Staggered vs. Blunt Cutting of Cas9 in the Context of Targeted Sequencing

Rachel Case

Research Mentor(s): Brian Athey
Mentor Department: Computational Medicine & Bioinformatics
Authors: Greg Farnum, Brian Athey, Monica Holmes, Rachel Case
Session: Session 1 (9:00am – 9:50am)
Presentation Type: Poster 97

Abstract

CYP2D6 plays a critical role in drug metabolism, affecting about 25% of medications that are processed in the body. As such, it is taken into important consideration when genetic profiles are being evaluated for medications. However, CYP2D6 has many variations, which are hard to detect with the current technology. This project aims to create verifiable assays that provide the necessary information about CYP2D6 through Oxford Nanopore Sequencing and targeted sequencing using Cas9. Originally, Cas9 was regarded as a blunting enzyme (an enzyme that cuts DNA with no base pair overhangs) and used to cut DNA at certain sites in the genome, allowing for targeted regions to be modified with sequencing adapters and sequenced. During our initial studies, it was noticed that the number of reads are unequally distributed from the initial cut site, leading to the idea that Cas9 does not actually cut blunt ends and instead leaves the DNA with various overhang structures. This experiment is meant to verify the presence and ligation efficiency of adaptors attached to DNA fragments using gel electrophoresis, with focus on detecting 1, 2, 3, and 4 base pair overhangs on both the 5’ and 3’ ends after Cas9 cutting. By using duplexed adaptors with various known overhangs and ligating them to Cas9 cut DNA and Eco53KI cut DNA (the control), it should be possible to determine the resulting pattern of overhang structures. A combination of Oxford Nanopore Sequencing, Capillary, and Gel Electrophoresis will be used to determine the results. The expected result is showing Cas9’s cutting mode pattern and the presence of specific overhang structures. In conclusion, having a set assay will provide a better understanding of Cas9 and more accuracy in clinical lab results, leading to improved drug metabolism predictions.