Novel biological insights by utilizing mitochondrial genome information from HuBMAP resources

利用 HuBMAP 资源中的线粒体基因组信息获得新颖的生物学见解

基本信息

批准号：
10530847
负责人：
Liming Pei
金额：
$ 52.8万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10530847
关键词：
ATAC-seq Address Affect Age Aging Area Biochemistry Bioinformatics Biological Biology Cardiovascular Diseases Cell Nucleus Cell physiology Cells Cellular biology DNA Sequence DNA analysis Data Data Set Databases Disease Gender Gene Expression Genetic Genome Genomics Health Human Human BioMolecular Atlas Program Human Biology Human Genome Imaging Techniques Immune Immune System Diseases Knowledge Limes Link Maps Mitochondria Mitochondrial DNA Mitochondrial Diseases Nerve Degeneration Nuclear Organ Physiology RNA Race Research Personnel Resolution Resources Role Spleen Technology Testing Thymus Gland Tissues Variant Visualization software cell type cohort data portal data resource epigenome gene function genomic data human disease improved insight lymph nodes mitochondrial DNA mutation mitochondrial genome multiple omics neglect novel single-cell RNA sequencing tool transcriptome

项目摘要

PROJECT SUMMARY Although typical scRNA-seq or scATAC-seq data contain 5-25% of reads that map to the mitochondrial DNA (mtDNA) genome, such mtDNA mapped reads are often filtered out or ignored during downstream analysis. The mitochondria generate over 90% of the cellular energy and are central to health and disease. mtDNA mutations directly cause mitochondrial disease. In addition, random somatic mtDNA mutations accumulate with age and are associated with a broad range of aging-related diseases such as immune disorders, cardiovascular disease and neurodegeneration. However, little is understood about whether accumulation of specific mtDNA variants during aging occurs at the same rate across different cell types, organs, age, gender and race. Such insights would substantially improve our understanding of how mtDNA mutations contribute to various human diseases. Accordingly, significant gaps of knowledge in the single-cell biology field include the lack of robust mtDNA analysis tools and how to utilize the rich mtDNA information often neglected in the ever-increasing datasets to obtain new biological insights. We propose to address these knowledge gaps by: (1) develop robust mtDNA analysis workflows and tools integrated with the HuBMAP Portal; (2) systemically analyze mtDNA from single- cell datasets generated by HuBMAP and other resources; (3) determine whether and how prevalent human mtDNA variants impact mitochondrial and cellular function.

项目概要尽管典型的 scRNA-seq 或 scATAC-seq 数据包含 5-25% 的读数映射到线粒体 DNA (mtDNA) 基因组，此类 mtDNA 映射读数在下游分析过程中经常被过滤掉或忽略。这线粒体产生 90% 以上的细胞能量，对健康和疾病至关重要。线粒体DNA突变直接引起线粒体疾病。此外，随机体细胞 mtDNA 突变会随着年龄的增长而积累与多种与衰老相关的疾病有关，例如免疫紊乱、心血管疾病和神经退行性变。然而，对于特定 mtDNA 变体的积累是否会产生影响，人们知之甚少。在衰老过程中，不同细胞类型、器官、年龄、性别和种族的衰老速度相同。这样的见解将大大提高我们对 mtDNA 突变如何导致各种人类疾病的理解。因此，单细胞生物学领域的知识存在重大差距，包括缺乏稳健的 mtDNA 分析工具以及如何利用在不断增加的数据集中经常被忽视的丰富线粒体DNA信息来获得新的生物学见解。我们建议通过以下方式解决这些知识差距：(1) 开发强大的 mtDNA 与 HuBMAP 门户集成的分析工作流程和工具； (2) 系统分析单细胞线粒体DNA HuBMAP 和其他资源生成的细胞数据集； (3) 确定人类是否流行以及如何流行 mtDNA 变异影响线粒体和细胞功能。