Abstract
Major depressive disorder (MDD) is a leading cause of disability worldwide and results tragically in the loss of almost one million lives in Western societies every year. This is due to poor understanding of the disease pathophysiology and lack of empirical medical tests for accurate diagnosis or for guiding antidepressant treatment strategies. Here, we have used shotgun proteomics in the analysis of post-mortem dorsolateral prefrontal cortex brain tissue from 24 MDD patients and 12 matched controls. Brain proteomes were pre-fractionated by gel electrophoresis and further analyzed by shotgun data-independent label-free liquid chromatography-mass spectrometry. This led to identification of distinct proteome fingerprints between MDD and control subjects. Some of these differences were validated by Western blot or selected reaction monitoring mass spectrometry. This included proteins associated with energy metabolism and synaptic function and we also found changes in the histidine triad nucleotide-binding protein 1 (HINT1), which has been implicated recently in regulation of mood and behavior. We also found differential proteome profiles in MDD with (n=11) and without (n=12) psychosis. Interestingly, the psychosis fingerprint showed a marked overlap to changes seen in the brain proteome of schizophrenia patients. These findings suggest that it may be possible to contribute to the disease understanding by distinguishing different subtypes of MDD based on distinct brain proteomic profiles.
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Introduction
Major depressive disorder (MDD) is a serious psychiatric condition affecting approximately 10% of the world population with a lifetime prevalence of 17%.1 The effects of MDD are wide-ranging, including a negative impact on families, work and relationships, and has been associated with debilitating co-morbidities such as general ill health, substance abuse and anxiety disorders. Together, these factors contribute to an enormous significant financial burden on the healthcare services.2 In addition, MDD subjects account for 60% of suicides in the United States.3 Although some molecular aspects of MDD have been identified, such as hypothalamic–pituitary–adrenal axis dysfunction,4 effects on memory5 and volume reduction of certain brain regions such as hippocampus6 and prefrontal cortex,7 the underlying pathophysiology of this disorder has only been partially elucidated. The association between inflammation and MDD has been supported by the fact that treatment of hepatitis C and certain cancer patients with interferon-alpha,8, 9 frequently induces depressive symptoms as side-effects. In addition, MDD is also associated with auto-immune diseases10 and metabolic disorders,11 and several studies have shown that the efficacy of antidepressants may be partly attributable to their anti-inflammatory properties.12 As a consequence, currently available antidepressant medications often show only medium efficacy and can have serious side-effects.13 Therefore, it is important to increase our understanding of the physiological factors underlying this condition before more effective medications can be developed.
Studies of MDD are complicated by the fact that it is a systemic, multifactorial disorder. Current hypotheses suggest that MDD most likely arises from complex interactions between genetic predisposition,14 disturbance of key molecular pathways including neurotransmitter systems and synaptic plasticity,4 along with the impact of environmental factors such as stressful life events.15, 16 Therefore, an increased understanding of this disorder is likely to be gained by application of molecular profiling analyses of relevant brain regions using approaches such as transcriptomics,17, 18 lipidomics19 and proteomics.20 Of these platforms, proteomics may be the most appropriate for studies of psychiatric conditions considering that it is better suited for capturing the dynamic nature of perturbed biological systems.20, 21
MDD patients can present with a great variety of symptoms including low mood, low self-esteem, loss of interest or pleasure in normally enjoyable activities and melancholia. MDD patients may also present with severe psychotic symptoms, including delusions and hallucinations. Similar symptoms are often observed, for example, in schizophrenia albeit with subtle differences. Therefore, the identification of symptom-related potential biomarker candidates22 could be useful in combination with the existing clinical assessment for more accurate classification of MDD patients and also to provide a biochemical comprehension of the molecular processes involved. These could be useful not only for potential diagnostic purposes but also to provide a molecular means of distinguishing patients to facilitate much-needed personalized medicine strategies.
With this in mind, we have carried out shotgun proteome analyses as represented in Supplementary Material 1 on post-mortem dorsolateral prefrontal cortex (DLPFC) tissue from MDD patients. Features such as volume reduction7 and reduced activation23 have previously suggested that effects on this brain region are associated with MDD.24 We used label-free liquid chromatography mass spectrometry in data-independent mode (LC-MSE) to analyze DLPFC tissue from 24 MDD patients and 12 control subjects. The DLPFC is known to be involved in executive and intellectual functions, integration of sensory and mnemonic information and in working memory.25 Additionally, we have re-evaluated the data considering those MDD patients who presented with (n=11) or without (n=12) psychotic symptoms, in an attempt to identify a MDD psychosis-specific signature.
Materials and methods
Clinical samples
DLPFC tissues (Brodmann area 9) were collected post-mortem and frozen from MDD patients (n=24), either with psychosis (n=11) or without psychosis (n=12) plus matched control subjects (n=12) by the Stanley Medical Research Institute (Bethesda, MD, USA). One of the MDD patients could not be defined as psychotic or not. The diagnosis of MDD had been given ante-mortem. Consent was obtained by questionnaire-based telephone interview and signed by the interviewer and a witness. The Institutional Review Board at the Uniformed Services University of Health Sciences determined that the procedure was exempt from federal and state regulations governing human research, as specimens were obtained from cadavers and anonymized with respect to personal information. General patient information is given in Table 1 and detailed information in Supplementary Material 2.
Sample and proteome preparation
Tissue samples (20 mg) were homogenized individually in 100 μl of 7 M urea, 2 M thiourea, 4% CHAPS, 2% ASB-14 and 70 mM DTT, as described previously,26 using the Sample Grinding Kit (GE Healthcare, Little Chalfont, Bucks, UK). Samples were centrifuged for 10 min at 16 000 × g, the supernatants were collected and protein concentrations were determined using the Bradford dye-binding assay (Sigma, Poole, Dorset, UK). For enhancing proteome coverage, SDS–polyacrylamide gel electrophoresis (SDS-PAGE) pre-fractionation was used for separation of proteins according to apparent molecular size. Extracted protein samples (15 μg) were diluted in SDS-PAGE sample loading buffer (2% w/v SDS, 100 mM Tris (pH 6.8), 10% glycerol, 100 mM DTT and 0.001% w/v bromophenol blue). Samples were heated for 5 min at 95 °C before electrophoresis on NuPAGE 4–12% bis-tris polyacrylamide gels (Invitrogen, Paisley, UK). Protein bands were visualized using Coomassie blue staining. Each lane containing stained protein bands was sliced in the horizontal direction to produce four sections corresponding to different apparent molecular weight ranges. Each gel section was then subjected to trypsin digestion in situ as described previously,27 and resulting peptide mixtures were lyophilized.
Nano-high-performance liquid chromatography—mass spectrometry analyses
Lyophilized peptides were dissolved in 0.1% formic acid and 0.5 μg injected in duplicate into a nanoUltra-Performance Liquid Chromatography instrument containing a BEH-130 C18 column (75 μm × 200 mm) at a flow rate of 0.3 μl min–1 connected online to a Q-TOF Premier Mass Spectrometer (Waters Corporation, Manchester, UK). The following gradient was applied over 140 min comprised of solvent A (0.1% aqueous formic acid) and solvent B (acetonitrile with 0.1% formic acid): 97/3% (A/B) to 70/30% in 90 min; 70/30 to 10/90% in 25 min; 10/90 to 3/97% in 5 min; 10 min at 3/97%; returning to the initial condition in 1 min. Eluted peptides were measured in MSE mode (data-independent analysis) using the ion accounting algorithm28 for data processing. Analysis of the resulting chromatograms/mass spectra and database searching were performed using the ProteinLynx Global Server (PLGS) v.2.4 (Waters Corp.). Firstly, raw data were processed and chromatograms aligned in time. Aligned peaks were extracted and abundance measurements obtained by integration of time, mass/charge (m/z) and intensity volumes, with normalization to the total ion current. Data were searched against the SwissProt human database (version 57.4; http://www.uniprot.org/) and also against a randomized database to exclude false-positives. The maximum false identification rate was set to 4% and peptides had to be detected in >70% of samples to ensure biological reproducibility. The criteria for protein identifications were set at a minimum of three ion fragments per peptide, seven ion fragments per protein and one peptide per protein. However, we only considered for differential expression analyses proteins identified by at least two peptides. Modifications considered were carbamidomethylation of cysteines and oxidation of methionine.
Quantitative protein expression and statistical analyses were performed using the Rosetta Elucidator system v.3.3.0.1.SP3.19 (Rosetta Inpharmatics, Seattle, WA, USA) and processed data from PLGS analysis. We established a fold change cutoff of ±1.15 based on the following facts: (1) coefficient of variation calculated for all identified proteins was 0.18±0.3 (mean±s.d.); (2) label-free proteomics has been shown to underestimate fold changes,29 which is supported by the fact that a protein with a fold change of 1.13 was validated by Western blot analysis revealing a 1.47-fold change.
Statistical analyses
Differences in protein expression between MDD patients and controls were accessed using Wilcoxon signed-rank test, as the data were not assumed to be normally distributed. Only differences with a P<0.05 were considered significant. False discovery rate (FDR) was calculated according to Benjamini and Hochberg.30 No adjustments were made for multiple comparisons in order to not exclude possible true positives.31 This approach will lead to fewer errors of interpretation as proteomic data are not necessarily random but can be physiologically inter-dependent observations. Nevertheless, a FDR threshold of approximately 0.4 and a fold change cutoff of 15% for the shotgun proteome analyses was established.
Although groups are matched for demographic variables (Table 1), the influence of gender, age, alcohol abuse, smoking, post-mortem interval and refrigeration interval on the data were accessed by using principal component analysis (PCA) as previously described.32 The PCA results are presented in Supplementary Material 3. Differentially expressed proteins were not found to be correlated to demographics variables.
Selected reaction monitoring
Quantitative differences in the levels of histidine triad nucleotide-binding protein 1 (HINT1) and synaptosomal-associated protein 25 (SNAP25) were validated using whole tissue lysates by selected reaction monitoring (SRM) mass spectrometry. Three SRM transitions of the HINT1 peptides ‘IIFEDDR’, ‘HISQISVAEDDDESLLGHLMIVGK’ and ‘MVVNEGSDGGQSVYHVHLHVLGGR’ were analyzed, as well as three SRM transitions of the SNAP25 peptides ‘NELEEMQR’, ‘AWGNNQDGVVASQPAR’ and ‘IEEGMDQINK’. Peptides were selected based on identification in the LC-MSE dataset with a high spectral quality, and if those peptides were proteotypic, which means an experimentally observable peptide that uniquely identifies a specific protein or protein isoforms.33
Samples (0.2 μg) were prepared exactly like for LC-MSE analyses. Whole digested lysates were injected in duplicate into an identical LC system, as above, coupled to a Xevo triple-quadrupole mass spectrometer (Waters). For separation of peptides, the following 48 min gradient was applied: 97/3% (A/B) to 60/40% B in 30 min; 60/40 to 15/85% in 2 min; 5 min at 15/85%; returning to the initial condition in 1 min. Eluted peptides were measured in SRM mode using an electrospray voltage of 22 kV and a cone voltage of 35 V. All SRM functions had a 2 min window of the predicted retention time and the scan time was 20 milliseconds. The collision energy for each transition was optimized using Skyline software34 based on the equation: CE=0.034*m/z+3.314. Acquired data were processed using TargetLynx (Waters). Differences in protein levels between MDD and controls were determined using Student's t-tests considering P<0.05 as significant.
Western blot
Quantitative differences in the levels of amphiphysin (AMPH) and growth-associated protein 43 (GAP43) were assessed for validation of LC-MSE findings by Western blot analyses due to their involvement in synapses. Total tissue lysates from MDD and controls were arranged in randomized order such that each of the groups were represented on both gels. For each sample, 20 μg total protein was electrophoresed using pre-cast Novex 10–20% Tricine polyacrylamide gels (Invitrogen) at 125 V for 60 min, followed by semi-dry transfer to Immobilon-FL polyvinyldiphenyl fluoride membranes (Millipore, Watford, UK). The membranes were incubated in a 1:1 mixture of Odyssey blocking buffer (Li-COR Biosciences, Cambridge, UK) and phosphate-buffered saline containing 0.1% Tween-20 for 1 h at room temperature. Membranes were then incubated overnight at 4 °C with the following primary antibodies (both from Abcam, Cambridge, UK): anti-amphiphysin (1:2000) and anti-GAP-43 (1/5000). Membranes were washed in Tris-buffered saline containing EDTA for 30 min, in phosphate-buffered saline for 2 × 5 min and then for 1 h at room temperature with the appropriate IR dye-conjugated secondary antibodies (1:7500 for secondary rabbit antibody and 1:15000 for secondary mouse antibody Li-COR Biosciences) in blocking buffer. Immunoreactive protein bands were visualized using the Odyssey Infra-red imaging system (Li-COR Biosciences) and the integrated intensities of the bands measured. Values outlying by more than two standard deviations from the mean were excluded from the analysis. Differences in protein levels between MDD and controls were determined using Student's t-tests considering P<0.05 as significant.
ATP assay
In order to validate the differential expression of several oxidative phosphorylation protein subunits, ATP levels were measured using the ATP Assay Quantification Kit according the manufacturers’ instructions (BioVision, San Francisco, CA, USA). Differences in ATP levels per 5 μg of brain tissue between MDD and controls were determined using Student's t-tests considering P<0.05 as significant.
Classification of differentially expressed proteins
Differentially expressed proteins in MDD DLPFC were classified according to their biological pathways and sub-cellular localization using the Human Protein Reference Database (http://www.hprd.org) and KEGG database (www.genome.jp/kegg). For interpretation of the functional significance of differentially expressed proteins, the associated SwissProt accession numbers were uploaded into the Ingenuity Pathways Knowledgebase (IPKB) (www.ingenuity.com) and these were analyzed to identify potential interactions between these proteins and other proteins in the IPKB.
Results
Shotgun proteomics results
The shotgun LC-MSE strategy for protein profiling of post-mortem DLPFC samples from MDD (n=24) and control subjects (n=12) is outlined in Supplementary Material 1. This resulted in identification of 18 422 unique peptides in the 36 analyzed samples, which led to identification of 1422 non-redundant proteins. From these, 526 proteins were identified with a minimum of two distinct peptides and detected at least in >75% of samples. Overall, 35 of these were discarded, as they were keratin and trypsin contaminations. Therefore, 491 proteins were considered for differential analyses. These proteins, their peptides and mass spectra data are deposited at the PRIDE database35 (http://www.ebi.ac.uk/pride; accession number: 15310). There were no significant differences between MDD patients and controls with regards to gender, age, post-mortem and refrigeration interval and brain pH (Table 1). In addition, PCA of the data showed no overt effects of any of these parameters (Supplementary Material 3).
Proteome differences between MDD and control subjects
Comparing all MDD patient samples to controls, significant differences (P<0.05; Wilcoxon test) in protein levels were observed for 39 proteins with a fold change cutoff of ±1.15 and a FDR of approximately 0.4 although most were approximately 0.350 (Table 2). No adjustment for FDR was used31 as described in material and methods section. AMPH is the only exception for the fold change cutoff as it has been validated by Western Blot. Two of these proteins were decreased and 37 increased in MDD patients compared with controls. These proteins were classified according to their biological processes and cellular localization. The major pathways that are associated with MDD were metabolic/energy pathways (32%) and transport of molecules (22%) (Supplementary Material 4A). The majority of the differentially expressed proteins (72%) were identified as cytoplasmic, and 13% were membrane-associated proteins (Supplementary Material 4B). We also identified several proteins such as profilin 1 and 2 (PFN1 and PFN2), SNAP25 and gamma aminobutyric acid receptor-associated protein like 2 (GABARAPL2) that are involved in synaptic transmission (Figure 1). Also, some of these proteins have been previously associated with other neurological or psychiatric conditions such as Huntington's disease (27%), schizophrenia (18%) and Alzheimer's disease (3%) (Supplementary Material 5).
Proteome differences in MDD patients with and without psychosis
Shotgun LC-MSE analysis of MDD patients with psychosis (MDD-P) and without psychosis (MDD-NP) showed significant differences (P<0.05; Wilcoxon test) in proteomic profiles with an FDR of approximately 0.4. Proteomic comparison of MDD-NP patients (n=12) with control subjects (n=12) led to identification of 18 altered proteins. However, comparison of MDD-P patients (n=11) with controls resulted in identification of 36 altered proteins and direct comparison of the MDD-P and MDD-NP proteomes resulted in identification of 30 significantly different proteins. All of the differentially expressed proteins associated with the categories above are listed in Table 3 and classified according to their biological processes and cellular localization (Figure 2).
The overlapping proteins across these different comparisons are shown in Figure 3. Five proteins (cystatin C (CST3), fatty acid-binding protein (FABP3), rho-related GTP-binding protein (RHOC), gamma synuclein (SNCG) and peptidylprolyl isomerase A (PPIA)) were changed in common between the MDD-P and MDD-NP patients relative to controls (Figure 3a). A total of 13 proteins were altered specifically in the MDD-NP subjects, including the HINT1 and the mitochondrial ATP synthase subunits b and e (ATP5F1 and ATP5I). Overall, 31 proteins were altered specifically in the MDD-P patients including the synaptic vesicle-related proteins AMPH and SNAP25. Direct comparison of the MDD-P and MDD-NP proteomes resulted in identification of 25 altered proteins, which were not found in either of the MDD-P vs control or MDD-NP vs control comparisons (Figure 3b). These proteins included ADP-ribosylation factor 1 and 3 (ARF1 and ARF3), synaptotagmin 2 (SYT2) and stress-induced phosphoprotein 1 (STIP1). Moreover, there was no overlap of this signature with that found in the total MDD vs control comparison.
Comparison of these datasets with those obtained from previous proteomic studies of post-mortem brain tissue from schizophrenia patients36, 37, 38 showed a marked overlap between the MDD-P and schizophrenia proteomes (Figure 3a). Also, the psychosis-specific fingerprint obtained by direct comparison of MDD-P and MDD-NP subjects showed a significant overlap with the schizophrenia fingerprint (Figure 3b). This included proteins such as SYT2, septin 2 (SEPT2), syntaxin-binding protein 1 (STXBP1) and calreticulin (CALR).
Validation experiments
Considering the impairment of synaptic transmission associated with MDD, as observed here and previously,1, 4 we carried out technical validation of three such differentially expressed candidate proteins identified by the shotgun LC-MSE molecular profiling method. Using Western blot, we analyzed the proteins AMPH and GAP43 in whole tissue lysates as well as with SRM-MS to validate the differential expression of SNAP25. Considering the suggested role of histidine triad nucleotide-binding protein-1 (HINT1) in MDD,39, 40 we also carried out SRM-MS experiments for this protein. Apart from GAP43, differential expression of all of these proteins was validated (Figure 4). Of note, AMPH was only found to be significant in the MDD-NP versus MDD-P comparison with a fold change of 1.13 based on MS profiling and Western blot analysis revealed that this was increased by 1.47 fold in all MDD patients. This lends support to our choice of a cutoff of 1.15-fold in the LC-MSE screening phase and also suggests that the quantification of label-free proteomics data may underestimate fold changes, as demonstrated previously.29
The results also suggested that the oxidative phosphorylation pathway was dysregulated in MDD through the differential expression of several subunits of the mitochondrial complexes (Supplementary Material 6). Therefore, we carried out a functional validation experiment of whole tissue lysates to test this possibility by measuring the levels of ATP in the DLPFC samples. This analysis showed that lower levels of ATP were observed in MDD tissues compared with controls, although this was not related to psychotic status (Figure 4).
Discussion
This is the first shotgun proteomics study of brain tissue samples from MDD patients. We employed an LC-MSE method in combination with gel-based protein fractionation to increase proteome coverage, including the identification of membrane proteins,26 which are mediators of signal transduction and potential therapeutic targets. We also applied LC-SRM-MS for the first time in proteome studies of psychiatric disorders. This method was used here as a means of validating differences in the levels of identified proteins with greater level of sensitivity and specificity compared to the conventional LC-MSE profiling. The multiplex nature of SRM allows the accurate quantification of tens of proteins in a single experimental run allowing its use in both pre-clinical and clinical trials as well as in clinical pipelines for detection of diagnostic, prognostic and treatment-related biomarkers. GAP43 could not be validated most probably due to the different sensitivity of mass spectrometry and Western blot.41
One of the major pathways associated with the DLPFC proteomic differences in MDD patients was related to energy metabolism, consistent with previous imaging findings. Previous studies have shown a reduction in glucose metabolism in brains from MDD patients using a positron emission tomography approach.42 This is interesting as an increase in glucose metabolism was found in this same brain region of MDD patients after administration of the anti-depressant paroxetine.43 Such effects on energy are likely to be common in psychiatric disorders,44 suggesting that these may be nonspecific features of these conditions. However, our data suggest some specificity in energy metabolism dysfunction at the protein level. Although in schizophrenia the main affected metabolic pathway appears to be glycolysis,38 the most affected pathway in MDD appeared to be oxidative phosphorylation. However, it should be noted that some components of the oxidative phosphorylation pathway are also affected in schizophrenia,45, 46 but not to the same extent as glycolysis. We found that 20 different subunits of the oxidative phosphorylation complexes showed increased levels in MDD brains (Supplementary Material 6). Interestingly, oxidative phosphorylation proteins are mostly decreased in schizophrenia.37 Considering that the main product of oxidative phosphorylation is ATP, we measured ATP concentrations in MDD DLPFC samples and verified that there were significantly lower levels in comparison with those found in control subjects (Figure 4). One possible explanation for this is that ATP may be depleted in this brain area of MDD subjects due to the presence of oxidative stress47 and the oxidative phosphorylation complexes may be over-expressed to compensate for this effect.
Our findings also support an impairment of synaptic function in MDD, especially for components of soluble N-ethylmaleimidesensitive (NSF) sensitive factor attachment protein receptor (SNARE) function. For example, we identified and validated changes in SNAP25, which is a pre-synaptic membrane protein involved in anchoring synaptic vesicles in the SNARE complex. We also found changes in the GABARAPL2, a cytosolic regulator of SNAREs involved in stimulation of the ATPase activity of NSF that signals disassembly and recycling of SNARE complexes.48 Syntaxin 1B (STX1B), which was found significantly upregulated (P=0.0291, fold change 1.07), but discarded from our list according to our fold change cutoff, is a cellular receptor for transport vesicles (Figure 1), which inhibits SNARE function49, 50 and thereby controls neurotransmitter release.51 STX1B has a role in the calcium-dependent synaptic transmission52 and has also been found to be increased in schizophrenia.53 Changes were also found and validated for AMPH, which is present on the cytoplasmic surface of synaptic vesicles.54 In addition, we found alterations in alpha-synuclein (SNCA) and SNCG, which are involved in integration of presynaptic signaling and membrane trafficking. Altered levels of these proteins has been associated previously with Parkinson's and Alzheimer's diseases.55
Dysregulation of phospholipids and fatty acids, which are critical components of synaptic vesicle membranes, has also been linked to depression.56 Our proteomic findings identified changes in expression of arachidonic acid and phospholipase D2 (PLD2), which are components in membrane structure and function. Antidepressant treatment reduces arachidonic acid turnover,57 and the release of this molecule is decreased when PLD2 is inactivated.58 In this regard, we found that the levels of SNCA were increased, which inhibits PLD2. Moreover, PLD2 is inhibited by AMPH,59 which was found to be increased. PLD2 interacts directly with protein kinase C (PKC). PKC is a component of intracellular signal transduction and its activity has been shown to be reduced in depression.60 PKC is also activated in the cytoplasm by diacylglycerol, which is anchored in the membrane, facilitating PKC translocation from the cytosol to membranes. Diacylglycerol and inositol trisphosphate are hydrolytic products of membrane phospholipid phosphatidyl inositol-bisphosphate resulting from activity of the enzyme phospholipase C. In this study, we found changes in several proteins in MDD brains, which are involved in PKC regulation, including PFN1, PFN2 and actin-binding proteins61 (Figure 1).
We found that the levels of HINT1 were increased in MDD patients, which was confirmed by SRM mass spectrometry analysis. Interestingly, the levels of this protein were found to be deceased in the DLPFC in previous studies of schizophrenia.62 HINT1 was originally described as a PKC inhibitor, although its precise function has yet to be confirmed. It is widely expressed in a number of tissues including liver, kidney and brain, where it has been associated with control of transcriptional processes along with tumor suppression and susceptibility.63 A potential role of HINT1 in MDD and anxiety disorders was described recently from studies of Hint1 knockout (KO) mice. The absence of HINT1 expression resulted in altered postsynaptic dopamine transmission in striatum and nucleus accumbens as well as an elevation of circulating corticosterone levels, suggesting effects on the hypothalamic-pituitary-adrenal axis. In addition, anxiety-like behavior has been observed in this model.39, 40, 64 Our findings not only support those of previous studies, which suggested that HINT1 may be a novel biomarker for MDD, but suggest that this could be specific for subjects without associated psychosis. This was shown by the fact that this protein was only increased in those MDD subjects without psychosis.
It has been known for decades that most neuropsychiatric disorders have a significant overlap of symptoms. It is likely that this will be reflected by an overlap of molecules and pathways in affected tissues such as the brain and only subtle differences are likely to be responsible for differentiating one disorder from another. For example, psychotic symptoms, which are a hallmark for schizophrenia, may occur in the course of MDD, although in a slightly different manner. Even so, the current findings suggest that there may be common molecular changes in MDD-P and patients with schizophrenia. In all, 11 of the 36 differentially expressed proteins in DLPFC of MDD-P patients compared with controls were identified in previous studies of schizophrenia.38, 65, 66 This included calmodulin (CALM1), which is involved in synaptic metabolism and calcium homeostasis and regulates the function of D1 and D2 dopamine receptors67 and the neurofilament medium polypeptide (NEFM), which belongs to the dopamine receptor-interacting protein (DRIP) family of proteins.67 Another protein that was altered in common between MDD-P and schizophrenia subjects was peroxiredoxin-6 (PRDX6).66, 68 This protein has been associated with schizophrenia susceptibility in Taiwanese families69 and has a role in phospholipid metabolism with phospholipase A2 (PLA2) activity.
The identification of proteome fingerprints has the potential to lead to the development of more specific classification tests compared with those depending on single molecules. In this case, testing for correlative changes in these protein fingerprints in peripheral tissues such as CSF and even serum might lead to the development of multiplex biomarker assays for psychosis. Moreover, direct comparison of MDD-P and MDD-NP patients resulted in identification of 12 differentially expressed proteins, which have also been found previously in schizophrenia (Table 3c). It was also of interest that comparison of MDD-P patients to controls identified differentially expressed proteins that were associated with energy metabolism, whereas comparison of MDD-NP patients and controls resulted in identification of proteins, which were mostly involved in cell growth and maintenance. This suggested that different mechanisms may be involved in the development of these two forms of MDD. Further experiments in this area should be conducted, which may eventually help with patient stratification for generating more personalized treatments.
Interestingly, 53.7% of the altered proteins in MDD-P overlapped with those in total MDD patients. This suggests that there may be a proteomic signature in MDD that exists independent of the presence of psychosis. Of the proteins unique to the MDD-P patients, two could not be associated with a biological class, although there are some leads about their function. One of these proteins was fanconi anemia group I protein (FANCI) that participates in protein ubiquitination and its differential expression can lead to defective DNA repair.70 The other was small VCP p97-interacting protein (SVIP), which is an 8.5 KDa membrane protein involved in ubiquitination of endoplasmic reticulum misfolded proteins.71 Considering the novelty of these proteins in psychiatric studies and their role in ubiquitination, their function could be further investigated in MDD-P.
Curiously, none of the proteins that were found to be differentially expressed between MDD-P and MDD-NP patients were altered in total MDD patients. This suggested that the MDD-P vs MDD-NP signature was more strongly related to psychosis and also more similar to previous findings in schizophrenia brain proteomes. In the MDD-P and MDD-NP comparison, we also identified protein targets with unknown biological function such as formin-like protein 2 (FMNL2), sterile alpha motif domain-containing protein 9 (SAMD9), spindle assembly abnormal protein 6 homolog (SASS6) and neurochondrin (NCDN). NCDN seems to interact with phosphatidic acid, which is a metabolite related to phospholipid metabolism (Figure 1) and SASS6 is likely to be involved in ubiquitination.72
One protein changed in common in all comparisons shown in Table 3 was PPIA (also known as cyclophilin A). PPIA accelerates the folding of proteins that are involved in cyclosporin A-mediated immunosuppression.73 Interestingly, this protein has also been found to be increased in schizophrenia thalamus,38 which suggests some relationship with the psychosis status. However, considering the fact that this protein is also differentially expressed in MDD-NP compared with controls, it does not seem to be a specific biomarker candidate to psychosis, but most likely associated to psychiatric conditions. On the other hand, the protein peptidylprolyl cis-trans isomerase (FKBP1A), which is from the same family of PPIA, was specifically altered in MDD-P samples. FKBP1A acts on immunoregulation and cellular processes involved in protein folding and trafficking and it also interacts with several intracellular signal transduction proteins.74 Moreover, FKBP1A binds to the immunosuppressants FK506 and rapamycin. Interestingly, the mammalian target of rapamycin (mTOR) signaling pathway in the prefrontal cortex is compromised in MDD,75 and mTOR interacts with FKBP1A bound to rapamycin.76 FKBP1A also interacts with the transforming growth factor (TGF)-beta receptor, which is critical for modulation of GABA synaptic transmission and dendritic homeostasis.77 Moreover, FMNL2, which we also found increased in MDD-P, is also part of TGF-beta pathway.78 These findings warrant further studies on the involvement of these proteins in psychiatric disorders.
Figure 2 represents a broader perspective of the unique findings of each of the compared groups. Changes in energy metabolism are notable in both MDD-P and MDD-NP. Although protein metabolism processes are more related to MDD-NP, changes in cell growth/maintenance, transport and regulation of nucleic acids are more related to MDD-P. When the two groups of MDD patients are compared, defects in cell signaling are pronounced so as are proteins with unknown biological processes. This last group of proteins should be further studied for providing more leads about the stratification of different MDD subtypes.
It is widely known that factors such as age, gender, postmortem interval, drug treatment and others may have confounding effects on global proteomic studies involving post-mortem samples.79 None of the factors considered here seem to have had a significant effect on the analyses. Compared groups are matched for demographic variables and these have not shown significant differences (Table 1). In addition, no unusual segregation of subjects using principle component analyses has been observed (Supplementary material 3). However, it should be noted that information was not available regarding the number of patients who were relapsed or who were on or off medication at the time of death. Although some effects on proteomic changes were observed when comparing MDD patients that committed suicide (n=17) with non-suicide MDD patients (n=6), we could not explore this any further due to the low numbers of subjects who did not die from suicide. Moreover, in the non-suicide group, some of these subjects actually attempted suicide although they were not successful.
The static nature of post-mortem brain tissue and limited sample sizes can be a drawback in studies such as the one presented here. Therefore, a replication of this study in an independent sample would be essential. However, we concur with a recent report that the analysis of post-mortem tissue from patients in brain disorders is indispensable, especially considering that it has generated important and unique insights for psychiatric studies.80
Our systems biology analyses of the MDD brain proteome showed that some of the differentially expressed proteins found in subjects with MDD have been associated previously with other diseases such as Huntington's and Alzheimer's disease and schizophrenia. This supports the notion that neurological and psychiatric disorders may share common pathways at the molecular level. Therefore, identification of multiple components of these conditions may be required in order to identify unique biomarker fingerprints. This may require identification of differences in the expression of several genes and proteins, together with consideration of potential environmental factors. Nevertheless, we and others have identified peculiarities for different diseases, as seen by differential expression of specific proteins. In fact, we showed that the changes in HINT1 expression were specific for MDD-NP in the DLPFC. Moreover, there appears to be distinct energy metabolism signatures for MDD and schizophrenia, with MDD affected more by changes in oxidative phosphorylation and schizophrenia through glycolysis-related pathways.
The current findings support the known impairment on synaptic transmission and especially on SNARE-related proteins in MDD. We also attempted to identify biomarker-specific signatures for subgroups of MDD through studies on depression associated with psychosis and even with suicide. Interestingly, we found a significant overlap of differentially expressed proteins in post-mortem brain tissue from MDD subjects with psychosis, with that of schizophrenia subjects.
These findings suggest that it may be possible to distinguish different subtypes of MDD patients based on differences in brain proteomic profiles. Translating such findings to the periphery might lead to novel personalized medicine strategies based on patient stratification according to molecular profiles. Also, the identification of new models for MDD based on brain and serum molecular profiles could lead to the development of such models for use in drug discovery. This could lead to more targeted treatment approaches and reduce the rate of drug attrition within the field of neuropsychiatric disorders.
References
Fava M, Kendler KS . Major depressive disorder. Neuron 2000; 28: 335–341.
Kendler KS . Major depression and generalised anxiety disorder. Same genes, (partly)different environments--revisited. Br J Psychiatry Suppl 1996; 30: 68–75.
Henriksson MM, Aro HM, Marttunen MJ, Heikkinen ME, Isometsa ET, Kuoppasalmi KI et al. Mental disorders and comorbidity in suicide. Am J Psychiatry 1993; 150: 935–940.
Harrison PJ . The neuropathology of primary mood disorder. Brain 2002; 125 (Part 7): 1428–1449.
Berke JD, Hyman SE . Addiction, dopamine, and the molecular mechanisms of memory. Neuron 2000; 25: 515–532.
McKinnon MC, Yucel K, Nazarov A, MacQueen GM . A meta-analysis examining clinical predictors of hippocampal volume in patients with major depressive disorder. J Psychiatry Neurosci 2009; 34: 41–54.
Drevets WC, Ongur D, Price JL . Neuroimaging abnormalities in the subgenual prefrontal cortex: implications for the pathophysiology of familial mood disorders. Mol Psychiatry 1998; 3: 220–226, 190-221.
Lotrich FE . Major depression during interferon-alpha treatment: vulnerability and prevention. Dialogues Clin Neurosci 2009; 11: 417–425.
Capuron L, Ravaud A, Dantzer R . Early depressive symptoms in cancer patients receiving interleukin 2 and/or interferon alfa-2b therapy. J Clin Oncol 2000; 18: 2143–2151.
Marques-Deak A, Cizza G, Sternberg E . Brain-immune interactions and disease susceptibility. Mol Psychiatry 2005; 10: 239–250.
Gans RO . The metabolic syndrome, depression, and cardiovascular disease: interrelated conditions that share pathophysiologic mechanisms. Med Clin North Am 2006; 90: 573–591.
Roumestan C, Michel A, Bichon F, Portet K, Detoc M, Henriquet C et al. Anti-inflammatory properties of desipramine and fluoxetine. Respir Res 2007; 8: 35.
Fava GA . Subclinical symptoms in mood disorders: pathophysiological and therapeutic implications. Psychol Med 1999; 29: 47–61.
McGuffin P, Katz R . The genetics of depression and manic-depressive disorder. Br J Psychiatry 1989; 155: 294–304.
Agid O, Kohn Y, Lerer B . Environmental stress and psychiatric illness. Biomed Pharmacother 2000; 54: 135–141.
Lundberg P, Cantor-Graae E, Rukundo G, Ashaba S, Ostergren PO . Urbanicity of place of birth and symptoms of psychosis, depression and anxiety in Uganda. Br J Psychiatry 2009; 195: 156–162.
Choudary PV, Molnar M, Evans SJ, Tomita H, Li JZ, Vawter MP et al. Altered cortical glutamatergic and GABAergic signal transmission with glial involvement in depression. Proc Natl Acad Sci USA 2005; 102: 15653–15658.
Sequeira A, Mamdani F, Ernst C, Vawter MP, Bunney WE, Lebel V et al. Global brain gene expression analysis links glutamatergic and GABAergic alterations to suicide and major depression. PLoS One 2009; 4: e6585.
Lalovic A, Levy E, Canetti L, Sequeira A, Montoudis A, Turecki G . Fatty acid composition in postmortem brains of people who completed suicide. J Psychiatry Neurosci 2007; 32: 363–370.
Martins-de-Souza D, Harris LW, Guest PC, Turck CW, Bahn S . The role of proteomics in depression research. Eur Arch Psychiatry Clin Neurosci 2010; 260: 499–506.
Filiou MD, Turck CW, Martins-de-Souza D . Quantitative proteomics for investigating psychiatric disorders. Proteomics Clin Appl 2011; 5: 38–49.
Martins-de-Souza D . Is the word ‘biomarker’ being properly used by proteomics research in neuroscience? Eur Arch Psychiatry Clin Neurosci 2010; 260: 561–562.
Halari R, Simic M, Pariante CM, Papadopoulos A, Cleare A, Brammer M et al. Reduced activation in lateral prefrontal cortex and anterior cingulate during attention and cognitive control functions in medication-naive adolescents with depression compared to controls. J Child Psychol Psychiatry 2009; 50: 307–316.
Chang CC, Yu SC, McQuoid DR, Messer DF, Taylor WD, Singh K et al. Reduction of dorsolateral prefrontal cortex gray matter in late-life depression. Psychiatry Res 2011; 193: 1–6.
Miller EK, Cohen JD . An integrative theory of prefrontal cortex function. Annu Rev Neurosci 2001; 24: 167–202.
Martins-de-Souza D, Menezes de Oliveira B, dos Santos Farias A, Horiuchi RS, Crepaldi Domingues C, de Paula E et al. The use of ASB-14 in combination with CHAPS is the best for solubilization of human brain proteins for two-dimensional gel electrophoresis. Brief Funct Genomic Proteomic 2007; 6: 70–75.
Martins-de-Souza D, Guest PC, Steeb H, Pietsch S, Rahmoune H, Harris LW et al. Characterizing the proteome of the human dorsolateral prefrontal cortex by shotgun mass spectrometry. Proteomics 2011; 11: 2347–2353.
Li GZ, Vissers JP, Silva JC, Golick D, Gorenstein MV, Geromanos SJ . Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 2009; 9: 1696–1719.
Levin Y, Hradetzky E, Bahn S . Quantification of proteins using data-independent analysis (MSE) in simple andcomplex samples: a systematic evaluation. Proteomics 2011; 11: 3273–3287.
Benjamini Y, Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc 1995; 57: 289–300.
Rothman KJ . No adjustments are needed for multiple comparisons. Epidemiology 1990; 1: 43–46.
Levin Y, Wang L, Schwarz E, Koethe D, Leweke FM, Bahn S . Global proteomic profiling reveals altered proteomic signature in schizophrenia serum. Mol Psychiatry 2010; 15: 1088–1100.
Kuster B, Schirle M, Mallick P, Aebersold R . Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol 2005; 6: 577–583.
MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010; 26: 966–968.
Vizcaino JA, Cote R, Reisinger F, Foster JM, Mueller M, Rameseder J et al. A guide to the Proteomics Identifications Database proteomics data repository. Proteomics 2009; 9: 4276–4283.
Martins-De-Souza D, Dias-Neto E, Schmitt A, Falkai P, Gormanns P, Maccarrone G et al. Proteome analysis of schizophrenia brain tissue. World J Biol Psychiatry 2010; 11: 110–120.
Martins-de-Souza D, Harris LW, Guest PC, Bahn S . The role of energy metabolism dysfunction and oxidative stress in schizophrenia revealed by proteomics. Antioxid Redox Signal 2011; 15: 2067–2079.
Martins-de-Souza D, Maccarrone G, Wobrock T, Zerr I, Gormanns P, Reckow S et al. Proteome analysis of the thalamus and cerebrospinal fluid reveals glycolysis dysfunction and potential biomarkers candidates for schizophrenia. J Psychiatr Res 2010; 44: 1176–1189.
Barbier E, Wang JB . Anti-depressant and anxiolytic like behaviors in PKCI/HINT1 knockout mice associated with elevated plasma corticosterone level. BMC Neurosci 2009; 10: 132.
Barbier E, Zapata A, Oh E, Liu Q, Zhu F, Undie A et al. Supersensitivity to amphetamine in protein kinase-C interacting protein/HINT1 knockout mice. Neuropsychopharmacology 2007; 32: 1774–1782.
Martins-de-Souza D, Guest PC, Vanattou-Saifoudine N, Harris LW, Bahn S . Proteomic Technologies for Biomarker Studies in Psychiatry: Advances and Needs. Int Rev Neurobiol 2011; 101: 33.
Baxter Jr LR, Schwartz JM, Phelps ME, Mazziotta JC, Guze BH, Selin CE et al. Reduction of prefrontal cortex glucose metabolism common to three types of depression. Arch Gen Psychiatry 1989; 46: 243–250.
Kennedy SH, Evans KR, Kruger S, Mayberg HS, Meyer JH, McCann S et al. Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. Am J Psychiatry 2001; 158: 899–905.
Rezin GT, Amboni G, Zugno AI, Quevedo J, Streck EL . Mitochondrial dysfunction and psychiatric disorders. Neurochem Res 2009; 34: 1021–1029.
Karry R, Klein E, Ben Shachar D . Mitochondrial complex I subunits expression is altered in schizophrenia: a postmortem study. Biol Psychiatry 2004; 55: 676–684.
Martins-de-Souza D, Gattaz WF, Schmitt A, Maccarrone G, Hunyadi-Gulyas E, Eberlin MN et al. Proteomic analysis of dorsolateral prefrontal cortex indicates the involvement of cytoskeleton, oligodendrocyte, energy metabolism and new potential markers in schizophrenia. J Psychiatr Res 2009; 43: 978–986.
Cumurcu BE, Ozyurt H, Etikan I, Demir S, Karlidag R . Total antioxidant capacity and total oxidant status in patients with major depression: impact of antidepressant treatment. Psychiatry Clin Neurosci 2009; 63: 639–645.
Sagiv Y, Legesse-Miller A, Porat A, Elazar Z . GATE-16, a membrane transport modulator, interacts with NSF and the Golgi v-SNARE GOS-28. EMBO J 2000; 19: 1494–1504.
Sorensen JB . SNARE complexes prepare for membrane fusion. Trends Neurosci 2005; 28: 453–455.
Yang B, Steegmaier M, Gonzalez Jr LC, Scheller RH . nSec1 binds a closed conformation of syntaxin1A. J Cell Biol 2000; 148: 247–252.
Mitchell SJ, Ryan TA . Munc18-dependent regulation of synaptic vesicle exocytosis by syntaxin-1A in hippocampal neurons. Neuropharmacology 2005; 48: 372–380.
Smirnova T, Stinnakre J, Mallet J . Characterization of a presynaptic glutamate receptor. Science 1993; 262: 430–433.
Behan AT, Byrne C, Dunn MJ, Cagney G, Cotter DR . 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 2009; 14: 601–613.
Mathew D, Popescu A, Budnik V . Drosophila amphiphysin functions during synaptic Fasciclin II membrane cycling. J Neurosci 2003; 23: 10710–10716.
Lavedan C . The synuclein family. Genome Res 1998; 8: 871–880.
Bennett CN, Horrobin DF . Gene targets related to phospholipid and fatty acid metabolism in schizophrenia and other psychiatric disorders: an update. Prostaglandins Leukot Essent Fatty Acids 2000; 63: 47–59.
Tassoni D, Kaur G, Weisinger RS, Sinclair AJ . The role of eicosanoids in the brain. Asia Pac J Clin Nutr 2008; 17 (Suppl 1): 220–228.
Kim JH, Lee BD, Kim Y, Lee SD, Suh PG, Ryu SH . Cytosolic phospholipase A2-mediated regulation of phospholipase D2 in leukocyte cell lines. J Immunol 1999; 163: 5462–5470.
Lee C, Kim SR, Chung JK, Frohman MA, Kilimann MW, Rhee SG . Inhibition of phospholipase D by amphiphysins. J Biol Chem 2000; 275: 18751–18758.
Shelton RC, Hal Manier D, Lewis DA . Protein kinases A and C in post-mortem prefrontal cortex from persons with major depression and normal controls. Int J Neuropsychopharmacol 2009; 12: 1223–1232.
Goldschmidt-Clermont PJ, Machesky LM, Baldassare JJ, Pollard TD . The actin-binding protein profilin binds to PIP2 and inhibits its hydrolysis by phospholipase C. Science 1990; 247: 1575–1578.
Varadarajulu J, Schmitt A, Falkai P, Alsaif M, Turck CW, Martins-de-Souza D . Differential expression of HINT1 in schizophrenia brain tissue. Eur Arch Psychiatry Clin Neurosci 2011; doi: 10.1007/s00406-011-0216-4; PMID: 21553311 [e-pub ahead of print].
Su T, Suzui M, Wang L, Lin CS, Xing WQ, Weinstein IB . Deletion of histidine triad nucleotide-binding protein 1/PKC-interacting protein in mice enhances cell growth and carcinogenesis. Proc Natl Acad Sci USA 2003; 100: 7824–7829.
Varadarajulu J, Lebar M, Krishnamoorthy G, Habelt S, Lu J, Bernard Weinstein I et al. Increased anxiety-related behaviour in Hint1 knockout mice. Behav Brain Res 2011; 220: 305–311.
Martins-de-Souza D, Gattaz WF, Schmitt A, Rewerts C, Maccarrone G, Dias-Neto E et al. Prefrontal cortex shotgun proteome analysis reveals altered calcium homeostasis and immune system imbalance in schizophrenia. Eur Arch Psychiatry Clin Neurosci 2009; 259: 151–163.
Martins-de-Souza D, Gattaz WF, Schmitt A, Novello JC, Marangoni S, Turck CW et al. Proteome analysis of schizophrenia patients Wernicke's area reveals an energy metabolism dysregulation. BMC Psychiatry 2009; 9: 17.
Bergson C, Levenson R, Goldman-Rakic PS, Lidow MS . Dopamine receptor-interacting proteins: the Ca(2+) connection in dopamine signaling. Trends Pharmacol Sci 2003; 24: 486–492.
Martins-de-Souza D, Schmitt A, Roder R, Lebar M, Schneider-Axmann T, Falkai P et al. Sex-specific proteome differences in the anterior cingulate cortex of schizophrenia. J Psychiatr Res 2010; 44: 989–991.
Hwu HG, Liu CM, Fann CS, Ou-Yang WC, Lee SF . Linkage of schizophrenia with chromosome 1q loci in Taiwanese families. Mol Psychiatry 2003; 8: 445–452.
Dorsman JC, Levitus M, Rockx D, Rooimans MA, Oostra AB, Haitjema A et al. Identification of the Fanconi anemia complementation group I gene, FANCI. Cell Oncol 2007; 29: 211–218.
Ballar P, Zhong Y, Nagahama M, Tagaya M, Shen Y, Fang S . Identification of SVIP as an endogenous inhibitor of endoplasmic reticulum-associated degradation. J Biol Chem 2007; 282: 33908–33914.
Sowa ME, Bennett EJ, Gygi SP, Harper JW . Defining the human deubiquitinating enzyme interaction landscape. Cell 2009; 138: 389–403.
Colgan J, Asmal M, Yu B, Luban J . Cyclophilin A-deficient mice are resistant to immunosuppression by cyclosporine. J Immunol 2005; 174: 6030–6038.
Matsuda A, Suzuki Y, Honda G, Muramatsu S, Matsuzaki O, Nagano Y et al. Large-scale identification and characterization of human genes that activate NF-kappaB and MAPK signaling pathways. Oncogene 2003; 22: 3307–3318.
Jernigan CS, Goswami DB, Austin MC, Iyo AH, Chandran A, Stockmeier CA et al. The mTOR signaling pathway in the prefrontal cortex is compromised in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2011; 35: 1774–1779.
Jacinto E, Loewith R, Schmidt A, Lin S, Ruegg MA, Hall A et al. Mammalian TOR complex 2 controls the actin cytoskeleton and is rapamycin insensitive. Nat Cell Biol 2004; 6: 1122–1128.
Sun M, Gewirtz JC, Bofenkamp L, Wickham RJ, Ge H, O’Connor MB . Canonical TGF-beta signaling is required for the balance of excitatory/inhibitory transmission within the hippocampus and prepulse inhibition of acoustic startle. J Neurosci 2010; 30: 6025–6035.
Li Y, Zhu X, Zeng Y, Wang J, Zhang X, Ding YQ et al. FMNL2 enhances invasion of colorectal carcinoma by inducing epithelial-mesenchymal transition. Mol Cancer Res 2010; 8: 1579–1590.
Kleinman JE, Law AJ, Lipska BK, Hyde TM, Ellis JK, Harrison PJ et al. Genetic neuropathology of schizophrenia: new approaches to an old question and new uses for postmortem human brains. Biol Psychiatry 2011; 69: 140–145.
Harrison PJ . Using our brains: the findings, flaws, and future of postmortem studies of psychiatric disorders. Biol Psychiatry 2011; 69: 102–103.
Acknowledgements
We would like to sincerely thank all tissue donors and their families for comprehending how important their consent is to our research and to the lives of patients. We also thank Professor Fuller Torrey and the Stanley Medical Research Institute for material donation and awarded grants.
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PCG, HR and SB are consultants for Psynova Neurotech Ltd.
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Martins-de-Souza, D., Guest, P., Harris, L. et al. Identification of proteomic signatures associated with depression and psychotic depression in post-mortem brains from major depression patients. Transl Psychiatry 2, e87 (2012). https://doi.org/10.1038/tp.2012.13
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DOI: https://doi.org/10.1038/tp.2012.13
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