Filipović Dragana, Inderhees Julica, Korda Alexandra, Tadić Predrag, Schwaninger Markus, Inta Dragoš, Borgwardt Stefan
Department of Molecular Biology and Endocrinology, "VINČA", Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia.
Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany.
Metabolites. 2024 Jul 25;14(8):405. doi: 10.3390/metabo14080405.
Metabolic perturbation has been associated with depression. An untargeted metabolomics approach using liquid chromatography-high resolution mass spectrometry was employed to detect and measure the rat serum metabolic changes following chronic social isolation (CSIS), an animal model of depression, and effective antidepressant fluoxetine (Flx) treatment. Univariate and multivariate statistics were used for metabolic data analysis and differentially expressed metabolites (DEMs) determination. Potential markers and predictive metabolites of CSIS-induced depressive-like behavior and Flx efficacy in CSIS were evaluated by the receiver operating characteristic (ROC) curve, and machine learning (ML) algorithms, such as support vector machine with linear kernel (SVM-LK) and random forest (RF). Upregulated choline following CSIS may represent a potential marker of depressive-like behavior. Succinate, stachydrine, guanidinoacetate, kynurenic acid, and 7-methylguanine were revealed as potential markers of effective Flx treatment in CSIS rats. RF yielded better accuracy than SVM-LK (98.50% vs. 85.70%, respectively) in predicting Flx efficacy in CSIS vs. CSIS, however, it performed almost identically in classifying CSIS vs. control (75.83% and 75%, respectively). Obtained DEMs combined with ROC curve and ML algorithms provide a research strategy for assessing potential markers or predictive metabolites for the designation or classification of stress-induced depressive phenotype and mode of drug action.
代谢紊乱与抑郁症有关。采用液相色谱-高分辨率质谱的非靶向代谢组学方法，来检测和测量慢性社会隔离（CSIS，一种抑郁症动物模型）以及有效抗抑郁药氟西汀（Flx）治疗后大鼠血清的代谢变化。单变量和多变量统计用于代谢数据分析和差异表达代谢物（DEM）的确定。通过受试者工作特征（ROC）曲线以及机器学习（ML）算法，如线性核支持向量机（SVM-LK）和随机森林（RF），评估CSIS诱导的抑郁样行为和Flx在CSIS中的疗效的潜在标志物和预测性代谢物。CSIS后胆碱上调可能代表抑郁样行为的潜在标志物。琥珀酸、水苏碱、胍基乙酸、犬尿烯酸和7-甲基鸟嘌呤被揭示为CSIS大鼠中Flx有效治疗的潜在标志物。在预测CSIS与对照中Flx的疗效时，RF的准确率（分别为98.50%和85.70%）优于SVM-LK，然而，在区分CSIS与对照时，二者表现几乎相同（分别为75.83%和75%）。获得的DEM与ROC曲线和ML算法相结合，为评估应激诱导的抑郁表型和药物作用模式的指定或分类的潜在标志物或预测性代谢物提供了一种研究策略。
