采用液相色谱-质谱联用技术对老年小鼠的血清代谢组学进行分析。
Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.
机构信息
Department of Endocrinology, Institute of Endocrine and Metabolic Diseases, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Clinical Research Hospital of Chinese Academy of Sciences (Hefei), University of Science and Technology of China, Hefei 230001, China.
出版信息
Biomolecules. 2022 Oct 29;12(11):1594. doi: 10.3390/biom12111594.
BACKGROUND
The process of aging and metabolism are intricately linked, thus rendering the identification of reliable biomarkers related to metabolism crucial for delaying the aging process. However, research of reliable markers that reflect aging profiles based on machine learning is scarce.
METHODS
Serum samples were obtained from aged mice (18-month-old) and young mice (3-month-old). LC-MS was used to perform a comprehensive analysis of the serum metabolome and machine learning was used to screen potential aging-related biomarkers.
RESULTS
In total, aging mice were characterized by 54 different metabolites when compared to control mice with criteria: VIP ≥ 1, -value < 0.05, and Fold-Change ≥ 1.2 or ≤0.83. These metabolites were mostly involved in fatty acid biosynthesis, cysteine and methionine metabolism, D-glutamine and D-glutamate metabolism, and the citrate cycle (TCA cycle). We merged the comprehensive analysis and four algorithms (LR, GNB, SVM, and RF) to screen aging-related biomarkers, leading to the recognition of oleic acid. In addition, five metabolites were identified as novel aging-related indicators, including oleic acid, citric acid, D-glutamine, trypophol, and L-methionine.
CONCLUSIONS
Changes in the metabolism of fatty acids and conjugates, organic acids, and amino acids were identified as metabolic dysregulation related to aging. This study revealed the metabolic profile of aging and provided insights into novel potential therapeutic targets for delaying the effects of aging.
背景
衰老和代谢过程是错综复杂地联系在一起的，因此，识别与代谢相关的可靠生物标志物对于延缓衰老过程至关重要。然而，基于机器学习的研究可靠的标志物来反映衰老特征的研究还很少。
方法
从老年小鼠（18 个月大）和年轻小鼠（3 个月大）中获取血清样本。使用 LC-MS 对血清代谢组进行全面分析，并使用机器学习筛选潜在的与衰老相关的生物标志物。
结果
与对照小鼠相比，衰老小鼠的特征是有 54 种不同的代谢物，标准为：VIP≥1，-值<0.05，倍数变化≥1.2 或≤0.83。这些代谢物主要涉及脂肪酸的生物合成、半胱氨酸和蛋氨酸代谢、D-谷氨酰胺和 D-谷氨酸代谢以及柠檬酸循环（TCA 循环）。我们将综合分析和四种算法（LR、GNB、SVM 和 RF）合并起来筛选与衰老相关的生物标志物，从而识别出油酸。此外，还确定了 5 种代谢物为新的与衰老相关的指标，包括油酸、柠檬酸、D-谷氨酰胺、色氨酸和 L-蛋氨酸。
结论
脂肪酸和缀合物、有机酸和氨基酸代谢的变化被确定为与衰老相关的代谢失调。本研究揭示了衰老的代谢特征，并为延缓衰老影响的新的潜在治疗靶点提供了思路。
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