RESUMO
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.
RESUMO
Overexpression of proteins by introducing a DNA vector is among the most important tools for the metabolic engineering of microorganisms such as Escherichia coli. Protein overexpression imposes a burden on metabolism because metabolic pathways must supply building blocks for protein and DNA synthesis. Different E. coli strains have distinct metabolic capacities. In this study, two proteins were overexpressed in four E. coli strains (MG1655(DE3), W3110(DE3), BL21star(DE3), and Rosetta(DE3)), and their effects on metabolic burden were investigated. Metabolomic analysis showed that E. coli strains overexpressing green fluorescent protein had decreased levels of several metabolites, with a positive correlation between the number of reduced metabolites and green fluorescent protein expression levels. Moreover, nucleic acid-related metabolites decreased, indicating a metabolic burden in the E. coli strains, and the growth rate and protein expression levels were improved by supplementation with the five nucleosides. In contrast, two strains overexpressing delta rhodopsin, a microbial membrane rhodopsin from Haloterrigena turkmenica, led to a metabolic burden and decrease in the amino acids Ala, Val, Leu, Ile, Thr, Phe, Asp, and Trp, which are the most frequent amino acids in the delta rhodopsin protein sequence. The metabolic burden caused by protein overexpression was influenced by the metabolic capacity of the host strains and the sequences of the overexpressed proteins. Detailed characterization of the effects of protein expression on the metabolic state of engineered cells using metabolomics will provide insights into improving the production of target compounds.