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Application of N-Edited H-C Correlation NMR Spectroscopy─Toward Fragment-Based Metabolite Identification and Screening via HCN Constructs. | LitMetric

AI Article Synopsis

  • * With recent advancements in isotope labeling, nitrogen and carbon enriched metabolites are increasingly studied, but existing NMR techniques for metabolite analysis still struggle with deconvoluting data from mixtures.
  • * A new NMR experiment, called broadband N-edited H-C HSQC, has been developed to better identify nitrogen-containing metabolites, resulting in significantly improved peak resolution compared to standard methods.

Article Abstract

Many key building blocks of life contain nitrogen moieties. Despite the prevalence of nitrogen-containing metabolites in nature, N nuclei are seldom used in NMR-based metabolite assignment due to their low natural abundance and lack of comprehensive chemical shift databases. However, with advancements in isotope labeling strategies, C and N enriched metabolites are becoming more common in metabolomic studies. Simple multidimensional nuclear magnetic resonance (NMR) experiments that correlate H and N via single bond or multiple bond couplings using heteronuclear single quantum coherence (HSQC) or heteronuclear multiple bond coherence are well established and routinely applied for structure elucidation. However, a H-N correlation spectrum of a metabolite mixture can be difficult to deconvolute, due to the lack of a N specific database. In order to bridge this gap, we present here a broadband N-edited H-C HSQC NMR experiment that targets metabolites containing N moieties. Through this approach, nitrogen-containing metabolites, such as amino acids, nucleotide bases, and nucleosides, are identified based on their C, H, and N chemical shift information. This approach was tested and validated using a [N, C] enriched (water flea) metabolite extract, where the number of clearly resolved N-containing peaks increased from only 11 in a standard HSQC to 51 in the N-edited HSQC, and the number of obscured peaks decreased from 59 to just 7. The approach complements the current repertoire of NMR techniques for mixture deconvolution and holds considerable potential for targeted metabolite NMR in N, C enriched systems.

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http://dx.doi.org/10.1021/acs.analchem.3c01362DOI Listing

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