Abstract
SummaryMass isotopolome analysis for mode of action identification (MIAMI) combines the strengths of targeted and non-targeted approaches to detect metabolic flux changes in gas chromatography/mass spectrometry datasets. Based on stable isotope labeling experiments, MIAMI determines a mass isotopomer distribution-based (MID) similarity network and incorporates the data into metabolic reference networks. By identifying MID variations of all labeled compounds between different conditions, targets of metabolic changes can be detected.Availability and implementationWe implemented the data processing in C++17 with Qt5 back-end using MetaboliteDetector and NTFD libraries. The data visualization is implemented as web application. Executable binaries and visualization are freely available for Linux operating systems, the source code is licensed under General Public License version 3.
Highlights
The identification of metabolic flux changes in biological systems is of high value in various scientific fields, like health, nutrition, pharmacology, biotechnology and chemistry
Based on the algorithms of MIA (Weindl et al, 2016b), NTFD (Hiller et al, 2013) and MetaboliteDetector (Hiller et al, 2009), we extended the contextualization beyond pure non-targeted mass isotopomer distribution-based (MID)-based analysis
Metabolites with no matching reference spectrum or identified metabolites with no KEGG identifier are mapped on the pathway based on MID similarity
Summary
Cellular metabolism can quickly adapt to exogenous stimuli. For this reason, the identification of metabolic flux changes in biological systems is of high value in various scientific fields, like health, nutrition, pharmacology, biotechnology and chemistry. Various software tools and packages are available for data analysis of either targeted or non-targeted datasets of stable isotope labeling experiments (Misra, 2018). All these tools either focus on targeted or non-targeted methods exclusively, without considering the strength of the other method. While the discovery of previously unconsidered metabolic interactions is quite important, it is essential to link these data to the current biological knowledge To address this shortcoming, we developed mass isotopolome analysis for mode of action identification (MIAMI) as a comprehensive tool for the detection, analysis and visualization of global metabolic flux changes. Detailed experimental procedures are described in Weindl et al (2016a)
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