Endothelial TGF-β signalling drives vascular inflammation and atherosclerosis


Atherosclerosis is a progressive vascular disease triggered by interplay between abnormal shear stress and endothelial lipid retention. A combination of these and, potentially, other factors leads to a chronic inflammatory response in the vessel wall, which is thought to be responsible for disease progression characterized by a buildup of atherosclerotic plaques. Yet molecular events responsible for maintenance of plaque inflammation and plaque growth have not been fully defined. Here we show that endothelial transforming growh factor β (TGF-β) signalling is one of the primary drivers of atherosclerosis-associated vascular inflammation. Inhibition of endothelial TGF-β signalling in hyperlipidemic mice reduces vessel wall inflammation and vascular permeability and leads to arrest of disease progression and regression of established lesions. These proinflammatory effects of endothelial TGF-β signalling are in stark contrast with its effects in other cell types and identify it as an important driver of atherosclerotic plaque growth and show the potential of cell-type-specific therapeutic intervention aimed at control of this disease.

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Fig. 1: TGF-β endothelial-specific induction of inflammatory response.
Fig. 2: Endothelial cell Tgfbr1/Tgfbr2 knockout inhibits atherosclerosis plaque development.
Fig. 3: Endothelial cell Tgfbr1/Tgfbr2 knockout facilitates regression of advanced murine atherosclerotic plaques.
Fig. 4: scRNA-seq analysis of endothelial gene expression in atherosclerosis.
Fig. 5: Endothelial cell Tgfbr1/Tgfbr2 deletion decreases inflammation.
Fig. 6: 7C1-siTgfbr1/2 therapy suppresses atherosclerosis lesion development and facilitates regression of advanced atherosclerotic plaques.

Data availability

High-throughput sequencing data associated with this study have been deposited. The GEO accession number is GSE134557.

Code availability

The code and the version of the dependencies are available at https://github.com/wxncailab/NatureMetabolism_Endothelial.


  1. 1.

    Baeyens, N. & Schwartz, M. A. Biomechanics of vascular mechanosensation and remodeling. Mol. Biol. Cell 27, 7–11 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Tabas, I., Garcia-Cardena, G. & Owens, G. K. Recent insights into the cellular biology of atherosclerosis. J. Cell Biol. 209, 13–22 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Schwartz, M. A., Vestweber, D. & Simons, M. A unifying concept in vascular health and disease. Science 360, 270–271 (2018).

    CAS  Article  Google Scholar 

  4. 4.

    Chen, P.-Y. et al. Endothelial-to-mesenchymal transition drives atherosclerosis progression. J. Clin. Investig. 125, 4529–4543 (2015).

    Article  Google Scholar 

  5. 5.

    Evrard, S. M. et al. Endothelial to mesenchymal transition is common in atherosclerotic lesions and is associated with plaque instability. Nat. Commun. 7, 11853 (2016).

    CAS  Article  Google Scholar 

  6. 6.

    Feaver, R. E., Gelfand, B. D., Wang, C., Schwartz, M. A. & Blackman, B. R. Atheroprone hemodynamics regulate fibronectin deposition to create positive feedback that sustains endothelial inflammation. Circulation Res. 106, 1703–1711 (2010).

    CAS  Article  Google Scholar 

  7. 7.

    Rohwedder, I. et al. Plasma fibronectin deficiency impedes atherosclerosis progression and fibrous cap formation. EMBO Mol. Med. 4, 564–576 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Iwata, J. et al. Modulation of noncanonical TGF-beta signaling prevents cleft palate in Tgfbr2 mutant mice. J. Clin. Investig. 122, 873–885 (2012).

    CAS  Article  Google Scholar 

  9. 9.

    Yang, P. et al. Smooth muscle cell-specific Tgfbr1 deficiency promotes aortic aneurysm formation by stimulating multiple signaling events. Sci. Rep. 6, 35444 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Lutgens, E. et al. Deficient CD40-TRAF6 signaling in leukocytes prevents atherosclerosis by skewing the immune response toward an antiinflammatory profile. J. Exp. Med. 207, 391–404 (2010).

    CAS  Article  Google Scholar 

  11. 11.

    Virmani, R., Kolodgie, F. D., Burke, A. P., Farb, A. & Schwartz, S. M. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb. Vasc. Biol. 20, 1262–1275 (2000).

    CAS  Article  Google Scholar 

  12. 12.

    Yu, P. et al. FGF-dependent metabolic control of vascular development. Nature 545, 224–228 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Dahlman, J. E. et al. In vivo endothelial siRNA delivery using polymeric nanoparticles with low molecular weight. Nat. Nanotechnol. 9, 648–655 (2014).

    CAS  Article  Google Scholar 

  14. 14.

    Sager, H. B. et al. RNAi targeting multiple cell adhesion molecules reduces immune cell recruitment and vascular inflammation after myocardial infarction. Sci. Transl. Med. 8, 342ra380 (2016).

    Article  Google Scholar 

  15. 15.

    Pardali, E. & Ten Dijke, P. TGFbeta signaling and cardiovascular diseases. Int. J. Biol. Sci. 8, 195–213 (2012).

    CAS  Article  Google Scholar 

  16. 16.

    Akhurst, R. J. & Hata, A. Targeting the TGFbeta signalling pathway in disease. Nat. Rev. Drug Discov. 11, 790–811 (2012).

    CAS  Article  Google Scholar 

  17. 17.

    McCaffrey, T. A. TGF-beta signaling in atherosclerosis and restenosis. Front. Biosci. 1, 236–245 (2009).

    Article  Google Scholar 

  18. 18.

    Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

    CAS  Article  Google Scholar 

  19. 19.

    Toma, I. & McCaffrey, T. A. Transforming growth factor-beta and atherosclerosis: interwoven atherogenic and atheroprotective aspects. Cell Tissue Res. 347, 155–175 (2012).

    CAS  Article  Google Scholar 

  20. 20.

    Mallat, Z. et al. Inhibition of transforming growth factor-beta signaling accelerates atherosclerosis and induces an unstable plaque phenotype in mice. Circulation Res. 89, 930–934 (2001).

    CAS  Article  Google Scholar 

  21. 21.

    Lutgens, E. et al. Transforming growth factor-beta mediates balance between inflammation and fibrosis during plaque progression. Arterioscler Thromb. Vasc. Biol. 22, 975–982 (2002).

    CAS  Article  Google Scholar 

  22. 22.

    Lievens, D. et al. Abrogated transforming growth factor beta receptor II (TGFbetaRII) signalling in dendritic cells promotes immune reactivity of T cells resulting in enhanced atherosclerosis. Eur. heart J. 34, 3717–3727 (2013).

    CAS  Article  Google Scholar 

  23. 23.

    Gistera, A. et al. Transforming growth factor-beta signaling in T cells promotes stabilization of atherosclerotic plaques through an interleukin-17-dependent pathway. Sci. Transl. Med. 5, 196ra100 (2013).

    Article  Google Scholar 

  24. 24.

    Robertson, A. K. et al. Disruption of TGF-beta signaling in T cells accelerates atherosclerosis. J. Clin. Investig. 112, 1342–1350 (2003).

    CAS  Article  Google Scholar 

  25. 25.

    Chen, P. Y., Qin, L., Li, G., Tellides, G. & Simons, M. Smooth muscle FGF/TGFbeta cross talk regulates atherosclerosis progression. EMBO Mol. Med. 8, 712–728 https://doi.org/10.15252/emmm.201506181 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    van Meeteren, L. A. & ten Dijke, P. Regulation of endothelial cell plasticity by TGF-beta. Cell tissue Res. 347, 177–186 (2012).

    Article  Google Scholar 

  27. 27.

    Deleavey, G. F. & Damha, M. J. Designing chemically modified oligonucleotides for targeted gene silencing. Chem. Biol. 19, 937–954 (2012).

    CAS  Article  Google Scholar 

  28. 28.

    Wang, Z. et al. A Non-canonical BCOR-PRC1.1 Complex Represses Differentiation Programs in Human ESCs. Cell Stem Cell 22, 235–251 e239 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic acids Res. 44, W160–W165 (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  31. 31.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).

    CAS  Article  Google Scholar 

  32. 32.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  33. 33.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  34. 34.

    Tomczak, J. M. & Welling, M. VAE with a VampPrior. Preprint at https://arxiv.org/abs/1705.07120 (2017).

  35. 35.

    Doersch, C. Tutorial on variational autoencoders. Preprint at https://arxiv.org/abs/1606.05908v2 (2016).

  36. 36.

    Zhao, S., Song, J. & Ermon, S. InfoVAE: Information maximizing variational autoencoders. Preprint at https://arxiv.org/abs/1706.02262 (2017).

  37. 37.

    Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS  Article  Google Scholar 

  38. 38.

    Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J. P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).

    Article  Google Scholar 

  39. 39.

    Robinson, M. D. & Smyth, G. K. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321–332 (2008).

    Article  Google Scholar 

  40. 40.

    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).

    Google Scholar 

  41. 41.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML Reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  Article  Google Scholar 

  42. 42.

    Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 e1330 (2016).

    CAS  Article  Google Scholar 

  43. 43.

    Strehl, A. & Ghosh, J. Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002).

    Google Scholar 

  44. 44.

    Shi, J. & Malik, J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000).

    Article  Google Scholar 

  45. 45.

    Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    Article  Google Scholar 

  46. 46.

    Chittenden, T. W. et al. nEASE: a method for gene ontology subclassification of high-throughput gene expression data. Bioinformatics 28, 726–728 (2012).

    CAS  Article  Google Scholar 

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We are grateful to J. Fang for aortic endothelial cell isolation used in scRNA-seq. We thank K. Hirschi for helpful discussions, R. Webber and L. Coon for maintaining mice colonies used in this study. This work was supported, in part, by National Nature Science Foundation of China grant no. 81600365 (G.L.) and NIH grant no. R01 HL135582 (M.S. and M.A.S.).

Author information




P.Y.C., L.Q., G.L., Z.W., X.Z., B.A., A.C.D., R.L., P.K. and Y.J. performed experiments and generated data. L.S. and H.S. performed sequencing. J.M.L., S.G., N.A.C., J.G., T.W.C. carried out bioinformatics studies. J.E.D., K.J.K., A.S. and D.G.A. designed and synthesized nanoparticles and RNA chemical modifications for delivery studies. C.F.H., E.L., M.A.S. and J.S.P. assisted with studies of inflammation. P.Y.C. and M.S. wrote the manuscript. M.A.S., G.T., T.W.C. and M.S. supervised the project and provided funding.

Corresponding author

Correspondence to Michael Simons.

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Competing interests

M.S. and P.Y.C. are holders of a provisional US Patent Application 62/311,086 and 62/406,732 dealing with endothelial-specific treatment of atherosclerosis. M.S., P.Y.C. and D.G.A. are scientific founders of VasoRx, Inc. M.S. and D.G.A. are members of VasoRx, Inc. Scientific Advisory Board. The other authors declare no competing interests.

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Peer review information: Primary Handling Editor: Pooja Jha.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Tables 1–3

Reporting Summary

Supplementary Dataset 1

Genes used in the analysis of the single-cell RNA-seq data. The values at every mouse genotype correspond to the total number of cells on which the gene was expressed.

Supplementary Dataset 2

Nested-GO terms functional enrichment. Accession number, nested-GO-term and their corresponding functional annotated genes for which the nested GOseq functional enrichment analysis was conducted (see Methods).

Supplementary Dataset 3

Cells used in the analysis of the single-cell RNA-seq data. For each cell barcode with its corresponding genotype we have the following information: VAE_ClusterLabel, cluster label as obtained from the single-cell RNA-seq analysis; LibraryCount, total cell’s library UMI count; FeatureCount, total number of genes expressed at the cell.

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Chen, PY., Qin, L., Li, G. et al. Endothelial TGF-β signalling drives vascular inflammation and atherosclerosis. Nat Metab 1, 912–926 (2019). https://doi.org/10.1038/s42255-019-0102-3

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