Natural Variation and Systems-Level Properties of Gene Regulation in Drosophila

Regulation of gene expression is essential for normal animal development, yet its complexity makes it difficult to fully understand. The genetic regulatory network (GRN) is thought to control precise cell fate decisions. To further elucidate the GRN, this project will use naturally-occurring genomic polymorphisms to explore correlations between DNA elements and gene expression patterns in early Drosophila embryos. This research will aim to discover novel DNA elements regulating expression patterns, in addition to transcriptomic data which could uncover previously unknown components of the GRN. Successful results will allow for a more complete model of the GRN, which could result in model-generated, testable predictions regarding its behavior and emergent properties. This work could lead to a deeper, quantitative understanding of developmental GRNs.

Sponsor

Principle Investigators

Cranos Williams

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Regulation of gene expression is of paramount importance in animal development, with improper regulation resulting in developmental defects and disease states. At the DNA level, gene regulation can be achieved by transcription factors binding to their cognate sequences, which are often clustered together. In developing tissues, several genes coding for transcription factors regulate each other in a complex web of interactions known as the genetic regulatory network (GRN). The structure of a GRN is thought to be responsible for the robust and precise cell fate decisions required in a developing tissue. However, there remain unknown components participating in the native GRN, limiting a full understanding of how the structure of the GRN results in robust cell fate decisions. The long-term goal is to deduce the genetic regulatory interactions necessary for robust patterns of gene expression. The overall objective in this proposal is to use the natural variation that occurs in a panel of wild-caught fly lines to characterize the GRN responsible for precise anterior-posterior (AP) patterning the early Drosophila embryo. This will test the central hypothesis that gene expression patterns in the AP patterning system have undiscovered regulation that can be found by examining the correlation between gene expression and natural variation in the genomes of these flies. Specific Aim 1: Use naturally-occurring genomic polymorphisms to correlate DNA elements to gene expression patterns. Based on our preliminary data, our working hypothesis is that novel DNA elements --- outside of standard, well-characterized enhancers --- exert control on the expression patterns of AP network genes. To test this hypothesis, we will measure gene expression patterns in DGRP lines and correlate the measurements to genomic sequences. If successful, our work in this Aim will result in discovery of novel DNA elements, which would advance our understanding of general mechanisms of gene regulation. Specific Aim 2: Use naturally-occurring genomic polymorphisms to correlate DNA elements to transcript levels. In complement to the previous aim, the goal in this aim is to correlate global transcriptomic data to natural genomic variation in order to discover novel AP patterning targets. Next-Gen sequencing will be used to generate large transcriptomic data sets for discovery. Specific Aim 3: Build a comprehensive model of the AP patterning network. The goal of this Aim is to synthesize large-scale data from the literature and from DGRP lines to build a comprehensive model of the AP patterning network. If successful, our work in this Aim will result in model-generated, testable predictions regarding emergent properties of networks, and advance our understanding of developmental GRNs at a quantitative level. The following outcomes are expected: First, novel regulation of known AP components will be discovered. Conversely, previously unknown components of the AP patterning network will be discovered. Moreover, this work will lead to a quantitative understanding of GRN behavior. Additionally, large sets of transcriptomic data will be made available through this work.