The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Foxp3 and Toll-like receptor signaling balance Treg cell anabolic metabolism for suppression
Open accessAbstract
CD4+ effector T cells (Teff cells) and regulatory T cells (Treg cells) undergo metabolic reprogramming to support proliferation and immunological function. Although signaling via the lipid kinase PI(3)K (phosphatidylinositol-3-OH kinase), the serine-threonine kinase Akt and the metabolic checkpoint kinase complex mTORC1 induces both expression of the glucose transporter Glut1 and aerobic glycolysis for Teff cell proliferation and inflammatory function, the mechanisms that regulate Treg cell metabolism and function remain unclear. We found that Toll-like receptor (TLR) signals that promote Treg cell proliferation increased PI(3)K-Akt-mTORC1 signaling, glycolysis and expression of Glut1. However, TLR-induced mTORC1 signaling also impaired Treg cell suppressive capacity. Conversely, the transcription factor Foxp3 opposed PI(3)K-Akt-mTORC1 signaling to diminish glycolysis and anabolic metabolism while increasing oxidative and catabolic metabolism. Notably, Glut1 expression was sufficient to increase the number of Treg cells, but it reduced their suppressive capacity and Foxp3 expression. Thus, inflammatory signals and Foxp3 balance mTORC1 signaling and glucose metabolism to control the proliferation and suppressive function of Treg cells.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fundamentals of Cancer Metabolism
DeBerardinis, R. J., & Chandel, N. S. (2016) · Science Advances · doi:10.1126/sciadv.1600200 · Cited by 3,013
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-citationThe Immunological Genome Project: networks of gene expression in immune cells
The Immunological Genome Project Consortium, Heng, T. S. P., Painter, M. W., Elpek, K., Lukacs-Kornek, V., Mauermann, N., Turley, S. J., Koller, D., Kim, F. S., Wagers, A. J., et al. (2008) · Nature Immunology · doi:10.1038/ni1008-1091 · Cited by 1,864
Shi, 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
Everts, B., Amiel, E., Huang, S. C.-C., Smith, A. M., Chang, C.-H., Lam, W. Y., Redmann, V., Freitas, T. C., Blagih, J., van der Windt, G. J. W., et al. (2014) · Nature Immunology · doi:10.1038/ni.2833 · Cited by 1,064
Panduro, M., Benoist, C., & Mathis, D. (2016) · Annual Review of Immunology · doi:10.1146/annurev-immunol-032712-095948 · Cited by 536
Cutting Edge: Regulatory T Cells Facilitate Cutaneous Wound Healing
Nosbaum, A., Prevel, N., Truong, H.-A., Mehta, P., Ettinger, M., Scharschmidt, T. C., Ali, N. H., Pauli, M. L., Abbas, A. K., & Rosenblum, M. D. (2016) · The Journal of Immunology · doi:10.4049/jimmunol.1502139 · Cited by 418
Tissue adaptation of regulatory and intraepithelial CD4 + T cells controls gut inflammation
Sujino, T., London, M., Hoytema van Konijnenburg, D. P., Rendon, T., Buch, T., Silva, H. M., Lafaille, J. J., Reis, B. S., & Mucida, D. (2016) · Science · doi:10.1126/science.aaf3892 · Cited by 250
Wang, S., Charbonnier, L.-M., Noval Rivas, M., Georgiev, P., Li, N., Gerber, G., Bry, L., & Chatila, T. A. (2015) · Immunity · doi:10.1016/j.immuni.2015.06.014 · Cited by 161
Cited by
AKT/PKB Signaling: Navigating the Network
Manning, B. D., & Toker, A. (2017) · Cell · doi:10.1016/j.cell.2017.04.001 · Cited by 3,772
Macrophages and metabolism in the tumor microenvironment
Vitale, I., Manic, G., Coussens, L. M., Kroemer, G., & Galluzzi, L. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.06.001 · Cited by 1,837
The regulation of immune tolerance by FOXP3
Lu, L., Barbi, J., & Pan, F. (2017) · Nature Reviews Immunology · doi:10.1038/nri.2017.75 · Cited by 580
Metabolic Regulation of T Cell Longevity and Function in Tumor Immunotherapy
Kishton, R. J., Sukumar, M., & Restifo, N. P. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.06.016 · Cited by 548
Self-citationSaravia, J., Chapman, N. M., & Chi, H. (2019) · Cellular & Molecular Immunology · doi:10.1038/s41423-019-0220-6 · Cited by 489
Metadata
- Authors
- Gerriets, V. A., Kishton, R. J., Johnson, M. O., Cohen, S., Siska, P. J., Nichols, A. G., Warmoes, M. O., de Cubas, A. A., MacIver, N. J., Locasale, J. W., Turka, L. A., Wells, A. D., & Rathmell, J. C.
- Venue
- Nature Immunology
- Published
- 2016-10-03
- Volume
- 17
- Issue
- 12
- Pages
- 1459-1466
- ISSN
- 1529-2908 (ISSN-L)
- 1529-2916
- OpenAlex
- W2529711900
- Cited by
- 539
- References
- 10
- FWCI
- 22.71
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
