Use of Bioinformatics to Extract Pharmacogenomic Information

Despite advances in heart research, knowledge about the cardiac conduction system and sinoatrial node (SAN) remains limited, with no effective treatments for SAN dysfunction currently available. Artificial pacemakers and defibrillators are commonly used but can lead to complications, emphasizing the need for better strategies and therapies. Human induced pluripotent stem cells (hiPSCs) offer a promising source for generating SAN-like cells, which can help in studying and treating genetic arrhythmias and modeling cardiac diseases. Publicly available transcriptomic datasets were sourced from the Gene Expression Omnibus (GEO) repository using specific keywords based criteria. Differential gene expression analysis was conducted using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}$</tex> software environment with the DESeq2 package, involving pre-filtering and normalization of raw counts. Our bioinformatic strategy facilitates disease modeling and drug discovery by analyzing expression profiles. Principal component analysis revealed distinct cell populations, suggesting different responses to drugs, while Gene Ontology enrichment highlighted affected biological pathways for targeted therapy. This method, applicable to various transcriptomic databases, aids in identifying potential therapies.