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

Systems metabolic engineering of Escherichia coli for high-yield production of Para-hydroxybenzoic acid

Para-hydroxybenzoic acid (PHBA) is extensively used as an additive in the food and cosmetics industries, significantly enhancing product shelf life and stability. While microbial fermentation offers an environment-friendly and sustainable method for producing PHBA, the titer and productivity are limited due to product toxicity and complex metabolic flux distributions. Here, we initially redesigned a L-phenylalanine-producing Escherichia coli by employing rational metabolic engineering strategies, resulting in the production of PHBA reached the highest reported level of 14.17 g/L. Subsequently, a novel accelerated evolution system was devised comprising deaminase, the alpha subunit of RNA polymerase, an uracil-DNA glycosylase inhibitor, and the PHBA-responsive promoter PyhcN. This system enabled us to obtain a mutant strain exhibiting a 47% increase in the half-inhibitory concentration (IC50) for PHBA within 15 days. Finally, the evolved strain achieved a production of 21.35 g/L PHBA in a 5-L fermenter, with a yield of 0.19 g/g glucose and a productivity rate of 0.44 g/L/h. This engineered strain emerges as a promising candidate for industrial production of PHBA through an eco-friendly approach.

Dynamic and Static Regulation of Nicotinamide Adenine Dinucleotide Phosphate: Strategies, Challenges, and Future Directions in Metabolic Engineering

Reduced nicotinamide adenine dinucleotide phosphate (NADPH) is a crucial cofactor in metabolic networks. The efficient regeneration of NADPH is one of the limiting factors for productivity in biotransformation processes. To date, many metabolic engineering tools and static regulation strategies have been developed to regulate NADPH regeneration. However, traditional static regulation methods often lead to the NADPH/NADP+ imbalance, causing disruptions in cell growth and production. These methods also fail to provide real-time monitoring of intracellular NADP(H) or NADPH/NADP+ levels. In recent years, various biosensors have been developed for the detection, monitoring, and dynamic regulate of the intracellular NADP(H) levels or the NADPH/NADP+ balance. These NADPH-related biosensors are mainly used in the cofactor engineering of bacteria, yeast, and mammalian cells. This review analyzes and summarizes the NADPH metabolic regulation strategies from both static and dynamic perspectives, highlighting current challenges and potential solutions, and discusses future directions for the advanced regulation of the NADPH/NADP+ balance.

Multidimensional combinatorial screening for high-level production of erythritol in Yarrowia lipolytica

Yarrowia lipolytica was successfully engineered to synthesize erythritol from crude glycerol, a cheap by-product of biodiesel production, but the yield remained low. Here, a biosensor-guided adaptive evolution screening platform was constructed to obtain mutant strains which could efficiently utilize crude glycerol to produce erythritol. Erythrose reductase D46A (M1) was identified as a key mutant through whole-genome sequencing of the strain G12, which exhibited higher catalytic activity (1.6-fold of the wild-type). M1 was further modified to obtain a combinatorial mutant with 4.1-fold enhancement of catalytic activity. Finally, the metabolic network was reconfigured to redirect carbon fluxes toward erythritol synthesis. The erythritol titer of the engineered strain G31 reached 220.5 g/L with a productivity of 1.8 g/L/h in a 5-L bioreactor. The study provides valuable guidance for biosensor-based ultra-high-throughput screening strategies in Y. lipolytica, as well as presenting a new paradigm for the sustainable valorization of crude glycerol.

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.

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.

Enhancing glucaric acid production from myo-inositol in Escherichia coli by eliminating cell-to-cell variation

Glucaric acid (GA) is a value-added chemical and can be used to manufacture food additives, anticancer drugs, and polymers. The non-genetic cell-to-cell variations in GA biosynthesis are naturally inherent, indicating the presence of both high- and low-performance cells in culture. Low-performance cells can lead to nutrient waste and inefficient production. Furthermore, myo-inositol oxygenase (MIOX) is a key rate-limiting enzyme with the problem of low stability and activity in GA production. Therefore, eliminating cell-to-cell variations and increasing MIOX stability can select high-performance cells and improve GA production. In this study, an in vivo GA bioselector was constructed based on GA biosensor and tetracycline efflux pump protein TetA to continuously select GA-efficient production strains. Additionally, the upper limit of the GA biosensor was improved to 40 g/L based on ribosome-binding site optimization, achieving efficient enrichment of GA high-performance cells. A small ubiquitin-like modifier (SUMO) enhanced MIOX stability and activity. Overall, we used the GA bioselector and SUMO-MIOX fusion in fed-batch GA production and achieved a 5.52-g/L titer in Escherichia coli, which was 17-fold higher than that of the original strain.IMPORTANCEGlucaric acid is a non-toxic valuable product that was mainly synthesized by chemical methods. Due to the problems of non-selectivity, inefficiency, and environmental pollution, GA biosynthesis has attracted significant attention. The non-genetic cell-to-cell variations and MIOX stability were both critical factors for GA production. In addition, the high detection limit of the GA biosensor was a key condition for performing high-throughput screening of GA-efficient production strains. To increase GA titer, this work eliminated the cell-to-cell variations by GA bioselector constructed based on GA biosensor and TetA, and improved the stability and activity of MIOX in the GA biosynthetic pathway through fusing the SUMO to MIOX. Finally, these approaches improved the GA production by 17-fold to 5.52 g/L at 65 h. This study represents a significant step toward the industrial application of GA biosynthetic pathways in E. coli.

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.

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

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

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.

Recent Advances in Electrochemical Detection of Cell Energy Metabolism

Cell energy metabolism is a complex and multifaceted process by which some of the most important nutrients, particularly glucose and other sugars, are transformed into energy. This complexity is a result of dynamic interactions between multiple components, including ions, metabolic intermediates, and products that arise from biochemical reactions, such as glycolysis and mitochondrial oxidative phosphorylation (OXPHOS), the two main metabolic pathways that provide adenosine triphosphate (ATP), the main source of chemical energy driving various physiological activities. Impaired cell energy metabolism and perturbations or dysfunctions in associated metabolites are frequently implicated in numerous diseases, such as diabetes, cancer, and neurodegenerative and cardiovascular disorders. As a result, altered metabolites hold value as potential disease biomarkers. Electrochemical biosensors are attractive devices for the early diagnosis of many diseases and disorders based on biomarkers due to their advantages of efficiency, simplicity, low cost, high sensitivity, and high selectivity in the detection of anomalies in cellular energy metabolism, including key metabolites involved in glycolysis and mitochondrial processes, such as glucose, lactate, nicotinamide adenine dinucleotide (NADH), reactive oxygen species (ROS), glutamate, and ATP, both in vivo and in vitro. This paper offers a detailed examination of electrochemical biosensors for the detection of glycolytic and mitochondrial metabolites, along with their many applications in cell chips and wearable sensors.

Design of synthetic bacterial biosensors

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

Mechanisms and biotechnological applications of transcription factors

Transcription factors play an indispensable role in maintaining cellular viability and finely regulating complex internal metabolic networks. These crucial bioactive functions rely on their ability to respond to effectors and concurrently interact with binding sites. Recent advancements have brought innovative insights into the understanding of transcription factors. In this review, we comprehensively summarize the mechanisms by which transcription factors carry out their functions, along with calculation and experimental-based methods employed in their identification. Additionally, we highlight recent achievements in the application of transcription factors in various biotechnological fields, including cell engineering, human health, and biomanufacturing. Finally, the current limitations of research and provide prospects for future investigations are discussed. This review will provide enlightening theoretical guidance for transcription factors engineering.

An Intelligent Synthetic Bacterium for Chronological Toxicant Detection, Biodegradation, and Its Subsequent Suicide

Modules, toolboxes, and synthetic biology systems may be designed to address environmental bioremediation. However, weak and decentralized functional modules require complex control. To address this issue, an integrated system for toxicant detection and biodegradation, and subsequent suicide in chronological order without exogenous inducers is constructed. Salicylic acid, a typical pollutant in industrial wastewater, is selected as an example to demonstrate this design. Biosensors are optimized by regulating the expression of receptors and reporters to get 2-fold sensitivity and 6-fold maximum output. Several stationary phase promoters are compared, and promoter Pfic is chosen to express the degradation enzyme. Two concepts for suicide circuits are developed, with the toxin/antitoxin circuit showing potent lethality. The three modules are coupled in a stepwise manner. Detection and biodegradation, and suicide are sequentially completed with partial attenuation compared to pre-integration, except for biodegradation, being improved by the replacements of ribosome binding site. Finally, a long-term stability test reveals that the engineered strain maintained its function for ten generations. The study provides a novel concept for integrating and controlling functional modules that can accelerate the transition of synthetic biology from conceptual to practical applications.

Applications of artificial intelligence and machine learning in dynamic pathway engineering

Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.

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.

Tunable T7 Promoter Orthogonality on T7RNAP for cis-Aconitate Decarboxylase Evolution via Base Editor and Screening from Itaconic Acid Biosensor

The deaminase-fused T7 RNA polymerase (T7RNAP) presents a promising toolkit for in vivo target-specific enzyme evolution, offering the unique advantage of simultaneous DNA modification and screening. Previous studies have reported the mutation efficiency of base editors relying on different resources. In contrast, the mechanism underlying the T7RNAP/T7 system is well-understood. Therefore, this study aimed to establish a new platform, termed dT7-Muta, by tuning the binding efficiency between T7RNAP and the T7 promoter for gene mutagenesis. The strategy for proof-of-concept involves alterations in the fluorescence distribution through dT7-Muta and screening of the mutants via flow cytometry. The cis-aconitate decarboxylase from Aspergillus terreus (AtCadA) was evolved and screened via an itaconate-induced biosensor as proof-of-function of enzyme evolution. First, the degenerated codons were designed within the binding and initial region of T7 promoters (dT7s), including upstream (U), central (C), and downstream (D) regions. Three strength variants of dT7 promoter from each design, i.e., strong (S), medium (M), and weak (W), were used for evaluation. Mutation using dT7s of varying strength resulted in a broader fluorescence distribution in sfGFP mutants from the promoters CW and DS. On the other hand, broader fluorescence distribution was observed in the AtCadA mutants from the original promoter T7, UW, and DS, with the highest fluorescence and itaconic acid titer at 860 a.u. and 0.51 g/L, respectively. The present platform introduces a novel aspect of the deaminase-based mutagenesis, emphasizing the potential of altering the binding efficiency between T7RNAP and the T7 promoter for further efforts in enzyme evolution.

Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium

Translation efficiency has been mainly studied by ribosome profiling, which only provides an incomplete picture of translation kinetics. Here, we integrated the absolute quantifications of tRNAs, mRNAs, RNA half-lives, proteins, and protein half-lives with ribosome densities and derived the initiation and elongation rates for 475 genes (67% of all genes), 73 with high precision, in the bacterium Mycoplasma pneumoniae (Mpn). We found that, although the initiation rate varied over 160-fold among genes, most of the known factors had little impact on translation efficiency. Local codon elongation rates could not be fully explained by the adaptation to tRNA abundances, which varied over 100-fold among tRNA isoacceptors. We provide a comprehensive quantitative view of translation efficiency, which suggests the existence of unidentified mechanisms of translational regulation in Mpn.

Transcriptome and Metabolome Analyses Reveal the Mechanism of Corpus Luteum Cyst Formation in Pigs

Corpus luteum cysts are a serious reproductive disorder that affects the reproductive performance of sows. In this study, transcriptome and metabolome datasets of porcine normal and cyst luteal granulosa cells were generated to explore the molecular mechanism of luteal cyst formation. We obtained 28.9 Gb of high-quality transcriptome data from luteum tissue samples and identified 1048 significantly differentially expressed genes between the cyst and normal corpus luteum samples. Most of the differentially expressed genes were involved in cancer and immune signaling pathways. Furthermore, 22,622 information-containing positive and negative ions were obtained through gas chromatography-mass spectrometry, and 1106 metabolites were successfully annotated. Important differentially abundant metabolites and pathways were identified, among which abnormal lipid and choline metabolism were involved in the formation of luteal cysts. The relationships between granulosa cells of luteal cysts and cancer, immune-related signaling pathways, and abnormalities of lipid and choline metabolism were elaborated, providing new entry points for studying the pathogenesis of porcine luteal cysts.

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.

Metabolic engineering and fermentation optimization strategies for producing organic acids of the tricarboxylic acid cycle by microbial cell factories

The organic acids of the tricarboxylic acid (TCA) pathway are important platform compounds and are widely used in many areas. The high-productivity strains and high-efficient and low-cost fermentation are required to satisfy a huge market size. The high metabolic flux of the TCA pathway endows microorganisms potential to produce high titers of these organic acids. Coupled with metabolic engineering and fermentation optimization, the titer of the organic acids has been significantly improved in recent years. Herein, we discuss and compare the recent advances in synthetic pathway engineering, cofactor engineering, transporter engineering, and fermentation optimization strategies to maximize the biosynthesis of organic acids. Such engineering strategies were mainly based on the TCA pathway and glyoxylate pathway. Furthermore, organic-acid-secretion enhancement and renewable-substrate-based fermentation are often performed to assist the biosynthesis of organic acids. Further strategies are also discussed to construct high-productivity and acid-resistant strains for industrial large-scale production.

Efficient Production of Glucaric Acid by Engineered Saccharomyces cerevisiae

Glucaric acid is a valuable chemical with applications in the detergent, polymer, pharmaceutical and food industries. In this study, two key enzymes for glucaric acid biosynthesis, MIOX4 (myo-inositol oxygenase) and Udh (uronate dehydrogenase), were fused and expressed with different peptide linkers. It was found that a strain harboring the fusion protein MIOX4-Udh linked by the peptide (EA3K)3 produced the highest glucaric acid titer and thereby resulted in glucaric acid production that was 5.7-fold higher than that of the free enzymes. Next, the fusion protein MIOX4-Udh linked by (EA3K)3 was integrated into delta sequence sites of the Saccharomyces cerevisiae opi1 mutant, and a strain, GA16, that produced a glucaric acid titer of 4.9 g/L in a shake flask fermentation was identified by a high-throughput screening method using an Escherichia coli glucaric acid biosensor. Strain improvement by further engineering was performed to regulate the metabolic flux of myo-inositol to increase the supply of glucaric acid precursors. The downregulation of ZWF1 and the overexpression of INM1 and ITR1 increased glucaric acid production significantly, and glucaric acid production was increased to 8.49 g/L in the final strain GA-ZII in a shake flask fermentation. Finally, in a 5-L bioreactor, GA-ZII produced a glucaric acid titer of 15.6 g/L through fed-batch fermentation. IMPORTANCE Glucaric acid is a value-added dicarboxylic acid that was synthesized mainly through the oxidation of glucose chemically. Due to the problems of the low selectivity, by-products, and highly polluting waste of this process, producing glucaric acid biologically has attracted great attention. The activity of key enzymes and the intracellular myo-inositol level were both rate-limiting factors for glucaric acid biosynthesis. To increase glucaric acid production, this work improved the activity of the key enzymes in the glucaric acid biosynthetic pathway through the expression of a fusion of Arabidopsis thaliana MIOX4 and Pseudomonas syringae Udh as well as a delta sequence-based integration. Furthermore, intracellular myo-inositol flux was optimized by a series of metabolic strategies to increase the myo-inositol supply, which improved glucaric acid production to a higher level. This study provided a way for constructing a glucaric acid-producing strain with good synthetic performance, making glucaric acid production biologically in yeast cells much more competitive.

Developing high-dimensional machine learning models to improve generalization ability and overcome data insufficiency for mixed sugar fermentation simulation

Biorefinery can be promoted by building accurate machine learning models. This work proposed a strategy to enhance model's generalization ability and overcome insufficient data conditions for mixed sugar fermentation simulation. Multiple inputs single output models, using initial glucose, initial xylose, and time together as inputs, have higher generalization ability than single input single output models with time as sole input in predicting glucose, xylose, ethanol, or biomass separately. Multiple inputs multiple outputs models, integrating outputs, enhanced model accuracy and resulted in an average R² at 0.99. To overcome data insufficiency conditions, consensus yeast (CY) model, through consolidating data from 4 yeasts, obtained R² at 0.90. By adjusting the pretrained CY model, the model can save more than 50% data and get R² at 0.95 and 0.93 for yeast and bacterial fermentation simulation. The strategy can expand the application range and save costs of data curation for ANN models.

Screening and Constructing a Library of Promoter-5'-UTR Complexes with Gradient Strength in Pediococcus acidilactici

The GRAS (generally recognized as safe) strain Pediococcus acidilactici is well known for its antibacterial and probiotic functions. Furthermore, as P. acidilactici has excellent high temperature and salt resistance, it is an ideal host for the production of food enzymes, food additives, and pharmaceuticals. In this regard, it is desirable and feasible to enhance the production of these products through the metabolic engineering of P. acidilactici. However, the rare gene expression elements greatly obstruct the development of engineering P. acidilactici. In this study, we screened and constructed a library of promoter-5'-UTR (PUTR) complexes in P. acidilactici DY15 for regulating gene expression at the transcription and translation levels. In the post-log phase, the mRNA and protein expression level ranges of the 90 screened native PUTRs were 0.059-2010% and 0.77-245%, respectively, of the P32 promoter. Besides, several PUTRs exhibited great expression stability under high temperature, salt, and ethanol stress. We analyzed the structure of PUTRs and obtained the conserved regions of the promoter and 5'-UTR. Based on the identified core regions of PUTRs, we constructed a panel of combinatorial PUTRs with higher and stable protein expression levels. The strongest combinatorial PUTR was 853% of the P32 promoter in the protein expression level. Finally, the obtained PUTRs were applied to optimize the expression level of aminotransferase and improve the phenyllactic acid (PLA) production in P. acidilactici DY15. The achieved yield was 950.6 mg/L, which was 79.2% higher than the wild-type strain. These results indicated that the obtained PUTRs with gradient strength had great potential for precisely regulating gene expression to achieve various goals in P. acidilactici.

Cell-Free Biosensing Genetic Circuit Coupled with Ribozyme Cleavage Reaction for Rapid and Sensitive Detection of Small Molecules

Synthetic biological systems have been utilized to develop a wide range of genetic circuits and components that enhance the performance of biosensing systems. Among them, cell-free systems are emerging as important platforms for synthetic biology applications. Genetic circuits play an essential role in cell-free systems, mainly consisting of sensing modules, regulation modules, and signal output modules. Currently, fluorescent proteins and aptamers are commonly used as signal outputs. However, these signal output modes cannot simultaneously achieve faster signal output, more accurate and reliable performance, and signal amplification. Ribozyme is a highly structured and catalytic RNA molecule that can specifically recognize and cut specific substrate sequences. Here, by adopting ribozyme as the signal output, we developed a cell-free biosensing genetic circuit coupled with the ribozyme cleavage reaction, enabling rapid and sensitive detection of small molecules. More importantly, we have also successfully constructed a 3D-printed sensor array and thereby achieved high-throughput analysis of an inhibitory drug. Furthermore, our method will help expand the application range of ribozyme in the field of synthetic biology and also optimize the signal output system of cell-free biosensing, thus promoting the development of cell-free synthetic biology in biomedical research, clinical diagnosis, environmental monitoring, and food inspection.

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.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Machine learning for metabolic pathway optimization: A review

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

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.

Molecular Defense Response of Bursaphelenchus xylophilus to the Nematophagous Fungus Arthrobotrys robusta

Bursaphelenchus xylophilus causes pine wilt disease, which poses a serious threat to forestry ecology around the world. Microorganisms are environmentally friendly alternatives to the use of chemical nematicides to control B. xylophilus in a sustainable way. In this study, we isolated a nematophagous fungus-Arthrobotrys robusta-from the xylem of diseased Pinus massoniana. The nematophagous activity of A. robusta against the PWNs was observed after just 6 h. We found that B. xylophilus entered the trap of A. robusta at 24 h, and the nervous system and immunological response of B. xylophilus were stimulated by metabolites that A. robusta produced. At 30 h of exposure to A. robusta, B. xylophilus exhibited significant constriction, and we were able to identify xenobiotics. Bursaphelenchus xylophilus activated xenobiotic metabolism, which expelled the xenobiotics from their bodies, by providing energy through lipid metabolism. When PWNs were exposed to A. robusta for 36 h, lysosomal and autophagy-related genes were activated, and the bodies of the nematodes underwent disintegration. Moreover, a gene co-expression pattern network was constructed by WGCNA and Cytoscape. The gene co-expression pattern network suggested that metabolic processes, developmental processes, detoxification, biological regulation, and signaling were influential when the B. xylophilus specimens were exposed to A. robusta. Additionally, bZIP transcription factors, ankyrin, ATPases, innexin, major facilitator, and cytochrome P450 played critical roles in the network. This study proposes a model in which mobility improved whenever B. xylophilus entered the traps of A. robusta. The model will provide a solid foundation with which to understand the molecular and evolutionary mechanisms underlying interactions between nematodes and nematophagous fungi. Taken together, these findings contribute in several ways to our understanding of B. xylophilus exposed to microorganisms and provide a basis for establishing an environmentally friendly prevention and control strategy.

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.

Efficient biosynthesis of (R)-mandelic acid from styrene oxide by an adaptive evolutionary Gluconobacter oxydans STA

BACKGROUND

(R)-mandelic acid (R-MA) is a highly valuable hydroxyl acid in the pharmaceutical industry. However, biosynthesis of optically pure R-MA remains significant challenges, including the lack of suitable catalysts and high toxicity to host strains. Adaptive laboratory evolution (ALE) was a promising and powerful strategy to obtain specially evolved strains.

RESULTS

Herein, we report a new cell factory of the Gluconobacter oxydans to biocatalytic styrene oxide into R-MA by utilizing the G. oxydans endogenous efficiently incomplete oxidization and the epoxide hydrolase (SpEH) heterologous expressed in G. oxydans. With a new screened strong endogenous promoter P₁₂₇₈₀, the production of R-MA was improved to 10.26 g/L compared to 7.36 g/L of using P_lac. As R-MA showed great inhibition for the reaction and toxicity to cell growth, adaptive laboratory evolution (ALE) strategy was introduced to improve the cellular R-MA tolerance. The adapted strain that can tolerate 6 g/L R-MA was isolated (named G. oxydans STA), while the wild-type strain cannot grow under this stress. The conversion rate was increased from 0.366 g/L/h of wild type to 0.703 g/L/h by the recombinant STA, and the final R-MA titer reached 14.06 g/L. Whole-genome sequencing revealed multiple gene-mutations in STA, in combination with transcriptome analysis under R-MA stress condition, we identified five critical genes that were associated with R-MA tolerance, among which AcrA overexpression could further improve R-MA titer to 15.70 g/L, the highest titer reported from bulk styrene oxide substrate.

CONCLUSIONS

The microbial engineering with systematic combination of static regulation, ALE, and transcriptome analysis strategy provides valuable solutions for high-efficient chemical biosynthesis, and our evolved G. oxydans would be better to serve as a chassis cell for hydroxyl acid production.

Recent Advances in Electrochemical Biosensors for Monitoring Animal Cell Function and Viability

Redox reactions in live cells are generated by involving various redox biomolecules for maintaining cell viability and functions. These qualities have been exploited in the development of clinical monitoring, diagnostic approaches, and numerous types of biosensors. Particularly, electrochemical biosensor-based live-cell detection technologies, such as electric cell-substrate impedance (ECIS), field-effect transistors (FETs), and potentiometric-based biosensors, are used for the electrochemical-based sensing of extracellular changes, genetic alterations, and redox reactions. In addition to the electrochemical biosensors for live-cell detection, cancer and stem cells may be immobilized on an electrode surface and evaluated electrochemically. Various nanomaterials and cell-friendly ligands are used to enhance the sensitivity of electrochemical biosensors. Here, we discuss recent advances in the use of electrochemical sensors for determining cell viability and function, which are essential for the practical application of these sensors as tools for pharmaceutical analysis and toxicity testing. We believe that this review will motivate researchers to enhance their efforts devoted to accelerating the development of electrochemical biosensors for future applications in the pharmaceutical industry and stem cell therapeutics.

Sequence and thermodynamic characteristics of terminators revealed by FlowSeq and the discrimination of terminators strength

The intrinsic terminator in prokaryotic forms secondary RNA structure and terminates the transcription. However, leaking transcription is common due to varied terminator strength. Besides of the representative hairpin and U-tract structure, detailed sequence and thermodynamic features of terminators were not completely clear, and the effect of terminator on the upstream gene expression was unclearly. Thus, it is still challenging to use terminator to control expression with higher precision. Here, in E. Coli, we firstly determined the effect of the 3′-end sequences including spacer sequences and terminator sequences on the expression of upstream and downstream genes. Secondly, terminator mutation library was constructed, and the thermodynamic and sequence features differing in the termination efficiency were analyzed using the FlowSeq technique. The result showed that under the regulation of terminators, a negative correlation was presented between the expression of upstream and downstream genes (r=−0.60), and the terminators with lower free energy corelated with higher upstream gene expression. Meanwhile, the terminator with longer stem length, more compact loop and perfect U-tract structure was benefit to the transcription termination. Finally, a terminator strength classification model was established, and the verification experiment based on 20 synthetic terminators indicated that the model can distinguish strong and weak terminators to certain extent. The results help to elucidate the role of terminators in gene expression, and the key factors identified are crucial for rational design of terminators, and the model provided a method for terminator strength prediction.

Precise Genomic Riboregulator Control of Metabolic Flux in Microbial Systems

Engineered microbes can be used for producing value-added chemicals from renewable feedstocks, relieving the dependency on nonrenewable resources such as petroleum. These microbes often are composed of synthetic metabolic pathways; however, one major problem in establishing a synthetic pathway is the challenge of precisely controlling competing metabolic routes, some of which could be crucial for fitness and survival. While traditional gene deletion and/or coarse overexpression approaches do not provide precise regulation, cis-repressors (CRs) are RNA-based regulatory elements that can control the production levels of a particular protein in a tunable manner. Here, we describe a protocol for a generally applicable fluorescence-activated cell sorting technique used to isolate eight subpopulations of CRs from a semidegenerate library in Escherichia coli, followed by deep sequencing that permitted the identification of 15 individual CRs with a broad range of protein production profiles. Using these new CRs, we demonstrated a change in production levels of a fluorescent reporter by over two orders of magnitude and further showed that these CRs are easily ported from E. coli to Pseudomonas putida. We next used four CRs to tune the production of the enzyme PpsA, involved in pyruvate to phosphoenolpyruvate (PEP) conversion, to alter the pool of PEP that feeds into the shikimate pathway. In an engineered P. putida strain, where carbon flux in the shikimate pathway is diverted to the synthesis of the commodity chemical cis,cis-muconate, we found that tuning PpsA translation levels increased the overall titer of muconate. Therefore, CRs provide an approach to precisely tune protein levels in metabolic pathways and will be an important tool for other metabolic engineering efforts.

Efficient Bacterial Genome Engineering throughout the Central Dogma Using the Dual-Selection Marker tetAOPT

Engineering of bacterial genomes is a fundamental craft in contemporary biotechnology. The ability to precisely edit chromosomes allows for the development of cells with specific phenotypes for metabolic engineering and for the creation of minimized genomes. Genetic tools are needed to select for cells that underwent editing, and dual-selection markers that enable both positive and negative selection are highly useful. Here, we present an optimized and easy-to-use version of the tetA dual-selection marker and demonstrate how this tetA^OPT can be used efficiently to engineer at different stages of the central dogma of molecular biology. On the DNA level, tetA^OPT can be used to create scarless knockouts across the Escherichia coli genome with efficiency above 90%, whereas recombinant gene integrations can be achieved with approximately 50% efficiency. On the RNA and protein level, we show that tetA^OPT enables advanced genome engineering of both gene translation and transcription by introducing sequence variation in the translation initiation region or by exchanging promoters. Finally, we demonstrate the use of tetA^OPT for genome engineering in the industrially relevant probiotic strain E. coli Nissle.

Understanding and application of Bacillus nitrogen regulation: A synthetic biology perspective

BACKGROUND

Nitrogen sources play an essential role in maintaining the physiological and biochemical activity of bacteria. Nitrogen metabolism, which is the core of microorganism metabolism, makes bacteria able to autonomously respond to different external nitrogen environments by exercising complex internal regulatory networks to help them stay in an ideal state. Although various studies have been put forth to better understand this regulation in Bacillus, and many valuable viewpoints have been obtained, these views need to be presented systematically and their possible applications need to be specified.

AIM OF REVIEW

The intention is to provide a deep and comprehensive understanding of nitrogen metabolism in Bacillus, an important industrial microorganism, and thereby apply this regulatory logic to synthetic biology to improve biosynthesis competitiveness. In addition, the potential researches in the future are also discussed.

KEY SCIENTIFIC CONCEPT OF REVIEW

Understanding the meticulous regulation process of nitrogen metabolism in Bacillus not only could facilitate research on metabolic engineering but also could provide constructive insights and inspiration for studies of other microorganisms.

Surveying the Genetic Design Space for Transcription Factor-Based Metabolite Biosensors: Synthetic Gamma-Aminobutyric Acid and Propionate Biosensors in E. coli Nissle 1917

Engineered probiotic bacteria have been proposed as a next-generation strategy for noninvasively detecting biomarkers in the gastrointestinal tract and interrogating the gut-brain axis. A major challenge impeding the implementation of this strategy has been the difficulty to engineer the necessary whole-cell biosensors. Creation of transcription factor-based biosensors in a clinically-relevant strain often requires significant tuning of the genetic parts and gene expression to achieve the dynamic range and sensitivity required. Here, we propose an approach to efficiently engineer transcription-factor based metabolite biosensors that uses a design prototyping construct to quickly assay the gene expression design space and identify an optimal genetic design. We demonstrate this approach using the probiotic bacterium Escherichia coli Nissle 1917 (EcN) and two neuroactive gut metabolites: the neurotransmitter gamma-aminobutyric acid (GABA) and the short-chain fatty acid propionate. The EcN propionate sensor, utilizing the PrpR transcriptional activator from E. coli, has a large 59-fold dynamic range and >500-fold increased sensitivity that matches biologically-relevant concentrations. Our EcN GABA biosensor uses the GabR transcriptional repressor from Bacillus subtilis and a synthetic GabR-regulated promoter created in this study. This work reports the first known synthetic microbial whole-cell biosensor for GABA, which has an observed 138-fold activation in EcN at biologically-relevant concentrations. Using this rapid design prototyping approach, we engineer highly functional biosensors for specified in vivo metabolite concentrations that achieve a large dynamic range and high output promoter activity upon activation. This strategy may be broadly useful for accelerating the engineering of metabolite biosensors for living diagnostics and therapeutics.

Efficient extracellular production of recombinant proteins in E. coli via enhancing expression of dacA on the genome

D, D-carboxypeptidase DacA plays an important role in the synthesis and stabilization of Escherichia coli cell wall peptidoglycan. The production level of extracellular recombinant proteins in E. coli can be enhanced by high D, D-carboxypeptidase activity. Construction of expression systems under optimal promoters is one of the main strategies to realize high protein production in E. coli. In this study, the promoter PdacA-3 from DacA on the genome of E. coli BL21 (DE3) was verified to be efficient for recombinant green fluorescent protein using the plasmid mutant pET28a-PdacA with PdacA-3. Meanwhile, the promoter PdacA-3 was engineered to increase the production level of proteins via inserting one or two Shine-Dalgarno (SD) sequences between the promoter PdacA-3 and the target genes. The expression level of dacA on the genome was increased by the improved transcription of the engineered promoters (especially after inserting one additional SD sequence). The engineered promoters increased cell membrane permeabilities to significantly enhance the secretion production of extracellular recombinant proteins in E. coli. Among them, the extracellular recombinant amylase activities in E. coli BL21::1SD-pET28a-amyK and E. coli BL21::2SD-pET28a-amyK were increased by 2.0- and 1.6-fold that of the control (E. coli BL21-pET28a-amyK), respectively. Promoter engineering also affected the morphology and growth of the E. coli mutants. It was indicated that the engineered promoters enhanced the expression of dacA on the genome to disturb the synthesis and structural stability of cell wall peptidoglycans.

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.

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

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.

Refactoring transcription factors for metabolic engineering

Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review introduced the categories, action principles, prediction strategies, and related databases of transcription factors. Then, the application of global transcription machinery engineering technology and the transcription factor-based biosensors and quorum sensing systems are overviewed. In addition, strategies for optimizing the transcriptional regulatory tools' performance by refactoring transcription factors are summarized. Finally, the current limitations and prospects of constructing various regulatory tools based on transcription factors are discussed. This review will provide theoretical guidance for the rational design and construction of transcription factor-based metabolic regulation systems.

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

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

Episome-Based Gene Expression Modulation Platform in the Model Diatom Phaeodactylum tricornutum

Chemically inducible gene expression systems have been an integral part of the advanced synthetic genetic circuit design and are employed for precise dynamic control over genetically engineered traits. However, the current systems for controlling transgene expression in most algae are limited to endogenous promoters that respond to different environmental factors. We developed a highly efficient, tunable, and reversible episome-based transcriptional control system in the model diatom alga, Phaeodactylum tricornutum. We assessed the time- and dose-response dynamics of each expression system using a reporter protein (eYFP) as a readout. Using our circuit configuration, we found two inducible expression systems with a high dynamic range and confirmed the suitability of an episome expression platform for synthetic biological applications in diatoms. These systems are controlled by the presence of β-estradiol and digoxin. Addition of either chemical to transgenic strains activates transcription with a dynamic range of up to ∼180-fold and ∼90-fold, respectively. We demonstrated that our episome-based transcriptional control systems are tunable and reversible in a dose- and time-dependent manner. Using droplet digital polymerase chain reaction (PCR), we also confirmed that inducer-dependent transcriptional activation starts within minutes of inducer application without any detectable transcript in the uninduced controls. The system described here expands the molecular and synthetic biology toolkits in algae and will facilitate future gene discovery and metabolic engineering efforts.

Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning

Promoters are one of the most critical regulatory elements controlling metabolic pathways. However, the fast and accurate prediction of promoter strength remains challenging, leading to time- and labor-consuming promoter construction and characterization processes. This dilemma is caused by the lack of a big promoter library that has gradient strengths, broad dynamic ranges, and clear sequence profiles that can be used to train an artificial intelligence model of promoter strength prediction. To overcome this challenge, we constructed and characterized a mutant library of Trc promoters (P_trc) using 83 rounds of mutation-construction-screening-characterization engineering cycles. After excluding invalid mutation sites, we established a synthetic promoter library that consisted of 3665 different variants, displaying an intensity range of more than two orders of magnitude. The strongest variant was ∼69-fold stronger than the original P_trc and 1.52-fold stronger than a 1 mM isopropyl-β-d-thiogalactoside-driven P_T7 promoter, with an ∼454-fold difference between the strongest and weakest expression levels. Using this synthetic promoter library, different machine learning models were built and optimized to explore the relationships between promoter sequences and transcriptional strength. Finally, our XgBoost model exhibited optimal performance, and we utilized this approach to precisely predict the strength of artificially designed promoter sequences (R² = 0.88, mean absolute error = 0.15, and Pearson correlation coefficient = 0.94). Our work provides a powerful platform that enables the predictable tuning of promoters to achieve optimal transcriptional strength.

Metabolic engineering of Escherichia coli for efficient production of l-arginine

As a semi-essential amino acid, l-arginine (l-Arg) plays an important role in food, health care, and medical treatment. At present, the main method of producing l-Arg is the use of microbial fermentation. Therefore, the selection and breeding of high-efficiency microbial strains is the top priority. To continuously improve the l-Arg production performance of the strains, a series of metabolic engineering strategies have been tried to transform the strains. The production of l-Arg by metabolically engineered Corynebacterium glutamicum (C. glutamicum) reached a relatively high level. Escherichia coli (E. coli), as a strain with great potential for l-Arg production, also has a large number of research strategies aimed at screening effective E. coli for producing l-Arg. E. coli also has a number of advantages over C. glutamicum in producing l-Arg. Therefore, it is of great significance to screen out excellent and stable E. coli to produce l-Arg. Here, based on recent research results, we review the metabolic pathways of l-Arg production in E. coli, the research progress of l-Arg production in E. coli, and various regulatory strategies implemented in E. coli.

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.

Translation initiation from sequence variants of the bacteriophage T7 g10RBS in Escherichia coli and Agrobacterium fabrum

BACKGROUND

The bacteriophage T7 gene 10 ribosome binding site (g10RBS) has long been used for robust expression of recombinant proteins in Escherichia coli. This RBS consists of a Shine-Dalgarno (SD) sequence augmented by an upstream translational "enhancer" (Enh) element, supporting protein production at many times the level seen with simple synthetic SD-containing sequences. The objective of this study was to dissect the g10RBS to identify simpler derivatives that exhibit much of the original translation efficiency.

METHODS AND RESULTS

Twenty derivatives of g10RBS were tested using multiple promoter/reporter gene contexts. We have identified one derivative (which we call "CON_G") that maintains 100% activity in E. coli and is 33% shorter. Further minimization of CON_G results in variants that lose only modest amounts of activity. Certain nucleotide substitutions in the spacer region between the SD sequence and initiation codon show strong decreases in translation. When testing these 20 derivatives in the alphaproteobacterium Agrobacterium fabrum, most supported strong reporter protein expression that was not dependent on the Enh.

CONCLUSIONS

The g10RBS derivatives tested in this study display a range of observed activity, including a minimized version (CON_G) that retains 100% activity in E. coli while being 33% shorter. This high activity is evident in two different promoter/reporter sequence contexts. The array of RBS sequences presented here may be useful to researchers in need of fine-tuned expression of recombinant proteins of interest.