Evolution of cis-regulatory sequences

描述（由申请人提供）：编码序列和非编码序列的进化明显不同。大多数蛋白质是高度保守的，即使是在远亲物种之间，如酵母和人类之间。相比之下，顺式调控序列很少在远亲物种之间保持保守。此外，在从酵母到人类的生物体内和物种之间发现了基因表达的高遗传变异率。这些观察结果以及其他证据表明，区分个体或物种的许多表型差异可能是由基因调控的变化引起的。然而，鉴定顺式调控序列和这些序列中的功能突变仍然是理解基因表达变化的进化和生物学意义的主要障碍。我们建议以酿酒酵母为模型系统来解决这一问题。首先，我们将使用作图、互补和等位基因替代实验来确定对药物活性化合物抗性的遗传变异是否由编码序列或非编码序列的突变引起。其次，我们将测试非编码序列中的非中性多态性模式是否表明邻近基因表达的变化。第三，我们将开发概率模型来识别物种之间顺式调控序列的功能变化，并通过基因表达分析来测试这些预测。对顺式调控序列的功能变化的检测不仅可以深入了解基因表达的变化是如何进化的，而且还可以提高我们识别人类有害的、潜在的致病调控突变的能力。

DESCRIPTION (provided by applicant): Evolution within coding and noncoding sequences is markedly different. Most proteins are highly conserved, even between distantly related species such as yeast and human. In contrast, cis-regulatory sequences rarely if ever remain conserved between distantly related species. Furthermore, high rates of genetic variation in gene expression have been found within and between species in organisms ranging from yeast to humans. These observations along with other evidence suggest that many of the phenotypic differences that distinguish individuals or species may be caused by changes in gene regulation. However, the identification of cis-regulatory sequences and functional mutations within these sequences remains a major obstacle in understanding the evolution and biological significance of changes in gene expression. We propose to address this issue using Saccharomyces cerevisiae as a model system. First, we will use mapping, complementation and allele replacement experiments to determine whether genetic variation in resistance to pharmacologically active compounds is caused by mutations in coding or noncoding sequences. Second, we will test whether non-neutral patterns of polymorphism within noncoding sequences are indicative of changes in the expression of adjacent genes. Third, we will develop probabilistic models to identify functional changes in cis-regulatory sequences between species and test these predictions by means of gene expression assays. The detection of functional changes in cis-regulatory sequences will not only provide insight into how changes in gene expression have evolved, but will also advance our ability to identify deleterious, potential disease-causing regulatory mutations in humans.