Pontificia Universidad Católica del Perú, Departamento de Ciencias - Química, CERMN, Av. Universitaria 1801, Lima, 32, Peru.
Sci Rep. 2019 May 3;9(1):6900. doi: 10.1038/s41598-019-43374-5.
Even though Pure Shift NMR methods have conveniently been used in the assessment of crowded spectra, they are not commonly applied to the analysis of metabolomics data. This paper exploits the recently published SAPPHIRE-PSYCHE methodology in the context of plant metabolome. We compare single pulse, PSYCHE, and SAPPHIRE-PSYCHE spectra obtained from aqueous extracts of Physalis peruviana fruits. STOCSY analysis with simplified SAPPHIRE-PSYCHE spectra of six types of Cape gooseberry was carried out and the results attained compared with classical STOCSY data. PLS coefficients analysis combined with 1D-STOCSY was performed in an effort to simplify biomarker identification. Several of the most compromised proton NMR signals associated with critical constituents of the plant mixture, such as amino acids, organic acids, and sugars, were more cleanly depicted and their inter and intra correlation better reveled by the Pure Shift methods. The simplified data allowed the identification of glutamic acid, a metabolite not observed in previous studies of Cape gooseberry due to heavy overlap of its NMR signals. Overall, the results attained indicated that Ultra-Clean Pure Shift spectra increase the performance of metabolomics data analysis such as STOCSY and multivariate coefficients analysis, and therefore represent a feasible and convenient additional tool available to metabolomics.
尽管纯位移 NMR 方法已被方便地用于评估拥挤的光谱，但它们通常不适用于代谢组学数据的分析。本文在植物代谢组学的背景下利用最近发表的 SAPPHIRE-PSYCHE 方法。我们比较了从秘鲁酸橘果实的水提物中获得的单脉冲、PSYCHE 和 SAPPHIRE-PSYCHE 光谱。对六种刺梨的简化 SAPPHIRE-PSYCHE 图谱进行了 STOCSY 分析，并将获得的结果与经典 STOCSY 数据进行了比较。PLS 系数分析与 1D-STOCSY 相结合，旨在简化生物标志物的识别。与植物混合物的关键成分（如氨基酸、有机酸和糖）相关的几个受干扰最大的质子 NMR 信号通过纯位移方法得到了更清晰的描绘，它们之间和内部的相关性也得到了更好的揭示。简化的数据允许鉴定谷氨酸，由于其 NMR 信号的严重重叠，在以前对刺梨的研究中未观察到该代谢物。总的来说，结果表明，Ultra-Clean 纯位移谱增加了代谢组学数据分析（如 STOCSY 和多元系数分析）的性能，因此代表了一种可行且方便的附加工具，可供代谢组学使用。
