Microbial 1-butanol production: Identification of non-native production routes andin silicoengineering interventions

Metabolic flux from the chloroplast provides signals controlling photosynthetic acclimation to cold in Arabidopsis thaliana

Photosynthesis is especially sensitive to environmental conditions, and the composition of the photosynthetic apparatus can be modulated in response to environmental change, a process termed photosynthetic acclimation. Previously, we identified a role for a cytosolic fumarase, FUM2 in acclimation to low temperature in Arabidopsis thaliana. Mutant lines lacking FUM2 were unable to acclimate their photosynthetic apparatus to cold. Here, using gas exchange measurements and metabolite assays of acclimating and non-acclimating plants, we show that acclimation to low temperature results in a change in the distribution of photosynthetically fixed carbon to different storage pools during the day. Proteomic analysis of wild-type Col-0 Arabidopsis and of a fum2 mutant, which was unable to acclimate to cold, indicates that extensive changes occurring in response to cold are affected in the mutant. Metabolic and proteomic data were used to parameterize metabolic models. Using an approach called flux sampling, we show how the relative export of triose phosphate and 3-phosphoglycerate provides a signal of the chloroplast redox state that could underlie photosynthetic acclimation to cold.

Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks

Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.

Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production

Many platform chemicals can be produced from renewable biomass by microorganisms, with organic acids making up a large fraction. Intolerance to the resulting low pH growth conditions, however, remains a challenge for the industrial production of organic acids by microorganisms. Issatchenkia orientalis SD108 is a promising host for industrial production because it is tolerant to acidic conditions as low as pH 2.0. With the goal to systematically assess the metabolic capabilities of this non-model yeast, we developed a genome-scale metabolic model for I. orientalis SD108 spanning 850 genes, 1826 reactions, and 1702 metabolites. In order to improve the model’s quantitative predictions, organism-specific macromolecular composition and ATP maintenance requirements were determined experimentally and implemented. We examined its network topology, including essential genes and flux coupling analysis and drew comparisons with the Yeast 8.3 model for Saccharomyces cerevisiae. We explored the carbon substrate utilization and examined the organism’s production potential for the industrially-relevant succinic acid, making use of the OptKnock framework to identify gene knockouts which couple production of the targeted chemical to biomass production. The genome-scale metabolic model iIsor850 is a data-supported curated model which can inform genetic interventions for overproduction.

•

Genome-scale metabolic model iIsor850 describes metabolism of I. orientalis SD108.

•

Customized biomass reaction highlights differences with S. cerevisiae.

•

Chemostat data elucidate growth-associated ATP maintenance.

•

Substrate utilization and CRISPR/Cas9 gene knockout phenotypes validate model.

•

Model pinpoints candidate gene deletions coupling succinic acid production to growth.

Metabolic engineering strategies for butanol production in Escherichia coli

The global market of butanol is increasing due to its growing applications as solvent, flavoring agent, and chemical precursor of several other compounds. Recently, the superior properties of n-butanol as a biofuel over ethanol have stimulated even more interest. (Bio)butanol is natively produced together with ethanol and acetone by Clostridium species through acetone-butanol-ethanol fermentation, at noncompetitive, low titers compared to petrochemical production. Different butanol production pathways have been expressed in Escherichia coli, a more accessible host compared to Clostridium species, to improve butanol titers and rates. The bioproduction of butanol is here reviewed from a historical and theoretical perspective. All tested rational metabolic engineering strategies in E. coli to increase butanol titers are reviewed: manipulation of central carbon metabolism, elimination of competing pathways, cofactor balancing, development of new pathways, expression of homologous enzymes, consumption of different substrates, and molecular biology strategies. The progress in the field of metabolic modeling and pathway generation algorithms and their potential application to butanol production are also summarized here. The main goals are to gather all the strategies, evaluate the respective progress obtained, identify, and exploit the outstanding challenges.

Genetic engineering of non-native hosts for 1-butanol production and its challenges: a review

BACKGROUND

Owing to the increase in energy consumption, fossil fuel resources are gradually depleting which has led to the growing environmental concerns; therefore, scientists are being urged to produce sustainable and ecofriendly fuels. Thus, there is a growing interest in the generation of biofuels from renewable energy resources using microbial fermentation.

MAIN TEXT

Butanol is a promising biofuel that can substitute for gasoline; unfortunately, natural microorganisms pose challenges for the economical production of 1-butanol at an industrial scale. The availability of genetic and molecular tools to engineer existing native pathways or create synthetic pathways have made non-native hosts a good choice for the production of 1-butanol from renewable resources. Non-native hosts have several distinct advantages, including using of cost-efficient feedstock, solvent tolerant and reduction of contamination risk. Therefore, engineering non-native hosts to produce biofuels is a promising approach towards achieving sustainability. This paper reviews the currently employed strategies and synthetic biology approaches used to produce 1-butanol in non-native hosts over the past few years. In addition, current challenges faced in using non-native hosts and the possible solutions that can help improve 1-butanol production are also discussed.

CONCLUSION

Non-native organisms have the potential to realize commercial production of 1- butanol from renewable resources. Future research should focus on substrate utilization, cofactor imbalance, and promoter selection to boost 1-butanol production in non-native hosts. Moreover, the application of robust genetic engineering approaches is required for metabolic engineering of microorganisms to make them industrially feasible for 1-butanol production.

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.

MapMaker and PathTracer for tracking carbon in genome-scale metabolic models

Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites.

Computational methods to identify metabolic sub-networks based on metabolomic profiles

Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

Applications of genome-scale metabolic network model in metabolic engineering

Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

Engineering biofuel tolerance in non-native producing microorganisms

Large-scale production of renewable biofuels through microbiological processes has drawn significant attention in recent years, mostly due to the increasing concerns on the petroleum fuel shortages and the environmental consequences of the over-utilization of petroleum-based fuels. In addition to native biofuel-producing microbes that have been employed for biofuel production for decades, recent advances in metabolic engineering and synthetic biology have made it possible to produce biofuels in several non-native biofuel-producing microorganisms. Compared to native producers, these non-native systems carry the advantages of fast growth, simple nutrient requirements, readiness for genetic modifications, and even the capability to assimilate CO2 and solar energy, making them competitive alternative systems to further decrease the biofuel production cost. However, the tolerance of these non-native microorganisms to toxic biofuels is naturally low, which has restricted the potentials of their application for high-efficiency biofuel production. To address the issues, researches have been recently conducted to explore the biofuel tolerance mechanisms and to construct robust high-tolerance strains for non-native biofuel-producing microorganisms. In this review, we critically summarize the recent progress in this area, focusing on three popular non-native biofuel-producing systems, i.e. Escherichia coli, Lactobacillus and photosynthetic cyanobacteria.

Rapid construction of metabolic models for a family of Cyanobacteria using a multiple source annotation workflow

Background

Cyanobacteria are photoautotrophic prokaryotes that exhibit robust growth under diverse environmental conditions with minimal nutritional requirements. They can use solar energy to convert CO₂ and other reduced carbon sources into biofuels and chemical products. The genus Cyanothece includes unicellular nitrogen-fixing cyanobacteria that have been shown to offer high levels of hydrogen production and nitrogen fixation. The reconstruction of quality genome-scale metabolic models for organisms with limited annotation resources remains a challenging task.

Results

Here we reconstruct and subsequently analyze and compare the metabolism of five Cyanothece strains, namely Cyanothece sp. PCC 7424, 7425, 7822, 8801 and 8802, as the genome-scale metabolic reconstructions iCyc792, iCyn731, iCyj826, iCyp752, and iCyh755 respectively. We compare these phylogenetically related Cyanothece strains to assess their bio-production potential. A systematic workflow is introduced for integrating and prioritizing annotation information from the Universal Protein Resource (Uniprot), NCBI Protein Clusters, and the Rapid Annotations using Subsystems Technology (RAST) method. The genome-scale metabolic models include fully traced photosynthesis reactions and respiratory chains, as well as balanced reactions and GPR associations. Metabolic differences between the organisms are highlighted such as the non-fermentative pathway for alcohol production found in only Cyanothece 7424, 8801, and 8802.

Conclusions

Our development workflow provides a path for constructing models using information from curated models of related organisms and reviewed gene annotations. This effort lays the foundation for the expedient construction of curated metabolic models for organisms that, while not being the target of comprehensive research, have a sequenced genome and are related to an organism with a curated metabolic model. Organism-specific models, such as the five presented in this paper, can be used to identify optimal genetic manipulations for targeted metabolite overproduction as well as to investigate the biology of diverse organisms.

Mathematical optimization applications in metabolic networks

Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.

Expanding the chemical palate of cells by combining systems biology and metabolic engineering

The field of Metabolic Engineering has recently undergone a transformation that has led to a rapid expansion of the chemical palate of cells. Now, it is conceivable to produce nearly any organic molecule of interest using a cellular host. Significant advances have been made in the production of biofuels, biopolymers and precursors, pharmaceuticals and nutraceuticals, and commodity and specialty chemicals. Much of this rapid expansion in the field has been, in part, due to synergies and advances in the area of systems biology. Specifically, the availability of functional genomics, metabolomics and transcriptomics data has resulted in the potential to produce a wealth of new products, both natural and non-natural, in cellular factories. The sheer amount and diversity of this data however, means that uncovering and unlocking novel chemistries and insights is a non-obvious exercise. To address this issue, a number of computational tools and experimental approaches have been developed to help expedite the design process to create new cellular factories. This review will highlight many of the systems biology enabling technologies that have reduced the design cycle for engineered hosts, highlight major advances in the expanded diversity of products that can be synthesized, and conclude with future prospects in the field of metabolic engineering.

MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

BACKGROUND

Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).

DESCRIPTION

MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.

CONCLUSIONS

The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).

An algorithm for efficient identification of branched metabolic pathways

This article presents a new graph-based algorithm for identifying branched metabolic pathways in multi-genome scale metabolic data. The term branched is used to refer to metabolic pathways between compounds that consist of multiple pathways that interact biochemically. A branched pathway may produce a target compound through a combination of linear pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While branched metabolic pathways predominate in metabolic networks, most previous work has focused on identifying linear metabolic pathways. The ability to automatically identify branched pathways is important in applications that require a deeper understanding of metabolism, such as metabolic engineering and drug target identification. The algorithm presented in this article utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on several well-characterized metabolic pathways that demonstrate that the new merging approach can efficiently find biologically relevant branched metabolic pathways.

Engineering of microorganisms for the production of biofuels and perspectives based on systems metabolic engineering approaches

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our interest in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to develop high performance microbes that are capable of producing biofuels with very high efficiency in order to compete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolic engineering, which can be considered as metabolic engineering integrated with systems biology and synthetic biology, have been developed. Systems metabolic engineering allows successful development of microbes that are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodiesel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers by metabolic engineering and systems metabolic engineering approaches are reviewed with relevant example cases. It is expected that systems metabolic engineering will be employed as an essential strategy for the development of microbial strains for industrial applications.

Butanol production from renewable biomass: rediscovery of metabolic pathways and metabolic engineering

Biofuel from renewable biomass is one of the answers to help solve the problems associated with limited fossil resources and climate change. Butanol has superior liquid-fuel characteristics, with similar properties to gasoline, and thus, has the potential to be used as a substitute for gasoline. Clostridia are recognized as a good butanol producers and are employed in the industrial-scale production of solvents. Due to the difficulty of performing genetic manipulations on clostridia, however, strain improvement has been rather slow. Furthermore, complex metabolic characteristics of acidogenesis followed by solventogenesis in this strain have hampered the development of engineered clostridia strains with highly efficient and selective butanol-production capabilities. In recent years, the butanol-producing characteristics in clostridia have been further characterized and alternative pathways discovered. More recently, systems-level metabolic engineering approaches were taken to develop superior strains. Herein, we review recent discoveries of metabolic pathways for butanol production and the metabolic engineering strategies being developed.

Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA

Malonyl-coenzyme A is an important precursor metabolite for the biosynthesis of polyketides, flavonoids and biofuels. However, malonyl-CoA naturally synthesized in microorganisms is consumed for the production of fatty acids and phospholipids leaving only a small amount available for the production of other metabolic targets in recombinant biosynthesis. Here we present an integrated computational and experimental approach aimed at improving the intracellular availability of malonyl-CoA in Escherichia coli. We used a customized version of the recently developed OptForce methodology to predict a minimal set of genetic interventions that guarantee a prespecified yield of malonyl-CoA in E. coli strain BL21 Star™. In order to validate the model predictions, we have successfully constructed an E. coli recombinant strain that exhibits a 4-fold increase in the levels of intracellular malonyl-CoA compared to the wild type strain. Furthermore, we demonstrate the potential of this E. coli strain for the production of plant-specific secondary metabolites naringenin (474mg/L) with the highest yield ever achieved in a lab-scale fermentation process. Combined effect of the genetic interventions was found to be synergistic based on a developed analysis method that correlates genetic modification to cell phenotype, specifically the identified knockout targets (ΔfumC and ΔsucC) and overexpression targets (ACC, PGK, GAPD and PDH) can cooperatively force carbon flux towards malonyl-CoA. The presented strategy can also be readily expanded for the production of other malonyl-CoA-derived compounds like polyketides and biofuels.

Engineering microbes to produce biofuels

The current biofuels landscape is chaotic. It is controlled by the rules imposed by economic forces and driven by the necessity of finding new sources of energy, particularly motor fuels. The need is bringing forth great creativity in uncovering new candidate fuel molecules that can be made via metabolic engineering. These next generation fuels include long-chain alcohols, terpenoid hydrocarbons, and diesel-length alkanes. Renewable fuels contain carbon derived from carbon dioxide. The carbon dioxide is derived directly by a photosynthetic fuel-producing organism(s) or via intermediary biomass polymers that were previously derived from carbon dioxide. To use the latter economically, biomass depolymerization processes must improve and this is a very active area of research. There are competitive approaches with some groups using enzyme based methods and others using chemical catalysts. With the former, feedstock and end-product toxicity loom as major problems. Advances chiefly rest on the ability to manipulate biological systems. Computational and modular construction approaches are key. For example, novel metabolic networks have been constructed to make long-chain alcohols and hydrocarbons that have superior fuel properties over ethanol. A particularly exciting approach is to implement a direct utilization of solar energy to make a usable fuel. A number of approaches use the components of current biological systems, but re-engineer them for more direct, efficient production of fuels.

Resolving the TCA cycle and pentose-phosphate pathway of Clostridium acetobutylicum ATCC 824: Isotopomer analysis, in vitro activities and expression analysis

Solventogenic clostridia are an important class of microorganisms that can produce various biofuels. One of the bottlenecks in engineering clostridia stems from the fact that central metabolic pathways remain poorly understood. Here, we utilized the power of (13) C-based isotopomer analysis to re-examine central metabolic pathways of Clostridium acetobutylicum ATCC 824. We demonstrate using [1,2-(13) C]glucose, MS analysis of intracellular metabolites, and enzymatic assays that C. acetobutylicum has a split TCA cycle where only Re-citrate synthase (CS) contributes to the production of α-ketoglutarate via citrate. Furthermore, we show that there is no carbon exchange between α-ketoglutarate and fumarate and that the oxidative pentose-phosphate pathway (oxPPP) is inactive. Dynamic gene expression analysis of the putative Re-CS gene (CAC0970), its operon, and all glycolysis, pentose-phosphate pathway, and TCA cycle genes identify genes and their degree of involvement in these core pathways that support the powerful primary metabolism of this industrial organism.