Abstract
Transcriptional master regulators drive cell fate transitions. Peroxisome proliferator-activated receptor γ (PPARγ) is the master regulator of adipogenesis, and its expression must therefore be tightly regulated and efficiently induced in response to adipogenic cues. Here we decipher the regulatory mechanisms of the highly connected enhancer community driving activation of the PPARG locus during adipocyte differentiation of human mesenchymal stem cells. By systematically deleting nine individual enhancers, spanning upstream, promoter-proximal, and downstream super-enhancer constituents, we demonstrate elaborate enhancer crosstalk in cis involving stabilization of C/EBPβ recruitment prior to chromatin remodeling. We show that the super-enhancer constituent E + 102 plays a dual role in cis crosstalk and feedback activation and is obligate for activation of PPARG expression. Non-coding genetic variants associated with cardiometabolic traits and predicted to regulate PPARG expression map to E + 102 and other essential enhancers in the community, thereby supporting the importance of these enhancers in human physiology and disease.
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Data availability
All data generated in this study have been deposited in the GEO repository under accession code GSE300907 and they are publicly available. In addition to the data generated for this study, raw sequencing data from RNA-sequencing, DNase-sequencing and MED1 ChIP-sequencing are available from GEO under accession code GSE113253, and raw sequencing data from ECHi-C from GEO under accession code GSE140782. Raw data used for Hi-C analysis are available from GEO under accession code GSE140782 and GSE52457. Source data are provided with this paper.
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No custom code was generated for this study.
References
Fiorenza, C. G., Chou, S. H. & Mantzoros, C. S. Lipodystrophy: Pathophysiology and advances in treatment. Nat. Rev. Endocrinol. 7, 137–150 (2011).
Hagberg, C. E. & Spalding, K. L. White adipocyte dysfunction and obesity-associated pathologies in humans. Nat. Rev. Mol. Cell Biol. 25, 270–289 (2024).
Rauch, A. & Mandrup, S. Transcriptional networks controlling stromal cell differentiation. Nat. Rev. Mol. Cell Biol. 22, 465–482 (2021).
Rosen, E. D. & Spiegelman, B. M. What we talk about when we talk about fat. Cell 156, 20–44 (2014).
Ghaben, A. L. & Scherer, P. E. Adipogenesis and metabolic health. Nat. Rev. Mol. Cell Biol. 20, 242–258 (2019).
Lefterova, M. I., Haakonsson, A. K., Lazar, M. A. & Mandrup, S. PPARγ and the global map of adipogenesis and beyond. Trends Endocrinol. Metab. 25, 293–302 (2014).
Siersbaek, R. & Mandrup, S. Transcriptional Networks Controlling Adipocyte Differentiation. Cold Spring Harb. Symposia Quant. Biol. 76, 247–255 (2011).
Madsen, M. S. et al. PPARγ lipodystrophy mutants reveal intermolecular interactions required for enhancer activation. Nat. Commun. 13, 7090 (2022).
Bugge, A., Grøntved, L., Aagaard, M. M., Borup, R. & Mandrup, S. The PPARgamma2 A/B-domain plays a gene-specific role in transactivation and cofactor recruitment. Mol. Endocrinol. 23, 794–808 (2009).
Nielsen, R., Grøntved, L., Stunnenberg, H. G. & Mandrup, S. Peroxisome proliferator-activated receptor subtype- and cell-type-specific activation of genomic target genes upon adenoviral transgene delivery. Mol. Cell Biol. 26, 5698–5714 (2006).
Hamm, J. K., Park, B. H. & Farmer, S. R. A role for C/EBPbeta in regulating peroxisome proliferator-activated receptor gamma activity during adipogenesis in 3T3-L1 preadipocytes. J. Biol. Chem. 276, 18464–18471 (2001).
Siersbæk, R. et al. Molecular architecture of transcription factor hotspots in early adipogenesis. Cell Rep. 7, 1434–1442 (2014).
Rauch, A. et al. Osteogenesis depends on commissioning of a network of stem cell transcription factors that act as repressors of adipogenesis. Nat. Genet. 51, 716–727 (2019).
Siersbaek, R. et al. Extensive chromatin remodelling and establishment of transcription factor ‘hotspots’ during early adipogenesis. EMBO J. 30, 1459–1472 (2011).
Siersbæk, R. et al. Dynamic Rewiring of Promoter-Anchored Chromatin Loops during Adipocyte Differentiation. Mol. Cell 66, 420–435.e5 (2017).
Madsen, J. G. S. et al. Highly interconnected enhancer communities control lineage-determining genes in human mesenchymal stem cells. Nat. Genet. 52, 1227–1238 (2020).
Blayney, J. W. et al. Super-enhancers include classical enhancers and facilitators to fully activate gene expression. Cell 186, 5826–5839.e18 (2023).
Brosh, R. et al. Synthetic regulatory genomics uncovers enhancer context dependence at the Sox2 locus. Mol. Cell 83, 1140–1152.e7 (2023).
Choi, J. et al. Evidence for additive and synergistic action of mammalian enhancers during cell fate determination. Elife 10, e65381 (2021).
Hay, D. et al. Genetic dissection of the α-globin super-enhancer in vivo. Nat. Genet. 48, 895–903 (2016).
Loubiere, V., de Almeida, B. P., Pagani, M. & Stark, A. Developmental and housekeeping transcriptional programs display distinct modes of enhancer-enhancer cooperativity in Drosophila. Nat. Commun. 15, 8584 (2024).
Shin, H. Y. et al. Hierarchy within the mammary STAT5-driven Wap super-enhancer. Nat. Genet. 48, 904–911 (2016).
Thomas, H. F. et al. Temporal dissection of an enhancer cluster reveals distinct temporal and functional contributions of individual elements. Mol. Cell 81, 969–982.e13 (2021).
Thomas, H. F. et al. Enhancer cooperativity can compensate for loss of activity over large genomic distances. Mol. Cell 85, 362–375.e9 (2025).
Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).
Fajas, L. et al. The Organization, Promoter Analysis, and Expression of the Human PPARγ Gene. J. Biol. Chem. 272, 18779–18789 (1997).
Mueller, E. et al. Genetic analysis of adipogenesis through peroxisome proliferator-activated receptor gamma isoforms. J. Biol. Chem. 277, 41925–41930 (2002).
Ren, D., Collingwood, T. N., Rebar, E. J., Wolffe, A. P. & Camp, H. S. PPARgamma knockdown by engineered transcription factors: Exogenous PPARgamma2 but not PPARgamma1 reactivates adipogenesis. Genes Dev. 16, 27–32 (2002).
Koutnikova, H. et al. Compensation by the muscle limits the metabolic consequences of lipodystrophy in PPARγ hypomorphic mice. Proc. Natl. Acad. Sci. 100, 14457–14462 (2003).
Medina-Gomez, G. et al. The link between nutritional status and insulin sensitivity is dependent on the adipocyte-specific peroxisome proliferator–activated receptor-γ2 isoform. Diabetes 54, 1706–1716 (2005).
Zhang, J. et al. Selective disruption of PPARγ2 impairs the development of adipose tissue and insulin sensitivity. Proc. Natl. Acad. Sci. 101, 10703–10708 (2004).
Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).
Warren et al. Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes. Cell 153, 307–319 (2013).
Cao, J. et al. An easy and efficient inducible CRISPR/Cas9 platform with improved specificity for multiple gene targeting. Nucleic Acids Res. 44, e149 (2016).
DeKelver, R. C. et al. Functional genomics, proteomics, and regulatory DNA analysis in isogenic settings using zinc finger nuclease-driven transgenesis into a safe harbor locus in the human genome. Genome Res. 20, 1133–1142 (2010).
Siersbæk, R. et al. Transcription factor cooperativity in early adipogenic hotspots and super-enhancers. Cell Rep. 7, 1443–1455 (2014).
Schmidt, S. F. et al. Cross species comparison of C/EBPα and PPARγ profiles in mouse and human adipocytes reveals interdependent retention of binding sites. BMC Genomics 12, 152 (2011).
Salma, N., Xiao, H. & Imbalzano, A. N. Temporal recruitment of CCAAT/enhancer-binding proteins to early and late adipogenic promoters in vivo. J. Mol. Endocrinol. 36, 139–151 (2006).
Lefterova, M. I. et al. PPARγ and C/EBP factors orchestrate adipocyte biology via adjacent binding on a genome-wide scale. Genes Dev. 22, 2941–2952 (2008).
Avsec, Ž et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).
Lee, H. et al. Allele-specific quantitative proteomics unravels molecular mechanisms modulated by cis-regulatory PPARG locus variation. Nucleic Acids Res. 45, 3266–3279 (2017).
Bhimsaria, D. et al. Hidden modes of DNA binding by human nuclear receptors. Nat. Commun. 14 (2023).
Haakonsson, A. K., Stahl Madsen, M., Nielsen, R., Sandelin, A. & Mandrup, S. Acute genome-wide effects of rosiglitazone on PPARγ transcriptional networks in adipocytes. Mol. Endocrinol. 27, 1536–1549 (2013).
Avsec, Ž et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021).
Grubert, F. et al. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell 162, 1051–1065 (2015).
Tanaka, T. Defective adipocyte differentiation in mice lacking the C/EBPbeta and/or C/EBPdelta gene. EMBO J. 16, 7432–7443 (1997).
Dittmar, G. et al. PRISMA: Protein Interaction Screen on Peptide Matrix Reveals Interaction Footprints and Modifications- Dependent Interactome of Intrinsically Disordered C/EBPβ. iScience 13, 351–370 (2019).
Broekema, M. F., Savage, D. B., Monajemi, H. & Kalkhoven, E. Gene-gene and gene-environment interactions in lipodystrophy: Lessons learned from natural PPARγ mutants. Biochimica et. Biophysica Acta (BBA) - Mol. Cell Biol. Lipids 1864, 715–732 (2019).
Soccio, R. E. et al. Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo. Cell 162, 33–44 (2015).
Abdallah, B. M. et al. Maintenance of differentiation potential of human bone marrow mesenchymal stem cells immortalized by human telomerase reverse transcriptase gene despite of extensive proliferation. Biochem. Biophys. Res. Commun. 326, 527–538 (2005).
Simonsen, J. L. et al. Telomerase expression extends the proliferative life-span and maintains the osteogenic potential of human bone marrow stromal cells. Nat. Biotechnol. 20, 592–596 (2002).
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).
Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinforma. 18, 529 (2017).
Lopez-Delisle, L. et al. pyGenomeTracks: Reproducible plots for multivariate genomic datasets. Bioinformatics 37, 422–423 (2021).
Wingett, S. et al. HiCUP: pipeline for mapping and processing Hi-C data. F1000Res 4, 1310 (2015).
Cairns, J. et al. CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127 (2016).
Zhou, X. et al. The Human Epigenome Browser at Washington University. Nat. Methods 8, 989–990 (2011).
Xiaotao, W. unHiC: a user-friendly Hi-C data processing software based on hiclib. https://doi.org/10.5281/zenodo.55324 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Zhang, Y. et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: A flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).
Oudelaar, A. M. et al. Single-allele chromatin interactions identify regulatory hubs in dynamic compartmentalized domains. Nat. Genet. 50, 1744–1751 (2018).
Telenius, J. M. et al. CaptureCompendium: A comprehensive toolkit for 3C analysis. bioRxiv, 2020.02.17.952572 (2020).
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in. R. Bioinforma. 30, 2811–2812 (2014).
Costanzo, M. C. et al. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab. 35, 695–710.e6 (2023).
Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).
Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).
Mahajan, A. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).
Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci. Rep. 9, 9354 (2019).
Shrikumar, A. T. et al. on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5. Preprint arXiv (2020).
Rauluseviciute, I. et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 52, D174–d182 (2024).
Gupta, S., Stamatoyannopoulos, J. A., Bailey, T. L. & Noble, W. S. Quantifying similarity between motifs. Genome Biol. 8, R24 (2007).
Acknowledgements
The work was supported by grants from the Independent Research Fund Denmark to S.M. (No 12-125524 (Sapere Aude Advanced grant) and No 2034-00335B); the Danish National Research Foundation to S.M. to establish the Center for Functional Genomics and Tissue Plasticity (ATLAS) (DNRF grant No 141); the Novo Nordisk Foundation to S.M. (NNF21OC0071613; NNF18OC0033444 to the Challenge Grant Center for Adipocyte Signaling (ADIPOSIGN)) and to M.C., J.G.S.M. and S.M. (NNF21SA0072102 to the NNF Center for Genomic Mechanisms of Disease); the Lundbeck Foundation to S.M. (grant No R218-2016-1450); the European Union’s Horizon 2020 research and innovation program to S.M. and A.C. (ENHPATHY Consortium grant agreement No 860002); the Novo Scholarship Program to M.H.; the Fuhrmann Foundation to K.M.; NIDDK to M.C. (UM1DK126185; P30 DK040561); and the Villum Foundation through support to the Villum Center for Bioanalytical Sciences. We want to acknowledge the participants and investigators of the FinnGen study. We thank Q. Yan from Yale School of Medicine for providing the Lenti-iCas9-neo plasmid and A. M. Oudelaar from the Max Planck Institute for Multidisciplinary Sciences, Göttingen for valuable discussions on Capture-C protocols and data analysis. We are grateful to our colleagues in the Functional Genomics and Metabolism Research Unit, as well as Eileen Furlong and Tim Pollex, for critical discussions that helped improve the study.
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Conceptualization: A.C., M.H., S.M., M.C., H.D., A.R., J.G.S.M.; Experimental work: A.C., M.H., M.N., L.K.H., K.M., M.S.M., V.S., E.D.M.; Formal analysis, investigation, and data curation: A.C., M.H., M.N., B.V.H., H.D., and J.G.S.M.; Visualization: A.C., M.H.; Writing: A.C., M.H., S.M.; Funding acquisition: S.M., M.C., J.G.S.M.; Supervision: S.M., A.R., J.G.S.M., M.C.
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Cetnarowska, A., Hyldahl, M., Nygård, M. et al. Extensive enhancer crosstalk controls PPARG2 activation during adipogenesis. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70401-7
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DOI: https://doi.org/10.1038/s41467-026-70401-7
