Issue 2, 2016

Genome-wide metabolic model to improve understanding of CD4+ T cell metabolism, immunometabolism and application in drug design

Abstract

CD4+ T cells play a critical role in adaptive immunity and have been well studied in past decades. However, the systematic metabolism features are less clear. Here, we reconstructed the genome-wide metabolic network of naïve CD4+ T cells, CD4T1670, by integrating transcriptome and metabolism data. We performed simulations for three critical metabolic subsystems (carbohydrate metabolism, fatty acid metabolism and glutaminolysis). The results were consistent with most experimental observations. Furthermore, we found that depletion of either glucose or glutamine did not significantly affect ATP production and biomass, but dramatically unbalanced the metabolic network and increased the release of some inflammation or anti-inflammation related factors, such as lysophosphatidylcholine, leukotriene and hyaluronan. Genome-wide single gene knockout analysis showed that acetyl-CoA carboxylase 1 (ACC1) was essential for T cell activation. We further investigated the role of immunometabolic genes in metabolic network stability, and found that over 25% of them were essential. The results also showed that although PTEN is a well-studied proliferation inhibitor, it was essential for maintaining the stability of CD4 metabolic networks. Finally, we applied CD4T1670 to evaluate the side-effects of certain drugs in preclinical experiments. These results suggested that CD4T1670 would be useful in understanding CD4+ T cells and drug design systematically.

Graphical abstract: Genome-wide metabolic model to improve understanding of CD4+ T cell metabolism, immunometabolism and application in drug design

Supplementary files

Article information

Article type
Paper
Submitted
17 Jul 2015
Accepted
16 Nov 2015
First published
18 Nov 2015

Mol. BioSyst., 2016,12, 431-443

Author version available

Genome-wide metabolic model to improve understanding of CD4+ T cell metabolism, immunometabolism and application in drug design

F. Han, G. Li, S. Dai and J. Huang, Mol. BioSyst., 2016, 12, 431 DOI: 10.1039/C5MB00480B

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