Lysak Daniel H, Wolff William W, Soong Ronald, Bermel Wolfgang, Kupče E Riks, Jenne Amy, Biswas Rajshree Ghosh, Lane Daniel, Gasmi-Seabrook Genevieve, Simpson Andre
University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario M1C1A4, Canada.
Bruker BioSpin GmbH, Rudolf-Plank-Str. 23, Ettlingen 76275, Germany.
Anal Chem. 2023 Aug 15;95(32):11926-11933. doi: 10.1021/acs.analchem.3c01362. Epub 2023 Aug 3.
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.
许多生命的关键组成部分都含有氮基团。尽管含氮代谢物在自然界中普遍存在,但由于氮核的天然丰度低且缺乏全面的化学位移数据库,氮核很少用于基于核磁共振的代谢物归属。然而,随着同位素标记策略的进步,碳和氮富集的代谢物在代谢组学研究中越来越普遍。通过异核单量子相干(HSQC)或异核多键相干,利用单键或多键耦合将氢和氮相关联的简单多维核磁共振(NMR)实验已经成熟,并常规应用于结构解析。然而,由于缺乏氮特异性数据库,代谢物混合物的氢-氮相关谱可能难以解卷积。为了弥补这一差距,我们在此展示一种宽带氮编辑的氢-碳HSQC NMR实验,该实验针对含有氮基团的代谢物。通过这种方法,基于含氮代谢物的碳、氢和氮化学位移信息来识别氨基酸、核苷酸碱基和核苷等含氮代谢物。使用富含[氮,碳]的(水蚤)代谢物提取物对该方法进行了测试和验证,其中在标准HSQC中仅能清晰分辨出11个含氮峰,而在氮编辑的HSQC中增加到51个,模糊峰的数量从59个减少到仅7个。该方法补充了当前用于混合物解卷积的NMR技术库,并且在富含氮、碳的系统中进行靶向代谢物NMR分析具有相当大的潜力。