MetExploreViz: web component for interactive metabolic network visualization

Summary: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc

A computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by “supervising” the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective

Integrated transcriptomics and metabolomics reveal signatures of lipid metabolism dysregulation in HepaRG liver cells exposed to PCB 126.

Chemical pollutant exposure is a risk factor contributing to the growing epidemic of non-alcoholic fatty liver disease (NAFLD) affecting human populations that consume a western diet. Although it is recognized that intoxication by chemical pollutants can lead to NAFLD, there is limited information available regarding the mechanism by which typical environmental levels of exposure can contribute to the onset of this disease. Here, we describe the alterations in gene expression profiles and metabolite levels in the human HepaRG liver cell line, a validated model for cellular steatosis, exposed to the polychlorinated biphenyl (PCB) 126, one of the most potent chemical pollutants that can induce NAFLD. Sparse partial least squares classification of the molecular profiles revealed that exposure to PCB 126 provoked a decrease in polyunsaturated fatty acids as well as an increase in sphingolipid levels, concomitant with a decrease in the activity of genes involved in lipid metabolism. This was associated with an increased oxidative stress reflected by marked disturbances in taurine metabolism. A gene ontology analysis showed hallmarks of an activation of the AhR receptor by dioxin-like compounds. These changes in metabolome and transcriptome profiles were observed even at the lowest concentration (100 pM) of PCB 126 tested. A decrease in docosatrienoate levels was the most sensitive biomarker. Overall, our integrated multi-omics analysis provides mechanistic insight into how this class of chemical pollutant can cause NAFLD. Our study lays the foundation for the development of molecular signatures of toxic effects of chemicals causing fatty liver diseases to move away from a chemical risk assessment based on in vivo animal experiments

Hypoxia Induces VEGF-C Expression in Metastatic Tumor Cells via a HIF-1α-Independent Translation-Mediated Mechanism

SummaryVarious tumors metastasize via lymph vessels and lymph nodes to distant organs. Even though tumors are hypoxic, the mechanisms of how hypoxia regulates lymphangiogenesis remain poorly characterized. Here, we show that hypoxia reduced vascular endothelial growth factor C (VEGF-C) transcription and cap-dependent translation via the upregulation of hypophosphorylated 4E-binding protein 1 (4E-BP1). However, initiation of VEGF-C translation was induced by hypoxia through an internal ribosome entry site (IRES)-dependent mechanism. IRES-dependent VEGF-C translation was independent of hypoxia-inducible factor 1α (HIF-1α) signaling. Notably, the VEGF-C IRES activity was higher in metastasizing tumor cells in lymph nodes than in primary tumors, most likely because lymph vessels in these lymph nodes were severely hypoxic. Overall, this transcription-independent but translation-dependent upregulation of VEGF-C in hypoxia stimulates lymphangiogenesis in tumors and lymph nodes and may contribute to lymphatic metastasis

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites

Hypoxia Induces VEGF-C Expression in Metastatic Tumor Cells via a HIF-1 α-Independent Translation-Mediated Mechanism.

Various tumors metastasize via lymph vessels and lymph nodes to distant organs. Even though tumors are hypoxic, the mechanisms of how hypoxia regulates lymphangiogenesis remain poorly characterized. Here, we show that hypoxia reduced vascular endothelial growth factor C (VEGF-C) transcription and cap-dependent translation via the upregulation of hypophosphorylated 4E-binding protein 1 (4E-BP1). However, initiation of VEGF-C translation was induced by hypoxia through an internal ribosome entry site (IRES)-dependent mechanism. IRES-dependent VEGF-C translation was independent of hypoxia-inducible factor 1α (HIF-1α) signaling. Notably, the VEGF-C IRES activity was higher in metastasizing tumor cells in lymph nodes than in primary tumors, most likely because lymph vessels in these lymph nodes were severely hypoxic. Overall, this transcription-independent but translation-dependent upregulation of VEGF-C in hypoxia stimulates lymphangiogenesis in tumors and lymph nodes and may contribute to lymphatic metastasis

A unified conceptual framework for metabolic phenotyping in diagnosis and prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help generate new diagnostic and prognostic biomarkers and models with the potential to impact patient management. Here we present a suite of conceptual approaches for the generation, analysis and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of: i) metabolite levels; ii) metabolic trajectories; iii) metabolic entropies and iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalised medicine, with the aim of improving clinical outcomes for patients, but it will have value and utility in all areas of metabolic profiling well beyond this exemplar

Système de recommandation basé sur les réseaux pour l'interprétation de résultats de métabolomique

Metabolomics allows large-scale studies of the metabolic profile of an individual, which is representative of its physiological state. Metabolic markers characterising a given condition can be obtained through the comparison of those profiles. Therefore, metabolomics reveals a great potential for the diagnosis as well as the comprehension of mechanisms behind metabolic dysregulations, and to a certain extent the identification of therapeutic targets. However, in order to raise new hypotheses, those applications need to put metabolomics results in the light of global metabolism knowledge. This contextualisation of the results can rely on metabolic networks, which gather all biochemical transformations that can be performed by an organism. The major bottleneck preventing this interpretation stems from the fact that, currently, no single metabolomic approach allows monitoring all metabolites, thus leading to a partial representation of the metabolome. Furthermore, in the context of human health related experiments, metabolomics is usually performed on bio-fluid samples. Consequently, those approaches focus on the footprints left by impacted mechanisms rather than the mechanisms themselves. This thesis proposes a new approach to overcome those limitations, through the suggestion of relevant metabolites, which could fill the gaps in a metabolomics signature. This method is inspired by recommender systems used for several on-line activities, and more specifically the recommendation of users to follow on social networks. This approach has been used for the interpretation of the metabolic signature of the hepatic encephalopathy. It allows highlighting some relevant metabolites, closely related to the disease according to the literature, and led to a better comprehension of the impaired mechanisms and as a result the proposition of new hypothetical scenario. It also improved and enriched the original signature by guiding deeper investigation of the raw data, leading to the addition of missed compounds. Models and data characterisation, alongside technical developments presented in this thesis, can also offer generic frameworks and guidelines for metabolic networks topological analysis.La métabolomique permet une étude à large échelle du profil métabolique d'un individu, représentatif de son état physiologique. La comparaison de ces profils conduit à l'identification de métabolites caractéristiques d'une condition donnée. La métabolomique présente un potentiel considérable pour le diagnostic, mais également pour la compréhension des mécanismes associés aux maladies et l'identification de cibles thérapeutiques. Cependant, ces dernières applications nécessitent d'inclure ces métabolites caractéristiques dans un contexte plus large, décrivant l'ensemble des connaissances relatives au métabolisme, afin de formuler des hypothèses sur les mécanismes impliqués. Cette mise en contexte peut être réalisée à l'aide des réseaux métaboliques, qui modélisent l'ensemble des transformations biochimiques opérables par un organisme. L'une des limites de cette approche est que la métabolomique ne permet pas à ce jour de mesurer l'ensemble des métabolites, et ainsi d'offrir une vue complète du métabolome. De plus, dans le contexte plus spécifique de la santé humaine, la métabolomique est usuellement appliquée à des échantillons provenant de biofluides plutôt que des tissus, ce qui n'offre pas une observation directe des mécanismes physiologiques eux-mêmes, mais plutôt de leur résultante. Les travaux présentés dans cette thèse proposent une méthode pour pallier ces limitations, en suggérant des métabolites pertinents pouvant aider à la reconstruction de scénarios mécanistiques. Cette méthode est inspirée des systèmes de recommandations utilisés dans le cadre d'activités en ligne, notamment la suggestion d'individus d'intérêt sur les réseaux sociaux numériques. La méthode a été appliquée à la signature métabolique de patients atteints d'encéphalopathie hépatique. Elle a permis de mettre en avant des métabolites pertinents dont le lien avec la maladie est appuyé par la littérature scientifique, et a conduit à une meilleure compréhension des mécanismes sous-jacents et à la proposition de scénarios alternatifs. Elle a également orienté l'analyse approfondie des données brutes de métabolomique et enrichie par ce biais la signature de la maladie initialement obtenue. La caractérisation des modèles et des données ainsi que les développements techniques nécessaires à la création de la méthode ont également conduit à la définition d'un cadre méthodologique générique pour l'analyse topologique des réseaux métaboliques