Prefrontal cortical synaptoproteome profile combined with machine learning predicts resilience towards chronic social isolation in rats

Abstract

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.