Construction and application of natural stable isotope correction matrix in 13C-labeled metabolic flux analysis.
- Author:
Shiyuan ZHENG
1
;
Junfeng JIANG
1
;
Jianye XIA
1
Author Information
- Publication Type:Journal Article
- Keywords: Python; mass spectrometry; metabolic flux analysis; natural isotope correction matrix
- MeSH: Metabolic Flux Analysis; Isotope Labeling/methods*; Carbon Isotopes/metabolism*; Mass Spectrometry/methods*; Metabolic Networks and Pathways
- From: Chinese Journal of Biotechnology 2022;38(10):3940-3955
- CountryChina
- Language:Chinese
- 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.
