Applications and advances of metabolite biosensors for metabolic engineering

Molecular process control for industrial biotechnology

The development of sustainable and economically competitive biotechnological processes is a central challenge of modern industrial biotechnology. Conventional strategies such as macroscopic and molecular bioprocess design are often insufficient to exploit their full potential. To circumvent this, molecular process control provides the missing link to further consolidate various optimization strategies to achieve multilayered process design. This review highlights the molecular mechanisms that can be exploited for molecular process control. These can either be endogenous or specifically implemented into the organism, and comprise regulatory mechanisms at the transcriptional, translational, and system levels. In addition to serving as a design tool to enhance existing bioprocesses, molecular process control is the gateway to biotechnological advances that will extend the boundaries of future process design.

Precision Quantification and Rational Regulation of Protein Expression with Bicistronic Cassette for Efficient Biotin Production

Identifying optimal enzyme expression levels is critical for microbial cell factories, as metabolic imbalances can impede the synthesis of target products. However, current screening strategies often rely on trial-and-error approaches, which are labor-intensive and have limited applicability. Here we developed a quantitative strategy utilizing a bicistronic design (BCD) library for enzyme expression screening, requiring no more than 17 tests in two steps: expression profiling and focused selection. The BCD library encoded a 992-fold expression range, and protein abundances were quantified based on fluorescence intensities due to a strong correlation (r = 0.96). This strategy was employed to fine-tune the expression of the rate-limiting enzyme BioB in biotin synthesis, whose overexpression inhibits cell growth and biotin production. Consequently, BCD6 was identified the optimal expression strength for the overexpressed bio operon, while BCD7 was optimal for the overexpressed bio + isc operons, resulting in 1.47-fold and 3.03-fold increases in biotin titer compared to original strain. Western Blot analysis confirmed a 2.38-fold and 2.71-fold increase in BioB abundance, respectively. The pioneering application of BCD establishes it as a versatile tool for the rational tuning of enzyme expression in the construction of any microbial cell factory.

De novo 2'-fucosyllactose biosynthesis using glucose as the sole carbon source by multiple engineered Bacillus subtilis

2'-Fucosyllactose (2'-FL) is the most abundant human milk oligosaccharide and plays significant roles in gut microbiome balance, neural development, and immunoregulation. However, current fermentation schemes using multiple carbon sources increase production cost and metabolism burden. This study reported the development of an engineered Bacillus subtilis strain that produces 2'-FL using glucose as the sole carbon source. First, a lactose biosynthesis module was constructed by expressing β-1,4-galactosyltransferase gene from Neisseria meningitidis. A 2'-FL titer of 2.53 ± 0.07 g/L was subsequently achieved using glucose as the sole carbon source by the combination of lactose and GDP-L-fucose (GDP-Fuc) biosynthesis modules. Introducing an exogenous nonphosphorylated transport system enhanced the supply of intracellular nonphosphorylated glucose, and the 2'-FL titer increased to 4.94 ± 0.35 g/L. Next, a transcription factor screening platform was designed. Based on this platform, the ligand of the transcription factor LacI was changed from isopropyl β-D-thiogalactoside to lactose. A lactose-responsive genetic circuit was then constructed and used for the dynamic regulation of metabolic fluxes between lactose and GDP-Fuc biosynthesis modules. Ultimately, the 2'-FL titer of the dynamically regulated strain improved by 107% to 9.67 ± 0.65 g/L in shake-flask, and the titer and yield in a 3-L bioreactor reached 30.1 g/L and 0.15 g/g using glucose as the sole carbon source. By using multidimensional engineering strategies, this study constructed a B. subtilis strain capable of efficiently producing 2'-FL with glucose as the sole carbon source, paving the way for the industrial production of 2'-FL with low cost in the future.

Emergent biotechnology applications in urology: a mini review

Technological advances have significantly impacted the field of urology, providing innovative solutions for diagnosis, treatment, and management of various urological disorders and diseases. This article highlights four groundbreaking technologies: whole-cell biosensors, optogenetic interventions for neuromodulation, bioengineered urinary bladder, and 3D bioprinting. Each technology plays a crucial role in enhancing patient care and improving clinical outcomes in urology. Advances in these fields underscore a shift towards precision diagnostics, personalized treatments, and enhanced regenerative strategies, ultimately aiming to enhance patient outcomes and address unmet clinical needs in urological diseases.

Biosensor-Guided Engineering of a Baeyer-Villiger Monooxygenase for Aliphatic Ester Production

Esters are valuable aroma compounds and can be produced enzymatically by Baeyer-Villiger monooxygenases (BVMOs) from (aliphatic) ketone precursors. However, a genetically encoded biosensor system for the assessment of BVMO activity and the detection of reaction products is missing. In this work, we assembled a synthetic enzyme cascade - featuring an esterase, an alcohol dehydrogenase, and LuxAB - in the heterologous host Escherichia coli. Target esters are produced by a BVMO, subsequently cleaved, and the corresponding alcohol oxidized through the artificial pathway. Ultimately, aldehyde products are detected in vivo by LuxAB, a luciferase from Photorhabdus luminescens that emits bioluminescence upon the oxidation of aldehydes to the corresponding carboxylates. This biosensor system greatly accelerated the screening and selection of active BVMO variants from a focused library, omitting commonly used low-throughput chromatographic analysis. Engineered enzymes accepted linear aliphatic ketones such as 2-undecanone and 2-dodecanone and exhibited improved ester formation.

Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors

Transcription factor (TF)-based biosensors (TFBs) have received considerable attention in various fields due to their capability of converting biosignals, such as molecule concentrations, into analyzable signals, thereby bypassing the dependence on time-consuming and laborious detection techniques. Natural TFs are evolutionarily optimized to maintain microbial survival and metabolic balance rather than for laboratory scenarios. As a result, native TFBs often exhibit poor performance, such as low specificity, narrow dynamic range, and limited sensitivity, hindering their application in laboratory and industrial settings. This work analyzes four types of regulatory mechanisms underlying TFBs and outlines strategies for constructing efficient sensing systems. Recent advances in TFBs across various usage scenarios are reviewed with a particular focus on the challenges of commercialization. The systematic improvement of TFB performance by modifying the constituent elements is thoroughly discussed. Additionally, we propose future directions of TFBs for developing rapid-responsive biosensors and addressing the challenge of application isolation. Furthermore, we look to the potential of artificial intelligence (AI) technologies and various models for programming TFB genetic circuits. This review sheds light on technical suggestions and fundamental instructions for constructing and engineering TFBs to promote their broader applications in Industry 4.0, including smart biomanufacturing, environmental and food contaminants detection, and medical science.

Construction of Whole Cell Bacterial Biosensors as an Alternative Environmental Monitoring Technology to Detect Naphthenic Acids in Oil Sands Process-Affected Water

After extraction of bitumen from oil sands deposits, the oil sand process-affected water (OSPW) is stored in tailings ponds. Naphthenic acids (NA) in tailings ponds have been identified as the primary contributor to toxicity to aquatic life. As an alternative to other analytical methods, here we identify bacterial genes induced after growth in naphthenic acids and use synthetic biology approaches to construct a panel of candidate biosensors for NA detection in water. The main promoters of interest were the atuAR promoters from a naphthenic acid degradation operon and upstream TetR regulator, the marR operon which includes a MarR regulator and downstream naphthenic acid resistance genes, and a hypothetical gene with a possible role in fatty acid biology. Promoters were printed and cloned as transcriptional lux reporter plasmids that were introduced into a tailings pond-derived Pseudomonas species. All candidate biosensor strains were tested for transcriptional responses to naphthenic acid mixtures and individual compounds. The three priority promoters respond in a dose-dependent manner to simple, acyclic, and complex NA mixtures, and each promoter has unique NA specificities. The limits of NA detection from the various NA mixtures ranged between 1.5 and 15 mg/L. The atuA and marR promoters also detected NA in small volumes of OSPW samples and were induced by extracts of the panel of OSPW samples. While biosensors have been constructed for other hydrocarbons, here we describe a biosensor approach that could be employed in environmental monitoring of naphthenic acids in oil sands mining wastewater.

Engineering two-component systems for advanced biosensing: From architecture to applications in biotechnology

Two-component systems (TCSs) are prevalent signaling pathways in bacteria. These systems mediate phosphotransfer between histidine kinase and a response regulator, facilitating responses to diverse physical, chemical, and biological stimuli. Advancements in synthetic and structural biology have repurposed TCSs for applications in monitoring heavy metals, disease-associated biomarkers, and the production of bioproducts. However, the utility of many TCS biosensors is hindered by undesired performance due to the lack of effective engineering methods. Here, we briefly discuss the architectures and regulatory mechanisms of TCSs. We also summarize the recent advancements in TCS engineering by experimental or computational-based methods to fine-tune the biosensor functional parameters, such as response curve and specificity. Engineered TCSs have great potential in the medical, environmental, and biorefinery fields, demonstrating a crucial role in a wide area of biotechnology.

Investigating and Engineering an 1,2-Propanediol-Responsive Transcription Factor-Based Biosensor

Transcription factor (TF)-based biosensors have arisen as powerful tools in the advancement of metabolic engineering. However, with the emergence of numerous bioproduction targets, the variety of applicable TF-based biosensors remains severely limited. In this study, we investigated and engineered an 1,2-propanediol (1,2-PD)-responsive transcription activator, PocR, from Salmonella typhimurium to enrich the current biosensor repertoire. Heterologous characterization of PocR in E. coli revealed a significantly limited operational range and dynamic range, primarily attributed to the leaky binding between PocR and its corresponding promoters in the absence of the 1,2-PD inducer. Promiscuity characterization uncovered the minor responsiveness of PocR toward glycerol and 1,2-butanediol (1,2-BD). Using AlphaFold-predicted structure and protein mutagenesis, we preliminarily explored the underlying mechanism of PocR. Based on the investigated mechanism, we engineered a PcoR-F46R/G105D variant with an altered inducer specificity to glycerol, as well as a PocR-ARE (Q107A/S192R/A203E) variant with nearly a 4-fold higher dynamic range (6.7-fold activation) and a 20-fold wider operational range (0-20 mM 1,2-PD). Finally, we successfully converted PocR to a repressor through promoter engineering. Integrating the activation and repression functions established a versatile 1,2-PD-induced bifunctional regulation system based on PocR-ARE. Our work showcases the exploration and exploitation of an underexplored type of transcriptional activator capable of recruiting RNA polymerase. It also expands the biosensor toolbox by providing a 1,2-PD-responsive bifunctional regulator and glycerol-responsive activator.

Thermostable in vitro transcription-translation compatible with microfluidic droplets

BACKGROUND

In vitro expression involves the utilization of the cellular transcription and translation machinery in an acellular context to produce one or more proteins of interest and has found widespread application in synthetic biology and in pharmaceutical biomanufacturing. Most in vitro expression systems available are active at moderate temperatures, but to screen large libraries of natural or artificial genetic diversity for highly thermostable enzymes or enzyme variants, it is instrumental to enable protein synthesis at high temperatures.

OBJECTIVES

Develop an in vitro expression system operating at high temperatures compatible with enzymatic assays and with technologies that enable ultrahigh-throughput protein expression in reduced volumes, such as microfluidic water-in-oil (w/o) droplets.

RESULTS

We produced cell-free extracts from Thermus thermophilus for in vitro translation including thermostable enzymatic cascades for energy regeneration and a moderately thermostable RNA polymerase for transcription, which ultimately limited the temperature of protein synthesis. The yield was comparable or superior to other thermostable in vitro expression systems, while the preparation procedure is much simpler and can be suited to different Thermus thermophilus strains. Furthermore, these extracts have enabled in vitro expression in microfluidic droplets at high temperatures for the first time.

CONCLUSIONS

Cell-free extracts from Thermus thermophilus represent a simpler alternative to heavily optimized or pure component thermostable in vitro expression systems. Moreover, due to their compatibility with droplet microfluidics and enzyme assays at high temperatures, the reported system represents a convenient gateway for enzyme screening at higher temperatures with ultrahigh-throughput.

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

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

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Streptomyces-based whole-cell biosensors for detecting diverse cell envelope-targeting antibiotics

Cell envelope-targeting antibiotics are potent therapeutic agents against various bacterial infections. The emergence of multiple antibiotic-resistant strains underscores the significance of identifying potent antimicrobials specifically targeting the cell envelope. However, current drug screening approaches are tedious and lack sufficient specificity and sensitivity, warranting the development of more efficient methods. Genetic circuit-based whole-cell biosensors hold great promise for targeted drug discovery from natural products. Here, we performed comparative transcriptomic analysis of Streptomyces coelicolor M1146 exposed to diverse cell envelope-targeting antibiotics, aiming to identify regulatory elements involved in perceiving and responding to these compounds. Differential gene expression analysis revealed significant activation of VanS/R two-component system in response to the glycopeptide class of cell envelope-acting antibiotics. Therefore, we engineered a pair of VanS/R-based biosensors that exhibit functional complementarity and possess exceptional sensitivity and specificity for glycopeptides detection. Additionally, through promoter screening and characterization, we expanded the biosensor's detection range to include various cell envelope-acting antibiotics beyond glycopeptides. Our genetically engineered biosensor exhibits superior performance, including a dynamic range of up to 887-fold for detecting subtle antibiotic concentration changes in a rapid 2-h response time, enabling high-throughput screening of natural product libraries for antimicrobial agents targeting the bacterial cell envelope.

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

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

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.

Microbial engineering for shikimate biosynthesis

Shikimate, a precursor to the antiviral drug oseltamivir (Tamiflu®), can influence aromatic metabolites and finds extensive use in antimicrobial, antitumor, and cardiovascular applications. Consequently, various strategies have been developed for chemical synthesis and plant extraction to enhance shikimate biosynthesis, potentially impacting environmental conditions, economic sustainability, and separation and purification processes. Microbial engineering has been developed as an environmentally friendly approach for shikimate biosynthesis. In this review, we provide a comprehensive summary of microbial strategies for shikimate biosynthesis. These strategies primarily include chassis construction, biochemical optimization, pathway remodelling, and global regulation. Furthermore, we discuss future perspectives on shikimate biosynthesis and emphasize the importance of utilizing advanced metabolic engineering tools to regulate microbial networks for constructing robust microbial cell factories.

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.

Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast

Dynamic metabolic engineering is a strategy to switch key metabolic pathways in microbial cell factories from biomass generation to accumulation of target products. Here, we demonstrate that optogenetic intervention in the cell cycle of budding yeast can be used to increase production of valuable chemicals, such as the terpenoid β-carotene or the nucleoside analog cordycepin. We achieved optogenetic cell-cycle arrest in the G2/M phase by controlling activity of the ubiquitin-proteasome system hub Cdc48. To analyze the metabolic capacities in the cell cycle arrested yeast strain, we studied their proteomes by timsTOF mass spectrometry. This revealed widespread, but highly distinct abundance changes of metabolic key enzymes. Integration of the proteomics data in protein-constrained metabolic models demonstrated modulation of fluxes directly associated with terpenoid production as well as metabolic subsystems involved in protein biosynthesis, cell wall synthesis, and cofactor biosynthesis. These results demonstrate that optogenetically triggered cell cycle intervention is an option to increase the yields of compounds synthesized in a cellular factory by reallocation of metabolic resources.

Riboswitches, from cognition to transformation

Riboswitches are functional RNA elements that regulate gene expression by directly detecting metabolites. Twenty years have passed since it was first discovered, researches on riboswitches are becoming increasingly standardized and refined, which could significantly promote people's cognition of RNA function as well. Here, we focus on some representative orphan riboswitches, enumerate the structural and functional transformation and artificial design of riboswitches including the coupling with ribozymes, hoping to attain a comprehensive understanding of riboswitch research.

Influence of Varying Pre-Culture Conditions on the Level of Population Heterogeneity in Batch Cultures with an Escherichia coli Triple Reporter Strain

When targeting robust, high-yielding bioprocesses, phenomena such as population heterogeneity have to be considered. Therefore, the influence of the conditions which the cells experience prior to the main culture should also be evaluated. Here, the influence of a pre-culture medium (complex vs. minimal medium), optical density for inoculation of the main culture (0.005, 0.02 and 0.0125) and harvest time points of the pre-culture in exponential growth phase (early, mid and late) on the level of population heterogeneity in batch cultures of the Escherichia coli triple reporter strain G7_BL21(DE3) in stirred-tank bioreactors was studied. This strain allows monitoring the growth (rrnB-EmGFP), general stress response (rpoS-mStrawberry) and oxygen limitation (nar-TagRFP657) of single cells through the expression of fluorescent proteins. Data from batch cultivations with varying pre-culture conditions were analysed with principal component analysis. According to fluorescence data, the pre-culture medium had the largest impact on population heterogeneities during the bioprocess. While a minimal medium as a pre-culture medium elevated the differences in cellular growth behaviour in the subsequent batch process, a complex medium increased the general stress response and led to a higher population heterogeneity. The latter was promoted by an early harvest of the cells with low inoculation density. Seemingly, nar-operon expression acted independently of the pre-culture conditions.

Streptomyces and their specialised metabolites for phytopathogen control - comparative in vitro and in planta metabolic approaches

In the search for new crop protection microbial biocontrol agents, isolates from the genus Streptomyces are commonly found with promising attributes. Streptomyces are natural soil dwellers and have evolved as plant symbionts producing specialised metabolites with antibiotic and antifungal activities. Streptomyces biocontrol strains can effectively suppress plant pathogens via direct antimicrobial activity, but also induce plant resistance through indirect biosynthetic pathways. The investigation of factors stimulating the production and release of Streptomyces bioactive compounds is commonly conducted in vitro, between Streptomyces sp. and a plant pathogen. However, recent research is starting to shed light on the behaviour of these biocontrol agents in planta, where the biotic and abiotic conditions share little similarity to those of controlled laboratory conditions. With a focus on specialised metabolites, this review details (i) the various methods by which Streptomyces biocontrol agents employ specialised metabolites as an additional line of defence against plant pathogens, (ii) the signals shared in the tripartite system of plant, pathogen and biocontrol agent, and (iii) an outlook on new approaches to expedite the identification and ecological understanding of these metabolites under a crop protection lens.

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

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

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.

De novo design of the global transcriptional factor Cra-regulated promoters enables highly sensitive glycolysis flux biosensor for dynamic metabolic control

Glycolytic flux is a fundamental index in microbial cell factories. A glycolytic flux biosensor that can monitor glucose metabolism efficiency is a promising strategy in rewiring metabolic flux to balance growth and biosynthesis. A key design feature of the glycolytic flux biosensors is the interaction between the global transcriptional factor Cra and its regulated promoters. However, overexpression and mutation of Cra has unpredictable effects on global metabolism in Escherichia coli. Therefore, new orthogonal biosensor design strategies should be developed to circumvent metabolic issues. In this report, the promoters in glycolytic flux biosensor were replaced with synthetic promoters of varying strengths or phage-derived promoters, and the Cra DNA-binding sites were deployed into promoters at different positions and distances to yield biosensors. The de nova biosensors that depended on Cra could sense Fructose-1,6-diphosphate (FBP) with broad dynamic ranges and low basal leakage. Then the negative-response biosensors were applied to fine-tune the target ATP synthesis gene, leading to the desired increase in pyruvate production (the highest 9.66 g/L) and cell growth. Moreover, the membrane synthesis gene plsC was also dynamically activated by the positive-response biosensor, leading to effective accumulation of lycopene in the cell membrane and a 50-fold increase in lycopene titre (100.3 mg/L) when compared with the control strain, demonstrating the effective and broader usages of our biosensors.

Rapid combinatorial rewiring of metabolic networks for enhanced poly(3-hydroxybutyrate) production in Corynebacterium glutamicum

BACKGROUND

The disposal of plastic waste is a major environmental challenge. With recent advances in microbial genetic and metabolic engineering technologies, microbial polyhydroxyalkanoates (PHAs) are being used as next-generation biomaterials to replace petroleum-based synthetic plastics in a sustainable future. However, the relatively high production cost of bioprocesses hinders the production and application of microbial PHAs on an industrial scale.

RESULTS

Here, we describe a rapid strategy to rewire metabolic networks in an industrial microorganism, Corynebacterium glutamicum, for the enhanced production of poly(3-hydroxybutyrate) (PHB). A three-gene PHB biosynthetic pathway in Rasltonia eutropha was refactored for high-level gene expression. A fluorescence-based quantification assay for cellular PHB content using BODIPY was devised for the rapid fluorescence-activated cell sorting (FACS)-based screening of a large combinatorial metabolic network library constructed in C. glutamicum. Rewiring metabolic networks across the central carbon metabolism enabled highly efficient production of PHB up to 29% of dry cell weight with the highest cellular PHB productivity ever reported in C. glutamicum using a sole carbon source.

CONCLUSIONS

We successfully constructed a heterologous PHB biosynthetic pathway and rapidly optimized metabolic networks across central metabolism in C. glutamicum for enhanced production of PHB using glucose or fructose as a sole carbon source in minimal media. We expect that this FACS-based metabolic rewiring framework will accelerate strain engineering processes for the production of diverse biochemicals and biopolymers.

Advances in riboswitch-based biosensor as food samples detection tool

Food safety has always been a hot issue of social concern, and biosensing has been widely used in the field of food safety detection. Compared with traditional aptamer-based biosensors, aptamer-based riboswitch biosensing represents higher precision and programmability. A riboswitch is an elegant example of controlling gene expression, where the target is coupled to the aptamer domain, resulting in a conformational change in the downstream expression domain and determining the signal output. Riboswitch-based biosensing can be extensively applied to the portable real-time detection of food samples. The numerous key features of riboswitch-based biosensing emphasize their sustainability, renewable, and testing, which promises to transform engineering applications in the field of food safety. This review covers recent developments in riboswitch-based biosensors. The brief history, definition, and modular design (regulatory mode, reporter, and expression platform) of riboswitch-based biosensors are explained for better insight into the design and construction. We summarize recent advances in various riboswitch-based biosensors involving theophylline, malachite green, tetracycline, neomycin, fluoride, thrombin, naringenin, ciprofloxacin, and paromomycin, aiming to provide general guidance for the design of riboswitch-based biosensors. Finally, the challenges and prospects are also summarized as a way forward stratagem and signs of progress.

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

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

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Directed Evolution of Herbicide Biosensors in a Fluorescence-Activated Cell-Sorting-Compatible Yeast Two-Hybrid Platform

Developing sensory modules for specific molecules of interest represents a fundamental challenge in synthetic biology and its applications. A somewhat generalizable approach for this challenge is demonstrated here by evolving a naturally occurring chemically induced heterodimer into a genetically encoded sensor for herbicides. The interaction between PYRABACTIN-RESISTANT-like receptors and type-2C protein phosphatases is induced by abscisic acid─a small-molecule hormone in plants. We considered abscisic acid receptors as a potential scaffold for the development of biosensors because of past successes in their engineering, a structurally defined ligand cavity and the availability of large-scale assays for their activation. A panel of 475 receptor variants, mutated at ligand-proximal residues, was screened for activation by 37 herbicides from several classes. Twelve compounds activated at least one member of the mutant panel. To facilitate the subsequent improvement of herbicide receptors through directed evolution, we engineered a yeast two-hybrid platform optimized for sequential positive and negative selection using fluorescence-activated cell sorting. By utilizing this system, we were able to isolate receptors with low nanomolar sensitivity and a broad dynamic range in sensing a ubiquitous group of chloroacetamide herbicides. Aside from its possible applicative value, this work lays down conceptual groundwork and provides infrastructure for the future development of biosensors through directed evolution.

A Gene Circuit Combining the Endogenous I-E Type CRISPR-Cas System and a Light Sensor to Produce Poly-β-Hydroxybutyric Acid Efficiently

‘Metabolic burden,’ which arises when introducing exogenic synthesizing pathways into a host strain, remains a challenging issue in metabolic engineering. Redirecting metabolic flux from cell growth to product synthesis at an appropriate culture timepoint is ideal for resolving this issue. In this report, we introduce optogenetics—which is capable of precise temporal and spatial control—as a genetic switch, accompanied by the endogenous type I-E CRISPRi system in Escherichia coli (E. coli) to generate a metabolic platform that redirects metabolic flux. Poly-β-hydroxybutyric acid (PHB) production was taken as an example to demonstrate the performance of this platform. A two-to-three-fold increase in PHB content was observed under green light when compared with the production of PHB under red light, confirming the regulatory activity of this platform and its potential to redirect metabolic flux to synthesize target products.

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

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

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

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

Biosensor and chemo-enzymatic one-pot cascade applications to detect and transform PET-derived terephthalic acid in living cells

Plastic waste imposes a serious problem to the environment and society. Hence, strategies for a circular plastic economy are demanded. One strategy is the engineering of polyester hydrolases toward higher activity for the biotechnological recycling of polyethylene terephthalate (PET). To provide tools for the rapid characterization of PET hydrolases and the detection of degradation products like terephthalic acid (TPA), we coupled a carboxylic acid reductase (CAR) and the luciferase LuxAB. CAR converted TPA into the corresponding aldehydes in Escherichia coli, which yielded bioluminescence that not only semiquantitatively reflected amounts of TPA in hydrolysis samples but is suitable as a high-throughput screening assay to assess PET hydrolase activity. Furthermore, the CAR-catalyzed synthesis of terephthalaldehyde was combined with a reductive amination cascade in a one-pot setup yielding the corresponding diamine, suggesting a new strategy for the transformation of TPA as a product obtained from PET biodegradation.

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First bioreduction of terephthalic acid (TPA) by a carboxylic acid reductase in vivo

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Real-time, high-throughput detection of TPA-derived aldehydes by luciferase LuxAB

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Bioluminescence reflects TPA amounts, assessing (engineered) PET hydrolase activity

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Transformation of TPA into the diamine through chemo-enzymatic one-pot cascade

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.

Engineering Transcription Factor BmoR Mutants for Constructing Multifunctional Alcohol Biosensors

Native transcription factor-based biosensors (TFBs) have the potential for the in situ detection of value-added chemicals or byproducts. However, their industrial application is limited by their ligand promiscuity, low sensitivity, and narrow detection range. Alcohols exhibit similar structures, and no reported TFB can distinguish a specific alcohol from its analogues. Here, we engineered an alcohol-regulated transcription factor, BmoR, and obtained various mutants with remarkable properties. For example, the generated signal-molecule-specific BmoRs could distinguish the constitutional isomers n-butanol and isobutanol, with insensitivity up to an ethanol concentration of 800 mM (36.9 g/L). Linear detection of 0-60 mM of a specific higher alcohol could be achieved in the presence of up to 500 mM (23.0 g/L) ethanol as background noise. Furthermore, we obtained two mutants with raised outputs and over 10⁷-fold higher sensitivity and one mutant with an increased upper detection limit (14.8 g/L n-butanol or isobutanol). Using BmoR as an example, this study systematically explored the ultimate detection limit of a TFB toward its small-molecule ligands, paving the way for in situ detection in biofuel and wine industries.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

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.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

Engineered l-Lactate Responding Promoter System Operating in Glucose-Rich and Anoxic Environments

Bacteria equipped with genetically encoded lactate biosensors are promising tools for biopharmaceutical production, diagnostics, and cellular therapies. However, many applications involve glucose-rich and anoxic environments, in which current whole-cell lactate biosensors show low performance. Here we engineer an optimized, synthetic lactate biosensor system by repurposing the natural LldPRD promoter regulated by the LldR transcriptional regulator. We removed glucose catabolite and anoxic repression by designing a hybrid promoter, containing LldR operators and tuned both regulator and reporter gene expressions to optimize biosensor signal-to-noise ratio. The resulting lactate biosensor, termed ALPaGA (A Lactate Promoter Operating in Glucose and Anoxia), can operate in glucose-rich, aerobic and anoxic conditions. We show that ALPaGA works reliably in the probiotic chassisEscherichia coliNissle 1917 and can detect endogenous l-lactate produced by 3D tumor spheroids with an improved dynamic range. In the future, the ALPaGA system could be used to monitor bioproduction processes and improve the specificity of engineered bacterial cancer therapies by restricting their activity to the lactate-rich microenvironment of solid tumors.

Microscopy imaging of living cells in metabolic engineering

Microscopy imaging of living cells is becoming a pivotal, noninvasive, and highly specific tool in metabolic engineering to visualize molecular dynamics in industrial microorganisms. This review describes the different microscopy methods, from fluorescence to super resolution, with application in microbial bioengineering. Firstly, the role and importance of microscopy imaging is analyzed in the context of strain design. Then, the advantages and disadvantages of different microscopy technologies are discussed, including confocal laser scanning microscopy (CLSM), spatial light interference microscopy (SLIM), and super-resolution microscopy, followed by their applications in synthetic biology. Finally, the future perspectives of live-cell imaging and their potential to transform microbial systems are analyzed. This review provides theoretical guidance and highlights the importance of microscopy in understanding and engineering microbial metabolism.

Programming living sensors for environment, health and biomanufacturing

Synthetic biology offers new tools and capabilities of engineering cells with desired functions for example as new biosensing platforms leveraging engineered microbes. In the last two decades, bacterial cells have been programmed to sense and respond to various input cues for versatile purposes including environmental monitoring, disease diagnosis and adaptive biomanufacturing. Despite demonstrated proof-of-concept success in the laboratory, the real-world applications of microbial sensors have been restricted due to certain technical and societal limitations. Yet, most limitations can be addressed by new technological developments in synthetic biology such as circuit design, biocontainment and machine learning. Here, we summarize the latest advances in synthetic biology and discuss how they could accelerate the development, enhance the performance and address the present limitations of microbial sensors to facilitate their use in the field. We view that programmable living sensors are promising sensing platforms to achieve sustainable, affordable and easy-to-use on-site detection in diverse settings.

Modular tuning engineering and versatile applications of genetically encoded biosensors

Genetically encoded biosensors have a diverse range of detectable signals and potential applications in many fields, including metabolism control and high-throughput screening. Their ability to be used in situ with minimal interference to the bioprocess of interest could revolutionize synthetic biology and microbial cell factories. The performance and functions of these biosensors have been extensively studied and have been rapidly improved. We review here current biosensor tuning strategies and attempt to unravel how to obtain ideal biosensor functions through experimental adjustments. Strategies for expanding the biosensor input signals that increases the number of detectable compounds have also been summarized. Finally, different output signals and their practical requirements for biotechnology and biomedical applications and environmental safety concerns have been analyzed. This in-depth review of the responses and regulation mechanisms of genetically encoded biosensors will assist to improve their design and optimization in various application scenarios.

Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis

Ligand-dependent biosensors are valuable tools for coupling the intracellular concentrations of small molecules to easily detectable readouts such as absorbance, fluorescence, or cell growth. While ligand-dependent biosensors are widely used for monitoring the production of small molecules in engineered cells and for controlling or optimizing biosynthetic pathways, their application to directed evolution for biocatalysts remains underexplored. As a consequence, emerging continuous evolution technologies are rarely applied to biocatalyst evolution. Here, we develop a panel of ligand-dependent biosensors that can detect a range of small molecules. We demonstrate that these biosensors can link enzymatic activity to the production of an essential phage protein to enable biocatalyst-dependent phage-assisted continuous evolution (PACE) and phage-assisted continuous selection (PACS). By combining these phage-based evolution and library selection technologies, we demonstrate that we can evolve enzyme variants with improved and expanded catalytic properties. Finally, we show that the genetic diversity resulting from a highly mutated PACS library is enriched for active enzyme variants with altered substrate scope. These results lay the foundation for using phage-based continuous evolution and selection technologies to engineer biocatalysts with novel substrate scope and reactivity.

LuxAB-Based Microbial Cell Factories for the Sensing, Manufacturing and Transformation of Industrial Aldehydes

The application of genetically encoded biosensors enables the detection of small molecules in living cells and has facilitated the characterization of enzymes, their directed evolution and the engineering of (natural) metabolic pathways. In this work, the LuxAB biosensor system from Photorhabdus luminescens was implemented in Escherichia coli to monitor the enzymatic production of aldehydes from primary alcohols and carboxylic acid substrates. A simple high-throughput assay utilized the bacterial luciferase—previously reported to only accept aliphatic long-chain aldehydes—to detect structurally diverse aldehydes, including aromatic and monoterpene aldehydes. LuxAB was used to screen the substrate scopes of three prokaryotic oxidoreductases: an alcohol dehydrogenase (Pseudomonas putida), a choline oxidase variant (Arthrobacter chlorophenolicus) and a carboxylic acid reductase (Mycobacterium marinum). Consequently, high-value aldehydes such as cinnamaldehyde, citral and citronellal could be produced in vivo in up to 80% yield. Furthermore, the dual role of LuxAB as sensor and monooxygenase, emitting bioluminescence through the oxidation of aldehydes to the corresponding carboxylates, promises implementation in artificial enzyme cascades for the synthesis of carboxylic acids. These findings advance the bio-based detection, preparation and transformation of industrially important aldehydes in living cells.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways

The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.

Establishment of a Biosensor-based High-Throughput Screening Platform for Tryptophan Overproduction

With the flexibility to fold into complex structures, RNA is well-suited to act as a cellular sensor to recognize environmental fluctuations and respond to changes by regulating the corresponding genes. In this study, we established a high-throughput screening platform to screen tryptophan high-producing strains from a large repertoire of candidate strains. This platform consists of a tryptophan-specific aptamer-based biosensor and fluorescence-activated droplet sorting technology. One mutant strain, with a 165.9% increase in Trp titer compared with the parental strain, was successfully screened from a random mutagenesis library. Sequencing results revealed that a total of 10 single-nucleotide polymorphisms were discovered in the genome of the mutant strain, among which CRP(T29K) was proven to significantly increase Trp production through improving the strain's tolerance of the harsh environment during the stationary phase of the fermentation process. Our results indicate that this strategy has great potential for improving the production of other amino acids in Escherichia coli.