transcription factor-based inducible system PpGalR/P_{PP_RS13150} that responds to the extracellular gallic acid in a dose-dependent manner in Pseudomonas putida KT2440. Surprisingly, this compound does not mediate induction when PpGalR/P_{PP_RS13150} is used in non-native host background. We show that the activation of the inducible system requires gallate dioxygenase activity encoded by galA gene. The 4-oxalomesaconic acid, an intermediate of gallic acid-metabolism, is identified as the effector molecule that interacts with the transcription factor GalR mediating activation of gene expression. Introduction of galA gene along galR enables development of biosensors suitable for detection and monitoring of gallic acid extracellularly using non-native hosts such as E. coli and C. necator. Moreover, the P. putida-based biosensor's applicability is demonstrated by detecting and measuring gallic acid in extracts of Camellia sinensis leaves. This study reports the strategy, which can be applied for developing gallic acid biosensors using bacterial species outside Pseudomonas genus.

Rapid and Finely-Tuned Expression for Deployable Sensing Applications

Organisms from across the tree of life have evolved highly efficient mechanisms for sensing molecules of interest using biomolecular machinery that can in turn be quite valuable for the development of biosensors. However, purification of such machinery for use in in vitro biosensors is costly, while the use of whole cells as in vivo biosensors often leads to long sensor response times and unacceptable sensitivity to the chemical makeup of the sample. Cell-free expression systems overcome these weaknesses by removing the requirements associated with maintaining living sensor cells, allowing for increased function in toxic environments and rapid sensor readout at a production cost that is often more reasonable than purification. Here, we focus on the challenge of implementing cell-free protein expression systems that meet the stringent criteria required for them to serve as the basis for field-deployable biosensors. Fine-tuning expression to meet these requirements can be achieved through careful selection of the sensing and output elements, as well as through optimization of reaction conditions via tuning of DNA/RNA concentrations, lysate preparation methods, and buffer conditions. Through careful sensor engineering, cell-free systems can continue to be successfully used for the production of tightly regulated, rapidly expressing genetic circuits for biosensors.

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.

Circuit-guided population acclimation of a synthetic microbial consortium for improved biochemical production

Microbial consortia have been considered potential platforms for bioprocessing applications. However, the complexity in process control owing to the use of multiple strains necessitates the use of an efficient population control strategy. Herein, we report circuit-guided synthetic acclimation as a strategy to improve biochemical production by a microbial consortium. We designed a consortium comprising alginate-utilizing Vibrio sp. dhg and 3-hydroxypropionic acid (3-HP)-producing Escherichia coli strains for the direct conversion of alginate to 3-HP. We introduced a genetic circuit, named "Population guider", in the E. coli strain, which degrades ampicillin only when 3-HP is produced. In the presence of ampicillin as a selection pressure, the consortium was successfully acclimated for increased 3-HP production by 4.3-fold compared to that by a simple co-culturing consortium during a 48-h fermentation. We believe this concept is a useful strategy for the development of robust consortium-based bioprocesses.

Olfactory Receptors as an Emerging Chemical Sensing Scaffold

Chemical biosensors are an increasingly ubiquitous part of our lives. Beyond enzyme-coupled assays, recent synthetic biology advances now allow us to hijack more complex biosensing systems to respond to difficult to detect analytes, such as chemical small molecules. Here, we briefly overview recent advances in the biosensing of small molecules, including nucleic acid aptamers, allosteric transcription factors, and two-component systems. We then look more closely at a recently developed chemical sensing system, G protein-coupled receptor (GPCR)-based sensors. Finally, we consider the chemical sensing capabilities of the largest GPCR subfamily, olfactory receptors (ORs). We examine ORs' role in nature, their potential as a biomedical target, and their ability to detect compounds not amenable for detection using other biological scaffolds. We conclude by evaluating the current challenges, opportunities, and future applications of GPCR- and OR-based sensors.

Striving for sustainable biosynthesis: discovery, diversification, and production of antimicrobial drugs in Escherichia coli

New antimicrobials need to be discovered to fight the advance of multidrug-resistant pathogens. A promising approach is the screening for antimicrobial agents naturally produced by living organisms. As an alternative to studying the native producer, it is possible to use genetically tractable microbes as heterologous hosts to aid the discovery process, facilitate product diversification through genetic engineering, and ultimately enable environmentally friendly production. In this mini-review, we summarize the literature from 2017 to 2022 on the application of Escherichia coli and E. coli-based platforms as versatile and powerful systems for the discovery, characterization, and sustainable production of antimicrobials. We highlight recent developments in high-throughput screening methods and genetic engineering approaches that build on the strengths of E. coli as an expression host and that led to the production of antimicrobial compounds. In the last section, we briefly discuss new techniques that have not been applied to discover or engineer antimicrobials yet, but that may be useful for this application in the future.

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.

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.

Engineering of a TrpR-Based Biosensor for Altered Dynamic Range and Ligand Preference

Transcriptional factors play a crucial role in regulating cellular functions. Understanding and altering the dynamic behavior of the transcriptional factor-based biosensors will expand our knowledge in investigating biomolecular interactions and facilitating biosynthetic applications. In this study, we characterized and engineered a TrpR-based tryptophan repressor system in Escherichia coli. We found that the reconstructed TrpR1-PtrpO1 biosensor system exhibited low basal expression and narrow dynamic range in the presence of tryptophan or its analogue 5-hydroxytryptophan (5-HTP). Given the application potential of the biosensor, we introduced engineering approaches in multiple levels to optimize its dynamic behavior. First, the I57 and V58 residues in the ligand-binding pocket were rationally mutated in search of variants with altered ligand specificity. Two TrpR1 variants, V58E and V58K, successfully acquired ligand preference toward tryptophan and 5-HTP, respectively. The biosensor-induced expression levels were increased up to 10-fold with those variants. Furthermore, to pursue broader operational range, we tuned the regulator-operator binding affinity by mutating the binding box of TrpR1. Collectively, we demonstrated that the biosynthesis-significant biosensor TrpR1-PtrpO1 can be engineered to acquire extended dynamic ranges and improved ligand preference. The engineered biosensor variants with remarkable dynamic behavior can serve as key genetic elements in high-throughput screening and dynamic regulation in biosynthetic scenarios.

Advances, Challenges and Future Trends of Cell-Free Transcription-Translation Biosensors

In recent years, the application of cell-free protein synthesis systems in biosensing has been developing rapidly. Cell-free synthetic biology, with its advantages of high biosafety, fast material transport, and high sensitivity, has overcome many defects of cell-based biosensors and provided an abiotic substitute for biosensors. In addition, the application of freeze-drying technology has improved the stability of such systems, making it possible to realize point-of-care application of field detection and broadening the application prospects of cell-free biosensors. However, despite these advancements, challenges such as the risk of sample interference due to the lack of physical barriers, maintenance of activity during storage, and poor robustness still need to be addressed before the full potential of cell-free biosensors can be realized on a larger scale. In this review, current strategies and research results for improving the performance of cell-free biosensors are summarized, including a comprehensive discussion of the existing challenges, future trends, and potential investments needed for improvement.

Directed evolution of the PobR allosteric transcription factor to generate a biosensor for 4-hydroxymandelic acid

Hydroxy-mandelic acid (HMA) is widely applied in pharmaceuticals, food and cosmetics. In this study, we aimed to develop an allosteric transcription factors (aTFs) based biosensor for HMA. PobR, an aTF for HMA analog 4-hydroxybenzoic acid, was used to alter its selectivity and create novel aTFs responsive to HMA by directed evolution. We established a PobR mutant library with a capacity of 550,000 mutants using error-prone PCR and Megawhop PCR. Through our screening, two mutants were obtained with responsiveness to HMA. Analysis of each missense mutation indicating residues 122-126 were involved in its PobR ligand specificity. These results showed the effectiveness of directed evolution in switching the ligand specificity of a biosensor and improving HMA production.

An Allosteric Transcription Factor DNA-Binding Electrochemical Biosensor for Progesterone

We describe an electrochemical strategy to transduce allosteric transcription factor (aTF) binding affinity to sense steroid hormones. Our approach utilizes square wave voltammetry to monitor changes in current output as a progesterone (PRG)-specific aTF (SRTF1) unbinds from the cognate DNA sequence in the presence of PRG. The sensor detects PRG in artificial urine samples with sufficient sensitivity suitable for clinical applications. Our results highlight the capability of using aTFs as the biorecognition elements to develop electrochemical point-of-care biosensors for the detection of small-molecule biomarkers and analytes.

Development and Characterization of Indole-Responsive Whole-Cell Biosensor Based on the Inducible Gene Expression System from Pseudomonas putida KT2440

Indole is a biologically active compound naturally occurring in plants and some bacteria. It is an important specialty chemical that is used as a precursor by the pharmaceutical and chemical industries, as well as in agriculture. Recently, indole has been identified as an important signaling molecule for bacteria in the mammalian gut. The regulation of indole biosynthesis has been studied in several bacterial species. However, this has been limited by the lack of in vivo tools suitable for indole-producing species identification and monitoring. The genetically encoded biosensors have been shown to be useful for real-time quantitative metabolite analysis. This paper describes the identification and characterization of the indole-inducible system PpTrpI/P_{PP_RS00425} from Pseudomonas putida KT2440. Indole whole-cell biosensors based on Escherichia coli and Cupriavidus necator strains are developed and validated. The specificity and dynamics of biosensors in response to indole and its structurally similar derivatives are investigated. The gene expression system PpTrpI/P_{PP_RS00425} is shown to be specifically induced up to 639.6-fold by indole, exhibiting a linear response in the concentration range from approximately 0.4 to 5 mM. The results of this study form the basis for the use of whole-cell biosensors in indole metabolism-relevant bacterial species screening and characterization.

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

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

Identification and characterization of L- and D-lactate-inducible systems from Escherichia coli MG1655, Cupriavidus necator H16 and Pseudomonas species

Lactic acid is an important platform chemical used for the production of various compounds including polylactic acid (PLA). Optically pure L- and D-lactic acids are required to obtain high quality PLA. To advance the development and selection of microbial strains for improved production of lactic acid enantiomers, a high-throughput screening, dynamic pathway control, or real-time monitoring are often applied. Inducible gene expression systems and their application in the genetically encoded biosensors contribute to the development of these techniques and are important devices for the advancement of lactic acid biotechnology. Here, we identify and characterize eleven lactate-inducible systems from Escherichia coli, Cupriavidus necator, and Pseudomonas spp. The specificity and dynamics of these systems in response to L- and D-lactate, or structurally similar compounds are investigated. We demonstrate that the inducible systems EcLldR/P_lldP and CnGntR/P_{H16_RS19190} respond only to the L-lactate, exhibiting approximately 19- and 24-fold induction, respectively. Despite neither of the examined bacteria possess the D-lactate-specific inducible system, the PaPdhR/P_lldP and PfPdhR/P_lldP are induced approximately 37- and 366-fold, respectively, by D-lactate and can be used for developing biosensor with improved specificity. The findings of this study provide an insight into understanding of L- and D-lactate-inducible systems that can be employed as sensing and tuneable devices in synthetic biology.

Force in numbers: high-throughput screening approaches to unlock microbial transport

Biological membranes are inherently complex, making transport processes in microbial cell factories a significant bottleneck. Lack of knowledge on transport proteins' characteristics and the need for advanced technical equipment often hamper transporter identification and optimization. For these reasons, moving away from individual characterization and towards high-throughput mining, engineering, and screening of transporters is an increasingly attractive approach. Superior transporters can be selected from large libraries by coupling their activity to growth, for substrates that function as feedstocks or toxic compounds. Other compounds can be screened thanks to recent advances in the design and deployment of synthetic genetic circuits (biosensors). Furthermore, novel strategies are rapidly increasing the repertoire of biomolecule transporters susceptible to high-throughput selection methods.

Rational design of allosterically regulated toehold mediated strand displacement circuits for sensitive and on-site detection of small molecule metabolites

Development of small molecule biosensors enables rapid and de-centralized small molecule detection that meets the demands of routine health monitoring and rapid diagnosis. Among them, allosteric transcription factor (aTF)-based biosensors have shown potential in modular design of small molecule detection platforms due to their ligand-regulated DNA binding activity. Here, we expand the capabilities of a biosensor that leverages the aTF-based regulation of toehold-mediated strand displacement (TMSD) circuits for uric acid (UA) detection in non-invasive salivary samples by utilizing the UA-responsive aTF HucR. The impact of the low ligand affinity of the native HucR was addressed by engineering a two-pass TMSD circuit with in silico rational design. This combined strategy achieved enrichment of the output signal and overcame the negative impact of the matrix effect on the sensitivity and overall response of the biosensor when using real samples, which enabled semi-quantitative detection in the normal salivary UA levels. As well, enhancements provided by the two-pass design halved the turnaround time to less than 15 minutes. To sum up, the two-cycle DNA circuit design enabled aTF-based simple, rapid and one-step non-invasive salivary UA detection, showing its potential in metabolite detection for health monitoring.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Development of Genetically Encoded Biosensors for Reporting the Methyltransferase-Dependent Biosynthesis of Semisynthetic Macrolide Antibiotics

Clarithromycin is an improved semisynthetic analogue of the naturally occurring macrolide, erythromycin. The subtle modification of a methyl group on the C-6 hydroxyl group endows the molecule with improved acid stability and results in a clinically useful antibiotic. Here, we show that the effector specificity of the biosensor protein, MphR, can be evolved to selectively recognize clarithromycin and therefore report on the production of this molecule in vivo. In addition, a crystal structure of the evolved variant reveals the molecular basis for selectivity and provides a guide for the evolution of a new metabolic function using this biosensor.

A comprehensive review on the applications of nano-biosensor-based approaches for non-communicable and communicable disease detection

The outstretched applications of biosensors in diverse domains has become the reason for their attraction for scientific communities. Because they are analytical devices, they can detect both quantitative and qualitative biological components through the generation of detectable signals. In the recent past, biosensors witnessed significant changes and developments in their design as well as features. Nanotechnology has revolutionized sensing phenomena by increasing biodiagnostic capacity in terms of specificity, size, and cost, resulting in exceptional sensitivity and flexibility. The steep increase of non-communicable diseases across the world has emerged as a matter of concern. In parallel, the abrupt outbreak of communicable diseases poses a serious threat to mankind. For decreasing the morbidity and mortality associated with various communicable and non-communicable diseases, early detection and subsequent treatment are indispensable. Detection of different biological markers generates quantifiable signals that can be electrochemical, mass-based, optical, thermal, or piezoelectric. Speculating on the incumbent applicability and versatility of nano-biosensors in large disciplines, this review highlights different types of biosensors along with their components and detection mechanisms. Moreover, it deals with the current advancements made in biosensors and the applications of nano-biosensors in detection of various non-communicable and communicable diseases, as well as future prospects of nano-biosensors for diagnostics.

Recent advances in tuning the expression and regulation of genes for constructing microbial cell factories

To overcome environmental problems caused by the use of fossil resources, microbial cell factories have become a promising technique for the sustainable and eco-friendly development of valuable products from renewable resources. Constructing microbial cell factories with high titers, yields, and productivity requires a balance between growth and production; to this end, tuning gene expression and regulation is necessary to optimise and precisely control complicated metabolic fluxes. In this article, we review the current trends and advances in tuning gene expression and regulation and consider their engineering at each of the three stages of gene regulation: genomic, mRNA, and protein. In particular, the technological approaches utilised in a diverse range of genetic-engineering-based tools for the construction of microbial cell factories are reviewed and representative applications of these strategies are presented. Finally, the prospects for strategies and systems for tuning gene expression and regulation are discussed.

The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes

Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Protein engineering for natural product biosynthesis and synthetic biology applications

As protein engineering grows more salient, many strategies have emerged to alter protein structure and function, with the goal of redesigning and optimizing natural product biosynthesis. Computational tools, including machine learning and molecular dynamics simulations, have enabled the rational mutagenesis of key catalytic residues for enhanced or altered biocatalysis. Semi-rational, directed evolution and microenvironment engineering strategies have optimized catalysis for native substrates and increased enzyme promiscuity beyond the scope of traditional rational approaches. These advances are made possible using novel high-throughput screens, including designer protein-based biosensors with engineered ligand specificity. Herein, we detail the most recent of these advances, focusing on polyketides, non-ribosomal peptides and isoprenoids, including their native biosynthetic logic to provide clarity for future applications of these technologies for natural product synthetic biology.