Workflow
Research Directions
Dissect the molecular mechanisms of familial cardiomyopathies
The main cause of familial cardiomyopathies is known to be genetic mutations. However, due to the lack of understanding of the pathological mechanisms, our current treatments of cardiomyopathies mostly focus on relieving symptoms, rather than targeting the etiology. The combined application of patient-specific induced pluripotent stem cells (iPSCs) and CRISPR/Cas9 genome editing technology has enabled the study of single mutation-related cardiac dysfunction. A major focus of the Wu lab is to use patient-specific and isogenic iPSC models to understand the pathological mechanisms of familial cardiomyopathies, and to identify new therapeutic targets that are specific to the patient and mutation.
Understand the relation between functional SNPs and cardiac risks of disease and aging
Genetic polymorphism is one of the main contributing factors for the heterogeneity of cardiac risks and aging in different populations. And there are a great number of SNPs that have been identified as cardiac risk-related from our Genome-Wide Association Study (GWAS) study. However, the understanding of whether and how these SNPs contribute to the pathogenesis, phenotype presentation, and drug response is very limited. Mainly due to the lack of screening methods and standardized platforms. Using a specialized Reel-sequencing method (REF), the unbalanced interaction of wide type and mutated alleles with nuclear protein factors in the target cell types can be identified. These allele-specific interactions of protein factors and SNPs in the non-coding DNA region indicate potential roles in transcriptional regulation, which can be further validated with CRISPR/Cas9 genome-editing, iPSCs, and animal models. These studies will decode the relation between SNP and cardiac risks and help us develop better therapeutic strategies for different populations.
Establish a platform for better diagnosis and drug discovery in cardiac arrhythmia
Cardiac arrhythmias are related to genetic mutations and drug exposure, which affects the ion homeostasis in the cardiomyocytes. In drug development, cardiotoxicity is a major cause of failure and may lead to serious consequences in patients (eg. cisapride caused at least 175 cases of death due to ventricular arrhythmia). The iPSCs provide us a unique platform to better evaluate the arrhythmic risk of both genetic mutations and drugs. The dataset from the different patient-specific iPSC line and drug treatment groups can be used to train a machine learning model (classifier), which will help us identify the potential arrhythmia risks based on the experimental data. The computational tool will optimize the utility of the massive dataset in modern iPSC modeling research, help to identify cardiotoxicity during drug development, and facilitate the evaluation of potential cardiac risk and drug response with the patient-specific iPSC model.
