Isotopic Ratio Outlier Analysis (IROA) of the S. cerevisiae metabolome using accurate mass GC-TOF/MS: A new method for discovery

摘要

Isotopic Ratio Outlier Analysis (IROA) is a ¹³C metabolomics profiling method that eliminates sample-to-sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass LC/MS. This is the first report using IROA technology in combination with accurate mass GC-TOFMS, here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% ¹³C, or 5%¹³C 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%¹³C extracts, or light isotopologues in the 95%¹³C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the ¹²C monoisotopic and the ¹³C 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 formulae generation, a 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 co-eluting metabolites, and allowed us to characterize 126 metabolites in the current study.