Isotopic Ratio Outlier Analysis of the S. cerevisiae Metabolome Using Accurate Mass Gas Chromatography/Time-of-Flight Mass

Analyte identification is a major bottleneck in metabolomics studies. The isotopic ratio outlier analysis (IROA) technique, which utilizes full metabolic ¹³C labeling to distinguish between two cell populations (i.e., experimental and control), has proven valuable in LC-MS analysis to discriminate between metabolites of biological and nonbiological (i.e., chemical artifacts and noise) origin. Here, Kurland, I. et al. demonstrates the application of the IROA labeling technology to GC-MS profiling (via electron impact and chemical ionization) of the yeast (S. cerevisiae) metabolome. Using accurate mass GC-MS data in combination with a newly developed algorithm for formula elucidation, the structure/identity of 126 yeast metabolites was characterized. This catalog represents a first step toward the creation of clean spectral libraries for improved metabolite identification of experimental samples.

Abstract

Isotopic ratio outlier analysis (IROA) is a (13)C metabolomics profiling method that eliminates sample to sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass liquid chromatography/mass spectrometry (LC/MS). This is the first report using IROA technology in combination with accurate mass gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS), here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% (13)C, or 5%(13)C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%(13)C extracts, or light isotopologues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the (12)C monoisotopic and the (13)C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both chemical and electron ionization, extends the information acquired from the isotopic peak patterns for formulas generation. The process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations are used as search constraints. In electron impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of chemical ionization (CI) IROA and EI/IROA affords a metabolite identification procedure that enables the identification of coeluting metabolites, and allowed us to characterize 126 metabolites in the current study.

Qiu Y, Moir R, Willis I, Beecher C, et al.

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