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通过整合基因组学和转录组学绘制阿尔茨海默病的基因网络图谱。

Mapping the gene network landscape of Alzheimer's disease through integrating genomics and transcriptomics.

机构信息

Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, California, United States of America.

Center for Multimodal Imaging and Genetics, Department of Radiology, University of California San Diego, La Jolla, California, United States of America.

出版信息

PLoS Comput Biol. 2022 Feb 25;18(2):e1009903. doi: 10.1371/journal.pcbi.1009903. eCollection 2022 Feb.

Abstract

Integration of multi-omics data with molecular interaction networks enables elucidation of the pathophysiology of Alzheimer's disease (AD). Using the latest genome-wide association studies (GWAS) including proxy cases and the STRING interactome, we identified an AD network of 142 risk genes and 646 network-proximal genes, many of which were linked to synaptic functions annotated by mouse knockout data. The proximal genes were confirmed to be enriched in a replication GWAS of autopsy-documented cases. By integrating the AD gene network with transcriptomic data of AD and healthy temporal cortices, we identified 17 gene clusters of pathways, such as up-regulated complement activation and lipid metabolism, down-regulated cholinergic activity, and dysregulated RNA metabolism and proteostasis. The relationships among these pathways were further organized by a hierarchy of the AD network pinpointing major parent nodes in graph structure including endocytosis and immune reaction. Control analyses were performed using transcriptomics from cerebellum and a brain-specific interactome. Further integration with cell-specific RNA sequencing data demonstrated genes in our clusters of immunoregulation and complement activation were highly expressed in microglia.

摘要

多组学数据与分子相互作用网络的整合能够阐明阿尔茨海默病(AD)的病理生理学。利用最新的全基因组关联研究(GWAS),包括代理病例和 STRING 相互作用网络,我们确定了一个由 142 个风险基因和 646 个网络近端基因组成的 AD 网络,其中许多基因与通过小鼠敲除数据注释的突触功能有关。近端基因被证实富集在尸检记录病例的复制 GWAS 中。通过将 AD 基因网络与 AD 和健康颞叶皮层的转录组数据整合,我们鉴定了 17 个基因簇的途径,如上调的补体激活和脂质代谢,下调的胆碱能活性,以及失调的 RNA 代谢和蛋白质稳态。通过 AD 网络的层次结构进一步组织这些途径之间的关系,突出了包括内吞作用和免疫反应在内的图结构中的主要父节点。使用小脑的转录组和大脑特异性相互作用网络进行了对照分析。进一步与细胞特异性 RNA 测序数据的整合表明,我们免疫调节和补体激活簇中的基因在小胶质细胞中高度表达。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f2b/8906581/b3b55a4ac4d8/pcbi.1009903.g001.jpg

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