Precise and reliable gene expression via standard transcription and translation initiation elements

Real-Time Optogenetics System for Controlling Gene Expression Using a Model-Based Design

Optimization of engineered biological systems requires precise control over the rates and timing of gene expression. Optogenetics is used to dynamically control gene expression as an alternative to conventional chemical-based methods since it provides a more convenient interface between digital control software and microbial culture. Here, we describe the construction of a real-time optogenetics platform, which performs closed-loop control over the CcaR-CcaS two-plasmid system in Escherichia coli. We showed the first model-based design approach by constructing a nonlinear representation of the CcaR-CcaS system, tuned the model through open-loop experimentation to capture the experimental behavior, and applied the model in silico to inform the necessary changes to build a closed-loop optogenetic control system. Our system periodically induces and represses the CcaR-CcaS system while recording optical density and fluorescence using image processing techniques. We highlight the facile nature of constructing our system and how our model-based design approach will potentially be used to model other systems requiring closed-loop optogenetic control.

In-situ generation of large numbers of genetic combinations for metabolic reprogramming via CRISPR-guided base editing

Reprogramming complex cellular metabolism requires simultaneous regulation of multigene expression. Ex-situ cloning-based methods are commonly used, but the target gene number and combinatorial library size are severely limited by cloning and transformation efficiencies. In-situ methods such as multiplex automated genome engineering (MAGE) depends on high-efficiency transformation and incorporation of heterologous DNA donors, which are limited to few microorganisms. Here, we describe a Base Editor-Targeted and Template-free Expression Regulation (BETTER) method for simultaneously diversifying multigene expression. BETTER repurposes CRISPR-guided base editors and in-situ generates large numbers of genetic combinations of diverse ribosome binding sites, 5’ untranslated regions, or promoters, without library construction, transformation, and incorporation of DNA donors. We apply BETTER to simultaneously regulate expression of up to ten genes in industrial and model microorganisms Corynebacterium glutamicum and Bacillus subtilis. Variants with improved xylose catabolism, glycerol catabolism, or lycopene biosynthesis are respectively obtained. This technology will be useful for large-scale fine-tuning of multigene expression in both genetically tractable and intractable microorganisms.

To obtain optimal yield and productivity in bioproduction, expression of pathway genes must be appropriately coordinated. Here, the authors report repurposing of base editors for simultaneous regulation of multiple gene expression and demonstrate its application in industrially important and model microorganisms.

Isobutene production in Synechocystis sp. PCC 6803 by introducing α-ketoisocaproate dioxygenase from Rattus norvegicus

Cyanobacteria can be utilized as a platform for direct phototrophic conversion of CO₂ to produce several types of carbon-neutral biofuels. One promising compound to be produced photobiologically in cyanobacteria is isobutene. As a volatile compound, isobutene will quickly escape the cells without building up to toxic levels in growth medium or get caught in the membranes. Unlike liquid biofuels, gaseous isobutene may be collected from the headspace and thus avoid the costly extraction of a chemical from culture medium or from cells. Here we investigate a putative synthetic pathway for isobutene production suitable for a photoautotrophic host. First, we expressed α-ketoisocaproate dioxygenase from Rattus norvegicus (RnKICD) in Escherichia coli. We discovered isobutene formation with the purified RnKICD with the rate of 104.6 ​± ​9 ​ng (mg protein)^-1 min^-1 using α-ketoisocaproate as a substrate. We further demonstrate isobutene production in the cyanobacterium Synechocystis sp. PCC 6803 by introducing the RnKICD enzyme. Synechocystis strain heterologously expressing the RnKICD produced 91 ​ng ​l⁻¹ OD₇₅₀⁻¹ ​h⁻¹. Thus, we demonstrate a novel sustainable platform for cyanobacterial production of an important building block chemical, isobutene. These results indicate that RnKICD can be used to further optimize the synthetic isobutene pathway by protein and metabolic engineering efforts.

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Photosynthetic isobutene production is demonstrated in a cyanobacterium.

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A Synechocystis strain capable of continuous direct conversion of CO₂ to isobutene.

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α-ketoisocaproate dioxygenase from R. norvegicus (RnKICD) is determined to form isobutene.

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RnKICD can convert α-ketoisocaproate to isobutene both in vitro and in vivo.

Systematical Engineering of Synthetic Yeast for Enhanced Production of Lycopene

Synthetic biology allows the re-engineering of biological systems and promotes the development of bioengineering to a whole new level, showing great potential in biomanufacturing. Here, in order to make the heterologous lycopene biosynthesis pathway compatible with the host strain YSy 200, we evolved YSy200 using a unique Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) system that is built in the Sc2.0 synthetic yeast. By inducing SCRaMbLE, we successfully identified a host strain YSy201 that can be served as a suitable host to maintain the heterologous lycopene biosynthesis pathway. Then, we optimized the lycopene biosynthesis pathway and further integrated into the rDNA arrays of YSy201 to increase its copy number. In combination with culturing condition optimization, we successfully screened out the final yeast strain YSy222, which showed a 129.5-fold increase of lycopene yield in comparison with its parental strain. Our work shows that, the strategy of combining the engineering efforts on both the lycopene biosynthesis pathway and the host strain can improve the compatibility between the heterologous pathway and the host strain, which can further effectively increase the yield of the target product.

Fine-Tuning Multi-Gene Clusters via Well-Characterized Gene Expression Regulatory Elements: Case Study of the Arginine Synthesis Pathway in C. glutamicum

Promoters and ribosome binding sites (RBSs) are routinely applied in gene expression regulation, but their orthogonality and combinatorial effects have not yet been systematically studied in Corynebacterium glutamicum. Here, 17 core promoters and 29 RBSs in C. glutamicum were characterized, which exhibited 470-fold and 430-fold in transcriptional and translational activity, respectively. By comparing the expression of two reporter genes regulated by multiple RBSs, the RBS efficacy showed significant dependence on the gene context, besides the RBSs' strength, reflecting the poor orthogonality of RBSs. Bicistron-modified RBS (referred as bc-RBS) was adapted to C. glutamicum, which improved RBS reliability. By coupling a series of promoters with RBSs/bc-RBSs, a much broader regulation range that spanned 4 orders of magnitude was observed compared with that of a sole element, and the contribution to gene expression of RBS was more than that of promoter. Finally, promoters and RBSs were applied as built-in elements to fine-tune the gene cluster in the arginine synthesis pathway in C. glutamicum. Compared with the original strain, more arginine (1.61-fold) or citrulline (2.35-fold) was accumulated in a 7 L bioreactor by strains with the gene expression regulation system rationally engineered. We demonstrated that, via combination of well-characterized gene elements, and overall consideration for both transcription and translation, the biosynthesis pathway can be effectively balanced, and the yield of a target metabolite can be further improved.

Characterizing Genetic Parts and Devices Using RNA Sequencing

Synthetic genetic circuits are composed of many parts that must interact and function together to produce a desired pattern of gene expression. A challenge when assembling circuits is that genetic parts often behave differently within a circuit, potentially impacting the desired functionality. Existing debugging methods based on fluorescent reporter proteins allow for only a few internal states to be monitored simultaneously, making diagnosis of the root cause impossible for large systems. Here, we present a tool called the Genetic Analyzer which uses RNA sequencing data to simultaneously characterize all transcriptional parts (e.g., promoters and terminators) and devices (e.g., sensors and logic gates) in complex genetic circuits. This provides a complete picture of the inner workings of a genetic circuit enabling faults to be easily identified and fixed. We construct a complete workflow to coordinate the execution of the various data processing and analysis steps and explain the options available when adapting these for the characterization of new systems.

Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters

Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.

Integrative characterization of the near-minimal bacterium Mesoplasma florum

The near-minimal bacterium Mesoplasma florum is an interesting model for synthetic genomics and systems biology due to its small genome (~ 800 kb), fast growth rate, and lack of pathogenic potential. However, fundamental aspects of its biology remain largely unexplored. Here, we report a broad yet remarkably detailed characterization of M. florum by combining a wide variety of experimental approaches. We investigated several physical and physiological parameters of this bacterium, including cell size, growth kinetics, and biomass composition of the cell. We also performed the first genome-wide analysis of its transcriptome and proteome, notably revealing a conserved promoter motif, the organization of transcription units, and the transcription and protein expression levels of all protein-coding sequences. We converted gene transcription and expression levels into absolute molecular abundances using biomass quantification results, generating an unprecedented view of the M. florum cellular composition and functions. These characterization efforts provide a strong experimental foundation for the development of a genome-scale model for M. florum and will guide future genome engineering endeavors in this simple organism.

Multidimensional Characterization of Parts Enhances Modeling Accuracy in Genetic Circuits

Mathematical models can aid the design of genetic circuits, but may yield inaccurate results if individual parts are not modeled at the appropriate resolution. To illustrate the importance of this concept, we study transcriptional cascades consisting of two inducible synthetic transcription factors connected in series. Despite the simplicity of this design, we find that accurate prediction of circuit behavior requires mapping the dose responses of each circuit component along the dimensions of both its expression level and its inducer concentration. Using this multidimensional characterization, we were able to computationally explore the behavior of 16 different circuit designs. We experimentally verified a subset of these predictions and found substantial agreement. This method of biological part characterization enables the use of models to identify (un)desired circuit behaviors prior to experimental implementation, thus shortening the design-build-test cycle for more complex circuits.

Tunable Gene Expression System Independent of Downstream Coding Sequence

Fine control of the expression levels of proteins constitutes a major challenge in synthetic biology and metabolic engineering. However, the dependence of translation initiation on the downstream coding sequence (CDS) obscures accurate prediction of the protein expression levels from mRNA sequences. Here, we present a tunable gene-expression system comprising 24 expression cassettes that produce predefined relative expression levels of proteins ranging from 0.001 to 1 without being influenced by the downstream CDS. To validate the practical utility of the tunable expression system, it was applied to a synthetic circuit displaying three states of fluorescence depending on the difference in protein expression levels. To demonstrate the suitability of application to metabolic engineering, this system was used to diversify the levels of key metabolic enzymes. As a result, expression-optimized strains were capable of producing 2.25 g/L of cadaverine, 2.59 g/L of L-proline, and 95.7 mg/L of 1-propanol. Collectively, the tunable expression system could be utilized to optimize genetic circuits for desired operation and to optimize metabolic fluxes through biosynthetic pathways for enhancing production yields of bioproducts. This tunable system will be useful for studying basic and applied biological sciences in addition to applications in synthetic biology and metabolic engineering.

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.

Repurposing a microfluidic formulation device for automated DNA construction

Microfluidic applications have expanded greatly over the past decade. For the most part, however, each microfluidics platform is developed with a specific task in mind, rather than as a general-purpose device with a wide-range of functionality. Here, we show how a microfluidic system, originally developed to investigate protein phase behavior, can be modified and repurposed for another application, namely DNA construction. We added new programable controllers to direct the flow of reagents across the chip. We designed the assembly of a combinatorial Golden Gate DNA library using TeselaGen DESIGN software and used the repurposed microfluidics platform to assemble the designed library from off-chip prepared DNA assembly pieces. Further experiments verified the sequences and function of the on-chip assembled DNA constructs.

Programmable cross-ribosome-binding sites to fine-tune the dynamic range of transcription factor-based biosensor

Currently, predictive translation tuning of regulatory elements to the desired output of transcription factor (TF)-based biosensors remains a challenge. The gene expression of a biosensor system must exhibit appropriate translation intensity, which is controlled by the ribosome-binding site (RBS), to achieve fine-tuning of its dynamic range (i.e. fold change in gene expression between the presence and absence of inducer) by adjusting the translation level of the TF and reporter. However, existing TF-based biosensors generally suffer from unpredictable dynamic range. Here, we elucidated the connections and partial mechanisms between RBS, translation level, protein folding and dynamic range, and presented a design platform that predictably tuned the dynamic range of biosensors based on deep learning of large datasets cross-RBSs (cRBSs). In doing so, a library containing 7053 designed cRBSs was divided into five sub-libraries through fluorescence-activated cell sorting to establish a classification model based on convolutional neural network in deep learning. Finally, the present work exhibited a powerful platform to enable predictable translation tuning of RBS to the dynamic range of biosensors.

Developing a pathway-independent and full-autonomous global resource allocation strategy to dynamically switching phenotypic states

A grand challenge of biological chemical production is the competition between synthetic circuits and host genes for limited cellular resources. Quorum sensing (QS)-based dynamic pathway regulations provide a pathway-independent way to rebalance metabolic flux over the course of the fermentation. Most cases, however, these pathway-independent strategies only have capacity for a single QS circuit functional in one cell. Furthermore, current dynamic regulations mainly provide localized control of metabolic flux. Here, with the aid of engineering synthetic orthogonal quorum-related circuits and global mRNA decay, we report a pathway-independent dynamic resource allocation strategy, which allows us to independently controlling two different phenotypic states to globally redistribute cellular resources toward synthetic circuits. The strategy which could pathway-independently and globally self-regulate two desired cell phenotypes including growth and production phenotypes could totally eliminate the need for human supervision of the entire fermentation.

A challenge for biological chemical production is the completion between synthetic circuits and host resources. Here the authors the authors use quorum sensing circuits and global mRNA decay to independently control two phenotypic states.

Synthetic gene-regulatory networks in the opportunistic human pathogen Streptococcus pneumoniae

Streptococcus pneumoniae is a major human pathogen responsible for enormous global morbidity and mortality. Despite this, the pneumococcus makes up part of the commensal nasopharyngeal flora. How the pneumococcus switches from this commensal to pathogenic state and causes disease is unclear and very likely involves variability in expression of its virulence factors. Here, we used synthetic biology approaches to generate complex gene-regulatory networks such as logic gates and toggle switches. We show that these networks are functional in vivo to control capsule production in an influenza-superinfection model. This opens the field of systematically testing the role of phenotypic variation in pneumococcal virulence. The approaches used here may serve as an example for synthetic biology projects in unrelated organisms.

Streptococcus pneumoniae can cause disease in various human tissues and organs, including the ear, the brain, the blood, and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control in S. pneumoniae. A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND gates and IMPLY gates. We demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors. Indeed, we were able to rewire gene expression of the capsule operon, the main pneumococcal virulence factor, to be externally inducible (YES gate) or to act as an IMPLY gate (only expressed in absence of inducer). Importantly, we demonstrate that these synthetic gene-regulatory networks are functional in an influenza A virus superinfection murine model of pneumonia, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity of S. pneumoniae.

Exploring the synthetic biology potential of bacteriophages for engineering non-model bacteria

Non-model bacteria like Pseudomonas putida, Lactococcus lactis and other species have unique and versatile metabolisms, offering unique opportunities for Synthetic Biology (SynBio). However, key genome editing and recombineering tools require optimization and large-scale multiplexing to unlock the full SynBio potential of these bacteria. In addition, the limited availability of a set of characterized, species-specific biological parts hampers the construction of reliable genetic circuitry. Mining of currently available, diverse bacteriophages could complete the SynBio toolbox, as they constitute an unexplored treasure trove for fully adapted metabolic modulators and orthogonally-functioning parts, driven by the longstanding co-evolution between phage and host.

Non-model bacteria offer unique and versatile metabolisms for synthetic biology. In this Perspective, the authors explore the limited availability of well-characterised biological parts in these species and argue that bacteriophages represent a diverse trove of orthogonal parts.

Identification, repair and characterization of a benzyl alcohol-inducible promoter for recombinant proteins overexpression in Corynebacterium glutamicum

Corynebacterium glutamicum is an important industrial organism for the production of a variety of biological commodities. We discovered a promoter encoded by the gene NCgl2319 in C. glutamicum, which could be induced by benzyl alcohol, could be used as an efficient tunable expression system. In initial attempts, this promoter failed to function in a recombinant expression system. This was remedied by extending the original genetic context of the promoter, generating a new version Pcat-B. The Pcat-B transcription initiation site, its critical active regions, and its effect of inducers were fully characterized resulting in tunable expression. This approach proved to be very efficient in producing a pharmaceutical protein, N-terminal pro-brain natriuretic peptide (NT-proBNP). Production of approximately 440.43 mg/L NT-proBNP was achieved with the Pcat-B expression system demonstrating its application for controllable pharmaceutical protein production in C. glutamicum.

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.

The Ongoing Quest to Crack the Genetic Code for Protein Production

Understanding the genetic design principles that determine protein production remains a major challenge. Although the key principles of gene expression were discovered 50 years ago, additional factors are still being uncovered. Both protein-coding and non-coding sequences harbor elements that collectively influence the efficiency of protein production by modulating transcription, mRNA decay, and translation. The influences of many contributing elements are intertwined, which complicates a full understanding of the individual factors. In natural genes, a functional balance between these factors has been obtained in the course of evolution, whereas for genetic-engineering projects, our incomplete understanding still limits optimal design of synthetic genes. However, notable advances have recently been made, supported by high-throughput analysis of synthetic gene libraries as well as by state-of-the-art biomolecular techniques. We discuss here how these advances further strengthen understanding of the gene expression process and how they can be harnessed to optimize protein production.

Improvement of Pseudoalteromonas haloplanktis TAC125 as a Cell Factory: IPTG-Inducible Plasmid Construction and Strain Engineering

Our group has used the marine bacterium Pseudoalteromonas haloplanktis TAC125 (PhTAC125) as a platform for the successful recombinant production of “difficult” proteins, including eukaryotic proteins, at low temperatures. However, there is still room for improvement both in the refinement of PhTAC125 expression plasmids and in the bacterium’s intrinsic ability to accumulate and handle heterologous products. Here, we present an integrated approach of plasmid design and strain engineering finalized to increment the recombinant expression and optimize the inducer uptake in PhTAC125. To this aim, we developed the IPTG-inducible plasmid pP79 and an engineered PhTAC125 strain called KrPL LacY⁺. This mutant was designed to express the E. coli lactose permease and to produce only a truncated version of the endogenous Lon protease through an integration-deletion strategy. In the wild-type strain, pP79 assured a significantly better production of two reporters in comparison to the most recent expression vector employed in PhTAC125. Nevertheless, the use of KrPL LacY⁺ was crucial to achieving satisfying production levels using reasonable IPTG concentrations, even at 0 °C. Both the wild-type and the mutant recombinant strains are characterized by an average graded response upon IPTG induction and they will find different future applications depending on the desired levels of expression.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Synthetic promoter design in Escherichia coli based on a deep generative network

Promoter design remains one of the most important considerations in metabolic engineering and synthetic biology applications. Theoretically, there are 4⁵⁰ possible sequences for a 50-nt promoter, of which naturally occurring promoters make up only a small subset. To explore the vast number of potential sequences, we report a novel AI-based framework for de novo promoter design in Escherichia coli. The model, which was guided by sequence features learned from natural promoters, could capture interactions between nucleotides at different positions and design novel synthetic promoters in silico. We combined a deep generative model that guides the search for artificial sequences with a predictive model to preselect the most promising promoters. The AI-designed promoters were optimized based on the promoter activity in E. coli and the predictive model. After two rounds of optimization, up to 70.8% of the AI-designed promoters were experimentally demonstrated to be functional, and few of them shared significant sequence similarity with the E. coli genome. Our work provided an end-to-end approach to the de novo design of novel promoter elements, indicating the potential to apply deep learning methods to de novo genetic element design.

Development of a genetic toolset for the highly engineerable and metabolically versatile Acinetobacter baylyi ADP1

One primary objective of synthetic biology is to improve the sustainability of chemical manufacturing. Naturally occurring biological systems can utilize a variety of carbon sources, including waste streams that pose challenges to traditional chemical processing, such as lignin biomass, providing opportunity for remediation and valorization of these materials. Success, however, depends on identifying micro-organisms that are both metabolically versatile and engineerable. Identifying organisms with this combination of traits has been a historic hindrance. Here, we leverage the facile genetics of the metabolically versatile bacterium Acinetobacter baylyi ADP1 to create easy and rapid molecular cloning workflows, including a Cas9-based single-step marker-less and scar-less genomic integration method. In addition, we create a promoter library, ribosomal binding site (RBS) variants and test an unprecedented number of rationally integrated bacterial chromosomal protein expression sites and variants. At last, we demonstrate the utility of these tools by examining ADP1’s catabolic repression regulation, creating a strain with improved potential for lignin bioprocessing. Taken together, this work highlights ADP1 as an ideal host for a variety of sustainability and synthetic biology applications.

Comprehensive study on Escherichia coli genomic expression: Does position really matter?

As a biorefinery platform host, Escherichia coli has been used extensively to produce metabolites of commercial interest. Integration of foreign DNA onto the bacterial genome allows for stable expression overcoming the need for plasmid expression and its associated instability. Despite the development of numerous tools and genome editing technologies, the question of where to incorporate a synthetic pathway remains unanswered. To address this issue, we studied the genomic expression in E. coli and linked it not only to 26 rationally selected genomic locations, but also to the gene direction in relation to the DNA replication fork, to the carbon and nitrogen source, to DNA folding and supercoiling, and to metabolic burden. To enable these experiments, we have designed a fluorescent expression cassette to eliminate specific local effects on gene expression. Overall it can be concluded that although the expression range obtained by changing the genomic location of a pathway is small compared to the range typically seen in promoter-RBS libraries, the effect of culture medium, environmental stress and metabolic burden can be substantial. The characterization of multiple effects on genomic expression, and the associated libraries of well-characterized strains, will only stimulate and improve the creation of stable production hosts fit for industrial settings.

Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads

Genetic circuits have many applications, from guiding living therapeutics to ordering process in a bioreactor, but to be useful they have to be genetically stable and not hinder the host. Encoding circuits in the genome reduces burden, but this decreases performance and can interfere with native transcription. We have designed genomic landing pads in Escherichia coli at high-expression sites, flanked by ultrastrong double terminators. DNA payloads >8 kb are targeted to the landing pads using phage integrases. One landing pad is dedicated to carrying a sensor array, and two are used to carry genetic circuits. NOT/NOR gates based on repressors are optimized for the genome and characterized in the landing pads. These data are used, in conjunction with design automation software (Cello 2.0), to design circuits that perform quantitatively as predicted. These circuits require fourfold less RNA polymerase than when carried on a plasmid and are stable for weeks in a recA+ strain without selection. This approach enables the design of synthetic regulatory networks to guide cells in environments or for applications where plasmid use is infeasible.

Design of orthogonal regulatory systems for modulating gene expression in plants

Agricultural biotechnology strategies often require the precise regulation of multiple genes to effectively modify complex plant traits. However, most efforts are hindered by a lack of characterized tools that allow for reliable and targeted expression of transgenes. We have successfully engineered a library of synthetic transcriptional regulators that modulate expression strength in planta. By leveraging orthogonal regulatory systems from Saccharomyces spp., we have developed a strategy for the design of synthetic activators, synthetic repressors, and synthetic promoters and have validated their use in Nicotiana benthamiana and Arabidopsis thaliana. This characterization of contributing genetic elements that dictate gene expression represents a foundation for the rational design of refined synthetic regulators. Our findings demonstrate that these tools provide variation in transcriptional output while enabling the concerted expression of multiple genes in a tissue-specific and environmentally responsive manner, providing a basis for generating complex genetic circuits that process endogenous and environmental stimuli.

Independent component analysis of E. coli's transcriptome reveals the cellular processes that respond to heterologous gene expression

Achieving the predictable expression of heterologous genes in a production host has proven difficult. Each heterologous gene expressed in the same host seems to elicit a different host response governed by unknown mechanisms. Historically, most studies have approached this challenge by manipulating the properties of the heterologous gene through methods like codon optimization. Here we approach this challenge from the host side. We express a set of 45 heterologous genes in the same Escherichia coli strain, using the same expression system and culture conditions. We collect a comprehensive RNAseq set to characterize the host's transcriptional response. Independent Component Analysis of the RNAseq data set reveals independently modulated gene sets (iModulons) that characterize the host response to heterologous gene expression. We relate 55% of variation of the host response to: Fear vs Greed (16.5%), Metal Homeostasis (19.0%), Respiration (6.0%), Protein folding (4.5%), and Amino acid and nucleotide biosynthesis (9.0%). If these responses can be controlled, then the success rate with predicting heterologous gene expression should increase.

From Microbial Communities to Distributed Computing Systems

A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.

Majority sensing in synthetic microbial consortia

As synthetic biocircuits become more complex, distributing computations within multi-strain microbial consortia becomes increasingly beneficial. However, designing distributed circuits that respond predictably to variation in consortium composition remains a challenge. Here we develop a two-strain gene circuit that senses and responds to which strain is in the majority. This involves a co-repressive system in which each strain produces a signaling molecule that signals the other strain to down-regulate production of its own, orthogonal signaling molecule. This co-repressive consortium links gene expression to ratio of the strains rather than population size. Further, we control the cross-over point for majority via external induction. We elucidate the mechanisms driving these dynamics by developing a mathematical model that captures consortia response as strain fractions and external induction are varied. These results show that simple gene circuits can be used within multicellular synthetic systems to sense and respond to the state of the population.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE's effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.

Multiplexing cell-cell communication

The engineering of advanced multicellular behaviors, such as the programmed growth of biofilms or tissues, requires cells to communicate multiple aspects of physiological information. Unfortunately, few cell-cell communication systems have been developed for synthetic biology. Here, we engineer a genetically encoded channel selector device that enables a single communication system to transmit two separate intercellular conversations. Our design comprises multiplexer and demultiplexer sub-circuits constructed from a total of 12 CRISPRi-based transcriptional logic gates, an acyl homoserine lactone-based communication module, and three inducible promoters that enable small molecule control over the conversations. Experimentally parameterized mathematical models of the sub-components predict the steady state and dynamical performance of the full system. Multiplexed cell-cell communication has applications in synthetic development, metabolic engineering, and other areas requiring the coordination of multiple pathways among a community of cells.

Influence of the spacer region between the Shine-Dalgarno box and the start codon for fine-tuning of the translation efficiency in Escherichia coli

Translation efficiency contributes several orders of magnitude difference in the overall yield of exogenous gene expression in bacteria. In diverse bacteria, the translation initiation site, whose sequence is the primary determinant of the translation performance, is comprised of the start codon and the Shine-Dalgarno box located upstream. Here, we have examined how the sequence of a spacer between these main components of the translation initiation site contributes to the yield of synthesized protein. We have created a library of reporter constructs with the randomized spacer region, performed fluorescently activated cell sorting and applied next-generation sequencing analysis (the FlowSeq protocol). As a result, we have identified sequence motifs for the spacer region between the Shine-Dalgarno box and AUG start codon that may modulate the translation efficiency in a 100-fold range.

Permutational analysis of Saccharomyces cerevisiae regulatory elements

Gene expression in Saccharomyces cerevisiae is regulated at multiple levels. Genomic and epigenomic mapping of transcription factors and chromatin factors has led to the delineation of various modular regulatory elements—enhancers (upstream activating sequences), core promoters, 5′ untranslated regions (5′ UTRs) and transcription terminators/3′ untranslated regions (3′ UTRs). However, only a few of these elements have been tested in combinations with other elements and the functional interactions between the different modular regulatory elements remain under explored. We describe a simple and rapid approach to build a combinatorial library of regulatory elements and have used this library to study 26 different enhancers, core promoters, 5′ UTRs and transcription terminators/3′ UTRs to estimate the contribution of individual regulatory parts in gene expression. Our combinatorial analysis shows that while enhancers initiate gene expression, core promoters modulate the levels of enhancer-mediated expression and can positively or negatively affect expression from even the strongest enhancers. Principal component analysis (PCA) indicates that enhancer and promoter function can be explained by a single principal component while UTR function involves multiple functional components. The PCA also highlights outliers and suggest differences in mechanisms of regulation by individual elements. Our data also identify numerous regulatory cassettes composed of different individual regulatory elements that exhibit equivalent gene expression levels. These data thus provide a catalog of elements that could in future be used in the design of synthetic regulatory circuits.

Characterization of Context-Dependent Effects on Synthetic Promoters

Understanding the composability of genetic elements is central to synthetic biology. Even for seemingly well-known elements such as a sigma 70 promoter the genetic context-dependent variability of promoter activity remains poorly understood. The lack of understanding of sequence to function results in highly limited de novo design of novel genetic element combinations. To address this issue, we characterized in detail concatenated "stacked" synthetic promoters including varying spacer sequence lengths and compared the transcription strength to the output of the individual promoters. The proxy for promoter activity, the msfGFP synthesis from stacked promoters was consistently lower than expected from the sum of the activities of the single promoters. While the spacer sequence itself had no activity, it drastically affected promoter activities when placed up- or downstream of a promoter. Single promoter-spacer combinations revealed a bivalent effect on msfGFP synthesis. By systematic analysis of promoter and spacer combinations, a semi-empirical correlation was developed to determine the combined activity of stacked promoters.

Dual UTR-A novel 5' untranslated region design for synthetic biology applications

Bacterial 5' untranslated regions of mRNA (UTR) involve in a complex regulation of gene expression; however, the exact sequence features contributing to gene regulation are not yet fully understood. In this study, we report the design of a novel 5' UTR, dual UTR, utilizing the transcriptional and translational characteristics of 5' UTRs in a single expression cassette. The dual UTR consists of two 5' UTRs, each separately leading to either increase in transcription or translation of the reporter, that are separated by a spacer region, enabling de novo translation initiation. We rationally create dual UTRs with a wide range of expression profiles and demonstrate the functionality of the novel design concept in Escherichia coli and Pseudomonas putida using different promoter systems and coding sequences. Overall, we demonstrate the application potential of dual UTR design concept in various synthetic biology applications ranging from fine-tuning of gene expression to maximization of protein production.

Enhanced production of recombinant proteins in Corynebacterium glutamicum by constructing a bicistronic gene expression system

BACKGROUND

Corynebacterium glutamicum is a traditional food-grade industrial microorganism, in which an efficient endotoxin-free recombinant protein expression factory is under developing in recent years. However, the intrinsic disadvantage of low recombinant protein expression level is still difficult to be solved. Here, according to the bacteria-specific polycistronic feature that multiple proteins can be translated in one mRNA, efforts have been made to insert a leading peptide gene upstream of target genes as an expression enhancer, and it is found that this can remarkably improve the expression level of proteins under the control of inducible tac promoter in C. glutamicum.

RESULTS

In this research, the Escherichia coli (E. coli) tac promoter combined with 24 different fore-cistron sequences were constructed in a bicistronic manner in C. glutamicum. Three strong bicistronic expression vectors were isolated and exhibited high efficiency under different culture conditions. The compatibility of these bicistronic vectors was further validated using six model proteins- aldehyde dehydrogenase (ALDH), alcohol dehydrogenase (ADH), RamA (regulator of acetate metabolism), Bovine interferon-α (BoIFN-α), glycoprotein D protein (gD) of infectious bovine rhinotracheitis virus (IBRV) and procollagen type Ι N-terminal peptide (PΙNP). All examined proteins were highly expressed compared with the original vector with tac promoter. Large-scale production of PΙNP was also performed in fed-batch cultivation, and the highest PΙNP production level was 1.2 g/L.

CONCLUSION

In this study, the strength of the inducible tac promoter for C. glutamicum was improved by screening and inserting fore-cistron sequences in front of the target genes. Those vectors with bicistronic expression patterns have strong compatibility for expressing various heterogeneous proteins in high yield. This new strategy could be used to further improve the performance of inducible promoters, achieving double competence of inducible control and high yield.

Rebalancing microbial carbon distribution for L-threonine maximization using a thermal switch system

In metabolic engineering, unbalanced microbial carbon distribution has long blocked the further improvement in yield and productivity of high-volume natural metabolites. Current studies mostly focus on regulating desired biosynthetic pathways, whereas few strategies are available to maximize L-threonine efficiently. Here, we present a strategy to guarantee the supply of reduced cofactors and actualize L-threonine maximization by regulating cellular carbon distribution in central metabolic pathways. A thermal switch system was designed and applied to divide the whole fermentation process into two stages: growth and production. This system could rebalance carbon substrates between pyruvate and oxaloacetate by controlling the heterogenous expression of pyruvate carboxylase and oxaloacetate decarboxylation that responds to temperature. The system was tested in an L-threonine producer Escherichia coli TWF001, and the resulting strain TWF106/pFT24rp overproduced L-threonine from glucose with 111.78% molar yield. The thermal switch system was then employed to switch off the L-alanine synthesis pathway, resulting in the highest L-threonine yield of 124.03%, which exceeds the best reported yield (87.88%) and the maximum available theoretical value of L-threonine production (122.47%). This inducer-free genetic circuit design can be also developed for other biosynthetic pathways to increase product conversion rates and shorten production cycles.

High density cultivation for efficient sesquiterpenoid biosynthesis in Synechocystis sp. PCC 6803

Cyanobacteria and microalgae are attractive photoautotrophic host systems for climate-friendly production of fuels and other value-added biochemicals. However, for economic applications further development and implementation of efficient and sustainable cultivation strategies are essential. Here, we present a comparative study on cyanobacterial sesquiterpenoid biosynthesis in Synechocystis sp. PCC 6803 using a commercial lab-scale High Density Cultivation (HDC) platform in the presence of dodecane as in-situ extractant. Operating in a two-step semi-batch mode over a period of eight days, volumetric yields of (E)-α-bisabolene were more than two orders of magnitude higher than previously reported for cyanobacteria, with final titers of 179.4 ± 20.7 mg * L⁻¹. Likewise, yields of the sesquiterpene alcohols (−)-patchoulol and (−)-α-bisabolol were many times higher than under reference conditions, with final titers of 17.3 ± 1.85 mg * L⁻¹ and 96.3 ± 2.2 mg * L⁻¹, respectively. While specific productivity was compromised particularly for (E)-α-bisabolene in the HDC system during phases of high biomass accumulation rates, volumetric productivity enhancements during linear growth at high densities were more pronounced for (E)-α-bisabolene than for the hydroxylated terpenoids. Together, this study provides additional insights into cell density-related process characteristics, introducing HDC as highly efficient strategy for phototrophic terpenoid production in cyanobacteria.

Growing a circular economy with fungal biotechnology: a white paper

Fungi have the ability to transform organic materials into a rich and diverse set of useful products and provide distinct opportunities for tackling the urgent challenges before all humans. Fungal biotechnology can advance the transition from our petroleum-based economy into a bio-based circular economy and has the ability to sustainably produce resilient sources of food, feed, chemicals, fuels, textiles, and materials for construction, automotive and transportation industries, for furniture and beyond. Fungal biotechnology offers solutions for securing, stabilizing and enhancing the food supply for a growing human population, while simultaneously lowering greenhouse gas emissions. Fungal biotechnology has, thus, the potential to make a significant contribution to climate change mitigation and meeting the United Nation’s sustainable development goals through the rational improvement of new and established fungal cell factories. The White Paper presented here is the result of the 2nd Think Tank meeting held by the EUROFUNG consortium in Berlin in October 2019. This paper highlights discussions on current opportunities and research challenges in fungal biotechnology and aims to inform scientists, educators, the general public, industrial stakeholders and policymakers about the current fungal biotech revolution.

Synthetic control of plasmid replication enables target- and self-curing of vectors and expedites genome engineering of Pseudomonas putida

Genome engineering of non-conventional microorganisms calls for the development of dedicated synthetic biology tools. Pseudomonas putida is a Gram-negative, non-pathogenic soil bacterium widely used for metabolic engineering owing to its versatile metabolism and high levels of tolerance to different types of stress. Genome editing of P. putida largely relies on homologous recombination events, assisted by helper plasmid-based expression of genes encoding DNA modifying enzymes. Plasmid curing from selected isolates is the most tedious and time-consuming step of this procedure, and implementing commonly used methods to this end in P. putida (e.g. temperature-sensitive replicons) is often impractical. To tackle this issue, we have developed a toolbox for both target- and self-curing of plasmid DNA in Pseudomonas species. Our method enables plasmid-curing in a simple cultivation step by combining in vivo digestion of vectors by the I-SceI homing nuclease with synthetic control of plasmid replication, triggered by the addition of a cheap chemical inducer (3-methylbenzoate) to the medium. The system displays an efficiency of vector curing >90% and the screening of plasmid-free clones is greatly facilitated by the use of fluorescent markers that can be selected according to the application intended. Furthermore, quick genome engineering of P. putida using self-curing plasmids is demonstrated through genome reduction of the platform strain EM42 by eliminating all genes encoding β-lactamases, the catabolic ben gene cluster, and the pyoverdine synthesis machinery. Physiological characterization of the resulting streamlined strain, P. putida SEM10, revealed advantageous features that could be exploited for metabolic engineering.

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Plasmid-curing is the most time-consuming step in genome engineering approaches.

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We have developed a system for easy target- and self-curing of plasmid DNA.

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Synthetic control of replication and highly-specific in vivo DNA digestion were used.

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Plasmid curing with this system displays an efficiency >90% in a 24-h cultivation.

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Quick genome engineering facilitated genome reduction of P. putida.

Optimized expression and enhanced production of alkaline protease by genetically modified Bacillus licheniformis 2709

BACKGROUND

Bacillus licheniformis 2709 is extensively applied as a host for the high-level production of heterologous proteins, but Bacillus cells often possess unfavorable wild-type properties, such as production of viscous materials and foam during fermentation, which seriously influenced the application in industrial fermentation. How to develop it from a soil bacterium to a super-secreting cell factory harboring less undomesticated properties always plays vital role in industrial production. Besides, the optimal expression pattern of the inducible enzymes like alkaline protease has not been optimized by comparing the transcriptional efficiency of different plasmids and genomic integration sites in B. licheniformis.

RESULT

Bacillus licheniformis 2709 was genetically modified by disrupting the native lchAC genes related to foaming and the eps cluster encoding the extracellular mucopolysaccharide via a markerless genome-editing method. We further optimized the expression of the alkaline protease gene (aprE) by screening the most efficient expression system among different modular plasmids and genomic loci. The results indicated that genomic expression of aprE was superior to plasmid expression and finally the transcriptional level of aprE greatly increased 1.67-fold through host optimization and chromosomal integration in the vicinity of the origin of replication, while the enzyme activity significantly improved 62.19% compared with the wild-type alkaline protease-producing strain B. licheniformis.

CONCLUSION

We successfully engineered an AprE high-yielding strain free of undesirable properties and its fermentation traits could be applied to bulk-production by host genetic modification and expression optimization. In summary, host optimization is an enabling technology for improving enzyme production by eliminating the harmful traits of the host and optimizing expression patterns. We believe that these strategies can be applied to improve heterologous protein expression in other Bacillus species.

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.

Bacterial growth physiology and RNA metabolism

Bacteria are sophisticated systems with high capacity and flexibility to adapt to various environmental conditions. Each prokaryote however possesses a defined metabolic network, which sets its overall metabolic capacity, and therefore the maximal growth rate that can be reached. To achieve optimal growth, bacteria adopt various molecular strategies to optimally adjust gene expression and optimize resource allocation according to the nutrient availability. The resulting physiological changes are often accompanied by changes in the growth rate, and by global regulation of gene expression. The growth-rate-dependent variation of the abundances in the cellular machineries, together with condition-specific regulatory mechanisms, affect RNA metabolism and fate and pose a challenge for rational gene expression reengineering of synthetic circuits. This article is part of a Special Issue entitled: RNA and gene control in bacteria, edited by Dr. M. Guillier and F. Repoila.

Pseudomonas mRNA 2.0: Boosting Gene Expression Through Enhanced mRNA Stability and Translational Efficiency

High gene expression of enzymes partaking in recombinant production pathways is a desirable trait among cell factories belonging to all different kingdoms of life. High enzyme abundance is generally aimed for by utilizing strong promoters, which ramp up gene transcription and mRNA levels. Increased protein abundance can alternatively be achieved by optimizing the expression on the post-transcriptional level. Here, we evaluated protein synthesis with a previously proposed optimized gene expression architecture, in which mRNA stability and translation initiation are modulated by genetic parts such as self-cleaving ribozymes and a bicistronic design, which have initially been described to support the standardization of gene expression. The optimized gene expression architecture was tested in Pseudomonas taiwanensis VLB120, a promising, novel microbial cell factory. The expression cassette was employed on a plasmid basis and after single genomic integration. We used three constitutive and two inducible promoters to drive the expression of two fluorescent reporter proteins and a short acetoin biosynthesis pathway. The performance was confronted with that of a traditional expression cassette harboring the same promoter and gene of interest but lacking the genetic parts for increased expression efficiency. The optimized expression cassette granted higher protein abundance independently of the expression basis or promoter used proving its value for applications requiring high protein abundance.

Accelerated genome engineering of Pseudomonas putida by I-SceI-mediated recombination and CRISPR-Cas9 counterselection

Pseudomonas species have become reliable platforms for bioproduction due to their capability to tolerate harsh conditions imposed by large-scale bioprocesses and their remarkable resistance to diverse physicochemical stresses. The last few years have brought forth a variety of synthetic biology tools for the genetic manipulation of pseudomonads, but most of them are either applicable only to obtain certain types of mutations, lack efficiency, or are not easily accessible to be used in different Pseudomonas species (e.g. natural isolates). In this work, we describe a versatile, robust and user-friendly procedure that facilitates virtually any kind of genomic manipulation in Pseudomonas species in 3-5 days. The protocol presented here is based on DNA recombination forced by double-stranded DNA cuts (through the activity of the I-SceI homing meganuclease from yeast) followed by highly efficient counterselection of mutants (aided by a synthetic CRISPR-Cas9 device). The individual parts of the genome engineering toolbox, tailored for knocking genes in and out, have been standardized to enable portability and easy exchange of functional gene modules as needed. The applicability of the procedure is illustrated both by eliminating selected genomic regions in the platform strain P. putida KT2440 (including difficult-to-delete genes) and by integrating different reporter genes (comprising novel variants of fluorescent proteins) into a defined landing site in the target chromosome.