Automated system for gene annotation and metabolic pathway reconstruction using general sequence databases

Despite the growing number of genomes published or currently being sequenced, there is a relative paucity of software for functional classification of newly discovered genes and their assignment to metabolic pathways. Available software for such analyses has a very steep learning curve and requires the installation, configuration, and maintenance of large amounts of complex infrastructure, including complementary software and databases. Many such tools are restricted to one or a few data sources and classification schemes. In this work, we report an automated system for gene annotation and metabolic pathway reconstruction (ASGARD), which was designed to be powerful and generalizable, yet simple for the biologist to install and run on centralized, commonly available computers. It avoids the requirement for complex resources such as relational databases and web servers, as well as the need for administrator access to the operating system. Our methodology contributes to a more rapid investigation of the potential biochemical capabilities of genes and genomes by the biological researcher, and is useful in biochemical as well as comparative and evolutionary studies of pathways and networks.     

Metabolite damage and repair in metabolic engineering design

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways – particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile ‘plug and play’ set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.     

Chemical and genomic evolution of enzyme-catalyzed reaction networks

There is a tendency that a unit of enzyme genes in an operon-like structure in the prokaryotic genome encodes enzymes that catalyze a series of consecutive reactions in a metabolic pathway. Our recent analysis shows that this and other genomic units correspond to chemical units reflecting chemical logic of organic reactions. From all known metabolic pathways in the KEGG database we identified chemical units, called reaction modules, as the conserved sequences of chemical structure transformation patterns of small molecules. The extracted patterns suggest co-evolution of genomic units and chemical units. While the core of the metabolic network may have evolved with mechanisms involving individual enzymes and reactions, its extension may have been driven by modular units of enzymes and reactions.     

Effect of substrate competition in kinetic models of metabolic networks

Substrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.     

Exploring De Novo metabolic pathways from pyruvate to propionic acid

Industrial biotechnology provides an efficient, sustainable solution for chemical production. However, designing biochemical pathways based solely on known reactions does not exploit its full potential. Enzymes are known to accept non‐native substrates, which may allow novel, advantageous reactions. We have previously developed a computational program named Biological Network Integrated Computational Explorer (BNICE) to predict promiscuous enzyme activities and design synthetic pathways, using generalized reaction rules curated from biochemical reaction databases. Here, we use BNICE to design pathways synthesizing propionic acid from pyruvate. The currently known natural pathways produce undesirable by‐products lactic acid and succinic acid, reducing their economic viability. BNICE predicted seven pathways containing four reaction steps or less, five of which avoid these by‐products. Among the 16 biochemical reactions comprising these pathways, 44% were validated by literature references. More than 28% of these known reactions were not in the BNICE training dataset, showing that BNICE was able to predict novel enzyme substrates. Most of the pathways included the intermediate acrylic acid. As acrylic acid bioproduction has been well advanced, we focused on the critical step of reducing acrylic acid to propionic acid. We experimentally validated that Oye2p from Saccharomyces cerevisiae can catalyze this reaction at a slow turnover rate (10^?3 s^?1), which was unknown to occur with this enzyme, and is an important finding for further propionic acid metabolic engineering. These results validate BNICE as a pathway‐searching tool that can predict previously unknown promiscuous enzyme activities and show that computational methods can elucidate novel biochemical pathways for industrial applications. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:303–311, 2016     

Inferring meaningful pathways in weighted metabolic networks

An approach is presented for computing meaningful pathways in the network of small molecule metabolism comprising the chemical reactions characterized in all organisms. The metabolic network is described as a weighted graph in which all the compounds are included, but each compound is assigned a weight equal to the number of reactions in which it participates. Path finding is performed in this graph by searching for one or more paths with lowest weight. Performance is evaluated systematically by computing paths between the first and last reactions in annotated metabolic pathways, and comparing the intermediate reactions in the computed pathways to those in the annotated ones. For the sake of comparison, paths are computed also in the un-weighted raw (all compounds and reactions) and filtered (highly connected pool metabolites removed) metabolic graphs, respectively. The correspondence between the computed and annotated pathways is very poor (<30%) in the raw graph; increasing to approximately 65% in the filtered graph; reaching approximately 85% in the weighted graph. Considering the best-matching path among the five lightest paths increases the correspondence to 92%, on average. We then show that the average distance between pairs of metabolites is significantly larger in the weighted graph than in the raw unfiltered graph, suggesting that the small-world properties previously reported for metabolic networks probably result from irrelevant shortcuts through pool metabolites. In addition, we provide evidence that the length of the shortest path in the weighted graph represents a valid measure of the "metabolic distance" between enzymes. We suggest that the success of our simplistic approach is rooted in the high degree of specificity of the reactions in metabolic pathways, presumably reflecting thermodynamic constraints operating in these pathways. We expect our approach to find useful applications in inferring metabolic pathways in newly sequenced genomes.     

Genetically constrained metabolic flux analysis

Significant progress has been made in using existing metabolic databases to estimate metabolic fluxes. Traditional metabolic flux analysis generally starts with a predetermined metabolic network. This approach has been employed successfully to analyze the behaviors of recombinant strains by manually adding or removing the corresponding pathway(s) in the metabolic map. The current work focuses on the development of a new framework that utilizes genomic and metabolic databases, including available genetic/regulatory network structures and gene chip expression data, to constrain metabolic flux analysis. The genetic network consisting of the sensing/regulatory circuits will activate or deactivate a specific set of genes in response to external stimulus. The activation and/or repression of this set of genes will result in different gene expression levels that will in turn change the structure of the metabolic map. Hence, the metabolic map will automatically "adapt" to the external stimulus as captured by the genetic network. This adaptation selects a subnetwork from the pool of feasible reactions and so performs what we term "environmentally driven dimensional reduction." The Escherichia coli oxygen and redox sensing/regulatory system, which controls the metabolic patterns connected to glycolysis and the TCA cycle, was used as a model system to illustrate the proposed approach.     

Exploring the differences in metabolic behavior of astrocyte and glioblastoma: a flux balance analysis approach

Brain cancers demonstrate a complex metabolic behavior so as to adapt the external hypoxic environment and internal stress generated by reactive oxygen species. To survive in these stringent conditions, glioblastoma cells develop an antagonistic metabolic phenotype as compared to their predecessors, the astrocytes, thereby quenching the resources expected for nourishing the neurons. The complexity and cumulative effect of the large scale metabolic functioning of glioblastoma is mostly unexplored. In this study, we reconstruct a metabolic network comprising of pathways that are known to be deregulated in glioblastoma cells as compared to the astrocytes. The network, consisted of 147 genes encoding for enzymes performing 247 reactions distributed across five distinct model compartments, was then studied using constrained-based modeling approach by recreating the scenarios for astrocytes and glioblastoma, and validated with available experimental evidences. From our analysis, we predict that glycine requirement of the astrocytes are mostly fulfilled by the internal glycine–serine metabolism, whereas glioblastoma cells demand an external uptake of glycine to utilize it for glutathione production. Also, cystine and glucose were identified to be the major contributors to glioblastoma growth. We also proposed an extensive set of single and double lethal reaction knockouts, which were further perturbed to ascertain their role as probable chemotherapeutic targets. These simulation results suggested that, apart from targeting the reactions of central carbon metabolism, knockout of reactions belonging to the glycine–serine metabolism effectively reduce glioblastoma growth. The combinatorial targeting of glycine transporter with any other reaction belonging to glycine–serine metabolism proved lethal to glioblastoma growth.

Electronic supplementary material

The online version of this article (doi:10.1007/s11693-015-9183-9) contains supplementary material, which is available to authorized users.     

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.     

Automated metabolic reconstruction for Methanococcus jannaschii

We present the computational prediction and synthesis of the metabolic pathways in Methanococcus jannaschii from its genomic sequence using the PathoLogic software. Metabolic reconstruction is based on a reference knowledge base of metabolic pathways and is performed with minimal manual intervention. We predict the existence of 609 metabolic reactions that are assembled in 113 metabolic pathways and an additional 17 super-pathways consisting of one or more component pathways. These assignments represent significantly improved enzyme and pathway predictions compared with previous metabolic reconstructions, and some key metabolic reactions, previously missing, have been identified. Our results, in the form of enzymatic assignments and metabolic pathway predictions, form a database (MJCyc) that is accessible over the World Wide Web for further dissemination among members of the scientific community.     

Encoding microbial metabolic logic: predicting biodegradation

Prediction of microbial metabolism is important for annotating genome sequences and for understanding the fate of chemicals in the environment. A metabolic pathway prediction system (PPS) has been developed that is freely available on the world wide web (), recognizes the organic functional groups found in a compound, and predicts transformations based on metabolic rules. These rules are designed largely by examining reactions catalogued in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) and are generalized based on metabolic logic. The predictive accuracy of the PPS was tested: (1) using a 113-member set of compounds found in the database, (2) against a set of compounds whose metabolism was predicted by human experts, and (3) for consistency with experimental microbial growth studies. First, the system correctly predicted known metabolism for 111 of the 113 compounds containing C and H, O, N, S, P and/or halides that initiate existing pathways in the database, and also correctly predicted 410 of the 569 known pathway branches for these compounds. Second, computer predictions were compared to predictions by human experts for biodegradation of six compounds whose metabolism was not described in the literature. Third, the system predicted reactions liberating ammonia from three organonitrogen compounds, consistent with laboratory experiments showing that each compound served as the sole nitrogen source supporting microbial growth. The rule-based nature of the PPS makes it transparent, expandable, and adaptable.     

Systematizing the generation of missing metabolic knowledge

Genome‐scale metabolic network reconstructions are built from all of the known metabolic reactions and genes in a target organism. However, since our knowledge of any organism is incomplete, these network reconstructions contain gaps. Reactions may be missing, resulting in dead‐ends in pathways, while unknown gene products may catalyze known reactions. New computational methods that analyze data, such as growth phenotypes or gene essentiality, in the context of genome‐scale metabolic networks, have been developed to predict these missing reactions or genes likely to fill these knowledge gaps. A growing number of experimental studies are appearing that address these computational predictions, leading to discovery of new metabolic capabilities in the target organism. Gap‐filling methods can thus be used to improve metabolic network models while simultaneously leading to discovery of new metabolic gene functions. Biotechnol. Bioeng. 2010;107: 403–412. © 2010 Wiley Periodicals, Inc.     

Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions

Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways’ side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl.gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.     

Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism

Elementary mode analysis is a useful metabolic pathway analysis tool to identify the structure of a metabolic network that links the cellular phenotype to the corresponding genotype. The analysis can decompose the intricate metabolic network comprised of highly interconnected reactions into uniquely organized pathways. These pathways consisting of a minimal set of enzymes that can support steady state operation of cellular metabolism represent independent cellular physiological states. Such pathway definition provides a rigorous basis to systematically characterize cellular phenotypes, metabolic network regulation, robustness, and fragility that facilitate understanding of cell physiology and implementation of metabolic engineering strategies. This mini-review aims to overview the development and application of elementary mode analysis as a metabolic pathway analysis tool in studying cell physiology and as a basis of metabolic engineering.     

The metabolic blueprint of Phaeodactylum tricornutum reveals a eukaryotic Entner-Doudoroff glycolytic pathway

Diatoms are one of the most successful groups of unicellular eukaryotic algae. Successive endosymbiotic events contributed to their flexible metabolism, making them competitive in variable aquatic habitats. Although the recently sequenced genomes of the model diatoms Phaeodactylum tricornutum and Thalassiosira pseudonana have provided the first insights into their metabolic organization, the current knowledge on diatom biochemistry remains fragmentary. By means of a genome‐wide approach, we developed DiatomCyc, a detailed pathway/genome database of P. tricornutum. DiatomCyc contains 286 pathways with 1719 metabolic reactions and 1613 assigned enzymes, spanning both the central and parts of the secondary metabolism of P. tricornutum. Central metabolic pathways, such as those of carbohydrates, amino acids and fatty acids, were covered. Furthermore, our understanding of the carbohydrate model in P. tricornutum was extended. In particular we highlight the discovery of a functional Entner–Doudoroff pathway, an ancient alternative for the glycolytic Embden–Meyerhof–Parnas pathway, and a putative phosphoketolase pathway, both uncommon in eukaryotes. DiatomCyc is accessible online ( http://www.diatomcyc.org ), and offers a range of software tools for the visualization and analysis of metabolic networks and ‘omics’ data. We anticipate that DiatomCyc will be key to gaining further understanding of diatom metabolism and, ultimately, will feed metabolic engineering strategies for the industrial valorization of diatoms.     

Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices

Genome-scale metabolic networks can be reconstructed. The systemic biochemical properties of these networks can now be studied. Here, genome-scale reconstructed metabolic networks were analysed using singular value decomposition (SVD). All the individual biochemical conversions contained in a reconstructed metabolic network are described by a stoichiometric matrix (S). SVD of S led to the definition of the underlying modes that characterize the overall biochemical conversions that take place in a network and rank-ordered their importance. The modes were shown to correspond to systemic biochemical reactions and they could be used to identify the groups and clusters of individual biochemical reactions that drive them. Comparative analysis of the Escherichia coli, Haemophilus influenzae, and Helicobacter pylori genome-scale metabolic networks showed that the four dominant modes in all three networks correspond to: (1) the conversion of ATP to ADP, (2) redox metabolism of NADP, (3) proton-motive force, and (4) inorganic phosphate metabolism. The sets of individual metabolic reactions deriving these systemic conversions, however, differed among the three organisms. Thus, we can now define systemic metabolic reactions, or eigen-reactions, for the study of systems biology of metabolism and have a basis for comparing the overall properties of genome-specific metabolic networks.