The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation
Open accessAbstract
Activation of CD4+ T cells results in rapid proliferation and differentiation into effector and regulatory subsets. CD4+ effector T cell (Teff) (Th1 and Th17) and Treg subsets are metabolically distinct, yet the specific metabolic differences that modify T cell populations are uncertain. Here, we evaluated CD4+ T cell populations in murine models and determined that inflammatory Teffs maintain high expression of glycolytic genes and rely on high glycolytic rates, while Tregs are oxidative and require mitochondrial electron transport to proliferate, differentiate, and survive. Metabolic profiling revealed that pyruvate dehydrogenase (PDH) is a key bifurcation point between T cell glycolytic and oxidative metabolism. PDH function is inhibited by PDH kinases (PDHKs). PDHK1 was expressed in Th17 cells, but not Th1 cells, and at low levels in Tregs, and inhibition or knockdown of PDHK1 selectively suppressed Th17 cells and increased Tregs. This alteration in the CD4+ T cell populations was mediated in part through ROS, as N-acetyl cysteine (NAC) treatment restored Th17 cell generation. Moreover, inhibition of PDHK1 modulated immunity and protected animals against experimental autoimmune encephalomyelitis, decreasing Th17 cells and increasing Tregs. Together, these data show that CD4+ subsets utilize and require distinct metabolic programs that can be targeted to control specific T cell populations in autoimmune and inflammatory diseases.
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Cites
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
Ivanov, I. I., McKenzie, B. S., Zhou, L., Tadokoro, C. E., Lepelley, A., Lafaille, J. J., Cua, D. J., & Littman, D. R. (2006) · Cell · doi:10.1016/j.cell.2006.07.035 · Cited by 5,145
Differentiation of Effector CD4 T Cell Populations
Zhu, J., Yamane, H., & Paul, W. E. (2010) · Annual Review of Immunology · doi:10.1146/annurev-immunol-030409-101212 · Cited by 3,452
Cua, D. J., Sherlock, J., Chen, Y., Murphy, C. A., Joyce, B., Seymour, B., Lucian, L., To, W., Kwan, S., Churakova, T., et al. (2003) · Nature · doi:10.1038/nature01355 · Cited by 2,906
The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation
Wang, R., Dillon, C. P., Shi, L. Z., Milasta, S., Carter, R., Finkelstein, D., McCormick, L. L., Fitzgerald, P., Chi, H., Munger, J., et al. (2011) · Immunity · doi:10.1016/j.immuni.2011.09.021 · Cited by 2,140
Michalek, R. D., Gerriets, V. A., Jacobs, S. R., Macintyre, A. N., MacIver, N. J., Mason, E. F., Sullivan, S. A., Nichols, A. G., & Rathmell, J. C. (2011) · The Journal of Immunology · doi:10.4049/jimmunol.1003613 · Cited by 2,091
Self-citationShi, L. Z., Wang, R., Huang, G., Vogel, P., Neale, G., Green, D. R., & Chi, H. (2011) · Journal of Experimental Medicine · doi:10.1084/jem.20110278 · Cited by 1,803
Control of TH17/Treg Balance by Hypoxia-Inducible Factor 1
Dang, E. V., Barbi, J., Yang, H.-Y., Jinasena, D., Yu, H., Zheng, Y., Bordman, Z., Fu, J., Kim, Y., Yen, H.-R., et al. (2011) · Cell · doi:10.1016/j.cell.2011.07.033 · Cited by 1,595
The CD28 Signaling Pathway Regulates Glucose Metabolism
Frauwirth, K. A., Riley, J. L., Harris, M. H., Parry, R. V., Rathmell, J. C., Plas, D. R., Elstrom, R. L., June, C. H., & Thompson, C. B. (2002) · Immunity · doi:10.1016/s1074-7613(02)00323-0 · Cited by 1,475
Self-citationInhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function
Sukumar, M., Liu, J., Ji, Y., Subramanian, M., Crompton, J. G., Yu, Z., Roychoudhuri, R., Palmer, D. C., Muranski, P., Karoly, E. D., et al. (2013) · Journal of Clinical Investigation · doi:10.1172/jci69589 · Cited by 952
A key role for mitochondrial gatekeeper pyruvate dehydrogenase in oncogene-induced senescence
Kaplon, J., Zheng, L., Meissl, K., Chaneton, B., Selivanov, V. A., Mackay, G., van der Burg, S. H., Verdegaal, E. M. E., Cascante, M., Shlomi, T., et al. (2013) · Nature · doi:10.1038/nature12154 · Cited by 633
Kidani, Y., Elsaesser, H., Hock, M. B., Vergnes, L., Williams, K. J., Argus, J. P., Marbois, B. N., Komisopoulou, E., Wilson, E. B., Osborne, T. F., et al. (2013) · Nature Immunology · doi:10.1038/ni.2570 · Cited by 552
An, J., Muoio, D. M., Shiota, M., Fujimoto, Y., Cline, G. W., Shulman, G. I., Koves, T. R., Stevens, R., Millington, D., & Newgard, C. B. (2004) · Nature Medicine · doi:10.1038/nm995 · Cited by 450
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Chang, C.-H., Qiu, J., O’Sullivan, D., Buck, M. D., Noguchi, T., Curtis, J. D., Chen, Q., Gindin, M., Gubin, M. M., van der Windt, G. J. W., et al. (2015) · Cell · doi:10.1016/j.cell.2015.08.016 · Cited by 3,331
A guide to immunometabolism for immunologists
O'Neill, L. A. J., Kishton, R. J., & Rathmell, J. (2016) · Nature Reviews Immunology · doi:10.1038/nri.2016.70 · Cited by 3,315
Self-citationFrom Krebs to clinic: glutamine metabolism to cancer therapy
Altman, B. J., Stine, Z. E., & Dang, C. V. (2016) · Nature Reviews Cancer · doi:10.1038/nrc.2016.71 · Cited by 2,119
Bile acid metabolites control TH17 and Treg cell differentiation
Hang, S., Paik, D., Yao, L., Kim, E., Trinath, J., Lu, J., Ha, S., Nelson, B. N., Kelly, S. P., Wu, L., et al. (2019) · Nature · doi:10.1038/s41586-019-1785-z · Cited by 1,457
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Metadata
- Authors
- Gerriets, V. A., Kishton, R. J., Nichols, A. G., Macintyre, A. N., Inoue, M., Ilkayeva, O., Winter, P. S., Liu, X., Priyadharshini, B., Slawinska, M. E., Haeberli, L., Huck, C., Turka, L. A., Wood, K. C., Hale, L. P., Smith, P. A., Schneider, M. A., MacIver, N. J., Locasale, J. W., Newgard, C. B., Shinohara, M. L., & Rathmell, J. C.
- Venue
- Journal of Clinical Investigation
- Published
- 2014-12-01
- Volume
- 125
- Issue
- 1
- Pages
- 194-207
- ISSN
- 0021-9738 (ISSN-L)
- OpenAlex
- W1994252478
- Cited by
- 731
- References
- 13
- FWCI
- 15.56
- Type
- journal article
- Language
- en
- Open access
- bronze
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