Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

Acute myocardial infarction preferentially alters low-abundant, long-chain unsaturated phospholipid and sphingolipid species in plasma high-density lipoprotein subpopulations

Aim

High-density lipoprotein (HDL) particles in ST-segment elevation myocardial infarction (STEMI) are deficient in their anti-atherogenic function. Molecular determinants of such deficiency remain obscure.

Methods

Five major HDL subpopulations were isolated using density-gradient ultracentrifugation from STEMI patients (n = 12) and healthy age- and sex-matched controls (n = 12), and 160 species of phosphatidylcholine, lysophosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, phosphatidylglycerol, phosphatidylserine, phosphatidic acid, sphingomyelin and ceramide were quantified by LC-MS/MS.

Results

Multiple minor species of proinflammatory phosphatidic acid and lysophosphatidylcholine were enriched by 1.7-27.2-fold throughout the majority of HDL subpopulations in STEMI. In contrast, minor phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, phosphatidylethanolamine, sphingomyelin and ceramide species were typically depleted up to 3-fold in STEMI vs. control HDLs, while abundances of their major species did not differ between the groups. Intermediate-to-long-chain phosphatidylcholine, phosphatidylinositol and phosphatidylglycerol species were more affected by STEMI than their short-chain counterparts, resulting in positive correlations between their fold decrease and the carbon chain length. Additionally, fold decreases in the abundances of multiple lipid species were positively correlated with the double bond number in their carbon chains. Finally, abundances of several phospholipid and ceramide species were positively correlated with cholesterol efflux capacity and antioxidative activity of HDL subpopulations, both reduced in STEMI vs controls. KEGG pathway analysis tied these species to altered glycerophospholipid and linoleic acid metabolism.

Conclusions

Minor unsaturated intermediate-to-long-chain phospholipid and sphingolipid species in HDL subpopulations are most affected by STEMI, reflecting alterations in glycerophospholipid and linoleic acid metabolism with the accumulation of proinflammatory lysolipids and maintenance of homeostasis of major phospholipid species.

Distinct phospholipid and sphingolipid species are linked to altered HDL function in apolipoprotein A-I deficiency

BACKGROUND

Familial apolipoprotein A-I (apoA-I) deficiency (FAID) involving low levels of both apoA-I and high-density lipoprotein (HDL) cholesterol is associated with accelerated atherosclerosis.

OBJECTIVE

The objective of this study was to define distinctive patterns in the lipidome of HDL subpopulations in FAID in relationship to antiatherogenic activities.

METHODS

Five HDL subfractions were isolated by ultracentrifugation from plasma of FAID Caucasian patients (n = 5) and age-matched healthy normolipidemic Caucasian controls (n = 8), and the HDL lipidome (160 molecular species of 9 classes of phospholipids and sphingolipids) was quantitatively evaluated.

RESULTS

Increased concentrations of numerous molecular species of lysophosphatidylcholine (up to 12-fold), ceramides (up to 3-fold), phosphatidylserine (up to 34-fold), phosphatidic acid (up to 71-fold), and phosphatidylglycerol (up to 20-fold) were detected throughout all five HDL subpopulations as compared with their counterparts from controls, whereas concentrations of phosphatidylethanolamine species were decreased (up to 5-fold). Moderately to highly abundant, within their lipid class, species of phosphatidylcholine, sphingomyelin, phosphatidylinositol, phosphatidylethanolamine, phosphatidylserine, and ceramide featuring multiple unsaturations were primarily affected by apoA-I deficiency; their HDL content, particularly that of phosphatidylcholine (34:2), was strongly correlated with HDL function, impaired in FAID. Metabolic pathway analysis revealed that sphingolipid, glycerophospholipid, and linoleic acid metabolism was significantly affected by FAID.

CONCLUSION

These data reveal that altered content of specific phospholipid and sphingolipid species is linked to deficient antiatherogenic properties of HDL in FAID.

Epigenetics as a mechanism linking developmental exposures to long-term toxicity

A variety of experimental and epidemiological studies lend support to the Developmental Origin of Health and Disease (DOHaD) concept. Yet, the actual mechanisms accounting for mid- and long-term effects of early-life exposures remain unclear. Epigenetic alterations such as changes in DNA methylation, histone modifications and the expression of certain RNAs have been suggested as possible mediators of long-term health effects of environmental stressors. This report captures discussions and conclusions debated during the last Prenatal Programming and Toxicity meeting held in Japan. Its first aim is to propose a number of criteria that are critical to support the primary contribution of epigenetics in DOHaD and intergenerational transmission of environmental stressors effects. The main criteria are the full characterization of the stressors, the actual window of exposure, the target tissue and function, the specificity of the epigenetic changes and the biological plausibility of the linkage between those changes and health outcomes. The second aim is to discuss long-term effects of a number of stressors such as smoking, air pollution and endocrine disruptors in order to identify the arguments supporting the involvement of an epigenetic mechanism. Based on the developed criteria, missing evidence and suggestions for future research will be identified. The third aim is to critically analyze the evidence supporting the involvement of epigenetic mechanisms in intergenerational and transgenerational effects of environmental exposure and to particularly discuss the role of placenta and sperm. While the article is not a systematic review and is not meant to be exhaustive, it critically assesses the contribution of epigenetics in the long-term effects of environmental exposures as well as provides insight for future research.

Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis

Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate.

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

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

Dynamic Metabolic Disruption in Rats Perinatally Exposed to Low Doses of Bisphenol-A

Along with the well-established effects on fertility and fecundity, perinatal exposure to endocrine disrupting chemicals, and notably to xeno-estrogens, is strongly suspected of modulating general metabolism. The metabolism of a perinatally exposed individual may be durably altered leading to a higher susceptibility of developing metabolic disorders such as obesity and diabetes; however, experimental designs involving the long term study of these dynamic changes in the metabolome raise novel challenges. ¹H-NMR-based metabolomics was applied to study the effects of bisphenol-A (BPA, 0; 0.25; 2.5, 25 and 250 μg/kg BW/day) in rats exposed perinatally. Serum and liver samples of exposed animals were analyzed on days 21, 50, 90, 140 and 200 in order to explore whether maternal exposure to BPA alters metabolism. Partial Least Squares-Discriminant Analysis (PLS-DA) was independently applied to each time point, demonstrating a significant pair-wise discrimination for liver as well as serum samples at all time-points, and highlighting unequivocal metabolic shifts in rats perinatally exposed to BPA, including those exposed to lower doses. In BPA exposed animals, metabolism of glucose, lactate and fatty acids was modified over time. To further explore dynamic variation, ANOVA-Simultaneous Component Analysis (A-SCA) was used to separate data into blocks corresponding to the different sources of variation (Time, Dose and Time*Dose interaction). A-SCA enabled the demonstration of a dynamic, time/age dependent shift of serum metabolome throughout the rats’ lifetimes. Variables responsible for the discrimination between groups clearly indicate that BPA modulates energy metabolism, and suggest alterations of neurotransmitter signaling, the latter finding being compatible with the neurodevelopmental effect of this xenoestrogen. In conclusion, long lasting metabolic effects of BPA could be characterized over 200 days, despite physiological (and thus metabolic) changes connected with sexual maturation and aging.

Metabolomics in chemical ecology

Chemical ecology elucidates the nature and role of natural products as mediators of organismal interactions. The emerging techniques that can be summarized under the concept of metabolomics provide new opportunities to study such environmentally relevant signaling molecules. Especially comparative tools in metabolomics enable the identification of compounds that are regulated during interaction situations and that might play a role as e.g. pheromones, allelochemicals or in induced and activated defenses. This approach helps overcoming limitations of traditional bioassay-guided structure elucidation approaches. But the power of metabolomics is not limited to the comparison of metabolic profiles of interacting partners. Especially the link to other -omics techniques helps to unravel not only the compounds in question but the entire biosynthetic and genetic re-wiring, required for an ecological response. This review comprehensively highlights successful applications of metabolomics in chemical ecology and discusses existing limitations of these novel techniques. It focuses on recent developments in comparative metabolomics and discusses the use of metabolomics in the systems biology of organismal interactions. It also outlines the potential of large metabolomics initiatives for model organisms in the field of chemical ecology.

Potential input from metabolomics for exploring and understanding the links between environment and health

Humans may be exposed via their environment to multiple chemicals as a consequence of human activities and use of synthetic products. Little knowledge is routinely generated on the hazards of these chemical mixtures. The metabolomic approach is widely used to identify metabolic pathways modified by diseases, drugs, or exposures to toxicants. This review, based on the state of the art of the current applications of metabolomics in environmental health, attempts to determine whether metabolomics might constitute an original approach to the study of associations between multiple, low-dose environmental exposures in humans. Studying the biochemical consequences of complex environmental exposures is a challenge demanding the development of careful experimental and epidemiological designs, in order to take into account possible confounders associated with the high level of interindividual variability induced by different lifestyles. The choices of populations studied, sampling and storage procedures, statistical tools used, and system biology need to be considered. Suggestions for improved experimental and epidemiological designs are described. Evidence indicates that metabolomics may be a powerful tool in environmental health in the identification of both complex exposure biomarkers directly in human populations and modified metabolic pathways, in an attempt to improve understanding the underlying environmental causes of diseases. Nevertheless, the validity of biomarkers and relevancy of animal-to-human extrapolation remain key challenges that need to be properly explored.

Quantitatively plotting the human face for multivariate data visualisation illustrated by health assessments using laboratory parameters

Objective. The purpose of this study was to describe a new data visualisation system by plotting the human face to observe the comprehensive effects of multivariate data. Methods. The Graphics Device Interface (GDI+) in the Visual Studio.NET development platform was used to write a program that enables facial image parameters to be recorded, such as cropping and rotation, and can generate a new facial image according to Z values from sets of normal data (Z > 3 was still counted as 3). The measured clinical laboratory parameters related to health status were obtained from senile people, glaucoma patients, and fatty liver patients to illustrate the facial data visualisation system. Results. When the eyes, nose, and mouth were rotated around their own axes at the same angle, the deformation effects were similar. The deformation effects for any abnormality of the eyes, nose, or mouth should be slightly higher than those for simultaneous abnormalities. The facial changes in the populations with different health statuses were significant compared with a control population. Conclusions. The comprehensive effects of multivariate may not equal the sum of each variable. The 3Z facial data visualisation system can effectively distinguish people with poor health status from healthy people.

A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype

Background

The study of metabolism has attracted much attention during the last years due to its relevance in various diseases. The advance in metabolomics platforms allows us to detect an increasing number of metabolites in abnormal high/low concentration in a disease phenotype. Finding a mechanistic interpretation for these alterations is important to understand pathophysiological processes, however it is not an easy task. The availability of genome scale metabolic networks and Systems Biology techniques open new avenues to address this question.

Results

In this article we present a novel mathematical framework to find enzymes whose malfunction explains the accumulation/depletion of a given metabolite in a disease phenotype. Our approach is based on a recently introduced pathway concept termed Carbon Flux Paths (CFPs), which extends classical topological definition by including network stoichiometry. Using CFPs, we determine the Connectivity Curve of an altered metabolite, which allows us to quantify changes in its pathway structure when a certain enzyme is removed. The influence of enzyme removal is then ranked and used to explain the accumulation/depletion of such metabolite. For illustration, we center our study in the accumulation of two metabolites (L-Cystine and Homocysteine) found in high concentration in the brain of patients with mental disorders. Our results were discussed based on literature and found a good agreement with previously reported mechanisms. In addition, we hypothesize a novel role of several enzymes for the accumulation of these metabolites, which opens new strategies to understand the metabolic processes underlying these diseases.

Conclusions

With personalized medicine on the horizon, metabolomic platforms are providing us with a vast amount of experimental data for a number of complex diseases. Our approach provides a novel apparatus to rationally investigate and understand metabolite alterations under disease phenotypes. This work contributes to the development of Systems Medicine, whose objective is to answer clinical questions based on theoretical methods and high-throughput “omics” data.

Determination of antiprotozoal drug mechanisms by metabolomics approaches

The discovery, development and optimal utilization of pharmaceuticals can be greatly enhanced by knowledge of their modes of action. However, many drugs currently on the market act by unknown mechanisms. Untargeted metabolomics offers the potential to discover modes of action for drugs that perturb cellular metabolism. Development of high resolution LC-MS methods and improved data analysis software now allows rapid detection of drug-induced changes to cellular metabolism in an untargeted manner. Several studies have demonstrated the ability of untargeted metabolomics to provide unbiased target discovery for antimicrobial drugs, in particular for antiprotozoal agents. Furthermore, the utilization of targeted metabolomics techniques has enabled validation of existing hypotheses regarding antiprotozoal drug mechanisms. Metabolomics approaches are likely to assist with optimization of new drug candidates by identification of drug targets, and by allowing detailed characterization of modes of action and resistance of existing and novel antiprotozoal drugs.

FT-ICR/MS and GC-EI/MS metabolomics networking unravels global potato sprout's responses to Rhizoctonia solani infection

The complexity of plant-pathogen interactions makes their dissection a challenging task for metabolomics studies. Here we are reporting on an integrated metabolomics networking approach combining gas chromatography/mass spectrometry (GC/MS) with Fourier transform ion cyclotron resonance/mass spectrometry (FT-ICR/MS) and bioinformatics analyses for the study of interactions in the potato sprout-Rhizoctonia solani pathosystem and the fluctuations in the global metabolome of sprouts. The developed bioanalytical and bioinformatics protocols provided a snapshot of the sprout's global metabolic network and its perturbations as a result of pathogen invasion. Mevalonic acid and deoxy-xylulose pathways were substantially up-regulated leading to the biosynthesis of sesquiterpene alkaloids such as the phytoalexins phytuberin, rishitin, and solavetivone, and steroidal alkaloids having solasodine and solanidine as their common aglycons. Additionally, the perturbation of the sprout's metabolism was depicted in fluctuations of the content of their amino acids pool and that of carboxylic and fatty acids. Components of the systemic acquired resistance (SAR) and hypersensitive reaction (HR) such as azelaic and oxalic acids were detected in increased levels in infected sprouts and strategies of the pathogen to overcome plant defense were proposed. Our metabolic approach has not only greatly expanded the multitude of metabolites previously reported in potato in response to pathogen invasion, but also enabled the identification of bioactive plant-derived metabolites providing valuable information that could be exploited in biotechnology, biomarker-assisted plant breeding, and crop protection for the development of new crop protection agents.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Pathos: a web facility that uses metabolic maps to display experimental changes in metabolites identified by mass spectrometry

This work describes a freely available web-based facility which can be used to analyse raw or processed mass spectrometric data from metabolomics experiments and display the metabolites identified--and changes in their experimental abundance--in the context of the metabolic pathways in which they occur. The facility, Pathos (http://motif.gla.ac.uk/Pathos/), employs Java servlets and is underpinned by a relational database populated from the Kyoto Encyclopaedia of Genes and Genomes (KEGG). Input files can contain either raw m/z values from experiments conducted in different modes, or KEGG or MetaCyc IDs assigned by the user on the basis of the m/z values and other criteria. The textual output lists the KEGG pathways on an XHTML page according to the number of metabolites or potential metabolites that they contain. Filtering by organism is also available. For metabolic pathways of interest, the user is able to retrieve a pathway map with identified metabolites highlighted. A particular feature of Pathos is its ability to process relative quantification data for metabolites identified under different experimental conditions, and to present this in an easily comprehensible manner. Results are colour-coded according to the degree of experimental change, and bar charts of the results can be generated interactively from either the text listings or the pathway maps. The visual presentation of the output from Pathos is designed to allow the rapid identification of metabolic areas of potential interest, after which particular results may be examined in detail.

Metabolomic analysis of trypanosomatid protozoa

Metabolomics aims to measure all low molecular weight chemicals within a given system in a manner analogous to transcriptomics, proteomics and genomics. In this review we highlight metabolomics approaches that are currently being applied to the kinetoplastid parasites, Trypanosoma brucei and Leishmania spp. The use of untargeted metabolomics approaches, made possible through advances in mass spectrometry and informatics, and stable isotope labelling has increased our understanding of the metabolism in these organisms beyond the views established using classical biochemical approaches. Set within the context of metabolic networks, predicted using genome-wide reconstructions of metabolism, new hypotheses on how to target aspects of metabolism to design new drugs against these protozoa are emerging.

Graph methods for the investigation of metabolic networks in parasitology

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks

High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis. The MetExplore web server is freely accessible at http://metexplore.toulouse.inra.fr.