Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer’s Disease mouse model and risk loci carriers

Abstract

AbstractAlzheimer’s Disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of important metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these datasets using gene ontology, and transcription factor, pathway and cell-type enrichment analysis. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic dataset derived from cortical tissue of ABCA7-null mice, a mouse model of one of the genes associated with late onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and AD.We found 203 DE transcripts, 164 DE proteins and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetics metabolic pathways were significantly over-represented across the AD multi-omics datasets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modelled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms (SNP)-metabolite associations, of which 70% corresponded to lipid classes.These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.