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A Doxorubicin-induced Cardiomyopathy Model in Adult Zebrafish
Published on: June 7, 2018
Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of
Jin Yang1, Francis Grafton1, Sara Ranjbarvaziri1
1Tenaya Therapeutics, South San Francisco, CA 94080, USA.
Insights
Researchers identified inhibiting histone deacetylase 6 (HDAC6) as a promising therapeutic strategy for dilated cardiomyopathy (DCM). This approach, validated in cellular and mouse models, targets the underlying molecular causes of genetic heart failure.
Area of Science:
- Cardiovascular Research
- Genetics
- Drug Discovery
Background:
- Dilated cardiomyopathy (DCM) involves heart enlargement and reduced function, often stemming from genetic factors.
- Current treatments for genetic DCM do not address the root molecular mechanisms.
- Novel therapeutic targets are needed for genetic heart failure.
Purpose of the Study:
- To develop novel therapeutics for DCM by targeting underlying molecular mechanisms.
- To identify cardioprotective drugs using a phenotypic screen and deep learning approach.
- To validate therapeutic targets in both in vitro and in vivo models of DCM.
Main Methods:
- Created an in vitro dilated cardiomyopathy (DCM) model using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) deficient in B-cell lymphoma 2 (BCL2)-associated athanogene 3 (BAG3).
- Conducted a phenotypic screen of 5500 bioactive compounds and siRNA validation to identify cardioprotective drugs.
- Utilized a BAG3 cardiomyocyte-knockout (BAG3cKO) mouse model to translate findings and test HDAC6 inhibitors.
Main Results:
- Inhibition of histone deacetylase 6 (HDAC6) demonstrated cardioprotective effects at the sarcomere level in BAG3-deficient iPSC-CMs.
- HDAC6 inhibitors (tubastatin A and TYA-018) improved cardiac function, including ejection fraction and reduced ventricular dimensions, in BAG3cKO and BAG3E455K mouse models.
- The novel inhibitor TYA-018 protected against sarcomere damage, reduced Nppb expression, and enhanced mitochondrial energetics in BAG3cKO mice.
Conclusions:
- Combining iPSC-CMs, phenotypic screening, and deep learning accelerates drug discovery for DCM.
- Inhibiting HDAC6 is a viable therapeutic strategy for genetic forms of DCM.
- Targeting molecular mechanisms offers a promising avenue for developing novel heart disease therapies.
Abstract:
Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enlargement of left ventricular chambers. These characteristics eventually lead to heart failure. Current standards of care do not target the underlying molecular mechanisms associated with genetic forms of heart failure, driving a need to develop novel therapeutics for DCM. To identify candidate therapeutics, we developed an in vitro DCM model using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) deficient in B-cell lymphoma 2 (BCL2)-associated athanogene 3 (BAG3). With these BAG3-deficient iPSC-CMs, we identified cardioprotective drugs using a phenotypic screen and deep learning. From a library of 5500 bioactive compounds and siRNA validation, we found that inhibiting histone deacetylase 6 (HDAC6) was cardioprotective at the sarcomere level. We translated this finding to a BAG3 cardiomyocyte-knockout (BAG3cKO) mouse model of DCM, showing that inhibiting HDAC6 with two isoform-selective inhibitors (tubastatin A and a novel inhibitor TYA-018) protected heart function. In BAG3cKO and BAG3E455K mice, HDAC6 inhibitors improved left ventricular ejection fraction and reduced left ventricular diameter at diastole and systole. In BAG3cKO mice, TYA-018 protected against sarcomere damage and reduced Nppb expression. Based on integrated transcriptomics and proteomics and mitochondrial function analysis, TYA-018 also enhanced energetics in these mice by increasing expression of targets associated with fatty acid metabolism, protein metabolism, and oxidative phosphorylation. Our results demonstrate the power of combining iPSC-CMs with phenotypic screening and deep learning to accelerate drug discovery, and they support developing novel therapies that address underlying mechanisms associated with heart disease.

