Department of Molecular Biology and Endocrinology, "VINČA", Institute of Nuclear Sciences - National Institute of thе Republic of Serbia, University of Belgrade, Belgrade, Serbia.
Proteomics and Biomarkers, Max Planck Institute of Psychiatry, Munich, Germany.
J Psychiatr Res. 2024 Apr;172:221-228. doi: 10.1016/j.jpsychires.2024.02.042. Epub 2024 Feb 21.
Chronic social isolation (CSIS) of rats serves as an animal model of depression and generates CSIS-resilient and CSIS-susceptible phenotypes. We aimed to investigate the prefrontal cortical synaptoproteome profile of CSIS-resilient, CSIS-susceptible, and control rats to delineate biochemical pathways and predictive biomarker proteins characteristic for the resilient phenotype. A sucrose preference test was performed to distinguish rat phenotypes. Class separation and machine learning (ML) algorithms support vector machine with greedy forward search and random forest were then used for discriminating CSIS-resilient from CSIS-susceptible and control rats. CSIS-resilient compared to CSIS-susceptible rat proteome analysis revealed, among other proteins, downregulated glycolysis intermediate fructose-bisphosphate aldolase C (Aldoc), and upregulated clathrin heavy chain 1 (Cltc), calcium/calmodulin-dependent protein kinase type II (Cam2a), synaptophysin (Syp) and fatty acid synthase (Fasn) that are involved in neuronal transmission, synaptic vesicular trafficking, and fatty acid synthesis. Comparison of CSIS-resilient and control rats identified downregulated mitochondrial proteins ATP synthase subunit beta (Atp5f1b) and citrate synthase (Cs), and upregulated protein kinase C gamma type (Prkcg), vesicular glutamate transporter 1 (Slc17a7), and synaptic vesicle glycoprotein 2 A (Sv2a) involved in signal transduction and synaptic trafficking. The combined protein differences make the rat groups linearly separable, and 100% validation accuracy is achieved by standard ML models. ML algorithms resulted in four panels of discriminative proteins. Proteomics-data-driven class separation and ML algorithms can provide a platform for accessing predictive features and insight into the molecular mechanisms underlying synaptic neurotransmission involved in stress resilience.
慢性社交隔离（CSIS）大鼠模型可用于研究抑郁症，并产生 CSIS 抗性和 CSIS 敏感表型。我们旨在研究 CSIS 抗性、CSIS 敏感和对照大鼠前额皮质突触蛋白组特征，以描绘出对抗性表型具有特征性的生化途径和预测生物标志物蛋白。通过蔗糖偏好测试来区分大鼠表型。然后使用分类分离和机器学习（ML）算法——支持向量机和随机森林，对 CSIS 抗性与 CSIS 敏感和对照大鼠进行区分。与 CSIS 敏感大鼠相比，CSIS 抗性大鼠的蛋白质组分析显示，糖酵解中间产物果糖二磷酸醛缩酶 C（Aldoc）下调，网格蛋白重链 1（Cltc）、钙/钙调蛋白依赖性蛋白激酶 II（Cam2a）、突触小泡蛋白（Syp）和脂肪酸合酶（Fasn）上调，这些蛋白参与神经元传递、突触小泡运输和脂肪酸合成。CSIS 抗性与对照大鼠的比较鉴定出参与信号转导和突触运输的线粒体蛋白 ATP 合酶亚基β（Atp5f1b）和柠檬酸合酶（Cs）下调，蛋白激酶 C γ型（Prkcg）、囊泡谷氨酸转运体 1（Slc17a7）和突触小泡糖蛋白 2A（Sv2a）上调。这些蛋白差异使大鼠组线性可分，标准 ML 模型的验证准确率达到 100%。ML 算法得到了四个有区别的蛋白面板。蛋白质组学数据驱动的分类分离和 ML 算法可以为获取预测特征和深入了解与压力抗性相关的突触神经传递的分子机制提供平台。
