Endothelial TGF-β signalling drives vascular inflammation and atherosclerosis

Abstract

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.

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Acknowledgements

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.

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.

Additional information

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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