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Developmental Functional Genomics in ASD Toddlers

自闭症谱系障碍 (ASD) 幼儿的发育功能基因组学

基本信息

批准号：
9980501
负责人：
ERIC COURCHESNE
金额：
$ 57.65万
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：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9980501
关键词：
2 year old Address Age Animal Model Behavior Biological Biological Assay Biological Markers Blood Brain CRISPR/Cas technology Categories Cell model Child Clinical Data Data Analyses Data Set Defect Development Developmental Delay Disorders Diagnostic Dimensions Disease Early Diagnosis Early Intervention Early identification Friendships Future Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genomics Goals Grant Growth Hematopoietic Hematopoietic stem cells Heterogeneity Human Immune Immunologic Factors Intervention Language Leukocytes Life Link Microglia Molecular Mother-Child Relations Network-based Outcome Parents Pathway Analysis Pathway interactions Pattern Process Protein Translation Pathway RNA Regression Analysis Research Sample Size Sampling Sensitivity and Specificity Signal Transduction Specificity Standardization Subgroup Symptoms Testing Toddler Uncertainty Validation Variant autism spectrum disorder autistic children base biological heterogeneity candidate marker cellular pathology clinical heterogeneity clinical phenotype clinical subtypes cohort developmental disease diagnostic biomarker disability disorder subtype early detection biomarkers feature selection functional genomics genetic variant impression improved individualized medicine knowledge base language outcome loss of function mutation molecular pathology multidimensional data nerve stem cell neurodevelopment non-genetic novel outcome forecast preference prognostic progression marker protein protein interaction relating to nervous system risk variant skills social stem cell model stem cells success transcription factor transcriptome sequencing transcriptomics treatment trial trend

项目摘要

In autism, early-age biomarkers are scarce. Research is urgently needed to identify markers that precede symptom onset, convey prognostic information, or indicate disorder subtypes. Our proposed functional genomics study of early development in ASD addresses many of these biomarker goals and is an essential early step in this discovery process. Robust biomarkers have been elusive presumably since ASD is a heterogeneous developmental disorder with thousands of speculated risk genes and potential non-genetic immune factors. We hypothesize that pathway-based transcriptomic biomarkers may be informative, as shown by our recent proof- of-concept study in which leukocyte-based gene expression provided an early diagnostic ASD classifier. Our findings are reasonable since many high confidence ASD genes (e.g., transcription factors, signaling genes, etc.) and networks are as strongly expressed in leukocytes as in brain. Furthermore, hypothesized immune disruptions in ASD should also be reflected in leukocytes, especially since microglia are a type of leukocyte that are established as a brain molecular and cellular pathology in ASD. In our proposed study, we will use 1,500 RNA-Seq datasets from 1,000 ASD and typically and atypically developing toddlers to identify biomolecular pathway biomarkers for early detection, prognosis, clinical progression and clinical subtyping. We will further study biomarker relationships to ASD gene defects and expression patterns in early neural development. Aim 1 will analyze RNA-Seq data from 1,000 1-2 year olds using data-driven and knowledge-based network approaches to identify early ASD diagnostic biomarkers that distinguish ASD (n=390) at ages 1-2 years from non-ASD (n=610) groups. Diagnostic biomarkers will include pathways and co-expression networks to address the heterogeneity across ASD subjects. Aim 2 will identify prognostic RNA-Seq expression patterns in the 390 ASD 1-2 year olds by analyzing gene expression levels to reveal pathways that predict good/poor social and language outcome at ages 3-4 years. Aim 2 will also look longitudinally at ASD (n=300) and typically developing (n=200) expression data to identify transcriptomic trajectories that underlie clinical progression from 1-2 years to 3-4 years in these different clinical outcome subgroups. Aim 3 will examine how variation in developmental functional genomic patterns relates to variation in social and language abilities across diagnostic categories (n=1,000) and within ASD (n=390) using dimensionality reduction and feature selecting regression. Multicollinear regressions will be used to combine multivariate trend observations of dimensionality reduction with the predictive power of regressions. Aim 4 will link key transcriptomic effects in Aims 1 to 3 to genetic variants in high-confidence and probable ASD genes that are linked to disrupted cellular pathways in our ASD subjects. Deleterious variants in those genes will be tested in hematopoietic and neural stem cells using CRISPR-Cas9 to introduce loss-of-function mutations in these genes. RNA-Seq will be used to assay the impact on ASD-relevant cellular pathways.

在自闭症中，早期生物标志物很少。迫切需要研究来识别先于症状发作、传达预后信息或指示疾病亚型。我们提出的功能基因组学自闭症谱系障碍 (ASD) 早期发育的研究解决了许多生物标志物目标，并且是自闭症谱系障碍 (ASD) 早期发展的重要早期步骤这个发现过程。可能由于自闭症谱系障碍是一种异质性，因此稳健的生物标志物一直难以捉摸。发育障碍，具有数千个推测的风险基因和潜在的非遗传免疫因素。我们假设基于通路的转录组生物标志物可能提供信息，正如我们最近的证据所示- 概念研究，其中基于白细胞的基因表达提供了早期诊断 ASD 分类器。我们的研究结果是合理的，因为许多高置信度 ASD 基因（例如转录因子、信号基因、等），网络在白细胞中的表达与在大脑中的表达一样强烈。此外，假设免疫 ASD 的破坏也应该反映在白细胞中，特别是因为小胶质细胞是一种白细胞，被确立为 ASD 的脑分子和细胞病理学。在我们提出的研究中，我们将使用 1,500 来自 1,000 个 ASD 以及典型和非典型发育幼儿的 RNA-Seq 数据集，用于识别生物分子用于早期检测、预后、临床进展和临床亚型的途径生物标志物。我们将进一步研究生物标志物与早期神经发育中 ASD 基因缺陷和表达模式的关系。目标1 将使用数据驱动和基于知识的网络分析 1,000 名 1-2 岁儿童的 RNA-Seq 数据识别早期 ASD 诊断生物标志物的方法，这些标志物可将 1-2 岁的 ASD (n=390) 与非 ASD (n=610) 组。诊断生物标志物将包括途径和共表达网络来解决 ASD 受试者之间的异质性。第390章目标2 通过分析基因表达水平来揭示预测 1-2 岁 ASD 儿童良好/不良社交和 3-4岁时的语言成果。目标 2 还将纵向研究 ASD (n=300) 和典型发展 (n=200) 表达数据，用于识别从 1-2 年到临床进展的转录组轨迹这些不同的临床结果亚组需要 3-4 年。目标 3 将研究发育差异如何功能基因组模式与跨诊断类别的社交和语言能力的变化有关 (n=1,000) 和 ASD 内 (n=390) 使用降维和特征选择回归。多重共线回归将用于将降维的多元趋势观察与回归的预测能力。目标 4 将目标 1 至 3 中的关键转录组效应与高可信度和可能的 ASD 基因与我们的 ASD 受试者中破坏的细胞通路有关。这些基因中的有害变异将使用 CRISPR-Cas9 在造血干细胞和神经干细胞中进行测试，以在这些基因中引入功能丧失突变。 RNA-Seq 将用于分析对 ASD 相关的影响细胞途径。