In vivo biosensors: mechanisms, development, and applications

Optimizing Escherichia coli strains and fermentation processes for enhanced L-lysine production: a review

lysine is an essential amino acid with significant importance, widely used in the food, feed, and pharmaceutical industries. To meet the increasing demand, microbial fermentation has emerged as an effective and sustainable method for L-lysine production. Escherichia coli has become one of the primary microorganisms for industrial L-lysine production due to its rapid growth, ease of genetic manipulation, and high production efficiency. This paper reviews the recent advances in E. coli strain engineering and fermentation process optimization for L-lysine production. Additionally, it discusses potential technological breakthroughs and challenges in E. coli-based L-lysine production, offering directions for future research to support industrial-scale production.

MicroLOCK: Highly stable microgel biosensor using locked nucleic acids as bioreceptors for sensitive and selective detection of let-7a

Chemically modified oligonucleotides can solve biosensing issues for the development of capture probes, antisense, CRISPR/Cas, and siRNA, by enhancing their duplex-forming ability, their stability against enzymatic degradation, and their specificity for targets with high sequence similarity as microRNA families. However, the use of modified oligonucleotides such as locked nucleic acids (LNA) for biosensors is still limited by hurdles in design and from performances on the material interface. Here we developed a fluorogenic biosensor for non-coding RNAs, represented by polymeric PEG microgels conjugated with molecular beacons (MB) modified with locked nucleic acids (MicroLOCK). By 3D modeling and computational analysis, we designed molecular beacons (MB) inserting spot-on LNAs for high specificity among targets with high sequence similarity (95%). MicroLOCK can reversibly detect microRNA targets in a tiny amount of biological sample (2 μL) at 25 °C with a higher sensitivity (LOD 1.3 fM) without any reverse transcription or amplification. MicroLOCK can hybridize the target with fast kinetic (about 30 min), high duplex stability without interferences from the polymer interface, showing high signal-to-noise ratio (up to S/N = 7.3). MicroLOCK also demonstrated excellent resistance to highly nuclease-rich environments, in real samples. These findings represent a great breakthrough for using the LNA in developing low-cost biosensing approaches and can be applied not only for nucleic acids and protein detection but also for real-time imaging and quantitative assessment of gene targeting both in vitro and in vivo.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

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

Strategies to improve the efficiency and quality of mutant breeding using heavy-ion beam irradiation

Heavy-ion beam irradiation (HIBI) is useful for generating new germplasm in plants and microorganisms due to its ability to induce high mutagenesis rate, broad mutagenesis spectrum, and excellent stability of mutants. However, due to the random mutagenesis and associated mutant breeding modalities, it is imperative to improve HIBI-based mutant breeding efficiency and quality. This review discusses and summarizes the findings of existing theoretical and technical studies and presents a set of tandem strategies to enable efficient and high-quality HIBI-based mutant breeding practices. These strategies: adjust the mutation-inducing techniques, regulate cellular response states, formulate high-throughput screening schemes, and apply the generated superior genetic elements to genetic engineering approaches, thereby, improving the implications and expanding the scope of HIBI-based mutant breeding. These strategies aim to improve the mutagenesis rate, screening efficiency, and utilization of positive mutations. Here, we propose a model based on the integration of these strategies that would leverage the advantages of HIBI while compensating for its present shortcomings. Owing to the unique advantages of HIBI in creating high-quality genetic resources, we believe this review will contribute toward improving HIBI-based breeding.

Searching for the optimal microbial factory: high-throughput biosensors and analytical techniques for screening small molecules

High-throughput screening technologies have been lacking in comparison to the plethora of high-throughput genetic diversification techniques developed in biotechnology. This review explores the challenges and advancements in high-throughput screening for high-value natural products, focusing on the critical need to expand ligand targets for biosensors and increase the throughput of analytical techniques in screening microbial cell libraries for optimal strain performance. The engineering techniques to broaden the scope of ligands for biosensors, such as transcription factors, G protein-coupled receptors and riboswitches are discussed. On the other hand, integration of microfluidics with traditional analytical methods is explored, covering fluorescence-activated cell sorting, Raman-activated cell sorting and mass spectrometry, emphasising recent developments in maximising throughput.

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.

Synthetic biology-based bioreactor and its application in biochemical analysis

In the past few years, synthetic biologists have established some biological elements and bioreactors composed of nucleotides under the guidance of engineering methods. Following the concept of engineering, the common bioreactor components in recent years are introduced and compared. At present, biosensors based on synthetic biology have been applied to water pollution monitoring, disease diagnosis, epidemiological monitoring, biochemical analysis and other detection fields. In this paper, the biosensor components based on synthetic bioreactors and reporters are reviewed. In addition, the applications of biosensors based on cell system and cell-free system in the detection of heavy metal ions, nucleic acid, antibiotics and other substances are presented. Finally, the bottlenecks faced by biosensors and the direction of optimization are also discussed.

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.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

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.

Development and characterization of a glycine biosensor system for fine-tuned metabolic regulation in Escherichia coli

Background

In vivo biosensors have a wide range of applications, ranging from the detection of metabolites to the regulation of metabolic networks, providing versatile tools for synthetic biology and metabolic engineering. However, in view of the vast array of metabolite molecules, the existing number and performance of biosensors is far from sufficient, limiting their potential applications in metabolic engineering. Therefore, we developed the synthetic glycine-ON and -OFF riboswitches for metabolic regulation and directed evolution of enzyme in Escherichia coli.

Results

The results showed that a synthetic glycine-OFF riboswitch (glyOFF6) and an increased-detection-range synthetic glycine-ON riboswitch (glyON14) were successfully screened from a library based on the Bacillus subtilis glycine riboswitch using fluorescence-activated cell sorting (FACS) and tetA-based dual genetic selection. The two synthetic glycine riboswitches were successfully used in tunable regulation of lactate synthesis, dynamic regulation of serine synthesis and directed evolution of alanine-glyoxylate aminotransferase in Escherichia coli, respectively. Mutants AGXT22 and AGXT26 of alanine-glyoxylate aminotransferase with an increase of 58% and 73% enzyme activity were obtained by using a high-throughput screening platform based on the synthetic glycine-OFF riboswitch, and successfully used to increase the 5-aminolevulinic acid yield of engineered Escherichia coli.

Conclusions

A synthetic glycine-OFF riboswitch and an increased-detection-range synthetic glycine-ON riboswitch were successfully designed and screened. The developed riboswitches showed broad application in tunable regulation, dynamic regulation and directed evolution of enzyme in E. coli.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01779-4.

Synthetic biology: a new frontier in food production

Concerns regarding food security arise from population growth, global warming, and reduction in arable land. With advances in synthetic biology, food production by microbes is considered to be a promising alternative that would allow rapid food production in an environmentally friendly manner. Moreover, synthetic biology can be adopted to the production of healthier or specifically designed food ingredients (e.g., high-value proteins, lipids, and vitamins) and broaden the utilization of feedstocks (e.g., methanol and CO₂), thereby offering potential solutions to high-quality food and the greenhouse effect. We first present how synthetic biology can facilitate the microbial production of various food components, and then discuss feedstock availability enabled by synthetic biology. Finally, we illustrate trends and key challenges in synthetic biology-driven food production.

The First FRET-Based RNA Aptamer NanoKit for Sensitively and Specifically Detecting c-di-GMP

An effective method to identify c-di-GMP may significantly facilitate the exploration of its signaling pathways and bacterial pathogenesis. Herein, we have developed the first conjugated polymer-amplified RNA aptamer NanoKit with a unique core-shell-shell architecture, which combines the advantages of high selectivity of RNA aptamers and high sensitivity of strong fluorescence resonance energy transfer (FRET) effect, for precisely detecting c-di-GMP. We identified that NanoKit could selectively detect c-di-GMP with a low detection limit of 50 pM. Importantly, NanoKit could identify bacterial species and physiological states, such as planktonic, biofilm, and even antibiotic-resistance, on the basis of their different c-di-GMP expression patterns. Particularly, NanoKit could distinguish bacterial infection and inflammation and identify Pseudomonas aeruginosa associated pneumonia and sepsis, thereby guiding treatment choice and monitoring antibiotic effects. Therefore, NanoKit provides a promising strategy to rapidly identify c-di-GMP and its associated diseases and may benefit for pathophoresis management.

Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.

In Vivo Screening Method for the Identification and Characterization of Prokaryotic, Metabolite-Responsive Transcription Factors

In prokaryotes, transcription factors (TFs) are of uttermost importance for the regulation of gene expression. However, the majority of TFs are not characterized today, which hampers both the understanding of fundamental processes and the development of TF-based applications, such as biosensors, used in metabolic engineering, synthetic biology, diagnostics, etc. One way of analyzing TFs is through in vivo screening, enabling the study of TF-promoter interactions, ligand inducibility, and ligand specificity in a high-throughput fashion. Here, an approach is described for the selection and cloning of TF-promoter pairs, the development of a reporter system, and the measurement and analysis of fluorescent reporter assays. Furthermore, the importance of a suitable inducible plasmid system is illustrated together with prospective adaptations to modify a reporter system's output signal. The given approach can be used for the investigation of native, heterologous, or even artificially created TFs in Escherichia coli, and can be extended toward use in other microorganisms.

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.

Biosensor-assisted evolution for high-level production of 4-hydroxyphenylacetic acid in Escherichia coli

4-Hydroxyphenylacetic acid (4HPAA) is an important building block for synthesizing drugs, agrochemicals, and biochemicals, and requires sustainable production to meet increasing demand. Here, we use a 4HPAA biosensor to overcome the difficulty of conventional library screening in identification of preferred mutants. Strains with higher 4HPAA production and tolerance are successfully obtained by atmospheric and room temperature plasma (ARTP) mutagenesis coupled with adaptive laboratory evolution using this biosensor. Genome shuffling integrates preferred properties in the strain GS-2-4, which produces 25.42 g/L 4HPAA. Chromosomal mutations of the strain GS-2-4 are identified by whole genome sequencing. Through comprehensive analysis and experimental validation, important genes, pathways and regulations are revealed. The best gene combination in inverse engineering, acrD-aroG, increases 4HPAA production of strain GS-2-4 by 37% further. These results emphasize precursor supply and stress resistance are keys to efficient 4HPAA biosynthesis. Our work shows the power of biosensor-assisted screening of mutants from libraries. The methods developed here can be easily adapted to construct cell factories for the production of other aromatic chemicals. Our work also provides many valuable target genes to build cell factories for efficient 4HPAA production in the future.

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.

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.

Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

High-Throughput Screening of an Octanoic Acid Producer Strain Library Enables Detection of New Targets for Increasing Titers in Saccharomyces cerevisiae

Octanoic acid is an industrially relevant compound with applications in antimicrobials or as a precursor for biofuels. Microbial biosynthesis through yeast is a promising alternative to current unsustainable production methods. To increase octanoic acid titers in Saccharomyces cerevisiae, we use a previously developed biosensor that is based on the octanoic acid responsive pPDR12 promotor coupled to GFP. We establish a biosensor strain amenable for high-throughput screening of an octanoic acid producer strain library. Through development, optimization, and execution of a high-throughput screening approach, we were able to detect two new genetic targets, KCS1 and FSH2, which increased octanoic acid titers through combined overexpression by about 55% compared to the parental strain. Neither target has yet been reported to be involved in fatty acid biosynthesis. The presented methodology can be employed to screen any genetic library and thereby more genes involved in improving octanoic acid production can be detected in the future.

Guanidine Biosensors Enable Comparison of Cellular Turn-on Kinetics of Riboswitch-Based Biosensor and Reporter

Cell-based sensors are useful for many synthetic biology applications, including regulatory circuits, metabolic engineering, and diagnostics. While considerable research efforts have been made toward recognizing new target ligands and increasing sensitivity, the analysis and optimization of turn-on kinetics is often neglected. For example, to our knowledge there has been no systematic study that compared the performance of a riboswitch-based biosensor versus reporter for the same ligand. In this study, we show the development of RNA-based fluorescent (RBF) biosensors for guanidine, a common chaotropic agent that is a precursor to both fertilizer and explosive compounds. Guanidine is cell permeable and nontoxic to E. coli at millimolar concentrations, which in contrast to prior studies enabled direct activation of the riboswitch-based biosensor and corresponding reporter with ligand addition to cells. Our results reveal that the biosensors activate fluorescence in the cell within 4 min of guanidine treatment, which is at least 15 times faster than a reporter derived from the same riboswitch, and this rapid sensing activity is maintained for up to 1.6 weeks. Together, this study describes the design of two new biosensor topologies and showcases the advantages of RBF biosensors for monitoring dynamic processes in cell biology, biotechnology, and synthetic biology.

Designing Artificial Cells towards a New Generation of Biosensors

The combination of biological and synthetic materials has great potential to generate new types of biosensors. Toward this goal, recent advances in artificial cell development have demonstrated the capacity to detect a variety of analytes and environmental changes by encapsulating genetically encoded sensors within bilayer membranes, expanding the contexts within which biologically based sensing can operate. This chassis not only acts as a container for cell-free sensors, but can also play an active role in artificial cell sensing by serving as an additional gate mediating the transfer of environmental information. Here, we focus on recent progress toward stimuli-responsive artificial cells and discuss strategies for membrane functionalization in order to expand cell-free biosensing capabilities and applications.

In vivo nano-biosensing element of red blood cell-mediated delivery

Biosensors based on nanotechnology are developing rapidly and are widely applied in many fields including biomedicine, environmental monitoring, national defense and analytical chemistry, and have achieved vital positions in these fields. Novel nano-materials are intensively developed and manufactured for potential biosensing and theranostic applications while lacking comprehensive assessment of their potential health risks. The integration of diagnostic in vivo biosensors and the DDSs for delivery of therapeutic drugs holds an enormous potential in next-generation theranostic platforms. Controllable, precise, and safe delivery of diagnostic biosensing devices and therapeutic agents to the target tissues, organs, or cells is an important determinant in developing advanced nanobiosensor-based theranostic platforms. Particularly, inspired by the comprehensive biological investigations on the red blood cells (RBCs), advanced strategies of RBC-mediated in vivo delivery have been developed rapidly and are currently in different stages of transforming from research and design to pre-clinical and clinical investigations. In this review, the RBC-mediated delivery of in vivo nanobiosensors for applications of bio-imaging at the single-cell level, advanced medical diagnostics, and analytical detection of biomolecules and cellular activities are presented. A comprehensive perspective of the technical framework of the state-of-the-art RBC-mediated delivery systems is explained in detail to inspire the design and implementation of advanced nanobiosensor-based theranostic platforms taking advantage of RBC-delivery modalities.

Developing an endogenous quorum-sensing based CRISPRi circuit for autonomous and tunable dynamic regulation of multiple targets in Streptomyces

Quorum-sensing (QS) mediated dynamic regulation has emerged as an effective strategy for optimizing product titers in microbes. However, these QS-based circuits are often created on heterologous systems and require careful tuning via a tedious testing/optimization process. This hampers their application in industrial microbes. Here, we design a novel QS circuit by directly integrating an endogenous QS system with CRISPRi (named EQCi) in the industrial rapamycin-producing strain Streptomyces rapamycinicus. EQCi combines the advantages of both the QS system and CRISPRi to enable tunable, autonomous, and dynamic regulation of multiple targets simultaneously. Using EQCi, we separately downregulate three key nodes in essential pathways to divert metabolic flux towards rapamycin biosynthesis and significantly increase its titers. Further application of EQCi to simultaneously regulate these three key nodes with fine-tuned repression strength boosts the rapamycin titer by ∼660%, achieving the highest reported titer (1836 ± 191 mg/l). Notably, compared to static engineering strategies, which result in growth arrest and suboptimal rapamycin titers, EQCi-based regulation substantially promotes rapamycin titers without affecting cell growth, indicating that it can achieve a trade-off between essential pathways and product synthesis. Collectively, this study provides a convenient and effective strategy for strain improvement and shows potential for application in other industrial microorganisms.

Biosensor-Guided Atmospheric and Room-Temperature Plasma Mutagenesis and Shuffling for High-Level Production of Shikimic Acid from Sucrose in Escherichia coli

Here, we first developed a combined strain improvement strategy of biosensor-guided atmospheric and room-temperature plasma mutagenesis and genome shuffling. Application of this strategy resulted in a 2.7-fold increase in the production of shikimic acid (SA) and a 2.0-fold increase in growth relative to those of the starting strain. Whole-cell resequencing of the shuffled strain and confirmation using CRISPRa/CRISPRi revealed that some membrane protein-related mutant genes are identified as being closely related to the higher SA titer. The engineered shuffling strain produced 18.58 ± 0.56 g/L SA from glucose with a yield of 68% (mol/mol) by fed-batch whole-cell biocatalysis, achieving 79% of the theoretical maximum. Sucrose-utilizing Escherichia coli was engineered for SA production by introducing Mannheimia succiniciproducens β-fructofuranosidase gene. The resulting sucrose-utilizing E. coli strain produced 24.64 ± 0.32 g/L SA from sucrose with a yield of 1.42 mol/mol by fed-batch whole-cell biocatalysis, achieving 83% of the theoretical maximum.

Biosensor-enabled droplet microfluidic system for the rapid screening of 3-dehydroshikimic acid produced in Escherichia coli

Genetically encoded biosensors are powerful tools used to screen metabolite-producing microbial strains. Traditionally, biosensor-based screening approaches also use fluorescence-activated cell sorting (FACS). However, these approaches are limited by the measurement of intracellular fluorescence signals in single cells, rather than the signals associated with populations comprising multiple cells. This characteristic reduces the accuracy of screening because of the variability in signal levels among individual cells. To overcome this limitation, we introduced an approach that combined biosensors with droplet microfluidics (i.e., fluorescence-activated droplet sorting, FADS) to detect labeled cells at a multi-copy level and in an independent droplet microenvironment. We used our previously reported genetically encoded biosensor, 3-dehydroshikimic acid (3-DHS), as a model with which to establish the biosensor-based FADS screening method. We then characterized and compared the effects of the sorting method on the biosensor-based screening system by subjecting the same mutant library to FACS and FADS. Notably, our developed biosensor-enabled, droplet microfluidics-based FADS screening system yielded an improved positive mutant enrichment rate and increased productivity by the best mutant, compared with the single-cell FACS system. In conclusion, the combination of a biosensor and droplet microfluidics yielded a more efficient screening method that could be applied to the biosensor-based high-throughput screening of other metabolites.

Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories

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

Ultrasensitive Field-Effect Biosensors Enabled by the Unique Electronic Properties of Graphene

This review provides a critical overview of current developments on nanoelectronic biochemical sensors based on graphene. Composed of a single layer of conjugated carbon atoms, graphene has outstanding high carrier mobility and low intrinsic electrical noise, but a chemically inert surface. Surface functionalization is therefore crucial to unravel graphene sensitivity and selectivity for the detection of targeted analytes. To achieve optimal performance of graphene transistors for biochemical sensing, the tuning of the graphene surface properties via surface functionalization and passivation is highlighted, as well as the tuning of its electrical operation by utilizing multifrequency ambipolar configuration and a high frequency measurement scheme to overcome the Debye screening to achieve low noise and highly sensitive detection. Potential applications and prospectives of ultrasensitive graphene electronic biochemical sensors ranging from environmental monitoring and food safety, healthcare and medical diagnosis, to life science research, are presented as well.

Discovery and identification of medium-chain fatty acid responsive promoters in Saccharomyces cerevisiae

Medium-chain fatty acids (MCFAs) and their derivatives are important chemicals that can be used in lubricants, detergents, and cosmetics. MCFAs can be produced in several microbes, although production is not high. Dynamic regulation by synthetic biology is a good method of improving production of chemicals that avoids toxic intermediates, but chemical-responsive promoters are required. Several MCFA sensors or promoters have been reported in Saccharomyces cerevisiae. In this study, by using transcriptomic analysis of S. cerevisiae exposed to fatty acids with 6-, 12-, and 16-carbon chains, we identified 58 candidate genes that may be responsive to MCFAs. Using a fluorescence-based screening method, we identified MCFA-responsive promoters, four that upregulated gene expression, and three that downregulated gene expression. Dose-response analysis revealed that some of the promoters were sensitive to fatty acid concentrations as low as 0.02-0.06 mM. The MCFA-responsive promoters reported in this study could be used in dynamic regulation of fatty acids and fatty acid-derived products in S. cerevisiae.

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

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

Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling

Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (∼4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

Biosensor-based enzyme engineering approach applied to psicose biosynthesis

Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.

Genetically encoded biosensors for lignocellulose valorization

Modern society is hugely dependent on finite oil reserves for the supply of fuels and chemicals. Moving our dependence away from these unsustainable oil-based feedstocks to renewable ones is, therefore, a critical factor towards the development of a low carbon bioeconomy. Lignin derived from biomass feedstocks offers great potential as a renewable source of aromatic compounds if methods for its effective valorization can be developed. Synthetic biology and metabolic engineering offer the potential to synergistically enable the development of cell factories with novel biosynthetic routes to valuable chemicals from these sustainable sources. Pathway design and optimization is, however, a major bottleneck due to the lack of high-throughput methods capable of screening large libraries of genetic variants and the metabolic burden associated with bioproduction. Genetically encoded biosensors can provide a solution by transducing the target metabolite concentration into detectable signals to provide high-throughput phenotypic read-outs and allow dynamic pathway regulation. The development and application of biosensors in the discovery and engineering of efficient biocatalytic processes for the degradation, conversion, and valorization of lignin are paving the way towards a sustainable and economically viable biorefinery.

Optimizing Cell-Free Biosensors to Monitor Enzymatic Production

Cell-free systems are promising platforms for rapid and high-throughput prototyping of biological parts in metabolic engineering and synthetic biology. One main limitation of cell-free system applications is the low fold repression of transcriptional repressors. Hence, prokaryotic biosensor development, which mostly relies on repressors, is limited. In this study, we demonstrate how to improve these biosensors in cell-free systems by applying a transcription factor (TF)-doped extract, a preincubation strategy with the TF plasmid, or reinitiation of the cell-free reaction (two-step cell-free reaction). We use the optimized biosensor to sense the enzymatic production of a rare sugar, D-psicose. This work provides a methodology to optimize repressor-based systems in cell-free to further increase the potential of cell-free systems for bioproduction.

Development of a Genetically Encoded Biosensor for Detection of Polyketide Synthase Extender Units in Escherichia coli

The scaffolds of polyketides are constructed via assembly of extender units based on malonyl-CoA and its derivatives that are substituted at the C2-position with diverse chemical functionality. Subsequently, a transcription-factor-based biosensor for malonyl-CoA has proven to be a powerful tool for detecting malonyl-CoA, facilitating the dynamic regulation of malonyl-CoA biosynthesis and guiding high-throughput engineering of malonyl-CoA-dependent processes. Yet, a biosensor for the detection of malonyl-CoA derivatives has yet to be reported, severely restricting the application of high-throughput synthetic biology approaches to engineering extender unit biosynthesis and limiting the ability to dynamically regulate the biosynthesis of polyketide products that are dependent on such α-carboxyacyl-CoAs. Herein, the FapR biosensor was re-engineered and optimized for a range of mCoA concentrations across a panel of E. coli strains. The effector specificity of FapR was probed by cell-free transcription-translation, revealing that a variety of non-native and non-natural acyl-thioesters are FapR effectors. This FapR promiscuity proved sufficient for the detection of the polyketide extender unit methylmalonyl-CoA in E. coli, providing the first reported genetically encoded biosensor for this important metabolite. As such, the previously unknown broad effector promiscuity of FapR provides a platform to develop new tools and approaches that can be leveraged to overcome limitations of pathways that construct diverse α-carboxyacyl-CoAs and those that are dependent on them, including biofuels, antibiotics, anticancer drugs, and other value-added products.