13C标记代谢流分析中天然稳定性同位素修正矩阵构建方法及应用

Construction and application of natural stable isotope correction matrix in 13C-labeled metabolic flux analysis

摘要:

稳定性同位素¹³C标记实验是分析细胞代谢流的一种重要手段，主要通过质谱检测胞内代谢物中¹³C标记的同位素分布，并作为胞内代谢流计算时的约束条件，进而通过代谢流分析算法得到相应代谢网络中的通量分布。然而在自然界中，并非只有C元素存在天然稳定性同位素¹³C，其他元素如O元素也有其天然稳定性同位素¹⁷O、¹⁸O等，这使得质谱方法所测得的同位素分布中会夹杂除¹³C标记之外的其他元素的同位素信息，特别是分子中含有较多其他元素的分子，这将导致很大的实验误差，因此需要在进行代谢流计算前进行质谱数据的矫正。本研究提出了一种基于Python语言的天然同位素修正矩阵的构建方法，用于修正同位素分布测量值中由于天然同位素分布引起的测定误差。文中提出的基本修正矩阵幂方法用于构建各元素修正矩阵，结构简单、易于编码实现，可直接应用于¹³C代谢流分析软件数据前处理。将该修正方法应用于¹³C标记的黑曲霉（Aspergillus niger）胞内代谢流分析，结果表明本研究提出的方法准确有效，为准确获取微生物胞内代谢流分析提供了可靠的数据修正方法。

Abstract:

Stable isotope ¹³C labeling is an important tool to analyze cellular metabolic flux. The ¹³C distribution in intracellular metabolites can be detected via mass spectrometry and used as a constraint in intracellular metabolic flux calculations. Then, metabolic flux analysis algorithms can be employed to obtain the flux distribution in the corresponding metabolic reaction network. However, in addition to carbon, other elements such as oxygen in the nature also have natural stable isotopes (e.g., ¹⁷O, ¹⁸O). This makes the isotopic information of elements other than the ¹³C marker interspersed in the isotopic distribution measured by the mass spectrometry, especially that of the molecules containing many other elements, which leads to large errors. Therefore, it is essential to correct the mass spectrometry data before performing metabolic flux calculations. In this paper, we proposed a method for construction of correction matrix based on Python language for correcting the measurement errors due to natural isotope distribution. The method employed a basic power method for constructing the correction matrix with simple structure and easy coding implementation, which can be directly applied to data pre-processing in ¹³C metabolic flux analysis. The correction method was then applied to the intracellular metabolic flux analysis of ¹³C-labeled Aspergillus niger. The results showed that the proposed method was accurate and effective, which can serve as a reliable data correction method for accurate microbial intracellular metabolic flux analysis.