# Elucidating mechanisms of hereditary hemorrhagic telangiectasia 2 gene-mediated arteriovenous malformation development via eNOS signaling using in vivo two-photon imaging in a preclinical mouse model

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $14,439

## Abstract

Project Summary
The long-term goal of this project is to identify an appropriate preclinical mouse model to characterize the etiology
of Hereditary hemorrhagic telangiectasia (HHT) and to develop novel treatment options for the disease. HHT is
a genetic disorder with arteriovenous malformations (AVMs) as a core pathology throughout the body. AVMs are
abnormal direct connections from arteries to veins without intervening capillaries. Symptoms of HHT include
telangiectasias, epistaxis, severe headache, epilepsy, stroke, gastrointestinal bleeding, and AVMs in brain, lung,
and liver. The worldwide estimated incidence of HHT is 1:50002, but 90% of people with HHT are undiagnosed.
Currently, there is no prevention or cure for the major clinical manifestations of HHT, calling for the development
of better treatment strategies. The TGFβ type I receptor activin A receptor-like type 1 (ACVRL1 or Alk1) gene
has been implicated in the etiology of HHT type 2 (HHT2), and mutation of this gene is estimated to underlie 25-
57% of all HHT cases3–7. Alk1 is primarily expressed in endothelial cells and is essential for vascular
development8–10. Previous mouse models of HHT2, based on deletion of Alk1, exhibited certain disease
aspects11–14. However, the existing animal models are not ideal animal models for HHT, as animals either die
early by postnatal day 5 (homozygous deletion) or develop vascular lesions with long latency and incomplete
penetration (heterozygous deletion) lacking key HHT symptoms, e. g. nosebleed15. Therefore, there is a critical
need to develop a better preclinical animal model that better reflects the vascular lesions presented in HHT2
patients, aiding the efforts to uncover the mechanisms of the disease progression and develop potential
preventative and treatment strategies for HHT therapy. To resolve these gaps in knowledge, our lab has created
a novel mouse model in which Alk1 is deleted postnatally in arterial endothelial cells. Preliminary data suggest
that these mice survived beyond postnatal day 27 with HHT-like vascular lesions in multiple organs, offering a
valuable animal model of HHT2 to study the disease processes and underlying mechanisms. I will characterize
the HHT-like lesions present in these mice by examining the gross pathology, histopathology, animal health, and
behavior of the animals. Further, I will examine the vascular structure and function through microscopic
imagining, including two-photon in vivo live imaging. Finally, I will characterize the oxidative stress profile of the
mice by measuring superoxide and NO levels, while also treating the mice with a superoxide dismutase mimetic,
TEMPOL and a synthetic BH4 compound, sepiapterin to determine the effects of oxidative stress in the formation
and prevention of AVMs in this model. The successful completion of this project will provide a novel tool for the
investigation of mechanism underlying Alk1-mediated HHT formation and for the development of novel
preventative and...

## Key facts

- **NIH application ID:** 10588596
- **Project number:** 3F32HL146025-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Kayla Branyan-Schroer
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $14,439
- **Award type:** 3
- **Project period:** 2019-02-19 → 2022-04-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10588596

## Citation

> US National Institutes of Health, RePORTER application 10588596, Elucidating mechanisms of hereditary hemorrhagic telangiectasia 2 gene-mediated arteriovenous malformation development via eNOS signaling using in vivo two-photon imaging in a preclinical mouse model (3F32HL146025-03S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10588596. Licensed CC0.

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