Researchers at Stanford University developed the tool, which combines gene editing and disease modeling.
Of the 135 genes associated with cardiomyopathy and congenital heart conditions, many have been identified as causing disease or being benign. Several, however, have unknown significance, so a simple genetic screening cannot determine which people are at highest risk.
To solve this dilemma, scientists at the Stanford University Cardiovascular Institute have turned to the gene-editing technology CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats-CRISPR-associated protein-9 nuclease). The team used in CRISPR-Cas9 editing of stem cells in combination with disease modeling techniques to model patient hearts and evaluate whether the cardiac cells exhibited indications of disease. The hope is to create a personalized screening tool that can determine when unspecified gene variants have a high probability of causing disease.
The first step in the research study was to sequence DNA from healthy people for the genes that code for sudden cardiac death. Of the 592 unique variants that were identified, 78% were found to be benign or of unknown significance. Focusing on the MYL3 gene, which is associated with hypertrophic cardiomyopathy, an abnormal thickening of the cardiac muscle that can make it difficult for the heart to pump blood, the scientists used CRISPR to identify the benign and disease-causing variants.
This step was achieved by reprogramming peripheral blood mononuclear cells collected from the study participants into pluripotent stem cells (iPSCs). The IPSCs were edited with CRISPR to create four stem cell lines - three with MYL3 variations and a control -and then determined which variations caused the disease.
The use of CRISPR in diagnostic tools is of growing interest, and these Stanford researchers are not the first to try it. Scientists at the Broad Institute developed a CRISPR-based tool for the detection of Zika virus in body fluid, and Mammoth Biosciences is developing a CRSPR-based diagnostic technology for the detection of multiple diseases.
For cardiac diseases, the ability to determine if a genetic variant has a high probability of causing diseases would be a significant advance. Currently, doctors cannot determine the level of risk, so patients with both high and low risk often take medications and/or undergo surgery. Ideally, using this therapy, unnecessary treatments could be eliminated for patients with low risk.