Complexity and evolution of splicing-regulatory networks

剪接调控网络的复杂性与演化 项目摘要 选择性剪接（AS）从单个基因产生多种转录异构体，并对基因的转录起关键作用。 哺乳动物的分子、细胞和表型复杂性。这一过程受到RNA结合的严格调控 识别其靶转录物中的特定调控元件的RBP。一个长期存在的 从进化的角度假设AS调节的变化是由于顺式调节基因的突变， 序列提供了哺乳动物物种形成的主要驱动力，包括密切相关的物种，如 人类和黑猩猩事实上，不同的AS事件在不同的哺乳动物物种中普遍存在， 广泛剪接的数量性状基因座（sQTL）。尽管引人注目 在过去的十年里，剪接调控网络的研究取得了进展，我们对剪接密码的理解 仍然是非常不完整的，留下了关键的问题，如1）哪些进化剪接改变不同的 物种或人群中有功能性影响吗？2)什么是潜在的突变/遗传 导致不同剪接模式的变异基于我们之前在剪接调控方面的工作， 网络，这项研究计划旨在解决这些问题。我们将开发计算方法， 在特定谱系和图谱中识别适应性选择下AS事件的实验模型系统 突变导致剪接调节元件的变化，而剪接调节元件是剪接趋异的基础。学到的见解 从代表自然实验的进化变化中获得的信息将被用来开发一种更具预测性的 拼接码在这些研究中，计算和实验方法，包括新技术， 我们的多学科团队紧密结合。如果成功，这项研究将提供 对AS进化对不同物种之间潜在表型差异的贡献的巨大见解， 哺乳动物物种，人类的健康和疾病，以及对RNA剪接的基本理解 调控

Complexity and evolution of splicing-regulatory networks Project Summary Alternative splicing (AS) generates multiple transcript isoforms from single genes and contributes critically to the molecular, cellular and phenotypic complexity of mammals. This process is tightly regulated by RNA-binding proteins (RBPs) which recognize specific regulatory elements in their target transcripts. A long-standing hypothesis from the evolutionary perspective is that changes of AS regulation due to mutations in cis-regulatory sequences provide a major driving force of speciation in mammals, including closely related species such as human and Chimpanzee. Indeed, divergent AS events are pervasive in different mammalian species, as well as in human populations as evident from widespread splicing quantitative trait loci (sQTLs). Despite remarkable progress in studies of splicing-regulatory networks over the past decade, our understanding of the splicing code remains very incomplete, leaving critical questions such as 1) which evolutionary splicing changes in different species or in human populations have functional implications? 2) what are the underlying mutations/genetic variations that led to the divergent splicing patterns? Building on our previous work on splicing-regulatory networks, this research program aims to address these questions. We will develop computational methods and experimental model systems to identify AS events under adaptive selection in specific lineages and map mutations leading to changes in splicing-regulatory elements that underlie splicing divergence. Insights learned from evolutionary changes that represent nature’s experiments will be leveraged to develop a more predictive splicing code. In these studies, computational and experimental approaches including new technology development are closely integrated by our multidisciplinary team. If successful, this study will provide tremendous insights into the contribution of AS evolution to potential phenotypic differences among different mammalian species, to health and disease in humans, and to fundamental understanding of RNA splicing regulation.