Hsiai, Tzung K.

RXRA is an endothelial- and early stage–predominant therapeutic target for treating anthracycline-induced cardiotoxicity.

ABSTRACT The adult zebrafish is an emerging vertebrate model for studying human cardiomyopathies; however, whether the simple zebrafish heart can model different subtypes of cardiomyopathies, such as dilated cardiomyopathy (DCM), remains elusive. Here, we generated and characterized an inherited DCM model in adult zebrafish and used this model to search for therapeutic strategies. We employed transcription activator-like effector nuclease (TALEN) genome editing technology to generate frame-shift mutants for the zebrafish ortholog of human BCL2-associated athanogene 3 (BAG3), an established DCM-causative gene. As in mammals, the zebrafish bag3 homozygous mutant (bag3e2/e2) exhibited aberrant proteostasis, as indicated by impaired autophagy flux and elevated ubiquitinated protein aggregation. Through comprehensive phenotyping analysis of the mutant, we identified phenotypic traits that resembled DCM phenotypes in mammals, including cardiac chamber enlargement, reduced ejection fraction characterized by increased end-systolic volume/body weight (ESV/BW), and reduced contractile myofibril activation kinetics. Nonbiased transcriptome analysis identified the hyperactivation of the mechanistic target of rapamycin (mTOR) signaling in bag3e2/e2 mutant hearts. Further genetic studies showed that mtorxu015/+, an mTOR haploinsufficiency mutant, repaired abnormal proteostasis, improved cardiac function and rescued the survival of the bag3e2/e2 mutant. This study established the bag3e2/e2 mutant as a DCM model in adult zebrafish and suggested mtor as a candidate therapeutic target gene for BAG3 cardiomyopathy.

Zebrafish are increasingly utilized to model cardiomyopathies and regeneration. Current methods evaluating cardiac function have known limitations, fail to reliably detect focal mechanics, and are not readily feasible in zebrafish. We developed a semiautomated, open-source method — displacement analysis of myocardial mechanical deformation (DIAMOND) — for quantitative assessment of 4D segmental cardiac function. We imaged transgenic embryonic zebrafish in vivo using a light-sheet fluorescence microscopy system with 4D cardiac motion synchronization. Our method permits the derivation of a transformation matrix to quantify the time-dependent 3D displacement of segmental myocardial mass centroids. Through treatment with doxorubicin, and by chemically and genetically manipulating the myocardial injury–activated Notch signaling pathway, we used DIAMOND to demonstrate that basal ventricular segments adjacent to the atrioventricular canal display the highest 3D displacement and are also the most susceptible to doxorubicin-induced injury. Thus, DIAMOND provides biomechanical insights into in vivo segmental cardiac function scalable to high-throughput research applications.

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine.