Natural variation and systems-level properties of gene regulation in Drosophila

项目总结。基因表达调控在动物发育中至关重要， 不适当的调节会导致发育缺陷和疾病状态。在DNA水平上，基因调控- 转录因子可以通过与它们的同源序列结合来实现，这些同源序列通常聚集到- 太好了。在发育中的组织中，几个编码转录因子的基因在一个复合体中相互调节 相互作用的网络被称为基因调控网络(GRN)。GRN的结构被认为是 负责在发育中的组织中所需的强大和精确的细胞命运决定。然而，还有- 主要未知成分参与了原生GRN，限制了对结构如何的充分了解 GRN导致了强大的细胞命运决策。 其长期目标是推断出强健的基因模式所必需的基因调控相互作用。 压迫感。这项提案的总体目标是利用发生在一组野生- 捕捉苍蝇线条以描述负责精确的前后部(AP)早期模式的GRN 果蝇胚胎。这将检验AP模式系统中基因表达模式的中心假设。 TM有未被发现的规则，可以解释基因表达的健壮性，可以通过以下方式找到 研究这些果蝇基因组中的基因表达和自然变异之间的相关性。 具体目标1：利用自然变异将DNA元素与基因表达模式联系起来。基于 我们的初步数据，我们的工作假设是，新的DNA分子-在标准之外-- 特征增强子-对AP网络基因的表达模式进行控制。为了验证这一假设- Esis，我们将测量DGRP系的基因表达模式，并将测量结果与基因组相关联 序列。如果成功，我们在这一目标上的工作将导致发现新的DNA元素，这将使- 万斯介绍了我们对基因调控一般机制的理解。具体目标2：利用自然变异 将DNA元件与转录调控联系起来。作为对先前目标的补充，这一目标 目的是将全球转录数据与自然基因组变异联系起来，以发现新的AP模式- ING目标。下一代测序将用于生成用于发现的大型转录数据集。SPE- 具体目标3：构建AP图案化网络的综合模型。这个目标的目标是合成- 调整文献和DGRP系列中的大规模数据，以构建AP PAT的全面模型 终端网络。如果成功，我们在这一目标方面的工作将导致以下方面的模型生成的、可检验的预测： 基因表达的稳健性，在数量水平上促进了我们对GRN的理解。 预期的结果如下：第一，将发现对已知AP成分的新调节。 相反，AP构图网络的先前未知组件将被发现。此外，这一点 这项工作将导致对GRN行为的定量理解。

PROJECT SUMMARY. 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 regula- tion can be achieved by transcription factors binding to their cognate sequences, which are often clustered to- gether. 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 re- main 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 ex- pression. 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 sys- tem have undiscovered regulation that may explain the robustness of gene expression, and can be found by examining the correlation between gene expression and natural variation in the genomes of these flies. Specific Aim 1: Use natural variation 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 hypoth- esis, 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 ad- vance our understanding of general mechanisms of gene regulation. Specific Aim 2: Use natural variation to correlate DNA elements to transcriptomic regulation. 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 pattern- ing targets. Next-Gen sequencing will be used to generate large transcriptomic data sets for discovery. Spe- cific Aim 3: Build a comprehensive model of the AP patterning network. The goal of this Aim is to synthe- size large-scale data from the literature and from DGRP lines to build a comprehensive model of the AP pat- terning network. If successful, our work in this Aim will result in model-generated, testable predictions regard- ing robustness of gene expression and advance our understanding of 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.