...
首页> 外文期刊>Biotechnology Progress >GC-MS Analysis of amino acids rapidly provides rich information for isotopomer balancing
【24h】

GC-MS Analysis of amino acids rapidly provides rich information for isotopomer balancing

机译:氨基酸的GC-MS快速分析为同位素异构体平衡提供了丰富的信息

获取原文
获取原文并翻译 | 示例
           

摘要

Gas chromatography-mass spectrometry (GC-MS) is a rapid method that provides rich information on isotopomer distributions for metabolic flux analysis. First, we established a fast and reliable experimental protocol for AC-MS analysis of amino acidsfrom total biomass hydrolyzates, and common experimental pitfalls are discussed. Second, a suitable interface for the use of mass isotopomer data is presented. For this purpose, a general, matrix-based correction procedure that accounts for naturally occurring isotopes is introduced. Simulated and experimentally determined mass distributions of unlabeled amino acids showed a deviation of less than 3% for 89% of the analyzed fragments. Third, to investigate general properties of GC-MS-based isotopomer balancing, altered flux ratios through glycolysis and pentose phosphate pathway and/or exchange fluxes were simulated. Different fluxes were found to exert specific and significant influence on the mass isotopomer distributions, thus indicating that GC-MSdata contain valuable information for metabolic flux analysis. Fourth, comparison of different methods revealed that GC-MS analysis provides the largest number of independent constraints on amino acid isotopomer abundance, followed by correlation spectroscopy and fractional enrichment analysis by nuclear magnetic resonance.
机译:气相色谱-质谱(GC-MS)是一种快速方法,可为代谢通量分析提供有关同位素异构体分布的丰富信息。首先,我们建立了一种快速可靠的实验方案,用于从总生物质水解物中进行氨基酸的AC-MS分析,并讨论了常见的实验陷阱。其次,给出了适合使用质量同位素数据的界面。为此,引入了一种一般的,基于矩阵的校正程序,该程序考虑了自然存在的同位素。仿真和实验确定的未标记氨基酸的质量分布显示,对于89%的分析片段,其偏差小于3%。第三,为了研究基于GC-MS的同位素平衡的一般性质,模拟了通过糖酵解和戊糖磷酸途径和/或交换通量改变的通量比。发现不同的通量对质量同位素异构体的分布有特定且显着的影响,因此表明GC-MSdata包含用于代谢通量分析的有价值的信息。第四,对不同方法的比较表明,GC-MS分析对氨基酸同工异构体的丰度提供了最大数量的独立约束,其次是相关光谱和核磁共振的分馏富集分析。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号