From DNA sequence to expression: A quantitative approach from yeast to human

描述（由申请人提供）：控制不同基因活性水平的能力是发育和分化等基本生物学过程的关键，许多人类疾病都是由这一调控过程的缺陷引起的。这种调节是在基因组的特定区域内编码的，称为调节区域，实际上，在许多关于癌症和其他疾病以及人类表型的研究中，与疾病状态密切相关的基因活动的变化反过来又与基因调节区域的DNA序列的变化有关。然而，我们目前对基因活性如何被DNA序列编码的了解甚少，因此，我们不了解这些与疾病相关的序列变化是通过什么机制导致观察到的基因活性变化。考虑到已经开展的许多基因调控研究，实际上令人惊讶的是，我们对基因活性和DNA序列之间的映射所知甚少。原则上，这些问题可以通过精确测量各种序列元件系统变化的调控区域来直接回答。然而，目前还不存在这样的数据，很可能是由于在构建这样的序列和准确测量它们的活性方面存在技术困难。在这里，我们的目标是推导出基因活性模式如何在DNA序列中编码的机制理解，并得出一个定量模型来描述整个过程，从被称为转录因子的调节蛋白的活性到它们与调节区域的结合，通过DNA包装在这一过程中的重要作用，以及由调节转录因子的DNA结合活性产生的基因活性模式。对这种相互作用的系统研究需要有效合成和准确测量许多不同调控序列的活性的能力。我们最近开发了这样的功能，我们将在这个项目中使用。具体来说，我们将设计调控序列，系统地测试各种类型的序列元件对基因活性的定量贡献，测量它们的活性，将结果数据整合到基因调控的统一模型中，然后使用该模型来研究如何在天然启动子中使用这些调控序列元件来实现具有生物学意义的活性模式。以及在进化过程中这些序列元素的变化如何促进基因活性的进化变化。最后，我们将应用该模型来预测人类个体之间的基因活性变化，使用正在迅速收集的新兴基因型数据。如果成功，我们的项目将产生深远的影响。最值得注意的是，由于基因活动水平的变化在癌症和许多其他疾病的发展中起着关键作用，因此，即使是根据正在迅速收集的基因型信息预测人类个体之间基因活动变化的部分能力，也可能具有重要的医学意义。

DESCRIPTION (provided by applicant): The ability to control the activity level of different genes is key to fundamental biological processes such as development and differentiation, and many human diseases are caused by defects in this regulatory process. This regulation is encoded within specific regions of the genome, termed regulatory regions, and indeed, in many studies of cancer and of other diseases and human phenotypes, changes in gene activity that are tightly linked to the disease state have in turn been linked to changes in the DNA sequence of the genes' regulatory regions. However, we currently have a poor understanding of the how gene activity is encoded by DNA sequence, and thus, we do not understand by what mechanism these disease-linked sequence changes cause the observed changes in gene activities. Given the many studies of gene regulation that have been carried out, it is actually surprising how little we know about this mapping between gene activity and DNA sequence. In principle, such questions can be directly answered through accurate measurements of regulatory regions in which various sequence elements are varied systematically. However, such data does not currently exist, most likely due to the technical difficulties in constructing such sequences and accurately measuring their activity. Here, we aim to derive a mechanistic understanding of how gene activity patterns are encoded in DNA sequence, and arrive at a quantitative model that describes the entire process, from the activity of the regulating proteins, termed transcription factors, to their binding to regulatory regions, through the important role of DNA packaging in this process, and up to the gene activity patterns resulting from the DNA binding activity of the regulating transcription factors. A systematic study of such interactions requires the ability to efficiently synthesize and accurately measure the activity of many different regulatory sequences. We have recently developed such capabilities, which we will utilize in this project. Specifically, we will design regulatory sequences that systematically test the quantitative contribution of various types of sequence elements to gene activity, measure their activity, integrate the resulting data into a unified model of gene regulation, and then use this model to examine how such regulatory sequence elements are used in native promoters to achieve biologically meaningful activity patterns, and how changes in these sequence elements during evolution contribute to evolutionary changes in gene activity. Finally, we will apply the model to predict gene activity changes among human individuals, using the emerging genotype data that is rapidly being collected. If successful, our project should have far reaching implications. Most notably, since changes in gene activity levels play a key role in the development of cancer and of many other diseases, even a partial ability to predict gene activity changes among human individuals from the genotype information that is rapidly being collected for them, could have important medical implications.