Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D ¹H NMR Database Search

Abstract

NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D 1H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D 1H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D 1H J-resolved (JRES) spectra, p-JRES, which are similar to 1D 1H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to “ConQuer ABC”, which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.

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“…Chemical Shift Multiplet Database (CSMDB), is a database that uses JRES spectra obtained from the Birmingham Metabolite Library (BML), to provide scores by accounting for both matched and unmatched peaks from a query list and the database hits (Charris-Molina et al 2020 ). This input list is generated from a projection of a 2D statistical correlation analysis on the J-RESolved (JRES) spectra, p-[JRES- Statistical TOtal Correlation SpectroscopY (STOCSY)], being able to compare the multiplets for the matched peaks.…”

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“…However, STOCSY performance may be compromised where peaks overlap. JRES can efficiently reduce overlap by separating chemical shifts and multiplicities to two dimensions, but JRES databases are limited and difficult for peak matching . The Hoijemberg group introduced two strategies to circumvent this challenge by querying peaks from the projection of JRES for 1D databases (Figure ).…”

“…(A) Adapted with permission from Charris-Molina, A.; Riquelme, G.; Burdisso, P.; Hoijemberg, P. A. J. Proteome Res. 2020 , 19 (8), 2977–2988 (ref ). Copyright 2020 American Chemical Society.…”

“…JRES can efficiently reduce overlap by separating chemical shifts and multiplicities to two dimensions, but JRES databases are limited and difficult for peak matching. 148 The Hoijemberg group introduced two strategies to circumvent this challenge by querying peaks from the projection of JRES for 1D databases (Figure 6). 148,149 The first strategy (Figure 6A) uses projection of STOCSY traces from tilted and symmetrized JRES (p-(JRES-STOCSY)) as pseudospectra.…”

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“…Metabolomics, both targeted and untargeted, generally adopts techniques like nuclear magnetic resonance (NMR) [40] or mass spectrometry (MS) [41]. The original spectrogram is processed and analyzed to obtain the data, compared with a standard database (HMDB, KEGG) to obtain the results [41,42]. Currently, a combination of multiple platforms has been used for comprehensive analysis, such as the liquid chromatography-mass spectrometry (LC-MS)-based metabolomics [43,44].…”