Data-driven map of the postsynaptic density to decipher autism pathogenesis

项目总结 自闭症谱系障碍(Asd)是一组复杂的神经发育疾病，导致巨大的 社会、情感和经济影响。几十年的研究已经证明了强大的基因 对疾病病因学的贡献。许多高度自信的自闭症基因定位于 突触后密度(PSD)，这是一种复杂的蛋白质密度结构，通常位于树突棘中 兴奋性突触。它由一组不同的蛋白质组成，包括主支架， 神经递质受体和细胞骨架调节器。即使研究表明，对人类的破坏 PSD是自闭症的核心机制，目前尚不清楚这些基因如何聚集在蛋白质途径和 扰乱突触功能。为了更好地了解突触疾病的分子机制， 拟议的研究将构建一个全面的数据驱动的PSD模型，以破译关键 自闭症的治疗途径和新的候选疾病的优先顺序。在目标1中，随机森林模型将是 训练以预测新的PSD基因，这将通过体外实验进行验证。机器学习 模型将整合广泛的不同数据类型，以基于其生物学特性识别PSD基因 特性，如表达谱、蛋白质结构等。预测的PSD基因将得到验证 通过免疫细胞化学和Western印迹分析人诱导多能干细胞(HiPSC)- 衍生的神经元。目标2将识别的PSD网络组织成分层本体以启用路径 疾病基因的分析。Logistic回归和基因富集法将应用于新型PSD 以确定自闭症发病机制中的关键途径。目标3将利用PSD本体来预测 这种蛋白质的邻居最有可能被ASD基因破坏。为了验证预测，CRISPR/Cas9 DNA编辑系统将用于删除HiPSC来源神经元中的高置信度ASD基因；量化 将完成聚合酶链式反应(QPCR)和生化分析以表征预测的 蛋白质邻居。总之，这些目标将揭示ASD发病机制中的关键突触通路和 为看似不同的疾病基因如何导致相同的疾病提供了一张综合地图 表型。这些多学科的研究将是突触中的第一个此类研究，并将使 ASD新的治疗策略的发展。拟议的研究将在《Trey博士》杂志上完成 加州大学圣迭戈分校的Ideker实验室配备了最先进的仪器，使计算和 描述了实验工作。拟议的培训计划侧重于获得综合研究方面的专门知识， 生物信息学、神经科学、导师、领导力和沟通。完成这些目标将 在所有五个领域提供丰富的经验，并促进向学术独立的过渡。

PROJECT SUMMARY Autism spectrum disorders (ASD) are a group of complex neurodevelopmental diseases that lead to enormous social, emotional, and economic impact. Decades of research have demonstrated the strong genetic contribution to the disease etiology. Many of the high-confidence autism genes are localized to the postsynaptic density (PSD), which is a complex protein-dense structure typically located in the dendritic spine of excitatory synapses. It is comprised of a diverse panel of proteins including master scaffolds, neurotransmitter receptors, and cytoskeleton regulators. Even though studies have shown that disruption of the PSD is a central mechanism of autism, it remains unclear how these genes aggregate in protein pathways and disrupt synaptic function. To better understand the molecular mechanisms of disease in the synapse, the proposed study will construct a comprehensive data-driven model of the PSD to decipher critical pathways in autism and prioritize novel disease candidates. In Aim 1, a random forest model will be trained to predict novel PSD genes, which will be validated through in vitro experiments. The machine learning model will integrate a broad spectrum of different data types to identify PSD genes based on their biological properties such as expression profile, protein structure, and others. The predicted PSD genes will be validated through immunocytochemistry and Western blot analysis in human induced pluripotent stem cell (hiPSC)- derived neurons. Aim 2 will organize the identified PSD network into a hierarchical ontology to enable pathway analysis of disease genes. Logistic regression and gene enrichment analysis will be applied to the novel PSD hierarchy to determine key pathways in autism pathogenesis. Aim 3 will leverage the PSD ontology to predict the protein neighbors most likely to be disrupted by ASD genes. To validate the predictions, CRISPR/Cas9 DNA editing system will be used to delete high-confidence ASD genes in hiPSC-derived neurons; quantitative polymerase chain reaction (qPCR) and biochemical analysis will be completed to characterize the predicted protein neighbors. Collectively, these aims will reveal the critical synaptic pathways in ASD pathogenesis and provide an integrative map for how seemingly disparate disease genes can lead to the same disease phenotypes. These multidisciplinary studies will be the first of their kind in the synapse and will enable the development of novel therapeutic strategies for ASD. The proposed studies will be completed in Dr. Trey Ideker’s lab at UCSD, which is equipped with state-of-the-art instruments to enable the computational and experimental work described. The proposed training plan focuses on gaining expertise in integrative studies, bioinformatics, neuroscience, mentorship, leadership, and communication. Completion of these aims will provide significant experience in all five domains, and facilitate the transition to academic independence.