nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria.

Cross-feeding interactions, in which bacterial cells exchange costly metabolites to the benefit of both interacting partners, are very common in the microbial world. However, it generally remains unclear what maintains this type of interaction in the presence of non-cooperating types. We investigate this problem using synthetic cross-feeding interactions: by simply deleting two metabolic genes from the genome of Escherichia coli, we generated genotypes that require amino acids to grow and release other amino acids into the environment. Surprisingly, in a vast majority of cases, cocultures of two cross-feeding strains showed an increased Darwinian fitness (that is, rate of growth) relative to prototrophic wild type cells--even in direct competition. This unexpected growth advantage was due to a division of metabolic labour: the fitness cost of overproducing amino acids was less than the benefit of not having to produce others when they were provided by their partner. Moreover, frequency-dependent selection maintained cross-feeding consortia and limited exploitation by non-cooperating competitors. Together, our synthetic study approach reveals ecological principles that can help explain the widespread occurrence of obligate metabolic cross-feeding interactions in nature.

KNIME-CDK: Workflow-driven cheminformatics.

BACKGROUND: Cheminformaticians have to routinely process and analyse libraries of small molecules. Among other things, that includes the standardization of molecules, calculation of various descriptors, visualisation of molecular structures, and downstream analysis. For this purpose, scientific workflow platforms such as the Konstanz Information Miner can be used if provided with the right plug-in. A workflow-based cheminformatics tool provides the advantage of ease-of-use and interoperability between complementary cheminformatics packages within the same framework, hence facilitating the analysis process. RESULTS: KNIME-CDK comprises functions for molecule conversion to/from common formats, generation of signatures, fingerprints, and molecular properties. It is based on the Chemistry Development Toolkit and uses the Chemical Markup Language for persistence. A comparison with the cheminformatics plug-in RDKit shows that KNIME-CDK supports a similar range of chemical classes and adds new functionality to the framework. We describe the design and integration of the plug-in, and demonstrate the usage of the nodes on ChEBI, a library of small molecules of biological interest. CONCLUSIONS: KNIME-CDK is an open-source plug-in for the Konstanz Information Miner, a free workflow platform. KNIME-CDK is build on top of the open-source Chemistry Development Toolkit and allows for efficient cross-vendor structural cheminformatics. Its ease-of-use and modularity enables researchers to automate routine tasks and data analysis, bringing complimentary cheminformatics functionality to the workflow environment.

Selection of human tissue-specific elementary flux modes using gene expression data.

MOTIVATION: The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising. RESULTS: In this article, we exploit and generalize the K-shortest EFM algorithm to determine a subset of EFMs in a human genome-scale metabolic network. This subset of EFMs involves a wide number of reported human metabolic pathways, as well as potential novel routes, and constitutes a valuable database where high-throughput data can be mapped and contextualized from a metabolic perspective. To illustrate this, we took expression data of 10 healthy human tissues from a previous study and predicted their characteristic EFMs based on enrichment analysis. We used a multivariate hypergeometric test and showed that it leads to more biologically meaningful results than standard hypergeometric. Finally, a biological discussion on the characteristic EFMs obtained in liver is conducted, finding a high level of agreement when compared with the literature.

Detecting and investigating substrate cycles in a genome-scale human metabolic network.

Substrate cycles, also known as futile cycles, are cyclic metabolic routes that dissipate energy by hydrolysing cofactors such as ATP. They were first described to occur in the muscles of bumblebees and brown adipose tissue in the 1970s. A popular example is the conversion of fructose 6-phosphate to fructose 1,6-bisphosphate and back. In the present study, we analyze a large number of substrate cycles in human metabolism that consume ATP and discuss their statistics. For this purpose, we use two recently published methods (i.e. EFMEvolver and the K-shortest EFM method) to calculate samples of 100,000 and 15,000 substrate cycles, respectively. We find an unexpectedly high number of substrate cycles in human metabolism, with up to 100 reactions per cycle, utilizing reactions from up to six different compartments. An analysis of tissue-specific models of liver and brain metabolism shows that there is selective pressure that acts against the uncontrolled dissipation of energy by avoiding the coexpression of enzymes belonging to the same substrate cycle. This selective force is particularly strong against futile cycles that have a high flux as a result of thermodynamic principles.

Against the stream: relevance of gluconeogenesis from fatty acids for natives of the arctic regions.

BACKGROUND: The question whether even-chain fatty acids can be converted into glucose has a long-standing tradition in biochemistry. Since the glyoxylate shunt is absent from mammals, the question has been considered to be solved. It is of particular relevance for understanding the metabolic state of natives of the arctic regions due to the very high fat content of their traditional diet only containing negligible amounts of carbohydrates. METHODS & RESULTS: Using an in silico approach, we discovered several hitherto unknown routes in human metabolism that allow the conversion of even-chain fatty acids into carbohydrates in humans. These pathways proceed via ketogenesis over the intermediate of acetone and produce the gluconeogenic precursor pyruvate. While these pathways can make a contribution to glucose production during times of limited carbohydrate supply, we found that their capacity might be limited due to a high demand in reducing equivalents in acetone degradation. Considering the traditional diet of natives of the arctic regions, the detected pathways are not only important in order to improve carbohydrate supply, but moreover reduce the amount of protein that needs to be used for gluconeogenesis. CONCLUSION: In summary, our study sheds new light on our understanding of the metabolic state of natives from the arctic regions on their traditional diet. Moreover, they provide an avenue for new analyses that can reveal how humans have adapted metabolically to a practically carbohydrate-free diet.

NAD(+) biosynthesis and salvage--a phylogenetic perspective.

NAD is best known as an electron carrier and a cosubstrate of various redox reactions. However, over the past 20 years, NAD(+) has been shown to be a key signaling molecule that mediates post-translational protein modifications and serves as precursor of ADP-ribose-containing messenger molecules, which are involved in calcium mobilization. In contrast to its role as a redox carrier, NAD(+)-dependent signaling processes involve the release of nicotinamide (Nam) and require constant replenishment of cellular NAD(+) pools. So far, very little is known about the evolution of NAD(P) synthesis in eukaryotes. In the present study, genes involved in NAD(P) metabolism in 45 species were identified and analyzed with regard to similarities and differences in NAD(P) synthesis. The results show that the Preiss-Handler pathway and NAD(+) kinase are present in all organisms investigated, and thus seem to be ancestral routes. Additionally, two pathways exist that convert Nam to NAD(+); we identified several species that have apparently functional copies of both biosynthetic routes, which have been thought to be mutually exclusive. Furthermore, our findings suggest the parallel phylogenetic appearance of Nam N-methyltransferase, Nam phosphoribosyl transferase, and poly-ADP-ribosyltransferases.

In silico evidence for gluconeogenesis from fatty acids in humans.

The question whether fatty acids can be converted into glucose in humans has a long standing tradition in biochemistry, and the expected answer is "No". Using recent advances in Systems Biology in the form of large-scale metabolic reconstructions, we reassessed this question by performing a global investigation of a genome-scale human metabolic network, which had been reconstructed on the basis of experimental results. By elementary flux pattern analysis, we found numerous pathways on which gluconeogenesis from fatty acids is feasible in humans. On these pathways, four moles of acetyl-CoA are converted into one mole of glucose and two moles of CO2. Analyzing the detected pathways in detail we found that their energetic requirements potentially limit their capacity. This study has many other biochemical implications: effect of starvation, sports physiology, practically carbohydrate-free diets of inuit, as well as survival of hibernating animals and embryos of egg-laying animals. Moreover, the energetic loss associated to the usage of gluconeogenesis from fatty acids can help explain the efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet.

Pathway analysis of NAD+ metabolism.

NAD(+) is well known as a crucial cofactor in the redox balance of metabolism. Moreover, NAD(+) is degraded in ADP-ribosyl transfer reactions, which are important components of multitudinous signalling reactions. These include reactions linked to DNA repair and aging. In the present study, using the concept of EFMs (elementary flux modes), we established all of the potential routes in a network describing NAD(+) biosynthesis and degradation. All known biosynthetic pathways, which include de novo synthesis starting from tryptophan as well as the classical Preiss-Handler pathway and NAD(+) synthesis from other vitamin precursors, were detected as EFMs. Moreover, several EFMs were found that degrade NAD(+), represent futile cycles or have other functionalities. The systematic analysis and comparison of the networks specific for yeast and humans document significant differences between species with regard to the use of precursors, biosynthetic routes and NAD(+)-dependent signalling.

Combining metabolic pathway analysis with Evolutionary Game Theory: explaining the occurrence of low-yield pathways by an analytic optimization approach.

Elementary-mode analysis is a powerful method for detecting all potential pathways in a metabolic network and computing the associated molar yields. Metabolic pathways can be interpreted as different strategies of organisms. Thus, methods from Evolutionary Game Theory can be employed. In Flux Balance Analysis (FBA), it is usually assumed that molar yields of relevant products (such as biomass or ATP) have been maximized during evolution. This has been questioned on game-theoretical grounds. In particular, in situations that can be characterized as a Prisoner's Dilemma, maximization of flux is not in line with maximization of yield. Under other conditions (that is, for other parameter values of maximal velocities), a Harmony game can result, where the above two maximization criteria give the same result. Here, we analyse the optimal situations under varying conditions. In particular, we consider the case where the cell can allocate a certain amount of protein on several enzymes in a varying distribution and model this by a linear programming problem in which not only the rates but also the maximal velocities are variable. It turns out that in the case of low or moderate synthesis costs for the enzymes of the high-yield pathway, maximizing pathway flux is in line with maximizing molar yield while in the case of high costs, it is not. This may explain the observation that many cells such as striated muscle cells, tumour cells, activated lymphocytes and several yeasts do not reallocate protein away from glycolytic enzymes towards TCA cycle and respiratory chain enzymes, in spite of the higher efficiency of respiration. This provides a straightforward explanation of the Warburg effect in tumour cells.

Predicting novel pathways in genome-scale metabolic networks.

Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.

Metabolic reconstruction, constraint-based analysis and game theory to probe genome-scale metabolic networks.

With the advent of modern omics technologies, it has become feasible to reconstruct (quasi-) whole-cell metabolic networks and characterize them in more and more detail. Computer simulations of the dynamic behavior of such networks are difficult due to a lack of kinetic data and to computational limitations. In contrast, network analysis based on appropriate constraints such as the steady-state condition (constraint-based analysis) is feasible and allows one to derive conclusions about the system's metabolic capabilities. Here, we review methods for the reconstruction of metabolic networks, modeling techniques such as flux balance analysis and elementary flux modes and current progress in their development and applications. Game-theoretical methods for studying metabolic networks are discussed as well.

Computing the shortest elementary flux modes in genome-scale metabolic networks.

MOTIVATION: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. RESULTS: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Can sugars be produced from fatty acids? A test case for pathway analysis tools.

MOTIVATION: In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis. RESULTS: Elementary modes analysis (EMA) of a set of enzymes corresponding to the Krebs cycle, glycolysis and gluconeogenesis supports the scientific evidence showing that there is no pathway capable of converting acetyl-CoA to glucose at steady state. This conversion is possible after the addition of isocitrate lyase and malate synthase (forming the glyoxylate shunt) to the system. Dealing with the same example, we compare EMA with two tools based on graph theory available online, PathFinding and Pathway Hunter Tool. These automated network generating tools do not succeed in predicting the conversions known from experiment. They sometimes generate unbalanced paths and reveal problems identifying side metabolites that are not responsible for the carbon net flux. This shows that, for metabolic pathway analysis, it is important to consider the topology (including bimolecular reactions) and stoichiometry of metabolic systems, as is done in EMA.

Can sugars be produced from fatty acids? A test case for pathway analysis tools.

MOTIVATION: In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis. RESULTS: Elementary modes analysis (EMA) of a set of enzymes corresponding to the Krebs cycle, glycolysis and gluconeogenesis supports the scientific evidence showing that there is no pathway capable of converting acetyl-CoA to glucose at steady state. This conversion is possible after the addition of isocitrate lyase and malate synthase (forming the glyoxylate shunt) to the system. Dealing with the same example, we compare EMA with two tools based on graph theory available online, PathFinding and Pathway Hunter Tool. These automated network generating tools do not succeed in predicting the conversions known from experiment. They sometimes generate unbalanced paths and reveal problems identifying side metabolites that are not responsible for the carbon net flux. This shows that, for metabolic pathway analysis, it is important to consider the topology (including bimolecular reactions) and stoichiometry of metabolic systems, as is done in EMA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.