Abstract
BackgroundMetabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. However, classical metabolomics techniques only provide a very static view on metabolism. Metabolic flux analysis methods, on the other hand, are highly targeted and require detailed knowledge on metabolism beforehand.ResultsWe present a novel workflow to analyze non-targeted metabolome-wide stable isotope labeling data to detect metabolic flux changes in a non-targeted manner. Furthermore, we show how similarity-analysis of isotopic enrichment patterns can be used for pathway contextualization of unidentified compounds. We illustrate our approach with the analysis of changes in cellular metabolism of human adenocarcinoma cells in response to decreased oxygen availability. Starting without a priori knowledge, we detect metabolic flux changes, leading to an increased glutamine contribution to acetyl-CoA production, reveal biosynthesis of N-acetylaspartate by N-acetyltransferase 8-like (NAT8L) in lung cancer cells and show that NAT8L silencing inhibits proliferation of A549, JHH-4, PH5CH8, and BEAS-2B cells.ConclusionsDifferential stable isotope labeling analysis provides qualitative metabolic flux information in a non-targeted manner. Furthermore, similarity analysis of enrichment patterns provides information on metabolically closely related compounds. N-acetylaspartate and NAT8L are important players in cancer cell metabolism, a context in which they have not received much attention yet.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0150-z) contains supplementary material, which is available to authorized users.
Highlights
Metabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points
Locating flux changes by non-targeted mass isotopomer abundance variation analysis Since changes in mass isotopomer distributions (MIDs) can only be a consequence of altered metabolic fluxes, we can reveal metabolic flux changes by detecting changes in the mass isotopolome [29]
MIDs of identical compounds are matched across different experimental conditions to detect differences in relative mass isotopomer abundances
Summary
Metabolism gained increasing interest for the understanding of diseases and to pinpoint therapeutic intervention points. Classical metabolomics techniques only provide a very static view on metabolism. Cellular metabolism gained increasing interest to pinpoint potential therapeutic intervention points to treat complex diseases. Metabolomics research, analyzing changes in metabolite levels, deepened our understanding of cellular metabolism, which led to the discovery of unanticipated metabolites [1] and disease biomarkers [2, 3]. To that end metabolic flux analysis techniques such as flux balance analysis (FBA) and 13C metabolic flux analysis (13C-MFA) have been developed. FBA employs genome-scale metabolic networks [6] and aims to balance cellular influxes and effluxes with an optimal set of intracellular fluxes [7, 8].
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