Stability of metabolic correlations under changing environmental conditions in Escherichia coli--a systems approach

Soil Metabolomics Predict Microbial Taxa as Biomarkers of Moisture Status in Soils from a Tidal Wetland

We present observations from a laboratory-controlled study on the impacts of extreme wetting and drying on a wetland soil microbiome. Our approach was to experimentally challenge the soil microbiome to understand impacts on anaerobic carbon cycling processes as the system transitions from dryness to saturation and vice-versa. Specifically, we tested for impacts on stress responses related to shifts from wet to drought conditions. We used a combination of high-resolution data for small organic chemical compounds (metabolites) and biological (community structure based on 16S rRNA gene sequencing) features. Using a robust correlation-independent data approach, we further tested the predictive power of soil metabolites for the presence or absence of taxa. Here, we demonstrate that taking an untargeted, multidimensional data approach to the interpretation of metabolomics has the potential to indicate the causative pathways selecting for the observed bacterial community structure in soils.

Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics

Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.

Mycorrhiza-Induced Alterations in Metabolome of Medicago lupulina Leaves during Symbiosis Development

The present study is aimed at disclosing metabolic profile alterations in the leaves of the Medicago lupulina MlS-1 line that result from high-efficiency arbuscular mycorrhiza (AM) symbiosis formed with Rhizophagus irregularis under condition of a low phosphorus level in the substrate. A highly effective AM symbiosis was established in the period from the stooling to the shoot branching initiation stage (the efficiency in stem height exceeded 200%). Mycorrhization led to a more intensive accumulation of phosphates (glycerophosphoglycerol and inorganic phosphate) in M. lupulina leaves. Metabolic spectra were detected with GS-MS analysis. The application of complex mathematical analyses made it possible to identify the clustering of various groups of 320 metabolites and thus demonstrate the central importance of the carbohydrate and carboxylate-amino acid clusters. The results obtained indicate a delay in the metabolic development of mycorrhized plants. Thus, AM not only accelerates the transition between plant developmental stages but delays biochemical “maturation” mainly in the form of a lag of sugar accumulation in comparison with non-mycorrhized plants. Several methods of statistical modeling proved that, at least with respect to determining the metabolic status of host-plant leaves, stages of phenological development have priority over calendar age.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

Diurnal regulation of the function of the rat brain glutamate dehydrogenase by acetylation and its dependence on thiamine administration

Glutamate dehydrogenase (GDH) is essential for the brain function and highly regulated, according to its role in metabolism of the major excitatory neurotransmitter glutamate. Here we show a diurnal pattern of the GDH acetylation in rat brain, associated with specific regulation of GDH function. Mornings the acetylation levels of K84 (near the ADP site), K187 (near the active site), and K503 (GTP-binding) are highly correlated. Evenings the acetylation levels of K187 and K503 decrease, and the correlations disappear. These daily variations in the acetylation adjust the GDH responses to the enzyme regulators. The adjustment is changed when the acetylation of K187 and K503 shows no diurnal variations, as in the rats after a high dose of thiamine. The regulation of GDH function by acetylation is confirmed in a model system, where incubation of the rat brain GDH with acetyl-CoA changes the enzyme responses to GTP and ADP, decreasing the activity at subsaturating concentrations of substrates. Thus, the GDH acetylation may support cerebral homeostasis, stabilizing the enzyme function during diurnal oscillations of the brain metabolome. Daytime and thiamine interact upon the (de)acetylation of GDH in vitro. Evenings the acetylation of GDH from control animals increases both IC₅₀ ^GTP and EC₅₀ ^ADP . Mornings the acetylation of GDH from thiamine-treated animals increases the enzyme IC₅₀ ^GTP . Molecular mechanisms of the GDH regulation by acetylation of specific residues are proposed. For the first time, diurnal and thiamine-dependent changes in the allosteric regulation of the brain GDH due to the enzyme acetylation are shown.

Hypoxic Adaptation of Mitochondrial Metabolism in Rat Cerebellum Decreases in Pregnancy

Function of brain amino acids as neurotransmitters or their precursors implies changes in the amino acid levels and/or metabolism in response to physiological and environmental challenges. Modelling such challenges by pregnancy and/or hypoxia, we characterize the amino acid pool in the rat cerebellum, quantifying the levels and correlations of 15 amino acids and activity of 2-oxoglutarate dehydrogenase complex (OGDHC). The parameters are systemic indicators of metabolism because OGDHC limits the flux through mitochondrial TCA cycle, where amino acids are degraded and their precursors synthesized. Compared to non-pregnant state, pregnancy increases the cerebellar content of glutamate and tryptophan, decreasing interdependence between the quantified components of amino acid metabolism. In response to hypoxia, the dependence of cerebellar amino acid pool on OGDHC and the average levels of arginine, glutamate, lysine, methionine, serine, phenylalanine, and tryptophan increase in non-pregnant rats only. This is accompanied by a higher hypoxic resistance of the non-pregnant vs. pregnant rats, pointing to adaptive significance of the hypoxia-induced changes in the cerebellar amino acid metabolism. These adaptive mechanisms are not effective in the pregnancy-changed metabolic network. Thus, the cerebellar amino acid levels and OGDHC activity provide sensitive markers of the physiology-dependent organization of metabolic network and its stress adaptations.

Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms

Biological networks play a paramount role in our understanding of complex biological phenomena, and metabolite-metabolite association networks are now commonly used in metabolomics applications. In this study we evaluate the performance of several network inference algorithms (PCLRC, MRNET, GENIE3, TIGRESS, and modifications of the MRNET algorithm, together with standard Pearson's and Spearman's correlation) using as a test case data generated using a dynamic metabolic model describing the metabolism of arachidonic acid (consisting of 83 metabolites and 131 reactions) and simulation individual metabolic profiles of 550 subjects. The quality of the reconstructed metabolite-metabolite association networks was assessed against the original metabolic network taking into account different degrees of association among the metabolites and different sample sizes and noise levels. We found that inference algorithms based on resampling and bootstrapping perform better when correlations are used as indexes to measure the strength of metabolite-metabolite associations. We also advocate for the use of data generated using dynamic models to test the performance of algorithms for network inference since they produce correlation patterns that are more similar to those observed in real metabolomics data.

Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism

The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling

Blood is one of the most used biofluids in metabolomics studies, and the serum and plasma fractions are routinely used as a proxy for blood itself. Here we investigated the association networks of an array of 29 metabolites identified and quantified via NMR in the plasma and serum samples of two cohorts of ∼1000 healthy blood donors each. A second study of 377 individuals was used to extract plasma and serum samples from the same individual on which a set of 122 metabolites were detected and quantified using FIA–MS/MS. Four different inference algorithms (ARANCE, CLR, CORR, and PCLRC) were used to obtain consensus networks. The plasma and serum networks obtained from different studies showed different topological properties with the serum network being more connected than the plasma network. On a global level, metabolite association networks from plasma and serum fractions obtained from the same blood sample of healthy people show similar topologies, and at a local level, some differences arise like in the case of amino acids.

Bacterial metabolism in immediate response to nutritional perturbation with temporal and network view of metabolites

In this study, the initial propagation of metabolic perturbation in Escherichia coli was visualized to understand the dynamic characteristics of the metabolic pathways without the association of transcription alterations. E. coli cells were exposed to the sudden relief of glucose starvation, and time-dependent variances in metabolite balances were traced in the second scale. The acquired time-course data were represented by structural variations of the metabolite-metabolite correlation network. The initial correlation structure was altered immediately by the glucose pulse, followed by further structural variations within a few minutes. It was demonstrated that one metabolite temporally correlated with distinct metabolites with different timings, and such a behavior could imply a regulatory role for the metabolite in the metabolic network. Centrality analysis of the networks and partial correlation analysis indicated that preparation for growth and oxidative stress could be coupled as a structural property of the metabolic pathways.

A metabolomics study delineating geographical location-associated primary metabolic changes in the leaves of growing tobacco plants by GC-MS and CE-MS

Ecological conditions and developmental senescence significantly affect the physiological metabolism of plants, yet relatively little is known about the influence of geographical location on dynamic changes in plant leaves during growth. Pseudotargeted gas chromatography-selected ion monitoring-mass spectrometry and capillary electrophoresis-mass spectrometry were used to investigate a time course of the metabolic responses of tobacco leaves to geographical location. Principal component analysis revealed obvious metabolic discrimination between growing districts relative to cultivars. A complex carbon and nitrogen metabolic network was modulated by environmental factors during growth. When the Xuchang and Dali Districts in China were compared, the results indicated that higher rates of photosynthesis, photorespiration and respiration were utilized in Xuchang District to generate the energy and carbon skeletons needed for the biosynthesis of nitrogen-containing metabolites. The increased abundance of defense-associated metabolites generated from the shikimate-phenylpropanoid pathway in Xuchang relative to Dali was implicated in protection against stress.

Dependency of cellular decision making in physiology and influence of preceding growth conditions

Events from the past growth conditions influence the course of physiology, and it gets reflected in the present cell behaviour. During this process, cells acquire a metabolic option which carries signature of the past, and it dictates the performance in the present situation. Study uses Escherichia coli as a sample organism wherein three scenarios of preceding growth conditions were created with varying nutritional status and pre-treatment strategies. This exercise leads to different seed cultures, which were subjected to growth using the four different substrates. The different seed culture behaviours were analysed by observing the respirometric rate of the seed culture and were followed by growth dynamics with different substrates. These two data sets were independently analysed by three-way ANOVA to arrive at strategic coupling of programming conditions to relate the available (respirometric rates) and executable physiology (growth kinetics).

Rationales and approaches for studying metabolism in eukaryotic microalgae

The generation of efficient production strains is essential for the use of eukaryotic microalgae for biofuel production. Systems biology approaches including metabolite profiling on promising microalgal strains, will provide a better understanding of their metabolic networks, which is crucial for metabolic engineering efforts. Chlamydomonas reinhardtii represents a suited model system for this purpose. We give an overview to genetically amenable microalgal strains with the potential for biofuel production and provide a critical review of currently used protocols for metabolite profiling on Chlamydomonas. We provide our own experimental data to underpin the validity of the conclusions drawn.

Deciphering the metabolic changes associated with diapause syndrome and cold acclimation in the two-spotted spider mite Tetranychus urticae

Diapause is a common feature in several arthropod species that are subject to unfavorable growing seasons. The range of environmental cues that trigger the onset and termination of diapause, in addition to associated hormonal, biochemical, and molecular changes, have been studied extensively in recent years; however, such information is only available for a few insect species. Diapause and cold hardening usually occur together in overwintering arthropods, and can be characterized by recording changes to the wealth of molecules present in the tissue, hemolymph, or whole body of organisms. Recent technological advances, such as high throughput screening and quantification of metabolites via chromatographic analyses, are able to identify such molecules. In the present work, we examined the survival ability of diapausing and non-diapausing females of the two-spotted spider mite, Tetranychus urticae, in the presence (0 or 5°C) or absence of cold acclimation. Furthermore, we examined the metabolic fingerprints of these specimens via gas chromatography-mass spectrophotometry (GC-MS). Partial Least Square Discriminant Analysis (PLS-DA) of metabolites revealed that major metabolic variations were related to diapause, indicating in a clear cut-off between diapausing and non-diapausing females, regardless of acclimation state. Signs of metabolic depression were evident in diapausing females, with most amino acids and TCA cycle intermediates being significantly reduced. Out of the 40 accurately quantified metabolites, seven metabolites remained elevated or were accumulated in diapausing mites, i.e. cadaverine, gluconolactone, glucose, inositol, maltose, mannitol and sorbitol. The capacity to accumulate winter polyols during cold-acclimation was restricted to diapausing females. We conclude that the induction of increased cold hardiness in this species is associated with the diapause syndrome, rather than being a direct effect of low temperature. Our results provide novel information about biochemical events related to the cold hardening process in the two-spotted spider mite.

Habitat phenotyping of two sub-Antarctic flies by metabolic fingerprinting: evidence for a species outside its home?

Metabolic fingerprinting can elucidate rearrangements of metabolic networks in organisms exposed to various environmental conditions. Maintenance of organismal performance occurs by alterations in metabolic fluxes and pathways, resulting in habitat-specific metabolic signatures. Several insects of sub-Antarctic Islands, including the wingless flies Anatalanta aptera and Calycopteryx moseleyi, are exposed to saline organic matter accumulated along littoral margins. However, C. moseleyi has long been considered restricted to a habitat of lower salinity, the Kerguelen cabbage. High C. moseleyi densities identified in saline decaying seaweeds are intriguing, and may involve osmoregulatory adjustments including accumulation of osmoprotectants. In the present work, we examined quantitative metabotypes (metabolic phenotypes) among wild C. moseleyi individuals from seaweeds versus non-saline Kerguelen cabbages. They were compared to metabotypes from wild A. aptera, a common fly on seaweed. Statistical procedures designed to magnify between-class differences failed to clearly separate C. moseleyi metabotypes from cabbage and seaweed, despite contrasted morphotypes, diets, and salinities. A. aptera exhibited higher glycerol, inositol, trehalose, and other osmoprotectants concentrations that may enhance its performance under saline environments. Seaweed may represent a secondary niche in C. moseleyi, promoted by the marked reduction in Kerguelen cabbage frequency subsequent to climate change, and herbivorous pressures caused by rabbit invasion.

High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions

The time-resolved response of Arabidopsis thaliana towards changing light and/or temperature at the transcriptome and metabolome level is presented. Plants grown at 21°C with a light intensity of 150 μE m⁻² sec⁻¹ were either kept at this condition or transferred into seven different environments (4°C, darkness; 21°C, darkness; 32°C, darkness; 4°C, 85 μE m⁻² sec⁻¹; 21 °C, 75 μE m⁻² sec⁻¹; 21°C, 300 μE m⁻² sec⁻¹ ; 32°C, 150 μE m⁻² sec⁻¹). Samples were taken before (0 min) and at 22 time points after transfer resulting in (8×) 22 time points covering both a linear and a logarithmic time series totaling 177 states. Hierarchical cluster analysis shows that individual conditions (defined by temperature and light) diverge into distinct trajectories at condition-dependent times and that the metabolome follows different kinetics from the transcriptome. The metabolic responses are initially relatively faster when compared with the transcriptional responses. Gene Ontology over-representation analysis identifies a common response for all changed conditions at the transcriptome level during the early response phase (5-60 min). Metabolic networks reconstructed via metabolite-metabolite correlations reveal extensive environment-specific rewiring. Detailed analysis identifies conditional connections between amino acids and intermediates of the tricarboxylic acid cycle. Parallel analysis of transcriptional changes strongly support a model where in the absence of photosynthesis at normal/high temperatures protein degradation occurs rapidly and subsequent amino acid catabolism serves as the main cellular energy supply. These results thus demonstrate the engagement of the electron transfer flavoprotein system under short-term environmental perturbations.

Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling

Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 µl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell--two important parameters for systems biology model development.

Staphylococcus aureus physiological growth limitations: insights from flux calculations built on proteomics and external metabolite data

Comparing proteomics and metabolomics allows insights into Staphylococcus aureus physiological growth. We update genome and proteome information and deliver strain-specific metabolic models for three S. aureus strains (COL, N315, and Newman). We find a number of differences in metabolism and enzymes. Growth experiments (glucose or combined with oxygen limitation) were conducted to measure external metabolites. Fluxes of the central metabolism were calculated from these data with low error. In exponential phase, glycolysis is active and amino acids are used for growth. In later phases, dehydroquinate synthetase is suppressed and acetate metabolism starts. There are strain-specific differences for these phases. A time series of 2-D gel protein expression data on COL strain delivered a second data set (glucose limitation) on which fluxes were calculated. The comparison with the metabolite-predicted fluxes shows, in general, good correlation. Outliers point to different regulated enzymes for S. aureus COL under these limitations. In exponential growth, there is lower activity for some enzymes in upper glycolysis and pentose phosphate pathway and stronger activity for some in lower glycolysis. In transition phase, aspartate kinase is expressed to meet amino acid requirements and in later phases there is high expression of glyceraldehyde-3-phosphate dehydrogenase and lysine synthetase. Central metabolite fluxes and protein expression of their enzymes correlate in S. aureus.

Comparative functional genomics of salt stress in related model and cultivated plants identifies and overcomes limitations to translational genomics

One of the objectives of plant translational genomics is to use knowledge and genes discovered in model species to improve crops. However, the value of translational genomics to plant breeding, especially for complex traits like abiotic stress tolerance, remains uncertain. Using comparative genomics (ionomics, transcriptomics and metabolomics) we analyzed the responses to salinity of three model and three cultivated species of the legume genus Lotus. At physiological and ionomic levels, models responded to salinity in a similar way to crop species, and changes in the concentration of shoot Cl⁻ correlated well with tolerance. Metabolic changes were partially conserved, but divergence was observed amongst the genotypes. Transcriptome analysis showed that about 60% of expressed genes were responsive to salt treatment in one or more species, but less than 1% was responsive in all. Therefore, genotype-specific transcriptional and metabolic changes overshadowed conserved responses to salinity and represent an impediment to simple translational genomics. However, ‘triangulation’ from multiple genotypes enabled the identification of conserved and tolerant-specific responses that may provide durable tolerance across species.

Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach

BACKGROUND

Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways.

RESULTS

In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 Arabidopsis genotypes, Col-0 wild-type, methionine over-accumulation 1 (mto1), and transparent testa4 (tt4) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways.

CONCLUSIONS

We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.

KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data

Correlations of gene-to-gene co-expression and metabolite-to-metabolite co-accumulation calculated from large amounts of transcriptome and metabolome data are useful for uncovering unknown functions of genes, functional diversities of gene family members and regulatory mechanisms of metabolic pathway flows. Many databases and tools are available to interpret quantitative transcriptome and metabolome data, but there are only limited ones that connect correlation data to biological knowledge and can be utilized to find biological significance of it. We report here a new metabolic pathway database, KaPPA-View4 (http://kpv.kazusa.or.jp/kpv4/), which is able to overlay gene-to-gene and/or metabolite-to-metabolite relationships as curves on a metabolic pathway map, or on a combination of up to four maps. This representation would help to discover, for example, novel functions of a transcription factor that regulates genes on a metabolic pathway. Pathway maps of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and maps generated from their gene classifications are available at KaPPA-View4 KEGG version (http://kpv.kazusa.or.jp/kpv4-kegg/). At present, gene co-expression data from the databases ATTED-II, COXPRESdb, CoP and MiBASE for human, mouse, rat, Arabidopsis, rice, tomato and other plants are available.

When metabolism meets topology: Reconciling metabolite and reaction networks

The search for a systems-level picture of metabolism as a web of molecular interactions provides a paradigmatic example of how the methods used to characterize a system can bias the interpretation of its functional meaning. Metabolic maps have been analyzed using novel techniques from network theory, revealing some non-trivial, functionally relevant properties. These include a small-world structure and hierarchical modularity. However, as discussed here, some of these properties might actually result from an inappropriate way of defining network interactions. Starting from the so-called bipartite organization of metabolism, where the two meaningful subsets (reactions and metabolites) are considered, most current works use only one of the subsets by means of so-called graph projections. Unfortunately, projected graphs often ignore relevant biological and chemical constraints, thus leading to statistical artifacts. Some of these drawbacks and alternative approaches need to be properly addressed.