A sigma factor toolbox for orthogonal gene expression in Escherichia coli

Generating information-dense promoter sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs sets of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts.

Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes

Promoter sequences are important genetic control elements. Through their interaction with RNA polymerase they determine transcription strength and specificity, thereby regulating the first step in gene expression. Consequently, they can be targeted as elements to control predictability and tuneability of a genetic circuit, which is essential in applications such as the development of robust microbial cell factories. This review considers the promoter elements implicated in the three stages of transcription initiation, detailing the complex interplay of sequence-specific interactions that are involved, and highlighting that DNA sequence features beyond the core promoter elements work in a combinatorial manner to determine transcriptional strength. In particular, we emphasize that, aside from promoter recognition, transcription initiation is also defined by the kinetics of open complex formation and promoter escape, which are also known to be highly sequence specific. Significantly, we focus on how insights into these interactions can be manipulated to lay the foundation for a more rational approach to promoter engineering.

A MoClo-Compatible Toolbox of ECF Sigma Factor-Based Regulatory Switches for Proteobacterial Chassis

The construction of complex synthetic gene circuits with predetermined and reliable output depends on orthogonal regulatory parts that do not inadvertently interfere with the host machinery or with other circuit components. Previously, extracytoplasmic function sigma factors (ECFs), a diverse group of alternative sigma factors with distinct promoter specificities, were shown to have great potential as context-independent regulators, but so far, they have only been used in a few model species. Here, we show that the alphaproteobacterium Sinorhizobium meliloti, which has been proposed as a plant-associated bacterial chassis for synthetic biology, has a similar phylogenetic ECF acceptance range as the gammaproteobacterium Escherichia coli. A common set of orthogonal ECF-based regulators that can be used in both bacterial hosts was identified and used to create 2-step delay circuits. The genetic circuits were implemented in single copy in E. coli by chromosomal integration using an established method that utilizes bacteriophage integrases. In S. meliloti, we demonstrated the usability of single-copy pABC plasmids as equivalent carriers of the synthetic circuits. The circuits were either implemented on a single pABC or modularly distributed on 3 such plasmids. In addition, we provide a toolbox containing pABC plasmids compatible with the Golden Gate (MoClo) cloning standard and a library of basic parts that enable the construction of ECF-based circuits in S. meliloti and in E. coli. This work contributes to building a context-independent and species-overarching ECF-based toolbox for synthetic biology applications.

Flux optimization using multiple promoters in Halomonas bluephagenesis as a model chassis of the next generation industrial biotechnology

Predictability and robustness are challenges for bioproduction because of the unstable intracellular synthetic activities. With the deeper understanding of the gene expression process, fine-tuning has become a meaningful tool for biosynthesis optimization. This study characterized several gene expression elements and constructed a multiple inducible system that responds to ten different small chemical inducers in halophile bacterium Halomonas bluephagenesis. Genome insertion of regulators was conducted for the purpose of gene cluster stabilization and regulatory plasmid simplification. Additionally, dynamic ranges of the multiple inducible systems were tuned by promoter sequence mutations to achieve diverse scopes for high-resolution gene expression control. The multiple inducible system was successfully employed to precisely control chromoprotein expression, lycopene and poly-3-hydroxybutyrate (PHB) biosynthesis, resulting in colorful bacterial pictures, optimized cell growth, lycopene and PHB accumulation. This study demonstrates a desirable approach for fine-tuning of rational and efficient gene expressions, displaying the significance for metabolic pathway optimization.

Deep Learning-Assisted Design of Novel Promoters in Escherichia coli

Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.

Taming CRISPRi: Dynamic range tuning through guide RNA diversion

CRISPRi is a powerful technique to repress gene expression in a targeted and highly efficient manner. However, this potency is a double-edged sword in inducible systems, as even leaky expression of guide RNA results in a repression phenotype, complicating applications such as dynamic metabolic engineering. We evaluated three methods to enhance the controllability of CRISPRi by modulating the level of free and DNA-bound guide RNA complexes. Overall repression can be attenuated through rationally designed mismatches in the reversibility determining region of the guide RNA sequence; decoy target sites can selectively modulate repression at low levels of induction; and the implementation of feedback control not only enhances the linearity of induction, but broadens the dynamic range of the output as well. Furthermore, feedback control significantly enhances the recovery rate after induction is removed. Used in combination, these techniques enable the fine-tuning of CRISPRi to meet restrictions imposed by the target and match the input signal required for induction.

Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins

Domain insertion engineering is a promising approach to recombine the functions of evolutionarily unrelated proteins. Insertion of light-switchable receptor domains into a selected effector protein, for instance, can yield allosteric effectors with light-dependent activity. However, the parameters that determine domain insertion tolerance and allostery are poorly understood. Here, an unbiased screen is used to systematically assess the domain insertion permissibility of several evolutionary unrelated proteins. Training machine learning models on the resulting data allow to dissect features informative for domain insertion tolerance and revealed sequence conservation statistics as the strongest indicators of suitable insertion sites. Finally, extending the experimental pipeline toward the identification of switchable hybrids results in opto-chemogenetic derivatives of the transcription factor AraC that function as single-protein Boolean logic gates. The study reveals determinants of domain insertion tolerance and yielded multimodally switchable proteins with unique functional properties.

Regulation strategies for two-output biomolecular networks

Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.

The σB alternative sigma factor circuit modulates noise to generate different types of pulsing dynamics

Single-cell approaches are revealing a high degree of heterogeneity, or noise, in gene expression in isogenic bacteria. How gene circuits modulate this noise in gene expression to generate robust output dynamics is unclear. Here we use the Bacillus subtilis alternative sigma factor σB as a model system for understanding the role of noise in generating circuit output dynamics. σB controls the general stress response in B. subtilis and is activated by a range of energy and environmental stresses. Recent single-cell studies have revealed that the circuit can generate two distinct outputs, stochastic pulsing and a single pulse response, but the conditions under which each response is generated are under debate. We implement a stochastic mathematical model of the σB circuit to investigate this and find that the system's core circuit can generate both response types. This is despite one response (stochastic pulsing) being stochastic in nature, and the other (single response pulse) being deterministic. We demonstrate that the main determinant for whichever response is generated is the degree with which the input pathway activates the core circuit, although the noise properties of the input pathway also biases the system towards one or the other type of output. Thus, our work shows how stochastic modelling can reveal the mechanisms behind non-intuitive gene circuit output dynamics.

Understanding Autologous Spliceostatin Transcriptional Regulation to Derive Parts for Heterologous Expression in a Burkholderia Bacterial Host

Burkholderia β-Proteobacteria are emerging sources of natural products. We are interested in developing Burkholderia sp. FERM BP-3421 into a synthetic biology chassis to facilitate natural product discovery. FERM BP-3421 produces autologous spliceostatins on gram per liter scale. We reasoned that transcription factors and promoters that regulate spliceostatin biosynthesis would provide valuable parts for heterologous expression. Herein we demonstrate that fr9A encodes a pathway-specific transcriptional activator of spliceostatin biosynthesis. In-frame deletion of fr9A abolished spliceostatin production, which was restored by genetic complementation. Using transcriptomics and green fluorescent protein (GFP) reporter assays, we identified four fr9 promoters, three of which are activated by LuxR-type regulator Fr9A. We then constructed an Fr9A-regulated promoter system that was compared to benchmarks and effectively applied for GFP and capistruin lasso peptide expression in an optimized host background. Our findings enrich the genetic toolbox for optimizing heterologous expression and promoting the discovery and development of natural products from Burkholderia bacteria.

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.

Using CIVT-SELEX to Select Aptamers as Genetic Parts to Regulate Gene Circuits in a Cell-Free System

The complexity of genetic circuits has not seen a significant increase over the last decades, even with the rapid development of synthetic biology tools. One of the bottlenecks is the limited number of orthogonal transcription factor-operator pairs. Researchers have tried to use aptamer-ligand pairs as genetic parts to regulate transcription. However, most aptamers selected using traditional methods cannot be directly applied in gene circuits for transcriptional regulation. To that end, we report a new method called CIVT-SELEX to select DNA aptamers that can not only bind to macromolecule ligands but also undergo significant conformational changes, thus affecting transcription. The single-stranded DNA library with affinity to our example ligand human thrombin protein is first selected and enriched. Then, these ssDNAs are inserted into a genetic circuit and tested in the in vitro transcription screening to obtain the ones with significant inhibitory effects on downstream gene transcription when thrombins are present. These aptamer-thrombin pairs can inhibit the transcription of downstream genes, demonstrating the feasibility and robustness of their use as genetic parts in both linear DNAs and plasmids. We believe that this method can be applied to select aptamers of any target ligands and vastly expand the genetic part library for transcriptional regulation.

Versatile Strategy for the Construction of a Transcription Factor-Based Orthogonal Gene Expression Toolbox in Monascus spp

Gene expression is needed to be conducted in an orthogonal manner and controllable independently from the host's native regulatory system. However, there is a shortage of gene expression regulatory toolboxes that function orthogonally from each other and toward the host. Herein, we developed a strategy based on the mutant library to generate orthogonal gene expression toolboxes. A transcription factor, MaR, located in the Monascus azaphilone biosynthetic gene cluster, was taken as a typical example. Nine DNA-binding residues of MaR were identified by molecular simulation and site-directed mutagenesis. We created five MaR multi-site saturation mutagenesis libraries consisting of 10743 MaR variants on the basis of five cognate promoters. A functional analysis revealed that all five tested promoters were orthogonally regulated by five different MaR variants, respectively. Furthermore, fine gene expression tunability and high signal sensitivity of this toolbox are demonstrated by introducing chemically inducible expression modules, designing synthetic promoter elements, and creating protein-protein interaction between MaRs. This study paves the way for a bottom-up approach to build orthogonal gene expression toolboxes.

Dynamic feedback regulation for efficient membrane protein production using a small RNA-based genetic circuit in Escherichia coli

BACKGROUND

Membrane proteins (MPs) are an important class of molecules with a wide array of cellular functions and are part of many metabolic pathways. Despite their great potential-as therapeutic drug targets or in microbial cell factory optimization-many challenges remain for efficient and functional expression in a host such as Escherichia coli.

RESULTS

A dynamically regulated small RNA-based circuit was developed to counter membrane stress caused by overexpression of different MPs. The best performing small RNAs were able to enhance the maximum specific growth rate with 123%. On culture level, the total MP production was increased two-to three-fold compared to a system without dynamic control. This strategy not only improved cell growth and production of the studied MPs, it also suggested the potential use for countering metabolic burden in general.

CONCLUSIONS

A dynamically regulated feedback circuit was developed that can sense metabolic stress caused by, in casu, the overexpression of an MP and responds to it by balancing the metabolic state of the cell and more specifically by downregulating the expression of the MP of interest. This negative feedback mechanism was established by implementing and optimizing simple-to-use genetic control elements based on post-transcriptional regulation: small non-coding RNAs. In addition to membrane-related stress when the MP accumulated in the cytoplasm as aggregates, the sRNA-based feedback control system was still effective for improving cell growth but resulted in a decreased total protein production. This result suggests promiscuity of the MP sensor for more than solely membrane stress.

Plasmids for Controlled and Tunable High-Level Expression in E. coli

Genetic systems for protein overexpression are required tools in microbiological and biochemical research. Ideally, these systems include standardized genetic parts with predictable behavior, enabling the construction of stable expression systems in the host organism.

Unlocking the bacterial domain for industrial biotechnology applications using universal parts and tools

Synthetic biology can play a major role in the development of sustainable industrial biotechnology processes. However, the development of economically viable production processes is currently hampered by the limited availability of host organisms that can be engineered for a specific production process. To date, standard hosts such as Escherichia coli and Saccharomyces cerevisiae are often used as starting points for process development since parts and tools allowing their engineering are readily available. However, their suboptimal metabolic background or impaired performance at industrial scale for a desired production process, can result in increased costs associated with process development and/or disappointing production titres. Building a universal and portable gene expression system allowing genetic engineering of hosts across the bacterial domain would unlock the bacterial domain for industrial biotechnology applications in a highly standardized manner and doing so, render industrial biotechnology processes more competitive compared to the current polluting chemical processes. This review gives an overview of a selection of bacterial hosts highly interesting for industrial biotechnology based on both their metabolic and process optimization properties. Moreover, the requirements and progress made so far to enable universal, standardized, and portable gene expression across the bacterial domain is discussed.

The Transcription Activator AtxA from Bacillus Anthracis was Employed for Developing a Tight-Control, High-Level, Modulable, and Stationary-Phase Specific Transcription Activity in Escherichia Coli

The strong transcriptional activity of the virulent gene pagA in Bacillus anthracis has been proven to be anthrax toxin activator (AtxA)-regulated. However, the obscure pagA transcription mechanism hinders practical applications of this strong promoter. In this study, a 509-bp DNA fragment [termed 509sequence, (−508)-(+1) relative to the P2 transcription start site] was cloned upstream of rbs-GFPuv as pTOL02B to elucidate the AtxA-regulated transcription. The 509sequence was dissected into the −10 sequence, −35 sequence, AT_rich tract, SLI/SLII and upstream site. In conjunction with the heterologous co-expression of AtxA (under the control of the T7 promoter), the −10 sequence (TATACT) was sufficient for the AtxA-regulated transcription. Integration of pTOL02F + pTOLAtxA as pTOL03F showed that the AtxA-regulated transcription exhibited a strong specific fluorescence intensity/common analytical chemistry term (OD₆₀₀) of 40 597 ± 446 and an induction/repression ratio of 122. An improved induction/repression ratio of 276 was achieved by cultivating Escherichia coli/pTOL03F in M9 minimal medium. The newly developed promoter system termed P_AtxA consists of AtxA, the −10 sequence and Escherichia RNA polymerase. These three elements synergistically and cooperatively formed a previously undiscovered transcription system, which exhibited a tight-control, high-level, modulable and stationary-phase-specific transcription. The P_AtxA was used for phaCAB expression for the stationary-phase polyhydroxybutyrate production, and the results showed that a PHB yield, content and titer of 0.20 ± 0.27 g/g-glucose, 68 ± 11% and 1.5 ± 0.4 g/l can be obtained. The positive inducible P_AtxA, in contrast to negative inducible, should be a useful tool to diversify the gene information flow in synthetic biology.

Graphical Abstract

Cell-free production of personalized therapeutic phages targeting multidrug-resistant bacteria

Bacteriophages are potent therapeutics against biohazardous bacteria, which rapidly develop multidrug resistance. However, routine administration of phage therapy is hampered by a lack of rapid production, safe bioengineering, and detailed characterization of phages. Thus, we demonstrate a comprehensive cell-free platform for personalized production, transient engineering, and proteomic characterization of a broad spectrum of phages. Using mass spectrometry, we validated hypothetical and non-structural proteins and could also monitor the protein expression during phage assembly. Notably, a few microliters of a one-pot reaction produced effective doses of phages against enteroaggregative Escherichia coli (EAEC), Yersinia pestis, and Klebsiella pneumoniae. By co-expressing suitable host factors, we could extend the range of cell-free production to phages targeting gram-positive bacteria. We further introduce a non-genomic phage engineering method, which adds functionalities for only one replication cycle. In summary, we expect this cell-free methodology to foster reverse and forward phage engineering and customized production of clinical-grade bacteriophages.

PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model

Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma (σ) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses σ70, σ24, σ32, σ38, σ28, and σ54. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30–90% for training, prediction, and hyperparameter optimization without compromising classification quality.

Sustainable production of 4-hydroxyisoleucine with minimised carbon loss by simultaneously utilising glucose and xylose in engineered Escherichia coli

4-Hydroxyisoleucine is a promising drug for diabetes therapy; however, microbial production of 4-hydroxyisoleucine is not economically efficient because of the carbon loss in the form of CO₂. This study aims to achieve de novo synthesis of 4-hydroxyisoleucine with minimised carbon loss in engineered Escherichia coli. Initially, an L-isoleucine-producing strain, ILE-5, was established, and the 4-hydroxyisoleucine synthesis pathway was introduced. The flux toward α-ketoglutarate was enhanced by reinforcing the anaplerotic pathway and disrupting competitive pathways. Subsequently, the metabolic flux for 4-hydroxyisoleucine synthesis was redistributed by dynamically modulating the α-ketoglutarate dehydrogenase complex activity, achieving a 4-hydroxyisoleucine production of 16.53 g/L. Finally, carbon loss was minimised by employing the Weimberg pathway, resulting in a 24.5% decrease in sugar consumption and a 31.6% yield increase. The 4-hydroxyisoleucine production by strain IEOH-11 reached 29.16 g/L in a 5-L fermenter. The 4-hydroxyisoleucine yield (0.29 mol/mol sugar) and productivity (0.91 g/(L⋅h)) were higher than those previously reported.

Construction and Application of a High-Throughput In Vivo Screening Platform for the Evolution of Nitrile Metabolism-Related Enzymes Based on a Desensitized Repressive Biosensor

Transcription factor (TF)-based biosensors are expected to serve as powerful tools for the high-throughput screening of biocatalytic systems; however, most of them respond to ligands in a narrow concentration range, which limits their application. In this study, we constructed a heterogenous niacin biosensor using the repressive TF BsNadR and its target promoters from Bacillus subtilis. The fine-tunable output of the niacin biosensor was expanded to a wide range of niacin concentrations (0-50 mM) through desensitization engineering, which was suitable for the accurate identification of differences in enzyme activity. Structural mechanism analysis indicated that weakening the affinity of BsNadR with the ligand niacin and with DNA alters its regulatory properties. Based on the desensitized niacin biosensor, a high-throughput in vivo screening platform was developed for evolving nitrile metabolism-related enzymes. The evolved nitrilase, amidase, and nitrile hydratase with 6.6-, 2.1-, and 21.3-fold improvements in activity were achieved, respectively. In addition, these mutants also exhibited elevated activity toward other cognate substrates, indicating the broad applicability of the screening platform. This study not only provided a universal high-throughput screening platform for different nitrile metabolism-related enzymes but also demonstrated the advantages of repressive biosensors and the vital role of desensitization engineering of the TF in the development of high-throughput screening platforms for enzymes.

Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway

Background

The rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration.

Results

The efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer.

Conclusions

An efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This “pathway architecture designer” workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01775-8.

Xenogeneic silencing strategies in bacteria are dictated by RNA polymerase promiscuity

Horizontal gene transfer facilitates dissemination of favourable traits among bacteria. However, foreign DNA can also reduce host fitness: incoming sequences with a higher AT content than the host genome can misdirect transcription. Xenogeneic silencing proteins counteract this by modulating RNA polymerase binding. In this work, we compare xenogeneic silencing strategies of two distantly related model organisms: Escherichia coli and Bacillus subtilis. In E. coli, silencing is mediated by the H-NS protein that binds extensively across horizontally acquired genes. This prevents spurious non-coding transcription, mostly intragenic in origin. By contrast, binding of the B. subtilis Rok protein is more targeted and mostly silences expression of functional mRNAs. The difference reflects contrasting transcriptional promiscuity in E. coli and B. subtilis, largely attributable to housekeeping RNA polymerase σ factors. Thus, whilst RNA polymerase specificity is key to the xenogeneic silencing strategy of B. subtilis, transcriptional promiscuity must be overcome to silence horizontally acquired DNA in E. coli.

Bacteria use specific silencing proteins to prevent spurious transcription of horizontally acquired DNA. Here, Forrest et al. describe differences in silencing strategies between E. coli and Bacillus subtilis, driven by the respective specificities of the silencing protein and the RNA polymerase in each organism.

Engineering transcriptional regulation in Escherichia coli using an archaeal TetR-family transcription factor

Synthetic biology requires well-characterized biological parts that can be combined into functional modules. One type of biological parts are transcriptional regulators and their cognate operator elements, which enable to either generate an input-specific response or are used as actuator modules. A range of regulators has already been characterized and used for orthogonal gene expression engineering, however, previous efforts have mostly focused on bacterial regulators. This work aims to design and explore the use of an archaeal TetR family regulator, FadR_Sa from Sulfolobus acidocaldarius, in a bacterial system, namely Escherichia coli. This is a challenging objective given the fundamental difference between the bacterial and archaeal transcription machinery and the lack of a native TetR-like FadR regulatory system in E. coli. The synthetic σ⁷⁰-dependent bacterial promoter proD was used as a starting point to design hybrid bacterial/archaeal promoter/operator regions, in combination with the mKate2 fluorescent reporter enabling a readout. Four variations of proD containing FadR_Sa binding sites were constructed and characterized. While expressional activity of the modified promoter proD was found to be severely diminished for two of the constructs, constructs in which the binding site was introduced adjacent to the -35 promoter element still displayed sufficient basal transcriptional activity and showed up to 7-fold repression upon expression of FadR_Sa. Addition of acyl-CoA has been shown to disrupt FadR_Sa binding to the DNA in vitro. However, extracellular concentrations of up to 2 mM dodecanoate, subsequently converted to acyl-CoA by the cell, did not have a significant effect on repression in the bacterial system. This work demonstrates that archaeal transcription regulators can be used to generate actuator elements for use in E. coli, although the lack of ligand response underscores the challenge of maintaining biological function when transferring parts to a phylogenetically divergent host.

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal (P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

ProD: A Tool for Predictive Design of Tailored Promoters in Escherichia coli

A major goal in synthetic biology is the engineering of synthetic gene circuits with a predictable, controlled and designed outcome. This creates a need for building blocks that can modulate gene expression without interference with the native cell system. A tool allowing forward engineering of promoters with predictable transcription initiation frequency is still lacking. Promoter libraries specific for σ⁷⁰ to ensure the orthogonality of gene expression were built in Escherichia coli and labeled using fluorescence-activated cell sorting to obtain high-throughput DNA sequencing data to train a convolutional neural network. We were able to confirm in vivo that the model is able to predict the promoter transcription initiation frequency (TIF) of new promoter sequences. Here, we provide an online tool for promoter design (ProD) in E. coli, which can be used to tailor output sequences of desired promoter TIF or predict the TIF of a custom sequence.

Biomolecular mechanisms for signal differentiation

Cells can sense temporal changes of molecular signals, allowing them to predict environmental variations and modulate their behavior. This paper elucidates biomolecular mechanisms of time derivative computation, facilitating the design of reliable synthetic differentiator devices for a variety of applications, ultimately expanding our understanding of cell behavior. In particular, we describe and analyze three alternative biomolecular topologies that are able to work as signal differentiators to input signals around their nominal operation. We propose strategies to preserve their performance even in the presence of high-frequency input signal components which are detrimental to the performance of most differentiators. We find that the core of the proposed topologies appears in natural regulatory networks and we further discuss their biological relevance. The simple structure of our designs makes them promising tools for realizing derivative control action in synthetic biology.

Reconstructing the transcription regulatory network to optimize resource allocation for robust biosynthesis

An ideal microbial cell factory (MCF) should deliver maximal resources to production, which conflicts with the microbe's native growth-oriented resource allocation strategy and can therefore lead to early termination of the high-yield period. Reallocating resources from growth to production has become a critical factor in constructing robust MCFs. Instead of strengthening specific biosynthetic pathways, emerging endeavors are focused on rearranging the gene regulatory network to fundamentally reprogram the resource allocation pattern. Combining this idea with transcriptional regulation within the hierarchical regulatory network, this review discusses recent engineering strategies targeting the transcription machinery, module networks, regulatory edges, and bottom network layer. This global view will help to construct a production-oriented phenotype that fully harnesses the potential of MCFs.

Importance of the 5' regulatory region to bacterial synthetic biology applications

The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of pre-characterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5' regulatory regions - promoters, untranslated regions and 5'-end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.

Development of a N-Acetylneuraminic Acid-Based Sensing and Responding Switch for Orthogonal Gene Regulation in Cyanobacterial Synechococcus Strains

Advances in synthetic biology have allowed photosynthetic cyanobacteria as promising "green cell factories" for sustainable production of biofuels and biochemicals. However, a limited of genetic switches developed in cyanobacteria restrict the complex and orthogonal metabolic regulation. In addition, suitable and controllable switches sensing and responding to specific inducers would allow for the separation of cellular growth and expression of exogenous genes or pathways that cause metabolic burden or toxicity. Here in this study, we developed a genetic switch repressed by NanR and induced by N-acetylneuraminic acid (Neu5Ac) in a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973 along with its highly homologous strain S. elongatus PCC 7942. First, nanR from Escherichia coli and a previously optimized cognate promoter P_J23119H10 were introduced into Syn2973 to control the expression of the reporter gene lacZ encoding β-galactosidase, achieving induction with negligible leakage. Second, the switch was systemically optimized to reach ∼738-fold induction by fine-tuning the expression level of NanR and introducing additional transporter of Neu5Ac. Finally, the orthogonality between the NanR/Neu5Ac switch and theophylline-responsive riboregulator was investigated, achieving a coordinated regulation or binary regulation toward the target gene. Our work here provided a new switch for transcriptional control and orthogonal regulation strategies in cyanobacteria, which would promote the metabolic regulation for the cyanobacterial chassis in the future.

Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit

Genetically identical individuals in bacterial populations can display significant phenotypic variability. This variability can be functional, for example by allowing a fraction of stress prepared cells to survive an otherwise lethal stress. The optimal fraction of stress prepared cells depends on environmental conditions. However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in Bacillus subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history and genetic perturbations. Using single-cell time-lapse microscopy and microfluidics, we find the fraction of cells that immediately activate σV under lysozyme stress depends on stress level and on a transcriptional memory of previous stress. Iteration between model and experiment reveals that this tunability can be explained by the autoregulatory feedback structure of the sigV operon. As predicted by the model, genetic perturbations to the operon also modulate the response variability. The conserved sigma-anti-sigma autoregulation motif is thus a simple mechanism for bacterial populations to modulate their heterogeneity based on their environment.

Data-Driven and in Silico-Assisted Design of Broad Host-Range Minimal Intrinsic Terminators Adapted for Bacteria

Efficient transcription termination relying on intrinsic terminators is critical to maintain cell fitness by avoiding unwanted read-through in bacteria. Natural intrinsic terminator (NIT) typically appears in mRNA as a hairpin followed by approximately eight conserved uridines (U-tract) at the 3' terminus. Owing to their simple structure, small size, and protein independence, assorted NITs have been redesigned as robust tools to construct gene circuits. However, most NITs exert functions to adapt to their physiological requirements rather than the demand for building synthetic gene circuits, rendering uncertain working performance when they are constructed intact in synthetic gene circuits. Here, rather than modifying NITs, we established a data-driven and in silico-assisted (DISA) design framework to forward engineer minimal intrinsic terminators (MITs). By comprehensively analyzing 75 natural intrinsic terminators from Bacillus subtilis, we revealed that two pivotal features, the length of the U-tract and the thermodynamics of the terminator hairpin, were involved in the sequence-activity relationship (SAR) of termination efficiency (TE). As per the SAR, we leveraged DISA to fabricate an array of MITs composed of in silico-assisted designed minimal hairpins and fixed U-tracts. Most of these MITs exhibited high TE in diverse Gram-positive and Gram-negative bacteria. In contrast, the TEs of the NITs were highly varied in different hosts. Moreover, TEs of MITs were flexibly tuned over a wide range by modulating the length of the U-tract. Overall, these results demonstrate an efficient framework to forward design functional and broad host-range terminators independent of tedious and iterative screening of mutagenesis libraries of natural terminators.

Cell-Free Characterization of Coherent Feed-Forward Loop-Based Synthetic Genetic Circuits

Regulatory pathways inside living cells employ feed-forward architectures to fulfill essential signal processing functions that aid in the interpretation of various types of inputs through noise-filtering, fold-change detection and adaptation. Although it has been demonstrated computationally that a coherent feed-forward loop (CFFL) can function as noise filter, a property essential to decoding complex temporal signals, this motif has not been extensively characterized experimentally or integrated into larger networks. Here we use post-transcriptional regulation to implement and characterize a synthetic CFFL in an Escherichia coli cell-free transcription-translation system and build larger composite feed-forward architectures. We employ microfluidic flow reactors to probe the response of the CFFL circuit using both persistent and short, noise-like inputs and analyze the influence of different circuit components on the steady-state and dynamics of the output. We demonstrate that our synthetic CFFL implementation can reliably repress background activity compared to a reference circuit, but displays low potential as a temporal filter, and validate these findings using a computational model. Our results offer practical insight into the putative noise-filtering behavior of CFFLs and show that this motif can be used to mitigate leakage and increase the fold-change of the output of synthetic genetic circuits.

A Computational Framework for Identifying Promoter Sequences in Nonmodel Organisms Using RNA-seq Data Sets

Engineering microorganisms into biological factories that convert renewable feedstocks into valuable materials is a major goal of synthetic biology; however, for many nonmodel organisms, we do not yet have the genetic tools, such as suites of strong promoters, necessary to effectively engineer them. In this work, we developed a computational framework that can leverage standard RNA-seq data sets to identify sets of constitutive, strongly expressed genes and predict strong promoter signals within their upstream regions. The framework was applied to a diverse collection of RNA-seq data measured for the methanotroph Methylotuvimicrobium buryatense 5GB1 and identified 25 genes that were constitutively, strongly expressed across 12 experimental conditions. For each gene, the framework predicted short (27-30 nucleotide) sequences as candidate promoters and derived -35 and -10 consensus promoter motifs (TTGACA and TATAAT, respectively) for strong expression in M. buryatense. This consensus closely matches the canonical E. coli sigma-70 motif and was found to be enriched in promoter regions of the genome. A subset of promoter predictions was experimentally validated in a XylE reporter assay, including the consensus promoter, which showed high expression. The pmoC, pqqA, and ssrA promoter predictions were additionally screened in an experiment that scrambled the -35 and -10 signal sequences, confirming that transcription initiation was disrupted when these specific regions of the predicted sequence were altered. These results indicate that the computational framework can make biologically meaningful promoter predictions and identify key pieces of regulatory systems that can serve as foundational tools for engineering diverse microorganisms for biomolecule production.

iPro2L-PSTKNC: A Two-Layer Predictor for Discovering Various Types of Promoters by Position Specific of Nucleotide Composition

Promoters are DNA regulatory elements located proximal to the transcription start site, which are in charge of the initiation of specific gene transcription. In Escherichia coli, promoters can be recognized by σ factors that have multiple families based on distinct function and structure, such as σ²⁴, σ²⁸, σ³², σ³⁸, σ⁵⁴ and σ⁷⁰. At present, biological methods are mainly used to identify these promoters. However, because it is time-consuming and material-consuming to do biological experiments, computational biology algorithm has emerged as a more effective way to predict the classification. In this study, we develop a novel two-layer seamless predictor called iPro2L-PSTKNC to identify the promoters of the E. coli genome, which based on the feature extraction model we newly proposed that is named as the position specific tendencies of k-mer nucleotide composition (PSTKNC). On the first layer, it is a binary classification predicting whether a sequence is promoter or not. And the second layer is a multiple classification identifying which type the identified promoter belongs to. The ensemble classification SVM performsbest comparing with other algorithms, which gets a promising accuracy and the Matthews correlation coefficient (MCC) at [Formula: see text] and [Formula: see text]. Our data and code are available at https://github.com/lyuyinuo/iPro2L-PSTKNC.

A native conjugative plasmid confers potential selective advantages to plant growth-promoting Bacillus velezensis strain GH1-13

The conjugative plasmid (pBV71) possibly confers a selective advantage to Bacillus velezensis strain GH1-13, although a selective marker gene is yet to be identified. Here we show that few non-mucoid wild-type GH1-13 cells are spontaneously converted to mucoid variants with or without the loss of pBV71. Mucoid phenotypes, which contain or lack the plasmid, become sensitive to bacitracin, gramicidin, selenite, and tellurite. Using the differences in antibiotic resistance and phenotype, we isolated a reverse complement (COM) and a transconjugant of strain FZB42 with the native pBV71. Transformed COM and FZB42p cells were similar to the wild-type strain GH1-13 with high antibiotic resistance and slow growth rates on lactose compared to those of mucoid phenotypes. RT-PCR analysis revealed that the expression of plasmid-encoded orphan aspartate phosphatase (pRapD) was coordinated with a new quorum-sensing (QS) cassette of RapF2-PhrF2 present in the chromosome of strain GH1-13, but not in strain FZB42. Multi-omics analysis on wild-type and plasmid-cured cells of strain GH1-13 suggested that the conjugative plasmid expression has a crucial role in induction of early envelope stress response that promotes cell morphogenesis, biofilm formation, catabolite repression, and biosynthesis of extracellular-matrix components and antibiotics for protection of host cell during exponential phase.

Extracytoplasmic Function σ Factors as Tools for Coordinating Stress Responses

The ability of bacterial core RNA polymerase (RNAP) to interact with different σ factors, thereby forming a variety of holoenzymes with different specificities, represents a powerful tool to coordinately reprogram gene expression. Extracytoplasmic function σ factors (ECFs), which are the largest and most diverse family of alternative σ factors, frequently participate in stress responses. The classification of ECFs in 157 different groups according to their phylogenetic relationships and genomic context has revealed their diversity. Here, we have clustered 55 ECF groups with experimentally studied representatives into two broad classes of stress responses. The remaining 102 groups still lack any mechanistic or functional insight, representing a myriad of systems yet to explore. In this work, we review the main features of ECFs and discuss the different mechanisms controlling their production and activity, and how they lead to a functional stress response. Finally, we focus in more detail on two well-characterized ECFs, for which the mechanisms to detect and respond to stress are complex and completely different: Escherichia coli RpoE, which is the best characterized ECF and whose structural and functional studies have provided key insights into the transcription initiation by ECF-RNAP holoenzymes, and the ECF15-type EcfG, the master regulator of the general stress response in Alphaproteobacteria.

Development of Host-Orthogonal Genetic Systems for Synthetic Biology

The construction of a host-orthogonal genetic system can not only minimize the impact of host-specific nuances on fine-tuning of gene expression, but also expand cellular functions such as in vivo continuous evolution of genes based on an error-prone DNA polymerase. It represents an emerging powerful approach for making biology easier to engineer. In this review, the recent advances are described on the design of genetic systems that can be stably inherited in the host cells and are responsible for important biological processes including DNA replication, RNA transcription, protein translation, and gene regulation. Their applications in synthetic biology are summarized and the future challenges and opportunities are discussed in developing such systems.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Synthetic Biological Circuits within an Orthogonal Central Dogma

Synthetic biology strives to reliably control cellular behavior, typically in the form of user-designed interactions of biological components to produce a predetermined output. Engineered circuit components are frequently derived from natural sources and are therefore often hampered by inadvertent interactions with host machinery, most notably within the host central dogma. Reliable and predictable gene circuits require the targeted reduction or elimination of these undesirable interactions to mitigate negative consequences on host fitness and develop context-independent bioactivities. Here, we review recent advances in biological orthogonalization, namely the insulation of researcher-dictated bioactivities from host processes, with a focus on systematic developments that may culminate in the creation of an orthogonal central dogma and novel cellular functions.

Metabolic engineering of an auto-regulated Corynebacterium glutamicum chassis for biosynthesis of 5-aminolevulinic acid

One challenge in metabolic engineering for industrial applications is the construction of highly efficient microbial cell factories. For this purpose, dynamic regulation of metabolic flux may be indispensable. In this study, an auto-regulated Corynebacterium glutamicum chassis for 5-aminolevulinic acid (5-ALA) biosynthesis was constructed. First, the expression of critical genes involved in 5-ALA synthesis and cofactor regeneration was precisely modulated. Furthermore, odhA expression was controlled using the strategies of static metabolic engineering (SME, with a weak promoter), dynamic metabolic engineering (DME, with a temperature-sensitive plasmid), and auto-inducible metabolic engineering (AME, with a growth-related promoter). The AME strategy showed the best effect and dynamically balanced the tradeoff between cell growth and 5-ALA synthesis. Additionally, the expression of exporter-encoding rhtA was regulated using AME strategy by the two-component system HrrSA in response to extracellular heme. The final strain A30 achieved the highest 5-ALA production (3.16 g/L) ever reported in C. glutamicum through C5 pathway.

Predictive design of sigma factor-specific promoters

To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.

Efficient Overproduction of Active Nitrile Hydratase by Coupling Expression Induction and Enzyme Maturation via Programming a Controllable Cobalt-Responsive Gene Circuit

A robust and portable expression system is of great importance in enzyme production, metabolic engineering, and synthetic biology, which maximizes the performance of the engineered system. In this study, a tailor-made cobalt-induced expression system (CIES) was developed for low-cost and eco-friendly nitrile hydratase (NHase) production. First, the strong promoter P_veg from Bacillus subtilis, the Ni(II)/Co(II) responsive repressor RcnR, and its operator were reorganized to construct a CIES. In this system, the expression of reporter green fluorescent protein (GFP) was specifically triggered by Co(II) over a broad range of concentration. The performance of the cobalt-induced system was evolved to version 2.0 (CIES 2.0) for adaptation to different concentrations of Co(II) through programming a homeostasis system that rebalances cobalt efflux and influx with RcnA and NiCoT, respectively. Harnessing these synthetic platforms, the induced expression of NHase was coupled with enzyme maturation by Co(II) in a synchronizable manner without requiring additional inducers, which is a unique feature relative to other induced systems for production of NHase. The yield of NHase was 111.2 ± 17.9 U/ml using CIES and 114.9 ± 1.4 U/ml using CIES 2.0, which has a producing capability equivalent to that of commonly used isopropyl thiogalactoside (IPTG)-induced systems. In a scale-up system using a 5-L fermenter, the yielded enzymatic activity reached 542.2 ± 42.8 U/ml, suggesting that the designer platform for NHase is readily applied to the industry. The design of CIES in this study not only provided a low-cost and eco-friendly platform to overproduce NHase but also proposed a promising pipeline for development of synthetic platforms for expression of metalloenzymes.

Realization of Robust and Precise Regulation of Gene Expression by Multiple Sigma Recognizable Artificial Promoters

Precise regulation of gene expression is fundamental for tailor-made gene circuit design in synthetic biology. Current strategies for this type of development are mainly based on directed evolution beginning with a native promoter template. The performances of engineered promoters are usually limited by the growth phase because only one promoter is recognized by one type of sigma factor (σ). Here, we constructed multiple-σ recognizable artificial hybrid promoters (AHPs) composed of tandems of dual and triple natural minimal promoters (NMPs). These NMPs, which use σ^A, σ^H and σ^W, had stable functions in different growth phases. The functions of these NMPs resulted from an effect called transcription compensation, in which AHPs sequentially use one type of σ in the corresponding growth phase. The strength of the AHPs was influenced by the combinatorial order of each NMP and the length of the spacers between the NMPs. More importantly, the output of the precise regulation was achieved by equipping AHPs with synthetic ribosome binding sites and by redesigning them for induced systems. This strategy might offer promising applications to rationally design robust synthetic promoters in diverse chassis to spur the construction of more complex gene circuits, which will further the development of synthetic biology.

Identification of σE-Dependent Promoter Upstream of clpB from the Pathogenic Spirochaete Leptospira interrogans by Applying an E. coli Two-Plasmid System

There is limited information on gene expression in the pathogenic spirochaete Leptospira interrogans and genetic mechanisms controlling its virulence. Transcription is the first step in gene expression that is often determined by environmental effects, including infection-induced stresses. Alterations in the environment result in significant changes in the transcription of many genes, allowing effective adaptation of Leptospira to mammalian hosts. Thus, promoter and transcriptional start site identification are crucial for determining gene expression regulation and for the understanding of genetic regulatory mechanisms existing in Leptospira. Here, we characterized the promoter region of the L. interrogans clpB gene (clpB_Li) encoding an AAA⁺ molecular chaperone ClpB essential for the survival of this spirochaete under thermal and oxidative stresses, and also during infection of the host. Primer extension analysis demonstrated that transcription of clpB in L. interrogans initiates at a cytidine located 41 bp upstream of the ATG initiation codon, and, to a lesser extent, at an adenine located 2 bp downstream of the identified site. Transcription of both transcripts was heat-inducible. Determination of clpB_Li transcription start site, combined with promoter transcriptional activity assays using a modified two-plasmid system in E. coli, revealed that clpB_Li transcription is controlled by the ECF σ^E factor. Of the ten L. interrogans ECF σ factors, the factor encoded by LIC_12757 (LA0876) is most likely to be the key regulator of clpB gene expression in Leptospira cells, especially under thermal stress. Furthermore, clpB expression may be mediated by ppGpp in Leptospira.

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.