Pathway‐based integration of multi‐omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers. Issue 1 (12th December 2022)

• Record Type: Journal Article
• Authors: Garcia‐Segura, Monica Emili; Durainayagam, Brenan R.; Liggi, Sonia; Graça, Gonçalo; Jimenez, Beatriz; Dehghan, Abbas; Tzoulaki, Ioanna; Karaman, Ibrahim; Elliott, Paul; Griffin, Julian L.
• Is Part Of: Journal of neurochemistry. Volume 164:Issue 1(2023)
• Page Start: 57
• Page End: 76
• Publication Date: 2022-12-12
• Subjects: Alzheimer's disease -- ATP‐binding‐cassette subfamily‐a member‐7 gene (ABCA7) -- lipidomics -- metabolome‐wide association study (MWAS) -- multi‐omics -- pathway‐based integration; Neurochemistry -- Periodicals; 616.8042
• DOI: 10.1111/jnc.15719
• Languages: English
• ISSNs: 0022-3042

Abstract

Abstract: Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of 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 data sets using gene ontology, transcription factor, pathway, and cell‐type enrichment analyses. A predicted lipid signature was extracted from genome‐scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA‐7 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 genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS‐derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over‐represented across the AD multi‐omics data sets. 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 modeled 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‐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. Abstract : A genome‐scale model network was built for Alzheimer's disease (AD) using data from publicly available genome‐wide association studies in humans, and transcriptomics and proteomics data from brain tissue from mouse models of AD. The derived network was characterized by gene ontology, transcription factor analysis, and pathway and cell‐type enrichment analysis. Lipid and bioenergetic pathways were over‐represented in the network, with microglia and astrocytes showing over‐enrichment of pathways. These network changes were validated in a mouse model of late‐onset AD (the ABCA7 null mouse) and a human metabolome‐wide association study (MWAS) of SNPs associated with late‐onset AD.