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

Author summaryAlzheimer's disease (AD) is recognized as the leading primary cause of dementia, resulting in a high socioeconomic burden. Understanding the disease pathogenesis serves as the cornerstone of exploring potential drug targets, therapeutic strategies and clinical intervention. As a complex disease, the development of AD involves pathological changes in multiple biological processes, and is impacted significantly by genetic factors. Through integration of the available genomic, protein-protein interactions (interactomic) and transcriptomic data, we identified a disease gene network that includes a total of 788 genes, and annotated 17 major gene clusters which encompassed the main categories of biological pathways with reported alterations in AD. The results revealed a landscape of AD etiology, with major pathological changes that extend from gene transcription and RNA metabolism, proteostasis, lipid metabolism, immune reactions to synaptic dysfunction. The systems-level approach of the present study can also be applied to other complex diseases with a significant genetic component. 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.