¹³C metabolic flux analysis (¹³C-MFA) enables the precise quantification of intracellular metabolic reaction rates by analyzing the distribution of mass isotopomers of proteinogenic amino acids or intracellular metabolites through ¹³C labeling experiments. ¹³C-MFA has received much attention as it can help systematically understand cellular metabolic characteristics, guide metabolic engineering design and gain mechanistic insights into pathophysiology. This article reviews the advances of ¹³C-MFA in the past 30 years and discusses its potential and future perspective, with a focus on its application in industrial biotechnology and biomedicine.
Amino Acids ; Carbon Isotopes ; Isotope Labeling ; Metabolic Engineering ; Metabolic Flux Analysis ; Models, Biological

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.
Metabolic Flux Analysis ; Isotope Labeling/methods* ; Carbon Isotopes/metabolism* ; Mass Spectrometry/methods* ; Metabolic Networks and Pathways

The aim of this study is to develop a synthetic medium suitable for 13C metabolic flux analysis (13C-MFA) of Streptomyces rimosus. The cell growth rate and oxytetracycline production by S. rimosus M4018 were compared when M4018 cells were growth on the optimized chemically defined media with organic nitrogen sources or inorganic nitrogen sources. First, a synthetic medium contained KNO3 as the main nitrogen source was screened, then optimized by a response surface method. Using this new medium, the oxytetracycline yield was increased from 75.2 to 145.6 mg/L. Furthermore, based on the 13C-MFA, we identified that Entner-Doudoroff pathway does not exist in S. rimosus cells cultured in a chemically defined medium with feed of 100% 1-13C labeled glucose. This study is helpful for subsequent 13C-MFA application of S. rimosus.
Carbon Isotopes ; analysis ; Culture Media ; chemistry ; Metabolic Flux Analysis ; Nitrogen ; chemistry ; Oxytetracycline ; biosynthesis ; Streptomyces rimosus ; metabolism