Abstract
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.
Highlights
Metabolomics is the real-time outcome of the organism metabolism
The chemical diversity of small molecules is vast as evidenced by the massive size of current databases such as PubChem (60,870,896 compounds, October 2015; Kim et al, 2016), eMolecules (4,840,559 compounds referenced in ChemSpider, October 2015; Pence and Williams, 2010), or MolPort (5,292,051 compounds referenced in ChemSpider, October 2015)
Metabolomics aims at deciphering metabolic modulations induced by environmental or genetic factors on this intracellular metabolism (Nicholson et al, 1999; Fiehn et al, 2000), researchers generally focus on endogenous metabolites and only monitor a small portion of these databases (Ramautar et al, 2013)
Summary
Metabolomics is the real-time outcome of the organism metabolism. To provide physiological interpretations and new hypotheses based on metabolomics datasets obtained on biofluids, tissue, or cellular extracts; it is of outmost importance to put the identified metabolites in a biological context. The chemical diversity of small molecules is vast as evidenced by the massive size of current databases such as PubChem (60,870,896 compounds, October 2015; Kim et al, 2016), eMolecules (4,840,559 compounds referenced in ChemSpider, October 2015; Pence and Williams, 2010), or MolPort (5,292,051 compounds referenced in ChemSpider, October 2015) Most of these compounds are drugs or synthetic compounds and are not necessarily related to the endogenous metabolism (in which metabolites are created or consumed by cellular processes). Metabolomics aims at deciphering metabolic modulations induced by environmental or genetic factors on this intracellular metabolism (Nicholson et al, 1999; Fiehn et al, 2000), researchers generally focus on endogenous metabolites and only monitor a small portion of these databases (Ramautar et al, 2013) This explains the success of biology-oriented chemical databases such as the Human Metabolome Database (HMDB, 41,993 compounds, October 2015; Wishart et al, 2013). Annotation can lead to ambiguities and requires a final identification step (to reach level 1 as described in Sumner et al, 2007) to provide high quality metabolite lists
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