In Biochemical and biophysical research communications
The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.
Komuro Jin, Tokuoka Yuta, Seki Tomohisa, Kusumoto Dai, Hashimoto Hisayuki, Katsuki Toshiomi, Nakamura Takahiro, Akiba Yohei, Kuoka Thukaa, Kimura Mai, Yamada Takahiro, Fukuda Keiichi, Funahashi Akira, Yuasa Shinsuke
Cardiac hypertrophy, Deep learning, Drug screening, Ezetimibe, Heart failure