Impairment in cholesterol metabolism is associated with many neurodegenerative disorders including Alzheimer's disease (AD). However, the lipid alterations underlying neurodegeneration and the connection between altered cholesterol levels and AD remains not fully understood. We recently showed that cholesterol accumulation in hippocampal neurons, induced by silencing Cyp46a1 gene expression, leads to neurodegeneration with a progressive neuronal loss associated with AD-like phenotype in wild-type mice. We used a targeted and non-targeted lipidomics approach by liquid chromatography coupled to high-resolution mass spectrometry to further characterize lipid modifications associated to neurodegeneration and cholesterol accumulation induced by CYP46A1 inhibition. Hippocampus lipidome of normal mice was profiled four weeks after cholesterol accumulation due to Cyp46a1 gene expression down- regulation at the onset of neurodegeneration. We showed that major membrane lipids, sphingolipids and specific enzymes involved in phosphatidylcholine and sphingolipid metabolism, were rapidly increased in the hippocampus of AAV-shCYP46A1 injected mice. This lipid accumulation was associated with alterations in the lysosomal cargoe, accumulation of phagolysosomes and impairment of endosome-lysosome trafficking. Altogether, we demonstrated that inhibition of cholesterol 24-hydroxylase, key enzyme of cholesterol metabolism leads to a complex dysregulation of lipid homeostasis. Our results contribute to dissect the potential role of lipids in severe neurodegenerative diseases like AD.

Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (t_R) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental t_R values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to t_R prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.