The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Lipid signalling enforces functional specialization of Treg cells in tumours
Open accessAbstract
Regulatory T cells (Treg cells) are essential for immune tolerance1, but also drive immunosuppression in the tumour microenvironment2. Therapeutic targeting of Treg cells in cancer will therefore require the identification of context-specific mechanisms that affect their function. Here we show that inhibiting lipid synthesis and metabolic signalling that are dependent on sterol-regulatory-element-binding proteins (SREBPs) in Treg cells unleashes effective antitumour immune responses without autoimmune toxicity. We find that the activity of SREBPs is upregulated in intratumoral Treg cells. Moreover, deletion of SREBP-cleavage-activating protein (SCAP)—a factor required for SREBP activity—in these cells inhibits tumour growth and boosts immunotherapy that is triggered by targeting the immune-checkpoint protein PD-1. These effects of SCAP deletion are associated with uncontrolled production of interferon-γ and impaired function of intratumoral Treg cells. Mechanistically, signalling through SCAP and SREBPs coordinates cellular programs for lipid synthesis and inhibitory receptor signalling in these cells. First, de novo fatty-acid synthesis mediated by fatty-acid synthase (FASN) contributes to functional maturation of Treg cells, and loss of FASN from Treg cells inhibits tumour growth. Second, Treg cells in tumours show enhanced expression of the PD-1 gene, through a process that depends on SREBP activity and signals via mevalonate metabolism to protein geranylgeranylation. Blocking PD-1 or SREBP signalling results in dysregulated activation of phosphatidylinositol-3-kinase in intratumoral Treg cells. Our findings show that metabolic reprogramming enforces the functional specialization of Treg cells in tumours, pointing to new ways of targeting these cells for cancer therapy. Identification of a metabolic checkpoint involving lipid signalling that is specific to regulatory T cells (Treg cells) in the tumour microenvironment raises the possibility of targeting this checkpoint for treatment of cancer.
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
A SIMPLE METHOD FOR THE ISOLATION AND PURIFICATION OF TOTAL LIPIDES FROM ANIMAL TISSUES
Folch, J., Lees, M., & Stanley, G. H. S. (1957) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)64849-5 · Cited by 65,189
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy
Sharma, P., Hu-Lieskovan, S., Wargo, J. A., & Ribas, A. (2017) · Cell · doi:10.1016/j.cell.2017.01.017 · Cited by 5,588
SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver
Horton, J. D., Goldstein, J. L., & Brown, M. S. (2002) · Journal of Clinical Investigation · doi:10.1172/jci15593 · Cited by 4,794
SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver
Horton, J. D., Goldstein, J. L., & Brown, M. S. (2002) · Journal of Clinical Investigation · doi:10.1172/jci0215593 · Cited by 4,092
CELLULAR RESPONSES TO INTERFERON-γ
Boehm, U., Klamp, T., Groot, M., & Howard, J. C. (1997) · Annual Review of Immunology · doi:10.1146/annurev.immunol.15.1.749 · Cited by 2,890
Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
Azizi, E., Carr, A. J., Plitas, G., Cornish, A. E., Konopacki, C., Prabhakaran, S., Nainys, J., Wu, K., Kiseliovas, V., Setty, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.060 · Cited by 1,976
Curran, M. A., Montalvo, W., Yagita, H., & Allison, J. P. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0915174107 · Cited by 1,874
Cited by
Kumagai, S., Koyama, S., Itahashi, K., Tanegashima, T., Lin, Y.-T., Togashi, Y., Kamada, T., Irie, T., Okumura, G., Kono, H., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.01.001 · Cited by 892
Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy
Tay, C., Tanaka, A., & Sakaguchi, S. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.014 · Cited by 580
Turning cold tumors hot: from molecular mechanisms to clinical applications
Zhang, J., Huang, D., Saw, P. E., & Song, E. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.04.010 · Cited by 534
The role of lipid metabolic reprogramming in tumor microenvironment
Yang, K., Wang, X., Song, C., He, Z., Wang, R., Xu, Y., Jiang, G., Wan, Y., Mei, J., & Mao, W. (2023) · Theranostics · doi:10.7150/thno.82920 · Cited by 270
PD-L1 checkpoint blockade promotes regulatory T cell activity that underlies therapy resistance
van Gulijk, M., van Krimpen, A., Schetters, S., Eterman, M., van Elsas, M., Mankor, J., Klaase, L., de Bruijn, M., van Nimwegen, M., van Tienhoven, T., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.abn6173 · Cited by 110
Metadata
- Authors
- Lim, S. A., Wei, J., Nguyen, T.-L. M., Shi, H., Su, W., Palacios, G., Dhungana, Y., Chapman, N. M., Long, L., Saravia, J., Vogel, P., & Chi, H.
- Venue
- Nature
- Published
- 2021-02-24
- Volume
- 591
- Issue
- 7849
- Pages
- 306-311
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3131861897
- Cited by
- 426
- References
- 52
- FWCI
- 19.86
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Lipid signalling enforces functional specialization of Treg cells in tumours. https://app.cochat.ai/papers/10.1038/s41586-021-03235-6
- The paper
- Lim, S. A., Wei, J., Nguyen, T.-L. M., Shi, H., Su, W., Palacios, G., Dhungana, Y., Chapman, N. M., Long, L., Saravia, J., Vogel, P., & Chi, H. (2021). Lipid signalling enforces functional specialization of Treg cells in tumours. Nature. https://doi.org/10.1038/s41586-021-03235-6
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.