November 19 2014

12:00 LSB 2320

Sergey Nuzhdin
Department of Biology, USC

Variation and evolution of gene regulatory networks


Cis regulatory polymorphisms leading to changes in gene expression have been shown to play an important role in phenotypic variation in a variety of complex traits including disease. Allele-specific expression can be used to identify cis- regulatory polymorphisms affecting expression. In a diploid individual, two alleles are exposed to the same cellular environment. Within an individual allelic imbalance (AI), the expression of one allele over the other, is caused by polymorphisms in the cis-regulatory regions. Similarly, when a single allele is compared between two individuals AI indicates trans-regulatory effects. Here we harness the power of natural variation and transcriptomics to identify intraspecific AI on a genome-wide scale in Drosophila melanogaster. These data are used to annotate variation in well-described gene regulatory networks (GRNs) and to predict the GRNs de novo. For example, the Drosophila sex determination regulatory hierarchy consists of a pre-mRNA splicing cascade that ultimately produces sex-specific transcription factors that direct sexual dimorphism in development, morphology, physiology and behavior. Our understanding of this regulatory cascade is the result of decades of detailed molecular-genetic studies in which network structure was perturbed using elegant experimental approaches. Yet, even in one of the most well developed molecular-genetic model systems, these approaches are time consuming and expensive. For these reasons, there is still a substantial amount we do not understand about this pathway. Natural variants are a window into subtle interactions among alleles, given that they are smaller-effect genetic perturbations than are typically examined in molecular-genetic studies. Structural equation models, a supervised approach based on Sewell Wright’s path analysis, readily identifies known key relationships among genes in the sex hierarchy, and adds new elements. Overall, we transition from gene to GRN-centric description of variation and evolution.











































































































































































































































































































































































































































































































































































































this is idtest: