PPAR-γ is a major driver of the accumulation and phenotype of adipose tissue Treg cells
Open accessAbstract
Obesity and type-2 diabetes have increased markedly over the past few decades, in parallel. One of the major links between these two disorders is chronic, low-grade inflammation. Prolonged nutrient excess promotes the accumulation and activation of leukocytes in visceral adipose tissue (VAT) and ultimately other tissues, leading to metabolic abnormalities such as insulin resistance, type-2 diabetes and fatty-liver disease. Although invasion of VAT by pro-inflammatory macrophages is considered to be a key event driving adipose-tissue inflammation and insulin resistance, little is known about the roles of other immune system cell types in these processes. A unique population of VAT-resident regulatory T (Treg) cells was recently implicated in control of the inflammatory state of adipose tissue and, thereby, insulin sensitivity. Here we identify peroxisome proliferator-activated receptor (PPAR)-γ, the 'master regulator' of adipocyte differentiation, as a crucial molecular orchestrator of VAT Treg cell accumulation, phenotype and function. Unexpectedly, PPAR-γ expression by VAT Treg cells was necessary for complete restoration of insulin sensitivity in obese mice by the thiazolidinedione drug pioglitazone. These findings suggest a previously unknown cellular mechanism for this important class of thiazolidinedione drugs, and provide proof-of-principle that discrete populations of Treg cells with unique functions can be precisely targeted to therapeutic ends.
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Treg cells support tissue homeostasis and regeneration, ranging from regulating metabolic parameters in adipose tissue to potentiating tissue repair in skeletal muscles, lung tissue, or brain.
“They have been shown to support tissue homeostasis and regeneration, ranging from regulating metabolic parameters in the adipose tissue to potentiating tissue repair [444-446], e.g., in skeletal muscles [447], lung tissue [448], or brain [449].”
From Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition) (2021) · cites this paper as [445]
PPARγ is known to regulate VAT Treg cell development.
“Transcription factors whose activity were increased in both liver and V AT Treg cells compared to spleen Treg cells included PPAR γ (known to regulate VAT Treg cell development 40 ), AHR, AR, HIF1 α , and ROR γ t, while transcription factors with increased activity in liver Treg cells but not VAT Treg cells compared to spleen Treg cells included HOXB4, IRF4, IRF8, and STAT5 (Fig. 2G).”
From Amphiregulin from regulatory T cells promotes liver fibrosis and insulin resistance in non-alcoholic steatohepatitis (2024) · cites this paper as [40]
The numerical increase in hepatic Tregs in mice with MASH, also observed by single-cell RNA-sequencing in MASH patients, is consistent with recent reports of Treg enrichment in this setting.
“The numerical increase in hepatic Tregs in mice with MASH, also observed via scRNA-seq in MASH patients, is consistent with recent reports of Treg enrichment in such a setting (26).”
From Regulatory T cells safeguard liver health during metabolic-associated steatohepatitis (2026) · cites this paper as [26]
The passage on pioglitazone-treated, high-fat-diet-fed mice (section labeled Abstract; page unavailable) reports Treg enrichment in visceral fat, not liver. Our copy does not address hepatic Treg enrichment in MASH mice or scRNA-seq findings in MASH patients; searches for MASH, NASH, and liver Tregs found no passage establishing either point. (LLM, conf=0.75)
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Giganti, G., Atif, M., Mohseni, Y., Mastronicola, D., Grageda, N., Povoleri, G. A., Miyara, M., & Scottà, C. (2021) · European Journal of Immunology · doi:10.1002/eji.201948131 · Cited by 82 · see citing works
From neutrophils to macrophages: Differences in regional adipose tissue depots
Dam, V., Sikder, T., & Santosa, S. (2016) · Obesity Reviews · doi:10.1111/obr.12335 · Cited by 65 · see citing works
The dark side of Tregs during aging
Palatella, M., Guillaume, S. M., Linterman, M. A., & Huehn, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.940705 · Cited by 58 · see citing works
3D Immunocompetent Organ‐on‐a‐Chip Models
Maharjan, S., Cecen, B., & Zhang, Y. S. (2020) · Small Methods · doi:10.1002/smtd.202000235 · Cited by 62 · see citing works
Characterization of regulatory T cells in obese omental adipose tissue in humans
Wu, D., Han, J. M., Yu, X., Lam, A. J., Hoeppli, R. E., Pesenacker, A. M., Huang, Q., Chen, V., Speake, C., Yorke, E., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201847570 · Cited by 55 · see citing works
Yates, K., Bi, K., Haining, W. N., Cantor, H., & Kim, H.-J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720447115 · Cited by 55 · see citing works
Innate Control of Tissue-Reparative Human Regulatory T Cells
Lam, A. J., MacDonald, K. N., Pesenacker, A. M., Juvet, S. C., Morishita, K. A., Bressler, B., iGenoMed Consortium, Pan, J. G., Sidhu, S. S., Rioux, J. D., et al. (2019) · The Journal of Immunology · doi:10.4049/jimmunol.1801330 · Cited by 47 · see citing works
The immune cell transcription factor T-bet: A novel metabolic regulator
Stolarczyk, E., Lord, G. M., & Howard, J. K. (2014) · Adipocyte · doi:10.4161/adip.26220 · Cited by 32 · see citing works
Regulatory T cells safeguard liver health during metabolic-associated steatohepatitis
Hanna, B. S., Langston, P. K., Marin-Rodero, M., Ramirez, R. N., Wan, M., Benoist, C., & Mathis, D. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2536314123 · Cited by 1 · see citing works
Self-citation
- Authors
- Cipolletta, D., Feuerer, M., Li, A., Kamei, N., Lee, J., Shoelson, S. E., Benoist, C., & Mathis, D.
- Affiliations
- Harvard University (US)
- Joslin Diabetes Center (US)
- Corresponding author
- Mathis, Diane
- Venue
- Nature
- Published
- 2012-05-16
- Volume
- 486
- Issue
- 7404
- Pages
- 549-553
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3387339
- OpenAlex
- W2041544510
- Cited by
- 1,143
- References
- 23
- Citations by year
- 2026: 47 · 2025: 85 · 2024: 74 · 2023: 56 · 2022: 90 · 2021: 104 · 2020: 99 · 2019: 93 · 2018: 96 · 2017: 92 · 2016: 78 · 2015: 80 · 2014: 65 · 2013: 76 · 2012: 7
- FWCI
- 29.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of General Medical Sciences (T32GM007753)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK 51729, DK092541, R01DK051729, P30-DK36836, R37DK051729, P30DK036836, R01DK092541)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Peroxisome Proliferator-Activated Receptors
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Medicine › Physiology › Adipose Tissue and Metabolism
- Keywords
- PPARγ, regulatory T cells, visceral adipose tissue, thiazolidinediones, pioglitazone, insulin sensitivity, obesity, type 2 diabetes, adipose tissue inflammation, insulin resistance
- MeSH terms
- Pioglitazone, Adipose Tissue, Animals, Cell Differentiation, Diabetes Mellitus, Type 2, Epididymis, Hypoglycemic Agents, Inflammation, Insulin Resistance, Male, Mice, Inbred C57BL, Mice, Obese, Obesity, Phenotype, RNA, Messenger, Transcription, Genetic, Gene Expression, Lymphocyte Count, Thiazolidinediones, PPAR gamma, T-Lymphocytes, Regulatory, Mice, Forkhead Transcription Factors
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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.
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