Aged Brain Metabolomics Study by Metabolic Profiling Analysis of Amino Acids, Organic Acids, and Fatty Acids in Cortex, Cerebellum, Hypothalamus, and Hippocampus of Rats.

ABSTRACT

BACKGROUND: Aging is a progressive process characterized by weakness in brain function. Although metabolomics studies on the brain related with aging have been conducted, it is not yet fully understood. A systematic metabolomics study was performed to search for biomarkers and monitor altered metabolism in various brain tissues of the cortex, cerebellum, hypothalamus, and hippocampus of young (8 months old) and old rats (22 months old). METHODS: Simultaneous profiling analysis of amino acids (AAs), organic acids (OAs), and fatty acids (FAs) in the brain tissues of young and old rats were performed by gas chromatography-tandem mass spectrometry. RESULTS: Under optimal conditions, AA, OA, and FA profiling methods showed good linearity (r ≥0.995) with limit of detection of ≤30 and 73.2 ng and limit of quantification of ≤90.1 and 219.5 ng, respectively. Repeatability varied from 0.4 to 10.4 and 0.8 to 14.8% relative standard deviation and accuracy varied from -11.3 to 10.3 and -12.8 to 14.1% relative error, respectively. In the profiling analysis, total 32, 43, 45, and 30 metabolites were determined in cortex, cerebellum, hypothalamus, and hippocampus, respectively. In statistical analysis, eight AAs (alanine, valine, leucine, isoleucine, threonine, serine, proline, and phenylalanine) in the cortex and four metabolites (alanine, phenylalanine, 3-hydoxypropionic acid, and eicosadienoic acid) in the cerebellum were significantly evaluated (Q-value <0.05, variable importance in projection scores ≥1.0). In all brain tissues, the score plots of orthogonal partial least square discriminant analysis were clearly separated between the young and old groups. CONCLUSIONS: Metabolomics results indicate that mechanistic targets of rapamycin complex 1, branched chain-amino acid, and energy metabolism are related to inflammation and mitochondrial dysfunction in the brain during aging. Thus, these results may explain the characteristic metabolism of brain aging.