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.
Document Type
Article
Publication Date
7-28-2014
Digital Object Identifier (DOI)
http://dx.doi.org/10.3389/fgene.2014.00237
Funding Information
This work was supported in part by National Science Foundation NSF 1252893 (Hunter N. B. Moseley), NIH R01ES022191-01 (Teresa W.-M. Fan, Richard M. Higashi, and Hunter N. B. Moseley), NIH P01CA163223-01A1 (Andrew N. Lane and Teresa W.-M. Fan), NIH 1U24DK097215-01A1 (Richard M. Higashi, Teresa W.-M. Fan, Andrew N. Lane, and Hunter N. B. Moseley) and NIH R25-CA134283 (David W. Hein).
Repository Citation
Mitchell, Joshua M.; Fan, Teresa W-M; Lane, Andrew N.; and Moseley, Hunter N. B., "Development and In Silico Evaluation of Large-Scale Metabolite Identification Methods Using Functional Group Detection for Metabolomics" (2014). Molecular and Cellular Biochemistry Faculty Publications. 72.
https://uknowledge.uky.edu/biochem_facpub/72
Supplemental Data
Notes/Citation Information
Published in Frontiers in Genetics, v. 5, article 237, p. 1-18.
© 2014 Mitchell, Fan, Lane and Moseley.
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