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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

基本信息

批准号：
10551191
负责人：
Yuan NA Mei
金额：
$ 7.84万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
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/10551191
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 DLG4 gene DNA Data Dendritic Spines Detection Development Disease Emotional Enzymes Etiology Excitatory Synapse Financial Hardship Gene Structure Genes Genetic Genetic Transcription Human In Vitro Induced pluripotent stem cell derived neurons Knowledge 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 Research SYNGAP1 Sampling Signal Transduction Source Stains Structure Synapses System Training Update Validation Western Blotting Work autism spectrum disorder candidate validation 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 protein complex protein expression protein protein interaction protein structure random forest scaffold social supervised learning synaptic function therapy development transcriptome sequencing

项目摘要

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.

项目总结