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

项目摘要。基因表达的调节在动物发育中至关重要， 调节不当导致发育缺陷和疾病状态。在DNA水平上，基因调控- 可以通过转录因子与它们的同源序列结合来实现，所述同源序列通常聚集到- 在一起。在发育中的组织中，编码转录因子的几个基因在一个复合体中相互调节 基因调控网络（Genetic Regulatory Network，GRN）。GRN的结构被认为是 负责发育组织中所需的稳健和精确的细胞命运决定。然而，有- 参与本地GRN的主要未知组件，限制了对GRN结构的全面理解。 GRN导致稳健的细胞命运决定。 长期目标是推导出基因表达的强大模式所必需的遗传调控相互作用， 压力。本提案的总体目标是利用一组野生- 捕捉飞线，以表征负责精确的前后（AP）图案的GRN的早期 果蝇胚胎。这将检验核心假设，即AP模式系统中的基因表达模式。 TEM具有未发现的调节，可以解释基因表达的稳健性，并且可以通过以下方法发现： 研究这些果蝇基因组中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元件与基因表达模式相关联。基于 我们的初步数据，我们的工作假设是，新的DNA元素-在标准之外，嗯-- 特征增强子-对AP网络基因的表达模式施加控制。为了验证这个假设- 因此，我们将测量DGRP系中的基因表达模式，并将测量结果与基因组数据相关联。 序列的如果成功的话，我们在这个目标上的工作将导致发现新的DNA元件，这将有助于- 万斯我们对基因调控的一般机制的理解。具体目标2：使用自然变异 将DNA元件与转录组调控相关联。作为对前一目标的补充， 目的是将全球转录组数据与自然基因组变异相关联，以发现新的AP模式- 目标。下一代测序将用于生成用于发现的大型转录组数据集。斯佩 目标3：建立AP模式网络的综合模型。这个目标的目的是合成- 从文献和DGRP线的大小大规模数据，建立一个全面的模型的AP帕特， terning网络如果成功的话，我们在这个目标中的工作将导致模型生成的，可测试的预测， 增强基因表达的鲁棒性，并在定量水平上推进我们对GRNs的理解。 预期以下结果：首先，将发现已知AP组分的新调节。 相反，AP图案化网络的先前未知的组件将被发现。而且这 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.