UMR1331, Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300, Toulouse, France.
MetaToul-Lipidomic Core Facility, MetaboHUB, Inserm I2MC, 31000, Toulouse, France.
Metabolomics. 2020 Mar 25;16(4):44. doi: 10.1007/s11306-020-01663-5.
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.
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.
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.
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.
This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.
为了解释代谢组学和脂质组学的谱图，有必要确定连接所测分子的代谢反应。这可以通过将它们置于基因组规模的代谢网络重建的背景下实现。然而，由于数据和代谢网络之间标识符和注释水平的差异，特别是对于脂质，将实验测量的分子映射到代谢网络是具有挑战性的。
为了帮助将脂质组学数据集中的脂质与代谢网络中的脂质联系起来，我们开发了一种基于 ChEBI 本体的新匹配方法。该实现可作为 Python 库在 MetExplore 网络服务器上免费获得。
我们的匹配方法比基于精确标识符的对应方法更灵活，因为它允许即使在数据集和代谢网络中提供不同的精度水平，也可以在分子之间建立联系。例如，它可以将网络中存在的脂质的通用类别与脂质组学数据集中详细的分子物种联系起来。这种映射是基于在 ChEBI 本体中计算分子之间的距离。
我们将我们的方法应用于化学文库（968 种脂质）和实验数据集（32 种调制脂质），并表明使用基于本体的映射可以提高并促进与基因组规模代谢网络的连接。除了网络映射之外，结果还为网络整理和脂质组学数据注释方面的改进提供了途径。
由于这种新方法是通用的，因此可以应用于任何代谢组学数据，从而提高我们对代谢调节的理解。
