Insulated transcriptional elements enable precise design of genetic circuits

Expanding the σ54-dependent transcription process with orthogonal designs

The significance of orthogonal gene expression lies in its ability to ensure consistent and predictable operation of genetic pathways in synthetic biology. In bacteria, σ factors are responsible for promoter recognition, where the recognition pattern of σ54 is distinct from that of σ70. Moreover, σ54-dependent promoters require bacterial enhancer-binding proteins (bEBPs) for transcription initiation, which are stringently regulated and strongly activated. Thus, σ54 appears to be a promising candidate for orthogonal designs. In this study, through knowledge-based screening and rewiring of the RpoN box in σ54, together with its partnered promoter, we identified three sets of orthogonal expression systems based on σ54-R456H, R456Y, and R456L, with different promoter preferences and ideal mutual orthogonality toward each other and the native σ54. The orthogonality is transferable, as specific transcription via σ54-R456H was demonstrated in three non-model bacteria. When combined with different bEBPs, the system can be employed to control orthogonal downstream output in response to environmental or chemical signals. The orthogonal σ54 factors proved to be capable of orthogonalizing complex biological pathways and genetic circuits. Therefore, the orthogonal transcription system will contribute to the expansion of synthetic biology toolkits, thereby providing reliable and diversified gene expression in a wide range of hosts.

Developing Quorum Sensing-Based Collaborative Dynamic Control System in Halomonas TD01

Dynamic control exhibits increasing significance in microbial cell factory engineering by precisely manipulating gene expression over time and levels. However, the practical uses of most dynamic control tools still remain challenging because of poor scale-up robustness, especially for non-model chassis. Herein, a quorum sensing (QS)-based collaborative dynamic control system is constructed in Halomonas TD by regrouping two orthogonal quorum-sensing modules into two cell types, namely cell-A harboring cinR-luxI and cell-B harboring luxR-cinI together with sfGFP driven by Pcin and Plux promoters, respectively. Effective gene expression control with over 15-time dynamic foldchange is achieved by mixing cells A and B at different ratios and time points in a lab-scale fed-batch study. Besides, dynamic inhibitory and amplified control is further developed by cascading CRISPRi/dCas9 system and MmP1 RNA polymerase, respectively, yielding up to 80% repression efficiency and 30-time amplification foldchange under high cell density fermentation. Moreover, 500 mg L-1 indigo and 4.7 g L-1 superoxide dismutase (SOD) are obtained by engineered Halomonas using QS-based control tools in the fed-batch study, showing 1.5- and 1.0-fold higher, respectively, than the yields by recombinants induced by IPTG. This study exemplifies a standardized and streamlined inducer-free dynamic control pattern for metabolic engineering with promising robustness in scale-up fermentation contexts.

Quantitative Characterization of Gene Regulatory Circuits Associated With Fungal Secondary Metabolism to Discover Novel Natural Products

Microbial genetic circuits are vital for regulating gene expression and synthesizing bioactive compounds. However, assessing their strength and timing, especially in multicellular fungi, remains challenging. Here, an advanced microfluidic platform is combined with a mathematical model enabling precise characterization of fungal gene regulatory circuits (GRCs) at the single-cell level. Utilizing this platform, the expression intensity and timing of 30 transcription factor-promoter combinations derived from two representative fungal GRCs, using the model fungus Aspergillus nidulans are determined. As a proof of concept, the selected GRC combination is utilized to successfully refactor the biosynthetic pathways of bioactive molecules, precisely control their production, and activate the expression of the silenced biosynthetic gene clusters (BGCs). This study provides insights into microbial gene regulation and highlights the potential of platform in fungal synthetic biology applications and the discovery of novel natural products.

Assembly of functional microbial ecosystems: from molecular circuits to communities

Microbes compete and cooperate with each other via a variety of chemicals and circuits. Recently, to decipher, simulate, or reconstruct microbial communities, many researches have been engaged in engineering microbiomes with bottom-up synthetic biology approaches for diverse applications. However, they have been separately focused on individual perspectives including genetic circuits, communications tools, microbiome engineering, or promising applications. The strategies for coordinating microbial ecosystems based on different regulation circuits have not been systematically summarized, which calls for a more comprehensive framework for the assembly of microbial communities. In this review, we summarize diverse cross-talk and orthogonal regulation modules for de novo bottom-up assembling functional microbial ecosystems, thus promoting further consortia-based applications. First, we review the cross-talk communication-based regulations among various microbial communities from intra-species and inter-species aspects. Then, orthogonal regulations are summarized at metabolites, transcription, translation, and post-translation levels, respectively. Furthermore, to give more details for better design and optimize various microbial ecosystems, we propose a more comprehensive design-build-test-learn procedure including function specification, chassis selection, interaction design, system build, performance test, modeling analysis, and global optimization. Finally, current challenges and opportunities are discussed for the further development and application of microbial ecosystems.

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Energy Aware Technology Mapping of Genetic Logic Circuits

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits- shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

GOLDBAR: A Framework for Combinatorial Biological Design

With the rise of new DNA part libraries and technologies for assembling DNA, synthetic biologists are increasingly constructing and screening combinatorial libraries to optimize their biological designs. As combinatorial libraries are used to generate data on design performance, new rules for composing biological designs will emerge. Most formal frameworks for combinatorial design, however, do not yet support formal comparison of design composition, which is needed to facilitate automated analysis and machine learning in massive biological design spaces. To address this need, we introduce a combinatorial design framework called GOLDBAR. Compared with existing frameworks, GOLDBAR enables synthetic biologists to intersect and merge the rules for entire classes of biological designs to extract common design motifs and infer new ones. Here, we demonstrate the application of GOLDBAR to refine/validate design spaces for TetR-homologue transcriptional logic circuits, verify the assembly of a partial nif gene cluster, and infer novel gene clusters for the biosynthesis of rebeccamycin. We also discuss how GOLDBAR could be used to facilitate grammar-based machine learning in synthetic biology.

Pangenomic landscapes shape performances of a synthetic genetic circuit across Stutzerimonas species

Engineering identical genetic circuits into different species typically results in large differences in performance due to the unique cellular environmental context of each host, a phenomenon known as the "chassis-effect" or "context-dependency". A better understanding of how genomic and physiological contexts underpin the chassis-effect will improve biodesign strategies across diverse microorganisms. Here, we combined a pangenomic-based gene expression analysis with quantitative measurements of performance from an engineered genetic inverter device to uncover how genome structure and function relate to the observed chassis-effect across six closely related Stutzerimonas hosts. Our results reveal that genome architecture underpins divergent responses between our chosen non-model bacterial hosts to the engineered device. Specifically, differential expression of the core genome, gene clusters shared between all hosts, was found to be the main source of significant concordance to the observed differential genetic device performance, whereas specialty genes from respective accessory genomes were not significant. A data-driven investigation revealed that genes involved in denitrification and components of trans-membrane transporter proteins were among the most differentially expressed gene clusters between hosts in response to the genetic device. Our results show that the chassis-effect can be traced along differences among the most conserved genome-encoded functions and that these differences create a unique biodesign space among closely related species.IMPORTANCEContemporary synthetic biology endeavors often default to a handful of model organisms to host their engineered systems. Model organisms such as Escherichia coli serve as attractive hosts due to their tractability but do not necessarily provide the ideal environment to optimize performance. As more novel microbes are domesticated for use as biotechnology platforms, synthetic biologists are urged to explore the chassis-design space to optimize their systems and deliver on the promises of synthetic biology. The consequences of the chassis-effect will therefore only become more relevant as the field of biodesign grows. In our work, we demonstrate that the performance of a genetic device is highly dependent on the host environment it operates within, promoting the notion that the chassis can be considered a design variable to tune circuit function. Importantly, our results unveil that the chassis-effect can be traced along similarities in genome architecture, specifically the shared core genome. Our study advocates for the exploration of the chassis-design space and is a step forward to empowering synthetic biologists with knowledge for more efficient exploration of the chassis-design space to enable the next generation of broad-host-range synthetic biology.

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

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

Emergent digital bio-computation through spatial diffusion and engineered bacteria

Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here we present work on the design of bacterial computers using spatial patterning to process information in the form of diffusible morphogen-like signals. We demonstrate, mathematically and experimentally, that single, modular, colonies can perform simple digital logic, and that complex functions can be built by combining multiple colonies, removing the need for further genetic engineering. We extend our experimental system to incorporate sender colonies as morphogen sources, demonstrating how one might integrate different biochemical inputs. Our approach will open up ways to perform biological computation, with applications in bioengineering, biomaterials and biosensing. Ultimately, these computational bacterial communities will help us explore information processing in natural biological systems.

Genetic Parts and Enabling Tools for Biocircuit Design

Synthetic biology aims to engineer biological systems for customized tasks through the bottom-up assembly of fundamental building blocks, which requires high-quality libraries of reliable, modular, and standardized genetic parts. To establish sets of parts that work well together, synthetic biologists created standardized part libraries in which every component is analyzed in the same metrics and context. Here we present a state-of-the-art review of the currently available part libraries for designing biocircuits and their gene expression regulation paradigms at transcriptional, translational, and post-translational levels in Escherichia coli. We discuss the necessary facets to integrate these parts into complex devices and systems along with the current efforts to catalogue and standardize measurement data. To better display the range of available parts and to facilitate part selection in synthetic biology workflows, we established biopartsDB, a curated database of well-characterized and useful genetic part and device libraries with detailed quantitative data validated by the published literature.

The dynamic-process characterization and prediction of synthetic gene circuits by dynamic delay model

Differential equation models are widely used to describe genetic regulations, predict multicomponent regulatory circuits, and provide quantitative insights. However, it is still challenging to quantitatively link the dynamic behaviors with measured parameters in synthetic circuits. Here, we propose a dynamic delay model (DDM) which includes two simple parts: the dynamic determining part and the doses-related steady-state-determining part. The dynamic determining part is usually supposed as the delay time but without a clear formula. For the first time, we give the detail formula of the dynamic determining function and provide a method for measuring all parameters of synthetic elements (include 8 activators and 5 repressors) by microfluidic system. Three synthetic circuits were built to show that the DDM can notably improve the prediction accuracy and can be used in various synthetic biology applications.

Customizing cellular signal processing by synthetic multi-level regulatory circuits

As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed "multi-level circuits". The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology.

Structure of the transcription open complex of distinct σI factors

Bacterial σI factors of the σ70-family are widespread in Bacilli and Clostridia and are involved in the heat shock response, iron metabolism, virulence, and carbohydrate sensing. A multiplicity of σI paralogues in some cellulolytic bacteria have been shown to be responsible for the regulation of the cellulosome, a multienzyme complex that mediates efficient cellulose degradation. Here, we report two structures at 3.0 Å and 3.3 Å of two transcription open complexes formed by two σI factors, SigI1 and SigI6, respectively, from the thermophilic, cellulolytic bacterium, Clostridium thermocellum. These structures reveal a unique, hitherto-unknown recognition mode of bacterial transcriptional promoters, both with respect to domain organization and binding to promoter DNA. The key characteristics that determine the specificities of the σI paralogues were further revealed by comparison of the two structures. Consequently, the σI factors represent a distinct set of the σ70-family σ factors, thus highlighting the diversity of bacterial transcription.

A novel toolbox for precise regulation of gene expression and metabolic engineering in Bacillus licheniformis

Despite industrial bio-manufacturing progress using Bacillus licheniformis, the absence of a well-characterized toolbox allowing precise regulation of multiple genes limits its expansion for basic research and application. Here, a novel gene expression toolbox (GET) was developed for precise regulation of gene expression and high-level production of 2-phenylethanol. Firstly, we established a novel promoter core region mosaic combination model to combine, characterize and analyze different core regions. Characterization and orthogonal design of promoter ribbons allowed convenient construction of an adaptable and robust GET, gene gfp expression intensity was 0.64%-16755.77%, with a dynamic range of 2.61 × 104 times, which is the largest regulatory range of GET in Bacillus based on modification of promoter P43. Then we verified the protein and species universality of GET using different proteins expressed in B. licheniformis and Bacillus subtilis. Finally, the GET for 2-phenylethanol metabolic breeding, resulting in a plasmid-free strain producing 6.95 g/L 2-phenylethanol with a yield and productivity of 0.15 g/g glucose and 0.14 g/L/h, respectively, the highest de novo synthesis yield of 2-phenylethanol reported. Taken together, this is the first report elucidating the impact of mosaic combination and tandem of multiple core regions to initiate transcription and improve the output of proteins and metabolites, which provides strong support for gene regulation and diversified product production in Bacillus.

Trumpet is an operating system for simple and robust cell-free biocomputing

Biological computation is becoming a viable and fast-growing alternative to traditional electronic computing. Here we present a biocomputing technology called Trumpet: Transcriptional RNA Universal Multi-Purpose GatE PlaTform. Trumpet combines the simplicity and robustness of the simplest in vitro biocomputing methods, adding signal amplification and programmability, while avoiding common shortcomings of live cell-based biocomputing solutions. We have demonstrated the use of Trumpet to build all universal Boolean logic gates. We have also built a web-based platform for designing Trumpet gates and created a primitive processor by networking several gates as a proof-of-principle for future development. The Trumpet offers a change of paradigm in biocomputing, providing an efficient and easily programmable biological logic gate operating system.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system

Context-dependency of mammalian transcriptional elements has hindered the quantitative investigation of multigene expression stoichiometry and its biological functions. Here, we describe a host- and local DNA context-independent transcription system to gradually fine-tune single and multiple gene expression with predictable stoichiometries. The mammalian transcription system is composed of a library of modular and programmable promoters from bacteriophage and its cognate RNA polymerase (RNAP) fused to a capping enzyme. The relative expression of single genes is quantitatively determined by the relative binding affinity of the RNAP to the promoters, while multigene expression stoichiometry is predicted by a simple biochemical model with resource competition. We use these programmable and modular promoters to predictably tune the expression of three components of an influenza A virus-like particle (VLP). Optimized stoichiometry leads to a 2-fold yield of intact VLP complexes. The host-independent orthogonal transcription system provides a platform for dose-dependent control of multiple protein expression which may be applied for advanced vaccine engineering, cell-fate programming and other therapeutic applications.

The Topological Characteristics of Biological Ratio-Sensing Networks

Ratio sensing is a fundamental biological function observed in signal transduction and decision making. In the synthetic biology context, ratio sensing presents one of the elementary functions for cellular multi-signal computation. To investigate the mechanism of the ratio-sensing behavior, we explored the topological characteristics of biological ratio-sensing networks. With exhaustive enumeration of three-node enzymatic and transcriptional regulatory networks, we found that robust ratio sensing was highly dependent on network structure rather than network complexity. Specifically, a set of seven minimal core topological structures and four motifs were deduced to be capable of robust ratio sensing. Further investigations on the evolutionary space of robust ratio-sensing networks revealed highly clustered domains surrounding the core motifs which suggested their evolutionary plausibility. Our study revealed the network topological design principles of ratio-sensing behavior and provided a design scheme for constructing regulatory circuits with ratio-sensing behavior in synthetic biology.

Design and Analysis of Massively Parallel Reporter Assays Using FORECAST

Machine learning is revolutionizing molecular biology and bioengineering by providing powerful insights and predictions. Massively parallel reporter assays (MPRAs) have emerged as a particularly valuable class of high-throughput technique to support such algorithms. MPRAs enable the simultaneous characterization of thousands or even millions of genetic constructs and provide the large amounts of data needed to train models. However, while the scale of this approach is impressive, the design of effective MPRA experiments is challenging due to the many factors that can be varied and the difficulty in predicting how these will impact the quality and quantity of data obtained. Here, we present a computational tool called FORECAST, which can simulate MPRA experiments based on fluorescence-activated cell sorting and subsequent sequencing (commonly referred to as Flow-seq or Sort-seq experiments), as well as carry out rigorous statistical estimation of construct performance from this type of experimental data. FORECAST can be used to develop workflows to aid the design of MPRA experiments and reanalyze existing MPRA data sets.

Autoinduction Expression Modules for Regulating Gene Expression in Bacillus subtilis

Although quorum sensing (QS) promoters that can autonomously activate gene expression have been identified and engineered in Bacillus subtilis, researchers focus on quantifying individual promoters while ignoring the interaction between other genetic regulatory elements. Here, we constructed the autoinduction expression modules consisting of promoters responsive to QS ComQXPA, ribosome binding sites (RBSs), and terminators. Using superfolder green fluorescent protein (sfGFP) as a reporter gene, three individual element libraries were generated from 945 promoters, 12,000 RBSs, and 425 terminators by random mutation, de novo design, and database mining strategies, respectively. Then, the efficiency of three libraries in regulating gene expression was further enhanced by engineering the core region of each optimal element. After hybridizing the element libraries, the generated expression modules exhibited a 627-fold range in regulating gene expression without significantly affecting the autoinduction initiation. Here, the hybrid modules with broad expression strength may benefit the application of QS-based autoinduction systems in B. subtilis.

Two-Layered Microfluidic Devices for High-Throughput Dynamic Analysis of Synthetic Gene Circuits in E. coli

Escherichia coli is a common chassis for synthetic gene circuit studies. In addition to the dose-response of synthetic gene circuits, the analysis of dynamic responses is also an important part of the future design of more complicated synthetic systems. Recently, microfluidic-based methods have been widely used for the analysis of gene expression dynamics. Here, we established a two-layered microfluidic platform for the systematic characterization of synthetic gene circuits (eight strains in eight different culture environments could be observed simultaneously with a 5 min time resolution). With this platform, both dose responses and dynamic responses with a high temporal resolution could be easily derived for further analysis. A controlled environment ensures the stability of the bacterial growth rate, excluding changes in gene expression dynamics caused by changes of the growth dilution rate. The precise environmental switch and automatic micrograph shooting ensured that there was nearly no time lag between the inducer addition and the data recording. We studied four four-node incoherent-feedforward-loop (IFFL) networks with different operators using this device. The experimental results showed that as the effect of inhibition increased, two of the IFFL networks generated pulselike dynamic gene expressions in the range of the inducer concentrations, which was different from the dynamics of the two other circuits with only a simple pattern of rising to the platform. Through fitting the dose-response curves and the dynamic response curves, corresponding parameters were derived and introduced to a simple model that could qualitatively explain the generation of pulse dynamics.

Minimization and optimization of α-amylase terminator for heterologous protein production in Bacillus licheniformis

Terminators serve as the regulatory role in gene transcription termination; however, few researches about terminator optimization have been conducted, which leads to the lack of available and universal terminator for gene expression regulation in Bacillus. To solve this problem and expand synthetic biology toolbox of Bacillus licheniformis, the terminator T1 of endogenous α-amylase gene (amyL) was characterized in this research, with a termination efficiency of 87.81%. Then, we explored and optimized the termination strength of terminator T1 from four aspects: the distance between stop codon and terminator, GC content at the bottom of stem structure, loop size, and U-tract length, and the best terminator T24 was attained by combination optimization strategy, which termination efficiency was increased to 97.97%, better than the commonly used terminator T7 (T7P) from Escherichia coli. Finally, terminator T24 was applied to protein expression, which, respectively, led to 33.00%, 25.93%, and 11.78% increases of green fluorescence intensity, red fluorescence intensity, and keratinase activity, indicating its universality in protein expression. Taken together, this research not only expands a plug-and-play synthetic biology toolbox in B. licheniformis but also provides a reference for the artificial design of versatile intrinsic terminator.

Mitigating Host Burden of Genetic Circuits by Engineering Autonegatively Regulated Parts and Improving Functional Prediction

Mitigating unintended interferences between circuits and host cells is key to realize applications of synthetic regulatory systems both for bacteria and mammalian cells. Here, we demonstrated that growth burden and circuit dysregulation occurred in a concentration-dependent manner for specific transcription factors (CymR*/CymR) in E.coli, and direct negative feedback modules were able to control the concentration of CymR*/CymR, mitigate growth burden, and restore circuit functions. A quantitative design scheme was developed for circuits embedded with autorepression modules. Four key parameters were theoretically identified to determine the performance of autoregulated switches and were experimentally modified by fine-tuning promoter architectures and cooperativity. Using this strategy, we synthesized a number of switches and demonstrated its improvement of product titers and host growth controlling the complex deoxyviolacein biosynthesis pathway. Furthermore, we restored functions of a dysregulated multilayer NOR gate by integrating autorepression modules. Our work provides a blueprint for engineering host-adaptable synthetic systems.

A microfluidic optimal experimental design platform for forward design of cell-free genetic networks

Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

Characterization of cell-free genetic networks is inherently difficult. Here the authors use optimal experimental design and microfluidics to improve characterization, demonstrating modularity and predictability of parts in applied test cases.

Novel switchable ECF sigma factor transcription system for improving thaxtomin A production in Streptomyces

The application of the valuable natural product thaxtomin A, a potent bioherbicide from the potato scab pathogenic Streptomyces strains, has been greatly hindered by the low yields from its native producers. Here, we developed an orthogonal transcription system, leveraging extra-cytoplasmic function (ECF) sigma (σ) factor 17 (ECF17) and its cognate promoter P_ecf17, to express the thaxtomin gene cluster and improve the production of thaxtomin A. The minimal P_ecf17 promoter was determined, and a P_ecf17 promoter library with a wide range of strengths was constructed. Furthermore, a cumate inducible system was developed for precise temporal control of the ECF17 transcription system in S. venezuelae ISP5230. Theoretically, the switchable ECF17 transcription system could reduce the unwanted influences from host and alleviate the burdens introduced by overexpression of heterologous genes. The yield of thaxtomin A was significantly improved to 202.1 ± 15.3 μ g/mL using the switchable ECF17 transcription system for heterologous expression of the thaxtomin gene cluster in S. venezuelae ISP5230. Besides, the applicability of this transcription system was also tested in Streptomyces albus J1074, and the titer of thaxtomin A was raised to as high as 239.3 ± 30.6 μg/mL. Therefore, the inducible ECF17 transcription system could serve as a complement of the generally used transcription systems based on strong native constitutive promoters and housekeeping σ factors for the heterologous expression of valuable products in diverse Streptomyces hosts.

Designing Biological Circuits: From Principles to Applications

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.

Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback

Unlike their natural counterparts, synthetic genetic circuits are usually fragile in the face of environmental perturbations and genetic mutations. Several theoretical robust genetic circuits have been designed, but their performance under real-world conditions has not yet been carefully evaluated. Here, we designed and synthesized a new robust perfect adaptation circuit composed of two-node negative feedback coupling with linear positive feedback on the buffer node. As a key feature, the linear positive feedback was fine-tuned to evaluate its necessity. We found that the desired function was robustly achieved when genetic parameters were varied by systematically perturbing all interacting parts within the topology, and the necessity of the completeness of the topological structures was evaluated by destroying key circuit features. Furthermore, different environmental perturbances were imposed onto the circuit by changing growth rates, carbon metabolic strategies and even chassis cells, and the designed perfect adaptation function was still achieved under all conditions. The successful design of a robust perfect adaptation circuit indicated that the top-down design strategy is capable of predictably guiding bottom-up engineering for robust genetic circuits. This robust adaptation circuit could be integrated as a motif into more complex circuits to robustly implement more sophisticated and critical biological functions.

Development of Synthetic DNA Circuit and Networks for Molecular Information Processing

Deoxyribonucleic acid (DNA), a genetic material, encodes all living information and living characteristics, e.g., in cell, DNA signaling circuits control the transcription activities of specific genes. In recent years, various DNA circuits have been developed to implement a wide range of signaling and for regulating gene network functions. In particular, a synthetic DNA circuit, with a programmable design and easy construction, has become a crucial method through which to simulate and regulate DNA signaling networks. Importantly, the construction of a hierarchical DNA circuit provides a useful tool for regulating gene networks and for processing molecular information. Moreover, via their robust and modular properties, DNA circuits can amplify weak signals and establish programmable cascade systems, which are particularly suitable for the applications of biosensing and detecting. Furthermore, a biological enzyme can also be used to provide diverse circuit regulation elements. Currently, studies regarding the mechanisms and applications of synthetic DNA circuit are important for the establishment of more advanced artificial gene regulation systems and intelligent molecular sensing tools. We therefore summarize recent relevant research progress, contributing to the development of nanotechnology-based synthetic DNA circuits. By summarizing the current highlights and the development of synthetic DNA circuits, this paper provides additional insights for future DNA circuit development and provides a foundation for the construction of more advanced DNA circuits.

Competitive dCas9 binding as a mechanism for transcriptional control

Catalytically dead Cas9 (dCas9) is a programmable transcription factor that can be targeted to promoters through the design of small guide RNAs (sgRNAs), where it can function as an activator or repressor. Natural promoters use overlapping binding sites as a mechanism for signal integration, where the binding of one can block, displace, or augment the activity of the other. Here, we implemented this strategy in Escherichia coli using pairs of sgRNAs designed to repress and then derepress transcription through competitive binding. When designed to target a promoter, this led to 27-fold repression and complete derepression. This system was also capable of ratiometric input comparison over two orders of magnitude. Additionally, we used this mechanism for promoter sequence-independent control by adopting it for elongation control, achieving 8-fold repression and 4-fold derepression. This work demonstrates a new genetic control mechanism that could be used to build analog circuit or implement cis-regulatory logic on CRISPRi-targeted native genes.

Engineered DNase-inactive Cpf1 variants to improve targeting scope for base editing in E. coli

The development of base editing (BE) technology has opened a new avenue for research studies in bacteriology, particularly for bacterial species in which the DNA double-strand breaks (DSBs) introduced by CRISPR/Cas system would lead to cell death. However, a major limitation of BE-mediated gene editing is the restricted editable sites in the target bacterial genome due to highly diverse genomic compositions, such as GC content. Herein, we developed a broad-spectrum DNase-inactive Cpf1 (dCpf1) variant from Francisella novicida (bsdFnCpf1) through directed evolution. The resulting optimized mutant showed a substantially expanded targeting range, including previously non-canonical protospacer-adjacent motifs (PAMs), especially the GC-rich PAMs. Cytidine deaminase APOBEC1 and uracil DNA glycosylase inhibitor (UGI) were fused with bsdFnCpf1 to achieve specific C to T mutations at multiple target sites with canonical or non-canonical PAMs in the E. coli genome without compromising cell growth. We anticipate that bsdFnCpf1 could be applied for multiplex gene regulation and BE in species that have been reported to be suitable for Cpf1.

Development of bifunctional biosensors for sensing and dynamic control of glycolysis flux in metabolic engineering

Glycolysis is the primary metabolic pathway in all living organisms. Maintaining the balance of glycolysis flux and biosynthetic pathways is the crucial matter involved in the microbial cell factory. Few regulation systems can address the issue of metabolic flux imbalance in glycolysis. Here, we designed and constructed a bifunctional glycolysis flux biosensor that can dynamically regulate glycolysis flux for overproduction of desired biochemicals. A series of positive-and negative-response biosensors were created and modified for varied thresholds and dynamic ranges. These engineered glycolysis flux biosensors were verified to be able to characterize in vivo fructose-1,6-diphosphate concentration. Subsequently, the biosensors were applied for fine-tuning glycolysis flux to effectively balance the biosynthesis of two chemicals: mevalonate and N-acetylglucosamine. A glycolysis flux-dynamically controlled Escherichia coli strain achieved a 111.3 g/L mevalonate titer in a 1L fermenter.

Construction and Characterization of a Gradient Strength Promoter Library for Fine-Tuned Gene Expression in Bacillus licheniformis

Bacillus licheniformis DW2 is an important industrial strain for bacitracin production, and it is also used for biochemical production, however, the lack of effective toolkit for precise regulation of gene expression hindered its application seriously. Here, a gradient strength promoter library was constructed based on bacitracin synthetase gene cluster promoter PbacA. First, different PbacA promoter variants were constructed via coupling PbacA with various 5'-UTRs, and expression ranges of 32.6-741.8% were attained among these promoters. Then, three promoters, PUbay (strong), PbacA (middle), and PUndh (weakest), were applied for red fluorescent protein (RFP) and keratinase expression assays, and these promoters were proven to have good universality for different proteins. Second, the promoter of bacitracin synthetase gene cluster was replaced by these three promoters, and bacitraicn titer was enhanced by 14.62% when PUbay was applied, which was decreased by 98.05% under the mediation of PUndh compared with that of the original strain DW2. Third, promoters PUbay, PUyvgO, and PUndh were selected to regulate the expression levels of critical genes that are responsible for pucheriminic acid synthesis, and pucheriminic acid yield was increased by 194.1% via manipulating synthetic and competitive pathways. Finally, promoters PUbay, PbacA, and PUndh were applied for green fluorescent protein (GFP) and RFP expression in Escherichia coli, and consistent effects were attained based on our results. Taken together, a gradient strength promoter library was constructed in this research, which provided an effective toolkit for fine-tuning gene expression and reprogramming metabolite metabolic flux in B. licheniformis.

An Integrative Toolbox for Synthetic Biology in Rhodococcus

The development of microbial cell factories requires robust synthetic biology tools to reduce design uncertainty and accelerate the design-build-test-learn process. Herein, we developed a suite of integrative genetic tools to facilitate the engineering of Rhodococcus, a genus of bacteria with considerable biocatalytic potential. We first created pRIME, a modular, copy-controlled integrative-vector, to provide a robust platform for strain engineering and characterizing genetic parts. This vector was then employed to benchmark a series of strong promoters. We found P_M6 to be the strongest constitutive rhodococcal promoter, 2.5- to 3-fold stronger than the next in our study, while overall promoter activities ranged 23-fold between the weakest and strongest promoters during exponential growth. Next, we used an optimized variant of P_M6 to develop hybrid-promoters and integrative vectors to allow for tetracycline-inducible gene expression in Rhodococcus. The best of the resulting hybrid-promoters maintained a maximal activity of ∼50% of P_M6 and displayed an induction factor of ∼40-fold. Finally, we developed and implemented a uLoop-derived Golden Gate assembly strategy for high-throughput DNA assembly in Rhodococcus. To demonstrate the utility of our approaches, pRIME was used to engineer Rhodococcus jostii RHA1 to grow on vanillin at concentrations 10-fold higher than what the wild-type strain tolerated. Overall, this study provides a suite of tools that will accelerate the engineering of Rhodococcus for various biocatalytic applications, including the sustainable production of chemicals from lignin-derived aromatics.

Bacteria-mediated cancer therapies: opportunities and challenges

In recent years, cancer therapy strategies utilizing live tumor-targeting bacteria have presented unique advantages. Engineered bacteria have the particular ability to distinguish tumors from normal tissues with less toxicity. Live bacteria are naturally capable of homing to tumors, resulting in high levels of local colonization because of insufficient oxygen and low pH in the tumor microenvironment. Bacteria initiate their antitumor effects by directly killing the tumor or by activating innate and adaptive antitumor immune responses. The bacterial vectors can be reprogrammed following advanced DNA synthesis, sophisticated genetic bioengineering, and biosensors to engineer microorganisms with complex functions, and then produce and deliver anticancer agents based on clinical needs. However, because of the lack of knowledge on the mechanisms and side effects of microbial cancer therapy, developing such smart microorganisms to treat or prevent cancer remains a significant challenge. In this review, we summarized the potential, status, opportunities and challenges of this growing field. We illustrated the mechanism of tumor regression induced by engineered bacteria and discussed the recent advances in the application of bacteria-mediated cancer therapy to improve efficacy, safety and drug delivery. Finally, we shared our insights into the future directions of tumor-targeting bacteria in cancer therapy.

Liquid-Phase Exfoliated Few-Layer Iodine Nanosheets for High-Rate Lithium-Iodine Batteries

Rechargeable lithium-iodine (Li-iodine) batteries attract significant attention owning to their high energy density, wide abundance and low cost of iodine resources. However, iodine suffers from low electrical conductivity and high solubility in aprotic electrolyte, leading to fast capacity degradation, low columbic efficiency, as well as poor rate capability. Herein, we propose a simple method for the large-scale production of two-dimensional (2D) few-layer iodine nanosheets (FLINs) via liquid-phase exfoliation of iodine in deionized water. 2D FLINs could effectively improve rate capability by providing sufficient active sites and shortening the Li ion diffusion path. Meanwhile, graphene oxide (GO)@carbon nanotubes (CNT) hosts are designed to suppress the dissolution of iodine and enhance the electrical conductivity. GO@CNT@FLINs film exhibits excellent rate capability (220 mAh g^-1 at 0.2 A g^-1 and 96 mAh g^-1 at 8 A g^-1 ) and outstanding cycle stability (93.2 mAh g^-1 at 2 A g^-1 after 1000 cycles) for lithium storage due to the synergistic effects of GO@CNT hosts and 2D structure of FLINs. The controllable synthesis of FLINs provides a bright prospect for achieving high rate capability of Li-iodine batteries and is of utmost importance to potential large-scale applications.

Harnessing the central dogma for stringent multi-level control of gene expression

Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided, but without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple solution to this challenge. By simultaneously regulating transcription and translation, we show how basal expression of an inducible system can be reduced, with little impact on the maximum expression rate. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction and show how multi-level regulation can suppress transcriptional noise and create digital-like switches between ‘on’ and ‘off’ states. These tools will aid those working with toxic genes or requiring precise regulation and propagation of cellular signals, plus illustrate the value of more diverse regulatory designs for synthetic biology.

Inducible gene expression systems should minimise leaky output and offer a large achievable range of expression. Here, the authors regulate transcription and translation together to suppress noise and create digital-like responses, while maintaining a large expression range in vivo and in vitro.

Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly

Synthetic Biology is a rapidly growing interdisciplinary field that is primarily built upon foundational advances in molecular biology combined with engineering design principles such as modularity and interoperability. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies and methodological advances. A key concept driving the field is the Design-Build-Test-Learn cycle which provides a systematic framework for building new biological systems. One major application area for synthetic biology is biosynthetic pathway engineering that requires the modular assembly of different genetic regulatory elements and biosynthetic enzymes. In this review we provide an overview of modular DNA assembly and describe and compare the plethora of in vitro and in vivo assembly methods for combinatorial pathway engineering. Considerations for part design and methods for enzyme balancing are also presented, and we briefly discuss alternatives to intracellular pathway assembly including microbial consortia and cell-free systems for biosynthesis. Finally, we describe computational tools and automation for pathway design and assembly and argue that a deeper understanding of the many different variables of genetic design, pathway regulation and cellular metabolism will allow more predictive pathway design and engineering.

Topology-dependent interference of synthetic gene circuit function by growth feedback

Growth-mediated feedback between synthetic gene circuits and host organisms leads to diverse emerged behaviors, including growth bistability and enhanced ultrasensitivity. However, the range of possible impacts of growth feedback on gene circuits remains underexplored. Here, we mathematically and experimentally demonstrated that growth feedback affects the functions of memory circuits in a network topology-dependent way. Specifically, the memory of the self-activation switch is quickly lost due to the growth-mediated dilution of the circuit products. Decoupling of growth feedback reveals its memory, manifested by its hysteresis property across a broad range of inducer concentration. On the contrary, the toggle switch is more refractory to growth-mediated dilution and can retrieve its memory after the fast-growth phase. The underlying principle lies in the different dependence of active and repressive regulations in these circuits on the growth-mediated dilution. Our results unveil the topology-dependent mechanism on how growth-mediated feedback influences the behaviors of gene circuits.

Programming Escherichia coli to function as a digital display

Synthetic genetic circuits offer the potential to wield computational control over biology, but their complexity is limited by the accuracy of mathematical models. Here, we present advances that enable the complete encoding of an electronic chip in the DNA carried by Escherichia coli (E. coli). The chip is a binary-coded digit (BCD) to 7-segment decoder, associated with clocks and calculators, to turn on segments to visualize 0-9. Design automation is used to build seven strains, each of which contains a circuit with up to 12 repressors and two activators (totaling 63 regulators and 76,000 bp DNA). The inputs to each circuit represent the digit to be displayed (encoded in binary by four molecules), and output is the segment state, reported as fluorescence. Implementation requires an advanced gate model that captures dynamics, promoter interference, and a measure of total power usage (RNAP flux). This project is an exemplar of design automation pushing engineering beyond that achievable "by hand", essential for realizing the potential of biology.

Structures and mechanism of transcription initiation by bacterial ECF factors

Bacterial RNA polymerase (RNAP) forms distinct holoenzymes with extra-cytoplasmic function (ECF) σ factors to initiate specific gene expression programs. In this study, we report a cryo-EM structure at 4.0 Å of Escherichia coli transcription initiation complex comprising σE-the most-studied bacterial ECF σ factor (Ec σE-RPo), and a crystal structure at 3.1 Å of Mycobacterium tuberculosis transcription initiation complex with a chimeric σH/E (Mtb σH/E-RPo). The structure of Ec σE-RPo reveals key interactions essential for assembly of E. coli σE-RNAP holoenzyme and for promoter recognition and unwinding by E. coli σE. Moreover, both structures show that the non-conserved linkers (σ2/σ4 linker) of the two ECF σ factors are inserted into the active-center cleft and exit through the RNA-exit channel. We performed secondary-structure prediction of 27,670 ECF σ factors and find that their non-conserved linkers probably reach into and exit from RNAP active-center cleft in a similar manner. Further biochemical results suggest that such σ2/σ4 linker plays an important role in RPo formation, abortive production and promoter escape during ECF σ factors-mediated transcription initiation.

Alternative σI/anti-σI factors represent a unique form of bacterial σ/anti-σ complex

The σ70 family alternative σI factors and their cognate anti-σI factors are widespread in Clostridia and Bacilli and play a role in heat stress response, virulence, and polysaccharide sensing. Multiple σI/anti-σI factors exist in some lignocellulolytic clostridial species, specifically for regulation of components of a multienzyme complex, termed the cellulosome. The σI and anti-σI factors are unique, because the C-terminal domain of σI (SigIC) and the N-terminal inhibitory domain of anti-σI (RsgIN) lack homology to known proteins. Here, we report structure and interaction studies of a pair of σI and anti-σI factors, SigI1 and RsgI1, from the cellulosome-producing bacterium, Clostridium thermocellum. In contrast to other known anti-σ factors that have N-terminal helical structures, RsgIN has a β-barrel structure. Unlike other anti-σ factors that bind both σ2 and σ4 domains of the σ factors, RsgIN binds SigIC specifically. Structural analysis showed that SigIC contains a positively charged surface region that recognizes the promoter -35 region, and the synergistic interactions among multiple interfacial residues result in the specificity displayed by different σI/anti-σI pairs. We suggest that the σI/anti-σI factors represent a distinctive mode of σ/anti-σ complex formation, which provides the structural basis for understanding the molecular mechanism of the intricate σI/anti-σI system.

Contemporary Tools for Regulating Gene Expression in Bacteria

Insights from novel mechanistic paradigms in gene expression control have led to the development of new gene expression systems for bioproduction, control, and sensing applications. Coupled with a greater understanding of synthetic burden and modern creative biodesign approaches, contemporary bacterial gene expression tools and systems are emerging that permit fine-tuning of expression, enabling greater predictability and maximisation of specific productivity, while minimising deleterious effects upon cell viability. These advances have been achieved by using a plethora of regulatory tools, operating at all levels of the so-called 'central dogma' of molecular biology. In this review, we discuss these gene regulation tools in the context of their design, prototyping, integration into expression systems, and biotechnological application.

Engineering orthogonal synthetic timer circuits based on extracytoplasmic function σ factors

The rational design of synthetic regulatory circuits critically hinges on the availability of orthogonal and well-characterized building blocks. Here, we focus on extracytoplasmic function (ECF) σ factors, which are the largest group of alternative σ factors and hold extensive potential as synthetic orthogonal regulators. By assembling multiple ECF σ factors into regulatory cascades of varying length, we benchmark the scalability of the approach, showing that these ‘autonomous timer circuits’ feature a tuneable time delay between inducer addition and target gene activation. The implementation of similar timers in Escherichia coli and Bacillus subtilis shows strikingly convergent circuit behavior, which can be rationalized by a computational model. These findings not only reveal ECF σ factors as powerful building blocks for a rational, multi-layered circuit design, but also suggest that ECF σ factors are universally applicable as orthogonal regulators in a variety of bacterial species.

Genetic Circuit-Assisted Smart Microbial Engineering

Rapid advances in DNA synthesis, genetic manipulation, and biosensors have greatly improved the ability to engineer microorganisms with complex functions. By accurately integrating quality biosensors and complex genetic circuits, recently emerged smart microorganisms have enabled exciting opportunities for dissecting complex signaling networks and making responses without artificial intervention. However, because of the lack of design principles, developing such smart microorganisms remains challenging. In this review, we propose the concept of smart microbial engineering (SME) and describe the general features of basic SME, including the circuit architecture, components, and design process. We also summarize the latest SME achievements, remaining challenges, and potential solutions in this growing field.

Aptamer-based regulation of transcription circuits

We propose synthetic DNA/RNA transcription circuits based on specific aptamer recognition. By mimicking transcription factor regulation, combined with specific enzyme/DNA aptamer binding, multiple biomolecules including DNA, RNA, polymerase, restriction enzymes and methylase were used as regulators. In addition, multi-level cascading networks and methylation-switch circuits were also established. This regulation strategy has the potential to expand the toolkit of in vitro synthetic biology.

Next-Generation Drug Discovery to Combat Antimicrobial Resistance

The widespread emergence of antibiotic-resistant pathogens poses a severe threat to public health. This problem becomes even worse with a coincident decline in the supply of new antibiotics. Conventional bioactivity-guided natural product discovery has failed to meet the urgent need for new antibiotics, largely due to limited resources and high rediscovery rates. Recent advances in cultivation techniques, analytical technologies, and genomics-based approaches have greatly expanded our access to previously underexploited microbial sources. These strategies will enable us to access new reservoirs of microorganisms and unleash their chemical potentials, thus opening new opportunities for the discovery of next-generation drugs to address the growing concerns of antimicrobial resistance.

Broad-Spectrum Gene Repression Using Scaffold Engineering of Synthetic sRNAs

Gene expression regulation in broad-spectrum range is critical for constructing cell factories and genetic circuits to balance and control system-wide fluxes. Synthetic small regulatory RNAs (sRNAs) effectively regulate gene expression at the translational level by modulating an mRNA-binding chance and sRNA abundance; however, it can control target gene expression only within the limit of the intrinsic repression ability of sRNAs. Here, we systematically mutated a SgrS scaffold as a model sRNA by dividing the Hfq-binding module of the sRNA into the three regions: the A/U-rich sequence, the stem, and the hairpin loop, and examined how efficiently the mutants suppressed DsRed2 expression. By doing this, we found that a scaffold with an altered A/U-rich sequence (CUUU) and stem length and that with altered A/U-rich sequence (GCAC) showed a 3-fold stronger and a 3-fold weaker repression than the original scaffold, respectively. For practical application of altered scaffolds, proof-of-concept experiments were performed by constructing a library of 67 synthetic sRNAs with the strongest scaffold, each one targeting a different rationally selected gene, and using this library to enhance cadaverine production in Escherichia coli, yielding in 27% increase (1.67 g/L in flask cultivation, 13.7 g/L in fed-batch cultivation). Synthetic sRNAs with engineered sRNA scaffolds could be useful in modulating gene expression for strain improvement.