Journal
PLOS ONE
Volume 12, Issue 12, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0189012
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Funding
- Spanish Ministry of Economy and Competitiveness [BFU2012-39521, BFU2015-68354, TEC2014-60337-R, SAF2011-30578, BFU2014-57466]
- Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM) of Institute de Investigacion Carlos III (ISCIII)
- Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) of Institute de Investigacion Carlos III (ISCIII)
- Finnish Cultural Society
- AGAUR Fl-scholarship programme
- Takeda Cambridge Ltd
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Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, meta-bolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of C-13 glucose labelling of these cells.
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