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

Autor: Filipović D; Department of Molecular Biology and Endocrinology, 'VINČA', Institute of Nuclear Sciences - National Institute of thе Republic of Serbia, University of Belgrade, Belgrade, Serbia. Electronic address: dragana@vinca.rs., Novak B; Proteomics and Biomarkers, Max Planck Institute of Psychiatry, Munich, Germany. Electronic address: novak@psych.mpg.de., Xiao J; Proteomics and Biomarkers, Max Planck Institute of Psychiatry, Munich, Germany. Electronic address: jxiao@biochem.mpg.de., Tadić P; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. Electronic address: ptadic@etf.bg.ac.rs., Turck CW; Proteomics and Biomarkers, Max Planck Institute of Psychiatry, Munich, Germany; Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China; National Resource Center for Non-human Primates, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China. Electronic address: turck@psych.mpg.de.
Jazyk: angličtina
Zdroj: Journal of psychiatric research [J Psychiatr Res] 2024 Apr; Vol. 172, pp. 221-228. Date of Electronic Publication: 2024 Feb 21.
DOI: 10.1016/j.jpsychires.2024.02.042
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest in relation to this manuscript.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: