Altered sphingolipid function in Alzheimer's disease; a gene regulatory network approach

C. Giovagnoni*, M. Ali, L.M.T. Eijssen, R. Maes, K. Choe, M. Mulder, J. Kleinjans, A. del Sol, E. Glaab, D. Mastroeni, E. Delvaux, P. Coleman, M. Losen, E. Pishva, P. Martinez-Martinez, D.L.A. van den Hove

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Sphingolipids (SLs) are bioactive lipids involved in various important physiological functions. The SL pathway has been shown to be affected in several brain-related disorders, including Alzheimer's disease (AD). Recent evidence suggests that epigenetic dysregulation plays an important role in the pathogenesis of AD as well. Here, we use an integrative approach to better understand the relationship between epigenetic and transcriptomic processes in regulating SL function in the middle temporal gyrus of AD patients. Transcriptomic analysis of 252 SL-related genes, selected based on GO term annotations, from 46 AD patients and 32 healthy age-matched controls, revealed 103 differentially expressed SL-related genes in AD patients. Additionally, methylomic analysis of the same subjects revealed parallel hydroxymethylation changes in PTGIS, GBA, and ITGB2 in AD.Subsequent gene regulatory network-based analysis identified 3 candidate genes, that is, SELPLG, SPHK1 and CAV1 whose alteration holds the potential to revert the gene expression program from a diseased towards a healthy state. Together, this epigenomic and transcriptomic approach highlights the importance of SL-related genes in AD, and may provide novel biomarkers and therapeutic alternatives to traditionally investigated biological pathways in AD.(c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( )
Original languageEnglish
Pages (from-to)178-187
Number of pages10
JournalNeurobiology of Aging
Publication statusPublished - 1 Jun 2021


  • Sphingolipids
  • Alzheimer's disease
  • Epigenetics
  • Gene regulatory network
  • Disease network analysis
  • RISK


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