Improving lipid mapping in Genome Scale Metabolic Networks using ontologies

Nathalie Poupin (1) , Florence Vinson (1) , Arthur Moreau (2) , Aurélie Batut (3) , Maxime Chazalviel (4) , Benoit Colsch (5, 6, 7, 8, 9, 10) , Laetitia Fouillen (11, 12) , Sarah Guez (3) , Spiro Khoury , Jessica Dalloux-Chioccioli (3) , Anthony Tournadre (3) , Pauline Le Faouder (3) , Corinne Pouyet (13) , Pierre van Delft (11, 12) , Fanny Viars (3) , Justine Bertrand-Michel (3) , Fabien Jourdan (1)

1 ToxAlim-MeX - Métabolisme et Xénobiotiques
2 ToxAlim - ToxAlim
3 MetaToul Lipidomics
4 MedDay Pharmaceuticals
5 MTS - Médicaments et Technologies pour la Santé
6 LI-MS - Laboratoire Innovations en Spectrométrie de Masse pour la santé
7 CEA - CEA- Saclay
8 Université Paris-Saclay
9 SPI - Service de Pharmacologie et Immunoanalyse
10 MetaboHUB-IDF
11 LBM - Laboratoire de biogenèse membranaire
12 Plateforme Bordeaux Metabolome
13 UNH - Unité de Nutrition Humaine

Introduction To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids.Objectives To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver.Methods Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology.Results We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation.Conclusion This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.

Mots clés: Metabolic networks; Ontology; Mapping; Lipidomics

Domaines: Bio-informatique [q-bio.QM]; Algorithme et structure de données [cs.DS]

Nathalie Poupin, Florence Vinson, Arthur Moreau, Aurélie Batut, Maxime Chazalviel, et al.. Improving lipid mapping in Genome Scale Metabolic Networks using ontologies. Metabolomics, 2020, 16 (4), ⟨10.1007/s11306-020-01663-5⟩. ⟨hal-02935172⟩