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

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.

Are trait‐associated genes clustered together in a gene network?

“…This result shows that the trait‐associated genes are more likely to form gene clusters, which can be leveraged to prioritize genes that are associated with certain phenotypes. Previous studies have shown that trait‐associated genes obtained using GWAS (with possibly problematic mappings of genetic variants to genes) have been clustered within monoplex and multiplex networks (D'haeseleer et al, 2000; Rosenthal et al, 2022). In this study, we have shown that gene clustering in the network can also be examined and better established in genes associated with complex traits by taking advantage of WES data.…”

The Proteome Profile of Olfactory Ecto-Mesenchymal Stem Cells-Derived from Patients with Familial Alzheimer’s Disease Reveals New Insights for AD Study

“…Some AD investigations have reported proteostasis network changes, with findings similar to ours. Vesicular transport, post-translational protein modifications, trafficking, and proteostasis were among the five classes of functionally related molecular pathways linked to AD that Rosenthal et al [ 178 ] proposed as part of their recent identification of an AD network integrating multi-omics data with the most recent genome-wide association studies (GWAS). Shokhirev and Johnson used machine learning and bioinformatic techniques in a massive multi-omic dataset of 4089 blood and brain human samples from microarray, RNA-Seq, proteomics, and miRNA-accessible data generated from AD and control participants [ 168 ].…”

Alzheimer’s Disease-Related Genes Identified by Linking Spatial Patterns of Pathology and Gene Expression

“…Differentially methylated positions on B3GALT4 are linked to late onset AD and have been associated with memory performance and CSF levels of Aβ and tau ( Madrid et al, 2018 ). CPSF3 is involved in the RNA life cycle and has been identified as part of the molecular interaction network for AD ( Rosenthal et al, 2022 ). COX17 codes for a cytochrome C oxidase copper chaperone involved in copper homeostastis, which has been tentatively linked to AD ( Ejaz et al, 2020 ).…”