Proper expression of ion channel genes is responsible for maintaining the electrical properties of cardiac tissue.  Ion channel expression changes across different areas of the heart.  These changes allow myocytes to specialize in signal propagation, contraction, or pace-making.  Further alterations in ion channel transcriptional expression are associated with diseases such as atrial fibrillation.  We study the regulation of ion channel gene expression at the transcriptional level using bioinformatic data mining approaches to analyze the core promoter of ion channel genes.  Our efforts to predict ion channel expression patterns may lead to novel drug targets in treating cardiovascular arrhythmias and disease.

 

            We have developed a bioinformatic method of identifying short stretches of DNA sequence that may be responsible for differences in gene expression of one group compared to a background group.  Our method involves identifying all motifs conserved in the DNA of any promoter sequence and subsequently filtering this list based on biological observations of the properties of transcription factor binding sites.

 

            Recently, our method proved effective in identifying known transcription factor binding sites in a photoreceptor model system.  Our method identified even smaller motifs, including NRE, when a group of rod-specific promoter sequences was compared with cone, and sequences without a photoreceptor-specific function.  These tests prove that our method is effective in real DNA sequence sets, and our present objective is to use our method in the analysis of ion channel gene expression.