bioRxivPathologyPreprint
Myeloid-Specific Thrombospondin-1 Deficiency Exacerbates Aortic Rupture via Broad Suppression of Extracellular Matrix Proteins
Ting ZhouiD, Huan Min YangiD, Carmel Rebecca Assa, Elise DeRooiD, Jack Bontekoe, Brian M. BurkeliD, Suzanne M. PonikiD, Hong S. LuiD, Alan DaughertyiD, Bo Liu
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Abstract
RationaleRupture of abdominal aortic aneurysms (AAA) is associated with high mortality. However, the precise molecular and cellular drivers of AAA rupture remain elusive. Our prior study showed that global and myeloid-specific deletion of matricellular protein thrombospondin-1 (TSP1) protects mice from aneurysm formation primarily by inhibiting vascular inflammation. ObjectiveTo investigate the cellular and molecular mechanisms that drive AAA rupture by testing how TSP1 deficiency in different cell populations affects the rupture event. Methods and ResultsWe deleted TSP1 in endothelial cells and macrophages --- the major TSP1-expressing cells in aneurysmal tissues ---- by crossbreeding Thbs1 flox/flox mice with VE-cadherin Cre and Lyz2-cre mice, respectively. Aortic aneurysm and rupture were induced by angiotensin II in mice with hypercholesterolemia. Myeloid-specific Thbs1 knockout, but not endothelial-specific knockout, increased the rate of lethal aortic rupture by more than 2 folds. Combined analyses of single-cell RNA sequencing and histology showed a unique cellular and molecular signature of the rupture-prone aorta that was characterized by a broad suppression in inflammation and extracellular matrix production. Visium spatial transcriptomic analysis on human AAA tissues showed a correlation between low TSP1 expression and aortic dissection. ConclusionsTSP1 expression by myeloid cells negatively regulates aneurysm rupture, likely through promoting the matrix repair phenotypes of vascular smooth muscle cells thereby increasing the strength of the vascular wall.
- organism: mouse
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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