Mapping the gene network landscape of Alzheimer's disease through integrating genomics and transcriptomics.
Sara Brin RosenthalHao WangDa ShiCin LiuRuben AbagyanLinda K McEvoyChi-Hua ChenPublished in: PLoS computational biology (2022)
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.
Keyphrases
- single cell
- genome wide identification
- genome wide
- rna seq
- transcription factor
- electronic health record
- genome wide analysis
- big data
- dna methylation
- copy number
- bioinformatics analysis
- cognitive decline
- squamous cell carcinoma
- gene expression
- genome wide association
- inflammatory response
- stem cells
- lymph node
- multiple sclerosis
- resting state
- brain injury
- functional connectivity
- mass spectrometry
- cerebral ischemia