Complexity and evolution of splicing-regulatory networks

项目摘要 选择性剪接(AS)从单个基因产生多个转录异构体，并在 哺乳动物的分子、细胞和表型的复杂性。这一过程受到RNA结合的严格调控 识别其目标转录本中特定调控元件的蛋白质(RBPs)。由来已久的 从进化论的观点来看，AS调控的变化是由于顺式调控突变引起的 序列是哺乳动物物种形成的主要驱动力，包括与之密切相关的物种，如 人类和黑猩猩。事实上，不同的哺乳动物物种中普遍存在着不同的AS事件 在人类群体中广泛存在的剪接数量性状基因座(SQTL)就是明显的证据。尽管不同寻常 近十年来剪接调控网络的研究进展，我们对剪接密码的理解 仍然非常不完整，留下了一些关键问题，如1)哪些进化剪接在不同的 物种或人类种群有功能影响吗？2)潜在的突变/基因是什么 导致不同剪接模式的变异？在我们之前关于剪接的工作的基础上-调控 网络，本研究计划旨在解决这些问题。我们将开发计算方法和 在特定谱系和地图中自适应选择下识别为事件的实验模型系统 突变导致剪接的变化--剪接分歧背后的调控元件。学到的洞察力 从代表自然实验的进化变化中将被利用来开发出更具预测性的 拼接代码。在这些研究中，包括新技术在内的计算和实验方法 我们的多学科团队将发展紧密结合在一起。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 监管。

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.