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聚苹果酸生产菌株出芽短梗霉菌代谢组学样品前处理方法的优化

范栩嘉 边艳慧 殷海松 乔长晟

生物技术通报2016,Vol.32Issue(6):54-59,6.
生物技术通报2016,Vol.32Issue(6):54-59,6.DOI:10.13560/j.cnki.biotech.bull.1985.2016.06.009

聚苹果酸生产菌株出芽短梗霉菌代谢组学样品前处理方法的优化

Optimization of Sample Pretreatment Method for Metabolomics Studies ofAureobasidium pullulansProducing Polymalic Acid

范栩嘉 1边艳慧 1殷海松 2乔长晟3

作者信息

  • 1. 天津科技大学生物工程学院 工业发酵微生物教育部重点实验室,天津 300457
  • 2. 天津现代职业技术学院生物工程学院,天津 300350
  • 3. 天津市轻工业化学研究所有限公司,天津 300350
  • 折叠

摘要

Abstract

In order to acquire more accurate and complete intracellular metabolite information,and therefore to more realistically reflect the microbial intracellular metabolism,the systematic optimization of metabolomic sample preparation process was carried out from 3 aspects of quencher,extraction and solvent agent based on the GC-MS platform. The leakage of intracellular metabolites and cell injury with different quenchers was compared,and the results showed that 60% methanol/0.9% NaCl had the least leakage. The effects of extraction and solvent agent on relative abundance of metabolite distribution were investigated,and 60% methanol presented the significant extraction effect, and pyridine had the dissolving effect.The optimal sample pretreatment method for metabolomic analysis of Aureobasidium pullulans was determined as follows:pre-cooled 60% methanol/0.9% NaCl as quencher,ground in liquid nitrogen and repeated freezing and thawing for cell disruption,pre-cooled 60% methanol for the extraction of metabolites,and then the extracts were dissolved by pyridine and derivatized for 90 min under MSTFA at 40 ℃ . By using this optimized protocol,more than 300 kinds of metabolic components were identified by GC-MS,including a large number of amino acids,organic acids and sugars. Thus our established method can be used to effectively analyze the A. pullulansmetabolites.

关键词

代谢组学/出芽短梗霉/GC-MS/样品前处理

Key words

metabolomics/Aureobasidium pullulans/GC-MS/pretreatment

引用本文复制引用

范栩嘉,边艳慧,殷海松,乔长晟..聚苹果酸生产菌株出芽短梗霉菌代谢组学样品前处理方法的优化[J].生物技术通报,2016,32(6):54-59,6.

基金项目

天津市科技支撑计划重点项目 ()

生物技术通报

OA北大核心CSCDCSTPCD

1002-5464

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