Subtyping the autisms using individualized protein network analysis

使用个体化蛋白质网络分析对自闭症进行亚型分类

基本信息

批准号：
10212205
负责人：
Stephen Edward Paucha Smith
金额：
$ 68.17万
依托单位：
SEATTLE CHILDREN'S HOSPITAL
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：
2020-07-09 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10212205
关键词：
Accounting Affect Age Behavior Behavior Therapy Behavioral Biochemistry Biocompatible Materials Biological Biological Assay Biological Markers Biological Process Cell Line Cells Child Clinical Collection Copy Number Polymorphism Custom DNA Sequence Alteration Data Development Diagnosis Diagnostic Disease Drug Targeting Electroencephalography FRAP1 gene Family Fibroblasts Gene Expression Profile Genes Genetic Genetic Diseases Genetic Heterogeneity Genetic Predisposition to Disease Genetic Transcription Genetic study Glutamates Goals Grain Grant Heterogeneity Human In Vitro Individual Knowledge Link Measurement Measures Methods Modality Molecular Molecular Abnormality Molecular Biology Morphology Mutation Network-based Neurons Outcome Measure Pathogenesis Pathology Pathway Analysis Pathway interactions Patient Recruitments Patients Pattern Pharmaceutical Preparations Phenotype Play Population Proteins Proteomics Protocols documentation Publishing Rare Diseases Research Personnel Research Subjects Role Route Sampling Signal Transduction Site Subgroup Synapses System Techniques Technology Testing Treatment outcome Universities Untranslated RNA Validation Washington Work autism spectrum disorder autistic autistic children base behavioral outcome biosignature cell type clinically relevant data analysis pipeline de novo mutation drug development druggable target exome sequencing gene environment interaction genetic information genome sequencing genome-wide genomic platform genotyped patients high dimensionality in vitro Model innovation member molecular pathology mouse model novel optimal treatments outcome prediction patient subsets phenotypic data preclinical study protein protein interaction research study sex single-cell RNA sequencing targeted treatment transcriptome sequencing treatment strategy

项目摘要

PROJECT SUMMARY Autism is a behaviorally-defined diagnosis that affects approximately 1 in 59 children in the US. Recent genetic studies have revealed that autism is an umbrella term for a large family of individually rare, collectively common genetic disorders, with each individual gene accounting for only a small portion of total cases. This presents a problem for researchers attempting to develop biologically-based drug or treatment strategies: the autism diagnostic entity is too broad to be biologically meaningful, but most individual genetic mutations are too rare to allow for sufficient patient recruitment or for commercially viable drug development. There is an urgent unmet need to develop a subtyping strategy that can assign patients into one of a small number of biologically meaningful subtypes that might be amenable to targeted treatment strategies. We have recently developed a novel proteomic strategy that makes high-dimensional measurements of protein-protein interaction networks (PINs). These measures reflect several relevant features of autism pathogenesis- synaptic content, recent activity, and developmental stage of the neuron. We postulate that different genetic autisms converge on two specific PINs and produce patterns of network disruption that, while individually unique, share common features that will allow clustering of PIN matrices into subtypes. Importantly, our clustering methods allow identification of specific signal transduction nodes that define each sub-type, linking biologically-relevant information with our proposed clusters. In published proof-of-concept work, we were able to cluster seven different mouse models and make predictions about previously unknown molecular pathologies. Here, we propose to extend this work to human neurons, using primary patient cells taken from genetically sequenced autistic research subjects with identified likely causative genetic mutations, or `idiopathic' autism patients who were sequenced but no mutation was identified, or age-and-sex-matched typically developing controls. This work will reveal new, biologically relevant relationships between autisms of different known and unknown genetic etiologies, and offers the opportunity to simultaneously identify sub-groups of patients and potential drug targets that may effectively treat each identified sub-group.

项目摘要自闭症是一种行为定义的诊断，在美国有59名儿童中约有1个。最近的遗传研究表明，自闭症是一个庞大的稀有，统称的大家庭的伞常见的遗传疾病，每个基因仅占总病例的一小部分。这为试图制定基于生物学的药物或治疗策略的研究人员提出了一个问题：自闭症诊断实体太广泛，无法在生物学上有意义，但是大多数个体基因突变也是很少有足够的患者招募或商业上可行的药物开发。有紧急未满足的需要制定可以将患者分配为少数生物学之一的亚型策略有意义的亚型可能适合有针对性的治疗策略。我们最近开发了新型的蛋白质组学策略，可以对蛋白质蛋白质相互作用网络进行高维测量（销）。这些措施反映了自闭症发病机理 - 突触含量的几个相关特征，最近活性和神经元的发育阶段。我们假设不同的遗传自闭症在两个特定的引脚和产生网络中断模式，尽管单独独特，但共享共同可以将销矩阵聚类为亚型的功能。重要的是，我们的聚类方法允许识别定义每个子类型的特定信号转导节点，与生物学相关的我们建议的集群信息。在出版的概念证明工作中，我们能够将七不同的小鼠模型，并对先前未知的分子病理做出预测。在这里，我们建议使用从遗传测序的原代患者细胞扩展到人神经元自闭症研究受试者具有可能导致遗传突变或“特发性”自闭症患者被测序但未鉴定出突变，或者是年龄和性别匹配的通常会发育的控制。这工作将揭示不同已知和未知的自闭症之间的新的，生物学上相关的关系遗传病因，并提供了同时识别患者亚组的机会可以有效治疗每个已鉴定的亚组的药物靶标。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Protein interaction network analysis of mTOR signaling reveals modular organization.

DOI：
10.1016/j.jbc.2023.105271
发表时间：
2023-11
期刊：
JOURNAL OF BIOLOGICAL CHEMISTRY
影响因子：
4.8
作者：
Wehle, Devin T;Bass, Carter S;Sulc, Josef;Mirzaa, Ghayda;Smith, Stephen E P
通讯作者：
Smith, Stephen E P

Altered network and rescue of human neurons derived from individuals with early-onset genetic epilepsy.

DOI：
10.1038/s41380-021-01104-2
发表时间：
2021-11
期刊：
Molecular psychiatry
影响因子：
11
作者：
Negraes PD;Trujillo CA;Yu NK;Wu W;Yao H;Liang N;Lautz JD;Kwok E;McClatchy D;Diedrich J;de Bartolome SM;Truong J;Szeto R;Tran T;Herai RH;Smith SEP;Haddad GG;Yates JR 3rd;Muotri AR
通讯作者：
Muotri AR