Abstract
Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks.
Highlights
Metabolomics has been widely used in disease and model organism research [1,2,3,4]
The chemical ionization (CI) reagent gas available for the GC-Orbitrap Isotopic Ratio Outlier Analysis (IROA) experiment was 10% ammonia in methane, as opposed to the 5% ammonia in methane we reported as used for GC-TOF/MS IROA determinations [10]
The results showed that seven chemical formulae generation (CFG) were suggested in GC-TOF/MS data and the last one is the correct formula for glutamic acid (Figure 2A)
Summary
A major challenge for the field of metabolomics is the identification of unknown or novel metabolites. Mass spectrometry has been significantly increased in terms of sensitivity and resolution over the past decade, while in general, tens to hundreds of thousands m/z features (ions) be detected by MS methods, many metabolites are not identified. It is estimated that rarely more than 30% of the compounds are identified in ESI MS/MS untargeted metabolomics profiling [5]. In general, only a fraction of the data that an untargeted mass spectrometric experiment provides can aid the formation of metabolic networks from which biological conclusions can be made. Challenges for global profiling including the creative use of algorithms for the separation of peaks from noise, optimal data mining paradigms and databases [1]
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