Engineering and application of a biosensor with focused ligand specificity

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

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

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Dynamics design of a non-natural transcription factor responding to androst-4-ene-3,17-dione

The production of androst-4-ene-3,17-dione (AD) by the steroidal microbial cell factory requires transcription factors (TFs) to participate in metabolic regulation. However, microbial cell factory lacks effective TFs that can respond to AD in its metabolic pathway. Additionally, finding and obtaining natural TFs that specifically respond to AD is a complex and onerous task. In this study, we devised an artificial TF that responds to AD, termed AdT, based on structure-guided molecular dynamics (MD) simulation. According to MD analysis of the conformational changes of AdT after binding to AD, an LBD in which the N- and C-termini exhibited convergence tendencies was used as a microswitch to guide the assembly of a DNA-binding domain lexA, a linker (GGGGS)2, and a transcription activation domain B42 into an artificial TF. As a proof of design, a AD biosensor was designed and constructed in yeast on the basis of the ligand-binding domain (LBD) of hormone receptor. In addition, the transcription factor activity of AdT was increased by 1.44-fold for its variant F320Y. Overall, we created non-natural TF elements for AD microbial cell factory, and expected that the design TF strategy will be applied to running in parallel to the signaling machinery of the host cell.

Design and Characterization of a Generalist Biosensor for Indole Derivatives

Transcription factor (TF)-based biosensors are useful synthetic biology tools for applications in a variety of areas of biotechnology. A major challenge of biosensor circuits is the limited repertoire of identified and well-characterized TFs for applications of interest, in addition to the challenge of optimizing selected biosensors. In this work, we implement the IclR family repressor TF TtgV from Pseudomonas putida DOT-T1E as an indole-derivative biosensor in Escherichia coli. We optimize the genetic circuit utilizing different components, providing insights into biosensor design and expanding on previous studies investigating this TF. We discover novel physiologically relevant ligands of TtgV, such as skatole. The broad specificity of TtgV makes it a useful target for directed evolution and protein engineering toward desired specificity. TtgV, as an indole-derivative biosensor, is a promising genetic component for the detection of compounds with biological activities relevant to health and the gut microbiome.

Engineering microbial metabolic homeostasis for chemicals production

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

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

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

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Recent Advances in Metabolic Engineering for the Biosynthesis of Phosphoenol Pyruvate-Oxaloacetate-Pyruvate-Derived Amino Acids

The phosphoenol pyruvate-oxaloacetate-pyruvate-derived amino acids (POP-AAs) comprise native intermediates in cellular metabolism, within which the phosphoenol pyruvate-oxaloacetate-pyruvate (POP) node is the switch point among the major metabolic pathways existing in most living organisms. POP-AAs have widespread applications in the nutrition, food, and pharmaceutical industries. These amino acids have been predominantly produced in Escherichia coli and Corynebacterium glutamicum through microbial fermentation. With the rapid increase in market requirements, along with the global food shortage situation, the industrial production capacity of these two bacteria has encountered two bottlenecks: low product conversion efficiency and high cost of raw materials. Aiming to push forward the update and upgrade of engineered strains with higher yield and productivity, this paper presents a comprehensive summarization of the fundamental strategy of metabolic engineering techniques around phosphoenol pyruvate-oxaloacetate-pyruvate node for POP-AA production, including L-tryptophan, L-tyrosine, L-phenylalanine, L-valine, L-lysine, L-threonine, and L-isoleucine. Novel heterologous routes and regulation methods regarding the carbon flux redistribution in the POP node and the formation of amino acids should be taken into consideration to improve POP-AA production to approach maximum theoretical values. Furthermore, an outlook for future strategies of low-cost feedstock and energy utilization for developing amino acid overproducers is proposed.

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.

Growth-Coupled Evolutionary Pressure Improving Epimerases for D-Allulose Biosynthesis Using a Biosensor-Assisted In Vivo Selection Platform

Fast screening strategies that enable high-throughput evaluation and identification of desired variants from diversified enzyme libraries are crucial to tailoring biocatalysts for the synthesis of D-allulose, which is currently limited by the poor catalytic performance of ketose 3-epimerases (KEases). Here, the study designs a minimally equipment-dependent, high-throughput, and growth-coupled in vivo screening platform founded on a redesigned D-allulose-dependent biosensor system. The genetic elements modulating regulator PsiR expression levels undergo systematic optimization to improve the growth-responsive dynamic range of the biosensor, which presents ≈30-fold facilitated growth optical density with a high signal-to-noise ratio (1.52 to 0.05) toward D-allulose concentrations from 0 to 100 mm. Structural analysis and evolutionary conservation analysis of Agrobacterium sp. SUL3 D-allulose 3-epimerase (ADAE) reveal a highly conserved catalytic active site and variable hydrophobic pocket, which together regulate substrate recognition. Structure-guided rational design and directed evolution are implemented using the growth-coupled in vivo screening platform to reprogram ADAE, in which a mutant M42 (P38N/V102A/Y201L/S207N/I251R) is identified with a 6.28-fold enhancement of catalytic activity and significantly improved thermostability with a 2.5-fold increase of the half-life at 60 °C. The research demonstrates that biosensor-assisted growth-coupled evolutionary pressure combined with structure-guided rational design provides a universal route for engineering KEases.

Engineering and application of LacI mutants with stringent expressions

Optimal transcriptional regulatory circuits are expected to exhibit stringent control, maintaining silence in the absence of inducers while exhibiting a broad induction dynamic range upon the addition of effectors. In the Plac /LacI pair, the promoter of the lac operon in Escherichia coli is characterized by its leakiness, attributed to the moderate affinity of LacI for its operator target. In response to this limitation, the LacI regulatory protein underwent engineering to enhance its regulatory properties. The M7 mutant, carrying I79T and N246S mutations, resulted in the lac promoter displaying approximately 95% less leaky expression and a broader induction dynamic range compared to the wild-type LacI. An in-depth analysis of each mutation revealed distinct regulatory profiles. In contrast to the wild-type LacI, the M7 mutant exhibited a tighter binding to the operator sequence, as evidenced by surface plasmon resonance studies. Leveraging the capabilities of the M7 mutant, a high-value sugar biosensor was constructed. This biosensor facilitated the selection of mutant galactosidases with approximately a seven-fold improvement in specific activity for transgalactosylation. Consequently, this advancement enabled enhanced biosynthesis of galacto-oligosaccharides (GOS).

Recent advances of nanopore technique in single cell analysis

Single cells and their dynamic behavior are closely related to biological research. Monitoring their dynamic behavior is of great significance for disease prevention. How to achieve rapid and non-destructive monitoring of single cells is a major issue that needs to be solved urgently. As an emerging technology, nanopores have been proven to enable non-destructive and label-free detection of single cells. The structural properties of nanopores enable a high degree of sensitivity and accuracy during analysis. In this article, we summarize and classify the different types of solid-state nanopores that can be used for single-cell detection and illustrate their specific applications depending on the size of the analyte. In addition, their research progress in material transport and microenvironment monitoring is also highlighted. Finally, a brief summary of existing research challenges and future trends in nanopore single-cell analysis is tentatively provided.

Development of nanobodies against Staphylococcus enterotoxin B through yeast surface display

Staphylococcus enterotoxin B (SEB) is one of the primary virulence factors of Staphylococcus aureus but there is still a lack of targeted drugs. SEB activates immune cells via interacting with MHC-II on antigen-presenting cells, leading to the production of large amounts of pro-inflammatory cytokines. Blocking the interaction between SEB and MHC-II can avert the overactivation of immune cells. Nanobodies are the smallest functional antibodies that can bind stably to antigens. In this study, an ideal approach to obtain specific nanobodies without immunizing camelids was introduced. We constructed a library containing up to 5 × 108 nanobodies, and then screened those targeting SEB by using yeast surface display (YSD) technique and fluorescence-activated cell sorting (FACS). A total of 8 nanobodies with divergent complementarity-determining regions (CDRs) sequences were identified and one candidate Nb8 with high affinity to SEB was isolated. In vitro study demonstrated that Nb8 significantly inhibited SEB-induced inflammatory response. Molecular docking simulation indicated that the unique CDR3 sequence contributed to the binding of Nb8 to the MHC-II binding domain of SEB and accordingly cut off the connection between SEB and MHC-II. Our efforts contributed to the development of specific nanobodies for eliminating the threats of SEB.

Biosensor-guided discovery and engineering of metabolic enzymes

A variety of chemicals have been produced through metabolic engineering approaches, and enhancing biosynthesis performance can be achieved by using enzymes with high catalytic efficiency. Accordingly, a number of efforts have been made to discover enzymes in nature for various applications. In addition, enzyme engineering approaches have been attempted to suit specific industrial purposes. However, a significant challenge in enzyme discovery and engineering is the efficient screening of enzymes with the desired phenotype from extensive enzyme libraries. To overcome this bottleneck, genetically encoded biosensors have been developed to specifically detect target molecules produced by enzyme activity at the intracellular level. Especially, the biosensors facilitate high-throughput screening (HTS) of targeted enzymes, expanding enzyme discovery and engineering strategies with advances in systems and synthetic biology. This review examines biosensor-guided HTS systems and highlights studies that have utilized these tools to discover enzymes in diverse areas and engineer enzymes to enhance their properties, such as catalytic efficiency, specificity, and stability.

L-lysine production by systems metabolic engineering of an NADPH auto-regulated Corynebacterium glutamicum

Here, the systems metabolic engineering of L-lysine-overproducing Corynebacterium glutamicum is described to create a highly efficient microorganism producer. The key chromosomal mutations associated with L-lysine synthesis were identified based on whole-genome sequencing. The carbon flux was subsequently redirected into the L-lysine synthesis pathway and increased the availability of energy and product transport systems required for L-lysine synthesis. In addition, a promoter library sensitive to intracellular L-lysine concentration was constructed and applied to regulate the NADPH pool dynamically. In the fed-batch fermentation experiment, the L-lysine titer of the final engineered strain was 223.4 ± 6.5 g/L. This study is the first to improve L-lysine production by enhancing ATP supply and NADPH self-regulation to improve the intracellular environment.

Structural basis of transcription factor YhaJ for DNT detection

Detection of landmines without harming personnel is a global issue. The bacterial transcription factor YhaJ selectively detects metabolites of explosives, and it can be used as a key component of DNT biosensors. However, the wild-type YhaJ has a binding affinity that is not sufficient for the detection of trace amounts of explosives leaked from landmines buried in the soil. Here, we report crystal structures of the effector-binding domain of YhaJ in both the apo- and effector-bound forms. A structural comparison of the two forms revealed that the loop above the primary effector-binding site significantly switches its conformation upon effector binding. The primary effector-binding site involves hydrophobic and polar interactions, having specificity to hydroxyl-substituted benzene compounds. The structures explain the mechanism of activity-enhancing mutations and provide information for the rational engineering of YhaJ biosensors for the sensitive detection of explosives.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Advances in ultrahigh-throughput screening technologies for protein evolution

Inspired by natural evolution, directed evolution randomly mutates the gene of interest through artificial evolution conditions with variants being screened for the required properties. Directed evolution is vital to the enhancement of protein properties and comprises the construction of libraries with considerable diversity as well as screening methods with sufficient efficiency as key steps. Owing to the various characteristics of proteins, specific methods are urgently needed for library screening, which is one of the main limiting factors in accelerating evolution. This review initially organizes the principles of ultrahigh-throughput screening from the perspective of protein properties. It then provides a comprehensive introduction to the latest progress and future trends in ultrahigh-throughput screening technologies for directed evolution.

LysR-type transcriptional regulators: state of the art

The LysR-type transcriptional regulators (LTTRs) are DNA-binding proteins present in bacteria, archaea, and in algae. Knowledge about their distribution, abundance, evolution, structural organization, transcriptional regulation, fundamental roles in free life, pathogenesis, and bacteria-plant interaction has been generated. This review focuses on these aspects and provides a current picture of LTTR biology.

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.

Design and application of artificial rare L-lysine codons in Corynebacterium glutamicum

Background: L-lysine is widely used in the feed, food, and pharmaceutical industries, and screening for high L-lysine-producing strains has become a key goal for the industry. Methods: We constructed the rare L-lysine codon AAA by corresponding tRNA promoter replacement in C. glutamicum. Additionally, a screening marker related to the intracellular L-lysine content was constructed by converting all L-lysine codons of enhanced green fluorescent protein (EGFP) into the artificial rare codon AAA. The artificial EGFP was then ligated into pEC-XK99E and transformed into competent Corynebacterium glutamicum 23604 cells with the rare L-lysine codon. After atmospheric and room-temperature plasma mutation and induction culture, 55 mutants (0.01% of total cells) with stronger fluorescence were sorted using flow cytometry, and further screened by fermentation in a 96-deep-well plate and 500 mL shaker. Results: The fermentation results showed that the L-lysine production was increased by up to 9.7% in the mutant strains with higher fluorescence intensities, and that the highest screening positive rate was 69%, compared with that in the wild-type strain. Conclusion: The application of artificially constructed rare codons in this study represents an efficient, accurate, and simple method for screening other amino acid-producing microorganisms.

Developing a Fluorescent Inducible System for Free Fucose Quantification in Escherichia coli

L-Fucose is a monosaccharide abundant in mammalian glycoconjugates. In humans, fucose can be found in human milk oligosaccharides (HMOs), mucins, and glycoproteins in the intestinal epithelium. The bacterial consumption of fucose and fucosylated HMOs is critical in the gut microbiome assembly of infants, dominated by Bifidobacterium. Fucose metabolism is important for the production of short-chain fatty acids and is involved in cross-feeding microbial interactions. Methods for assessing fucose concentrations in complex media are lacking. Here we designed and developed a molecular quantification method of free fucose using fluorescent Escherichia coli. For this, low- and high-copy plasmids were evaluated with and without the transcription factor fucR and its respective fucose-inducible promoter controlling the reporter gene sfGFP. E. coli BL21 transformed with a high copy plasmid containing pFuc and fucR displayed a high resolution across increasing fucose concentrations and high fluorescence/OD values after 18 h. The molecular circuit was specific against other monosaccharides and showed a linear response in the 0-45 mM range. Adjusting data to the Hill equation suggested non-cooperative, simple regulation of FucR to its promoter. Finally, the biosensor was tested on different concentrations of free fucose and the supernatant of Bifidobacterium bifidum JCM 1254 supplemented with 2-fucosyl lactose, indicating the applicability of the method in detecting free fucose. In conclusion, a bacterial biosensor of fucose was validated with good sensitivity and precision. A biological method for quantifying fucose could be useful for nutraceutical and microbiological applications, as well as molecular diagnostics.

Metabolic reprogramming and biosensor-assisted mutagenesis screening for high-level production of L-arginine in Escherichia coli

L-arginine is a value-added amino acid with promising applications in the pharmaceutical and nutraceutical industries. Further unleashing the potential of microbial cell factories to make L-arginine production more competitive remains challenging due to the sophisticated intracellular interaction networks and the insufficient knowledge of global metabolic regulation. Here, we combined multilevel rational metabolic engineering with biosensor-assisted mutagenesis screening to exploit the L-arginine production potential of Escherichia coli. First, multiple metabolic pathways were systematically reprogrammed to redirect the metabolic flux into L-arginine synthesis, including the L-arginine biosynthesis, TCA cycle, and L-arginine export. Specifically, a toggle switch responding to special cellular physiological conditions was designed to dynamically control the expression of sucA and pull more carbon flux from the TCA cycle toward L-arginine biosynthesis. Subsequently, a biosensor-assisted high-throughput screening platform was designed and applied to further exploit the L-arginine production potential. The best-engineered ARG28 strain produced 132 g/L L-arginine in a 5-L bioreactor with a yield of 0.51 g/g glucose and productivity of 2.75 g/(L ⋅ h), which were the highest values reported so far. Through whole genome sequencing and reverse engineering, Frc frameshift mutant, PqiB A78P mutant, and RpoB P564T mutant were revealed for enhancing the L-arginine biosynthesis. Our study exhibited the power of coupling rational metabolic reprogramming and biosensor-assisted mutagenesis screening to unleash the cellular potential for value-added metabolite production.

Emergence of an Auxin Sensing Domain in Plant-Associated Bacteria

Bacteria have evolved a sophisticated array of signal transduction systems that allow them to adapt their physiology and metabolism to changing environmental conditions. Typically, these systems recognize signals through dedicated ligand binding domains (LBDs) to ultimately trigger a diversity of physiological responses. Nonetheless, an increasing number of reports reveal that signal transduction receptors also bind antagonists to inhibit responses mediated by agonists. The mechanisms by which antagonists block the downstream signaling cascade remain largely unknown. To advance our knowledge in this field, we used the LysR-type transcriptional regulator AdmX as a model. AdmX activates the expression of an antibiotic biosynthetic cluster in the rhizobacterium Serratia plymuthica. AdmX specifically recognizes the auxin phytohormone indole-3-acetic acid (IAA) and its biosynthetic intermediate indole-3-pyruvic acid (IPA) as signals. However, only IAA, but not IPA, was shown to regulate antibiotic production in S. plymuthica. Here, we report the high-resolution structures of the LBD of AdmX in complex with IAA and IPA. We found that IAA and IPA compete for binding to AdmX. Although IAA and IPA binding does not alter the oligomeric state of AdmX, IPA binding causes a higher degree of compactness in the protein structure. Molecular dynamics simulations revealed significant differences in the binding modes of IAA and IPA by AdmX, and the inspection of the three-dimensional structures evidenced differential agonist- and antagonist-mediated structural changes. Key residues for auxin binding were identified and an auxin recognition motif defined. Phylogenetic clustering supports the recent evolutionary emergence of this motif specifically in plant-associated enterobacteria. IMPORTANCE Although antagonists were found to bind different bacterial signal transduction receptors, we are still at the early stages of understanding the molecular details by which these molecules exert their inhibitory effects. Here, we provide insight into the structural changes resulting from the binding of an agonist and an antagonist to a sensor protein. Our data indicate that agonist and antagonist recognition is characterized by small conformational differences in the LBDs that can be efficiently transmitted to the output domain to modulate the final response. LBDs are subject to strong selective pressures and are rapidly evolving domains. An increasing number of reports support the idea that environmental factors drive the evolution of sensor domains. Given the recent evolutionary history of AdmX homologs, as well as their narrow phyletic distribution within plant-associated bacteria, our results are in accordance with a plant-mediated evolutionary process that resulted in the emergence of receptor proteins that specifically sense auxin phytohormones.

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

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

Transcription factor-based biosensors for screening and dynamic regulation

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

Development of a Transcriptional Factor PuuR-Based Putrescine-Specific Biosensor in Corynebacterium glutamicum

Corynebacterium glutamicum is regarded as an industrially important microbial cell factory and is widely used to produce various value-added chemicals. Because of the importance of C. glutamicum applications, current research is increasingly focusing on developing C. glutamicum synthetic biology platforms. Because of its ability to condense with adipic acid to synthesize the industrial plastic nylon-46, putrescine is an important platform compound of industrial interest. Developing a high-throughput putrescine biosensor can aid in accelerating the design-build-test cycle of cell factories (production strains) to achieve high putrescine-generating strain production in C. glutamicum. This study developed a putrescine-specific biosensor (pSenPuuR) in C. glutamicum using Escherichia coli-derived transcriptional factor PuuR. The response characteristics of the biosensor to putrescine were further improved by optimizing the genetic components of pSenPuuR, such as the response promoter, reporter protein, and promoter for controlling PuuR expression. According to the findings of the study, pSenPuuR has the potential to be used to assess putrescine production in C. glutamicum and is suitable for high-throughput genetic variant screening.

An enzymatic colorimetric whole-cell biosensor for high-throughput identification of lysine overproducers

L-lysine is a crucial nutrient for both humans and animals, and its main commercial use is as a supplement in animal feed to promote chicken and other animal growth. Fluorescence biosensors based on the transcriptional regulator have been developed for high-throughput screening of L-lysine producers. However, due to its inability to specifically detect lysine, this fluorescent biosensor cannot be employed to screen high-yielding strains. Here, we present a novel technique for observing L-lysine concentrations within individual Corynebacterium glutamicum cells. The transcriptional regulator LysG and its binding site, as well as the phytoene desaturase that catalyzes the synthesis of the red pigment, make up the functional core of the biosensor. The lysine-sensitive mutant LysG^{(E123Y, E125A)}, which improved the sensitivity of biosensors, was generated by site-directed saturation mutagenesis. In addition, we increased the lysine-induced chromogenic biosensor response to 320 mM by optimizing the L-lysine export mechanism and the pathway for the synthesis of lycopene precursors. The direct identification of producers with elevated L-lysine accumulation is thus made straightforward by colorimetric screening. Lys-8, a lysine producer with a maximum lysine titer of 316.2 mM, was sorted out based on the biosensor. The enzymatic colorimetric biosensor constructed here is a simple tool with great potential for the development of high-level lysine-producing C. glutamicum.

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

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

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.

Rational Metabolic Engineering Combined with Biosensor-Mediated Adaptive Laboratory Evolution for l-Cysteine Overproduction from Glycerol in Escherichia coli

l-Cysteine is an important sulfur-containing amino acid with numerous applications in the pharmaceutical and cosmetic industries. The microbial production of l-cysteine has received substantial attention, and the supply of the precursor l-serine is important in l-cysteine biosynthesis. In this study, to achieve l-cysteine overproduction, we first increased l-serine production by deleting genes involved in the pathway of l-serine degradation to glycine (serine hydroxymethyl transferase, SHMT, encoded by glyA genes) in strain 4W (with l-serine titer of 1.1 g/L), thus resulting in strain 4WG with l-serine titer of 2.01 g/L. Second, the serine-biosensor based on the transcriptional regulator NCgl0581 of C. glutamicum was constructed in E. coli, and the validity and sensitivity of the biosensor were demonstrated in E. coli. Then 4WG was further evolved through adaptive laboratory evolution (ALE) combined with serine-biosensor, thus yielding the strain 4WGX with 4.13 g/L l-serine production. Moreover, the whole genome of the evolved strain 4WGX was sequenced, and ten non-synonymous mutations were found in the genome of strain 4WGX compared with strain 4W. Finally, 4WGX was used as the starting strain, and deletion of the l-cysteine desulfhydrases (encoded by tnaA), overexpression of serine acetyltransferase (encoded by cysE) and the key enzyme of transport pathway (encoded by ydeD) were performed in strain 4WGX. The recombinant strain 4WGX-∆tnaA-cysE-ydeD can produce 313.4 mg/L of l-cysteine using glycerol as the carbon source. This work provides an efficient method for the biosynthesis of value-added commodity products associated with glycerol conversion.

Spatiotemporally Resolved Protein Detection in Live Cells Using Nanopore Biosensors

Spatiotemporal detection of proteins in living cells is a persistent challenge but is the key to understanding their cellular biology and developing theranostic technologies. We develop a dual-nanopore biosensor using affinity-tunable peptide probes, which enables label-free and spatiotemporal monitoring of protein abundance and its concentration change in single live cells. We demonstrate that by screening for peptide probes with tunable affinities, the nanopore modified with a medium-affinity peptide allowed reversible and sensitive detection of the protein kinase A (PKA) catalytic subunit with a detection limit of 0.04 nM. The sensor is shown to have the ability to effectively eliminate interferences from cell membrane resistance and coexisting species in live cell detection. Moreover, our sensor is successfully implemented in monitoring of dynamic PKA activity changes (PKA catalytic subunit dynamic concentration changes) under different stimulations in single live cells. Our design may provide a paradigm for developing nanopore biosensors for spatiotemporally resolved protein analysis in live cells.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Effective use of biosensors for high-throughput library screening for metabolite production

The development of fast and affordable microbial production from recombinant pathways is a challenging endeavor, with targeted improvements difficult to predict due to the complex nature of living systems. To address the limitations in biosynthetic pathways, much work has been done to generate large libraries of various genetic parts (promoters, RBSs, enzymes, etc.) to discover library members that bring about significantly improved levels of metabolite production. To evaluate these large libraries, high throughput approaches are necessary, such as those that rely on biosensors. There are various modes of operation to apply biosensors to library screens that are available at different scales of throughput. The effectiveness of each biosensor-based method is dependent on the pathway or strain to which it is applied, and all approaches have strengths and weaknesses to be carefully considered for any high throughput library screen. In this review, we discuss the various approaches used in biosensor screening for improved metabolite production, focusing on transcription factor-based biosensors.

Biosensor-based isolation of amino acid-producing Vibrio natriegens strains

The marine bacterium Vibrio natriegens has recently been demonstrated to be a promising new host for molecular biology and next generation bioprocesses. V. natriegens is a Gram-negative, non-pathogenic slight-halophilic bacterium, with a high nutrient versatility and a reported doubling time of under 10 min. However, V. natriegens is not an established model organism yet, and further research is required to promote its transformation into a microbial workhorse. In this work, the potential of V. natriegens as an amino acid producer was investigated. First, the transcription factor-based biosensor LysG, from Corynebacterium glutamicum, was adapted for expression in V. natriegens to facilitate the detection of positively charged amino acids. A set of different biosensor variants were constructed and characterized, using the expression of a fluorescent protein as sensor output. After random mutagenesis, one of the LysG-based sensors was used to screen for amino acid producer strains. Here, fluorescence-activated cell sorting enabled the selective sorting of highly fluorescent cells, i.e. potential producer cells. Using this approach, individual L-lysine, L-arginine and L-histidine producers could be obtained producing up to 1 mM of the effector amino acid, extracellularly. Genome sequencing of the producer strains provided insight into the amino acid production metabolism of V. natriegens. This work demonstrates the successful expression and application of transcription factor-based biosensors in V. natriegens and provides insight into the underlying physiology, forming a solid basis for further development of this promising microbe.

Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets

The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference time for applications that require the folding of hundreds of sequences. The prediction of protein structure annotations, such as amino acid distances, can be achieved at a higher speed with existing tools, such as the ProSPr network. Here, we report on important updates to the ProSPr network, its performance in the recent Critical Assessment of Techniques for Protein Structure Prediction (CASP14) competition, and an evaluation of its accuracy dependency on sequence length and multiple sequence alignment depth. We also provide a detailed description of the architecture and the training process, accompanied by reusable code. This work is anticipated to provide a solid foundation for the further development of protein distance prediction tools.

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.

Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum

Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.

Directed Evolution Methods for Enzyme Engineering

Enzymes underpin the processes required for most biotransformations. However, natural enzymes are often not optimal for biotechnological uses and must be engineered for improved activity, specificity and stability. A rich and growing variety of wet-lab methods have been developed by researchers over decades to accomplish this goal. In this review such methods and their specific attributes are examined.

Development of a Biosensor Platform for Phenolic Compounds Using a Transition Ligand Strategy

The time-consuming and laborious characterization of protein or microbial strain designs limits the development of high-performance biocatalysts for biotechnological applications. Here, transcriptional biosensors emerged as valuable tools as they allow for rapid characterization of several thousand variants within a very short time. However, for many molecules of interest, no specific transcriptional regulator determining a biosensor's specificity is available. We present an approach for rapidly engineering biosensor specificities using a semirational transition ligand approach combined with fluorescence-activated cell sorting. In this two-step approach, a biosensor is first evolved toward a more relaxed-ligand specificity before using the resulting variant as the starting point in a second round of directed evolution toward high specificity for several chemically different ligands. By following this strategy, highly specific biosensors for 4-hydroxybenzoic acid, p-coumaric acid, 5-bromoferulic acid, and 6-methyl salicylic acid were developed, starting from a biosensor for the intracellular detection of trans-cinnamic acid.

Advances in metabolic engineering of Corynebacterium glutamicum to produce high-value active ingredients for food, feed, human health, and well-being

The soil microbe Corynebacterium glutamicum is a leading workhorse in industrial biotechnology and has become famous for its power to synthetise amino acids and a range of bulk chemicals at high titre and yield. The product portfolio of the microbe is continuously expanding. Moreover, metabolically engineered strains of C. glutamicum produce more than 30 high value active ingredients, including signature molecules of raspberry, savoury, and orange flavours, sun blockers, anti-ageing sugars, and polymers for regenerative medicine. Herein, we highlight recent advances in engineering of the microbe into novel cell factories that overproduce these precious molecules from pioneering proofs-of-concept up to industrial productivity.

Programming adaptive laboratory evolution of 4-hydroxyisoleucine production driven by a lysine biosensor in Corynebacterium glutamicum

4-Hydroxyisoleucine (4-HIL) is a promising drug for treating diabetes. In our previous study, 4-HIL was synthesized from self-produced L-isoleucine (Ile) in Corynebacterium glutamicum by expressing an Ile dioxygenase gene. Although the 4-HIL production of recombinant strain SZ06 increased significantly, a by-product, L-lysine (Lys) was accumulated because of the share of the first several enzymes in Ile and Lys biosynthetic pathways. In this study, programming adaptive laboratory evolution (ALE) was designed and conducted in SZ06 to promote 4-HIL biosynthesis. At first, a programming evolutionary system pMK was constructed, which contains a Lys biosensor LysG-P_lysE and an evolutionary actuator composed of a mutagenesis gene and a fluorescent protein gene. The evolutionary strain SZ06/pMK was then let to be evolved programmatically and spontaneously by sensing Lys concentration. After successive rounds of evolution, nine mutant strains K1 - K9 with significantly increased 4-HIL production and growth performance were obtained. The maximum 4-HIL titer was 152.19 ± 14.60 mM, 28.4% higher than that in SZ06. This titer was higher than those of all the metabolic engineered C. glutamicum strains ever constructed. The whole genome sequencing of the nine evolved strains revealed approximately 30 genetic mutations in each strain. Only one mutation was directly related to the Lys biosynthetic pathway. Therefore, programming ALE driven by Lys biosensor can be used as an effective strategy to increase 4-HIL production in C. glutamicum.