Chronic social isolation (CSIS) as a mild psychosocial stress has been recognized as a risk factor for developing a major depressive disorder-like phenotype (Brandt et al., 2022; Ge et al., 2017; Santini et al., 2020). From a neurobiological standpoint, depression is a complex and heterogeneous disorder that involves biological, genetic, social, and psychological components. Some individuals are less prone to the debilitating effects of stress, i.e., they are resistant to stress which is commonly believed to be a major cause for developing depression. The resilient phenotype is a dynamic neurobiological process that involves both physiological and psychological adaptations (Wood and Bhatnagar, 2015). For the investigation of the mechanisms involved in stress resilience, animal models are used. Thus, adult male rats exposed to CSIS show behavioral and neuroendocrine phenotypes (CSIS-susceptible rats) similar to depression in humans (Brenes and Fornaguera, 2009; Cacioppo et al., 2015; Filipović et al., 2017; Mumtaz et al., 2018). CSIS in rats induces diminished sensitivity to rewarding stimuli, which mimics anhedonia, a major symptom of depression. CSIS-induced anhedonic-like behavior in rats can be assessed by reduced sucrose preference (Willner et al., 1987). Moreover, CSIS as an animal model of depression fulfills construct, face, and predictive validity (Filipović et al., 2017). However, some rats do not show reduced sucrose preference following CSIS exposure and are designated as CSIS-resilient rats (Filipović et al., 2020). The existence of the resilient phenotype offers the chance to examine the various phenotypes of stress reactivity, and contribute important knowledge regarding stress coping mechanisms towards chronic psychosocial stress.

Evidence from animal models and human studies indicates that molecular alterations within the synapses, especially in the prefrontal cortex (PFC) region, are impaired in depression (Duman et al., 2016; Holmes et al., 2019; McEwen, 2007; Yoshino et al., 2021). Synaptosomes, as a subcellular fraction, are used as a model system for studying the molecular mechanisms of brain synaptic function (Evans, 2015). The study of synaptosomes as a way of investigating synaptic transmission is based on the fact that they contain the complete machinery for neurotransmitter trafficking, which is mediated by synaptic vesicles and storage (Bai and Witzmann, 2007). Moreover, neurotransmitter systems in different brain regions are implicated in social stress sensitivity and the pathophysiology of depression (Sandi and Haller, 2015; Hare and Duman, 2020; Pizzagalli and Roberts, 2022). Its dysfunction has been associated with impairment of cognitive functions and emotional regulation in depressed patients (Palazidou, 2012).

In our previous PFC synaptosome proteomic analysis, we revealed alterations in energy metabolism, mitochondrial transport, oxidative stress, and neurotransmission in CSIS-susceptible rats compared to controls (Filipović et al., 2023b). To explore the molecular mechanisms of resilience to CSIS, comparative non-hypothesis synaptoptroteome analysis of the PFC proteins was performed using liquid chromatography coupled to tandem mass spectrometry. The changes in the proteome profile between CSIS-resilient and CSIS-susceptible and CSIS-resilient and control rats were used to identify biochemical pathways and processes specific for the development of CSIS-resilience, and to identify a panel of potential predictive proteins for the resilient phenotype. The sucrose preference test (SPT) was used to segregate CSIS-resilient from CSIS-susceptible rats. Protein identification and quantification were carried out using liquid chromatography online tandem mass spectrometry followed by label-free (LF) quantification and STRING bioinformatics analysis. The potential predictive proteins for resilience were evaluated using class separation and ML algorithms such as Support Vector Machine (SVM) with greedy forward search and Random Forest (RF). To our knowledge, this is the first report that describes different classification models to identify the most predictive proteins contributing to the discrimination between CSIS-resilient and CSIS-susceptible and CSIS-resilient and controls groups.