A Big-Data Approach to Understand a Genetic Heart Disease 

What biomedical issue did you address in your research and what did your studies find?

My research focused on understanding a genetic heart disease called hypertrophic cardiomyopathy, or HCM, which causes the heart to become too large and can lead to abnormal heart rhythms, heart failure and sudden death. Even though HCM is the most common genetic heart condition and one of the most commonly identified causes of sudden death, the treatment options are limited and aimed mostly at relieving symptoms rather than preventing the progression of the disease. My study aimed to better understand the disease pathogenesis and identify novel therapeutic strategies.

For my research, we used samples of human cardiac tissue — from healthy hearts and hearts with HCM — to understand this disease from multiple vantage points. There are almost 20 genes that have been associated with HCM, but about half of the people with HCM have no identified genetic cause. We used a big-data approach to better understand HCM, from the way the genome is packaged, to the gene expression profile, and extending to protein expression and regulation. We utilized next-generation sequencing and mass spectrometry to characterize dysregulation in the cardiac tissue, allowing us to develop a comprehensive understanding of this disease and identify new therapeutic strategies.

In addition, we aimed to identify pathways related to disease severity. Previous studies have shown that people with known genetic variants have more severe disease than people with no identified genetic cause. I hypothesized that subtle differences in protein expression could contribute to these clinical differences. We found that people with genetic variants have more severe dysregulation at the protein level than those with no known variants. Further, we uncovered a unique disease signature; regardless of the underlying genetics, the progression of all types of HCM have a common, final, multi-omics profile. Our multi-omics profile data provide a roadmap for numerous novel therapeutic strategies for HCM. I am most excited about our identification of a specific hypertrophy pathway that we hypothesize could be a novel therapeutic target for HCM.

What aspects of Mayo's culture and approach to education contributed to your research and helped you grow as a scientist?

Mayo Clinic has a unique and exceptional culture that promotes patient-centered, translational research. The most valuable and critical resource for this project was access to cardiac tissue relevant to this disease. Mayo Clinic is a world leader and destination center for HCM, and the Windland Smith Rice Sudden Death Genomics Lab led by Dr. Michael Ackerman has one of the largest repositories of HCM myectomy tissue.

What's next?

My goal is to become a physician-scientist cardiologist who runs a research lab to develop novel therapies and improve patient care. Having completed my Ph.D. research, I am returning to clinical rotations to complete my M.D. degree at Mayo Clinic Alix School of Medicine. I’m still involved with Dr. Ackerman's research team and will continue advancing our findings and testing the novel therapeutic strategies we identified.