食品与发酵工业 >

2024 , Vol. 50 >Issue 2: 312 - 320

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.033846

分析与检测

刺梨不同提取物的非靶向代谢组学比较与分析

  • 刘含 ,
  • 郭银萍 ,
  • 穆兴燕 ,
  • 刘晓燕 ,
  • 岑顺友
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  • 1(贵阳学院 食品与制药工程学院,贵州 贵阳,550005)
    2(贵州省果品加工工程技术研究中心,贵州 贵阳,550005)
    3(贵州宏财聚农投资有限责任公司,贵州 六盘水,553535)
第一作者:硕士研究生(刘晓燕教授为通信作者,E-mail:1195296111@qq.com)

收稿日期: 2022-10-04

  修回日期: 2022-11-10

  网络出版日期: 2024-02-27

基金资助

2021年贵阳学院硕士研究生科研创新计划项目(GYU-YJS[2021]-50);六盘水市科技项目(52020-2019-N-02);中央引导地方科技发展项目(黔科中引地[2021]4009)

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Comparison and analysis of non-targeted metabolomics of different extracts of Rosa roxburghii Tratt

  • LIU Han ,
  • GUO Yinping ,
  • MU Xingyan ,
  • LIU Xiaoyan ,
  • CEN Shunyou
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  • 1(School of Food and Pharmaceutical Engineering, Guiyang University, Guiyang 550005, China)
    2(Guizhou Fruit Processing Engineering Technology Research Center, Guiyang 550005, China)
    3(Guizhou Hongcai Junong Investment Co.Ltd., Liupanshui 553535, China)

Received date: 2022-10-04

  Revised date: 2022-11-10

  Online published: 2024-02-27

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摘要

该文采用超高效液相色谱-高分辨质谱技术(ultra high performance liquid chromatography high resolution mass spectrometry, UPLC-HRMS)对刺梨不同提取物进行非靶向定性解析,通过数据库确认注释并鉴定代谢物,在二级质谱信息下共鉴定出316种代谢物。运用多元统计方法进行刺梨不同提取物数据分析并筛选出差异代谢物。主成分分析结果表明,刺梨不同提取物存在代谢物差异。依据正交偏最小二乘法判别分析模型的变量重要性投影(variable importance projection value, VIP,VIP>1.3)值、P<0.01和差异倍数(fold change, FC,FC≥3和FC≤0.1)可筛选出49种显著差异代谢物。通过MBRole 2.0通路分析功能,发现有11条代谢途径。其中类黄酮生物合成途径最为显著,共计4个代谢物参与类黄酮生物合成代谢通路。该文基于非靶向代谢组学分析表明,刺梨不同提取物之间的代谢物有一定差异,为进一步研究其功能提供了数据支持。

关键词： 非靶向代谢组学; 刺梨; 提取物; 多元统计分析; 代谢通路

本文引用格式

刘含 , 郭银萍 , 穆兴燕 , 刘晓燕 , 岑顺友 . 刺梨不同提取物的非靶向代谢组学比较与分析[J]. 食品与发酵工业, 2024 , 50(2) : 312 -320 . DOI: 10.13995/j.cnki.11-1802/ts.033846

Abstract

One of the more critical steps in metabolomics research is substance extraction. This paper intended to provide a data reference for subsequent metabolomics-based studies and efficient utilization of Rosa roxburghii Tratt. Ultra high-performance liquid chromatography high-resolution mass spectrometry (UPLC-HRMS) was used for the non-targeted qualitative analysis of different extracts of Rosa roxburghii Tratt, which was confirmed by database annotation and identification of metabolites. 316 metabolites were identified under secondary mass spectrometry information. The data of different extracts of Rosa roxburghii Tratt were analyzed by multivariate statistical methods and the different metabolites were screened. Principal component analysis showed that differences in metabolites existed among the different extracts of Rosa roxburghii Tratt. Forty-nine significantly different metabolites could be screened based on the variable importance projection value (VIP, VIP>1.3), P<0.01, and fold change (FC, FC≥3 and ≤0.1) of the orthogonal partial least squares discriminant analysis model. Through the MBRole 2.0 pathway analysis function, 11 metabolic pathways were identified. The biosynthesis pathway of flavonoids was the most significant, with a total of four metabolites involved in flavonoid biosynthesis metabolic pathways. In the present paper, metabolomics-based analysis showed that the metabolites of Rosa roxburghii Tratt had some differences between different extracts, providing data support for the further investigation of their functions.

Key words： non-targeted metabolomics; Rosa roxburghii Tratt; extract; multivariate statistical analysis; metabolic pathways

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