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

Monica Emili Garcia-Segura, Brenan R Durainayagam, Sonia Liggi, Gonçalo Graça, Beatriz Jimenez, Abbas Dehghan, Ioanna Tzoulaki, Ibrahim Karaman, Paul Elliott, Jules Griffin* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
2 Downloads (Pure)

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 datasets 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 dataset 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 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.

Original languageEnglish
Pages (from-to)57-76
Number of pages20
JournalJournal of Neurochemistry
Volume164
Issue number1
Early online date12 Dec 2022
DOIs
Publication statusPublished - 26 Dec 2022

Bibliographical note

Acknowledgements
The authors would like to acknowledge Dr. Tomonori Aikawa and Professor Takahisa Kanekiyo from the Mayo Clinic, Jacksonville, Florida for providing the ABCA7 cortical mouse tissue.

Funding Information
Alzheimer's Research UK, Grant/Award Number: Imperial College Dementia Research Institute; Alzheimer's Society; Medical Research Council

Data Availability Statement

Data Availability
The manuscript makes secondary use of data already available through public databases. We have listed all the datasets we have accessed allowing others to reproduce our analysis. A preprint of this article has been posted on MedRxiv on May 17, 2021: https://www.medrxiv.org/content/10.1101/2021.05.10.21255052v1.

Supporting Information
Additional supporting information can be found online in the Supporting Information section at the end of this article.

Keywords

  • Alzheimer's disease
  • ATP-binding-cassette subfamily-a member-7 gene (ABCA7)
  • lipidomics
  • metabolome-wide association study (MWAS)
  • multi-omics
  • pathway-based integration

Fingerprint

Dive into the research topics of 'Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer´s Disease mouse model and risk loci carriers'. Together they form a unique fingerprint.

Cite this