TY - JOUR
AU - Jiang,, Yan
AB - Abstract Aims Alcohol abuse has attracted public attention and chronic alcohol exposure can result in irreversible structural changes in the brain. The molecular mechanisms underlying alcohol neurotoxicity are complex, mandating comprehensive mining of spatial protein expression profile. Methods In this study, mice models of chronic alcohol intoxication were established after 95% alcohol vapor administration for 30 consecutive days. On Day 30, striatum (the dorsal and ventral striatum) and hippocampus, the two major brain regions responsible for learning and memorizing while being sensitive to alcohol toxicity, were collected. After that, isobaric tags for relative and absolute quantitation -based quantitative proteomic analysis were carried out for further exploration of the novel mechanisms underlying alcohol neurotoxicity. Results Proteomic results showed that in the striatum, 29 proteins were significantly up-regulated and 17 proteins were significantly down-regulated. In the hippocampus, 72 proteins were significantly up-regulated, while 2 proteins were significantly down-regulated. Analysis of the overlay proteins revealed that a total of 102 proteins were consistently altered (P < 0.05) in both hippocampus and striatum regions, including multiple keratins such as Krt6a, Krt17 and Krt5. Ingenuity pathway analysis revealed that previously reported diseases/biofunctions such as dermatological diseases and developmental disorders were enriched in those proteins. Interestingly, the glucocorticoid receptor (GR) signaling was among the top enriched pathways in both brain regions, while multiple keratins from the GR signaling such as Krt1 and Krt17 exhibited significantly opposite expression patterns in the two brain nuclei. Moreover, there are several other involved pathways significantly differed between the hippocampus and striatum. Conclusions Our data revealed brain regional differences upon alcohol consumption and indicated the critical involvement of keratins from GR signaling in alcohol neurotoxicity. The differences in proteomic results between the striatum and hippocampus suggested a necessity of taking into consideration brain regional differences and intertwined signaling pathways rather than merely focusing on single nuclei or molecule during the study of drug-induced neurotoxicity in the future. Introduction Excessive use of alcohol is a global issue. Abuse of alcohol has been linked with social, economic and health problems. According to a survey from Word Health Organization, there are ~3.3 million deaths in 2012 that are estimated to have been caused by alcohol consumption. Among these cases, ~132,000 deaths are directly related to alcohol-induced neuropsychiatric disorders (Fang et al., 2016). Alcohol, the main component of alcoholic beverages, is associated with more than 200 health problems, such as liver diseases (Godoy et al., 2012), cardiovascular diseases (Fernandez-Sola and Planavila, 2016) and brain disorders (Horton et al., 2015). Chronic alcohol exposure can result in alcohol-related metabolic injury, alcohol-related dementia, fetal alcohol spectrum disorder (de la Monte and Kril, 2014) leading to cognitive and motor impairments, memory and learning dysfunction, as well as irreversible structural changes in the brain. Despite extensive studies reporting the toxicological properties of alcohol in the past decades, few studies examined the effects of alcohol on specific brain nuclei but from the perspective of the brain as a whole. Conclusions drawn from previous studies might be debatable due to their limitations of focusing on single molecule or brain region. In fact, the causes and behavioral consequences vary across brain nuclei after alcohol insult (de la Monte and Kril, 2014). Generally, the hippocampus and striatum are two major brain regions sensitive to alcohol challenge. Hippocampus plays a critical role in establishing memory. It is more susceptible to damage from chronic alcohol consumption (Verbaten, 2009), including cognitive, learning and memory impairment (Vongvatcharanon et al., 2010). Chronic alcohol exposure leads to changes in hippocampal formation, including decreased excitability of neurons (Korkotian et al., 2015) and an increase of oxidative stress (Assuncao et al., 2007). Striatum, another brain region related to action, learning, cognition and emotion, is also susceptible to alcohol and it is the key area of motivation and reward processing with a close correlation to addiction. Alcohol has a regional specificity to the striatum that the dorsal striatum has a key role in habit formation and participates in goal-directed alcohol seeking actions, while the ventral striatum appears to have important roles in environmental control of alcohol drinking (Chen et al., 2011). After alcohol exposure, elevated dopamine (DA) levels in the striatum were associated with alcohol preference (Liu et al., 2017). Additional clue that supports the hypothesis of regional specificity of signaling transduction upon alcohol neurotoxicity is that a the nitric oxide(NO)-sensitive guanylyl cyclase (NO-GC) stimulator IWP-051 potentiated DEA/NO-induced cGMP increases in the cerebellum and striatum, but not in the hippocampal CA1 area or primary hippocampal neurons (Peters et al., 2018). The pathogenesis of alcohol neurotoxicity is complex, mandating comprehensive mining of the molecular mechanisms underlying its toxicity on both the hippocampus and striatum. High-throughput proteomic analysis has paved the way for comprehensive study and made valuable supplement to the pathogenesis of alcohol neurotoxicity (Filiou et al., 2012). Two-dimensional gel electrophoresis (2-DE) proteomics used to be a favorable approach for comparative analysis, which could reveal age difference from adolescence to adulthood through protein expression during chronic alcohol intake in hippocampus (Hargreaves et al., 2009). However, in the 2-DE approach, comigration and partial comigration of proteins could compromise the accuracy of quantification (Wu et al., 2006). Alternatively, isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques, which allows multiplexing multiple samples together in one run. Thus, by using iTRAQ isobaric tags, up to eight samples can be analyzed simultaneously (Aggarwal and Yadav, 2016), which can reduce batch-to-batch variation and save time (Popov et al., 2019). iTRAQ method can obtain quantitative information from thousands of proteins at the same time with an higher data reproducibility, which is more conducive for analysis of protein interaction and expression patterns. iTRAQ labeling combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) ensures a high accuracy in the measurement with fewer technical replicates required (Nunez et al., 2017). In the current study, iTRAQ technology was used to investigate the proteomic alteration in both the striatum and hippocampus collected from mice exposed to alcohol. The protein expression profile was then analyzed to reveal the brain regional difference of potential proteins. We identified multiple keratins (Krt16, Krt6a and Krt17, etc.) from the glucocorticoid signaling as novel molecules that significantly associated with alcohol toxicity in both brain nuclei. However, these keratins were asynchronously or even inversely expressed in the two brain nuclei, mirroring their differential responses upon chronic alcohol insult. Our data provided a comprehensive perspective into the mechanisms by which chronic alcohol induced the striatum and hippocampus toxicity, and paved novel way for developing region-specific intervention strategies against alcohol neurotoxicity. Materials and Methods Animals A total of 60 adult male C57BL/6 mice (weighing 18–20 g at the start of experiment) from the Shanghai Jiesijie Experimental Animal Center (Shanghai, China) were used in this study. All mice were housed in a standard condition (22–26 °C, 12 h light/dark cycle, 50–60% relative humidity), free access to a standard diet and water ad libitum. All experimental methods were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publication no. 8023). Experimental protocols were approved by the Ethical Reviewing Board at the School of Basic Medical Sciences, Fudan University (the specific protocol number was shown under the Ethics Statement below). In particular, humane endpoints were implemented in this study involving animals based on the National Guideline for Replacement, Refinement and Reduction of Animals in Research in China (GB/T 27416-2014). Experimental treatments After adaptive feeding, mice were randomly divided into alcohol treatment group and control group (n = 30/group). Mice in the treatment group received a week of alcohol adaptive administration and then were subjected to 95% alcohol vapor via a manmade chamber, which allows continuous transportation of alcohol vapor as described previously (Goldstein, 1972). Vapor inhalation is a method of alcohol administration that has been used extensively to produce alcohol dependence in mice (Mouton et al., 2016). It has been shown to reliably allow for the titration of blood–alcohol concentrations (BACs) that are sufficient for inducing physical alcohol dependence (Ehlers et al., 2018). In brief, mice in the treatment group were exposed to alcohol vapor in the chamber from 8:00 in the morning till 16:00 at afternoon followed by 16 h of withdrawal (no alcohol vapor inhalation) for a total of 30 days. Previous studies demonstrated that the intermittent exposure to alcohol was a valid method to induce alcohol dependence in rodents (Siciliano et al., 2018). The alcohol vapor rate and chamber configuration were as previously described (Goldstein, 1972). Alcohol concentration was adjusted by changing pump flow and monitored via a spectrometer. Mice that inhaled air were grouped into negative control. All mice had free access to water and food during treatment. Body weight was recorded at the end of each treatment day. Optimization of alcohol administration Co-administration of an alcohol dehydrogenase inhibitor (such as pyrazole) with alcohol is a manner used to keep BAC high and stable following gastric administration of alcohol or short-term alcohol vapor exposure (Shibasaki et al., 2012; Perez and De Biasi, 2015). Under the current alcohol vapor duration (30 days), the necessity of an alcohol dehydrogenase inhibitor was preliminarily assessed. Briefly, a total of 48 age-matched C57BL/6 mice were randomly divided into two groups: single alcohol treatment group and alcohol + pyrazole treatment group. Mice in the alcohol and pyrazole co-treatment group were administered with the pyrazole (8.21 μg/kg) intraperitoneally and immediately placed in the inhalation chambers. The two groups of mice received alcohol vapor in the inhalation chambers were kept under the same conditions. After 5 days of consecutive exposure, blood samples were collected at 0, 1, 4, 8, 8.5, 9, 10 and 12 h after entry into the inhalation chambers. For each mouse, an aliquot of 100 μL whole blood was collected from orbital venous sinus. There were at least three mice at each monitored time point for each group. BACs were determined by headspace gas chromatography with flame-ionization detection. Assessment of alcohol withdrawal symptoms Alcohol withdrawal syndrome is a typical manifestation of chronic alcohol intoxication, which mainly manifests as irritability, audiogenic seizures and other symptoms. The development of withdrawal symptoms is a direct evidence indicating chronic alcohol intoxication. Studies have suggested that the withdrawal symptoms generally peaked at 6 h after withdrawal of alcohol and relieved within 24 h (Erden et al., 1999) and 6 weeks of alcohol exposure was necessary to induce clear behavioral withdrawal signs (Matthews et al., 2018). In this study, the withdrawal behavior symptom scale (Erden et al., 1999) was utilized to assess mice withdrawal symptoms in order to determine whether the chronic alcohol intoxication model was successfully established. After 30 days of chronic alcohol administration, the mice were observed at 0 (the start of alcohol withdrawal), 4 and 6 h for 5 min after cessation of alcohol vapor. At the end of each observation period, mice were assessed simultaneously for the following comprehensive behavioral indicators: stereotypic behaviors, agitation, tail stiffness, abnormal posture and abnormal gait, which were then scored using a rating scale that was adapted from the literature (Erden et al., 1999). The ratings were done by experimentally naive observers. The sum of the five observation scores was used as a quantitative measure of withdrawal severity and the total scores from alcohol-exposed mice were compared among 0, 4 and 6 h. Collection and separation of brain tissues Alcohol-treated mice and control mice were sacrificed 24 h following the final exposure at the end of 30-day consecutive vapor exposure by cervical dislocation. Brains were quickly dissected on ice, and the hippocampus and striatum of each mouse were separated, washed in ice-cold PBS in order to avoid contamination from other impurities and rapidly frozen into liquid nitrogen. Samples were stored at −80°C until use. Protein extraction and normalization For protein loading in mass spectrometry, it requires sufficient tissue amounts (~200 mg/nuclei). Since both the striatum and hippocampus are small nuclei by weight, two biological replicates were required for samplings of control and alcohol treatment groups with each sample composed of a pool of 10 different individual brain tissues, which were used to conduct proteomic analysis by the iTRAQ technique. Each sample was ground with a high-throughput tissue grinder added with 8 M urea and 2% sodium dodecyl sulfate at a 1:15 (v/v) ratio. The lysates were then added with a protease inhibitor cocktail followed by rotation at 4°C for 30 min and centrifuge at 12,000 g for 10 min. The supernatant was then collected and protein concentrations were determined using the bicinchoninic acid method (Beyotime, Shanghai, China). Trypsin digestion and iTRAQ labeling A total of 120 μg protein from each group was added with 10 mM trichloroethyl phosphate and incubated at 37°C for 1 h. Samples were then added with 40 mM methyl methanethiosulfonate at room temperature for 40 min with light-proof reaction. Pre-cooled acetone was added to each sample (acetone:sample = 6:1, v/v) and samples were precipitated at −20°C overnight. Samples were then centrifuged at 10,000 g for 20 min. The precipitate was sufficiently dissolved with 100 μL of 100 mM triethyl ammonium bicarbonate (TEAB) and trypsin was added (trypsin:sample = 1:20, v/v) for incubation overnight at 37°C. After trypsinization, the peptide was blotted with a vacuum pump and reconstituted with 0.5 M TEAB. The peptides were labeled with a unique iTRAQ reagent (AB SCIEX, Massachusetts, USA, 4390812) according to the manufacturer’s instructions. The samples from the striatum were labeled with iTRAQ tag 113, iTRAQ tag 114, and the samples from the hippocampus were labeled with iTRAQ tag 115, iTRAQ tag 116, respectively. The labeled peptide samples were then pooled and lyophilized in a vacuum concentrator. High pH reverse phase separation and nano-LC-MS/MS analysis High pH reverse phase-separation chromatography was performed using a Waters ACQUITY Ultra performance liquid chromatography(UPLC) high-performance chromatography system. The iTRAQ labeled peptide mixture was reconstituted in UPLC loading buffer and loaded onto a reverse phase column (ACQUITY UPLC BEH C18 Column 1.7 μm, 2.1 mm × 150 mm) and was fractionated at a constant flow rate of 0.2 ml/min. Buffer A was 2% acetonitrile (ACN) (ammonia was adjusted to a pH of 10) and buffer B was 90% ACN (ammonia was adjusted to a pH of 10). The 60-min gradient starts from 100% A for 5 min, then a linear gradient from 5 to 25% B for 35 min, from 25 to 80% B for 5 min, maintained 80% B for 5 min, and finally 100% A for 10 min. The wavelength of the UV detector was set to 280 nm. A total of 36 fractions were collected according to peak type and time, and were combined into 12 fractions, concentrated by vacuum centrifugation, and dissolved in loading buffer for second-dimensional analysis. Peptides were separated by a linear gradient formed from 1% ACN with 0.1% formic acid (mobile phase A) and 95% ACN with 0.1% formic acid (mobile phase B), from 5 to 80% of mobile phase B in 120 min at a flow rate of 300 nL/min. The mass spectrometer (MS) analysis was performed on a Thermo Xcalibur 4.0 (Thermo, USA). MS spectra were acquired across the mass range of 350–1300 m/z in data-dependent acquisition mode (70,000). The resolution of the first-order MS was 70,000, and then the 20 strongest signals in the parent ion were selected for secondary fragmentation by high-energy collision dissociation, and the dynamic elimination was 18 s. The tandem mass spectrometry was recorded in high sensitivity mode (17,500). Three technical replicates were processed. Table 1 Primer sequences used for qRT-PCR analysis Gene name . Forward primer (5′–3′) . Reverse primer (5′–3′) . Krt6a AGAGAGGGGTCGCATGAACT TCATCTGTTAGACTGTCTGCCTT Krt42 GGAGCTGAACCGAGAAGTGG CCTTTCCATGTCACAACGCAG Krt17 ACCATCCGCCAGTTTACCTC CTACCCAGGCCACTAGCTGA Krt5 TCTGCCATCACCCCATCTGT CCTCCGCCAGAACTGTAGGA Krt1 TGGGAGATTTTCAGGAGGAGG GCCACACTCTTGGAGATGCTC 9530053A07Rik GGGAACGTCTCACCCTGTG CTGGCTTCTGAGTTGCAGGAG Krt78 GCAAGGGATACGGTTTGGG GTTGTTGAGGCTTCTGATCTCTC Krt76 AGTGCAGGTTGTCTGGAGAGT CCTCCGTAGTTACTGCCACC Krt72 GCATGAGAGTTACATCAGCAACC CGTGCGTCTGTTAATCTCCAC Krt13 TCATCTCGGTTTGTCACTGGA TGATCTTCTCGTTGCCAGAGAG Krt10 CTGGCGATGTGAACGTGGAA GTCCCTGAACAGTGCGTCTC Fga AGTCTGGACTACAGATACCGAAG CGTCAATCAACCCTTTCATCCTG Gapdh AGGTCGGTGTGAACGGATTTG TGTAGACCATGTAGTTGAGGTCA Gene name . Forward primer (5′–3′) . Reverse primer (5′–3′) . Krt6a AGAGAGGGGTCGCATGAACT TCATCTGTTAGACTGTCTGCCTT Krt42 GGAGCTGAACCGAGAAGTGG CCTTTCCATGTCACAACGCAG Krt17 ACCATCCGCCAGTTTACCTC CTACCCAGGCCACTAGCTGA Krt5 TCTGCCATCACCCCATCTGT CCTCCGCCAGAACTGTAGGA Krt1 TGGGAGATTTTCAGGAGGAGG GCCACACTCTTGGAGATGCTC 9530053A07Rik GGGAACGTCTCACCCTGTG CTGGCTTCTGAGTTGCAGGAG Krt78 GCAAGGGATACGGTTTGGG GTTGTTGAGGCTTCTGATCTCTC Krt76 AGTGCAGGTTGTCTGGAGAGT CCTCCGTAGTTACTGCCACC Krt72 GCATGAGAGTTACATCAGCAACC CGTGCGTCTGTTAATCTCCAC Krt13 TCATCTCGGTTTGTCACTGGA TGATCTTCTCGTTGCCAGAGAG Krt10 CTGGCGATGTGAACGTGGAA GTCCCTGAACAGTGCGTCTC Fga AGTCTGGACTACAGATACCGAAG CGTCAATCAACCCTTTCATCCTG Gapdh AGGTCGGTGTGAACGGATTTG TGTAGACCATGTAGTTGAGGTCA Open in new tab Table 1 Primer sequences used for qRT-PCR analysis Gene name . Forward primer (5′–3′) . Reverse primer (5′–3′) . Krt6a AGAGAGGGGTCGCATGAACT TCATCTGTTAGACTGTCTGCCTT Krt42 GGAGCTGAACCGAGAAGTGG CCTTTCCATGTCACAACGCAG Krt17 ACCATCCGCCAGTTTACCTC CTACCCAGGCCACTAGCTGA Krt5 TCTGCCATCACCCCATCTGT CCTCCGCCAGAACTGTAGGA Krt1 TGGGAGATTTTCAGGAGGAGG GCCACACTCTTGGAGATGCTC 9530053A07Rik GGGAACGTCTCACCCTGTG CTGGCTTCTGAGTTGCAGGAG Krt78 GCAAGGGATACGGTTTGGG GTTGTTGAGGCTTCTGATCTCTC Krt76 AGTGCAGGTTGTCTGGAGAGT CCTCCGTAGTTACTGCCACC Krt72 GCATGAGAGTTACATCAGCAACC CGTGCGTCTGTTAATCTCCAC Krt13 TCATCTCGGTTTGTCACTGGA TGATCTTCTCGTTGCCAGAGAG Krt10 CTGGCGATGTGAACGTGGAA GTCCCTGAACAGTGCGTCTC Fga AGTCTGGACTACAGATACCGAAG CGTCAATCAACCCTTTCATCCTG Gapdh AGGTCGGTGTGAACGGATTTG TGTAGACCATGTAGTTGAGGTCA Gene name . Forward primer (5′–3′) . Reverse primer (5′–3′) . Krt6a AGAGAGGGGTCGCATGAACT TCATCTGTTAGACTGTCTGCCTT Krt42 GGAGCTGAACCGAGAAGTGG CCTTTCCATGTCACAACGCAG Krt17 ACCATCCGCCAGTTTACCTC CTACCCAGGCCACTAGCTGA Krt5 TCTGCCATCACCCCATCTGT CCTCCGCCAGAACTGTAGGA Krt1 TGGGAGATTTTCAGGAGGAGG GCCACACTCTTGGAGATGCTC 9530053A07Rik GGGAACGTCTCACCCTGTG CTGGCTTCTGAGTTGCAGGAG Krt78 GCAAGGGATACGGTTTGGG GTTGTTGAGGCTTCTGATCTCTC Krt76 AGTGCAGGTTGTCTGGAGAGT CCTCCGTAGTTACTGCCACC Krt72 GCATGAGAGTTACATCAGCAACC CGTGCGTCTGTTAATCTCCAC Krt13 TCATCTCGGTTTGTCACTGGA TGATCTTCTCGTTGCCAGAGAG Krt10 CTGGCGATGTGAACGTGGAA GTCCCTGAACAGTGCGTCTC Fga AGTCTGGACTACAGATACCGAAG CGTCAATCAACCCTTTCATCCTG Gapdh AGGTCGGTGTGAACGGATTTG TGTAGACCATGTAGTTGAGGTCA Open in new tab Bioinformatic analysis Protein identification and quantification of iTRAQ experiments were performed using Protein Pilot 4.2 software (Applied Bio systems; MDS-Sciex). Ingenuity Pathway Analysis (IPA; http://ingenuity.com) was used to perform a systematic bioinformatic analysis, including canonical pathways, diseases and functions (bio functions and tox functions) and gene networks. A general criterion for differentially expressed proteins in iTRAQ labeling quantification was set as fold changes of ≥1.2 or ≤0.8 with adjusted P value < 0.05. Potentially affected biological pathways, protein function and possible toxic effects were confirmed by Fisher’s exact test (P < 0.05). Total RNA extraction and quantitative real-time PCR validation The quantitative real-time PCR (qRT-PCR) analysis was used to verify the results from the iTRAQ analysis. The total RNAs were extracted from the brain nucleus using the Trizol Reagent (Ambion, Texas, USA) according to the manufacturer’s instructions. The total RNAs were resuspended in RNase-free deionized water. After assessment of RNA quality and concentration, a total of 500 ng RNA was used for the reverse transcription (RT) assays with the HiScript II Q RT SuperMix (Vazyme Biotech., Nanjing, China). Gene-specific qRT-PCR primers were synthesized and the sequences were summarized in Table 1. The qRT-PCR was performed in a Light Cycler 480II (Roche, Basel, Switzerland) Real-Time System using the AceQ qPCR SYBR Green Master Mix (Vazyme Biotech, Nanjing, China). The final PCR reaction volume was 20 μL. The PCR reaction conditions were set as follows: initial denaturation at 95°C for 30 s, followed by 40 cycles of 95°C for 10 s, 60°C for 30 s. Finally, the melting curve analysis was performed at 95°C for 15 s, 60°C for 60 s and 95°C for 15 s. Each sample was tested in triplicate. For relative quantification of gene expression, Gapdh was used as an internal standard of mRNA expression, and the blank control was used as a reference sample, which was set to one. Statistical analysis Experimental data were presented as mean ± SEM and analyzed with GraphPad Prism 5 software (GraphPad Software Inc., San Diego, CA, USA). One-way analysis of variance was used for comparison of means among groups and followed by a Bonferroni post-hoc analysis. The students’ t-test was used for comparison of means between two groups in Fig. 4. The two-way analysis of variance was used to compare means in Supplementary Fig. 1. All statistical calculations were at 95% confidence limits and the value of P < 0.05 was considered statistically significant. Results Optimization and simplification of alcohol intoxication model To assess whether pyrazole co-administration is necessary, we compared the difference in the BACs between single alcohol treatment and alcohol + pyrazole co-treatment groups in a preliminary study. After 5-day consecutive exposure, single alcohol vapor was sufficient to produce BACs of 150–200 mg/dL, a level that was considered pharmacologically active (Rimondini et al., 2003), 1 h after initiation of alcohol vapor. Eight-hour exposure to alcohol vapor steadily increased BACs and peaked as high as 800 mg/dL that was far beyond the range of 150–200 mg/dL (Supplementary Fig. 1). When comparing the BACs between single alcohol group and alcohol + pyrazole group, our results showed that there was no significant difference in BACs (P = 0.385). In fact, simplification of alcohol administration by discarding the use of pyrazole was also denoted in other reports, which established the chronic alcohol intoxication models using a comparable duration of alcohol vapor as our study (Johnson et al., 2019). Meanwhile, some studies have shown that the NMDA receptors play a key role in the effects of alcohol in the CNS and the similarity of pyrazole and alcohol acting on NMDA receptor function raised the possibility of pharmacological antagonism resulting from interactions and competition of these drugs at the NMDA receptor site (Aracava et al., 1991). Combined all, the establishment of chronic alcohol intoxication model was optimized by administrating mere alcohol without pyrazole. Body weight loss Both the control and alcohol-treated mice had comparable body weight at the beginning of the experiment. However, after chronic alcohol exposure, the mean body weight of alcohol-exposed mice was significantly lower than that in the control group (P < 0.01, Fig. 1A). The significant body weight loss induced by chronic alcohol consumption was consistent with previous reports (Matthews and Mittleman, 2017), which validated the model efficiency. Fig. 1. Open in new tabDownload slide Summary of the body weight and the withdrawal signs scores. (A) Body weight (g) records of mice in control group and alcohol treatment group during 30 days. Data were expressed as mean ± SEM. n = 30 mice in each group **P < 0.01 vs. control group. (B) Results of withdrawal signs score at the 0, 4 and 6 h after alcohol withdrawal after 30 days of chronic alcohol administration. Values are expressed as mean ± SEM. n = 30 mice in each group **P < 0.01 vs. 0 h after cessation of alcohol vapor. Fig. 1. Open in new tabDownload slide Summary of the body weight and the withdrawal signs scores. (A) Body weight (g) records of mice in control group and alcohol treatment group during 30 days. Data were expressed as mean ± SEM. n = 30 mice in each group **P < 0.01 vs. control group. (B) Results of withdrawal signs score at the 0, 4 and 6 h after alcohol withdrawal after 30 days of chronic alcohol administration. Values are expressed as mean ± SEM. n = 30 mice in each group **P < 0.01 vs. 0 h after cessation of alcohol vapor. Table 2 Summary of differentially expressed proteins. The ratio of dysregulated protein number to the total identified proteins was calculated in parentheses Nucleus . Up-regulated proteins . Down-regulated proteins . Total differentially expressed proteins . Striatum 29 (1.09%) 17 (0.64%) 46 (1.73%) Hippocampus 72 (2.71%) 2 (0.08%) 74 (2.79%) Nucleus . Up-regulated proteins . Down-regulated proteins . Total differentially expressed proteins . Striatum 29 (1.09%) 17 (0.64%) 46 (1.73%) Hippocampus 72 (2.71%) 2 (0.08%) 74 (2.79%) Open in new tab Table 2 Summary of differentially expressed proteins. The ratio of dysregulated protein number to the total identified proteins was calculated in parentheses Nucleus . Up-regulated proteins . Down-regulated proteins . Total differentially expressed proteins . Striatum 29 (1.09%) 17 (0.64%) 46 (1.73%) Hippocampus 72 (2.71%) 2 (0.08%) 74 (2.79%) Nucleus . Up-regulated proteins . Down-regulated proteins . Total differentially expressed proteins . Striatum 29 (1.09%) 17 (0.64%) 46 (1.73%) Hippocampus 72 (2.71%) 2 (0.08%) 74 (2.79%) Open in new tab Alcohol withdrawal reaction After grading the withdrawal signs, we found that behavioral signs of alcohol withdrawal syndrome such as abnormal posture and gait like tail rigidity, head-down, back-hunched with hind legs being wide apart, tremors, stereotyped behaviors like sneezing developed during the whole observation period. Part of the withdrawal scoring record was shown in Supplementary Table 1. Observation suggested that abnormal posture and gait were prominent at the start of alcohol withdrawal (0 h). Four hours after withdrawal of alcohol, stereotyped behavior, irritability and tail stiffness increased significantly. The intensity of the behavioral changes reached a peak level 6 h after withdrawal. The total alcohol withdrawal score was significantly higher at the fourth and sixth hour compared with the 0 h (P < 0.01, Fig. 1B). All these withdrawal symptoms we observed were similar to other studies (Erden et al., 1999) and suggested the successful establishment of chronic alcohol intoxication model. Effects of chronic alcohol consumption on brain proteomics For the analysis of differential protein expression, different brain regions of control and alcohol-treated mice were subject to iTRAQ proteomics. A total of 2655 proteins were identified in the striatum and 2655 proteins were identified in the hippocampus. As shown in Table 2, 46 (1.73%) proteins from the striatum were significantly altered, among which 29 were up-regulated and 17 were down-regulated. In the hippocampus, a total of 74 (2.79%) proteins were significantly altered, consisting of 72 up-regulations and 2 down-regulations. The number of up-regulated proteins markedly exceeded that of down-regulated proteins in both brain nuclei (Fig. 2A). A total of 102 proteins differentially expressed in the striatum and hippocampus after chronic alcohol treatment through intersection analysis as described (Sun et al., 2018), a Venn diagram was used to determine common differential abundance proteins in striatum and hippocampus, 28 proteins were unique to the striatum, 56 proteins were unique to the hippocampus and 18 out of the 102 (17.65%) proteins were shared by the striatum and hippocampus (Fig. 2B). These shared proteins included multiple keratins such as Krt6a, Krt17, Krt5 and Krt1. In addition, the top regulated proteins were listed in Table 3. These altered proteins included many members from the keratin family. Interestingly, many of these proteins were inversely regulated in the two nuclei. For instance, Krt17 significantly decreased in striatum by up to 33% (P = 0.015), whereas it increased in hippocampus by up to 2.67-fold (P = 0.0006). Other members from the keratin family such as Krt5, 6a and 42 also showed the similar expression patterns as Krt17. Fig. 2. Open in new tabDownload slide Summary of the proteomic analysis in the striatum and hippocampus after chronic alcohol consumption. (A) General trend of protein alterations in the striatum and hippocampus. (B) Venn diagram showing significantly altered proteins in the striatum and hippocampus after 30-day alcohol insult. Fig. 2. Open in new tabDownload slide Summary of the proteomic analysis in the striatum and hippocampus after chronic alcohol consumption. (A) General trend of protein alterations in the striatum and hippocampus. (B) Venn diagram showing significantly altered proteins in the striatum and hippocampus after 30-day alcohol insult. Table 3 A list of major differentially expressed proteins in the brain nuclei from the proteomics analysis Accession no. . Entrez gene name . Gene symbol . Striatum . Hippocampus . . . . Fold change . P value . Fold change . P value . STRBP Spermatid perinuclear RNA-binding protein Strbp 2.30 0.033 - - K2C6A Keratin, type II cytoskeletal 6A Krt6a 0.53 0.009 2.00 0.0001 K1C42 Keratin, type I cytoskeletal 42 Krt42 0.60 0.030 1.52 0.009 K1C17 Keratin, type I cytoskeletal 17 Krt17 0.67 0.015 2.67 0.0006 K2C5 Keratin, type II cytoskeletal 5 Krt5 0.70 0.015 2.27 0.002 K2C1 Keratin, type II cytoskeletal 1 Krt1 0.70 0.049 2.89 0.004 E9PVG8 RIKEN cDNA 9530053A07 gene 9530053A07Rik 0.70 0.0001 2.67 0.004 Q6IFT3 Keratin Kb40 Krt78 0.80 0.0001 2.83 0.001 K22O Keratin, type II cytoskeletal 2 oral Krt76 0.80 0.0001 2.54 0.010 K2C72 Keratin, type II cytoskeletal 72 Krt72 0.80 0.0001 2.22 0.040 K1C13 Keratin, type I cytoskeletal 13 Krt13 1.30 - 1.73 0.0001 K2C79 Keratin, type II cytoskeletal 79 Krt79 - - 1.70 0.007 K1C10 Keratin, type I cytoskeletal 10 Krt10 1.27 0.010 1.66 0.005 B2RTP7 Krt2 protein Krt2 - - 1.58 0.050 FIBA Fibrinogen alpha chain Fga 1.40 0.0001 1.57 0.050 HBB0 Hemoglobin subunit beta-H0 Hbb-bh0 - - 1.50 0.030 MYPR Myelin proteolipid protein Plp1 - - 0.75 0.0001 Accession no. . Entrez gene name . Gene symbol . Striatum . Hippocampus . . . . Fold change . P value . Fold change . P value . STRBP Spermatid perinuclear RNA-binding protein Strbp 2.30 0.033 - - K2C6A Keratin, type II cytoskeletal 6A Krt6a 0.53 0.009 2.00 0.0001 K1C42 Keratin, type I cytoskeletal 42 Krt42 0.60 0.030 1.52 0.009 K1C17 Keratin, type I cytoskeletal 17 Krt17 0.67 0.015 2.67 0.0006 K2C5 Keratin, type II cytoskeletal 5 Krt5 0.70 0.015 2.27 0.002 K2C1 Keratin, type II cytoskeletal 1 Krt1 0.70 0.049 2.89 0.004 E9PVG8 RIKEN cDNA 9530053A07 gene 9530053A07Rik 0.70 0.0001 2.67 0.004 Q6IFT3 Keratin Kb40 Krt78 0.80 0.0001 2.83 0.001 K22O Keratin, type II cytoskeletal 2 oral Krt76 0.80 0.0001 2.54 0.010 K2C72 Keratin, type II cytoskeletal 72 Krt72 0.80 0.0001 2.22 0.040 K1C13 Keratin, type I cytoskeletal 13 Krt13 1.30 - 1.73 0.0001 K2C79 Keratin, type II cytoskeletal 79 Krt79 - - 1.70 0.007 K1C10 Keratin, type I cytoskeletal 10 Krt10 1.27 0.010 1.66 0.005 B2RTP7 Krt2 protein Krt2 - - 1.58 0.050 FIBA Fibrinogen alpha chain Fga 1.40 0.0001 1.57 0.050 HBB0 Hemoglobin subunit beta-H0 Hbb-bh0 - - 1.50 0.030 MYPR Myelin proteolipid protein Plp1 - - 0.75 0.0001 Proteins that altered by fold change ≥1.2 or ≤0.8 at P < 0.05 in at least one brain nuclei were listed. “-” indicates not being statistically significant. Open in new tab Table 3 A list of major differentially expressed proteins in the brain nuclei from the proteomics analysis Accession no. . Entrez gene name . Gene symbol . Striatum . Hippocampus . . . . Fold change . P value . Fold change . P value . STRBP Spermatid perinuclear RNA-binding protein Strbp 2.30 0.033 - - K2C6A Keratin, type II cytoskeletal 6A Krt6a 0.53 0.009 2.00 0.0001 K1C42 Keratin, type I cytoskeletal 42 Krt42 0.60 0.030 1.52 0.009 K1C17 Keratin, type I cytoskeletal 17 Krt17 0.67 0.015 2.67 0.0006 K2C5 Keratin, type II cytoskeletal 5 Krt5 0.70 0.015 2.27 0.002 K2C1 Keratin, type II cytoskeletal 1 Krt1 0.70 0.049 2.89 0.004 E9PVG8 RIKEN cDNA 9530053A07 gene 9530053A07Rik 0.70 0.0001 2.67 0.004 Q6IFT3 Keratin Kb40 Krt78 0.80 0.0001 2.83 0.001 K22O Keratin, type II cytoskeletal 2 oral Krt76 0.80 0.0001 2.54 0.010 K2C72 Keratin, type II cytoskeletal 72 Krt72 0.80 0.0001 2.22 0.040 K1C13 Keratin, type I cytoskeletal 13 Krt13 1.30 - 1.73 0.0001 K2C79 Keratin, type II cytoskeletal 79 Krt79 - - 1.70 0.007 K1C10 Keratin, type I cytoskeletal 10 Krt10 1.27 0.010 1.66 0.005 B2RTP7 Krt2 protein Krt2 - - 1.58 0.050 FIBA Fibrinogen alpha chain Fga 1.40 0.0001 1.57 0.050 HBB0 Hemoglobin subunit beta-H0 Hbb-bh0 - - 1.50 0.030 MYPR Myelin proteolipid protein Plp1 - - 0.75 0.0001 Accession no. . Entrez gene name . Gene symbol . Striatum . Hippocampus . . . . Fold change . P value . Fold change . P value . STRBP Spermatid perinuclear RNA-binding protein Strbp 2.30 0.033 - - K2C6A Keratin, type II cytoskeletal 6A Krt6a 0.53 0.009 2.00 0.0001 K1C42 Keratin, type I cytoskeletal 42 Krt42 0.60 0.030 1.52 0.009 K1C17 Keratin, type I cytoskeletal 17 Krt17 0.67 0.015 2.67 0.0006 K2C5 Keratin, type II cytoskeletal 5 Krt5 0.70 0.015 2.27 0.002 K2C1 Keratin, type II cytoskeletal 1 Krt1 0.70 0.049 2.89 0.004 E9PVG8 RIKEN cDNA 9530053A07 gene 9530053A07Rik 0.70 0.0001 2.67 0.004 Q6IFT3 Keratin Kb40 Krt78 0.80 0.0001 2.83 0.001 K22O Keratin, type II cytoskeletal 2 oral Krt76 0.80 0.0001 2.54 0.010 K2C72 Keratin, type II cytoskeletal 72 Krt72 0.80 0.0001 2.22 0.040 K1C13 Keratin, type I cytoskeletal 13 Krt13 1.30 - 1.73 0.0001 K2C79 Keratin, type II cytoskeletal 79 Krt79 - - 1.70 0.007 K1C10 Keratin, type I cytoskeletal 10 Krt10 1.27 0.010 1.66 0.005 B2RTP7 Krt2 protein Krt2 - - 1.58 0.050 FIBA Fibrinogen alpha chain Fga 1.40 0.0001 1.57 0.050 HBB0 Hemoglobin subunit beta-H0 Hbb-bh0 - - 1.50 0.030 MYPR Myelin proteolipid protein Plp1 - - 0.75 0.0001 Proteins that altered by fold change ≥1.2 or ≤0.8 at P < 0.05 in at least one brain nuclei were listed. “-” indicates not being statistically significant. Open in new tab Diseases and biological function analysis To gain a better understanding of 102 differentially expressed proteins between different brain nuclei, a detailed analysis of their biological functions was performed using IPA software and carried out on the aspects of diseases and disorders, molecular and cellular functions and physiological system development and function. The P-value was applied to analyze qualitative correlation. The top 10 enriched disease/biological functions were shown based on the P-value, and the most significantly enriched disease/biological function was dermatological disease and condition for both nuclei (Fig. 3A). Developmental disorder, hereditary disorder, organismal injury and abnormalities were the followed diseases/biological functions (Fig. 3A). The known toxicity of alcohol, namely inflammatory response, immunological disorders and inflammatory disease were also overrepresented in the striatum and hippocampus, mirroring the consistent toxicity of alcohol on both brain regions (Fig. 3A). Fig. 3. Open in new tabDownload slide The proteomic analysis data in details. (A) Bioinformatic analysis of proteomic data using IPA. Enriched diseases and biofunctions with their P-values (threshold set to 0.05, corresponding to 1.30 on the X-axis) were plotted for the striatum and hippocampus, respectively. (B) Canonical pathway analysis of proteomic data using IPA. Enriched pathways with their P-values (the threshold was set to 0.05, corresponding to 1.30 on the X-axis) were plotted for the striatum and hippocampus, respectively. (C) Functional networks of proteomic data. Networks of 35 proteins were generated by IPA for the data sets obtained from the striatum and hippocampus treated with alcohol. Up-regulation and down-regulation are indicated by red and green colors, respectively. Color intensity corresponds to the degree of alteration. Solid line represents direct interaction and dotted line represents indirect interaction. Fig. 3. Open in new tabDownload slide The proteomic analysis data in details. (A) Bioinformatic analysis of proteomic data using IPA. Enriched diseases and biofunctions with their P-values (threshold set to 0.05, corresponding to 1.30 on the X-axis) were plotted for the striatum and hippocampus, respectively. (B) Canonical pathway analysis of proteomic data using IPA. Enriched pathways with their P-values (the threshold was set to 0.05, corresponding to 1.30 on the X-axis) were plotted for the striatum and hippocampus, respectively. (C) Functional networks of proteomic data. Networks of 35 proteins were generated by IPA for the data sets obtained from the striatum and hippocampus treated with alcohol. Up-regulation and down-regulation are indicated by red and green colors, respectively. Color intensity corresponds to the degree of alteration. Solid line represents direct interaction and dotted line represents indirect interaction. Canonical pathways analysis IPA identified that many canonical pathways were significantly affected by alcohol insult in two brain regions. The top 10 canonical pathways, as ranked by P-value, were shown in Fig. 3B. The most significantly enriched pathway was the glucocorticoid receptor (GR) signaling in both brain regions (P < 0.0001). This finding reinforced the involvement of many keratins in alcohol toxicity in the two brain regions since keratins composed of key molecules involved in the GR Signaling. Strikingly, though both the striatum and hippocampus shared many keratins in the GR signaling, these keratins were inversely expressed in these nuclei (Table 4). In addition to the GR signaling, other enriched pathways differed significantly between the two brain regions. For instance, the following enriched canonical pathway was Granzyme A Signaling in the striatum and GABA Receptor Signaling in the hippocampus, respectively. Table 4 Molecules involved in the GR signaling in the striatum and hippocampus Nucleus . Molecules . Striatum Krt16, Krt13↑, Krt6B, Krt76↓, Krt17↓, Krt5↓, Krt72↓, Krt1↓, Krt78↓, UBE2I Hippocampus Krt16, Krt13↑, Krt6B, Krt76↑, Krt17↑, Krt5↑, Krt72↑, Krt1↑, Krt78↑, Krt79↑ Nucleus . Molecules . Striatum Krt16, Krt13↑, Krt6B, Krt76↓, Krt17↓, Krt5↓, Krt72↓, Krt1↓, Krt78↓, UBE2I Hippocampus Krt16, Krt13↑, Krt6B, Krt76↑, Krt17↑, Krt5↑, Krt72↑, Krt1↑, Krt78↑, Krt79↑ “↑” indicates up-regulation in nuclei and “↓“indicates down-regulation in nuclei. Open in new tab Table 4 Molecules involved in the GR signaling in the striatum and hippocampus Nucleus . Molecules . Striatum Krt16, Krt13↑, Krt6B, Krt76↓, Krt17↓, Krt5↓, Krt72↓, Krt1↓, Krt78↓, UBE2I Hippocampus Krt16, Krt13↑, Krt6B, Krt76↑, Krt17↑, Krt5↑, Krt72↑, Krt1↑, Krt78↑, Krt79↑ Nucleus . Molecules . Striatum Krt16, Krt13↑, Krt6B, Krt76↓, Krt17↓, Krt5↓, Krt72↓, Krt1↓, Krt78↓, UBE2I Hippocampus Krt16, Krt13↑, Krt6B, Krt76↑, Krt17↑, Krt5↑, Krt72↑, Krt1↑, Krt78↑, Krt79↑ “↑” indicates up-regulation in nuclei and “↓“indicates down-regulation in nuclei. Open in new tab Effects of chronic alcohol on striatum and hippocampus networks For striatum and hippocampus, IPA also generated statistically significant functional network of 35 related proteins, which were visualized in Fig. 3C. The network for the striatum was clustered in cellular and organismal function, while the network for the hippocampus clustered in cellular related functions. Analysis of these two network commonalities and differences found that only 6 out of the 35 proteins (17%) were throughout the striatum and hippocampal networks. The number of up-regulated proteins in the hippocampus network was significantly more than that in the striatum’s network. In particular, Krt17, one of the shared proteins, showed differential upstream regulators between the hippocampus and striatum (Fig. 3C). Combining all of the above analyses, the differences between the brain nuclei were highlighted. Validation of gene expression by qRT-PCR analysis To further verify the reliability of the iTRAQ results, 12 proteins with significant alterations in both hippocampus and striatum were selected and subjected to qRT-PCR analysis to confirm their mRNA expression based on the results of Table 3. Results showed that 9 (75%) of the 12 genes were significantly down-regulated in the striatum but up-regulated in the hippocampus, indicating an opposite expression pattern that was consistently observed in the iTRAQ analysis. The other three genes showed consistent up-regulation after alcohol consumption in the two nuclei, which were also similar with iTRAQ (Fig. 4). All these findings suggested that the iTRAQ results in the present study were reliable that the key keratins were inversely regulated in different nuclei by alcohol consumption (P < 0.05). Fig. 4. Open in new tabDownload slide Validation of the major expressed genes by qRT-PCR. The red color represented up-regulation and the blue color represented down-regulation. Data were expressed as mean ± SEM. n = 5 mice in each group *P < 0.05; **P < 0.01 alcohol vs. control. Fig. 4. Open in new tabDownload slide Validation of the major expressed genes by qRT-PCR. The red color represented up-regulation and the blue color represented down-regulation. Data were expressed as mean ± SEM. n = 5 mice in each group *P < 0.05; **P < 0.01 alcohol vs. control. Discussion Alcohol-induced chronic neurotoxicity remains a global health problem and the molecular mechanisms underlying the neuronal signaling changes in alcohol addiction are complex and multifaceted. One fact that hinders the understanding of alcohol neurotoxicity is that there is cellular heterogeneity between brain regions (Craft et al., 2013). Therefore, it is inadequate to focus on single nucleus or molecule. Alternatively, investigation of different brain nuclei proteome from a comprehensive perspective may confer a high-throughput mapping of molecular alterations in response to chronic alcohol insult. The current study utilized iTRAQ-based proteomics to study the protein expression profile after chronic alcohol exposure and found that 144 proteins were differentially expressed in the striatum and hippocampus under the same condition. Both nuclei shared the top enriched pathway, namely GR signaling, in response to alcohol insult. However, some significantly altered molecules (keratins) from the glucocorticoid signaling presented inverse expression pattern between brain regions. Among all the differentially expressed proteins, members of keratin family were significantly enriched, such as Krt17, Krt5 and Krt6. Keratins signal through the GR pathway (Bayo et al., 2008). GR modulates gene expression by DNA binding-dependent (transactivation) and independent (transrepression) mechanisms (Donet et al., 2008). GR can regulate the growth and apoptosis of keratinocytes in a cellular autonomous manner (Bayo et al., 2008). GR can regulate the expression of some basal keratins (Krt5 and Krt14) or migration-related keratins (Krt6 and Krt16) by binding to AP-1 and interaction of four GR monomers with Krt6 promoter, respectively (Radoja et al., 2000). As important components of the GR signaling, keratins were considered as key mediators in various carcinomas (Li et al., 2016). Keratins were identified in the brain since 1987 (Franko et al., 1987). Recent evidence suggests that keratin 9 may be associated with neurological disorders such as dementia (Jiang et al., 2012), and may be an independent biomarker for the diagnosis of Alzheimer’s disease (Richens et al., 2016). In this study, it was found that the hippocampus and striatum shared the same diseases and biofunctions, which included the dermatological disease and condition, developmental disorder, hereditary disorder, organismal injury and abnormalities and inflammatory responses. Moreover, the GR signaling was among the top enriched pathways by both nuclei. Interestingly, some studies have found that withdrawal from chronic alcohol intake raised brain glucocorticoid concentrations and led to the variation of GR (Beracochea et al., 2019) and GR played an important role in alcohol self-administration behavior by its interaction with the DA system in the brain (Rotter et al., 2012). Mifepristone, a type of GR antagonist, has been found to be a valuable pharmacotherapeutic strategy for preventing relapse to alcohol use disorders (Vendruscolo et al., 2015). Meanwhile, the findings of our research reinforced the involvement of GR signaling in alcohol toxicity and providing a theoretical basis for GR participation in chronic alcohol addiction. All these findings were conclusive that keratins from the GR signaling were associated with alcohol neurotoxicity. One interesting finding was that the major keratins exhibited opposite expression patterns between the two brain regions, which indicated the regional differences in alcohol neurotoxicity. In addition to the GR signaling, other pathways showed significant difference between the two nuclei, further validating the differential signal transduction between nuclei. Explanation of the issue might be multifaceted. On one hand, the GR signal was differently regulated across nuclei under the action of alcohol. Studies have shown that c-Jun N-terminal kinases (JNK) is a key negative regulator of GR-mediated signaling, while chronic alcohol exposure can lead to region-specific deficits in JNK phosphorylation. Decreased activity of p-JNK leads to greater GR activity (Pahng et al., 2019). On the other hand, the distinction of direct regulators of keratins might also account for alcohol-induced spatial differences. The upstream regulator of Krt17, for example, was nuclear factor kB (NF-kB) in the striatum as illustrated by the network. NF-kB is an inflammatory mediator known for its role in immune response. Recent studies have shown that alcohol abuse and other drugs can induce NF-kB activity (Hijazi et al., 2017) and the release of brain cytokines (Nennig and Schank, 2017). Previous studies have shown that there is an interaction between NF-kB and glucocorticoid in the pathogenesis of breast cancer, which plays a critical role in the occurrence and development of breast cancer (Ling and Kumar, 2012). The induction of NF-kB activity by alcohol in the striatum might be a causal contributor to down-regulated Krt17. By contrast, the upstream regulators of Krt17 were NF-kB and annexin A2 (ANXA2) in the hippocampus as illustrated by the network. ANXA2 is a protein related to hepatic fibrosis (Zhang et al., 2010) and was reported to be related to liver fibrosis caused by alcohol (Seth et al., 2003). Studies have also shown that alcohol exposure can significantly affect the hippocampal proteome, including ANXA2 (Davis-Anderson et al., 2018). Regulating the expression of ANXA2 can change the stability of Krt17, which proves that ANXA2 is a potential regulator of keratins (Chung et al., 2012). Although NF-kB is the upstream regulator of Krt17 in the striatum and hippocampus, ANXA2 only acts as the upstream regulator of Krt17 in the hippocampus and may interact with Krt17, resulting in the opposite expression of Krt17 in different brain nuclei. Therefore, albeit the hippocampus and striatum shared GR signaling as the top enriched pathway, the detailed specific molecule from the GR signaling went the opposite way in mediating alcohol neurotoxicity, suggesting that different nuclei might have opposing molecular networks, thus further verifying our hypothesis and highlighting the importance of performing comprehensive study of nuclei in response to alcohol insults. The identification of keratins from GR signaling as the novel molecules in alcohol neurotoxicity is of biological importance. Studies on the relationship between keratins and alcohol toxicity mostly focused on liver (Forsyth et al., 2017). Krt8 and Krt18 were abundant in hepatocytes and play a central role in guarding hepatocytes from apoptosis as cytoprotective stress proteins (Ku et al., 2007). Studies have shown that dephosphorylation of Krt8 and 18 induced by alcohol feeding may be an early step in alcoholic hepatitis (Eckert and Yeagle, 1996). In isolated hepatocytes, both Krt8 and 18 were found to be hyperphosphorylated after acute alcohol exposure (Kawahara et al., 1990). The expression of Krt8 and 18 can be considered as biomarkers for the progression of alcoholic liver disease (Shepard and Tuma, 2010). The current study broadened the link between keratins and alcohol toxicity and found that they were significantly associated in the brain nuclei, suggesting the wide involvement of keratins in alcohol neurotoxicity. A study of all regions of the striatum in alcoholic brains identified changes to several proteins not directly related to the function and metabolism of neurotransmitters in the dorsal striatum and these may reflect more general aspects of the damage caused by long-term heavy drinking (Kashem et al., 2016). The significant alterations of keratins in the striatum and hippocampus after chronic alcoholism found in this study not only supplemented the above findings, but also added a more solid foundation for the understanding of the relationship between alcohol addiction and alcohol-related disorders. Second, keratins in different nuclei were not always consistent as revealed by the present study. Some keratins were up-regulated in the hippocampus while they were down-regulated in the striatum. Our findings suggested that drugs targeting single molecule might not confer protection against all brain nuclei. In conclusion, the study of keratins in the striatum and hippocampus after chronic alcohol exposure suggested that keratins may be involved in the process of alcohol injury. Our data highlighted keratins from the GR signaling as novel mediators of alcohol neurotoxicity. It is also noteworthy that though identical proteins were altered in different nuclei, the function of specific protein was distinct or even opposite between different brain regions. Our data suggested that in the study of central nervous system impairment, we should not merely focus on single nucleus or molecule, but look into the drug-induced neurotoxicity from a comprehensive perspective. Supplementary Material Supplementary material is available at Alcohol and Alcoholism online. Acknowledgements This research was financially supported by the National Natural Science Foundation of China (Grant Nos. 81571849 and 81871525). Compliance with Ethical Standards Ethical approval All experimental procedures were conducted with the protocols for animal experiments approved by the Ethical Review Committee at the School of Basic Medical Sciences, Fudan University, Shanghai, China (Approval No. 20150119-091). Conflict of Interest The authors declare that they have no conflict of interest. References Aggarwal S , Yadav AK ( 2016 ) Dissecting the iTRAQ data analysis . Methods Mol Biol 1362 : 277 – 91 . Google Scholar Crossref Search ADS PubMed WorldCat Aracava Y , Froes-Ferrao MM , Pereira EF , et al. ( 1991 ) Sensitivity of N-methyl-D-aspartate (NMDA) and nicotinic acetylcholine receptors to ethanol and pyrazole . Ann N Y Acad Sci 625 : 451 – 72 . Google Scholar Crossref Search ADS PubMed WorldCat Assuncao M , Santos-Marques MJ , de Freitas V, et al. ( 2007 ) Red wine antioxidants protect hippocampal neurons against ethanol-induced damage: a biochemical, morphological and behavioral study . 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TI - Proteomic Analysis of Brain Regions Reveals Brain Regional Differences and the Involvement of Multiple Keratins in Chronic Alcohol Neurotoxicity
JO - Alcohol and Alcoholism
DO - 10.1093/alcalc/agaa007
DA - 2020-03-19
UR - https://www.deepdyve.com/lp/oxford-university-press/proteomic-analysis-of-brain-regions-reveals-brain-regional-differences-0VCdWofbva
DP - DeepDyve
ER -