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

数据驱动的突触后密度图破译自闭症发病机制

• 批准号: 10305595
• 负责人: Yuan NA Mei
• 金额: $ 7.39万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-11-16 至 2023-12-08
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10305595
• 关键词: Address; Affect; Algorithms; Animals; Architecture; Biochemical; Bioinformatics; Biological; CRISPR/Cas technology; Child; Chromosome Mapping; Code; Communication; Communities; Complex; Consensus; Critical Pathways; Cytoskeletal Proteins; Cytoskeleton; DNA; Data; Dendritic Spines; Detection; Development; Disease; Emotional; Enzymes; Etiology; Excitatory Synapse; Genes; Genetic; Genetic Transcription; Human; In Vitro; Induced pluripotent stem cell derived neurons; Knowledge; Lead; Leadership; Location; Logistic Regressions; Machine Learning; Maps; Mass Spectrum Analysis; Mentorship; Methodology; Modeling; Molecular; Mutation; Neurodevelopmental Disorder; Neurosciences; Neurotransmitter Receptor; Ontology; Pathogenesis; Pathway Analysis; Pathway interactions; Patients; Pattern; Polymerase Chain Reaction; Property; Proteins; Proteomics; Receptor Signaling; Research; SYNGAP1; Signal Transduction; Source; Stains; Structure; Synapses; System; Training; Update; Western Blotting; Work; autism spectrum disorder; base; cost; data-driven model; density; disease disparity; disease phenotype; economic impact; effective therapy; experience; experimental study; immunocytochemistry; in silico; insight; instrument; interdisciplinary approach; large scale data; machine learning model; multidisciplinary; multiple data types; novel; novel therapeutic intervention; postsynaptic; postsynaptic density protein; presynaptic density protein 95; protein complex; protein expression; protein protein interaction; protein structure; random forest; scaffold; social; supervised learning; synaptic function; therapy development; transcriptome sequencing; validation studies

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.

项目摘要 自闭症谱系障碍（ASD）是一组复杂的神经发育疾病， 社会、情感和经济影响。数十年的研究表明， 对疾病病因的贡献。许多高置信度的自闭症基因定位于 突触后致密物（PSD），其是典型地位于树突棘中的复杂蛋白质致密结构 兴奋性突触它由多种蛋白质组成，包括主支架， 神经递质受体和细胞骨架调节剂。尽管研究表明， PSD是自闭症的一个中心机制，目前还不清楚这些基因如何在蛋白质通路中聚集， 破坏突触功能为了更好地理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模型的PSD破译关键 自闭症的路径，并优先考虑新的疾病候选人。在目标1中，随机森林模型将是 训练预测新的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，以根据PSD基因的生物学特性识别PSD基因。 例如表达谱、蛋白质结构和其他性质。将验证预测的PSD基因 通过人诱导多能干细胞（hiPSC）的免疫细胞化学和蛋白质印迹分析- 衍生神经元目标2将组织识别的PSD网络到一个层次本体，使路径 疾病基因分析。Logistic回归和基因富集分析将应用于新型PSD 层次结构，以确定自闭症发病机制的关键途径。目标3将利用PSD本体来预测 最有可能被ASD基因破坏的蛋白质邻居。为了验证预测，CRISPR/Cas9 DNA编辑系统将用于删除hiPSC衍生神经元中的高置信度ASD基因;定量 将完成聚合酶链反应（qPCR）和生化分析，以表征预测的 蛋白质邻居总的来说，这些目标将揭示ASD发病机制中的关键突触通路， 提供了一个综合图谱，说明看似不同的疾病基因如何导致相同的疾病。 表型这些多学科研究将是突触领域的第一次， 开发ASD的新治疗策略。拟定研究将在Dr. Trey完成 Ideker在UCSD的实验室，配备了最先进的仪器，使计算和 实验工作描述。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、导师制、领导力和沟通。实现这些目标将 在所有五个领域提供重要的经验，并促进过渡到学术独立。