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胆固醇合成的同位素异构体光谱分析:在人肝癌细胞中的应用

Isotopomer spectral analysis of cholesterol synthesis: applications in human hepatoma cells.

作者信息

Kelleher J K, Kharroubi A T, Aldaghlas T A, Shambat I B, Kennedy K A, Holleran A L, Masterson T M

机构信息

Department of Physiology, George Washington University Medical Center, Washington, DC 20037.

出版信息

Am J Physiol. 1994 Mar;266(3 Pt 1):E384-95. doi: 10.1152/ajpendo.1994.266.3.E384.

DOI:10.1152/ajpendo.1994.266.3.E384
PMID:8166258
Abstract

Cholesterol synthesis from 13C-labeled precursors produces a discrete spectrum of mass isotopomers detectable using gas chromatography-mass spectrometry. The isotopomer spectral analysis (ISA) method matches the observed spectrum of cholesterol isotopomers with a mathematical model to obtain the best fit of model spectrum to data spectrum. The model was based on multinomial probability expressions that simulate cholesterol synthesis as a condensation of mevalonate fragments. As many as four unknown parameters, representing fluxes between compartments, were included in the model. Models were developed to assess cholesterol synthesis from 13C-enriched precursors including mevalonate, acetate, acetoacetate or octanoate. Models were tested in the human hepatoma cell line, Hep G2, which readily incorporated the 13C substrates into cholesterol. The ISA approach was used to estimate the fractional amount of the cholesterol precursors derived from the 13C substrate and the fraction of total cellular cholesterol synthesized in the presence of the 13C substrate. The study demonstrated the feasibility of the ISA approach for a condensation biosynthesis that is not a simple polymerization and for models with more than two unknown parameters.

摘要

利用气相色谱 - 质谱联用技术,由13C标记的前体合成胆固醇会产生一系列离散的质量同位素异构体谱。同位素异构体谱分析(ISA)方法将观察到的胆固醇同位素异构体谱与数学模型进行匹配,以使模型谱与数据谱达到最佳拟合。该模型基于多项概率表达式,将胆固醇合成模拟为甲羟戊酸片段的缩合反应。模型中包含多达四个代表不同区室间通量的未知参数。已开发出多种模型来评估由富含13C的前体(包括甲羟戊酸、乙酸盐、乙酰乙酸或辛酸盐)合成胆固醇的情况。这些模型在人肝癌细胞系Hep G2中进行了测试,该细胞系能轻易地将13C底物掺入胆固醇中。ISA方法用于估计源自13C底物的胆固醇前体的分数含量以及在13C底物存在下合成的总细胞胆固醇的分数。该研究证明了ISA方法对于非简单聚合的缩合生物合成以及具有两个以上未知参数的模型的可行性。

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