Zhang Jing, Zhang Ran, Peng Ying, Aa Jiye, Wang Guangji
Key Lab of Drug Metabolism & Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang 24, Nanjing 210009, China.
Brain Sci. 2023 May 5;13(5):763. doi: 10.3390/brainsci13050763.
(1) Background: there is an urgent clinical need for rapid and effective antidepressants. (2) Methods: We employed proteomics to profile proteins in two animal models ( = 48) of Chronic Unpredictable Stress and Chronic Social Defeat Stress. Additionally, partial least squares projection to latent structure discriminant analysis and machine learning were used to distinguish the models and the healthy control, extract and select protein features and build biomarker panels for the identification of different mouse models of depression. (3) Results: The two depression models were significantly different from the healthy control, and there were common changes in proteins in the depression-related brain regions of the two models; i.e., SRCN1 was down-regulated in the dorsal raphe nucleus in both models of depression. Additionally, SYIM was up-regulated in the medial prefrontal cortex in the two depression models. Bioinformatics analysis suggested that perturbed proteins are involved in energy metabolism, nerve projection, etc. Further examination confirmed that the trends of feature proteins were consistent with mRNA expression levels. (4) Conclusions: To the best of our knowledge, this is the first study to probe new targets of depression in multiple brain regions of two typical models of depression, which could be targets worthy of study.
(1) 背景：临床迫切需要快速有效的抗抑郁药。(2) 方法：我们采用蛋白质组学对慢性不可预测应激和慢性社会挫败应激两种动物模型（n = 48）中的蛋白质进行分析。此外，使用偏最小二乘判别分析和机器学习来区分模型与健康对照，提取和选择蛋白质特征，并构建生物标志物组以识别不同的抑郁症小鼠模型。(3) 结果：两种抑郁症模型与健康对照有显著差异，且两种模型与抑郁症相关脑区的蛋白质存在共同变化；即在两种抑郁症模型中，中缝背核中的SRCN1均下调。此外，在两种抑郁症模型中，内侧前额叶皮质中的SYIM均上调。生物信息学分析表明，受干扰的蛋白质参与能量代谢、神经投射等。进一步检查证实，特征蛋白的趋势与mRNA表达水平一致。(4) 结论：据我们所知，这是首次在两种典型抑郁症模型的多个脑区中探索抑郁症新靶点的研究，这些靶点可能值得研究。
