306B Poster - 03. Evolution
Friday April 08, 2:00 PM - 4:00 PM

Comparative Analysis of Node Degree on Gene Evolution in the Insulin Signaling Pathway


Authors:
Abigail Myers 1; Alyssa Koehler 2; Annie Backlund 3; Chinmay Rele 4; Laura Reed 5

Affiliations:
1) The University of Alabama; 2) The University of Alabama; 3) The University of Alabama; 4) The University of Alabama; 5) The University of Alabama

Keywords:
d. evolution of gene expression; n. networks

A biological pathway is a network of nodes (genes) interacting with each other and other regulating molecules in the cell which determines gene expression and therefore overall gene function of the cell.The insulin signaling pathway is extensively studied and includes a group of genes that regulate glucose metabolism. This, in addition to the pathway’s high conservation across species, makes it a beneficial model to study the function and evolution of biological pathways. In a network, node degree refers to the number of connections a gene has to other genes in the pathway. Previous studies using computational methods found genes with high node degree to be less evolutionarily constrained than genes with low node degree in this pathway. These results contradict the accepted hypothesis that genes with high node degree are under more selective constraint because connected genes have to adapt to any mutations that occur at the node. In this project we further investigate the impact of node degree on gene evolution using manually curated gene models across the Drosophila genus. We are looking at the ratio of nonsynonymous to synonymous mutations (dN/dS) of three genes with varying node degree. We chose to focus on GlyS, raptor, and Dsor1; which are genes with low, intermediate, and high node degree, respectively. A comparative analysis of the nonsynonymous changes in these genes across Drosophila species related to their node degree will provide a small-scale analysis of the impact of node degree on gene evolution using manually curated gene models. This analysis will grow as more genes in the insulin signaling pathway are annotated and analyzed in the future.