Non-Targeted Mass Isotopolome Analysis Using Stable Isotope Patterns to Identify Metabolic Changes

Gas chromatography coupled with mass spectrometry can provide an extensive overview of the metabolic state of a biological system. Analysis of raw mass spectrometry data requires powerful data processing software to generate interpretable results. Here we describe a data processing workflow to generate metabolite levels, mass isotopomer distribution, similarity and variability analysis of metabolites in a nontargeted manner, using stable isotope labeling. Using our data analysis software, no bioinformatic or programming background is needed to generate results from raw mass spectrometry data.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses

¹³C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary ¹³C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open ¹³C-MFA software that works in various operating systems will enable more researchers to perform ¹³C-MFA and to further modify and develop the package.

High-throughput quantification of the levels and labeling abundance of free amino acids by liquid chromatography tandem mass spectrometry

Accurate assessment of mass isotopomer distributions (MIDs) of intracellular metabolites, such as free amino acids (AAs), is crucial for quantifying in vivo fluxes. To date, the majority of studies that measured AA MIDs have relied on the analysis of proteinogenic rather than free AAs by: i) GC-MS, which involved cumbersome process of derivatization, or ii) NMR, which requires large quantities of biological sample. In this work, the development and validation of a high-throughput LC-MS/MS method allowing the quantification of the levels and labeling of free AAs is described. Sensitivity in the order of the femtomol was achieved using multiple reaction monitoring mode (MRM). The MIDs of all free AAs were assessed without the need of derivatization, and were validated (except for Trp) on a mixture of unlabeled AA standards. Finally, this method was applied to the determination of the ¹³C-labeling abundance in free AAs extracted from maize embryos cultured with ¹³C-glutamine or ¹³C-glucose. Although Cys was below the limit of detection in these biological samples, the MIDs of a total of 18 free AAs were successfully determined. Due to the increased application of tandem mass spectrometry for ¹³C-Metabolic Flux Analysis, this novel method will enable the assessment of more complete and accurate labeling information of intracellular AAs, and therefore a better definition of the fluxes.