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Research Article

Altered Functional Protein Networks in the Prefrontal Cortex and Amygdala of Victims of Suicide

  • Katalin Adrienna Kékesi mail,

    kakekesi@dec001.geobio.elte.hu

    Affiliations: Laboratory of Proteomics, Institute of Biology, Eötvös Loránd University, Budapest, Hungary, Department of Physiology and Neurobiology, Eötvös Loránd University, Budapest, Hungary

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  • Gábor Juhász,

    Affiliation: Laboratory of Proteomics, Institute of Biology, Eötvös Loránd University, Budapest, Hungary

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  • Attila Simor,

    Affiliation: Laboratory of Proteomics, Institute of Biology, Eötvös Loránd University, Budapest, Hungary

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  • Péter Gulyássy,

    Affiliation: Laboratory of Proteomics, Institute of Biology, Eötvös Loránd University, Budapest, Hungary

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  • Éva Mónika Szegő,

    Affiliation: Laboratory of Proteomics, Institute of Biology, Eötvös Loránd University, Budapest, Hungary

    Current address: Department of Neurodegeneration and Restorative Research, Georg-August University, DFG Research Center: Molecular Physiology of the Brain (CMPB), Göttingen, Waldweg, Germany

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  • Éva Hunyadi-Gulyás,

    Affiliation: Proteomics Research Group, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

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  • Zsuzsanna Darula,

    Affiliation: Proteomics Research Group, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary

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  • Katalin F. Medzihradszky,

    Affiliations: Proteomics Research Group, Institute of Biochemistry, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Hungary, Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, Calofornia, United States of America

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  • Miklós Palkovits,

    Affiliation: Neuromorphology Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary

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  • Botond Penke,

    Affiliation: Medical Chemistry Department, University of Szeged, Szeged, Hungary

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  • András Czurkó

    Affiliations: Laboratory of Proteomics, Institute of Biology, Eötvös Loránd University, Budapest, Hungary, Medical Chemistry Department, University of Szeged, Szeged, Hungary

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  • Published: December 06, 2012
  • DOI: 10.1371/journal.pone.0050532

Abstract

Probing molecular brain mechanisms related to increased suicide risk is an important issue in biological psychiatry research. Gene expression studies on post mortem brains indicate extensive changes prior to a successful suicide attempt; however, proteomic studies are scarce. Thus, we performed a DIGE proteomic analysis of post mortem tissue samples from the prefrontal cortex and amygdala of suicide victims to identify protein changes and biomarker candidates of suicide. Among our matched spots we found 46 and 16 significant differences in the prefrontal cortex and amygdala, respectively; by using the industry standard t test and 1.3 fold change as cut off for significance. Because of the risk of false discoveries (FDR) in these data, we also made FDR adjustment by calculating the q-values for all the t tests performed and by using 0.06 and 0.4 as alpha thresholds we reduced the number of significant spots to 27 and 9 respectively. From these we identified 59 proteins in the cortex and 11 proteins in the amygdala. These proteins are related to biological functions and structures such as metabolism, the redox system, the cytoskeleton, synaptic function, and proteolysis. Thirteen of these proteins (CBR1, DPYSL2, EFHD2, FKBP4, GFAP, GLUL, HSPA8, NEFL, NEFM, PGAM1, PRDX6, SELENBP1 and VIM,) have already been suggested to be biomarkers of psychiatric disorders at protein or genome level. We also pointed out 9 proteins that changed in both the amygdala and the cortex, and from these, GFAP, INA, NEFL, NEFM and TUBA1 are interacting cytoskeletal proteins that have a functional connection to glutamate, GABA, and serotonin receptors. Moreover, ACTB, CTSD and GFAP displayed opposite changes in the two examined brain structures that might be a suitable characteristic for brain imaging studies. The opposite changes of ACTB, CTSD and GFAP in the two brain structures were validated by western blot analysis.

Introduction

Suicide is a human attribute without a proper equivalent in animals; however, some behavioural traits, such as aggression, hopelessness, and impulsivity, are correlated with suicide and can be reproduced in animals [1]. Suicidal behaviour often occurs in conjunction with different psychiatric diseases, such as major depression or schizophrenia [2]. Major depression and bipolar disorder generally increase the incidence of suicide [3].

Although suicide is a complex behaviour that is often preceded by suicidal thoughts, it can occur as the outcome of an impulsive action [4]. The altered serotonergic transmission theory is the most widely emphasised cellular mechanism of suicide [4], [5]. Suicide is linked with the downregulation of serotonin (5HT) release and/or uptake [6] together with 5-HT1A receptor dysfunction. These dysfunctions are thought to be major factors in several mental disorders, including major depression [7]; however, the current gene expression data suggest that suicide is possibly correlated with extensive changes in the brain and is not restricted to only one neurotransmitter system [8], [9], [10]. In addition to changes that have been observed in the serotonergic system, studies on brain samples of people who have committed suicide suggest that GABAergic and glutamatergic transmissions are also involved [11], [12]. Furthermore, changes in the expression of glia-derived genes and glial fibrillary acidic protein (GFAP) in depression and other psychiatric illnesses indicate that suicide-related molecular alterations may not be restricted to neurons [13]. Most likely, molecular mechanisms in the brain that lead to suicide coexist with pathological changes along several functional protein networks. Suicide-brain studies that show that hyper-methylation of the ribosomal-RNA gene promoter could cause aberrant changes in protein synthesis [14] support this idea. Psychoactive drugs can change the risk of suicide, and there are ongoing efforts to find potential biomarkers to predict suicidal behaviours [15], [16], [17], [18], [19], [20]. Thus, understanding the molecular brain mechanisms involved in suicide is important for the development of both psychoactive drugs and predictive diagnostic tools.

Screening technology progress in the past two decades (e.g., the gene chip and the 2D gel-based and liquid-based proteomic techniques) have provided new insights into the molecular processes of the brain [21]. Because suicide cannot be observed in animals, investigating post mortem human brains with a relatively short post mortem delay is a good alternative. Particularly, the post mortem human brain proteome reflects the complex pathological changes of protein expression in the human brain while alive [21]. A homogeneous sample is usually unlikely in such studies because suicide and its associated psychiatric disorders and medications differentially influence various underlying molecular mechanisms. Therefore, in the present study we used brain samples from people who had hanged themselves and from individuals who died due to acute cardiac arrest to decrease the heterogeneity of data. We examined prefrontal cortex and amygdala samples because mood disorders invoke several neuronal mechanisms in these brain areas and are correlated with suicide [1], [7].

Our aim was to find changes in the proteome of the prefrontal cortex and amygdala that correlated with suicide. Changes in protein expression patterns may reflect molecular changes of psychopathological states and could provide biomarkers for suicide risk.

Methods

Ethics Statement

The human brains were obtained from the Lenhossek Human Brain Program, Human Brain Tissue Bank, Budapest. Brains were taken from persons who had died without any known neurodegenerative diseases. The collection of brains and the microdissection of the brain samples for research have been performed by the approval of the Regional Committee of Science and Research Ethics of the Semmelweis University, Budapest (TUKEB: 32/92) and the Ethics Committee of the Ministry of Health, Hungary, 2002 according to the principles expressed in the Declaration of Helsinki. Tissues were collected only after a family member gave informed (written) consent.

Sample Collection and Preparation for Proteomics

We used brain samples from male subjects. The age distributions of suicide (6 brains; age range: 41–79 years; mean age: 52.7; SD: 14.2) and control (6 brains; age range: 47–85 years; mean age average: 64.8; SD: 17.2) groups did not differ significantly (p = 0.1481, Wilcoxon test; Table 1). Suicide group brain samples came from subjects who had hanged themselves, control group brain samples came from victims of cardiac arrest. No information was available whether the cardiac arrest in control subjects happened during sleep or not. The post mortem interval (PMI) did not differ significantly between groups (p = 0.0683, Wilcoxon test; Table 1). We used two brain areas - the prefrontal cortex and the amygdala – to conduct proteomic analyses. We treated and handled brain samples as described in a previous publication [22]; briefly, brains were removed from the skull 2–6 hours after death, frozen, and sliced into 1– to 1.5 cm-thick coronal sections. We used the punch technique to micro-dissect the brain areas. Tissue samples were stored at –80°C until used. In this study, we processed one cortex and one amygdala samples from 6 suicide and 6 control subjects, meaning a total of 24 human post mortem brain tissue samples.

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Table 1. Description of participants in the present study.

doi:10.1371/journal.pone.0050532.t001

The brain sample preparation protocol was similar to previous studies [23], [24]; briefly, we mechanically homogenised tissue samples in a cooled lysis buffer (7 M urea; 2 M thiourea; 20 mM Tris; 5 mM magnesium acetate, 4% CHAPS; Protease Inhibitor Mix (1:1000), GE Healthcare, Uppsala, Sweden). Samples were then sonicated and centrifuged (1 h, 14000 g, 4°C). The pH of the supernatant was adjusted to 8.0 and protein concentrations of the samples were measured by PlusOne Quant Kit (GE Healthcare). We labelled 5 µg of each protein sample with CyDye™ DIGE Fluor Labelling kit for Scarce Samples (GE Healthcare) at a concentration of 4 nmol/5 µg proteins according to instructions.

We labelled the experimental samples (control and suicide samples) as Cy5 and the pooled internal standard samples (reference or standard sample, is a pool comprising equal amounts (2.5 µg) of each of the experimental samples being compared) as Cy3. The pooled standard represents the average of all the samples being analyzed and ensures all proteins present in the experimental samples are represented. The pooled standard is used to normalize protein abundance measurements across multiple gels in an experiment. As a consequence each gel will contain an image with a highly similar spot pattern, simplifying and improving the confidence of inter-gel spot matching and quantification [25].

We multiplexed the differently labelled samples in the same gel. Sample multiplexing in DIGE greatly refines the detection of changes at the protein level between samples [26], as variation in spot intensities due to experimental factors, for example protein loss during sample entry into the strip, will be the same for both samples within a single DIGE gel [25].

The multiplexed, differently labelled samples (5 µg protein of Cy5-labelled and 5 µg protein of Cy3-labelled reference) were dissolved in isoelectric focusing (IEF) buffer containing ampholytes (0.5 v/v %), DTT (0.5 m/v %), 8 M urea, 30% glycerine, 2% CHAPS, and rehydrated passively onto 24 cm nonlinear IPG strips (pH 3–10 NL, GE Healthcare) overnight at room temperature. After rehydration, the strips were placed to first dimension isoelectric focusing (IPGPhore, GE Healthcare) for 24 h to attain a total of 80 kVh. The applied currents were: 30 V for 3.5 h step, 500 V for 5 h gradient, 1000 V for 6 h gradient, 8000 V for 3 h gradient, and 8000 V for 6.5 h step mode. Focused proteins were reduced by equilibrating with buffer containing 1% (w/v) mercaptoethanol for 20 min. After reduction the IPG strips were loaded onto 10% polyacrylamide gels (24×20 cm), and SDS-PAGE was conducted at 2 W/gel for 1 h and at 10 W/gel in the second dimension.

We prepared 12 gels from both areas because one experimental sample and one pooled standard reference sample can be loaded into one gel with the Labelling kit for Scarce Samples (GE Healthcare). Following electrophoresis, gels were scanned by a Typhoon TRIO+ Variable Mode Imager (GE Healthcare) using appropriate lasers and filters with the photomultiplier tube (PMT) biased at 600 V. Cy3 images were scanned using a 532 nm laser and an emission filter of 580 nm BP (band pass) 30. Cy5 images were scanned using a 633 nm laser and a 670 nm BP30 emission filter. All gels were scanned at 100 µm resolution. Images in different channels were overlaid using selected colours, and differences were visualised using Image Quant software (GE Healthcare). We used the DeCyder 6.5 2D gel evaluation software (GE Healthcare); the Differential In-gel Analysis (DIA) module to perform differential protein analyses and the Biological Variance Analysis (BVA) module to gel-to-gel matching and statistical analysis of protein-abundance change between samples.

In the DIA module the scanned images of the sample and the internal standard were overlaid and the algorithms within the software co-detected the spots in the gel. The estimated number of spots for each co-detection procedure was set to 2500. When calculating the abundance ratios for spot pairs in co-detected sample images, the spot volumes of the component spot maps needed to be normalized and the log standardized abundances were calculated.

The statistical analysis of protein-abundance change between samples was made by the BVA module. The BVA matched the quantified spots of all gels to a chosen master gel. According to the standard proteomic protocol [25], the threshold for the differential expression was set at a minimum fold change of 1.3 as we used human samples and the quality of the gels were adequate. We determined the p-values (Student’s t-test) for each protein spot (p<0.05).

To identify proteins in the spots of interest, we performed preparative 2D electrophoresis using 800 µg of proteins per gel. We made four preparative gels and picked the relevant spots for protein identification.

Protein Identification

We extracted peptides from gel spots after in-gel digestion by Trypsin Gold (for a detailed protocol, see http://ms-facility.ucsf.edu/ingel.html). Peptide separation before MS analysis was done by HPLC started by inline trapping on to a nanoACQUITY UPLC trapping column (Symmetry, C18 5 µm, 180 µm × 20 mm; 15 µl/min with 3% solvent B) followed by a linear gradient elution (solvent B: 10% to 50% in 40 min, flow rate: 250 nl/min; nanoACQUITY UPLC BEH C18 Column, 1.7 µm, 75 µm × 200 mm). Solvent A was composed of 0.1% formic acid in water; solvent B was composed of 0.1% formic acid in acetonitrile. MS measurements started by using information-dependent acquisition mode, using a Waters nanoAcquity nanoUPLC system coupled to a Micromass qTOF tandem mass spectrometer (Waters, USA). Next, 3 s collision-induced dissociation (CID) analyses on multiple computer-selected ions were performed for amino acid sequence determination.

Database Search

We converted raw MS data into a Mascot generic file using the Mascot Distiller software (version 2.1.1.0). We used the Mascot search engine (version 2.2.2) to search the resulting peak lists against the NCBI non-redundant database without species restriction (6,833,826 sequences), to eliminate false positive hits. We submitted monoisotopic masses with a peptide mass tolerance of at least 50 ppm and a fragment mass tolerance of at least 0.1 Da. We set the carbamidomethylation of Cys as a fixed modification, and we permitted acetylation of the protein N-termini, methionine oxidation and pyroglutamic acid formation from N-terminal Gln residues as variable modifications. The acceptance criterion was the identification of at least two significant peptides per protein (i.e., peptide score >52, p<0.05).

Correction for False Discovery Rate (FDR)

When applying statistical tests to 2-D gel data, one is faced with the so-called multiple hypothesis testing problem: for each matched and quantified spot series, a separate test is done. Each test has a certain probability of giving a false positive result, and the large number of tests can produce a high number of false positives [27]. This has led to the application of methodologies to control the false discovery rate (FDR) where FDR is the rate of false positive results among all profiles that were tested positive (type I errors).

The original FDR methodology was considered to be too conservative for discovery experiments consequently, an extension to the FDR was developed by Storey that calculates a q-value [28].

The q-values were calculated from the p-values obtained for all features within the study with the statistics software, R (R Development Core Team (2011)). R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/) [29] by using an easy to use tool (QVALUE software ver. 1.0) developed by Storey and Tibshirani [28]. The frequency distributions of P-values were used to estimate the proportion of features that are unchanging; this is then used to estimate the false discovery rate (Fig. S1).

Careful observation of the P-values histograms suggested that the shape of the histograms were not the most desirable shape, although they were acceptable. Note, that the Student’s t test we used is a simple test that assumes the data are randomly sampled from normal distributions and shows homogeneity of variance. In DIGE with the traditional three-dye approach, Karp et al. demonstrated that the final standardized abundance data for the spots are not truly independent [30]. However, we used the two-dye design in this study where the Student’s t test was adequate [30], [31].

The histograms of P-values of the prefrontal cortex and amygdala were dense near zero and became less dense as the P-values increased. The amygdala P-histogram contained a wider peak indicating that less spots were detected as significantly changing. By observing their q-value cut-off histograms (Fig. S1) we used 0.06 and 0.4 as q-values alpha thresholds for FDR adjustment of significant spots of the prefrontal cortex and amygdala, respectively.

Functional Clustering of Identified Proteins

Following an extensive literature search, we formed the functional protein clusters using PDB (http://www.pdb.org, La Jolla, CA, USA), ExPASy and UniProt databases (http://www.expasy.org and http://www.uniprot.org, respectively; Swiss Institute of Bioinformatics, Switzerland). From our data pool we selected 11 proteins that changed in both the amygdala and the cortex for detailed protein interaction modelling analyses using PathwayStudio® 6.2 software (Ariadne Genomics, Inc., Rockville, MD, USA). The protein network model created was manually verified using the PubMed database (http://www.ncbi.nlm.nih.gov, MD, USA).

Western Blot

Frozen brain samples were homogenized as described earlier [24]. Protein lysates (20 µg) were resolved on a 10% polyacrylamide gel. Proteins were transferred onto a nitrocellulose membrane (Bio-Rad, USA). Membranes were blocked in 5% BSA in TRIS-Tween buffer (500 mM TRIS, 150 mM sodium chloride, pH 7.4, and 0.05% Tween 20 (Sigma)) for 1 h, incubated with polyclonal anti-cathepsin 1B (1:1000, Santa Cruz Biotechnology, CA, USA), anti-GFAP (1:1000, DAKO, Denmark) or anti-actin (1:5000, Sigma, Hungary) antibodies in TRIS-Tween buffer for 24 h at 4°C. After incubation with ECL-HRP-conjugated secondary antibody (1:5000, GE Healthcare, Germany), bands were visualized using a Chemiluminescence kit (BioRad, CA, USA). Ponceau staining was used as control for equal protein load and transfer.

Results

We used DIGE proteomics technology to investigate the differences in the protein expression pattern of suicide compared to control brain samples. We detected a total of 2,465 spots (after exclusion of false spots) from the prefrontal cortex and 2,115 from the amygdala on the master gels, defined to be the gel containing the most spots. Representative gel is shown in Figure 1. We performed the spot gel-to-gel matching with the DeCyder 6.5 software (GE Healthcare) BVA module and after careful and rigours manual validation we matched 681 spots in the prefrontal cortex samples and 696 in the amygdala samples. From these matched spots with the t test and 1.3 fold change as cut off we found 46 significant differences between the control and suicide prefrontal cortex samples from which we could identify 84 proteins (see Table S1). Regarding the amygdala, 16 matched spots showed significant differences, and 20 proteins were identified from them (see Table S2). After FDR adjustment we had 27 significant spots in the prefrontal cortex and 9 significant spots in the amygdala. This way the number of protein “hits” in the proposed profile reduced to 59 proteins in the prefrontal cortex and 11 proteins in the amygdala (see bold-italic gene names in Tables 2 and 3).

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Figure 1. Representative gel image.

The first dimension was carried out in pH 3–10 NL IPG strip and the second dimension was 24×20 cm 10% SDS PAGE. Part A shows the overlaid image, part B shows the standardized log abundance of a representative spot (2406, prefrontal cortex) on the different gels, part C shows 3D views of the individual spots (C1–C6: control brains; S1–S6: suicide brains).

doi:10.1371/journal.pone.0050532.g001
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Table 2. Functionally clustered protein changes in the prefrontal cortex.

doi:10.1371/journal.pcbi.1002817.t001

Clustering of proteins in the prefrontal cortex revealed the following categories: cytoskeleton, signalling, metabolism, protein processing, development, synapse and neuron, proteolysis, RNA/DNA metabolism, redox system, and glia cell marker (see Table 2). Changes in the protein expression pattern of the amygdala were smaller, but they formed almost the same clusters as the cortical protein changes (see Table 3). The direction of change in the two brain structures was the opposite for several proteins. We identified several proteins in more than one spot of the 2D gel, most likely due to posttranslational or post mortem processing. Thus, whenever more than one arrow is included, they represent the number of spots in which the protein was identified; the direction of each arrow shows the direction of change in a certain spot (see Tables 2 and 3). The numerical values of changes and p-values of significance are shown in the Supplementary material (see Table S1 and S2).

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Table 3. Functionally clustered changes in proteins of the amygdala.

doi:10.1371/journal.pone.0050532.t003

Functional protein clusters of the amygdala and prefrontal cortex demonstrated both similarities and differences in the brains of suicide victims compared to controls. Of the nine proteins whose levels were altered in both the brain structures (Table 4), three (actin (ACTB), glial fibrillary acidic protein (GFAP) and cathepsin D (CTSD)) showed altered levels in opposing directions; elevated in the amygdala and lower in the cortex.

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Table 4. Altered proteins in the prefrontal cortex and amygdala.

doi:10.1371/journal.pone.0050532.t004

In an attempt to validate our proteomic results, western blot analysis was carried out on the proteins that showed opposing directions of change in the two brain structures as these proteins are the most promising biomarker protein candidates, e.g. for brain imaging PET probe targets. Expressions of cathepsin (p = 0.0321) and GFAP (p = 0.0192) were significantly decreased in the suicide prefrontal cortex samples compared to the control samples, while in the amygdala, the expression of cathepsin (p = 0.0164) and GFAP (p = 0.0383) significantly increased in suicide samples (Figure 2). In case of the actin we also observed decreased level in the cortex and increased level in the amygdala of suicide samples although these changes were not significant because of high SD and low n (Figure 2).

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Figure 2. Western blot validation of GFAP, cathepsin and actin expressions in the cortex and amygdala of suicide and control subjects.

The expressions of GFAP and cathepsin were significantly decreased in the suicide prefrontal cortex compared to the control samples while in the amygdala their expressions were significantly increased. In case of the actin similar but non-significant changes were found. The loading control was Ponceau, mean ± SEM.

doi:10.1371/journal.pone.0050532.g002

Another set of proteins displayed parallel changes in both brain structures: creatin kinase B-type (CKB), alpha-internexin (INA), neurofilament light polypeptide (NEFL), neurofilament medium polypeptide (NEFM), tubulin alpha-1B chain (TUBA1A) and heat shock cognate 71 kDa protein (HSPA8). We did not find proteins change simultaneously in the prefrontal cortex and amygdala in functional categories: signalling, redox system and development.

Interestingly, nearly half of the altered proteins in the wider data pool had already been identified as indicative factors of suicide risk (see Table 2 and 3). In our study, we identified 35 proteins from the cortex and 16 proteins from the amygdala that had been previously linked to schizophrenia. We also identified 21 protein changes from the cortex and 9 from the amygdala that are related to depression as well as 5 proteins from the cortex and 2 proteins from the amygdala mentioned in the suicide literature (see Table 2 and 3). In this study, we identified 43 proteins from the cortex and 2 proteins from the amygdala that have never been connected to schizophrenia or depression.

Discussion

In this study, we found changes in the expression of several proteins in the amygdala and the prefrontal cortex of suicide victims using proteomics technology. Our data reflect the widely accepted idea that suicide is the result of complex interactions of psychopathology-related molecular events [32], [33], [34], [35], [36] because several of the altered proteins have already been linked to psychiatric disorders such as schizophrenia or depression (see Table 2 and 3). Thus, our results are in agreement with the clinical observations that report coexisting psychopathological symptoms that can lead to suicide [37], [38], [39]. The proteomic changes detected in our study and the results of gene chip studies [9], [11], [40] show little overlap, which is in agreement with the fact that only a fraction of transcribed genes result in protein expression. In addition, differences in sample preparation, differences in sensitivity of protein or DNA/RNA detection and differences in the brain structures sampled may explain these differences. Similarly, the hyper-methylation of ribosomal-RNA gene promoter observed in suicide victims [14] might explain the widespread protein changes observed. Therefore, our data complement gene-chip and target-oriented mRNA studies [11], [12].

Methodological Considerations

The applied proteomics methodology provides information on only a fraction of the proteome at one time; thus, although our results indicate certain functional processes, they do not reveal the complete functioning protein network [41]. The number of different proteins in a cell is estimated to be around 30,000, and the DIGE technology can detect only 2,000–4,000 (detecting 2,000 proteins is routine) [42], [43]. Nevertheless, the number of detected proteins is large enough to treat as a multi-spot index of change in the cellular protein network and suggests possible biomarker proteins of suicide. Additional information can be gained regarding the molecular mechanisms by linking identified proteins to known functional protein pathways of psychiatric diseases.

Limitation of the Post Mortem Study

The proteomic analysis of post mortem human brain samples has some inherent limitations. The post mortem human brain proteome reflects the changes of protein expression in the human brain while alive, including the changes resulted from the complex psycho-pathological processes leading to suicide. However, both pre- and post mortem factors can affect tissue quality that will influence the quantitative proteomics data [44]. These factors include prolonged agonal state, metabolic state, the use of drugs, infections, hypoxia and the post mortem interval (PMI), that is the period from death to freezing of the brain for long-term storage [45], [46].

In our present study to decrease the effect of these factors, we used brain samples from people who had died from hanging (suicide) and from individuals who died due to acute cardiac arrest. We have to be aware that the differentially altered proteins in our study may reflect the cause of death and not solely the intended vulnerability of suicide, but this relatively homogeneous experimental sample group design is a plus in our human post mortem study. Moreover, the pH of each sample was measured and they had fallen in a narrow range (7.1–7.3 in lysis buffer). This could be important because the post mortem brain pH is informative about certain types of ante mortem factors [45], [47]. Furthermore, PMI was relatively short in our study (suicide group 3–6 h; control group 2–5 h) that is an advantage, although it was repeatedly demonstrated that most human brain proteins are quite stable with respect to post mortem factors, such as PMI [44]. Even so, we have to be aware that certain protein abundance changes are dependant on the PMI duration [44] and these proteins include e.g. GFAP and INA that also changed in this study. However, the PMI duration was short and overlapping in our study, and the spot positions in the gel and the peptide coverage of the identified protein (see Fig. S2 and Table S3, S4), as well as the opposite change of some proteins in the two brain structures, do not suggest simple protein degradation. We think, that at least some of these cytoskeleton related protein abundance changes observed in our study could be in vivo existing protein isoforms reflecting the pathophysiological processes of psychiatric illnesses rather than protein degradation.

However, one question is open, whether the changes in protein expression present before the suicide or the result of the trauma from the suicide. Post mortem brain tissue studies on suicide brains can not elucidate this question. Protein expression changes presented here can be the result of pre-suicide psychotic state, or a longer major depressive agitated state because of the long turn-over time of proteins. The hypoxia caused by hanging might not have changed the brain proteome directly because hypoxia activated proteins were not found in great number. Since we are searching for biomarkers of suicide, it would be very important to know which biomarker protein candidates are correlating with the pre-suicide psychosis however we must leave the question open.

Extensive Protein Changes in the Brains of Suicide Victims Reflect an Altered State of Cellular Functions

Different psychiatric diseases, such as major depression [48], [49] and schizophrenia [50], may increase the risk of suicide; in turn, protein expression changes in the brains of suicide victims reflect several overlapping molecular mechanisms of different psychiatric illnesses. They may also reflect preceding psychiatric abnormalities, pre-suicide stress and/or psychopathology. Thus, we did not expect to find a pathway or protein network directly responsible for suicide; rather, we expected that molecular markers for predicting the risk for committing suicide can be uncovered. As expected, we identified several proteins already reported in the suicide and psychiatric disorder literature (see Tables 2 and 3).

Some of our results may probably indicate an altered monoaminergic neurotransmission [51] while mitochondrial enzymes, such as different ATP synthase subunits (ATP5B, ATP5C1, etc.), citrate synthase (CS), enoyl-CoA hydratase (ECHS1), and fumarate hydratase (FH) may reflect the glucose metabolism down-regulation theory of suicide [52]. On the other hand, lower amounts of peroxiredoxin 6 (PRDX6) and glutathione peroxidase (GPX1), in the brains of suicide victims support the relevance of the redox imbalance hypothesis in psychiatric patients [53]. We found changes in the expression of cytoskeleton proteins (see Tables 2 and 3), which probably reflects altered receptor trafficking and signalling [54]. Unbalanced glutamatergic and GABAergic neurotransmission are also important risk factors in developing suicide behaviour [11], [55]. Furthermore, changes in GABAA receptor subunits accompanied by alterations in NMDA and AMPA receptor signalling have been found in psychopathological states related to suicide [8], [56]. Contrary to our finding in the cortices of suicide victims, decreased glutamine synthetase (GLUL) levels have been detected in schizophrenia and depression models [57], and down-regulated GLUL gene has been found among depressed suicide victims [9], [11]. This discrepancy might indicate that a suicide by hanging and its associated stress elevates excitatory events, whereas depression decreases excitatory events. Increased GLUL levels may not only indicate increased glutamate-to-glutamine conversion, but also increased glutamatergic transmission [58]. We found other proteins that indicate that elevated excitatory events may play a role in suicide; e.g. decreased cortical levels of calbindin (CALB2) suggest - as a consequence of decreased Ca2+ binding capacity - an elevated concentration of free Ca2+ that can be excitatory above a certain level [59].

Glutamine synthetase is mainly located in astrocytes, and its changes in relative level were investigated after deprivation of paradoxical sleep in rats [60]. A significant increase in GLUL level was observed e.g. in the frontoparietal cortex after paradoxical sleep deprivation that rises the issue that stress and prolonged waking could affect the physiological regulation of GLUL. In our study, it can not be excluded that the cardiac arrest in control subjects would had happened during sleep (see methods section) and the difference between those who died asleep opposed to those who died awake could influence our result. The heterogeneity of data from this regard also could increase the data dispersion. Nevertheless, we think that that the stress and the prolonged waking in case of the suicide victims could be an important issue.

In accordance with previously published changes in the GFAP of suicide victims and patients with psychotic disorders [61], we also found an increased level of GFAP in the amygdala but decreased expression in the cortex. GFAP concentration is generally believed to be an index of the number of glia cells [13]; however, astrocyte dysfunction, without a reduction in cell number, may be a factor in suicide [52]. We identified GFAP from several different gel areas (see Table 2 and 3 and Table S1, S2, S3, S4 Fig. S2), which indicates that GFAP is probably highly processed. Furthermore, a lower level of PRDX6 is known to be present in astrocytes [62]. Therefore, our data suggest that focused studies on changes in glial morphology and glial protein functions in the brains of suicide victims could be beneficial in understanding the role of glia cells in suicide.

The extensive changes detected in the proteome of suicide brain are not surprising because the ribosomal RNA level is likely decreased in the brains of suicide victims due to hyper-methylation in the RNA-promoter region [14]. Epigenetic factors, such as DNA methylation, are known to exist in different psychiatric disorders related to high suicide risk [63], [64], [65].

Can Some Proteins be Used as Biomarker Molecules of Suicide?

Our proteomic study revealed that protein changes might be considered as a potential starting point for identifying biomarker candidates of suicide. Fifteen of the proteins we detected (carbonyl reductase [CBR1], dihydropyrimidinase-like 2 [DPYSL2], EF-hand domain family, member D2 [EFHD2], FK506-binding protein 4 [FKBP4], GFAP, GLUL, HSPA8, NEFL, NEFM, phosphoglycerate mutase 1 [PGAM1], PRDX6, SELENBP1, VIM, 14-3-3 protein eta [YWHAH] and 14-3-3 protein zeta/delta [YWHAZ]) have already been suggested as potential biomarker candidates for depression or schizophrenia [66], [67], [68], [69], [70], [71], [72], [73]. Additionally, 14-3-3 protein epsilon [YWHAE] was found to be a potential suicide susceptibility gene [74]. There were 9 protein expression changes in both the cortex and the amygdala in the brains of suicide victims compared to controls (Table 4), and four of these (GFAP, HSPA8, NEFL and NEFM) were overlapped with the previous fifteen. These 9 proteins indicate that at least some of the protein changes are global in the brains of suicide victims. Three of these proteins (ACTB, CTSD and GFAP) had opposite changes in the cortex compared to the amygdala and these opposite changes were validated by western blot analysis.

These proteins with opposite changes in the amygdala and prefrontal cortex could be particularly interesting in the scope of the functional neuroimaging studies of suicide. Greater fMRI activity of the amygdale were demonstrated on threatening stimuli in association with serotonin transporter gene promoter polymorphism [75], [76] that is known to be associated with suicidal behaviors in psychiatric patients, especially with violent suicides [77], [78]. In the prefrontal cortical regions however, lower metabolism (measured by PET) was found in association with greater suicidal ideation and greater lethality in suicide attempts in depressive patients [78], [79].

The protein interaction networks of the 9 proteins that changed both in the cortex and the amygdala (see Figure 3) contained a direct interaction sub-network of cytoskeletal proteins (INA, NEFL, NEFM and GFAP) which interact with binding or expression regulation. This direct interaction network of the cytoskeletal proteins is connected to the network of glutamate and serotonin receptors involved in psychotic illnesses through GRIN1 (Glutamate receptor, ionotropic, N-methyl-D-aspartate; NMDA receptor, e,g, [80]). CTSD connected to HSPA8, ACTB, CKB and TUBA1A were not directly linked to the other selected proteins. ACTB, CKB, NEFL, INA and GFAP had link to both schizophrenia and depression, while CTSD, HSPA8, NEFM and TUBA1A had link to schizophrenia.

thumbnail

Figure 3. The protein network of altered cytoskeleton proteins in the brains of suicide victims (green) is connected to the receptor-interaction network of glutamate and serotonin (red) via NEFL and GFAP.

Abbreviations: GRIA1– Glutamate receptor, ionotropic, AMPA1, GRIA3 - glutamate receptor, ionotrophic, AMPA 3, GRIK1– Glutamate receptor, ionotropic, kainate 1, GRIN1– Glutamate receptor, ionotropic, N-methyl-D-aspartate, HTR1A –5-Hydroxytryptamin (serotonin) receptor 1A, HTR2A (5-hydroxytryptamine (serotonin) receptor 2A, HTR1B (5-hydroxytryptamine (serotonin) receptor 1B, CKB - Creatine kinase B-type, ACTB - Actin, cytoplasmic 1, TUBA1A – Tubulin alpha-1B chain, NEFL – Neurofilament, light polypeptide 68 kDa, NEFM – Neurofilament, medium polypeptide, INA – Alpha-internexin, GFAP – Glial fibrillary acidic protein, CTSD - Cathepsin D, HSPA8 - Heat shock 70 kDa protein 8.

doi:10.1371/journal.pone.0050532.g003

We regard these 9 proteins as biomarker candidates of suicide risk. Furthermore, the development of quantitative brain imaging probes based on selected proteins shows promise. Prior to developing these, however, several additional studies must be performed to confirm the identity of candidate biomarkers (e.g., in other forms of suicide and in suicide trait behaviour in animals).

Conclusion

In this study, the proteome of the prefrontal cortex changed more extensively than the amygdala of suicide victims. This result is in accordance with the fact that the prefrontal cortex is highly involved in mental disorders and suicide [81]. Because the direct interaction network of cytoskeletal proteins is changed in the brains of suicide victims, new perspectives for studying suicide-related mechanisms in receptor anchoring and ultra-structural plasticity including glia cell function have been introduced.

Supporting Information

Figure S1.

The q-values were calculated from the p-values with the statistics software R (www.r-project.org; see text). The frequency distributions of P-values were used to estimate the proportion of features that are unchanging; this is then used to estimate the false discovery rate. The q-values were graphed twice for both p-value range 0.0–1.0 and 0.0–0.15.

doi:10.1371/journal.pone.0050532.s001

(DOC)

Figure S2.

Gel image from the prefrontal cortex, GFAP containing spots are highlighted with grey colour, the spot marked with orange is GFAP isoform containing spot. See. Table S3.

doi:10.1371/journal.pone.0050532.s002

(TIF)

Table S1.

The full list of the identified proteins by MS analysis according to spot numbers from the prefrontal cortex. Bold gene names highlighting those proteins that were found in those differently expressed protein spots that proved significant with both statistical tests.

doi:10.1371/journal.pone.0050532.s003

(DOC)

Table S2.

The full list of the identified proteins by MS analysis according to spot numbers from the amygdala. Bold gene names highlighting those proteins that were found in those differently expressed protein spots that proved significant with both statistical tests.

doi:10.1371/journal.pone.0050532.s004

(DOC)

Table S3.

The list of the identified triptic peptides of GFAP by MS analysis detected in different spots from the prefrontal cortex.

doi:10.1371/journal.pone.0050532.s005

(DOC)

Table S4.

The list of the identified triptic peptides of GFAP by MS analysis detected in different spots from the amygdala.

doi:10.1371/journal.pone.0050532.s006

(DOC)

Acknowledgments

We are grateful to Dr. Árpád Dobolyi and Prof. Zoltán Janka for improving our manuscript. Finally, we thank the referees for their constructive comments and suggestions which were extremely valuable in revising the paper.

Author Contributions

Conceived and designed the experiments: KAK GJ EMSZ MP BP AC. Performed the experiments: KAK GJ AS PG EMSZ EHG MP ZSD KFM. Analyzed the data: KAK GJ AS PG EHG ZSD KFM AC. Contributed reagents/materials/analysis tools: KAK GJ AS PG EMSZ EHG ZSD KFM MP BP AC. Wrote the paper: KAK GJ AC. Drafting the article: KAK GJ KFM AC. Critically revising the Manuscript: AS PG EMSZ EHG ZSD MP BP.

References

  1. 1. Malkesman O, Pine DS, Tragon T, Austin DR, Henter ID, et al. (2009) Animal models of suicide-trait-related behaviors. Trends Pharmacol Sci 30: 165–173. doi: 10.1016/j.tips.2009.01.004
  2. 2. Nordentoft M (2007) Prevention of suicide and attempted suicide in Denmark. Epidemiological studies of suicide and intervention studies in selected risk groups. Dan Med Bull 54: 306–369.
  3. 3. Bostwick JM, Pankratz VS (2000) Affective disorders and suicide risk: a reexamination. Am J Psychiatry 157: 1925–1932. doi: 10.1176/appi.ajp.157.12.1925
  4. 4. Mann JJ, Brent DA, Arango V (2001) The neurobiology and genetics of suicide and attempted suicide: a focus on the serotonergic system. Neuropsychopharmacology 24: 467–477. doi: 10.1016/s0893-133x(00)00228-1
  5. 5. Du L, Bakish D, Hrdina PD (2001) Tryptophan hydroxylase gene 218A/C polymorphism is associated with somatic anxiety in major depressive disorder. J Affect Disord 65: 37–44. doi: 10.1016/s0165-0327(00)00274-3
  6. 6. Roy A, De Jong J, Linnoila M (1989) Cerebrospinal fluid monoamine metabolites and suicidal behavior in depressed patients. A 5-year follow-up study. Arch Gen Psychiatry 46: 609–612. doi: 10.1001/archpsyc.1989.01810070035005
  7. 7. Savitz JB, Drevets WC (2009) Imaging phenotypes of major depressive disorder: genetic correlates. Neuroscience 164: 300–330. doi: 10.1016/j.neuroscience.2009.03.082
  8. 8. Akbarian S (2008) Approaching the molecular pathology of suicide. Biol Psychiatry 64: 643–644. doi: 10.1016/j.biopsych.2008.06.013
  9. 9. Klempan TA, Sequeira A, Canetti L, Lalovic A, Ernst C, et al. (2009) Altered expression of genes involved in ATP biosynthesis and GABAergic neurotransmission in the ventral prefrontal cortex of suicides with and without major depression. Mol Psychiatry 14: 175–189. doi: 10.1038/sj.mp.4002110
  10. 10. Crow TJ (2007) How and why genetic linkage has not solved the problem of psychosis: review and hypothesis. Am J Psychiatry 164: 13–21. doi: 10.1176/appi.ajp.164.1.13
  11. 11. Sequeira A, Mamdani F, Ernst C, Vawter MP, Bunney WE, et al. (2009) Global brain gene expression analysis links glutamatergic and GABAergic alterations to suicide and major depression. PLoS One 4: e6585. doi: 10.1371/journal.pone.0006585
  12. 12. Mann JJ, Arango VA, Avenevoli S, Brent DA, Champagne FA, et al. (2009) Candidate endophenotypes for genetic studies of suicidal behavior. Biol Psychiatry 65: 556–563. doi: 10.1016/j.biopsych.2008.11.021
  13. 13. Rajkowska G, Miguel-Hidalgo JJ (2007) Gliogenesis and glial pathology in depression. CNS Neurol Disord Drug Targets 6: 219–233. doi: 10.2174/187152707780619326
  14. 14. McGowan PO, Sasaki A, Huang TC, Unterberger A, Suderman M, et al. (2008) Promoter-wide hypermethylation of the ribosomal RNA gene promoter in the suicide brain. PLoS One 3: e2085. doi: 10.1371/journal.pone.0002085
  15. 15. Coryell W, Schlesser M (2007) Combined biological tests for suicide prediction. Psychiatry Res 150: 187–191. doi: 10.1016/j.psychres.2006.01.021
  16. 16. Falcone T, Fazio V, Lee C, Simon B, Franco K, et al. (2010) Serum S100B: a potential biomarker for suicidality in adolescents? PLoS One 5: e11089. doi: 10.1371/journal.pone.0011089
  17. 17. Hunter AM, Leuchter AF, Cook IA, Abrams M (2010) Brain functional changes (QEEG cordance) and worsening suicidal ideation and mood symptoms during antidepressant treatment. Acta Psychiatr Scand. 122: 461–469. doi: 10.1111/j.1600-0447.2010.01560.x
  18. 18. Magno LA, Miranda DM, Neves FS, Pimenta GJ, Mello MP, et al. (2010) Association between AKT1 but not AKTIP genetic variants and increased risk for suicidal behavior in bipolar patients. Genes Brain Behav 9: 411–418. doi: 10.1111/j.1601-183x.2010.00571.x
  19. 19. McGuffin P, Perroud N, Uher R, Butler A, Aitchison KJ, et al. (2010) The genetics of affective disorder and suicide. Eur Psychiatry 25: 275–277. doi: 10.1016/j.eurpsy.2009.12.012
  20. 20. Neves FS, Malloy-Diniz LF, Romano-Silva MA, Aguiar GC, de Matos LO, et al. (2010) Is the serotonin transporter polymorphism (5-HTTLPR) a potential marker for suicidal behavior in bipolar disorder patients? J Affect Disord 125: 98–102. doi: 10.1016/j.jad.2009.12.026
  21. 21. Robinson AA, Westbrook JA, English JA, Boren M, Dunn MJ (2009) Assessing the use of thermal treatment to preserve the intact proteomes of post-mortem heart and brain tissue. Proteomics 9: 4433–4444. doi: 10.1002/pmic.200900287
  22. 22. Kekesi KA, Kovacs Z, Szilagyi N, Bobest M, Szikra T, et al. (2006) Concentration of nucleosides and related compounds in cerebral and cerebellar cortical areas and white matter of the human brain. Cell Mol Neurobiol 26: 833–844. doi: 10.1007/s10571-006-9103-3
  23. 23. Szego EM, Janaky T, Szabo Z, Csorba A, Kompagne H, et al. (2010) A mouse model of anxiety molecularly characterized by altered protein networks in the brain proteome. Eur Neuropsychopharmacol 20: 96–111. doi: 10.1016/j.euroneuro.2009.11.003
  24. 24. Szego EM, Kekesi KA, Szabo Z, Janaky T, Juhasz GD (2010) Estrogen regulates cytoskeletal flexibility, cellular metabolism and synaptic proteins: A proteomic study. Psychoneuroendocrinology 35: 807–819. doi: 10.1016/j.psyneuen.2009.11.006
  25. 25. Alban A, David SO, Bjorkesten L, Andersson C, Sloge E, et al. (2003) A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard. Proteomics 3: 36–44. doi: 10.1002/pmic.200390006
  26. 26. Tonge R, Shaw J, Middleton B, Rowlinson R, Rayner S, et al. (2001) Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology. Proteomics 1: 377–396. doi: 10.1002/1615-9861(200103)1:3<377::aid-prot377>3.3.co;2-y
  27. 27. Berth M, Moser FM, Kolbe M, Bernhardt J (2007) The state of the art in the analysis of two-dimensional gel electrophoresis images. Appl Microbiol Biotechnol 76: 1223–1243. doi: 10.1007/s00253-007-1128-0
  28. 28. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440–9445. doi: 10.1073/pnas.1530509100
  29. 29. R_Development_Core_Team (2011) R: A language and environment for statistical computing Vienna, Austria: R Foundation for Statistical Computing.
  30. 30. Karp NA, McCormick PS, Russell MR, Lilley KS (2007) Experimental and statistical considerations to avoid false conclusions in proteomics studies using differential in-gel electrophoresis. Mol Cell Proteomics 6: 1354–1364. doi: 10.1074/mcp.m600274-mcp200
  31. 31. Karp NA, Lilley KS (2005) Maximising sensitivity for detecting changes in protein expression: experimental design using minimal CyDyes. Proteomics 5: 3105–3115. doi: 10.1002/pmic.200500083
  32. 32. Ben-Efraim YJ, Wasserman D, Wasserman J, Sokolowski M (2012) Family-based study of HTR2A in suicide attempts: observed gene, gene×environment and parent-of-origin associations. Mol Psychiatry in press.
  33. 33. Garbett K, Gal-Chis R, Gaszner G, Lewis DA, Mirnics K (2008) Transcriptome alterations in the prefrontal cortex of subjects with schizophrenia who committed suicide. Neuropsychopharmacol Hung 10: 9–14.
  34. 34. Gollan JK, Lee R, Coccaro EF (2005) Developmental psychopathology and neurobiology of aggression. Dev Psychopathol 17: 1151–1171. doi: 10.1017/s0954579405050546
  35. 35. Serretti A, Calati R, Mandelli L, De Ronchi D (2006) Serotonin transporter gene variants and behavior: a comprehensive review. Curr Drug Targets 7: 1659–1669. doi: 10.2174/138945006779025419
  36. 36. Yang CH, Huang CC, Hsu KS (2012) A critical role for protein tyrosine phosphatase nonreceptor type 5 in determining individual susceptibility to develop stress-related cognitive and morphological changes. J Neurosci 32: 7550–7562. doi: 10.1523/jneurosci.5902-11.2012
  37. 37. Gvion Y, Apter A (2011) Aggression, impulsivity, and suicide behavior: a review of the literature. Arch Suicide Res 15: 93–112. doi: 10.1080/13811118.2011.565265
  38. 38. Knittel D, Munn G, Simmer E (2008) Prodromal psychosis as an etiology of suicide: a case report and review of the literature. Am J Forensic Med Pathol 29: 238–241. doi: 10.1097/paf.0b013e3181834540
  39. 39. Taylor PJ, Gooding P, Wood AM, Tarrier N (2011) The role of defeat and entrapment in depression, anxiety, and suicide. Psychol Bull 137: 391–420. doi: 10.1037/a0022935
  40. 40. Sequeira A, Klempan T, Canetti L, ffrench-Mullen J, Benkelfat C, et al. (2007) Patterns of gene expression in the limbic system of suicides with and without major depression. Mol Psychiatry 12: 640–655. doi: 10.1038/sj.mp.4001969
  41. 41. Taurines R, Dudley E, Grassl J, Warnke A, Gerlach M, et al. (2011) Proteomic research in psychiatry. J Psychopharmacol. 25: 151–196. doi: 10.1177/0269881109106931
  42. 42. Marouga R, David S, Hawkins E (2005) The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem 382: 669–678. doi: 10.1007/s00216-005-3126-3
  43. 43. Lopez JL (2007) Two-dimensional electrophoresis in proteome expression analysis. J Chromatogr B Analyt Technol Biomed Life Sci 849: 190–202. doi: 10.1016/j.jchromb.2006.11.049
  44. 44. Crecelius A, Gotz A, Arzberger T, Frohlich T, Arnold GJ, et al. (2008) Assessing quantitative post-mortem changes in the gray matter of the human frontal cortex proteome by 2-D DIGE. Proteomics 8: 1276–1291. doi: 10.1002/pmic.200700728
  45. 45. Monoranu CM, Apfelbacher M, Grunblatt E, Puppe B, Alafuzoff I, et al. (2009) pH measurement as quality control on human postmortem brain tissue: A Study of the BrainNet Europe Consortium. Neuropathol Appl Neurobiol. 35: 329–337. doi: 10.1111/j.1365-2990.2008.01003a.x
  46. 46. Schmitt A, Bauer M, Heinsen H, Feiden W, Falkai P, et al. (2007) How a neuropsychiatric brain bank should be run: a consensus paper of Brainnet Europe II. J Neural Transm 114: 527–537. doi: 10.1007/s00702-006-0601-8
  47. 47. Lewis DA (2002) The human brain revisited: opportunities and challenges in postmortem studies of psychiatric disorders. Neuropsychopharmacology 26: 143–154. doi: 10.1016/s0893-133x(01)00393-1
  48. 48. Kasper S, Montgomery SA, Moller HJ, van Oers HJ, Schutte AJ, et al. (2010) Longitudinal analysis of the suicidal behaviour risk in short-term placebo-controlled studies of mirtazapine in major depressive disorder. World J Biol Psychiatry 11: 36–44. doi: 10.3109/15622970701691503
  49. 49. Brown LA, Gaudiano BA, Miller IW (2010) The impact of panic-agoraphobic comorbidity on suicidality in hospitalized patients with major depression. Depress Anxiety 27: 310–315. doi: 10.1002/da.20609
  50. 50. Meerwijk EL, van Meijel B, van den Bout J, Kerkhof A, de Vogel W, et al. (2010) Development and evaluation of a guideline for nursing care of suicidal patients with schizophrenia. Perspect Psychiatr Care 46: 65–73. doi: 10.1111/j.1744-6163.2009.00239.x
  51. 51. Panula P, Chen YC, Priyadarshini M, Kudo H, Semenova S, et al. (2010) The comparative neuroanatomy and neurochemistry of zebrafish CNS systems of relevance to human neuropsychiatric diseases. Neurobiol Dis 40: 46–57. doi: 10.1016/j.nbd.2010.05.010
  52. 52. Ernst C, Mechawar N, Turecki G (2009) Suicide neurobiology. Prog Neurobiol 89: 315–333. doi: 10.1016/j.pneurobio.2009.09.001
  53. 53. Vaccarino V, Brennan ML, Miller AH, Bremner JD, Ritchie JC, et al. (2008) Association of major depressive disorder with serum myeloperoxidase and other markers of inflammation: a twin study. Biol Psychiatry 64: 476–483. doi: 10.1016/j.biopsych.2008.04.023
  54. 54. English JA, Dicker P, Focking M, Dunn MJ, Cotter DR (2009) 2-D DIGE analysis implicates cytoskeletal abnormalities in psychiatric disease. Proteomics 9: 3368–3382. doi: 10.1002/pmic.200900015
  55. 55. Poulter MO, Du L, Weaver IC, Palkovits M, Faludi G, et al. (2008) GABAA receptor promoter hypermethylation in suicide brain: implications for the involvement of epigenetic processes. Biol Psychiatry 64: 645–652. doi: 10.1016/j.biopsych.2008.05.028
  56. 56. Rapoport SI, Basselin M, Kim HW, Rao JS (2009) Bipolar disorder and mechanisms of action of mood stabilizers. Brain Res Rev 61: 185–209. doi: 10.1016/j.brainresrev.2009.06.003
  57. 57. Steffek AE, McCullumsmith RE, Haroutunian V, Meador-Woodruff JH (2008) Cortical expression of glial fibrillary acidic protein and glutamine synthetase is decreased in schizophrenia. Schizophr Res 103: 71–82. doi: 10.1016/j.schres.2008.04.032
  58. 58. Kugler P (1993) Enzymes involved in glutamatergic and GABAergic neurotransmission. Int Rev Cytol 147: 285–336. doi: 10.1016/s0074-7696(08)60771-8
  59. 59. Gall D, Roussel C, Nieus T, Cheron G, Servais L, et al. (2005) Role of calcium binding proteins in the control of cerebellar granule cell neuronal excitability: experimental and modeling studies. Prog Brain Res 148: 321–328. doi: 10.1016/s0079-6123(04)48025-x
  60. 60. Sallanon-Moulin M, Touret M, Didier-Bazes M, Roudier V, Fages C, et al. (1994) Glutamine synthetase modulation in the brain of rats subjected to deprivation of paradoxical sleep. Brain Res Mol Brain Res 22: 113–120. doi: 10.1016/0169-328x(94)90038-8
  61. 61. Schlicht K, Buttner A, Siedler F, Scheffer B, Zill P, et al. (2007) Comparative proteomic analysis with postmortem prefrontal cortex tissues of suicide victims versus controls. J Psychiatr Res 41: 493–501. doi: 10.1016/j.jpsychires.2006.04.006
  62. 62. Power JH, Asad S, Chataway TK, Chegini F, Manavis J, et al. (2008) Peroxiredoxin 6 in human brain: molecular forms, cellular distribution and association with Alzheimer’s disease pathology. Acta Neuropathol 115: 611–622. doi: 10.1007/s00401-008-0373-3
  63. 63. Abdolmaleky HM, Cheng KH, Faraone SV, Wilcox M, Glatt SJ, et al. (2006) Hypomethylation of MB-COMT promoter is a major risk factor for schizophrenia and bipolar disorder. Hum Mol Genet 15: 3132–3145. doi: 10.1093/hmg/ddl253
  64. 64. Dong E, Guidotti A, Grayson DR, Costa E (2007) Histone hyperacetylation induces demethylation of reelin and 67-kDa glutamic acid decarboxylase promoters. Proc Natl Acad Sci U S A 104: 4676–4681. doi: 10.1073/pnas.0700529104
  65. 65. Widom CS, DuMont K, Czaja SJ (2007) A prospective investigation of major depressive disorder and comorbidity in abused and neglected children grown up. Arch Gen Psychiatry 64: 49–56. doi: 10.1001/archpsyc.64.1.49
  66. 66. Gudmundsson P, Skoog I, Waern M, Blennow K, Zetterberg H, et al. (2010) Is there a CSF biomarker profile related to depression in elderly women? Psychiatry Res 176: 174–178. doi: 10.1016/j.psychres.2008.11.012
  67. 67. Kanazawa T, Chana G, Glatt SJ, Mizuno H, Masliah E, et al. (2008) The utility of SELENBP1 gene expression as a biomarker for major psychotic disorders: replication in schizophrenia and extension to bipolar disorder with psychosis. Am J Med Genet B Neuropsychiatr Genet 147B: 686–689. doi: 10.1002/ajmg.b.30664
  68. 68. Martins-de-Souza D, Gattaz WF, Schmitt A, Novello JC, Marangoni S, et al. (2009) Proteome analysis of schizophrenia patients Wernicke’s area reveals an energy metabolism dysregulation. BMC Psychiatry 9: 17. doi: 10.1186/1471-244x-9-17
  69. 69. Martins-de-Souza D, Maccarrone G, Wobrock T, Zerr I, Gormanns P, et al. (2010) Proteome analysis of the thalamus and cerebrospinal fluid reveals glycolysis dysfunction and potential biomarkers candidates for schizophrenia. J Psychiatr Res. 44: 1176–1189. doi: 10.1016/j.jpsychires.2010.04.014
  70. 70. Tabares-Seisdedos R, Rubenstein JL (2009) Chromosome 8p as a potential hub for developmental neuropsychiatric disorders: implications for schizophrenia, autism and cancer. Mol Psychiatry 14: 563–589. doi: 10.1038/mp.2009.2
  71. 71. Tatro ET, Everall IP, Masliah E, Hult BJ, Lucero G, et al. (2009) Differential expression of immunophilins FKBP51 and FKBP52 in the frontal cortex of HIV-infected patients with major depressive disorder. J Neuroimmune Pharmacol 4: 218–226. doi: 10.1007/s11481-009-9146-6
  72. 72. Toyooka K, Muratake T, Tanaka T, Igarashi S, Watanabe H, et al. (1999) 14–3-3 protein eta chain gene (YWHAH) polymorphism and its genetic association with schizophrenia. Am J Med Genet 88: 164–167. doi: 10.1002/(sici)1096-8628(19990416)88:2<164::aid-ajmg13>3.3.co;2-v
  73. 73. Jia Y, Yu X, Zhang B, Yuan Y, Xu Q, et al. (2004) An association study between polymorphisms in three genes of 14–3-3 (tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein) family and paranoid schizophrenia in northern Chinese population. Eur Psychiatry 19: 377–379. doi: 10.1016/j.eurpsy.2004.07.006
  74. 74. Yanagi M, Shirakawa O, Kitamura N, Okamura K, Sakurai K, et al. (2005) Association of 14–3-3 epsilon gene haplotype with completed suicide in Japanese. J Hum Genet 50: 210–216. doi: 10.1007/s10038-005-0241-0
  75. 75. Hariri AR, Mattay VS, Tessitore A, Kolachana B, Fera F, et al. (2002) Serotonin transporter genetic variation and the response of the human amygdala. Science 297: 400–403. doi: 10.1126/science.1071829
  76. 76. Heinz A, Smolka MN, Braus DF, Wrase J, Beck A, et al. (2007) Serotonin transporter genotype (5-HTTLPR): effects of neutral and undefined conditions on amygdala activation. Biol Psychiatry 61: 1011–1014. doi: 10.1016/j.biopsych.2006.08.019
  77. 77. Gonda X, Fountoulakis KN, Harro J, Pompili M, Akiskal HS, et al. (2011) The possible contributory role of the S allele of 5-HTTLPR in the emergence of suicidality. J Psychopharmacol 25: 857–866. doi: 10.1177/0269881110376693
  78. 78. Jimenez-Trevino L, Blasco-Fontecilla H, Braquehais MD, Ceverino-Dominguez A, Baca-Garcia E (2011) Endophenotypes and suicide behaviour. Actas Esp Psiquiatr 39: 61–69.
  79. 79. Oquendo MA, Placidi GP, Malone KM, Campbell C, Keilp J, et al. (2003) Positron emission tomography of regional brain metabolic responses to a serotonergic challenge and lethality of suicide attempts in major depression. Arch Gen Psychiatry 60: 14–22. doi: 10.1001/archpsyc.60.1.14
  80. 80. Ehlers MD, Fung ET, O’Brien RJ, Huganir RL (1998) Splice variant-specific interaction of the NMDA receptor subunit NR1 with neuronal intermediate filaments. J Neurosci 18: 720–730.
  81. 81. Hercher C, Turecki G, Mechawar N (2009) Through the looking glass: examining neuroanatomical evidence for cellular alterations in major depression. J Psychiatr Res 43: 947–961. doi: 10.1016/j.jpsychires.2009.01.006
  82. 82. Matthews PR, Eastwood SL, Harrison PJ (2012) Reduced myelin basic protein and actin-related gene expression in visual cortex in schizophrenia. PLoS One 7: e38211. doi: 10.1371/journal.pone.0038211
  83. 83. Paulson L, Martin P, Nilsson CL, Ljung E, Westman-Brinkmalm A, et al. (2004) Comparative proteome analysis of thalamus in MK-801-treated rats. Proteomics 4: 819–825. doi: 10.1002/pmic.200300622
  84. 84. Paulson L, Martin P, Ljung E, Blennow K, Davidsson P (2007) Proteome analysis after co-administration of clozapine or haloperidol to MK-801-treated rats. J Neural Transm 114: 885–891. doi: 10.1007/s00702-007-0626-7
  85. 85. Behan AT, Byrne C, Dunn MJ, Cagney G, Cotter DR (2009) Proteomic analysis of membrane microdomain-associated proteins in the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder reveals alterations in LAMP, STXBP1 and BASP1 protein expression. Mol Psychiatry 14: 601–613. doi: 10.1038/mp.2008.7
  86. 86. Pennington K, Dicker P, Dunn MJ, Cotter DR (2008) Proteomic analysis reveals protein changes within layer 2 of the insular cortex in schizophrenia. Proteomics 8: 5097–5107. doi: 10.1002/pmic.200800415
  87. 87. Buckland PR, Hoogendoorn B, Guy CA, Coleman SL, Smith SK, et al. (2004) A high proportion of polymorphisms in the promoters of brain expressed genes influences transcriptional activity. Biochim Biophys Acta 1690: 238–249. doi: 10.1016/j.bbadis.2004.06.023
  88. 88. Clark D, Dedova I, Cordwell S, Matsumoto I (2007) Altered proteins of the anterior cingulate cortex white matter proteome in schizophrenia. Proteomics Clin Appl 1: 157–166. doi: 10.1002/prca.200600541
  89. 89. Sivagnanasundaram S, Crossett B, Dedova I, Cordwell S, Matsumoto I (2007) Abnormal pathways in the genu of the corpus callosum in schizophrenia pathogenesis: a proteome study. Proteomics Clin Appl 1: 1291–1305. doi: 10.1002/prca.200700230
  90. 90. Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, et al.. (2004) Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatry 9: 684–697, 643.
  91. 91. Beasley CL, Zhang ZJ, Patten I, Reynolds GP (2002) Selective deficits in prefrontal cortical GABAergic neurons in schizophrenia defined by the presence of calcium-binding proteins. Biol Psychiatry 52: 708–715. doi: 10.1016/s0006-3223(02)01360-4
  92. 92. Byne W, Dracheva S, Chin B, Schmeidler JM, Davis KL, et al. (2008) Schizophrenia and sex associated differences in the expression of neuronal and oligodendrocyte-specific genes in individual thalamic nuclei. Schizophr Res 98: 118–128. doi: 10.1016/j.schres.2007.09.034
  93. 93. Kim S, Webster MJ (2010) Correlation analysis between genome-wide expression profiles and cytoarchitectural abnormalities in the prefrontal cortex of psychiatric disorders. Mol Psychiatry 15: 326–336. doi: 10.1038/mp.2008.99
  94. 94. Newton SS, Collier EF, Bennett AH, Russell DS, Duman RS (2004) Regulation of growth factor receptor bound 2 by electroconvulsive seizure. Brain Res Mol Brain Res 129: 185–188. doi: 10.1016/j.molbrainres.2004.06.032
  95. 95. Dwivedi Y, Rizavi HS, Zhang H, Roberts RC, Conley RR, et al. (2009) Aberrant extracellular signal-regulated kinase (ERK)1/2 signalling in suicide brain: role of ERK kinase 1 (MEK1). Int J Neuropsychopharmacol 12: 1337–1354. doi: 10.1017/s1461145709990575
  96. 96. Musazzi L, Mallei A, Tardito D, Gruber SH, El Khoury A, et al. (2010) Early-life stress and antidepressant treatment involve synaptic signaling and Erk kinases in a gene-environment model of depression. J Psychiatr Res 44: 511–520. doi: 10.1016/j.jpsychires.2009.11.008
  97. 97. Yuan P, Zhou R, Wang Y, Li X, Li J, et al. (2010) Altered levels of extracellular signal-regulated kinase signaling proteins in postmortem frontal cortex of individuals with mood disorders and schizophrenia. J Affect Disord 124: 164–169. doi: 10.1016/j.jad.2009.10.017
  98. 98. Ikeda M, Hikita T, Taya S, Uraguchi-Asaki J, Toyo-oka K, et al. (2008) Identification of YWHAE, a gene encoding 14–3-3epsilon, as a possible susceptibility gene for schizophrenia. Hum Mol Genet 17: 3212–3222. doi: 10.1093/hmg/ddn217
  99. 99. Grover D, Verma R, Goes FS, Mahon PL, Gershon ES, et al. (2009) Family-based association of YWHAH in psychotic bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 150B: 977–983. doi: 10.1002/ajmg.b.30927
  100. 100. Beasley CL, Pennington K, Behan A, Wait R, Dunn MJ, et al. (2006) Proteomic analysis of the anterior cingulate cortex in the major psychiatric disorders: Evidence for disease-associated changes. Proteomics 6: 3414–3425. doi: 10.1002/pmic.200500069
  101. 101. Johnston-Wilson NL, Sims CD, Hofmann JP, Anderson L, Shore AD, et al. (2000) Disease-specific alterations in frontal cortex brain proteins in schizophrenia, bipolar disorder, and major depressive disorder. The Stanley Neuropathology Consortium. Mol Psychiatry 5: 142–149. doi: 10.1038/sj.mp.4000696
  102. 102. Novikova SI, He F, Cutrufello NJ, Lidow MS (2006) Identification of protein biomarkers for schizophrenia and bipolar disorder in the postmortem prefrontal cortex using SELDI-TOF-MS ProteinChip profiling combined with MALDI-TOF-PSD-MS analysis. Neurobiol Dis 23: 61–76. doi: 10.1016/j.nbd.2006.02.002
  103. 103. Huang JT, Wang L, Prabakaran S, Wengenroth M, Lockstone HE, et al. (2008) Independent protein-profiling studies show a decrease in apolipoprotein A1 levels in schizophrenia CSF, brain and peripheral tissues. Mol Psychiatry 13: 1118–1128. doi: 10.1038/sj.mp.4002108
  104. 104. Ji B, La Y, Gao L, Zhu H, Tian N, et al. (2009) A comparative proteomics analysis of rat mitochondria from the cerebral cortex and hippocampus in response to antipsychotic medications. J Proteome Res 8: 3633–3641. doi: 10.1021/pr800876z
  105. 105. McHugh PC, Rogers GR, Loudon B, Glubb DM, Joyce PR, et al. (2008) Proteomic analysis of embryonic stem cell-derived neural cells exposed to the antidepressant paroxetine. J Neurosci Res 86: 306–316. doi: 10.1002/jnr.21482
  106. 106. Burbaeva G, Savushkina OK, Boksha IS (2003) Creatine kinase BB in brain in schizophrenia. World J Biol Psychiatry 4: 177–183. doi: 10.1080/15622970310029916
  107. 107. Gardner A, Salmaso D, Nardo D, Micucci F, Nobili F, et al. (2008) Mitochondrial function is related to alterations at brain SPECT in depressed patients. CNS Spectr 13: 805–814.
  108. 108. Scaini G, Santos PM, Benedet J, Rochi N, Gomes LM, et al. (2010) Evaluation of Krebs cycle enzymes in the brain of rats after chronic administration of antidepressants. Brain Res Bull 82: 224–227. doi: 10.1016/j.brainresbull.2010.03.006
  109. 109. Karolewicz B, Szebeni K, Gilmore T, Maciag D, Stockmeier CA, et al. (2009) Elevated levels of NR2A and PSD-95 in the lateral amygdala in depression. Int J Neuropsychopharmacol 12: 143–153. doi: 10.1017/s1461145708008985
  110. 110. Bernard R, Kerman IA, Thompson RC, Jones EG, Bunney WE, et al. (2011) Altered expression of glutamate signaling, growth factor, and glia genes in the locus coeruleus of patients with major depression. Mol Psychiatry 16: 634–646. doi: 10.1038/mp.2010.44
  111. 111. Arai R, Ito K, Ohnishi T, Ohba H, Akasaka R, et al. (2007) Crystal structure of human myo-inositol monophosphatase 2, the product of the putative susceptibility gene for bipolar disorder, schizophrenia, and febrile seizures. Proteins 67: 732–742. doi: 10.1002/prot.21299
  112. 112. Sjoholt G, Ebstein RP, Lie RT, Berle JO, Mallet J, et al. (2004) Examination of IMPA1 and IMPA2 genes in manic-depressive patients: association between IMPA2 promoter polymorphisms and bipolar disorder. Mol Psychiatry 9: 621–629. doi: 10.1038/sj.mp.4001460
  113. 113. Ben-Shachar D, Karry R (2007) Sp1 expression is disrupted in schizophrenia; a possible mechanism for the abnormal expression of mitochondrial complex I genes, NDUFV1 and NDUFV2. PLoS One 2: e817. doi: 10.1371/journal.pone.0000817
  114. 114. Koene S, Kozicz TL, Rodenburg RJ, Verhaak CM, de Vries MC, et al. (2009) Major depression in adolescent children consecutively diagnosed with mitochondrial disorder. J Affect Disord 114: 327–332. doi: 10.1016/j.jad.2008.06.023
  115. 115. Tatro ET, Everall IP, Kaul M, Achim CL (2009) Modulation of glucocorticoid receptor nuclear translocation in neurons by immunophilins FKBP51 and FKBP52: implications for major depressive disorder. Brain Res 1286: 1–12. doi: 10.1016/j.brainres.2009.06.036
  116. 116. Kaneko M, Abe K, Kogure K, Saito H, Matsuki N (1993) Correlation between electroconvulsive seizure and HSC70 mRNA induction in mice brain. Neurosci Lett 157: 195–198. doi: 10.1016/0304-3940(93)90735-4
  117. 117. Arion D, Unger T, Lewis DA, Levitt P, Mirnics K (2007) Molecular evidence for increased expression of genes related to immune and chaperone function in the prefrontal cortex in schizophrenia. Biol Psychiatry 62: 711–721. doi: 10.1016/j.biopsych.2006.12.021
  118. 118. Carter CJ (2007) eIF2B and oligodendrocyte survival: where nature and nurture meet in bipolar disorder and schizophrenia? Schizophr Bull 33: 1343–1353. doi: 10.1093/schbul/sbm007
  119. 119. Pennington K, Beasley CL, Dicker P, Fagan A, English J, et al. (2008) Prominent synaptic and metabolic abnormalities revealed by proteomic analysis of the dorsolateral prefrontal cortex in schizophrenia and bipolar disorder. Mol Psychiatry 13: 1102–1117. doi: 10.1038/sj.mp.4002098
  120. 120. Glatt SJ, Everall IP, Kremen WS, Corbeil J, Sasik R, et al. (2005) Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia. Proc Natl Acad Sci U S A 102: 15533–15538. doi: 10.1073/pnas.0507666102
  121. 121. Kanazawa T, Glatt SJ, Faraone SV, Hwu HG, Yoneda H, et al. (2009) Family-based association study of SELENBP1 in schizophrenia. Schizophr Res 113: 268–272. doi: 10.1016/j.schres.2009.06.011
  122. 122. Suzuki G, Harper KM, Hiramoto T, Sawamura T, Lee M, et al. (2009) Sept5 deficiency exerts pleiotropic influence on affective behaviors and cognitive functions in mice. Hum Mol Genet 18: 1652–1660. doi: 10.1093/hmg/ddp086
  123. 123. Takahashi S, Cui YH, Han YH, Fagerness JA, Galloway B, et al. (2008) Association of SNPs and haplotypes in APOL1, 2 and 4 with schizophrenia. Schizophr Res 104: 153–164. doi: 10.1016/j.schres.2008.05.028
  124. 124. Shyn SI, Shi J, Kraft JB, Potash JB, Knowles JA, et al. (2011) Novel loci for major depression identified by genome-wide association study of Sequenced Treatment Alternatives to Relieve Depression and meta-analysis of three studies. Mol Psychiatry 16: 202–215. doi: 10.1038/mp.2009.125
  125. 125. Banay-Schwartz M, DeGuzman T, Faludi G, Lajtha A, Palkovits M (1998) Alteration of protease levels in different brain areas of suicide victims. Neurochem Res 23: 953–959. doi: 10.1023/a:1021028304481
  126. 126. Bernstein HG, Kirschke H, Wiederanders B, Khudoerkov RM, Hinz W, et al.. (1992) Lysosomal proteinases as putative diagnostic tools in human neuropathology: Alzheimer disease (AD) and schizophrenia. Acta Histochem Suppl 42: 19–24.
  127. 127. Wong ML, Dong C, Maestre-Mesa J, Licinio J (2008) Polymorphisms in inflammation-related genes are associated with susceptibility to major depression and antidepressant response. Mol Psychiatry 13: 800–812. doi: 10.1038/mp.2008.59
  128. 128. Nakatani N, Hattori E, Ohnishi T, Dean B, Iwayama Y, et al. (2006) Genome-wide expression analysis detects eight genes with robust alterations specific to bipolar I disorder: relevance to neuronal network perturbation. Hum Mol Genet 15: 1949–1962. doi: 10.1093/hmg/ddl118
  129. 129. Matsuzawa D, Hashimoto K, Hashimoto T, Shimizu E, Watanabe H, et al. (2009) Association study between the genetic polymorphisms of glutathione-related enzymes and schizophrenia in a Japanese population. Am J Med Genet B Neuropsychiatr Genet 150B: 86–94. doi: 10.1002/ajmg.b.30776
  130. 130. Kodydkova J, Vavrova L, Zeman M, Jirak R, Macasek J, et al. (2009) Antioxidative enzymes and increased oxidative stress in depressive women. Clin Biochem 42: 1368–1374. doi: 10.1016/j.clinbiochem.2009.06.006
  131. 131. McHugh PC, Rogers GR, Glubb DM, Joyce PR, Kennedy MA (2010) Proteomic analysis of rat hippocampus exposed to the antidepressant paroxetine. J Psychopharmacol 24: 1243–1251. doi: 10.1177/0269881109102786
  132. 132. Liu Q, Li B, Zhu HY, Wang YQ, Yu J, et al. (2009) Clomipramine treatment reversed the glial pathology in a chronic unpredictable stress-induced rat model of depression. Eur Neuropsychopharmacol 19: 796–805. doi: 10.1016/j.euroneuro.2009.06.010
  133. 133. Miguel-Hidalgo JJ, Waltzer R, Whittom AA, Austin MC, Rajkowska G, et al. (2010) Glial and glutamatergic markers in depression, alcoholism, and their comorbidity. J Affect Disord. 127: 230–240. doi: 10.1016/j.jad.2010.06.003
  134. 134. Altshuler LL, Abulseoud OA, Foland-Ross L, Bartzokis G, Chang S, et al. (2010) Amygdala astrocyte reduction in subjects with major depressive disorder but not bipolar disorder. Bipolar Disord 12: 541–549. doi: 10.1111/j.1399-5618.2010.00838.x
  135. 135. Kroes RA, Panksepp J, Burgdorf J, Otto NJ, Moskal JR (2006) Modeling depression: social dominance-submission gene expression patterns in rat neocortex. Neuroscience 137: 37–49. doi: 10.1016/j.neuroscience.2005.08.076
  136. 136. Sillaber I, Panhuysen M, Henniger MS, Ohl F, Kuhne C, et al. (2008) Profiling of behavioral changes and hippocampal gene expression in mice chronically treated with the SSRI paroxetine. Psychopharmacology (Berl) 200: 557–572. doi: 10.1007/s00213-008-1232-6
  137. 137. Musselman DL, Tomer A, Manatunga AK, Knight BT, Porter MR, et al. (1996) Exaggerated platelet reactivity in major depression. Am J Psychiatry 153: 1313–1317.
  138. 138. Taylor K (2000) Immune-biochemical interactions in schizophrenia. Schizophr Res 44: 245–246. doi: 10.1016/s0920-9964(99)00194-2
  139. 139. Smalla KH, Mikhaylova M, Sahin J, Bernstein HG, Bogerts B, et al. (2008) A comparison of the synaptic proteome in human chronic schizophrenia and rat ketamine psychosis suggest that prohibitin is involved in the synaptic pathology of schizophrenia. Mol Psychiatry 13: 878–896. doi: 10.1038/mp.2008.60
  140. 140. Casini A, Caccia S, Scozzafava A, Supuran CT (2003) Carbonic anhydrase activators. The selective serotonin reuptake inhibitors fluoxetine, sertraline and citalopram are strong activators of isozymes I and II. Bioorg Med Chem Lett 13: 2765–2768. doi: 10.1016/s0960-894x(03)00507-9
  141. 141. Vawter MP, Shannon Weickert C, Ferran E, Matsumoto M, Overman K, et al. (2004) Gene expression of metabolic enzymes and a protease inhibitor in the prefrontal cortex are decreased in schizophrenia. Neurochem Res 29: 1245–1255. doi: 10.1023/b:nere.0000023611.99452.47
  142. 142. Vawter MP, Ferran E, Galke B, Cooper K, Bunney WE, et al. (2004) Microarray screening of lymphocyte gene expression differences in a multiplex schizophrenia pedigree. Schizophr Res 67: 41–52. doi: 10.1016/s0920-9964(03)00151-8
  143. 143. Arnold SE, Han LY, Moberg PJ, Turetsky BI, Gur RE, et al. (2001) Dysregulation of olfactory receptor neuron lineage in schizophrenia. Arch Gen Psychiatry 58: 829–835. doi: 10.1001/archpsyc.58.9.829