The embryonic chicken (Gallus gallus) has proved an integral model organism for studying retina development. Furthermore, the embryonic chicken demonstrates the remarkable ability to regenerate retina in response to injury. Despite these important traits, a complete understanding of gene regulation during chick retinal development is lacking. Epigenetic regulation is an important class of gene regulation, denoting reversible changes to the genome that affect gene expression without affecting the underlying DNA sequence. DNA methylation, one form of epigenetic regulation, is highly dynamic throughout development, and likely has important regulatory roles during retina development and regeneration. However, the complex relationship between methylation and gene expression in the retina has yet to be fully resolved. Here we perform an in-silico analysis of available high-throughput sequencing data during chick retina development to characterize changes in gene expression and DNA methylation. This novel analysis compiles gene expression and methylation data from multiple studies to characterize their relationship at various time points across chicken retina development. Gene expression data was used to cluster genes into 8 major classes defining biological function during retina development. Furthermore, we integrated methylation and expression data to reveal a global inverse relationship between methylation and gene expression. This correlation suggests that DNA methylation is used as a repressive mechanism of modulating gene expression throughout development. Furthermore, we identify changes in DNA methylation proximal to genes important in retina development, such as LHX2. This study highlights previously undiscovered roles of methylation in retina development, and in the future, additional high-throughput data will expand our knowledge of how epigenetic changes affect gene expression. Through this study, I gained an improved understanding of bioinformatics and how to conduct cross-study analyses. I hope to use this experience to better interpret and design data-based solutions in my future career as an epidemiologist and public health worker.
Author: Emilio Bloch
Faculty Advisors: Dr. Katia Del Rio-Tsonis, Department of Biology,
Jared Tangeman, Department of Biology