Transcription Factor-Based Biosensors in High-Throughput Screening: Advances and Applications

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

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

Development of the IMP biosensor for rapid and stable analysis of IMP concentrations in fermentation broth

IMP (inosinic acid) is a crucial intermediate in the purine metabolic pathway and is continuously synthesized in all cells. Besides its role as a precursor for DNA and RNA, IMP also plays a critical or essential role in cell growth, energy storage, conversion, and metabolism. In our study, we utilized the circularly permuted fluorescent protein (cpFP) and IMP dehydrogenase to screen and develop the IMP biosensor, IMPCP1. By introducing a mutation in the catalytically active site of IMPCP1, from Cys to Ala, we disrupted its ability to catalyze IMP while retaining its capability to bind to IMP without affecting the IMP concentration in the sample. To immobilize IMPCP1, we employed the SpyCatcher/SpyTag system and securely attached it to Magarose-Epoxy, resulting in the development of the IMP rapid test kit, referred to as IMPTK. The biosensor integrated into IMPTK offers enhanced stability, resistance to degradation activity, and specific recognition of IMP. It is also resistant to peroxides and temperature changes. IMPTK serves as a rapid and stable assay for analyzing IMP concentrations in fermentation broth. Within the linear range of IMP concentrations, it can be utilized as a substitute for HPLC. The IMPTK biosensor provides a reliable and efficient alternative for monitoring IMP levels, offering advantages such as speed, stability, and resistance to environmental factors.

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.

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.

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.

Repurposing conformational changes in ANL superfamily enzymes to rapidly generate biosensors for organic and amino acids

Biosensors are powerful tools for detecting, real-time imaging, and quantifying molecules, but rapidly constructing diverse genetically encoded biosensors remains challenging. Here, we report a method to rapidly convert enzymes into genetically encoded circularly permuted fluorescent protein-based indicators to detect organic acids (GECFINDER). ANL superfamily enzymes undergo hinge-mediated ligand-coupling domain movement during catalysis. We introduce a circularly permuted fluorescent protein into enzymes hinges, converting ligand-induced conformational changes into significant fluorescence signal changes. We obtain 11 GECFINDERs for detecting phenylalanine, glutamic acid and other acids. GECFINDER-Phe3 and GECFINDER-Glu can efficiently and accurately quantify target molecules in biological samples in vitro. This method simplifies amino acid quantification without requiring complex equipment, potentially serving as point-of-care testing tools for clinical applications in low-resource environments. We also develop a GECFINDER-enabled droplet-based microfluidic high-throughput screening method for obtaining high-yield industrial strains. Our method provides a foundation for using enzymes as untapped blueprint resources for biosensor design, creation, and application.

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.

A tRNAModification-based strategy for Identifying amiNo acid Overproducers (AMINO)

Amino acids have a multi-billion-dollar market with rising demand, prompting the development of high-performance microbial factories. However, a general screening strategy applicable to all proteinogenic and non-proteinogenic amino acids is still lacking. Modification of the critical structure of tRNA could decrease the aminoacylation level of tRNA catalyzed by aminoacyl-tRNA synthetases. Involved in a two-substrate sequential reaction, amino acids with increased concentration could elevate the reduced aminoacylation rate caused by specific tRNA modification. Here, we developed a selection system for overproducers of specific amino acids using corresponding engineered tRNAs and marker genes. As a proof-of-concept, overproducers of five amino acids such as L-tryptophan were screened out by growth-based and/or fluorescence-activated cell sorting (FACS)-based screening from random mutation libraries of Escherichia coli and Corynebacterium glutamicum, respectively. This study provided a universal strategy that could be applied to screen overproducers of proteinogenic and non-proteinogenic amino acids in amber-stop-codon-recoded or non-recoded hosts.

Modulating Sensitivity of an Erythromycin Biosensor for Precise High-Throughput Screening of Strains with Different Characteristics

Genetically encoded biosensors are powerful tools for product-driven high-throughput screening in synthetic biology and metabolic engineering. However, most biosensors can only properly function in a limited concentration cutoff, and the incompatible performance characteristics of biosensors will lead to false positives or failure in screening. The transcription factor (TF)-based biosensors are usually organized in modular architecture and function in a regulator-depended manner, whose performance properties can be fine-tuned by modifying the expression level of the TF. In this study, we modulated the performance characteristics, including sensitivity and operating range, of an MphR-based erythromycin biosensor by fine-adjusting regulator expression levels via ribosome-binding site (RBS) engineering and obtained a panel of biosensors with varied sensitivities by iterative fluorescence-assisted cell sorting (FACS) in Escherichia coli to accommodate different screening purposes. To exemplify their application potential, two engineered biosensors with 10-fold different sensitivities were employed in the precise high-throughput screening by microfluidic-based fluorescence-activated droplet sorting (FADS) of Saccharopolyspora erythraea mutant libraries with different starting erythromycin productions, and mutants representing as high as 6.8 folds and over 100% of production improvements were obtained starting from the wild-type strain and the high-producing industrial strain, respectively. This work demonstrated a simple strategy to engineer biosensor performance properties, which was significant to stepwise strain engineering and production improvement.

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.

Heme biosensor-guided in vivo pathway optimization and directed evolution for efficient biosynthesis of heme

BACKGROUND

Heme has attracted much attention because of its wide applications in medicine and food. The products of genes hemBCDEFY convert 5-aminolevulinic acid to protoporphyrin IX (PPIX; the immediate precursor of heme); protoporphyrin ferrochelatase (FECH) inserts Fe²⁺ into PPIX to generate heme. Biosynthesis of heme is limited by the need for optimized expression levels of multiple genes, complex regulatory mechanisms, and low enzymatic activity; these problems need to be overcome in metabolic engineering to improve heme synthesis.

RESULTS

We report a heme biosensor-guided screening strategy using the heme-responsive protein HrtR to regulate tcR expression in Escherichia coli, providing a quantifiable link between the intracellular heme concentration and cell survival in selective conditions (i.e., the presence of tetracycline). This system was used for rapid enrichment screening of heme-producing strains from a library with random ribosome binding site (RBS) variants and from a FECH mutant library. Through up to four rounds of iterative evolution, strains with optimal RBS intensities for the combination of hemBCDEFY were screened; we obtained a PPIX titer of 160.8 mg/L, the highest yield yet reported in shaken-flask fermentation. A high-activity FECH variant was obtained from the saturation mutagenesis library. Fed-batch fermentation of strain SH20C, harboring the optimized hemBCDEFY and the FECH mutant, produced 127.6 mg/L of heme.

CONCLUSION

We sequentially improved the multigene biosynthesis pathway of PPIX and performed in vivo directed evolution of FECH, based on a heme biosensor, which demonstrated the effectiveness of the heme biosensor-based pathway optimization strategy and broadens our understanding of the mechanism of heme synthesis.

A novel biosensor-based method for the detection of p-nitrophenol in agricultural soil

Directly measurement of the bioavailable concentration of soil contaminants is essential for their accurate risk assessment. In this study, we successfully modified and identified the key genetic elements (pobR1-3) for the bio-detection of p-nitrophenol and synthesized five novel whole-cell biosensors (Escherichia coli BL21/pPNP-mrfp, E. coli BL21/pPNP-CFP, E. coli BL21/pPNP-YFP, E. coli BL21/pPNP-GFP, and E. coli BL21/pPNP-amilCP) to directly detect the concentration of p-nitrophenol in soils. These biosensor methods contained a simple biosensor activation and sample extraction step, a cost-effective detection means, and a fast detection process (5 h) by using a 96-microwell plate with a low background value and high-reliability equation for p-nitrophenol detection. These biosensors had a detection limit of 6.21-25.2 μg/kg and a linear range of 10-10000 μg/kg for p-nitrophenol in four soils. All biosensors showed better detection performance in the detection of p-nitrophenol in soil samples. The biosensors method can help to quickly and directly assess the actual bioavailable fractions of p-nitrophenol in soils, thus facilitating to understand the environmental cycling of p-nitrophenol.

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.

CRISPRi-based programmable logic inverter cascade for antibiotic-free selection and maintenance of multiple plasmids

Antibiotics have been widely used for plasmid-mediated cell engineering. However, continued use of antibiotics increases the metabolic burden, horizontal gene transfer risks, and biomanufacturing costs. There are limited approaches to maintaining multiple plasmids without antibiotics. Herein, we developed an inverter cascade using CRISPRi by building a plasmid containing a single guide RNA (sgRNA) landing pad (pSLiP); this inhibited host cell growth by repressing an essential cellular gene. Anti-sgRNAs on separate plasmids restored cell growth by blocking the expression of growth-inhibitory sgRNAs in pSLiP. We maintained three plasmids in Escherichia coli with a single antibiotic selective marker. To completely avoid antibiotic use and maintain the CRISPRi-based logic inverter cascade, we created a novel d-glutamate auxotrophic E. coli. This enabled the stable maintenance of the plasmid without antibiotics, enhanced the production of the terpenoid, (-)-α-bisabolol, and generation of an antibiotic-resistance gene-free plasmid. CRISPRi is therefore widely applicable in genetic circuits and may allow for antibiotic-free biomanufacturing.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Signal Amplification of araC pBAD Using a Standardised Translation Initiation Region

araC pBAD is a genetic fragment that regulates the expression of the araBAD operon in bacteria, which is required for the metabolism of L-arabinose. It is widely used in bioengineering applications because it can drive regulatable and titratable expression of genes and genetic pathways in microbial cell factories. A notable limitation of araC pBAD is that it generates a low signal when induced with high concentrations of L-arabinose (the maximum ON state). Herein we have amplified the maximum ON state of araC pBAD by coupling it to a synthetically evolved translation initiation region (TIR^EVOL). The coupling maintains regulatable and titratable expression from araC pBAD and yet increases the maximal ON state by >5-fold. The general principle demonstrated in the study can be applied to amplify the signal from similar genetic modules.

Graphical Abstract

Whole-Cell Biosensor and Producer Co-cultivation-Based Microfludic Platform for Screening Saccharopolyspora erythraea with Hyper Erythromycin Production

Actinomycetes are versatile secondary metabolite producers with great application potential in industries. However, industrial strain engineering has long been limited by the inefficient and labor-consuming plate/flask-based screening process, resulting in an urgent need for product-driven high-throughput screening methods for actinomycetes. Here, we combine a whole-cell biosensor and microfluidic platform to establish the whole-cell biosensor and producer co-cultivation-based microfluidic platform for screening actinomycetes (WELCOME). In WELCOME, we develop an MphR-based Escherichia coli whole-cell biosensor sensitive to erythromycin and co-cultivate it with Saccharopolyspora erythraea in droplets for high-throughput screening. Using WELCOME, we successfully screen out six erythromycin hyper-producing S. erythraea strains starting from an already high-producing industrial strain within 3 months, and the best one represents a 50% improved yield. WELCOME completely circumvents a major problem of industrial actinomycetes, which is usually genetic-intractable, and this method will revolutionize the field of industrial actinomycete engineering.

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.

Intracellular biosensor-based dynamic regulation to manipulate gene expression at the spatiotemporal level

The use of intracellular, biosensor-based dynamic regulation strategies to regulate and improve the production of useful compounds have progressed significantly over previous decades. By employing such an approach, it is possible to simultaneously realize high productivity and optimum growth states. However, industrial fermentation conditions contain a mixture of high- and low-performance non-genetic variants, as well as young and aged cells at all growth phases. Such significant individual variations would hinder the precise controlling of metabolic flux at the single-cell level to achieve high productivity at the macroscopic population level. Intracellular biosensors, as the regulatory centers of metabolic networks, can real-time sense intra- and extracellular conditions and, thus, could be synthetically adapted to balance the biomass formation and overproduction of compounds by individual cells. Herein, we highlight advances in the designing and engineering approaches to intracellular biosensors. Then, the spatiotemporal properties of biosensors associated with the distribution of inducers are compared. Also discussed is the use of such biosensors to dynamically control the cellular metabolic flux. Such biosensors could achieve single-cell regulation or collective regulation goals, depending on whether or not the inducer distribution is only intracellular.

Tuning the catalytic performances of a sucrose isomerase for production of isomaltulose with high concentration

Obtaining a sucrose isomerase (SIase) with high catalytic performance is of great importance in industrial production of isomaltulose (a reducing sugar). In order to obtain such SIase mutant, a high-throughput screening system in microtiter plate format was developed based on a widely used 2,4-dinitrosalicylic acid (DNS) method for determination of reducing sugar. An SIase from Erwinia sp. Ejp617 (ErSIase) was selected to improve its catalytic efficiency. After screening of ~ 8000 mutants from a random mutagenesis library, Q209 and R456 were identified as beneficial positions. Saturation mutagenesis of the two positions resulted in a double-site mutant ErSIase_Q209S-R456H that showed the highest catalytic efficiency, and its specific activity reached 684 U/mg that is 17.5-fold higher than that of the wild-type ErSIase. By employing the lyophilized Escherichia coli (E. coli) cells harboring ErSIase_Q209S-R456H, a high space-time yield (STY = 3.9 kg/(L·d)) was achieved toward 600 g/L sucrose. Furthermore, the in silico analysis suggested that the hydrogen bond network was improved and steric hindrance was reduced due to the beneficial substitutions.Key points• A sucrose isomerase mutant with high catalytic efficiency was obtained.• The highest space-time yield was achieved toward high-concentration sucrose.• The optimized H-bond network contributed to the enhanced catalytic efficiency.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

MarR-type transcription factor RosR regulates glutamate metabolism network and promotes accumulation of L-glutamate in Corynebacterium glutamicum G01

Transcription factors (TFs) perform a crucial function in the regulation of amino acids biosynthesis. Here, TFs involved in L-glutamate biosynthesis in Corynebacterium glutamicum were investigated. Compared to transcriptomic results of C. glutamicum 13032, 7 TFs regulated to glutamate biosynthesis were indentifed in G01 and E01. Among them, RosR was demonstrated to regulate L-glutamate metabolic network by binding to the promoters of glnA, pqo, ilvB, ilvN, ilvC, ldhA, odhA, dstr1, fas, argJ, ak and pta. Overexpression of RosR in G01 resulted in significantly decreased by-products yield and improved L-glutamate titer (130.6 g/L) and yield (0.541 g/g from glucose) in fed-batch fermentation. This study demonstrated the L-glutamate production improved by the expression of TFs in C. glutamicum, which provided a good reference for the transcriptional regulation engineering of strains for amino acid biosynthesis and suggested further metabolic engineering of C. glutamicum for L-glutamate production.

Development of a Transcription Factor-Based Diamine Biosensor in Corynebacterium glutamicum

Diamines serve as major platform chemicals that can be employed to a variety of industrial scenarios, particularly as monomers for polymer synthesis. High-throughput sensors for diamine biosynthesis can greatly improve the biological production of diamines. Here, we identified and characterized a transcription factor-driven biosensor for putrescine and cadaverine in Corynebacterium glutamicum. The transcriptional TetR-family regulatory protein CgmR (CGL2612) is used for the specific detection of diamine compounds. This study also improved the dynamic range and the sensitivity to putrescine by systematically optimizing genetic components of pSenPut. By a single cell-based screening strategy for a library of CgmR with random mutations, this study obtained the most sensitive variant CgmR_I152T, which possessed an experimentally determined limit of detection (LoD) of ≤0.2 mM, a K of 11.4 mM, and a utility of 720. Using this highly sensitive putrescine biosensor pSenPut_I152T, we demonstrated that CgmRI_152T can be used as a sensor to detect putrescine produced biologically in a C. glutamicum system. This high sensitivity and the range of CgmR will be an influential tool for rewiring metabolic circuits and facilitating the directed evolution of recombinant strains toward the biological synthesis of diamine compounds.

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.

Genome-Wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a large-scale application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a bacterium. We applied DAP-seq to > 90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA-, and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike toward a desired phenotype.

Bioproduction of l- and d-lactic acids: advances and trends in microbial strain application and engineering

Lactic acid is an important platform chemical used in the food, agriculture, cosmetic, pharmaceutical, and chemical industries. It serves as a building block for the production of polylactic acid (PLA), a biodegradable polymer, which can replace traditional petroleum-based plastics and help to reduce environmental pollution. Cost-effective production of optically pure l- and d-lactic acids is necessary to achieve a quality and thermostable PLA product. This paper evaluates research advances in the bioproduction of l- and d-lactic acids using microbial fermentation. Special emphasis is given to the development of metabolically engineered microbial strains and processes tailored to alternative and flexible feedstock concepts such as: lignocellulose, glycerol, C1-gases, and agricultural-food industry byproducts. Alternative fermentation concepts that can improve lactic acid production are discussed. The potential use of inducible gene expression systems for the development of biosensors to facilitate the screening and engineering of lactic acid-producing microorganisms is discussed.

Fluorescence-based screening for engineered aldo-keto reductase KmAKR with improved catalytic performance and extended substrate scope

BACKGROUND

Aldo-keto reductases-catalyzed transformations of ketones to chiral alcohols have become an established biocatalytic process step in the pharmaceutical industry. Previously, we have discovered an aldo-keto reductase (AKR) from Kluyveromyces marxianus that is active to the aliphatic tert-butyl 6-substituted (5R/S)-hydroxy-3-oxohexanoates, but it is inactive to aromatic ketones. In order to acquire an excellent KmAKRmutant for ensuring the simultaneous improvement of activity-thermostability toward tert-butyl 6-cyano-(5R)-hydroxy-3-oxohexanoate ((5R)-1) and broadening the universal application prospects toward more substrates covering both aliphatic and aromatic ketones, a fluorescence-based high-throughput (HT) screening technique was established.

MAIN METHODS AND MAJOR RESULTS

The directed evolution of KmAKR variant M5 (KmAKR-W297H/Y296W/K29H/Y28A/T63M) produced the "best" variant M5-Q213A/T23V. It exhibited enhanced activity-thermostability toward (5R)-1, improved activity toward all 18 test substrates and strict R-stereoselectivity toward 10 substrates in comparison to M5. The enhancement of enzymatic activity and the extension of substrate scope covering aromatic ketones are proposed to be largely attributed to pushing the binding pocket of M5-Q213A/T23V to the enzyme surface, decreasing the steric hindrance at the entrance and enhancing the flexibility of loops surrounding the active center. In addition, combined with 0.94 g dry cell weight (DCW) L^-1 glucose dehydrogenase from Exiguobacterium sibiricum (EsGDH) for NADPH regeneration, 2.81 g DCW L^-1 M5-Q213A/T23V completely converted (5R)-1 of up to 450 g L^-1 at 120 g g^-1 substrates/catalysts (S/C), yielding the corresponding optically pure tert-butyl 6-cyano-(3R,5R)-dihydroxyhexanoate ((3R,5R)-2, > 99.5% d.e._p ) with a space-time yield (STY) of 1.08 kg L^-1 day^-1 .

CONCLUSIONS

A fluorescence-based HT screening system was developed to tailor KmAKR's activity, thermostability and substrate scope. The "best" variant M5-Q213A/T23V holds great potential application for the synthesis of aliphatic/aromatic R-configuration alcohols.

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

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

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.

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

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

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

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

Design, Evolution, and Characterization of a Xylose Biosensor in Escherichia coli Using the XylR/xylO System with an Expanded Operating Range

Genetically encoded biosensors are extensively utilized in synthetic biology and metabolic engineering. However, reported xylose biosensors are far too sensitive with a limited operating range to be useful for most sensing applications. In this study, we describe directed evolution of Escherichia coli XylR, and construction of biosensors based on XylR and the corresponding operator xylO. The operating range of biosensors containing the mutant XylR was increased by nearly 10-fold comparing with the control. Two individual amino acid mutations (either L73P or N220T) in XylR were sufficient to extend the linear response range to upward of 10 g/L xylose. The evolved biosensors described here are well suited for developing whole-cell biosensors for detecting varying xylose concentrations across an expanded range. As an alternative use of this system, we also demonstrate the utility of XylR and xylO as a xylose inducible system to enable graded gene expression through testing with β-galactosidase gene and the lycopene synthetic pathway. This evolution strategy identified a less-sensitive biosensor for real applications, thus providing new insights into strategies for expanding operating ranges of other biosensors for synthetic biology applications.

Directed evolution of Aspergillus oryzae lipase for the efficient resolution of (R,S)-ethyl-2-(4-hydroxyphenoxy) propanoate

Aspergillus oryzae lipase (AOL) is a potential biocatalyst for industrial application. In this study, a mutant lipase AOL-3^F38N/V230R was screened through two rounds of directed evolution, resulting in a fourfold increase in lipase activity, and threefold in catalytic efficiency (k_cat/K_m), while maintaining its excellent stereoselectivity. AOL-3^F38N/V230R enzyme activity was maximum at pH 7.5 and also at 40 °C. And compared with wild-type AOL-3, AOL-3^F38N/V230R preferentially hydrolyzed the fatty acid ethyl ester carbon chain length from C4 to C6-C10. In the same catalytic reaction conditions, the conversion of (R,S)-ethyl-2-(4-hydroxyphenoxy) propanoate ((R,S)-EHPP) by AOL-3^F38N/V230R can be increased 169.7% compared to the original enzyme. The e.e._s of (R,S)-EHPP achieved 99.4% and conversion about 50.2% with E value being 829.0. Therefore, AOL-3^F38N/V230R was a potential biocatalyst for obtaining key chiral compounds for aryloxyphenoxy propionate (APP) herbicides.

An Update on Molecular Tools for Genetic Engineering of Actinomycetes-The Source of Important Antibiotics and Other Valuable Compounds

The first antibiotic-producing actinomycete (Streptomyces antibioticus) was described by Waksman and Woodruff in 1940. This discovery initiated the "actinomycetes era", in which several species were identified and demonstrated to be a great source of bioactive compounds. However, the remarkable group of microorganisms and their potential for the production of bioactive agents were only partially exploited. This is caused by the fact that the growth of many actinomycetes cannot be reproduced on artificial media at laboratory conditions. In addition, sequencing, genome mining and bioactivity screening disclosed that numerous biosynthetic gene clusters (BGCs), encoded in actinomycetes genomes are not expressed and thus, the respective potential products remain uncharacterized. Therefore, a lot of effort was put into the development of technologies that facilitate the access to actinomycetes genomes and activation of their biosynthetic pathways. In this review, we mainly focus on molecular tools and methods for genetic engineering of actinomycetes that have emerged in the field in the past five years (2015-2020). In addition, we highlight examples of successful application of the recently developed technologies in genetic engineering of actinomycetes for activation and/or improvement of the biosynthesis of secondary metabolites.

Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories

Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.

A New Biosensor for Stilbenes and a Cannabinoid Enabled by Genome Mining of a Transcriptional Regulator

In vivo biosensors are powerful tools for metabolic engineering and synthetic biology applications. However, the development of biosensors is hindered by the limited number of characterized transcriptional regulators. The versatile sensing abilities of microbes and genome sequences available hold great potential for developing novel biosensors via genome mining for new transcriptional regulators. Here we report the development and engineering of a new stilbene-responsive biosensor discovered by mining the Novosphingobium aromaticivorans DSM 12444 genome. The biosensor can distinguish resveratrol from its precursors, p-coumaric acid and trans-cinnamic acid. Remarkably, it can detect other biologically active stilbenes with resorcinol groups, and cannabidiolic acid with a β-resorcylic acid functional group. When coupled to resveratrol biosynthesis enzymes, the biosensor can sense altered resveratrol production in cells, demonstrating a 667-fold enrichment in one round of fluorescence-activated cell sorting. Our biosensor will be potentially applicable to metabolic engineering of microbial cell factories for production of stilbenes and cannabinoids.

The physical basis and practical consequences of biological promiscuity

Proteins interact with metabolites, nucleic acids, and other proteins to orchestrate the myriad catalytic, structural and regulatory functions that support life from the simplest microbes to the most complex multicellular organisms. These molecular interactions are often exquisitely specific, but never perfectly so. Adventitious "promiscuous" interactions are ubiquitous due to the thousands of macromolecules and small molecules crowded together in cells. Such interactions may perturb protein function at the molecular level, but as long as they do not compromise organismal fitness, they will not be removed by natural selection. Although promiscuous interactions are physiologically irrelevant, they are important because they can provide a vast reservoir of potential functions that can provide the starting point for evolution of new functions, both in nature and in the laboratory.

A Strategy Employing a TF-Splinting Duplex Nanoswitch to Achieve Single-Step, Enzyme-Free, Signal-On Detection of l-Tryptophan

Transcription factor (TF)-based metabolite detection mainly depends on TF-regulated gene expression in cells. From TF activation to gene transcription and translation, the signal travels a relatively long way before it is received. Here, we propose a TF-splinting duplex DNA nanoswitch to detect metabolites. We show its feasibility using tryptophan repressor (TrpR) to detect l-tryptophan as a model. The assay has been optimized and characterized after obtaining a proof of concept, and the detection of l-tryptophan in complex biological samples is feasible. Unlike an equivalent gene expression approach, the whole process is a single-step, enzyme-free, and signal-on method. It can be completed within 20 min. This proposed TF-splinting duplex has the potential to be applied to the quick and convenient detection of other metabolites or even TFs.

Development of a Novel Biosensor-Driven Mutation and Selection System via in situ Growth of Corynebacterium crenatum for the Production of L-Arginine

The high yield mutants require a high-throughput screening method to obtain them quickly. Here, we developed an L-arginine biosensor (ARG-Select) to obtain increased L-arginine producers among a large number of mutant strains. This biosensor was constructed by ArgR protein and argC promoter, and could provide the strain with the output of bacterial growth via the reporter gene sacB; strains with high L-arginine production could survive in 10% sucrose screening. To extend the screening limitation of 10% sucrose, the sensitivity of ArgR protein to L-arginine was decreased. Corynebacterium crenatum SYPA5-5 and its systems pathway engineered strain Cc6 were chosen as the original strains. This biosensor was employed, and L-arginine hyperproducing mutants were screened. Finally, the HArg1 and DArg36 mutants of C. crenatum SYPA5-5 and Cc6 could produce 56.7 and 95.5 g L^-1 of L-arginine, respectively, which represent increases of 35.0 and 13.5%. These results demonstrate that the transcription factor-based biosensor could be applied in high yield strains selection as an effective high-throughput screening method.

Tuning amino acid dehydrogenases with featured sequences for L-phosphinothricin synthesis by reductive amination

Biosynthesizing unnatural chiral amino acids is challenging due to the limited reductive amination activity of amino acid dehydrogenase (AADH). Here, for the asymmetric synthesis of l-phosphinothricin from 2-oxo-4-[(hydroxy)(methyl)phosphinoyl]butyric acid (PPO), a glutamate dehydrogenase gene (named GluDH3) from Pseudomonas monteilii was selected, cloned and expressed in Escherichia coli (E. coli). To boost its activity, a "two-step"-based computational approach was developed and applied to select the potential beneficial amino acid positions on GluDH3. l-phosphinothricin was synthesized by GluDH-catalyzed asymmetric amination using the d-glucose dehydrogenase from Exiguobacterium sibiricum (EsGDH) for NADPH regeneration. Using lyophilized E. coli cells that co-expressed GluDH3_V375S and EsGDH, up to 89.04 g L^-1 PPO loading was completely converted to l-phosphinothricin within 30 min at 35 °C with a space-time yield of up to 4.752 kg·L^-1·d^-1. The beneficial substitution V375S with increased polar interactions between K90, T193, and substrate PPO exhibited 168.2-fold improved catalytic efficiency (k_cat/K_M) and 344.8-fold enhanced specific activity. After the introduction of serine residues into other GluDHs at specific positions, forty engineered GluDHs exhibited the catalytic functions of "glufosinate dehydrogenase" towards PPO.

Recent advances in high-throughput mass spectrometry that accelerates enzyme engineering for biofuel research

Enzymes play indispensable roles in producing biofuels, a sustainable and renewable source of transportation fuels. Lacking rational design rules, the development of industrially relevant enzyme catalysts relies heavily on high-throughput screening. However, few universal methods exist to rapidly characterize large-scale enzyme libraries. Therefore, assay development is necessary on an ad hoc basis to link enzyme properties to spectrophotometric signals and often requires the use of surrogate, optically active substrates. On the other hand, mass spectrometry (MS) performs label-free enzyme assays that utilize native substrates and is therefore generally applicable. But the analytical speed of MS is considered rate limiting, mainly due to the use of time-consuming chromatographic separation in traditional MS analysis. Thanks to new instrumentation and sample preparation methods, direct analyte introduction into a mass spectrometer without a prior chromatographic step can be achieved by laser, microfluidics, and acoustics, so that each sample can be analyzed within seconds. Here we review recent advances in MS platforms that improve the throughput of enzyme library screening and discuss how these advances can potentially facilitate biofuel research by providing high sensitivity, selectivity and quantitation that are difficult to obtain using traditional assays. We also highlight the limitations of current MS assays in studying biofuel-related enzymes and propose possible solutions.

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

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

High-Throughput Screening Technology in Industrial Biotechnology

Based on the development of automatic devices and rapid assay methods, various high-throughput screening (HTS) strategies have been established for improving the performance of industrial microorganisms. We discuss the most significant factors that can improve HTS efficiency, including the construction of screening libraries with high diversity and the use of new detection methods to expand the search range and highlight target compounds. We also summarize applications of HTS for enhancing the performance of industrial microorganisms. Current challenges and potential improvements to HTS in industrial biotechnology are discussed in the context of rapid developments in synthetic biology, nanotechnology, and artificial intelligence. Rational integration will be an important driving force for constructing more efficient industrial microorganisms with wider applications in biotechnology.

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

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