中国海洋大学学报（自然科学版）2026，Vol.56Issue(7)：80-91,12.DOI:10.16441/j.cnki.hdxb.20250136
扇贝核磁共振波谱指纹图谱技术建立及图谱功能解析
An optimized NMR-Based Metabolic Fingerprinting and Annotation Method for Scallop
摘要
Abstract
This study presents an optimized protocol for acquiring and functionally annotating nuclear magnetic resonance(NMR)metabolic fingerprints of Mizuhopecten yessoensis.To overcome the chal-lenge of low metabolite annotation rates in NMR-based metabolomics,we developed an integrated anno-tation approach combining NMR and transcriptomic data using Weighted gene co-expression network a-nalysis(WGCNA).Through an orthogonal experimental design,we determined the optimal metabolite extraction parameters to be 1∶1 methanol/water,90 s homogenization,D2O at pH 7.4,yielding the highest number of detectable NMR signal peaks with over 90%reproducibility in both qualitative and quantitative analyses.Application of this protocol to various tissues of M.yessoensis revealed distinct metabolic profiles for each tissue.Notably,the hepatopancreas and striated muscle showed particularly higher levels of metabolism,such as fructose,fucose,and glucose,as well as energy metabolism-relat-ed substances like glycogen,ATP,acetoacetate,and carnitine.By constructing gene-metabolite co-oc-currence network using transcriptomic and NMR fingerprint data,we identified metabolite-gene mod-ules that were closely associated with specific tissues.The gene ontology(GO)annotations of genes within these modules were highly consistent with the physiological functions of corresponding tissues.This study provided a robust analytical framework for the functional annotation of NMR spectral and laid a foundation for applying NMR metabolomics in molluscan breeding programs.关键词
虾夷扇贝/代谢组学/NMR代谢指纹/正交实验/加权基因共表达网络分析Key words
Mizuhopecten yessoensis/metabolomics/NMR metabolic fingerprinting/orthogonal ex-periment/weighted gene co-expression network analysis分类
农业科技引用本文复制引用
孙晓璐,吕珍立,黄晓文,段续圆,马帅康,耿玘琳,王师,吕佳..扇贝核磁共振波谱指纹图谱技术建立及图谱功能解析[J].中国海洋大学学报（自然科学版）,2026,56(7):80-91,12.基金项目
国家自然科学基金项目(32130107,32102778) （32130107,32102778）
山东省重点研究发展计划项目(2021ZLGX03)资助 Supported by the National Natural Science Foundation of China(32130107,32102778) （2021ZLGX03）
the Key Research and Development Project of Shandong Province(2021ZLGX03) （2021ZLGX03）
