Back

Arterial endothelial deletion of hereditary hemorrhagic telangiectasia 2/Alk1 causes epistaxis and cerebral microhemorrhage with aberrant arteries and defective smooth muscle coverage

Zhang, X.; Jacobs, K. A.; Raygor, K. P.; Li, S.; Li, J.; Wang, R. A.

2024-12-04 pathology
10.1101/2024.11.25.622742 bioRxiv
Show abstract

Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant vascular disorder with manifestations including severe nose bleeding and microhemorrhage in brains. Despite being the second most common inherited bleeding disorder, the pathophysiological mechanism underlying HHT-associated hemorrhage is poorly understood. HHT pathogenesis is thought to follow a Knudsonian two-hit model, requiring a second somatic mutation for lesion formation. Mutations in activin receptor-like kinase 1 (ALK1) gene cause HHT type 2. We hypothesize that somatic mutation of Alk1 in arterial endothelial cells (AECs) leads to arterial defects and hemorrhage. Here, we mutated Alk1 in AECs in postnatal mice using Bmx(PAC)-CreERT2 and found that somatic arterial endothelial mutation of Alk1 was sufficient to induce spontaneous epistaxis and multifocal cerebral microhemorrhage. This bleeding occurred in the presence of tortuous and enlarged blood vessels, loss of arterial molecular marker Efnb2, disorganization of vascular smooth muscle, and impaired vasoregulation. Our data suggest that arterial endothelial deletion of Alk1 leading to reduced arterial identity and disrupted vascular smooth muscle cell coverage is a plausible molecular mechanism for HHT-associated severe epistaxis. This work provides the first evidence that somatic Alk1 mutation in AECs can cause hemorrhagic vascular lesions, offering a novel preclinical model critically needed for studying HHT-associated epistaxis, and delineating an arterial mechanism to HHT pathophysiology.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.