Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease

了解导致阿尔茨海默病神经精神症状的分子机制

• 批准号: 10406707
• 负责人: STEVEN M FINKBEINER
• 金额: $ 25.38万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10406707
• 关键词: Address; Adverse effects; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Bioconductor; Biological; Biology; Caregiver Burden; Cell Nucleus; Cells; Code; Cognitive; Communities; Complex; Computer software; Data; Data Set; Dementia; Development; Disease; Disease Progression; Documentation; Engineering; Environment; Etiology; Gene Expression; Gene Expression Profile; Genes; Genomics; Goals; Human; Institutionalization; Knowledge; Memory; Mind; Mining; Modeling; Molecular; Molecular Disease; Neurodegenerative Disorders; Pathway interactions; Quality of life; Research; Research Design; Resolution; Resources; Software Engineering; Source; Statistical Models; Testing; Time; Translating; Variant; Work; Writing; base; brain tissue; care giving burden; cell type; daily functioning; design; differential expression; epigenome; epigenomics; genomic data; high dimensionality; improved; large datasets; multiple data types; neuropsychiatric symptom; neuropsychiatry; new therapeutic target; novel therapeutics; open source; open source tool; parallel processing; parent grant; phenomics; phenotypic data; prevent; success; transcriptome; transcriptomics

PROJECT SUMMARY Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects 6.2M Americans, yet current therapies are not effective at preventing or slowing the cognitive decline1. Neuropsychiatric symptoms (NPS) are core features of AD and related dementias that are associated with major adverse effects on daily function and quality of life, and accelerate time to institutionalization. The overarching goal of the parent grant R01AG067025 is to integrate single nucleus transcriptome profiles with detailed NPS phenotype data from each donor and identify dysregulated genes associated with disease trajectory, identify clusters of donors with different gene expression disease signatures, and nominate genes and pathways for targeting with novel therapeutics. The compendium of single nucleus transcriptome profiles comprising ~7.2M nuclei from ~1,800 total donors generated by the parent grant R01AG067025 is a remarkable resource. Yet mining these transcriptome profiles to advance knowledge of AD etiology requires analytical workflows that scale to the unprecedented size of these and other emerging data. Existing workflows for multi-donor single cell and nucleus transcriptome data have either been 1) designed for a small number of donors and so cannot take advantage of the large-scale and complex study design used here, or 2) adapted from bulk transcriptome analyses and do not currently scale to hundreds of donors, dozens of cell types and millions of cells. The objective of addressing pressing biological hypotheses about AD biology necessitates the development of analytical workflows designed and engineered with the challenges of multi-donor single cell and nucleus transcriptome data in mind. In this Supplement, we propose developing a scalable, open source analytical workflow for multi-donor single cell/nucleus transcriptome data motivated by our previous work on linear mixed models2,3. We have previously applied linear mixed models to analyze bulk transcriptome profiles, and developed the open source variancePartition package to perform differential expression testing, account for technical batch effects and characterize the multiple biological and technical sources of expression variation. While the current software has facilitated analysis of bulk transcriptomic and epigenomic profiles by our group and many others, applying it to the multi-donor single nucleus data is currently limited by the ad hoc design of the variancePartition codebase. To address these limitations, here we propose (Aim 1) Scaling this analytical workflow to emerging datasets using best practices in software engineering, code refactoring, and empirical testing across multiple computing environments; and (Aim 2) Enabling broader use by (a) computational biologists by developing vignettes to illustrate applications of the software on public datasets, and by (b) open source developers by improving code design and documentation. Overall, reconceiving the analytical workflow of variancePartition will enable the powerful linear mixed model approach to scale to multi-donor single cell and nucleus transcriptome datasets in order to address questions about the etiology of AD and serve as an open source tool for the broader community.

项目总结 阿尔茨海默病(AD)是一种毁灭性的神经退行性疾病，影响着620万美国人，但目前 治疗在预防或减缓认知衰退方面并不有效。神经精神症状(NPS)是 阿尔茨海默病和相关痴呆的核心特征，与日常功能和 提高生活质量，加快实现制度化的时间。家长基金的首要目标R01AG067025 是将单核转录组图谱与来自每个捐赠者的详细的NPS表型数据相结合 识别与疾病轨迹相关的失调基因，识别具有不同基因的捐赠者群 表达疾病特征，并提名新疗法靶向的基因和途径。 ~1800名献血者~720万个核的单核转录组图谱概要 由父母资助产生的R01AG067025是一个非凡的资源。然而，挖掘这些转录组特征 要推进AD病因学的知识，需要能够扩展到前所未有的规模的分析工作流 以及其他新兴数据。多供体单细胞和核转录组数据的现有工作流程有 要么是为少数捐赠者设计的，所以不能利用大规模和 此处使用的复杂研究设计，或2)改编自批量转录组分析，目前未扩展到 数百名捐赠者，数十种细胞类型和数百万个细胞。解决紧迫的生物问题的目标 关于AD生物学的假设需要开发设计和工程设计的分析工作流 考虑到多捐赠者单细胞和核转录组数据的挑战。 在本增刊中，我们建议开发一个可扩展的、开放源码的分析工作流，用于多个捐赠者 细胞/细胞核转录组数据是由我们以前在线性混合模型上所做的工作2、3推动的。 将线性混合模型应用于批量转录组分析，并开发了开放源码 VarancePartition包用于执行差异表达测试，考虑技术批次效应和 描述表达变异的多种生物和技术来源。虽然当前的软件 促进了我们团队和其他许多人对批量转录和表观基因组图谱的分析，应用它 对于多捐赠者，单核数据目前受到varancePartition代码库的特别设计的限制。 为了解决这些限制，我们建议(目标1)将此分析工作流扩展到新兴数据集 在软件工程、代码重构和跨多个计算的经验测试中使用最佳实践 环境；和(目标2)使(A)计算生物学家能够通过开发小场景来更广泛地使用 说明软件在公共数据集上的应用，以及(B)开放源码开发人员通过改进代码 设计和文档。总体而言，协调varancePartition的分析工作流将使 用于多供体单细胞和核转录组数据集的高效线性混合模型方法 以解决有关AD病因的问题，并作为更广泛社区的开放源码工具。