Metabolic Profiling of Brain Tissue and Brain-Derived Extracellular Vesicles in Alzheimer's Disease

Abstract

Alzheimer´s disease (AD) is the most frequent neurodegenerative disorder in the world and is characterised by the loss of memory and other cognitive functions. Metabolic changes associated with AD are important players in the development of the disease. However, the mechanism underlying these changes is still unknown. Extracellular vesicles (EVs) are nano-sized particles that play an important role in regulating pathophysiological processes and are a non-invasive manner to obtain information of the cell that is secreting them. The analysis of brain-derived EVs (bdEVs) will provide new insights in the metabolic processes associated with AD. To characterize bdEVs in AD, we optimised a method to isolate them from tissue of different brain regions, obtaining the highest enrichment in isolations from the temporal cortex. We performed unbiased untargeted metabolomics analysis on post-mortem human temporal cortex tissue and bdEVs from the same region of AD patients and healthy controls. Both, univariate and multivariate statistical analysis were used to determine the metabolites that influence the separation between AD patients and controls. Interestingly, a clear separation between control and AD groups was obtained with bdEVs, which allowed to select 12 relevant features by a validated PLS-DA model. Furthermore, comparison of tissue and bdEVs identified 68 common features. The pathway enrichment analysis of the common metabolites showed that the alanine, aspartate and glutamate pathway and the arginine, phenylalanine, tyrosine pathway were the most significant ones in the separation between the AD patients and controls. The phenylalanine, tyrosine and tryptophan pathway, still had a very high influence in the separation between groups, albeit not significant. Notably, some metabolites were identified for the first time in bdEVs. For example, the N-acetyl aspartic acid (NAA) metabolite present in bdEVs was suitable to differentiate AD patients from healthy controls. Furthermore, the analysis of the hippocampus, midbrain, temporal and entorhinal cortex and their respective bdEVs indicated that the metabolic profiles of different brain areas were distinct and showed some correlation between the metabolome of the tissue and its respective bdEVs. Thus, our study highlights the potential of bdEVs to understand the metabolic fingerprint associated with AD and their potential use as diagnostic and therapeutic targets.