Abstract
Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Advances in metabolomics technologies, particularly ultra-high resolution mass spectrometry (MS) enable comprehensive and rapid analysis of metabolites. However, a significant barrier to meaningful data interpretation is the identification of a wide range of metabolites including unknowns and the determination of their role(s) in various metabolic networks. Chemoselective (CS) probes to tag metabolite functional groups combined with high mass accuracy provide additional structural constraints for metabolite identification and quantification. We have developed a novel algorithm, Chemically Aware Substructure Search (CASS) that efficiently detects functional groups within existing metabolite databases, allowing for combined molecular formula and functional group (from CS tagging) queries to aid in metabolite identification without a priori knowledge. Analysis of the isomeric compounds in both Human Metabolome Database (HMDB) and KEGG Ligand demonstrated a high percentage of isomeric molecular formulae (43 and 28%, respectively), indicating the necessity for techniques such as CS-tagging. Furthermore, these two databases have only moderate overlap in molecular formulae. Thus, it is prudent to use multiple databases in metabolite assignment, since each major metabolite database represents different portions of metabolism within the biosphere. In silico analysis of various CS-tagging strategies under different conditions for adduct formation demonstrate that combined FT-MS derived molecular formulae and CS-tagging can uniquely identify up to 71% of KEGG and 37% of the combined KEGG/HMDB database vs. 41 and 17%, respectively without adduct formation. This difference between database isomer disambiguation highlights the strength of CS-tagging for non-lipid metabolite identification. However, unique identification of complex lipids still needs additional information.
Highlights
Metabolomics is the comprehensive study of metabolomes, which comprise the entirety of metabolites interconverted by networks of chemical reactions in living systems that make life possible and can be regarded as the functional readout of the genome and proteome (Kaddurah-Daouk et al, 2008; Le et al, 2012)
ALGORITHM PERFORMANCE Chemically Aware Substructure Search (CASS) outperforms the older Ullmann algorithm significantly when searching for functional groups within molfile files
The pseudo-linear behavior of our new algorithm as shown in Figure 9 is stable for values of m up to 150 and remains sufficiently fast for large values of m during functional group searching, making CASS tractable for systematic functional group searches in KEGG and Human Metabolome Database (HMDB)
Summary
Metabolomics is the comprehensive study of metabolomes, which comprise the entirety of metabolites interconverted by networks of chemical reactions in living systems that make life possible and can be regarded as the functional readout of the genome and proteome (Kaddurah-Daouk et al, 2008; Le et al, 2012). The important step is the ability to track individual atoms of various metabolites through the metabolic network using isotopically enriched tracers (e.g., 13C, 15N, and/or 2H labeled precursors) coupled with stable isotope-resolved metabolomics (SIRM), from which metabolic networks can be robustly reconstructed (Fan et al, 2009, 2010, 2011, 2012; Moseley et al, 2011; Le et al, 2012) From such studies, we can acquire system biochemical insights across a broad spectrum of biological and biomedical problems (Lane et al, 2011; Ramautar et al, 2013; Armitage and Barbas, 2014; Wood, 2014; Zhang et al, 2014).
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