ABSTRACT
Neutrophils are innate immune cells and the first line of defense for the maintenance of homeostasis. However, our knowledge of the metabolic rewiring associated with their differentiation and immune stimulation is limited. Here, quantitative 13C-metabolic flux analysis was performed using HL-60 cells as the neutrophil model. A metabolic model for 13C-metabolic flux analysis of neutrophils was developed based on the accumulation of 13C in intracellular metabolites derived from 13C-labeled extracellular carbon sources and intracellular macromolecules. Aspartate and glutamate in the medium were identified as carbon sources that enter central carbon metabolism. Furthermore, the breakdown of macromolecules, estimated to be fatty acids and nucleic acids, was observed. Based on these results, a modified metabolic model was used for 13C-metabolic flux analysis of undifferentiated, differentiated, and lipopolysaccharide (LPS)-activated HL-60 cells. The glucose uptake rate and glycolytic flux decreased with differentiation, whereas the tricarboxylic acid (TCA) cycle flux remained constant. The addition of LPS to differentiated HL-60 cells activated the glucose uptake rate and pentose phosphate pathway (PPP) flux levels, resulting in an increased rate of total NADPH regeneration, which could be used to generate reactive oxygen species. The flux levels of fatty acid degradation and synthesis were also increased in LPS-activated HL-60 cells. Overall, this study highlights the quantitative metabolic alterations in multiple pathways via the differentiation and activation of HL-60 cells using 13C-metabolic flux analysis.
ABSTRACT
13C-based metabolic flux analysis (13C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy).
