Department of Chemistry, Michigan State University, East Lansing, Michigan, USA.
Magn Reson Chem. 2024 Apr;62(4):298-309. doi: 10.1002/mrc.5397. Epub 2023 Sep 19.
Solid-state nuclear magnetic resonance (ssNMR) measurements of intact cell walls and cellular samples often generate spectra that are difficult to interpret due to the presence of many coexisting glycans and the structural polymorphism observed in native conditions. To overcome this analytical challenge, we present a statistical approach for analyzing carbohydrate signals using high-resolution ssNMR data indexed in a carbohydrate database. We generate simulated spectra to demonstrate the chemical shift dispersion and compare this with experimental data to facilitate the identification of important fungal and plant polysaccharides, such as chitin and glucans in fungi and cellulose, hemicellulose, and pectic polymers in plants. We also demonstrate that chemically distinct carbohydrates from different organisms may produce almost identical signals, highlighting the need for high-resolution spectra and validation of resonance assignments. Our study provides a means to differentiate the characteristic signals of major carbohydrates and allows us to summarize currently undetected polysaccharides in plants and fungi, which may inspire future investigations.
固态核磁共振（ssNMR）测量完整细胞壁和细胞样品时，由于共存的许多聚糖以及在天然条件下观察到的结构多态性，通常会产生难以解释的光谱。为了克服这一分析挑战，我们提出了一种使用碳水化合物数据库索引的高分辨率 ssNMR 数据分析碳水化合物信号的统计方法。我们生成模拟光谱以展示化学位移分散，并将其与实验数据进行比较，以促进鉴定重要的真菌和植物多糖，如真菌中的几丁质和葡聚糖以及植物中的纤维素、半纤维素和果胶聚合物。我们还表明，来自不同生物体的化学上不同的碳水化合物可能产生几乎相同的信号，这突出了需要高分辨率光谱和共振分配验证。我们的研究提供了一种区分主要碳水化合物特征信号的方法，并使我们能够总结目前在植物和真菌中未检测到的多糖，这可能激发未来的研究。
