MetExplore: collaborative edition and exploration of metabolic networks

Cottret, Ludovic; Frainay, Clément; Chazalviel, Maxime; Cabanettes, Floréal; Gloaguen, Yoann; Camenen, Etienne; Merlet, Benjamin; Heux, Stéphanie; Portais, Jean‐Charles; Poupin, Nathalie; Vinson, Florence; Jourdan, Fabien

doi:10.1093/nar/gky301

Cited by 106 publications

(96 citation statements)

References 47 publications

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“…Using direct MS, myo-inositol could be resolved from glucose and other isobaric hexosaccharides at MS 2 level, though it cannot be distinguished from its epimers (30). Despite the relatively low intensity of the ion at m/z 203.526, the myo-inositol intensity was found to be stable across all seven patients analyzed (ANOVA: p = 0.4778; Fig S3B) (31,32) for metabolic network analysis. Decanoic acid was found to be part of a known metabolic pathway (fatty acid biosynthesis).…”

Section: Resultsmentioning

confidence: 97%

Sequential 3D OrbiSIMS and LESA-MS/MS-based metabolomics for prediction of brain tumor relapse from sample-limited primary tissue archives

Meurs¹,

Scurr²,

Lourdusamy³

et al. 2020

Preprint

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Surgically-resected biopsies from childhood brain tumors are archived in a formalin-fixed paraffin-embedded (FFPE) state. Identified key tumor regions are conserved through the fabrication of tissue microarrays (TMA). The vast amount of TMA libraries available have the potential to be used for metabolite profiling; however, due to incompatibility of TMAs with current analysis strategies, the potential has been unexplored. The recent emergence of liquid extraction surface analysis tandem mass spectrometry (LESA-MS/MS) and Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) allows for a novel untargeted metabolite profiling and imaging approach for characterization of tumor TMAs. This strategy employs serial mass spectrometry (MS) analysis, with the high mass resolving imaging power of 3D OrbiSIMS combining complementarity, with the detection of a greater number of metabolites using LESA-MS/MS. Using multivariate analysis on data generated from both techniques permits the identification of metabolites that group patients based on eventual tumor recurrence, leading to the discovery of novel affected metabolic pathways and the prediction of tumor recurrence with high confidence. Moreover, both techniques reveal a unique set of classifiers and metabolic pathways for predicting pediatric ependymoma relapse from a set of primary tumors. The serial MS strategy opens new opportunities to retrospectively link molecular data from any FFPE TMA library with clinical outcomes and to discover novel drug targets with the aim of developing stratified drug treatments.

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Section: Resultsmentioning

confidence: 97%

Sequential 3D OrbiSIMS and LESA-MS/MS-based metabolomics for prediction of brain tumor relapse from sample-limited primary tissue archives

Meurs¹,

Scurr²,

Lourdusamy³

et al. 2020

Preprint

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“…Using values of gene expression, Pathifier provides deregulation scores, which correspond to the extent to which the activity of a pathway in a biological sample differs from the activity of the same pathway estimated in a reference sample. A total of 478 sunflower pathways were extracted through MetExplore2 https://metexplore.toulouse.inra.fr/metexplore2/ (Cottret et al ).…”

Section: Methodsmentioning

confidence: 99%

A multiscale approach reveals regulatory players of water stress responses in seeds during germination

Saux

Ponnaiah

Langlade

et al. 2020

Plant Cell & Environment

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Seed germination is regulated by environmental factors, particularly water availability. Water deficits at the time of sowing impair the establishment of crop plants.Transcriptome and proteome profiling was used to document the responses of sunflower (Helianthus annuus) seeds to moderate water stress during germination in two hybrids that are nominally classed as drought sensitive and drought tolerant. Differences in the water stress-dependent accumulation reactive oxygen species and antioxidant enzymes activities were observed between the hybrids. A pathway-based analysis of the hybrid transcriptomes demonstrated that the water stress-dependent responses of seed metabolism were similar to those of the plant, with a decreased abundance of transcripts encoding proteins associated with metabolism and cell expansion. Moreover, germination under water stress conditions was associated with increased levels of transcripts encoding heat shock proteins. Exposure of germinating seeds to water stress specifically affected the abundance of a small number of proteins, including heat shock proteins. Taken together, these data not only identify factors that are likely to play a key role in drought tolerance during seed germination, but they also demonstrate the importance of the female parent in the transmission of water stress tolerance.

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“…Here we describe the introduction of Rhea as the reference vocabulary for enzyme annotation in UniProtKB. This development will significantly increase the utility of UniProtKB for applications such as the integrative analysis of metabolomics and other 'omics data (Kale, et al, 2016;Sud, et al, 2016), as illustrated here, as well as the study of enzyme chemistry and evolution (Rahman, et al, 2016;Tyzack, et al, 2019), the construction and annotation of metabolic models (Cottret, et al, 2018;King, et al, 2016;Moretti, et al, 2016), and the engineering of pathways for biosynthesis and bioremediation (Duigou, et al, 2019). Rhea will also provide a basis to further improve links with other knowledge resources, such as the Gene Ontology (The Gene Ontology Consortium, 2019) and Reactome (Fabregat, et al, 2018), which both plan to use Rhea as the reference for reaction chemistry (Chris Mungall and Peter D'Eustachio, personal communication).…”

Section: Conclusion and Futures Directionsmentioning

confidence: 99%

Enzyme annotation in UniProtKB using Rhea

Morgat

Lombardot

Coudert

et al. 2019

Preprint

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Motivation: To provide high quality computationally tractable enzyme annotation in UniProtKB using Rhea, a comprehensive expert-curated knowledgebase of biochemical reactions which describes reaction participants using the ontology ChEBI (Chemical Entities of Biological Interest). Results:We replaced existing textual descriptions of biochemical reactions in UniProtKB with their equivalents from Rhea, which is now the standard for annotation of enzymatic reactions in UniProtKB. We developed improved search and query facilities for the UniProt website, REST API, and SPARQL endpoint that leverage the chemical structure data, nomenclature, and classification that Rhea and ChEBI provide.Availability and Implementation: UniProtKB at https://www.uniprot.org/; UniProt REST API at https://www.uniprot.org/help/api; UniProt SPARQL endpoint at https://sparql.uniprot.org/sparql; Rhea at https://www.rhea-db.org/.Contact: anne.morgat@sib.swiss

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MetExplore: collaborative edition and exploration of metabolic networks

Cited by 106 publications

References 47 publications

Sequential 3D OrbiSIMS and LESA-MS/MS-based metabolomics for prediction of brain tumor relapse from sample-limited primary tissue archives

Sequential 3D OrbiSIMS and LESA-MS/MS-based metabolomics for prediction of brain tumor relapse from sample-limited primary tissue archives

A multiscale approach reveals regulatory players of water stress responses in seeds during germination

Enzyme annotation in UniProtKB using Rhea

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