Unified and Isomer-Specific NMR Metabolomics Database for the Accurate Analysis of ¹³C–¹H HSQC Spectra

Abstract

A new metabolomics database and query algorithm for the analysis of 13C–1H HSQC spectra is introduced, which unifies NMR spectroscopic information on 555 metabolites from both the Biological Magnetic Resonance Data Bank (BMRB) and Human Metabolome Database (HMDB). The new database, termed Complex Mixture Analysis by NMR (COLMAR) 13C–1H HSQC database, can be queried via an interactive, easy to use web interface at . Our new HSQC database separately treats slowly exchanging isomers that belong to the same metabolite, which permits improved query in cases where lowly populated isomers are below the HSQC detection limit. The performance of our new database and query web server compares favorably with the one of existing web servers, especially for spectra of samples of high complexity, including metabolite mixtures from the model organisms Drosophila melanogaster and Escherichia coli. For such samples, our web server has on average a 37% higher accuracy (true positive rate) and a 82% lower false positive rate, which makes it a useful tool for the rapid and accurate identification of metabolites from 13C–1H HSQC spectra at natural abundance. This information can be combined and validated with NMR data from 2D TOCSY-type spectra that provide connectivity information not present in HSQC spectra.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

“…This novel structure elucidation work can be clearly differentiated from the second type of identification problem, that of structure confirmation of known metabolites, for which spectroscopic data is available in databases such as the Human Metabolite Database (HMDB) [36], the BioMagResBank (BMRB) [37] and COLMAR. [38] The key question for this structure confirmation work is what confidence the investigator has in the identification of the known metabolites.…”

“…[39] However, these recommendations have not been widely adopted and are currently being updated. [40] In the meantime, new proposals have emerged for both MS- [41,42] and NMR-based [38,[43][44][45] known metabolite identification. It is expected that the Metabolite Identification Task Group of the Metabolomics Society (http://metabolomicssociety.org/board/scientific-task-groups/metaboliteidentification-task-group) will publish improved and unifying recommendations for this important area shortly.…”

NMR-based metabolomics reveals urinary metabolome modifications in female Sprague–Dawley rats by cranberry procyanidins

“…Fourier transformed 2D 1 H-13 C HSQC files were imported to MestReNova software (Version 9.0; Mestrelab Research, Santiago de Compostela, Spain) for peak picking. 1 H-13 C HSQC peak lists were transferred to the COLMAR 13 C-1 H HSQC query web server for metabolite identification [15].…”

“…The advantage of the S-line plot over the S-plot is that it displays the predictive loading in a form resembling the original NMR spectra. The discriminant metabolites were identified by comparing their NMR spectra with reported spectra [28,29], Human Metabolome Database [30] and the COLMAR 13 C-1 H HSQC query [15]. The 13 C-1 H HSQC of a urine sample and metabolites identified by COLMAR query is shown in Fig.…”

Positional Enrichment by Proton Analysis (PEPA): A One‐Dimensional 1H‐NMR Approach for 13C Stable Isotope Tracer Studies in Metabolomics

“…Overall, 84 different resonances underlying 46 polar metabolites were identified in 1D-1 H-NMR spectra (Table S1) using BBioref AMIX database from Bruker,C henomx, HMDB [18] and complex mixture analysis by NMR (COLMAR) [19] leading to the metabolic network coverage shown in Figure S4. The area of 1D-1 H-NMR resonances in Table S1 were quantified using DOLPHIN, [20] as pectral library-guided line shapefitting and deconvolution algorithm specially developed for quantifying partially overlapped and poorly resolved 1D-1 H-NMR resonances ( Figure S1 and S2).…”