Untargeted metabolomics of 3xTg-AD neurotoxic astrocytes

Alzheimer's disease (AD) is the most common form of dementia, affecting approximately 47 M people worldwide. Histological features and genetic risk factors, among other evidence, supported the amyloid hypothesis of the disease. This neuronocentric paradigm is currently undergoing a shift, considering evidence of the role of other cell types, such as microglia and astrocytes, in disease progression. Previously, we described a particular astrocyte subtype obtained from the 3xTg-AD murine model that displays neurotoxic properties in vitro. We continue here our exploratory analysis through the lens of metabolomics to identify potentially altered metabolites and biological pathways.

Cell extracts from neurotoxic and control astrocytes were compared using high-resolution mass spectrometry-based metabolomics. Around 12 % of metabolic features demonstrated significant differences between neurotoxic and control astrocytes, including alterations in the key metabolite glutamate. Consistent with our previous transcriptomic study, the present results illustrate many homeostatic and regulatory functions of metabolites, suggesting that neurotoxic 3xTg-AD astrocytes exhibit alterations in the Krebs cycle as well as the prostaglandin pathway.

This is the first metabolomic study performed in 3xTg-AD neurotoxic astrocytes. These results provide insight into metabolic alterations potentially associated with neurotoxicity and pathology progression in the 3xTg-AD mouse model and strengthen the therapeutic potential of astrocytes in AD.

Biological significance

Our study is the first high-resolution metabolomic characterization of the novel neurotoxic 3xTg-AD astrocytes. We propose key metabolites and pathway alterations, as well as possible associations with gene expression alterations in the model. Our results are in line with recent hypotheses beyond the amyloid cascade, considering the involvement of several stress response cascades during the development of Alzheimer's disease. This work could inspire other researchers to initiate similar studies in related models. Furthermore, this work illustrates a powerful workflow for metabolite annotation and selection that can be implemented in other studies.