Liver proteome alterations in psychologically distressed rats and a nootropic drug

Animals and experimental design

Twenty-eight healthy adult female Sprague-Dawley rats (Rattus novergicus) of 10–12 weeks of age, weighing 170 g to 270 g, were used. The animals were maintained in a controlled environment in light-dark cycle of 12/12 h, under room temperature conditions of 24 ± 2 °C, with food and water ad libitum. The animals were kept in pairs in standard plastic cages with no enriched environment. Five days before the experiment were housed individually to minimize the stress by separation. Rats were purchased in Rismart, S.A. de C.V., México.

The experimental design was carried out for six days. The induction of distress was performed in the first five days, and the rats were sacrificed on the sixth day. Each day, before distress exposure, rats belonging to the treatment group received piracetam and rats belonging to the control group received the same amount of water instead of the drug (Fig. S1A). The rats were randomly allocated by drawing lots into four experimental groups (n = 6–7 for each group): rats neither exposed to stressor nor with piracetam treatment (S−P−), rats exposed to stressor without piracetam treatment (S+P−), rats not exposed to stressor but treated with piracetam (S−P+), and rats exposed to stressor and treated with piracetam (S+P+) (Fig. S1C). No animal exclusions during the experiments were made in this study.

Administration of piracetam and distress induction

Piracetam (Nootropil^® (1 g/5 mL), UCB Pharma Belgium) was administered as a single oral dose of 600 mg/kg in a final volume of 600 µL using a cannula, for six days, always at the same time (7:30 am) (Fig. S1A).

Every day, the induction of distress was carried out after the administration of piracetam from 8:00 am to 9:00 am. For it, a special box (60 × 27 × 35 cm) made of plastic was used, with the third part of black walls (the part of the box where the animals can hide, the black box) and two parts with transparent walls, separated by a black room divider with a small entrance through which the animal can pass (Dielenberg & McGregor, 2001) (Fig. S1B). Rats were left inside the boxes for 20 min to habituate to the new habitat. After this time, the animals were returned to their housing cage for 20 min to relax. Meanwhile, the box was conditioned by placing a 20 × 20 cm white cloth in the transparent part: a clean cloth was placed for the control group while, for the distressed group, the piece of cloth was previously placed in contact with a domestic cat for several days (as a cat rug). Next, rats were transferred to the experimental boxes for another 20 min. Each piece of cloth was stored in hermetic plastic bags and stored at −20 °C until its next use. The rats were videotaped for further behavior analysis using JWatcher™ v0.9 software, and the obtained data were expressed as the full time spent in each specific position each day (Grigoruţă et al., 2018).

All experiments were approved by the Institutional Committee of Ethics and Bioethics from the Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Chihuahua, México (No.CIBE-2017-1-34), following the Official Mexican Norm (NOM-062-ZOO-1999) and the ARRIVE guidelines 2.0 (Du Sert et al., 2020). No humane endpoints were needed in this study.

Sample collection

On the sixth day, animals were intraperitoneally anesthetized using sodium pentobarbital (45 mg/kg). After the loss of sensitivity and motor reflexes, whole blood samples were collected by cardiac puncture using vacutainer EDTA tubes for plasma biochemical parameter analysis. Further, rats were decapitated and, finally, the liver was extracted and weighted. One gram of liver was aliquoted for enzymatic activity assays and the rest of it was stored at −80 °C for proteomic analysis (Fig. S1C).

Blood plasma biochemical parameter analysis

The whole blood was centrifugated at 1,500 × g for 10 min. Then, the fresh plasma was collected and measured using a Cobas^® c111 analyzer (Roche) for each biochemical parameter with the reagents provided by the manufacturer, following the manufacturer’s specifications (Fig. S1C).

Liver enzyme activity assays

Fresh liver aliquots (1 g) were homogenized (weight/volume 1:10) in cold 50 mM phosphate buffer (K₂HPO₄, KH₂PO₄, pH 7.4), with 1% phenylmethylsulfonyl fluoride (PMSF). After homogenization, the samples were sonicated three times for 30 s, with 1 min pause between each round, over the ice. Further, these samples were separated into aliquots for the different enzymatic activity methodology (n = 3–5). For catalase (CAT) activity analysis, the samples were centrifugated at 52 ×  g for 20 min at 4 °C. For the study of the enzymatic activity of superoxide dismutase (SOD), glutathione-S transferase (GST), glutathione peroxidase (GPX), and glutathione reductase (GR), the samples were centrifugated at 20,800 ×  g for 30 min at 4 °C, as previously described (Mejia-Carmona et al., 2014; Mejia-Carmona et al., 2015). In both cases, the supernatants were stored at −80 °C till use. Enzyme activity was measured by spectrophotometry using a microplate reader FLUOstar Omega (BMG) for 96-wells plates (Fig. S1C).

CAT activity was determined according to the method of Aebi (1984) with small modifications. As an oxidant medium was used 14 mM H₂O₂ (Sigma-Aldrich, H1009) in 0.1 M phosphate buffer (K₂HPO₄, KH₂PO₄, pH 7.4), and the sample was added in a final volume of 220 µL per well. Kinetic measurements were obtained at 240 nm (Mejia-Carmona et al., 2014). One unit of the enzyme represents the quantity of CAT used to neutralize one µM of peroxide per min.

SOD activity was determined by autooxidation of pyrogallol, a method developed by Marklund and Marklund (Marklund & Marklund, 1974). This method is based on the ability of pyrogallol for autooxidation, capturing the superoxide radicals at pH 8.2 and developing a yellow color that can be detected spectrophotometrically. SOD eliminates the radicals from de media and inhibits the pyrogallol oxidation. Total SOD and mitochondrial SOD (SOD2) activity were measured in 50 mM Tris–HCl, pH 8.2, containing 1 mM dietilentriaminopentaacetic acid (DTPA) as an oxidant medium. For the SOD2 activity, 110 mM sodium cyanide (NaCN) solution was also added to inhibit the specific activity of cytosolic SOD (SOD1). Finally, pyrogallol (Sigma 254002) solution was automatically injected by the microplate reader reaching a final concentration of 4 mM in a total volume of 300 µL per well, and kinetic measurements were obtained at 450 nm, as previously described (Mejia-Carmona et al., 2015). One unit of SOD was considered as the concentration of enzyme used to inhibit 50% of pyrogallol autooxidation. Specific enzyme activity of SOD1 was calculated as the difference between the enzyme activity of total SOD and SOD2.

The GST activity was determined according to the method of Habig, Pabst & Jakoby (1974) with some modifications. This method is based on the capacity of glutathione to combine with 1-chloro-2,4-dinitrobenzene (CDNB) to form the complex glutathione-CDNB. A 0.1 M Phosphate buffer (K₂HPO₄, KH₂PO₄, pH 6.5) containing 2.25 mM CDNB (Sigma, 237329) and 2.25 mM reduced glutathione (GSH, Sigma-Aldrich, G4251) was used as the substrate solution. Further, the sample was added in a final volume of 225 µL in each well. Kinetic measurements were obtained at 340 nm, as previously described (Mejia-Carmona et al., 2015). One unit of the enzyme activity was considered as the concentration of the GST necessary to obtain one µM of the complex glutathione-CDNB per min.

GPX activity was determined according to Weiss, Maker & Lehrer (1980) with some modifications. This method is based on the measurement of the disappearance of reduced nicotinamide adenine dinucleotide phosphate (NADPH). Fresh substrate solution was prepared in 0.1 M phosphate buffer (K₂HPO₄, KH₂PO₄, pH 7.4) containing 2 mM EDTA, 1.4 U glutathione reductase (Roche, 10105678001), 1 mM GSH, 0.2 mM NADPH, 0.5 mM H₂O₂ (Sigma-Aldrich, H1009), and 1 mM NaCN. The substrate solution and the sample were added to each well in a final volume of 300 µL. Kinetic measurements were obtained at 340 nm. One unit of enzyme activity represents the concentration of GPX that oxidizes one µM of NADPH per min.

The method used for the study of GR activity is based on the measurement of the reduction of NADPH, as previously published by Weiss, Maker & Lehrer (1980) with few modifications. The substrate solution was prepared from 20 mM oxidized glutathione (GSSG) (Abcam, ab141393) and 2 mM NADPH (Sigma-Aldrich, N5130) diluted in 0.1 M phosphate buffer (K₂HPO₄, KH₂PO₄, pH 7.4) with 2 mM EDTA. The substrate solution and the sample were added to each well in a final volume of 300 µL. Kinetic measurements were obtained at 340 nm. One unit of enzyme activity represents the concentration of GR that oxidizes one µM of NADPH per min.

Liver protein extraction

The remaining liver was lyophilized and grounded using a mortar and a pestle. For protein extraction, a TCA/acetone-based method was used. Briefly, 600 µL of cold precipitation solution (10% (w/v) TCA; 100% (v/v) acetone, 0.07% (w/v) DTT) was added on 40 mg of pulverized liver. Samples were sonicated 6 × 10 s at 30 W (6.9 kHz; Sonic Dismembrator, Model 100, Fischer Scientific) and let to precipitate at −20 °C overnight. Subsequently, the samples were centrifuged at 18,500  × g for 10 min at 4 °C and the supernatant was decanted. Then, pellets were twice washed using one mL of cold washing solution (80% (v/v) acetone, 0.07% (w/v) DTT) (González-Fernández et al., 2014). Finally, protein pellets were dried at room temperature to eliminate acetone residue. Proteins were solubilized using 100 µL of a solution containing 7 M urea, 2 M thiourea, 4% (w/v) CHAPS, 0.5% (w/v) Triton X-100, 20 mM DTT, 1 mM PMSF. Proteins were quantified using the Bradford assay (Bradford Reagent, B6916, Sigma-Aldrich^®), according the manufacturer’s procedure. For the protein identification, 50 µg of total proteins extracted from each liver were pooled (n = 6 − 7) for each experimental group (Fig. S1C).

GeLC-MS/MS analysis

The protein identification was performed using one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis (1D-SDS-PAGE) coupled by liquid chromatography-tandem mass spectrometry (GeLC-MS/MS) analysis (Fig. S1C). A total of 100 µg of pooled proteins from each experimental group was pre-separated by 1D-SDS-PAGE, gels were stained by the Coomassie method, and finally, each gel band was dissected manually in three equal parts. In-gel protein digestion and nano LC-MS/MS were performed according to the methodology previously described (Espinosa-Gómez et al., 2020) (for the complete description of the methodology carried out in this analysis, see Methodology S1). Briefly, firstly, each gel section was distained, and then dehydrated. Finally, proteins in gel sections were reduced with DTT and alkylated with iodoacetamide. For in-gel digestion, the gels sections were incubated in a solution containing 12.5 ng/µL mass spectrometry grade Trypsin Gold (Promega, Madison, WI, USA) in 5 mM NH₄HCO₃, at 37 °C overnight. Peptide samples were analyzed using an UltiMate 3000 RSnanoLC system (Thermo-Fisher Scientific, San Jose, CA) interfaced with Orbitrap Fusion™ Tribid™ (Thermo-Fisher Scientific, San Jose, CA) mass spectrometer. All MS data were obtained through Xcalibur 4.0.27.10 software (Thermo-Fisher Scientific) (Espinosa-Gómez et al., 2020). Each sample of pooled proteins for each experimental group was run on one time.

Proteomics data analysis and biological interpretation

Mass spectra were analyzed with Proteome Discoverer 2.1 software (PD, Thermo Fisher Scientific Inc.). The later searches were performed through Mascot server (version 2.4.1, Matrix Science, Boston, MA), SEQUEST HT (Eng, McCormack & Yates, 1994), and AMANDA (Dorfer et al., 2014). For protein identification, 25, 3.7, and 200 scores were considered, respectively, for each search engine. The search with each engine was conducted against the UniProt rat reference proteome (R. novergicus) database (https://www.uniprot.org/proteomes/UP000002494) (for the complete description of the protein search parameters in this analysis, see Methodology S1). The exponentially modified protein abundance index (emPAI) was calculated for the label-free relative quantitation of the proteins in each sample analyzed by nanoLC-MS/MS (Shinoda, Tomita & Ishihama, 2009). Only proteins identified with at least two unique peptides and a 1.5-fold difference in their relative abundance (0.66 ≤ fold change ≥ 1.50) were considered for further analysis.

Gene Ontology (GO) analysis and protein network were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (https://string-db.org/cgi/input?sessionId=b8yhKk9BbQSn&input_page_show_search=on) (Szklarczyk et al., 2019). Enrichment pathway analysis was carried out using the Gene Annotation & Analysis Resource Metascape (http://metascape.org/gp/index.html#/main/step1) (Zhou et al., 2019). The enrichment cluster analysis was made using the following settings: p-value < 0.01, minimum count of 3, enrichment factor > 1.5, and FDR > 0.05.

Statistical analysis

The normality of the animal behavior, the antioxidant enzyme activity, and the biochemical plasma data were tested using the Kolmogorov-Smirnoff (KS) test. Furthermore, for the descriptive data, one-way ANOVA and Pearson’s correlation coefficient (rho) were determined, considering a statistically significant of p ≤ 0.05. The statistical analysis was performed using SPSS v.8.0 software (SPSS Inc. Chicago, IL, USA).

Protein identification by GeLC-MS/MS approach

To evaluate the effect of the psychological distress and piracetam on female rat liver proteome, proteins were extracted using a TCA-acetone-based method. Protein samples from each liver were pooled (n = 6 − 7) and pools from each experimental condition were subjected to a pre-separation by 1D SDS-PAGE. Then, proteins were in-gel digested by trypsin, extracted from gel pieces and analyzed by a shotgun proteomic approach. Each experimental group was analyzed independently by label-free relative quantitation using the emPAI values. A total of 1,302 proteins were identified with at least one unique peptide and 1% FDR (Data S1). Of these, 894 proteins with at least two unique peptides were considered for further analysis. The intersection of proteins identified in all the experimental conditions was shown by a Venn diagram (Fig. 1A). A 1.5-fold change threshold was used as cut-off values for protein abundance changes (50% of value variation). According to this criterion, a total of 350 proteins exhibited qualitative differences between the four conditions: 112 proteins were found unique in one of the four experimental conditions, 103 were presented only in two of the four groups, and 135 in three groups (Fig. 1A). Regarding the quantitative differences, 313 proteins showed a change in their abundance comparing S+P− versus S−P−, 259 proteins in S+P− versus S+P+, and 315 proteins in S−P+ versus S−P− (Data S2).

S−Pntology through STRING search tool annotation was used to determine the subcellular localization of all proteins identified. The most highly represented categories in this study were membrane-associated, mitochondrial, cytosolic, and nuclear proteins, counting more than 100 proteins in each category (Fig. 1B).

Biological interpretation of differentially accumulated proteins

Proteins function was analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation through Metascape online tool (Fig. 2). Liver from rats exposed to distress (S+P−) showed significant enrichment in 16 pathways in which proteins were less abundant, and in 31 pathways in which proteins were more abundant compared to the liver from rats not exposed to distress (S−P−) (Fig. 2A). In general, proteins related to amino acids metabolism, glucose and fatty acid (FA) mobilization and degradation to produce energy (glycolysis/gluconeogenesis, TCA cycle, pyruvate metabolism, FA degradation, PPAR signaling pathway, dicarboxylate metabolism, butanoate metabolism), protein folding, trafficking and degradation, metabolism of xenobiotics by cytochrome P450, GSH metabolism, antioxidant defense mechanisms, and non-alcoholic fatty acid liver or Parkinson’s diseases, among others, were highly affected in distress (S+P−) when compared to S−P− (Fig. 2A, Data S3).

When piracetam was supplied to the distressed group (S+P+), 24 and 19 pathways showed a significant enrichment in which proteins were less and more abundant, respectively, compared to S+P− (Fig. 2B). Piracetam reverted the changes in metabolism caused by distress exposure (Figs. 2A and 2B, Data S3). When piracetam was administrated to non-distressed rats (S−P+), 16 and 34 pathways showed a significant enrichment in which proteins were less and more abundant, respectively, compared to the control group (S−P−) (Fig. 2C). The functional enrichment analysis showed that proteins involved in amino acid metabolism, both biosynthesis and especially degradation pathways, and in the oxidation of glucose and fatty acids were more abundant. In the same way, proteins involved in the metabolism of xenobiotics by cytochrome P450 and GSH showed an increase in their abundance (Figs. 2A and 2C, Data S3).

This study highlights that psychological distress and piracetam induce changes in protein abundances that are involved in redox metabolism, non-alcoholic fatty liver disease (NAFLD), ER stress, and metabolism of xenobiotics by Cyt P450.

Interaction network of differently accumulated proteins

A Markov Clustering (MCL) algorithm was applied to identified proteins with abundance changes involved in PD (rno05012), NAFLD (rno04932), selected antioxidant enzymes (included in Table 1), PPAR signaling pathway (rno03320), processing in ER (rno04141), proteasome (rno03050), cytochrome P450 metabolism (rno00980), and selected proteins involved in apoptosis (caspase 6 (CASP6) and apoptosis-inducing factor 1, mitochondrial (AIFM1)) to show the associations among them, using de STRING protein-protein interaction database. These proteins were grouped into sixteen clusters (Fig. 3, Data S5). The protein interaction network showed a close relationship among these pathways. These clusters can be grouped into five sets and are distributed around a central group with proteins involved in redox status homeostasis as DJ-1, SOD1, CAT, GLRX5, PRDX2, PRDX5, PRDX6, and TXNRD1 (proteins in cluster 6, colored like green; see cluster information in Data S5).

Effect of chronic psychological distress and piracetam on animal behavior

Four of the rat behaviors were more common during the 20 min exposure to distress each day and were expressed as the time spent in each position (Dielenberg & McGregor, 2001; Grigoruţă et al., 2018) (Fig. 4, Data S4): two defensive behaviors that included hide, referring to the time the rat spent in the hiding place (Fig. 4A), and head-out, like the time the rat is spending in alarm position with the head out or with half of the body out from the hiding place, observing the horizon (Fig. 4B); and two non-defensive behaviors that included exploration, like the time the rat spent exploring the transparent part of the box (Fig. 4C), and approach, like the time the rat spent smelling the piece of cloth with or without cat odor stimuli (Fig. 4D). Rats exposed to cat odor stimuli (S+P−) were concealed in the hiding place during almost all the time of the distress exposure in all five days (97% of the total time) (Fig. 4A), comparing to non-distressed rats (S−P−) (p ≤ 0.00001), that spent more time exploring the open place (4%, p ≤ 0.0001), in head out position (12%, p ≤ 0.0000001), and approaching the cloth (5%, p = 0.032), from the second day (Figs. 4B, 4C, 4D). These observations showed the efficiency of the distress induction model by cat odor exposure.

Piracetam did not change the behavior in distressed rats (S+P+) (Fig. 4). However, when the drug was administrated to non-distressed rats (S−P+), significant changes were observed compared to the control group (S−P−), increasing of 78% to 93% the time spent in the concealment, while the time used to explore, to approach the cloth and to be in the alert was decreased, as happened in S+P− group (Fig. 4).

Effect of chronic psychological distress and piracetam on plasma biomarkers

Several biochemical parameters including glucose, triglycerides, and cholesterol and other biomarkers that indicate hepatic injury and function (direct and indirect bilirubin, alanine aminotransferase, aspartate aminotransferase, total protein, and albumin levels) and kidney function (creatinine, urea, and blood urea nitrogen (BUN) levels) were measured in plasma in all experimental groups (Table 2, Data S4). The exposure to distress induced a statistically significant decrease in glucose levels in the distressed group (S+P−) (p = 0.012), and the administration of piracetam avoided such an effect. The same happened in the triglycerides level, albeit not significant changes. The specific biomarkers for hepatic injury and function were not affected by distress and/or piracetam, although the total bilirubin presented a tendency to decrease in distressed animals (S+P−) and with piracetam (S+P+) (p = 0.065). Moreover, a statistically significant increase in the BUN level was found in distressed animals (S+P−), and piracetam counteracted this effect reducing such levels, even more in rats exposed to distress (S+P−) (p = 0.025). Also, piracetam significantly decreased plasma urea levels in distressed rats (S+P+) compared to untreated rats (S+P−) (p = 0.025).

Correlational analysis among treatments, behavior, biochemical parameters, and antioxidant enzyme activity

To study the association between treatments and behavior, a normality test of the variables was first performed using Kolmogorov-Smirnoff (KS). Normal data distribution was shown and therefore the statistical test Pearson’s correlation coefficient (rho) was used, considering a statistically significant correlation p ≤ 0.05. A high correlation was found between treatments and behaviors (Table S4). The time spent hiding was positively correlated with the treatments (r = 0.82, p ≤ 0.00001), while the time spent in head-out position, exploring and approaching the piece of cloth were negatively correlated with the treatments (r =  − 0.73, r =  − 0.83, and r =  − 0.72, respectively; p ≤ 0.0001) (Fig. S2). These correlations showed that piracetam had no effect on distressed rats but induced defensive behavior in non-distressed rats (Grigoruţă et al., 2018).

Further, correlational studies among biochemical parameters, antioxidant enzyme activity, treatments, and rat behavior were performed. The indirect and total bilirubin were negatively correlated with the treatments (r =  − 0.63, p = 0.02 and r =  − 0.58, p = 0.01, respectively) (Table S4). Moreover, glucose level was negatively correlated with the time spent hiding (r =  − 0.49, p = 0.04) (Fig. S2A, Table S4), while it was positively correlated with the time spent in head out position (r = 0.57, p = 0.04) (Fig. S2B, Table S4); direct bilirubin level was positively correlated to the time spent approaching and exploring (r = 0.67, p = 0.01 and r = 0.59, p = 0.03, respectively) (Figs. S2C, S2D, Table S4); and total bilirubin level was negatively correlated with the time spent hiding (r =  − 0.45, p = 0.05) (Fig. S2E, Table S4), and positively correlated with the time spent exploring (r = 0.45, p = 0.05) (Fig. S2F, Table S4). These correlations between behavior and biochemical parameters show that bilirubin levels are related to stress behavior and can be useful as a biomarker of psychological distress.

The correlational analysis among antioxidant enzyme activity with rat behavior and treatments showed that only GPX activity had a positive association with the treatments (r = 0.61, p = 0.01) (Table S4) and with the time spent hiding (r = 0.55, p = 0.01) (Fig. S2G), and it had negative association with the exploration time (r =  − 0.61, p = 0.01) (Fig. S2H). These correlations support the relationship of this enzyme with the stress behavior of hiding and it can be considered in future studies as a distress biomarker.

Redox metabolism

The maintenance of optimal ROS levels is essential in the body due to the dual function of these species. ROS are signaling molecules that activate transcription factors that regulate the gene expression related to growth and cell differentiation, but, on the other hand, ROS excess has been associated with the development of numerous diseases such as type 2 diabetes, autoimmune diseases, cancer or neurodegenerative diseases, among all (Hackett & Steptoe, 2017; Wadhwa & Maurya, 2018; Sharif et al., 2018; Kruk et al., 2019). The relationship between psychological distress and the redox status alteration of the body has been previously shown in several studies using different murine models in the liver, brain, pancreas, kidney, lungs, and heart (Şahin & Gümüşlü, 2007; Jafari et al., 2014; Mejia-Carmona et al., 2014; Mejia-Carmona et al., 2015; López-López et al., 2016; De Sousa Rodrigues et al., 2017).

In the present study, we focused on the changes induced by distress on liver proteome. One of the liver functions is the regulation of the redox status. Although the hepatic antioxidant system is one of the most effective in the body, psychological distress can induce ineffective antioxidant defenses at this level. Our proteomic results showed several changes in proteins involved in the maintenance of redox homeostasis (Table S1). We observed changes induced by the distress in 19 proteins abundance from a total of 26 proteins described at this level, between them were the enzymes CAT, isoforms of GST, and SOD1. Also, enzyme activity was changed by oxidative stress in the liver, like the increase in GPX activity. This enzyme is involved in modulating cellular oxidant stress and redox-mediated responses (Lubos, Loscalzo & Handy, 2011). The GPX activity increase is probably due to the need of the cells to scavenge the lipid peroxides formed because of the increase ROS level due to psychological distress (Djordjevic et al., 2011), and piracetam performed its antioxidant function preventing this damage, as previously seen in immune (Grigoruţă et al., 2018) and neuronal cells (Gupta et al., 2014; Verma et al., 2018). In a CUMS rat model, an increase in oxidative proteins and lipids oxidation was observed in the liver after 40 and 60 days of stress (López-López et al., 2016) and, also, five days of psychological distress exposure altered proteins and lipids level in circulating immune cells (Grigoruţă et al., 2018). These alterations can be due to changes in RE and proteasome pathways involved in the modulation of cell response to high levels of glucocorticoids and catecholamines, as a response to stress (López-López et al., 2016). Moreover, several studies have found that distress promotes lipid peroxidation in the liver and other organs (Demirdaş, Nazıroğlu & , 2016; Duda et al., 2016; López-López et al., 2016). CUMS in male rats induced no changes in the SOD activity in the liver; however, CAT activity was significantly reduced after 20 days of stress (López-López et al., 2016). Besides, a chronic mild stress-induced hepatic oxidative stress model promoted the increase of ROS and lipid peroxidation and the decrease of GPX and CAT activity (Duda et al., 2016). The divergences among the different studies may be due to the type of the psychological stress model, the time of exposure to distress, and the gender-associated differences in response to oxidative stress (Brunelli et al., 2014).

Previous studies showed that piracetam decreases the antioxidant level, and therefore decreases the oxidative stress, and increases the proinflammatory responses (Singh et al., 2011; Liu et al., 2017; Verma et al., 2018). Moreover, this drug may protect the lipids from cellular membranes against oxidative stress due to its ability to scavenge free radicals, in this way avoiding mitochondrial dysfunction caused by such reactive molecules (Keil et al., 2006; Grigoruţă et al., 2018). In the present study, piracetam decreased the GPX and CAT activity in liver in distress conditions (Table 1) due to its ability to reduce the number of oxidant compounds, as observed in blood peripheral immune cells using SR-µFTIR (Grigoruţă et al., 2018).

However, in non-distressed conditions, possible alterations were shown in circulating mononuclear cells from rats treated with this drug (Grigoruţă et al., 2018). Piracetam has been labeled as a metabolic enhancer as it increases oxygen consumption and mitochondrial activity in stressed neuronal cells (Gupta et al., 2014; Verma et al., 2018). However, little evidence has been found about the effects of this drug on the metabolism under physiological conditions. In previous studies, piracetam has no impact on non-stressed subjects, both on the brain membrane fluidity of young animals (Müller et al., 1997) and cell viability and DNA damage in leukocytes and macrophages (Singh et al., 2011). In the present study, non-distressed groups showed changes in liver protein abundance and activity under treatment with this drug (Table 1 and Table S1). Our results suggest that piracetam induces an increase of catabolism rate guided to energy production, which can boost the ROS production and oxidative stress, under physiological conditions. These results support our previous observations made on circulating mononuclear cells from rats under the same distress conditions, in which a modest increase in oxidative stress in the control group (S−P+) was shown by using SR-µFTIR analysis (Grigoruţă et al., 2018). In the same way, the high fatty acid degradation observed in the present study can explain the decrease of lipids/proteins ratio reported in the immune cells from non-distressed rats, which confirms that this drug disturbs the metabolism (Grigoruţă et al., 2018). Therefore, the beneficial effect of piracetam seems to be closely related to the presence of oxidative stress (Keil et al., 2006; Verma et al., 2018).

Non-alcoholic fatty liver disease (NAFLD)

Recently, oxidative stress has been considered a factor that contributes to the NAFLD pathogenesis due to its effects on lipid metabolism. ROS overproduction in mitochondrial electron transport chain (ETC) and non-ETC ROS sources, as fatty acid β-oxidation, appears to contribute to hepatic metabolic diseases (Chen et al., 2020). Lipid peroxidation has been recently proposed as a possible mechanism for NAFLD development and progression because several circulating biomarkers of this process were identified in patients with NAFLD, as well as the presence of lipid peroxidation products (e.g., MDA and 4-HNE) were correlated with various histological features of non-alcoholic steatohepatitis (Chen et al., 2020). However, little evidence had been provided on the possible relationship between NAFLD and psychological distress. In a recent study, the effects of CUMS on metabolism were assessed in a mouse model of diet-induced obesity, and stress was not associated with changes in liver lipid deposition. However, a high-fat high-fructose diet promoted histological changes in the mouse liver that includes microvesicular lipid droplets, ballooning of hepatocytes, and cell infiltration (De Sousa Rodrigues et al., 2017). Our proteomic results may confirm that prolonged exposure to distress can lead to the development of NAFLD.

Parkinson’s disease (PD) (rno05012) was one of the enriched pathways. PD shares 86 gene entries with the NAFLD according to the KEGG pathway database (https://www.genome.jp/kegg/), which means that PD is associated with the oxidative stress and mitochondrial dysfunction induced by psychological distress (Grigoruţă et al., 2019; Grigoruţă et al., 2020), a molecular response that affects also other organs like the liver (López-López et al., 2016). Many proteins could be involved in the NAFLD progression. In this study, 38 proteins were found variable and belonging to the PD pathway, and 17 of these were not included in the NAFLD pathway (Table S2). One of these shared proteins is the PD protein 7 (PARK7) or protein DJ-1, which is a multifunctional protein with transcription modulatory and antioxidant activity, and acts as a cytoprotective in different cellular compartments (Junn et al., 2009). This protein is also required for the maintenance of mitochondrion homeostasis, like mitochondrial morphology and function, as well as for autophagy of dysfunctional mitochondria (Xiong et al., 2009). Loss of DJ-1 causes impaired mitochondrial respiration, increased intramitochondrial ROS, reduced mitochondrial membrane potential, and mitochondrial morphology alteration (Krebiehl et al., 2010). Moreover, DJ-1 is involved in the regulation of antioxidant gene expression through the Nrf2 transcription factor and of oxidative stress-induced apoptosis and has been related to the pathophysiology of immune and inflammatory diseases (Zhang et al., 2020). However, DJ-1 has an essential role in the progression of liver diseases due to its function in the inflammatory response initiation by modulating ROS generation and the immune response. In this study, DJ-1 abundance decreased by 50% in distress conditions. This observation shows the high use of this protein and the significant oxidative damage in the liver induced by psychological distress. A lower expression of DJ-1 was also found in the prefrontal cortex from rats exposed to the same model of distress (Grigoruţă et al., 2019) and in circulating immune cells from distressed PINK1-KO rats (Grigoruţă et al., 2020). In addition to the altered level of DJ-1, distressed rats showed anxiety, motor disfunction, altered mitochondrial respiration in the brain, and ineffective immune response (Grigoruţă et al., 2019; Grigoruţă et al., 2020). However, piracetam increased DJ-1 level in the liver, almost 80% in non-distressed animals, due to the metabolic enhancer effects of this drug. Nevertheless, a previous study showed that the lack of DJ-1 protected against hepatic steatosis because it enhanced the fatty acid oxidation that decreases the hepatic TG accumulation (Xu et al., 2018). Therefore, further studies are necessary for the role of this protein in the NAFLD progression in the liver, including gender-associated differences.

The connections of DJ-1 with members of the oxidative phosphorylation cluster have been reported in PD and other neurodegenerative diseases. This protein, along with others, assists the regulation of adequate ROS production in mitochondria by ETC and, therefore, maintains a proper mitochondrial function (Rekatsina et al., 2020). DJ-1 has been also related to the antioxidant enzymes CAT, GST, and SOD2 through the Nrf2 transcription factor, which regulates numerous antioxidant genes, being DJ-1 a positive regulator of Nrf2 (Yan et al., 2015).

ER stress

Distress and piracetam also induced alteration in protein processing in the ER. Both rough and smooth ER are widely developed in hepatocytes. The ER homeostasis alteration triggers the ER stress, which promotes the cumulation of misfolded or unfolded proteins in this organelle. ER stress has been associated with diverse pathophysiological changes, including inflammation, lipogenesis, insulin resistance, and apoptosis, both animal models and human subjects with NAFLD. The ER is also the origin of ROS, but their input into oxidative stress in NAFLD continues to be unclear (Chen et al., 2020). Our results suggest that distress exposure can induce an increase of the machinery to re-fold denatured proteins or to remove them from the ER-associated protein degradation pathway by the proteasome, probably to prevent the ER stress in the liver. In our previous study, the increase of protein denaturation or an abnormal protein aggregation was found in circulating mononuclear cells from stressed rats by SR-µFTIR analysis. Piracetam could not avoid such protein damage in the stress condition and it altered the proteins in the non-distressed group (S−P+), which was statistically grouped with S+P+ by principal component analysis (Grigoruţă et al., 2018). The ER stress has been associated with various liver diseases including NAFLD (Lebeaupin et al., 2018). Oxidative stress, lipotoxicity, and local inflammation induce the progression from hepatic steatosis to steatohepatitis and cirrhosis. To fight against cellular stress and inflammation, the UPR is a vital mechanism in patients with non-alcoholic steatohepatitis, especially in the mitochondria, because factors, such as mitochondrial UPR and mitohormesis, prevent the evolution of fatty liver to fibrosis (Solano-Urrusquieta et al., 2020). Previous studies showed that the PPAR signaling pathway (through PPARα receptor) can assist ER stress by regulating fatty acid β-oxidation pathways in peroxisomes and mitochondria, and also by inhibiting the inflammatory response in the liver (Wang et al., 2017). This pathway has been shown to mediate hepatic protection and the decrease of hepatocyte apoptosis, by regulating ER stress, in acute liver failure (Zhang et al., 2016). In this study, the PPAR signaling pathway was increased in stressed animals and by piracetam, which can confirm the ER stress.

Metabolism of xenobiotics by Cyt P450

The biotransformation process involving Phases I and II enzymes of xenobiotics and drug metabolism was also affected by distress in the present study. Similar results were found in a previous study, in which psychological stress alone impacts the regulation of liver proteins involved in Phase I and II, affecting the liver functionality, in a restraint stress mouse model (Flint et al., 2010). In our study, the cytochrome P450 2E1 (CYP2E1) was found more abundant in the rats’ liver after exposure to distress. This is one of the most abundant proteins in the liver of stressed rats and has been associated with steatohepatitis development. The functional stabilization of CYP2E1 induced hepatic oxidative stress, JNK1 signaling activation, insulin resistance, and accumulation of fatty acids and triglycerides, which results in liver injury, contributing to non-alcoholic steatohepatitis (Kim et al., 2016). This result supports the relationship between chronic exposure to psychological distress and the progress of NAFLD (Solano-Urrusquieta et al., 2020). In this study, piracetam avoided this change, but altered CYP2E1 expression in the liver of non-distressed rats, probably due to its stimulatory effect on metabolism (Müller et al., 1997; Keil et al., 2006; Kurz et al., 2010; Verma et al., 2018).

Effect of piracetam on animal behavior

Piracetam did not change the rat behavior under five days of exposure to distress, contrary to our previous study where nine days of drug administration diminished the anxiety behavior induced by distress (Grigoruţă et al., 2018), probably because a longer period of drug treatment is needed to obtain significant results. However, the drug induced a change in animal conduct when it was administered under physiological conditions. This result confirms our previous studies, where piracetam induced neurostimulation and psychomotor agitation in healthy individuals (Corazza et al., 2014; Grigoruţă et al., 2018), probably due to its actions as a metabolic enhancer (Müller et al., 1997; Keil et al., 2006; Kurz et al., 2010; Verma et al., 2018).

Effect of distress and piracetam on biochemical parameters

In the present study, none of the specific markers for liver injury, like ALT and AST, were affected by distress and/or piracetam, although the total bilirubin tended to decrease, regardless of whether rats were exposed or not to distress. Low bilirubin levels have been closely related to cardiovascular diseases or a high risk of cerebral deep white matter lesions (Higuchi et al., 2018). Contrary to our results, CUMS model induced an increase in the activity of ALT and AST in male rats, suggesting liver damage (Jia et al., 2016). Again, gender-associated studies need further investigations.

Related to specific markers associated with renal function, the exposure to distress induced a significant increase in BUN level, indicating that the sympathetic system activation by psychological distress stimulates protein degradation, or/and induces hemodynamic changes, that can lead to kidney function failure and increase the risk for cardiovascular disease (Kazory, 2010). Piracetam protected kidney functions by diminishing BUN and urea levels. Previous studies showed the beneficial effect of this drug on microcirculation not only in the brain but also in the periphery (Winblad, 2005).