JK: We see patients and the medical problems and the challenges they face. But we also see the limitations in terms of what we can do for them right now. Traditionally, there wasn't that much we could do for those with genetic forms of heart disease, but potentially in the future with gene editing — we could potentially correct causal genetic variants that contribute to their disease.
A recent example was these two patients. The father has PRKAG2 syndrome and has a variant of unknown significance in that gene. The son also has that same variant of unknown significance, and also has a similar phenotype as his father.
PRKAG2 syndrome is a condition where you have this triad of hypertrophic cardiomyopathy, glycogen accumulation in the cardiomyocytes, and accessory pathways that contribute to supraventricular tachyarrhythmias, including, Wolff-Parkinson-White syndrome.
The father developed end stage heart failure requiring a heart transplant.
And the son, who has the same mutation, developed supraventricular tachycardia leading to syncope. Nathan helped with some of the genetic analysis for these patients. In silico modeling predicted that this variant is deleterious.
We were wondering whether base editing or prime editing would be able to help correct that mutation. We’re now exploring that possibility with Dr. Liu’s lab members. And if it proves out in animal studies, this can potentially be a therapy for our patients with this condition.
NC: It’s been interesting to see how my background in data science and statistics applies to the world of bioinformatics. I’m working on a project looking into different somatic variant calling pipelines to determine which is the most effective for accurate variant calling, especially as it pertains to heart disease and heart failure.
JK: Nathan is investigating a new area of research methodology to help us identify the strengths and weaknesses of somatic variant calling algorithms and tweaking them to enable us to have the most complete picture possible. He's helping to innovate in the area of somatic variant detection in cardiovascular disease as well as the pathways somatic variants affect. Working together with Drs. Hongyu Zhao and Jianlei Gu, we’ve also been optimizing CHIP calls on the Yale Generations data in several 1000 patients and validating on larger datasets like UKB.