Evaluation of computational metabolic-pathway predictions for Helicobacter pylori

MOTIVATION: We seek to determine the accuracy of computational methods for predicting metabolic pathways in sequenced genomes, and to understand the contributions of both the prediction algorithms, and the reference pathway databases used by those algorithms, to the prediction accuracy. RESULTS: The comparisons we performed were as follows. (1) We compared two predictions of the pathway complements of Helicobacter pylori that were computed by an early version of our pathway-prediction algorithm: prediction A used the EcoCyc E. coli pathway DB as the reference database (DB) for prediction, and prediction B used the MetaCyc pathway DB (a superset of EcoCyc) as the reference pathway DB. The MetaCyc-based prediction contained 75% more pathway predictions, but we believe a significant number of those predictions were false positives. (2) We compared two predictions of the pathway complement of H. pylori that used MetaCyc as the reference pathway DB, but that used different algorithms: the original PathoLogic algorithm, and an enhanced version of the algorithm designed to eliminate false-positive pathway predictions. The improved algorithm predicted 30\% fewer metabolic pathways than the original algorithm; all of the eliminated pathways are believed to be false-positive predictions. (3) We compared the 98 pathways predicted by the enhanced algorithm with the results of a manual analysis of the pathways of H. pylori. Results: 40 of the computationally predicted pathways were consistent with the manual analysis, 13 pathways are considered false-positive predictions, and four pathways had partially overlapping topologies. Twenty-six predicted pathways were not mentioned in the manual analysis; we believe these are correct predictions by PathoLogic that were not found by the manual analysis. Five pathways from the manual analysis were not found computationally. Agreement between the computational and manual predictions was good overall, with the computational analysis inferring many pathways that the manual analysis did not identify. Ultimately the manual analysis is also partially speculative, and therefore is not an absolute measure of correctness. The algorithm is designed to err on the side of more false positives to bring more potential pathways to the user's attention. The resulting H. pylori pathway DB is freely available at http://ecocyc.org:1555/HPY/organism-summary?object=HPY. AVAILABILITY: The Pathway Tools software is freely available to academic users, and is available to commercial users for a fee. Contact pkarp@ai.sri.com for information on obtaining the software.     

PseudoCyc,a pathway-genome database for Pseudomonas aeruginosa

A pathway-genome database (PGDB) describes the entire genome of an organism, as well as its biochemical pathways, reactions, and enzymes. Our PathoLogic software can generate a PGDB from an annotated genome of an organism, predicting the metabolic reactions and pathways corresponding to the enzymes present in the annotation. We have used PathoLogic to generate a PGDB for PSEUDOMONAS AERUGINOSA, strain PAO1, called 'PseudoCyc', which includes 139 predicted pathways and 800 predicted reactions involving 623 chemical species and 718 enzymes. Analysis of the PathoLogic predictions of arginine metabolism and the beta-ketoadipate pathway, which are landmark pathways in P. AERUGINOSA, showed that they were for the most part correctly predicted. These studies also provided possible locations for two genes involved in the beta-ketoadipate pathway, PCAI and PCAJ, which are missing from the PAO1 annotation. PseudoCyc adds an extended dimension to the genome of P. AERUGINOSA, providing researchers with a helpful tool for the analysis of the genomic, proteomic, and metabolic information of the organism. The finding of the probable location of the PCAI and PCAJ genes is but one example of the discoveries facilitated by such a PGDB. PseudoCyc, along with PGDBs for 12 other organisms, is available at http://BioCyc.org/.     

Computational prediction of human metabolic pathways from the complete human genome

Background  

We present a computational pathway analysis of the human genome that assigns enzymes encoded therein to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary first step toward quantitative modeling of metabolism.     

Thermodynamic constraints shape the structure of carbon fixation pathways

Thermodynamics impose a major constraint on the structure of metabolic pathways. Here, we use carbon fixation pathways to demonstrate how thermodynamics shape the structure of pathways and determine the cellular resources they consume. We analyze the energetic profile of prototypical reactions and show that each reaction type displays a characteristic change in Gibbs energy. Specifically, although carbon fixation pathways display a considerable structural variability, they are all energetically constrained by two types of reactions: carboxylation and carboxyl reduction. In fact, all adenosine triphosphate (ATP) molecules consumed by carbon fixation pathways - with a single exception - are used, directly or indirectly, to power one of these unfavorable reactions. When an indirect coupling is employed, the energy released by ATP hydrolysis is used to establish another chemical bond with high energy of hydrolysis, e.g. a thioester. This bond is cleaved by a downstream enzyme to energize an unfavorable reaction. Notably, many pathways exhibit reduced ATP requirement as they couple unfavorable carboxylation or carboxyl reduction reactions to exergonic reactions other than ATP hydrolysis. In the most extreme example, the reductive acetyl coenzyme A (acetyl-CoA) pathway bypasses almost all ATP-consuming reactions. On the other hand, the reductive pentose phosphate pathway appears to be the least ATP-efficient because it is the only carbon fixation pathway that invests ATP in metabolic aims other than carboxylation and carboxyl reduction. Altogether, our analysis indicates that basic thermodynamic considerations accurately predict the resource investment required to support a metabolic pathway and further identifies biochemical mechanisms that can decrease this requirement.     

Synthetic Pathway for Production of Five-Carbon Alcohols from Isopentenyl Diphosphate

Synthetic biological pathways could enhance the development of novel processes to produce chemicals from renewable resources. On the basis of models that describe the evolution of metabolic pathways and enzymes in nature, we developed a framework to rationally identify enzymes able to catalyze reactions on new substrates that overcomes one of the major bottlenecks in the assembly of a synthetic biological pathway. We verified the framework by implementing a pathway with two novel enzymatic reactions to convert isopentenyl diphosphate into 3-methyl-3-butenol, 3-methyl-2-butenol, and 3-methylbutanol. To overcome competition with native pathways that share the same substrate, we engineered two bifunctional enzymes that redirect metabolic flux toward the synthetic pathway. Taken together, our work demonstrates a new approach to the engineering of novel synthetic pathways in the cell.     

Small-molecule metabolism: an enzyme mosaic

Escherichia coli has been a popular organism for studying metabolic pathways. In an attempt to find out more about how these pathways are constructed, the enzymes were analysed by defining their protein domains. Structural assignments and sequence comparisons were used to show that 213 domain families constitute 90% of the enzymes in the small-molecule metabolic pathways. Catalytic or cofactor-binding properties between family members are often conserved, while recognition of the main substrate with change in catalytic mechanism is only observed in a few cases of consecutive enzymes in a pathway. Recruitment of domains across pathways is very common, but there is little regularity in the pattern of domains in metabolic pathways. This is analogous to a mosaic in which a stone of a certain colour is selected to fill a position in the picture.     

The influence of metabolic network structures and energy requirements on xanthan gum yields

The metabolic network of Xanthomonas campestris is complex since a number of cyclic pathways are present making simple stoichiometric yield predictions difficult. The influence of certain pathway configurations and the resulting variations in flux have been examined as regards the maximum yield potential of this bacteria for xanthan gum production. These predictions have been compared with experimental results showing that the strain employed is functioning close to its theoretical maximum as regards yield criteria. The major constraint imposed on the network concerns energy availability which has a more pronounced effect on yield than carbon precursor supply. This can be attributed to the relatively high maintenance requirements determined experimentally and incorporated into the model. While some of this overall energy burden will undoubtedly be associated with incompressible metabolic requirements such as sugar uptake and xanthan efflux mechanisms, future strain improvement strategies will need to attack other non-essential energy-consuming reactions, if yields are to be further increased.     

The pathway tools pathway prediction algorithm

The PathoLogic component of the Pathway Tools software performs prediction of metabolic pathways in sequenced and annotated genomes. This article provides a detailed presentation of the PathoLogic algorithm. The algorithm consists of two phases. The reactome inference phase infers the reactions catalyzed by the organism from the set of enzymes present in the annotated genome. The pathway inference phase infers the metabolic pathways present in the organism from the reactions catalyzed by the organism. Both phases draw on the MetaCyc database of metabolic reactions and pathways. MetaCyc contains two data fields to support pathway inference: the expected taxonomic range of each pathway, and a list of key reactions for pathways. These fields have significantly increased the predictive accuracy of PathoLogic.     

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.     

Dissecting Germ Cell Metabolism through Network Modeling

Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.     

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.     

Minimal Escherichia coli cell for the most efficient production of ethanol from hexoses and pentoses

To obtain an efficient ethanologenic Escherichia coli strain, we reduced the functional space of the central metabolic network, with eight gene knockout mutations, from over 15,000 pathway possibilities to 6 pathway options that support cell function. The remaining pathways, identified by elementary mode analysis, consist of four pathways with non-growth-associated conversion of pentoses and hexoses into ethanol at theoretical yields and two pathways with tight coupling of anaerobic cell growth with ethanol formation at high yields. Elimination of three additional genes resulted in a strain that selectively grows only on pentoses, even in the presence of glucose, with a high ethanol yield. We showed that the ethanol yields of strains with minimized metabolic functionality closely matched the theoretical predictions. Remarkably, catabolite repression was completely absent during anaerobic growth, resulting in the simultaneous utilization of pentoses and hexoses for ethanol production.     

THE EVOLUTION OF GENES IN BRANCHED METABOLIC PATHWAYS

Simulation models of the evolution of genes in a branched metabolic pathway subject to stabilizing selection on flux are described and analyzed. The models are based either on metabolic control theory (MCT), with the assumption that enzymes are far from saturation, or on Michaelis–Menten kinetics, which allows for saturation and near saturation. Several predictions emerge from the models: (1) flux control evolves to be concentrated at pathway branch points, including the first enzyme in the pathway. (2) When flux is far from its optimum, adaptive substitutions occur disproportionately often in branching enzymes. (3) When flux is near its optimum, adaptive substitutions occur disproportionately often in nonbranching enzymes. (4) Slightly deleterious substitutions occur disproportionately often in nonbranching enzymes. (5) In terms of both flux control and patterns of substitution, pathway branches are similar to those predicted for linear pathways. These predictions provide null hypotheses for empirical examination of the evolution of genes in metabolic pathways.     

Design, construction and performance of the most efficient biomass producing E. coli bacterium

Inverse metabolic engineering based on elementary mode analysis was applied to maximize the biomass yield of Escherchia coli MG1655. Elementary mode analysis was previously employed to identify among 1691 possible pathways for cell growth the most efficient pathway with maximum biomass yield. The metabolic network analysis predicted that deletion of only 6 genes reduces the number of possible elementary modes to the most efficient pathway. We have constructed a strain containing these gene deletions and we evaluated its properties in batch and in chemostat growth experiments. The results show that the theoretical predictions are closely matched by the properties of the designed strain.     

The enzymatic and metabolic capabilities of early life

We introduce the concept of metaconsensus and employ it to make high confidence predictions of early enzyme functions and the metabolic properties that they may have produced. Several independent studies have used comparative bioinformatics methods to identify taxonomically broad features of genomic sequence data, protein structure data, and metabolic pathway data in order to predict physiological features that were present in early, ancestral life forms. But all such methods carry with them some level of technical bias. Here, we cross-reference the results of these previous studies to determine enzyme functions predicted to be ancient by multiple methods. We survey modern metabolic pathways to identify those that maintain the highest frequency of metaconsensus enzymes. Using the full set of modern reactions catalyzed by these metaconsensus enzyme functions, we reconstruct a representative metabolic network that may reflect the core metabolism of early life forms. Our results show that ten enzyme functions, four hydrolases, three transferases, one oxidoreductase, one lyase, and one ligase, are determined by metaconsensus to be present at least as late as the last universal common ancestor. Subnetworks within central metabolic processes related to sugar and starch metabolism, amino acid biosynthesis, phospholipid metabolism, and CoA biosynthesis, have high frequencies of these enzyme functions. We demonstrate that a large metabolic network can be generated from this small number of enzyme functions.     

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.