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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

Address Adverse effects Affect Alzheimer&apos s Disease Alzheimer&apos 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

展开

项目摘要

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.

项目摘要阿尔茨海默氏病（AD）是一种毁灭性的神经退行性疾病，影响了620万美国人，但目前疗法无效预防或减慢认知能力下降1。神经精神症状（NP）为 AD和相关痴呆症的核心特征与对日常功能的主要不利影响相关生活质量，并加速时间制度化。父母授予R01AG067025的总体目标是将单个核转录组曲线与每个供体的详细NP表型数据整合在一起鉴定与疾病轨迹相关的基因失调，鉴定具有不同基因的供体的簇表达疾病特征，并提名使用新型治疗剂靶向基因和途径。来自约1,800个供体的单核转录组轮廓的纲要由父授予R01AG067025生成的是一个非凡的资源。但是挖掘这些转录组轮廓为了促进对AD病因的了解，需要分析工作流程，以扩展到这些规模和其他新兴数据。现有的多符谱单单元和核转录组数据的工作流程已有要么是1）专为少数捐助者而设计的，因此无法利用大规模和此处使用的复杂研究设计，或2）根据批量转录组分析进行了改编，目前不扩展到数百个供体，数十种细胞类型和数百万个细胞。解决压力生物学的目的关于AD生物学的假设需要开发设计和设计的分析工作流程考虑到多主单元和核转录组数据的挑战。在此补充中，我们建议为多符合单一的单个单一开发可扩展的开源分析工作流程细胞/核转录组数据由我们先前在线性混合模型上的工作动机2,3。我们以前有应用线性混合模型来分析批量转录组轮廓，并开发了开源方差分配包进行差分表达测试，说明技术批处理效果和表征表达变化的多种生物学和技术来源。当前软件我们小组和其他许多人都促进了对批量转录组和表观基因组概况的分析，并应用了它当前，对于多主核单核数据，可受到方差分配代码库的临时设计的限制。为了解决这些限制，我们在这里提出（AIM 1）将此分析工作流程缩放到新兴数据集在多个计算中使用软件工程，代码重构和经验测试中的最佳实践环境；（目标2）（A）通过开发小插曲来实现（a）计算生物学家的广泛使用通过改进代码来说明该软件在公共数据集中的应用程序，以及（b）开源开发人员设计和文档。总体而言，重新考虑方差分配的分析工作流程将使强大的线性混合模型方法以扩展到多符谱单单元和核转录组数据集为了解决有关AD病因的问题，并作为更广泛社区的开源工具。