Design and optimization of genetically encoded biosensors for high-throughput screening of chemicals

Cell-free systems: A synthetic biology tool for rapid prototyping in metabolic engineering

Microbial cell factories provide sustainable alternatives to petroleum-based chemical production using cost-effective substrates. A deep understanding of their metabolism is essential to harness their potential along with continuous efforts to improve productivity and yield. However, the construction and evaluation of numerous genetic variants are time-consuming and labor-intensive. Cell-free systems (CFSs) serve as powerful platforms for rapid prototyping of genetic circuits, metabolic pathways, and enzyme functionality. They offer numerous advantages, including minimizing unwanted metabolic interference, precise control of reaction conditions, reduced labor, and shorter Design-Build-Test-Learn cycles. Additionally, the introduction of in vitro compartmentalization strategies in CFSs enables ultra-high-throughput screening in physically separated spaces, which significantly enhances prototyping efficiency. This review highlights the latest examples of using CFS to overcome prototyping limitations in living cells with a focus on rapid prototyping, particularly regarding gene regulation, enzymes, and multienzymatic reactions in bacteria. Finally, this review evaluates CFSs as a versatile prototyping platform and discusses its future applications, emphasizing its potential for producing high-value chemicals through microbial biosynthesis.

Synthetic biology meets Aspergillus: engineering strategies for next-generation organic acid production

Organic acids constitute a vital category of chemical raw materials. They have extensive applications in industries such as polymers, food, and pharmaceuticals. Currently, industrial production predominantly relies on microbial fermentation. Aspergillus, due to its unique metabolic capabilities, has become an important microbial resource for organic acid production. In recent years, there has been a growing emphasis on genetic engineering of Aspergillus to increase its yield of organic acids. This review provides a comprehensive overview of the current advancement and future directions in the application of genetic engineering techniques to enhance organic production in Aspergillus, specifically highlighting achievement in reconstructing metabolic pathways for desired products, eliminating by-products, modifying regulatory pathways, and engineering mycelial morphology. Furthermore, this review also focuses on the strategies and genetic tools applied in Aspergillus, with particular emphasis on the potential applications and challenges of CRISPR-based biosensors in organic acid fermentation. By providing insights into these developments, we aim to offer theoretical guidance and innovative approaches for enhancing the efficiency of Aspergillus strains in industrial organic acid production.

Directed evolution of highly sensitive and stringent choline-induced gene expression controllers

Gene expression controllers are useful tools for microbial production of recombinant proteins and valued bio-based chemicals. Despite its usefulness, they have rarely been applied to the practical industrial bioprocess, due to the lack of systems that meets the three requirements: low cost, safety, and tight control, to the inducer molecules. Previously, we have developed the high-spec gene induction system controlled by safe and cheap inducer choline. However, the system requires relatively high concentration (~100 mM) of choline to fully induce the gene under control. In this work, we attempted to drastically improve the sensitivity of this induction system to further reduce the induction costs. To this end, we devised a simple circuit which couples gene induction system with positive-feedback loop (P-loop) of choline importer protein BetT. After the tuning of translation level of BetT (strength of the P-loop) and deletion of endogenous betI (noise sources), highly active yet stringent control of gene expression was achieved using about 100 times less amount of inducer molecules. The choline induction system developed in this study has the lowest basal expression, the lowest choline needed to be activated, and the highest amplitude of induction as the highest available promoter such as those known as PT5 system. With this system, one can tightly control the expression level of genes of interest with negligible cost for inducer molecule, which has been the bottleneck for the application to the large-scale industrial processes.

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.

Fluorescent riboswitch-controlled biosensors for the genome scale analysis of metabolic pathways

Fluorescent detection in cells has been tremendously developed over the years and now benefits from a large array of reporters that can provide sensitive and specific detection in real time. However, the intracellular monitoring of metabolite levels still poses great challenges due to the often complex nature of detected metabolites. Here, we provide a systematic analysis of thiamin pyrophosphate (TPP) metabolism in Escherichia coli by using a TPP-sensing riboswitch that controls the expression of the fluorescent gfp reporter. By comparing different combinations of reporter fusions and TPP-sensing riboswitches, we determine key elements that are associated with strong TPP-dependent sensing. Furthermore, by using the Keio collection as a proxy for growth conditions differing in TPP levels, we perform a high-throughput screen analysis using high-density solid agar plates. Our study reveals several genes whose deletion leads to increased or decreased TPP levels. The approach developed here could be applicable to other riboswitches and reporter genes, thus representing a framework onto which further development could lead to highly sophisticated detection platforms allowing metabolic screens and identification of orphan riboswitches.

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.

Design of a dual-responding genetic circuit for high-throughput identification of L-threonine-overproducing Escherichia coli

L-threonine is a crucial amino acid that is extensively employed in the realms of food, animal feed and pharmaceuticals. Unfortunately, the lack of an appropriate biosensor has hindered the establishment of a robust high-throughput screening (HTS) system for the identification of the desired strains from random mutants. In this study, a dual-responding genetic circuit that capitalizes on the L-threonine inducer-like effect, the L-threonine riboswitch, and a signal amplification system was designed for the purpose of screening L-threonine overproducers. This platform effectively enhanced the performance of the enzyme and facilitated the identification of high L-threonine-producing strains from a random mutant library. Consequently, pathway optimization and directed evolution of the key enzyme enhanced L-threonine production by 4 and 7-fold, respectively. These results demonstrate the potential of biosensor design for dynamic metabolite detection and offer a promising tool for HTS and metabolic regulation for the development of L-threonine-hyperproducing strains.

Enhancing (2S)-naringenin production in Saccharomyces cerevisiae by high-throughput screening method based on ARTP mutagenesis

(2S)-Naringenin, a dihydro-flavonoid, serves as a crucial precursor for flavonoid synthesis due to its extensive medicinal values and physiological functions. A pathway for the synthesis of (2S)-naringenin from glucose has previously been constructed in Saccharomyces cerevisiae through metabolic engineering. However, this synthetic pathway of (2S)-naringenin is lengthy, and the genes involved in the competitive pathway remain unknown, posing challenges in significantly enhancing (2S)-naringenin production through metabolic modification. To address this issue, a novel high-throughput screening (HTS) method based on color reaction combined with a random mutagenesis method called atmospheric room temperature plasma (ARTP), was established in this study. Through this approach, a mutant (B7-D9) with a higher titer of (2S)-naringenin was obtained from 9600 mutants. Notably, the titer was enhanced by 52.3% and 19.8% in shake flask and 5 L bioreactor respectively. This study demonstrates the successful establishment of an efficient HTS method that can be applied to screen for high-titer producers of (2S)-naringenin, thereby greatly improving screening efficiency and providing new insights and solutions for similar product screenings.

Biosensor-guided discovery and engineering of metabolic enzymes

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

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

Organ-on-Chip: Advancing Nutraceutical Testing for Improved Health Outcomes

The recent global wave of organic food consumption and the vitality of nutraceuticals for human health benefits has driven the need for applying scientific methods for phytochemical testing. Advanced in vitro models with greater physiological relevance than conventional in vitro models are required to evaluate the potential benefits and toxicity of nutraceuticals. Organ-on-chip (OOC) models have emerged as a promising alternative to traditional in vitro models and animal testing due to their ability to mimic organ pathophysiology. Numerous studies have demonstrated the effectiveness of OOC models in identifying pharmaceutically relevant compounds and accurately assessing compound-induced toxicity. This review examines the utility of traditional in vitro nutraceutical testing models and discusses the potential of OOC technology as a preclinical testing tool to examine the biomedical potential of nutraceuticals by reducing the need for animal testing. Exploring the capabilities of OOC models in carrying out plant-based bioactive compounds can significantly contribute to the authentication of nutraceuticals and drug discovery and validate phytochemicals medicinal characteristics. Overall, OOC models can facilitate a more systematic and efficient assessment of nutraceutical compounds while overcoming the limitations of current traditional in vitro models.

Recent Progress in Metabolic Engineering of Escherichia coli for the Production of Various C4 and C5-Dicarboxylic Acids

As an alternative to petrochemical synthesis, well-established industrial microbes, such as Escherichia coli, are employed to produce a wide range of chemicals, including dicarboxylic acids (DCAs), which have significant potential in diverse areas including biodegradable polymers. The demand for biodegradable polymers has been steadily rising, prompting the development of efficient production pathways on four- (C4) and five-carbon (C5) DCAs derived from central carbon metabolism to meet the increased demand via the biosynthesis. In this context, E. coli is utilized to produce these DCAs through various metabolic engineering strategies, including the design or selection of metabolic pathways, pathway optimization, and enhancement of catalytic activity. This review aims to highlight the recent advancements in metabolic engineering techniques for the production of C4 and C5 DCAs in E. coli.

Molecular Complementarity of Proteomimetic Materials for Target-Specific Recognition and Recognition-Mediated Complex Functions

As biomolecules essential for sustaining life, proteins are generated from long chains of 20 different α-amino acids that are folded into unique 3D structures. In particular, many proteins have molecular recognition functions owing to their binding pockets, which have complementary shapes, charges, and polarities for specific targets, making these biopolymers unique and highly valuable for biomedical and biocatalytic applications. Based on the understanding of protein structures and microenvironments, molecular complementarity can be exhibited by synthesizable and modifiable materials. This has prompted researchers to explore the proteomimetic potentials of a diverse range of materials, including biologically available peptides and oligonucleotides, synthetic supramolecules, inorganic molecules, and related coordination networks. To fully resemble a protein, proteomimetic materials perform the molecular recognition to mediate complex molecular functions, such as allosteric regulation, signal transduction, enzymatic reactions, and stimuli-responsive motions; this can also expand the landscape of their potential bio-applications. This review focuses on the recognitive aspects of proteomimetic designs derived for individual materials and their conformations. Recent progress provides insights to help guide the development of advanced protein mimicry with material heterogeneity, design modularity, and tailored functionality. The perspectives and challenges of current proteomimetic designs and tools are also discussed in relation to future applications.

High-throughput screening of microbial strains in large-scale microfluidic droplets

The transformation of engineered microbial cells is a pivotal link in green biomanufacturing. Its distinctive research application involves genetic modification of microbial chassis to impart targeted traits and functions for effective synthesis of the desired products. Microfluidics, as an emerging complementary solution, focuses on controlling and manipulating fluid in channels at the microscopic scale. One of its subcategories is droplet-based microfluidics (DMF), which can generate discrete droplets using immiscible multiphase fluids at kHz frequencies. To date, droplet microfluidics has been successfully applied to a variety of microbes, including bacteria, yeast, and filamentous fungi, and the detection of massive metabolites of strain products, such as polypeptides, enzymes, and lipids, has been realized. In summary, we firmly believe that droplet microfluidics has evolved into a powerful technology that will pave the way for high-throughput screening of engineered microbial strains in the green biomanufacturing industry.

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

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

Riboswitch-guided chalcone synthase engineering and metabolic flux optimization for enhanced production of flavonoids

Flavonoids are a group of secondary metabolites from plants that have received attention as high value-added pharmacological substances. Recently, a robust and efficient bioprocess using recombinant microbes has emerged as a promising approach to supply flavonoids. In the flavonoid biosynthetic pathway, the rate of chalcone synthesis, the first committed step, is a major bottleneck. However, chalcone synthase (CHS) engineering was difficult because of high-level conservation and the absence of effective screening tools, which are limited to overexpression or homolog-based combinatorial strategies. Furthermore, it is necessary to precisely regulate the metabolic flux for the optimum availability of malonyl-CoA, a substrate of chalcone synthesis. In this study, we engineered CHS and optimized malonyl-CoA availability to establish a platform strain for naringenin production, a key molecular scaffold for various flavonoids. First, we engineered CHS through synthetic riboswitch-based high-throughput screening of rationally designed mutant libraries. Consequently, the catalytic efficiency (k_cat/K_m) of the optimized CHS enzyme was 62% higher than that of the wild-type enzyme. In addition to CHS engineering, we designed genetic circuits using transcriptional repressors to fine-tune the malonyl-CoA availability. The best mutant with synergistic effects of the engineered CHS and the optimized genetic circuit produced 98.71 mg/L naringenin (12.57 mg naringenin/g glycerol), which is the highest naringenin concentration and yield from glycerol in similar culture conditions reported to date, a 2.5-fold increase compared to the parental strain. Overall, this study provides an effective strategy for efficient production of flavonoids.

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.

Enzyme directed evolution using genetically encodable biosensors

Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.

Recent advances in high-throughput metabolic engineering: Generation of oligonucleotide-mediated genetic libraries

The preparation of genetic libraries is an essential step to evolve microorganisms and study genotype-phenotype relationships by high-throughput screening/selection. As the large-scale synthesis of oligonucleotides becomes easy, cheap, and high-throughput, numerous novel strategies have been developed in recent years to construct high-quality oligo-mediated libraries, leveraging state-of-art molecular biology tools for genome editing and gene regulation. This review presents an overview of recent advances in creating and characterizing in vitro and in vivo genetic libraries, based on CRISPR/Cas, regulatory RNAs, and recombineering, primarily for Escherichia coli and Saccharomyces cerevisiae. These libraries' applications in high-throughput metabolic engineering, strain evolution and protein engineering are also discussed.

Transporter-Driven Engineering of a Genetic Biosensor for the Detection and Production of Short-Branched Chain Fatty Acids in Saccharomyces cerevisiae

Biosensors can be used for real-time monitoring of metabolites and high-throughput screening of producer strains. Use of biosensors has facilitated strain engineering to efficiently produce value-added compounds. Following our recent work on the production of short branched-chain fatty acids (SBCFAs) in engineered Saccharomyces cerevisiae, here we harnessed a weak organic acid transporter Pdr12p, engineered a whole-cell biosensor to detect exogenous and intracellular SBCFAs and optimized the biosensor’s performance by varying PDR12 expression. We firstly constructed the biosensor and evaluated its response to a range of short-chain carboxylic acids. Next, we optimized its sensitivity and operational range by deletion and overexpression of PDR12. We found that the biosensor responded to exogenous SBCFAs including isovaleric acid, isobutyric acid and 2-methylbutanoic acid. PDR12 deletion enhanced the biosensor’s sensitivity to isovaleric acid at a low concentration and PDR12 overexpression shifted the operational range towards a higher concentration. Lastly, the deletion of PDR12 improved the biosensor’s sensitivity to the SBCFAs produced in our previously engineered SBCFA-overproducing strain. To our knowledge, our work represents the first study on employing an ATP-binding-cassette transporter to engineer a transcription-factor-based genetic biosensor for sensing SBCFAs in S. cerevisiae. Our findings provide useful insights into SBCFA detection by a genetic biosensor that will facilitate the screening of SBCFA-overproducing strains.

Cheating the Cheater: Suppressing False-Positive Enrichment during Biosensor-Guided Biocatalyst Engineering

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false-positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection) suppresses cheater population enrichment. Furthermore, we show that, if we couple the biosensor-based screen with an orthogonal prescreen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting. Using the approach developed here, we were successfully able to isolate PAL variants with ∼70% higher k_cat after a single sort. These mutants have tremendous potential in phenylketonuria (PKU) treatment and flavonoid production.

Development of Synthetic Riboswitches to Guide the Evolution of Metabolite Production in Microorganisms

The untranslated region (UTR) of prokaryotic mRNA contains riboswitches, which are gene regulating modules. Riboswitches can be used as biosensors to regulate the expression of a gene or an operon depending on the intracellular level of a target molecule and consequently modulate the cellular responses. In evolutionary engineering, riboswitch-based biosensors have been widely applied for high-throughput screening or selection of target phenotypes. Evolutionary approaches can overcome the limitations of rational approaches in metabolic engineering. Previous studies have reported synthetic riboswitches equipped with novel aptamers and marker genes based on a deep understanding of the operation mechanism of the riboswitch. Here, we introduce the development process of novel synthetic riboswitches for applications in metabolic engineering.

Toward the Development of Rapid, Specific, and Sensitive Microfluidic Sensors: A Comprehensive Device Blueprint

Recent advances in nano/microfluidics have led to the miniaturization of surface-based chemical and biochemical sensors, with applications ranging from environmental monitoring to disease diagnostics. These systems rely on the detection of analytes flowing in a liquid sample, by exploiting their innate nature to react with specific receptors immobilized on the microchannel walls. The efficiency of these systems is defined by the cumulative effect of analyte detection speed, sensitivity, and specificity. In this perspective, we provide a fresh outlook on the use of important parameters obtained from well-characterized analytical models, by connecting the mass transport and reaction limits with the experimentally attainable limits of analyte detection efficiency. Specifically, we breakdown when and how the operational (e.g., flow rates, channel geometries, mode of detection, etc.) and molecular (e.g., receptor affinity and functionality) variables can be tailored to enhance the analyte detection time, analytical specificity, and sensitivity of the system (i.e., limit of detection). Finally, we present a simple yet cohesive blueprint for the development of high-efficiency surface-based microfluidic sensors for rapid, sensitive, and specific detection of chemical and biochemical analytes, pertinent to a variety of applications.

Synthetic cellular communication-based screening for strains with improved 3-hydroxypropionic acid secretion

Although cellular secretion is important in industrial biotechnology, its assessment is difficult due to the lack of efficient analytical methods. This study describes a synthetic cellular communication-based microfluidic platform for screening strains with the improved secretion of 3-hydroxypropionic acid (3-HP), an industry-relevant platform chemical. 3-HP-secreting cells were compartmentalized in droplets, with receiving cells equipped with a genetic circuit that converts the 3-HP secretion level into an easily detectable signal. This platform was applied to identify Escherichia coli genes that enhance the secretion of 3-HP. As a result, two genes (setA, encoding a sugar exporter, and yjcO, encoding a Sel1 repeat-containing protein) found by this platform enhance the secretion of 3-HP and its production. Given the increasing design capability for chemical-detecting cells, this platform has considerable potential in identifying efflux pumps for not only 3-HP but also many important chemicals.

Response of root development and nutrient uptake of two chinese cultivars of hybrid rice to nitrogen and phosphorus fertilization in Sichuan Province, China

Background

Chemical fertilization helped modern agriculture in grain yield improvement to ensure food security. The response of chemical fertilization for higher hybrid rice production is highly dependent on optimal fertilization management in paddy fields. To assess such responses, in the current work we examine the yield, root growth, and expression of related genes responsible for stress metabolism of nitrogen (N) and phosphorus (P) in two hybrid-rice cultivars Deyou4727 (D47) and Yixiangyou2115 (Y21).

Methods and results

The experiment followed four nitrogen (N) (N₀, N₆₀, N_120, and N₁₈₀ kg/ha) and phosphorus (P) (P₀, P₆₀, P₉₀, and P₁₂₀ kg/ha) fertilizer levels. The grain yield in D47 was more sensitive to nitrogen application, while Y21 was more sensitive to phosphorus application, which resulted in comparatively higher biomass and yield. Our findings were corroborated by gene expression studies of glutamine synthetase OsGS1;1 and OsGS1;2 and phosphate starvation-related genes PHR1 and SPX, confirming sensitivity to N and P application. The number of roots was less sensitive to nitrogen application in D47 between N₀ and N₆₀, but the overall nutrient response difference was significantly higher due to the deep rooting system as compared to Y21.

Conclusions

The higher yield, high N and P use efficiency, and versatile root growth of D47 make it suitable to reduce unproductive usage of N and P from paddy fields, improving hybrid rice productivity, and environmental safety in the Sichuan basin area of China.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-021-06835-7.

Multi-level rebalancing of the naringenin pathway using riboswitch-guided high-throughput screening

Recombinant microbes have emerged as promising alternatives to natural sources of naringenin-a key molecular scaffold for flavonoids. In recombinant strains, expression levels of the pathway genes should be optimized at both transcription and the translation stages to precisely allocate cellular resources and maximize metabolite production. However, the optimization of the expression levels of naringenin generally relies on evaluating a small number of variants from libraries constructed by varying transcription efficiency only. In this study, we introduce a systematic strategy for the multi-level optimization of biosynthetic pathways. We constructed a multi-level combinatorial library covering both transcription and translation stages using synthetic T7 promoter variants and computationally designed 5'-untranslated regions. Furthermore, we identified improved strains through high-throughput screening based on a synthetic naringenin riboswitch. The most-optimized strain obtained using this approach exhibited a 3-fold increase in naringenin production, compared with the parental strain in which only the transcription efficiency was modulated. Furthermore, in a fed-batch bioreactor, the optimized strain produced 260.2 mg/L naringenin, which is the highest concentration reported to date using glycerol and p-coumaric acid as substrates. Collectively, this work provides an efficient strategy for the expression optimization of the biosynthetic pathways.

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

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

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

Signal amplification and optimization of riboswitch-based hybrid inputs by modular and titratable toehold switches

BACKGROUND

Synthetic biological circuits are widely utilized to control microbial cell functions. Natural and synthetic riboswitches are attractive sensor modules for use in synthetic biology applications. However, tuning the fold-change of riboswitch circuits is challenging because a deep understanding of the riboswitch mechanism and screening of mutant libraries is generally required. Therefore, novel molecular parts and strategies for straightforward tuning of the fold-change of riboswitch circuits are needed.

RESULTS

In this study, we devised a toehold switch-based modulator approach that combines a hybrid input construct consisting of a riboswitch and transcriptional repressor and de-novo-designed riboregulators named toehold switches. First, the introduction of a pair of toehold switches and triggers as a downstream signal-processing module to the hybrid input for coenzyme B12 resulted in a functional riboswitch circuit. Next, several optimization strategies that focused on balancing the expression levels of the RNA components greatly improved the fold-change from 260- to 887-fold depending on the promoter and host strain. Further characterizations confirmed low leakiness and high orthogonality of five toehold switch pairs, indicating the broad applicability of this strategy to riboswitch tuning.

CONCLUSIONS

The toehold switch-based modulator substantially improved the fold-change compared to the previous sensors with only the hybrid input construct. The programmable RNA-RNA interactions amenable to in silico design and optimization can facilitate further development of RNA-based genetic modulators for flexible tuning of riboswitch circuitry and synthetic biosensors.

A multiplexed, automated evolution pipeline enables scalable discovery and characterization of biosensors

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Here, using aptamer-coupled ribozyme libraries and a ribozyme regeneration method, de novo rapid in vitro evolution of RNA biosensors (DRIVER) enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identify and validate biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors are applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors are also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

CRISPRi-Mediated NIMPLY Logic Gate for Fine-Tuning the Whole-Cell Sensing toward Simple Urine Glucose Detection

Whole-cell biosensors have been regarded as a prominent alternative to chemical and physical biosensors due to their renewability, environmental friendliness, and biocompatibility. However, there is still a lack of noninvasive measurements of urine glucose, which plays a vital role in monitoring the risk of diabetes in the healthcare system, via whole-cell biosensors. In this study, we characterized a glucose-inducible promoter and further enhanced the sensing performance using three genetic effectors, which encompassed ribozyme regulator (RiboJ), clustered regularly interspaced short palindromic repeat interference (CRISPRi), and plasmid-based T7RNA polymerase (PDT7), to develop the noninvasive glucose biosensor by fluorescent signal. As a result, RiboJ increased dynamic range to 2989 au, but declined signal-to-noise (S/N) to 1.59, while CRISPRi-mediated NIMPLY gate intensified both dynamic range to 5720 au and S/N to 4.58. The use of single PDT7 orthogonal with T7 promoter in cells (i.e., P strain) achieved a 44 180 au of dynamic range with S/N at 3.08. By coupling the PDT7 and NIMPLY-mediated CRISPRi, we constructed an optimum PIGAS strain with the highest S/N value of 4.95. Finally, we adopted the synthetic bacteria into a microdevice to afford an integrative and portable system for daily urine glucose inspection, which would be an alternative approach for medical diagnosis in the future.

Does co-expression of Yarrowia lipolytica genes encoding Yas1p, Yas2p and Yas3p make a potential alkane-responsive biosensor in Saccharomyces cerevisiae?

Alkane-based biofuels are desirable to produce at a commercial scale as these have properties similar to current petroleum-derived transportation fuels. Rationally engineering microorganisms to produce a desirable compound, such as alkanes, is, however, challenging. Metabolic engineers are therefore increasingly implementing evolutionary engineering approaches combined with high-throughput screening tools, including metabolite biosensors, to identify productive cells. Engineering Saccharomyces cerevisiae to produce alkanes could be facilitated by using an alkane-responsive biosensor, which can potentially be developed from the native alkane-sensing system in Yarrowia lipolytica, a well-known alkane-assimilating yeast. This putative alkane-sensing system is, at least, based on three different transcription factors (TFs) named Yas1p, Yas2p and Yas3p. Although this system is not fully elucidated in Y. lipolytica, we were interested in evaluating the possibility of translating this system into an alkane-responsive biosensor in S. cerevisiae. We evaluated the alkane-sensing system in S. cerevisiae by developing one sensor based on the native Y. lipolytica ALK1 promoter and one sensor based on the native S. cerevisiae CYC1 promoter. In both systems, we found that the TFs Yas1p, Yas2p and Yas3p do not seem to act in the same way as these have been reported to do in their native host. Additional analysis of the TFs suggests that more knowledge regarding their mechanism is needed before a potential alkane-responsive sensor based on the Y. lipolytica system can be established in S. cerevisiae.

Synthetic protein switches: Combinatorial linker engineering with iFLinkC

Linker engineering constitutes a critical, yet frequently underestimated aspect in the construction of synthetic protein switches and sensors. Notably, systematic strategies to engineer linkers by predictive means remain largely elusive to date. This is primarily due to our insufficient understanding how the biophysical properties that underlie linker functions mediate the conformational transitions in artificially engineered protein switches and sensors. The construction of synthetic protein switches and sensors therefore heavily relies on experimental trial-and-error. Yet, methods for effectively generating linker diversity at the genetic level are scarce. Addressing this technical shortcoming, iterative functional linker cloning (iFLinkC) enables the combinatorial assembly of linker elements with functional domains from sequence verified repositories that are developed and stored in-house. The assembly process is highly scalable and given its recursive nature generates linker diversity in a combinatorial and exponential fashion based on a limited number of linker elements.

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.

Application of combinatorial optimization strategies in synthetic biology

In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.

Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms

Low yield and low titer of natural products are common issues in natural product biosynthesis through microbial cell factories. One effective way to resolve such bottlenecks is to design genetic biosensors to monitor and regulate the biosynthesis of target natural products. In this review, we evaluate the most recent advances in the design of genetic biosensors for natural product biosynthesis in microorganisms. In particular, we examine strategies for selection of genetic parts and construction principles for the design and evaluation of genetic biosensors. We also review the latest applications of transcription factor- and riboswitch-based genetic biosensors in natural product biosynthesis. Lastly, we discuss challenges and solutions in designing genetic biosensors for the biosynthesis of natural products in microorganisms.

Sensitive and Rapid Phenotyping of Microbes With Soluble Methane Monooxygenase Using a Droplet-Based Assay

Methanotrophs with soluble methane monooxygenase (sMMO) show high potential for various ecological and biotechnological applications. Here, we developed a high throughput method to identify sMMO-producing microbes by integrating droplet microfluidics and a genetic circuit-based biosensor system. sMMO-producers and sensor cells were encapsulated in monodispersed droplets with benzene as the substrate and incubated for 5 h. The sensor cells were analyzed as the reporter for phenol-sensitive transcription activation of fluorescence. Various combinations of methanotrophs and biosensor cells were investigated to optimize the performance of our droplet-integrated transcriptional factor biosensor system. As a result, the conditions to ensure sMMO activity to convert the starting material, benzene, into phenol, were determined. The biosensor signals were sensitive and quantitative under optimal conditions, showing that phenol is metabolically stable within both cell species and accumulates in picoliter-sized droplets, and the biosensor cells are healthy enough to respond quantitatively to the phenol produced. These results show that our system would be useful for rapid evaluation of phenotypes of methanotrophs showing sMMO activity, while minimizing the necessity of time-consuming cultivation and enzyme preparation, which are required for conventional analysis of sMMO activity.

Quantification, regulation and production of 5-aminolevulinic acid by green fluorescent protein in recombinant Escherichia coli

5-Aminolevulinic acid (5-ALA) is an unnatural amino acid and has been approved as a biodegradable, non-toxic pesticide and herbicide with applications in sustainable agriculture. 5-ALA can also be applied for cancer targeting via tumor localization and photodynamic therapy. Herein, we developed a feasible quantification, regulation and production method of 5-ALA in Escherichia coli is based on the chimera of 5-ALA synthetase from Rhodobacter sphaeroides (RshemA) and super-fold green fluorescent protein (sfGFP) under the control of dual promoters/double plasmids. 5-ALA production based on quantification with the reporter sfGFP was unsuccessfully for the RshemA-sfGFP fusion protein owing to a steric hindrance effect, but was effective using dual constitutive promoters (i.e., J23100 and PLacI) for RshemA and sfGFP independently. Moreover, a simple quantification method based on the linear relationship between 5-ALA concentration and the change in sfGFP intensity was calculated with the Hill equation according to the results of dual plasmids which composed of RshemA-threonine/homoserine exporter (RhtA) and the sensing plasmid pSU-T7-sfGFP. Compared with the conventional detection method for 5-ALA using Ehrlich's reagent, our proposed method is advantages in effectiveness, real-time detection, and outstanding sensitivity. Finally, the highest yield of 5-ALA was obtained in E. coli D2TT strain, reaching 2.46 g/L of 5-ALA produced in a 2.5-L baffle flask fermentation. Hence, this approach shows strong potential for improving 5-ALA production with appropriate regulation and detection based on the fluorescent signal.

Improvement of cis,cis-Muconic Acid Production in Saccharomyces cerevisiae through Biosensor-Aided Genome Engineering

Muconic acid is a potential platform chemical for the production of nylon, polyurethanes, and terephthalic acid. It is also an attractive functional copolymer in plastics due to its two double bonds. At this time, no economically viable process for the production of muconic acid exists. To harness novel genetic targets for improved production of cis,cis-muconic acid (CCM) in the yeast Saccharomyces cerevisiae, we employed a CCM-biosensor coupled to GFP expression with a broad dynamic response to screen UV-mutagenesis libraries of CCM-producing yeast. Via fluorescence activated cell sorting we identified a clone Mut131 with a 49.7% higher CCM titer and 164% higher titer of biosynthetic intermediate-protocatechuic acid (PCA). Genome resequencing of the Mut131 and reverse engineering identified seven causal missense mutations of the native genes (PWP2, EST2, ATG1, DIT1, CDC15, CTS2, and MNE1) and a duplication of two CCM biosynthetic genes, encoding dehydroshikimate dehydratase and catechol 1,2-dioxygenase, which were not recognized as flux controlling before. The Mut131 strain was further rationally engineered by overexpression of the genes encoding for PCA decarboxylase and AROM protein without shikimate dehydrogenase domain (Aro1p^ΔE), and by restoring URA3 prototrophy. The resulting engineered strain produced 20.8 g/L CCM in controlled fed-batch fermentation, with a yield of 66.2 mg/g glucose and a productivity of 139 mg/L/h, representing the highest reported performance metrics in a yeast for de novo CCM production to date and the highest production of an aromatic compound in yeast. The study illustrates the benefit of biosensor-based selection and brings closer the prospect of biobased muconic acid.

New Insight into Plasmid-Driven T7 RNA Polymerase in Escherichia coli and Use as a Genetic Amplifier for a Biosensor

T7 RNA polymerase (T7RNAP) and T7 promoter are powerful genetic components, thus a plasmid-driven T7 (PDT7) genetic circuit could be broadly applied for synthetic biology. However, the limited knowledge of the toxicity and instability of such a system still restricts its application. Herein, we constructed 16 constitutive genetic circuts of PDT7 and investigated the orthogonal effects in toxicity and instability. The T7 toxicity was elucidated from the construction processes and cell growth characterization, showing the importance of optimal orthogonality for PDT7. Besides, a protein analysis was performed to validate how the T7 system affected cell metabolism and led to the instability. The application of constitutive PDT7 in functional protein expressions, including carbonic anhydrase, lysine decarboxylase, and 5-ALA synthetase was demonstrated. Furthermore, PDT7 working as a genetic amplifier had been designed for E. coli cell-based biosensors, which illustrated the opportunities in the future of PDT7 used in synthetic biology.

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.

Auxotrophic Selection Strategy for Improved Production of Coenzyme B12 in Escherichia coli

The production of coenzyme B₁₂ using well-characterized microorganisms, such as Escherichia coli, has recently attracted considerable attention to meet growing demands of coenzyme B₁₂ in various applications. In the present study, we designed an auxotrophic selection strategy and demonstrated the enhanced production of coenzyme B₁₂ using a previously engineered coenzyme B₁₂-producing E. coli strain. To select a high producer, the coenzyme B₁₂-independent methionine synthase (metE) gene was deleted in E. coli, thus limiting its methionine synthesis to only that via coenzyme B₁₂-dependent synthase (encoded by metH). Following the deletion of metE, significantly enhanced production of the specific coenzyme B₁₂ validated the coenzyme B₁₂-dependent auxotrophic growth. Further precise tuning of the auxotrophic system by varying the expression of metH substantially increased the cell biomass and coenzyme B₁₂ production, suggesting that our strategy could be effectively applied to E. coli and other coenzyme B₁₂-producing strains.

•

The auxotrophic selection strategy was applied to coenzyme B₁₂ production

•

Coenzyme B₁₂-independent methionine synthase was deleted for auxotroph system

•

The auxotrophic strategy could significantly enhance the coenzyme B₁₂ production

•

Optimization of the auxotroph system further enhanced the coenzyme B₁₂ production

High-Throughput Screening Technology in Industrial Biotechnology

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

Air Plasma-Enhanced Covalent Functionalization of Poly(methyl methacrylate): High-Throughput Protein Immobilization for Miniaturized Bioassays

Miniaturized systems, such as integrated microarray and microfluidic devices, are constantly being developed to satisfy the growing demand for sensitive and high-throughput biochemical screening platforms. Owing to its recyclability, and robust mechanical and optical properties, poly(methyl methacrylate) (PMMA) has become the most sought after material for the large-scale fabrication of these platforms. However, the chemical inertness of PMMA entails the use of complex chemical surface treatments for covalent immobilization of proteins. In addition to being hazardous and incompatible for large-scale operations, conventional biofunctionalization strategies pose high risks of compromising the biomolecular conformations, as well as the stability of PMMA. By exploiting radio frequency (RF) air plasma and standard 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide (EDC) and N-hydroxysuccinimide (NHS) chemistry in tandem, we demonstrate a simple yet scalable PMMA functionalization strategy for covalent immobilization (chemisorption) of proteins, such as green fluorescent protein (GFP), while preserving the structural integrities of the proteins and PMMA. The surface density of chemisorbed GFP is shown to be highly dependent on the air plasma energy, initial GFP concentration, and buffer pH, where a maximum GFP surface density of 4 × 10^-7 mol/m² is obtained, when chemisorbed on EDC-NHS-activated PMMA exposed to 27 kJ of air plasma, at pH 7.4. Furthermore, antibody-binding studies validate the preserved biofunctionality of the chemisorbed GFP molecules. Finally, the coupled air plasma and EDC-NHS PMMA biofunctionalization strategy is used to fabricate microfluidic antibody assay devices to detect clinically significant concentrations of Chlamydia trachomatis specific antibodies. By coupling our scalable and tailored air plasma-enhanced PMMA biofunctionalization strategy with microfluidics, we elucidate the potential of fabricating sensitive, reproducible, and sustainable high-throughput protein screening systems, without the need for harsh chemicals and complex instrumentation.

Recent Advances in Synthetic Biology Approaches to Optimize Production of Bioactive Natural Products in Actinobacteria

Actinobacteria represent one of the most fertile sources for the discovery and development of natural products (NPs) with medicinal and industrial importance. However, production titers of actinobacterial NPs are usually low and require optimization for compound characterization and/or industrial production. In recent years, a wide variety of novel enabling technologies for engineering actinobacteria have been developed, which have greatly facilitated the optimization of NPs biosynthesis. In this review, we summarize the recent advances of synthetic biology approaches for overproducing desired drugs, as well as for the discovery of novel NPs in actinobacteria, including dynamic metabolic regulation based on metabolite-responsive promoters or biosensors, multi-copy chromosomal integration of target biosynthetic gene clusters (BGCs), promoter engineering-mediated rational BGC refactoring, and construction of genome-minimized Streptomyces hosts. Integrated with metabolic engineering strategies developed previously, these novel enabling technologies promise to facilitate industrial strain improvement process and genome mining studies for years to come.