Metabolic Engineering Projects Research Articles

A promoter-RBS library for fine-tuning gene expression in Methanosarcina acetivorans.

Methanogenic archaea are potent producers of the greenhouse gas methane and thus contribute substantially to global warming. Under controlled conditions, these microbes can catalyze the production of biogas, which is a renewable fuel, and might help counter global warming and its effects. Engineering the primary metabolism of Methanosarcina acetivorans to render it better and more useful requires controllable gene expression, yet only a few well-characterized promoters and RBSs are presently available. Our study rectifies this situation by providing a library of 33 different promoter-RBS combinations with a 140-fold dynamic range in expression strength. Future metabolic engineering projects can take advantage of this library by using these promoter-RBS combinations as an efficient and tunable gene expression system for M. acetivorans. Furthermore, the methodologies we developed in this study could also be utilized to construct promoter libraries for other types of methanogens.

Total optimization potential (TOP) approach based constrained design of isoprene and cis-abienol production in A. thaliana

Mathematical modelling is an important approach in metabolic engineering projects to support the design and adaptation of organisms for the needs of biotechnology. Assuming sufficient quality of the model, mechanistic mathematical descriptions of the processes enable the prediction of the systemic impact of metabolic pathway engineering on the organism’s metabolism. In this paper, modelling is used to design the necessary changes in Arabidopsis thaliana metabolism for the production of cis-abienol and isoprene. We developed a kinetic model of the A. thaliana 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway, including the production of plastoquinone, chlorophyll side chains and carotenoids. For the purpose of optimisation, it was extended with additional reactions leading to the production of cis-abienol (using geranylgeranyl diphosphate as precursor) and isoprene (using dimethylallyl pyrophosphate as precursor).A total optimisation potential (TOP) based search of reasonable enzyme concentration adjustments yielded sets of parameter combinations, from which those were selected that gave the largest increase in the product flux compared to parameter sets with a smaller number of parameters. Three designs (sets of required concentration changes of particular enzymes) are suggested for each product, based on optimisation of the 2048 possible combinations of adjustable parameters (enzyme concentrations). The resulting optimization of the model forecasts a cis-abienol production of 0.021 nmol.s−1.ml−1 and an isoprene production of 0.048 nmol.s−1.ml−1 in plastids of A. thaliana.

A machine learning Automated Recommendation Tool for synthetic biology

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

Improving protein solubility and activity by introducing small peptide tags designed with machine learning models

Improving catalytic ability of enzymes is critical to the success of many metabolic engineering projects, but the search space of possible protein mutants is too large to explore exhaustively through experiments. To some extent, highly soluble enzymes tend to exhibit high activity due to their better folding quality. Here, we demonstrate that an optimization algorithm based on a regression model can effectively design short peptide tags to improve solubility of a few model enzymes. Based on the protein sequence information, a support vector regression model we recently developed was used to evaluate protein solubility after small peptide tags were introduced to a target protein. The optimization algorithm guided the sequences of the tags to evolve towards variants that had higher solubility. The optimization results were validated successfully by measuring solubility and activity of the model enzyme with and without the identified tags. The solubility of one protein (tyrosine ammonia lyase) was more than doubled and its activity was improved by 250%. This strategy successfully increased solubility of another two enzymes (aldehyde dehydrogenase and 1-deoxy-D-xylulose-5-phosphate synthase) we tested. The presented optimization methodology thus provides a valuable tool for improving enzyme performance for metabolic engineering and other biotechnology projects.

Reinforcement Learning for Bioretrosynthesis.

Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.

Determination of growth-coupling strategies and their underlying principles

BackgroundMetabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.ResultsWe adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.ConclusionMaximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

Expanding Metabolic Capabilities Using Novel Pathway Designs: Computational Tools and Case Studies

Design and selection of efficient metabolic pathways is critical for the success of metabolic engineering endeavors. Convenient pathways should not only produce the target metabolite in high yields but also are required to be thermodynamically feasible under production conditions, and to prefer efficient enzymes. To support the design and selection of such pathways, different computational approaches have been proposed for exploring the feasible pathway space under many of the above constraints. In this review, an overview of recent constraint-based optimization frameworks for metabolic pathway prediction, as well as relevant pathway engineering case studies that highlight the importance of rational metabolic designs is presented. Despite the availability and suitability of in silico design tools for metabolic pathway engineering, scarce-although increasing-application of computational outcomes is found. Finally, challenges and limitations hindering the broad adoption and successful application of these tools in metabolic engineering projects are discussed.

Construction, Model-Based Analysis, and Characterization of a Promoter Library for Fine-Tuned Gene Expression in Bacillus subtilis.

Promoters are among the most-important and most-basic tools for the control of metabolic pathways. However, previous research mainly focused on the screening and characterization of some native promoters in Bacillus subtilis. To develop a broadly applicable promoter system for this important platform organism, we created a de novo synthetic promoter library (SPL) based on consensus sequences by analyzing the microarray transcriptome data of B. subtilis 168. A total of 214 potential promoters spanning a gradient of strengths was isolated and characterized by a green fluorescence assay. Among these, a detailed intensity analysis was conducted on nine promoters with different strengths by reverse-transcription polymerase chain reaction (RT-PCR)and sodium dodecyl sulfate polyacrylamide gel electrophoresis(SDS-PAGE). Furthermore, reconstructed promoters and promoter cassettes (tandem promoter cluster) were designed via statistical model-based analysis and tandem dual promoters, which showed strength that was increased 1.2- and 2.77-fold compared to that of promoter P43, respectively. Finally, the SPL was employed in the production of inosine and acetoin by repressing and over-expressing the relevant metabolic pathways, yielding a 700% and 44% increase relative to the respective control strains. This is the first report of a de novo synthetic promoter library for B. subtilis, which is independent of any native promoter. The strategy of improving and fine-tuning promoter strengths will contribute to future metabolic engineering and synthetic biology projects in B. subtilis.

Glycerol positive promoters for tailored metabolic engineering of the yeast Saccharomyces cerevisiae.

Glycerol offers several advantages as a substrate for biotechnological applications. An important step toward using the popular production host Saccharomyces cerevisiae for glycerol-based bioprocesses has been the fact that in recent studies commonly used S. cerevisiae strains were engineered to grow in synthetic medium containing glycerol as the sole carbon source. For metabolic engineering projects of S. cerevisiae growing on glycerol, characterized promoters are missing. In the current study, we used transcriptome analysis and a yECitrine-based fluorescence reporter assay to select and characterize 25 useful promoters. The promoters of the genes ALD4 and ADH2 showed 4.2-fold and 3-fold higher activities compared to the well-known strong TEF1 promoter. Moreover, the collection contains promoters with graded activities in synthetic glycerol medium and different degrees of glucose repression. To demonstrate the general applicability of the promoter collection, we successfully used a subset of the characterized promoters with graded activities in order to optimize growth on glycerol in an engineered derivative of CEN.PK, in which glycerol catabolism exclusively occurs via a non-native DHA pathway.

Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing.

Metabolic diversity in microorganisms can provide the basis for creating novel biochemical products. However, most metabolic engineering projects utilize a handful of established model organisms and thus, a challenge for harnessing the potential of novel microbial functions is the ability to either heterologously express novel genes or directly utilize non-model organisms. Genetic manipulation of non-model microorganisms is still challenging due to organism-specific nuances that hinder universal molecular genetic tools and translatable knowledge of intracellular biochemical pathways and regulatory mechanisms. However, in the past several years, unprecedented progress has been made in synthetic biology, molecular genetics tools development, applications of omics data techniques, and computational tools that can aid in developing non-model hosts in a systematic manner. In this review, we focus on concerns and approaches related to working with non-model microorganisms including developing molecular genetics tools such as shuttle vectors, selectable markers, and expression systems. In addition, we will discuss: (1) current techniques in controlling gene expression (transcriptional/translational level), (2) advances in site-specific genome engineering tools [homologous recombination (HR) and clustered regularly interspaced short palindromic repeats (CRISPR)], and (3) advances in genome-scale metabolic models (GSMMs) in guiding design of non-model species. Application of these principles to metabolic engineering strategies for consolidated bioprocessing (CBP) will be discussed along with some brief comments on foreseeable future prospects.

Computational Redesign of Acyl-ACP Thioesterase with Improved Selectivity toward Medium-Chain-Length Fatty Acids.

Enzyme and metabolic engineering offer the potential to develop biocatalysts for converting natural resources into a wide range of chemicals. To broaden the scope of potential products beyond natural metabolites, methods of engineering enzymes to accept alternative substrates and/or perform novel chemistries must be developed. DNA synthesis can create large libraries of enzyme-coding sequences, but most biochemistries lack a simple assay to screen for promising enzyme variants. Our solution to this challenge is structure-guided mutagenesis in which optimization algorithms select the best sequences from libraries based on specified criteria (i.e. binding selectivity). Here, we demonstrate this approach by identifying medium-chain (C6-C12) acyl-ACP thioesterases through structure-guided mutagenesis. Medium-chain fatty acids, products of thioesterase-catalyzed hydrolysis, are limited in natural abundance compared to long-chain fatty acids; the limited supply leads to high costs of C6-C10 oleochemicals such as fatty alcohols, amines, and esters. Here, we applied computational tools to tune substrate binding to the highly-active 'TesA thioesterase in Escherichia coli. We used the IPRO algorithm to design thioesterase variants with enhanced C12- or C8-specificity while maintaining high activity. After four rounds of structure-guided mutagenesis, we identified three thioesterases with enhanced production of dodecanoic acid (C12) and twenty-seven thioesterases with enhanced production of octanoic acid (C8). The top variants reached up to 49% C12 and 50% C8 while exceeding native levels of total free fatty acids. A comparably sized library created by random mutagenesis failed to identify promising mutants. The chain length-preference of 'TesA and the best mutant were confirmed in vitro using acyl-CoA substrates. Molecular dynamics simulations, confirmed by resolved crystal structures, of 'TesA variants suggest that hydrophobic forces govern 'TesA substrate specificity. We expect that the design rules we uncovered and the thioesterase variants identified will be useful to metabolic engineering projects aimed at sustainable production of medium-chain oleochemicals.

The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.

Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants), but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.

Molecular Cloning Designer Simulator (MCDS): All-in-one molecular cloning and genetic engineering design, simulation and management software for complex synthetic biology and metabolic engineering projects

Molecular Cloning Designer Simulator (MCDS) is a powerful new all-in-one cloning and genetic engineering design, simulation and management software platform developed for complex synthetic biology and metabolic engineering projects. In addition to standard functions, it has a number of features that are either unique, or are not found in combination in any one software package: (1) it has a novel interactive flow-chart user interface for complex multi-step processes, allowing an integrated overview of the whole project; (2) it can perform a user-defined workflow of cloning steps in a single execution of the software; (3) it can handle multiple types of genetic recombineering, a technique that is rapidly replacing classical cloning for many applications; (4) it includes experimental information to conveniently guide wet lab work; and (5) it can store results and comments to allow the tracking and management of the whole project in one platform. MCDS is freely available from https://mcds.codeplex.com.

Synthetic biology for production of natural and new-to-nature terpenoids in photosynthetic organisms.

With tens of thousands of characterized members, terpenoids constitute the largest class of natural compounds that are synthesized by all living organisms. Several terpenoids play primary roles in the maintenance of cell membrane fluidity, as pigments or as phytohormones, but most of them function as specialized metabolites that are involved in plant resistance to herbivores or plant-environment interactions. Terpenoids are an essential component of human nutrition, and many are economically important pharmaceuticals, aromatics and potential next-generation biofuels. Because of the often low abundance in their natural source, as well as the demand for novel terpenoid structures with new or improved bioactivities, terpenoid biosynthesis has become a prime target for metabolic engineering and synthetic biology projects. In this review we focus on the creation of new-to-nature or tailor-made plant-derived terpenoids in photosynthetic organisms, in particular by means of combinatorial biosynthesis and the activation of silent metabolism. We reflect on the characteristics of different potential photosynthetic host organisms and recent advances in synthetic biology and discuss their utility for the (heterologous) production of (novel) terpenoids.

Libraries of Synthetic TALE-Activated Promoters: Methods and Applications.

The discovery of proteins with programmable DNA-binding specificities triggered a whole array of applications in synthetic biology, including genome editing, regulation of transcription, and epigenetic modifications. Among those, transcription activator-like effectors (TALEs) due to their natural function as transcription regulators, are especially well-suited for the development of orthogonal systems for the control of gene expression. We describe here the construction and testing of libraries of synthetic TALE-activated promoters which are under the control of a single TALE with a given DNA-binding specificity. These libraries consist of a fixed DNA-binding element for the TALE, a TATA box, and variable sequences of 19 bases upstream and 43 bases downstream of the DNA-binding element. These libraries were cloned using a Golden Gate cloning strategy making them usable as standard parts in a modular cloning system. The broad range of promoter activities detected and the versatility of these promoter libraries make them valuable tools for applications in the fine-tuning of expression in metabolic engineering projects or in the design and implementation of regulatory circuits.

Systems analysis of methylerythritol-phosphate pathway flux in E. coli: insights into the role of oxidative stress and the validity of lycopene as an isoprenoid reporter metabolite.

BackgroundHigh-throughput screening methods assume that the output measured is representative of changes in metabolic flux toward the desired product and is not affected by secondary phenotypes. However, metabolic engineering can result in unintended phenotypes that may go unnoticed in initial screening. The red pigment lycopene, a carotenoid with antioxidant properties, has been used as a reporter of isoprenoid pathway flux in metabolic engineering for over a decade. Lycopene production is known to vary between wild-type Escherichia coli hosts, but the reasons behind this variation have never been fully elucidated.ResultsIn an examination of six E. coli strains we observed that strains also differ in their capacity for increased lycopene production in response to metabolic engineering. A combination of genetic complementation, quantitative SWATH proteomics, and biochemical analysis in closely-related strains was used to examine the mechanistic reasons for variation in lycopene accumulation. This study revealed that rpoS, a gene previously identified in lycopene production association studies, exerts its effect on lycopene accumulation not through modulation of pathway flux, but through alteration of cellular oxidative status. Specifically, absence of rpoS results in increased accumulation of reactive oxygen species during late log and stationary phases. This change in cellular redox has no effect on isoprenoid pathway flux, despite the presence of oxygen-sensitive iron-sulphur cluster enzymes and the heavy redox requirements of the methylerythritol phosphate pathway. Instead, decreased cellular lycopene in the ΔrpoS strain is caused by degradation of lycopene in the presence of excess reactive oxygen species.ConclusionsOur results demonstrate that lycopene is not a reliable indicator of isoprenoid pathway flux in the presence of oxidative stress, and suggest that caution should be exercised when using lycopene as a screening tool in genome-wide metabolic engineering studies. More extensive use of systems biology for strain analysis will help elucidate such unpredictable side-effects in metabolic engineering projects.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-015-0381-7) contains supplementary material, which is available to authorized users.

Development and analysis of a highly flexible multi-gene expression system for metabolic engineering in Arabidopsis seeds and other plant tissues.

Production of novel value-added compounds in transgenic crops has become an increasingly viable approach in recent years. However, in many cases, product yield still falls short of the levels necessary for optimal profitability. Determination of the limiting factors is thus of supreme importance for the long-term viability of this approach. A significant challenge to most metabolic engineering projects is the need for strong coordinated co-expression of multiple transgenes. Strong constitutive promoters have been well-characterized during the >30 years since plant transformation techniques were developed. However, organ- or tissue-specific promoters are poorly characterized in many cases. Oilseeds are one such example. Reports spanning at least 20 years have described the use of certain seed-specific promoters to drive expression of individual transgenes. Multi-gene engineering strategies are often hampered by sub-optimal expression levels or improper tissue-specificity of particular promoters, or rely on the use of multiple copies of the same promoter, which can result in DNA instability or transgene silencing. We describe here a flexible system of plasmids that allows for expression of 1-7 genes per binary plasmid, and up to 18 genes altogether after multiple rounds of transformation or sexual crosses. This vector system includes six seed-specific promoters and two constitutive promoters. Effective constitutive and seed-specific RNA interference gene-suppression cloning vectors were also constructed for silencing of endogenous genes. Taken together, this molecular toolkit allows combinatorial cloning for multiple transgene expression in seeds, vegetative organs, or both simultaneously, while also providing the means to coordinately overexpress some genes while silencing others.

Riboselector: riboswitch-based synthetic selection device to expedite evolution of metabolite-producing microorganisms.

Many successful metabolic engineering projects have utilized evolutionary approaches, which consist of generating phenotypic diversity and screening for desired phenotype. Since conventional screening methods suffer from low throughput and limited target metabolites, a universal high-throughput screening platform for selection of improved strains should be developed to facilitate evolution of metabolite high producer. Recently, riboswitches have received attention as attractive sensor-actuator hybrids that can control gene expression in response to intracellular metabolite concentration. Our group developed a riboswitch-based selection device called "Riboselector" which can give a growth advantage to metabolite-overproducing strains by modulating expression of a selectable marker gene. We applied the device to expedite evolution of lysine producing Escherichia coli, and the selected strain showed a dramatic improvement of lysine production compared to its parental strain. Moreover, a tryptophan-responsive Riboselector was also developed using synthetic tryptophan aptamer. In this chapter, we provide a step-by-step overview of developing synthetic RNA devices comprising a riboswitch and a selection module that specifically sense inconspicuous metabolites and enrich high producer strains out of library.

By‐passing the refinery for production of high‐value BTX derivatives

By‐passing the refinery for production of high‐value BTX derivatives

Achieving Diversity in the Face of Constraints: Lessons from Metabolism

Metabolic engineering of plants can reduce the cost and environmental impact of agriculture while providing for the needs of a growing population. Although our understanding of plant metabolism continues to increase at a rapid pace, relatively few plant metabolic engineering projects with commercial potential have emerged, in part because of a lack of principles for the rational manipulation of plant phenotype. One underexplored approach to identifying such design principles derives from analysis of the dominant constraints on plant fitness, and the evolutionary innovations in response to those constraints, that gave rise to the enormous diversity of natural plant metabolic pathways.