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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurochem. Author manuscript; available in PMC 2012 November 1.
Published in final edited form as:
PMCID: PMC3192943
NIHMSID: NIHMS317710

The effects of chronic treatment with mood stabilizers on the rat hippocampal postsynaptic density proteome

Abstract

Bipolar disorder is a devastating illness that is marked by recurrent episodes of mania and depression. There is growing evidence that the disease is correlated with disruptions in synaptic plasticity cascades involved in cognition and mood regulation. Alleviating the symptoms of bipolar disorder involves chronic treatment with mood stabilizers like lithium or valproate. These two structurally dissimilar drugs are known to alter prominent signaling cascades in the hippocampus, but their effects on the postsynaptic density complex remain undefined. In this work, we utilized mass spectrometry for quantitative profiling of the rat hippocampal postsynaptic proteome to investigate the effects of chronic mood stabilizer treatment. Our data shows that in response to chronic treatment of mood stabilizers there were not gross qualitative changes but rather subtle quantitative perturbations in PSD proteome linked to several key signaling pathways. Our data specifically support the changes in actin dynamics on valproate treatment. Using label free quantification methods, we report that lithium and valproate significantly altered the abundance of 21 and 43 proteins, respectively. Seven proteins were affected similarly by both lithium and valproate: Ank3, Grm3, Dyhc1, and four isoforms of the 14-3-3 family. Immunoblotting the same samples confirmed the changes in Ank3 and Grm3 abundance. Our findings support the hypotheses that BPD is a synaptic disorder and that mood stabilizers modulate the protein signaling complex in the hippocampal PSD.

Keywords: Hippocampus, postsynapse, lithium, valproate, ankyrin 3, Grm3

Introduction

The hippocampus and related limbic structures are critical to regulating affect, stress responses, and cognition related to memory formation. Dysfunction in these brain regions is hypothesized to contribute to affective illnesses like bipolar disorder. Observational and functional studies have investigated the neuroanatomical differences in the hippocampus of bipolar patients, though few differences in overall structure and cell type distributions have been noted (Videbech & Ravnkilde 2004, Knable et al. 2004, Frazier et al. 2005). However, the two most prominent drugs used to treat the manic and depressive elements of bipolar disorder, lithium and valproate, have been shown to have a significant impact on the hippocampus in both preclinical and clinical studies. These mood stabilizers enhance the rate of hippocampal neurogenesis in rodents (Chen et al. 2000), patients (Yucel et al. 2007, Yucel et al. 2008), and can drastically alter prominent signaling cascades as well. Previous studies have shown that lithium and valproate alter prominent intracellular signaling pathways, including the glycogen synthase kinase-3 (GSK-3), extracellular regulated kinase (ERK), B-cell CLL/lymphoma 2 (Bcl-2) signaling cascade and the wnt/β-catenin pathway (Hunsberger et al. 2009). While it is unclear whether altering these signaling cascades can affect the overall hippocampal structure, there is evidence that significant changes emerge at the subcellular level, particularly at the neuronal synapse.

Located at neuronal terminals, the postsynaptic density (PSD) comprises a complex network of cytoskeletal scaffolding and signaling proteins that facilitate the movement of receptor and signaling proteins within the synaptic active zone (Sheng & Hoogenraad 2007). It is thought to facilitate many functions that are critical to interneuronal signaling and to reflect cellular response to external environmental changes (Emes et al. 2008). Metabotropic glutamate receptors (mGluRs) in the PSD control long term potentiation or depression, providing the mechanisms thought to underlie cognitive and mood processes (Luscher & Huber 2010). Dysfunction in plasticity cascades has been implicated in patients suffering from bipolar disorder, as well as many other psychiatric disorders, and the therapeutic basis of conventional and novel mood stabilizers may be mediated through improvements in synaptic plasticity regulation (Zarate et al. 2006, Coyle & Duman 2003). Observational studies have examined the composition of the postsynaptic complex in the rodent brain (Collins et al. 2006, Trinidad et al. 2006), but methods for quantitatively evaluating protein expression are still emerging (Dosemeci et al. 2007). The overarching remodeling of the postsynaptic protein complex following chronic treatment with mood stabilizers is unknown. In this work, we used mass spectrometry to quantitatively investigate the composition of the rat hippocampal postsynaptic complex and examined how this proteomic network is affected by chronic treatment with lithium and valproate.

Materials & Methods

Animals and treatment with mood stabilizers

8-week old male Wistar Kyoto rats (Taconic, Germantown, NY) were housed in pairs and treated for 5 weeks with either unmedicated chow (control group, n=12), or medicated chow containing either lithium carbonate (2.4 gm/kg, n=12) or sodium valproate (20 gm/kg, n=10) (BioServ, Frenchtown, NJ). All animals were housed in a facility with constant temperature (22 ± 1°C) and 12 h light/dark cycle with ad libitum access to food and water. An additional water bottle containing 0.9% saline solution was provided to ensure electrolyte balance was maintained throughout the experiment. All experimental procedures were approved by the Animal Use Committee of the National Institute of Mental Health and were conducted according to the NIH guidelines.

Isolation of hippocampal postsynaptic densities

The postsynaptic density (PSD) fraction was isolated from hippocampal tissue of rats that were housed in the same cage using procedures previously published (Carlin et al. 1980, Dosemeci et al. 2000). Hippocampi were rapidly collected, pooled from two animals so that there would be sufficient final PSD yield (~60–100 µg) for proteolysis and peptide fractionation. Tissue was immediately homogenized using a chilled glass Teflon homogenizer in ice-cold buffer (0.3 M mannitol, 1mM EDTA, protease inhibitor cocktail (Gamm et al. 2008)), omitting aprotinin due to observed interference with subsequent analyses (AEBSF 104 mM, bestatin HCL 4 mM, E-64 1.4 mM, leupeptin 2 mM, pepstatin 1.5 mM and phosphatase inhibitors I & II diluted 1:100, all from Sigma Aldrich St. Louis, MO). The homogenate was centrifuged at 3,000 × g for 10 min at 4°C (SS-34 rotor, Sorvall) to remove nuclei and cell debris. The resulting supernatant was saved and pellet was resuspended in another 3 mL ice cold mannitol buffer and centrifuged as above. Pooled supernatants were centrifuged at 26,900 × g for 30 min at 4°C. The pellet was resuspended again in 0.7 mL ice cold mannitol buffer and fractionated on a 6-tier Ficoll® gradient (20, 16, 12, 8, 2, 0%) and centrifuged for 90 min at 59,600 × g at 4°C (SW-60ti rotor, Beckman XL-90 Ultra).

The fractions between the 8–12% and 12–16% interfaces were first collected, pooled together, and then diluted 1:4 in 4 mL mannitol buffer. These combined fractions were centrifuged at 26,900 × g (SS-34 rotor) for 20 min at 4°C. The synaptosomal pellet was resuspended in 0.9 mL of a 20mM HEPES solution (pH 7.2) containing 0.5% Triton X-100 detergent (supplemented with protease and phosphatase I & II inhibitors(diluted 1:100 as recommended)). The suspension was centrifuged at 20,800 × g (Eppendorf 5417R) for 30 min at 4°C. The pellet was resuspended in 0.5 mL of HEPES buffer (containing 0.5% Triton X-100 and 75mM KCl) and centrifuged again at 20,800 × g for 30 min at 4°C. Finally, the enriched PSD pellet was resuspended in 0.2 mL detergent-free 20mM HEPES buffer. The protein concentration was estimated by BCA protein assay (Thermo Scientific, Rockford, IL) and aliquots were frozen at −80 °C.

Western Blotting

Immunoblotting was done using a standard protocol. Briefly, membranes were probed with the primary antibodies to Ankyrin G (1:100, NB20, EMD Biosciences, Gibbstown, NJ), Grm3 (1:300, sc-47137, Santa Cruz Biotechnology, Santa Cruz, CA) and PSD-95 (1:5,000, #4970, Cell Signaling, Beverley, MA). Protein bands were visualized using ECL Plus enhanced chemiluminescent signal detection kit (GE Healthcare) and exposed to Kodak Biolight film (Rochester, NY). When necessary, membranes were stripped using Re-Blot Plus (Millipore, Waltham, MA). Protein levels were normalized to a loading control (PSD-95) and subsequently analyzed by densitometric film analysis using AlphaImager software (Alpha Innotech, San Leandro, CA). GraphPad Prism software was used for statistics, performing one-way ANOVA with a post hoc Tukey (compares all columns) and individual t-tests (Control vs. Lithium, Control vs. Valproate).

Strong cation exchange chromatography

From each treatment group, three different PSD preparations of 50 µg proteins were denatured by 8 M urea at 60°C for 45 min. To reduce denatured PSD proteins, 1M DTT (final concentration 45 mM) was added and incubated at 60°C for 15 min. The proteins were alklyated by adding 1M iodoacetamide (final concentration 100mM) and incubating in the dark for 15 min. Subsequently, the PSD sample was digested by diluting from 8M to 1M urea concentration by adding 100 mM NH4HCO3 and sequencing grade trypsin (Promega, Madison WI) (substrate to enzyme ratio of 10:1(w/w)). The sample was digested at 37°C overnight. The digested samples were desalted using an UltraMicroSpin™ reverse phase column (The Nest Group Inc., Southboro, MA) according to the manufacturer’s instructions. The desalted sample was concentrated to dryness in vacuo (SpeedVac, Thermo-Savant). The dried peptides were resuspended in 100 µl of 10mM ammonium formate, 10mM formic acid, and 25% acetonitrile by vortexing and sonicating for 15 min respectively. The resuspended sample was then loaded onto the PolySULFOETHYL A column (1.0 mm × 50 mm, PolyLC Inc., Columbia, MD) to separate peptides by strong cation exchange chromatography. The composition of solution A was 10mM ammonium formate, 10mM formic acid, 25% acetonitrile; solution B contained 500mM ammonium formate, 500mm formic acid, 25 % acetonitrile. The peptides were separated by 12 nonlinear gradient steps of 3 min each at 100 µl/min flow rate. The fractions were collected every minute for a total of 36 fractions. The concentration of solution B in each step of the gradient was 0%, 1%, 3%, 5%, 7 %, 10 %, 15%, 20%, 30%, 50%, 70% and 100%, respectively. Each fraction was dried in vacuo and then re-suspended in nanopure water to remove volatile ammonium formate salts in vacuo. After three cycles of aqueous suspension and in vacuo drying, the peptides were re-suspended in 30ul of 5% acetonitrile, 0.1% formic acid before analyzing by mass spectrometry.

Mass spectrometry

Samples were separated on reverse phase nanocolumn (PicoFritBioBasicC18 column with 75-m inner diameter and 15-m tip, New Objective, Woburn, MA) at 500 nl/min, running a 60-minute linear gradient from 5 % to 80% acetonitrile on an Eksigent nano LC 2D HPLC system. The LC unit was coupled to Triversa Nanomate (Advion, Ithaca, NY) spray source attached to LTQ-Orbitrap (Thermo Electron, San Jose, CA). To minimize the variation in liquid chromatography between fractions, individual fractions from each treatment were run in a set. That is, replicates of fraction 1 from the SCX of the three treatment groups (nine samples) were analyzed on LC/MS as a single set. In each set, between every sample analyzed, one blank with a short LC cycle of 30 minutes was run to minimize peptide carry-over. All of the fractions from all replicates and conditions were analyzed, a total of 324 LC/MS runs. The peptides were analyzed in positive ion mode; for each MS scan, the top five most intense ions were selected for collision-induced dissociation and MS/MS recording. The collision energy was set at 35%. The resolution for MS was set at 60,000 and data was collected in a centroid mode.

Data analysis

Data were searched using MASCOT version 2.1 (Matrix Science, Boston MA) (Perkins et al. 1999). The data was searched against the Swiss-Prot database (version 57. 15.0, release date March 24 2009) with the species filter “mammals”. The other parameters were as follows: 1. Enzyme specificity: trypsin; 2. One allowed missed cleavage; 3. Fixed modification: cysteine carbamidomethylation; 4. Variable modification: methionine oxidation; 5. Precursor mass tolerance was ± 50 ppm; 6. Fragment ion mass tolerance was ±0.8 Da. The false discovery rate (FDR) for each sample set was determined with Mascot using a concatenated reversed sequence decoy database. Control 1: 0.63 % (11 decoy hits/ 1751 peptides), Control 2: 0.27 % (6 decoys/2184 peptides), Control 3: 0.35 % (8 decoys/2306 peptides), Lithium 1: 0.43% (10 decoys/ 2314 peptides), Lithium 2: 0.32% (9 decoys /2175 peptides), Lithium 3: 0.47% (11 decoys/2343 peptides), Valproate 1: 0.46% (10 decoys/2193 peptides), Valproate 2: 0.23% (6 decoys/2598 peptides) Valproate 3: 0.25% (6 decoys/2418 peptides). It should be noted that most reversed sequence decoy database hits were single spectra observations. As a result, the FDR calculated for spectral hits is lower than that for peptides (average FDR for all nine sample sets based on spectral hits is 0.07 ± 0.021%). The protein inference from the peptide search data was done by parsimony analysis (Yang et al. 2004) using the NCBI software MassSieve (Slotta et al. 2010). In MassSieve, all single peptide hits are removed and only peptide identifications with MASCOT ion scores greater than or equal to their identity scores and with probability scores less than 0.05 accepted. The net effect of this filtering is that the calculable peptide FDR drops to zero. We used a broad species filter because the rat database is incomplete, and then reviewed all mammalian proteins designated as equivalent (i.e., containing identical sets of peptides) (Liska & Shevchenko 2003, Junqueira et al. 2008). Using this strategy, protein identifications were assigned to the species rat by default, but wherever a rat homolog was not found, a secondary rodent species (mouse) was preferentially selected, followed by human and bovine, respectively. We included proteins only if there was at least one unique peptide (observed multiple times) identified in each of the groups (control, lithium, valproate) based on the criteria described above. Mass spectrometric data files (raw data files, data files from the MASCOT search engine, and the protein inference list from MassSieve) associated with this paper have been deposited in the NCBI Peptidome Repository for public access (Accession PSE 150) (http://www.ncbi.nlm.nih.gov/projects/peptidome/).

Protein quantification and statistical analyses

The software program suite DBparser 3.0 was used for the protein quantification as described previously (Dosemeci et al. 2007, McFarland et al. 2008). The program extracts retention time and peak intensities from MS1 raw data based on the precursor mass assigned by MASCOT. Proteins were quantified by summing the ion current intensities of all constituent peptides, and have not been normalized. The log2-fold change between treated and control was calculated by comparing average intensities from three replicates. Proteins that were not identified in all three replicates were not considered for quantification. A two tailed t-test and Cohen’s d effect size correlation was performed based on log2 transformed intensities. The p value cutoff of > 0.9, effect size of > ± 0.8, and log2-fold change of ± 0.8 was considered significant. The effect size (Cohen’s d) is the number of standard deviations difference between conditions. So, a Cohen’s d of 1.0 means there is a one standard deviation difference between conditions. Thus, the effect sizes take into account the size of the standard deviation for the variable in question.

Network Analysis

The networks, functional analyses, and canonical pathways were generated using Ingenuity Pathway Analysis (Ingenuity® Systems, http://www.ingenuity.com). The composite data of post synaptic density proteome used is the dataset reported in Supplementary Table 1.

Results

Composition of hippocampal post synaptic density proteome

We identified 605 proteins in the PSD of the rat hippocampus, based on concatenated data sets derived from nine sets of mass spectrometric analyses (3 conditions × 1/2 (6 paired hippocampi) × 36 SCX fractions, or 324 runs) (Supplementary Table 1). Of the total, 584 proteins (96.5%) were found in both treatment groups and control (but not necessarily in all three replicates), while 332 (55%) of the proteins were found in all of the replicates of all three groups. The functional classification of the proteins, shown in Supplementary Table 1, follows the format used previously in a comprehensive literature survey of PSD proteins (Collins et al. 2006). Based on this classification scheme, our PSD preparation was predominantly composed of signaling proteins (23%), cytoskeletal and cell adhesion (20%) and synaptic vesicle proteins (13%) (Figure 1) similar to those reported in the survey of PSD proteomics literature (Collins et al. 2006). In the Collins study, a consensus list of 466 proteins was compiled from seven different studies profiling the rodent post synaptic density proteome. From this consensus list, we found 424 proteins in our PSD preparation from rat hippocampus (data not shown). The overlap of our dataset list with the published PSD consensus list indicates that our preparations are relatively specific, and typical of sucrose density gradient purified postsynaptic fractions (Dosemeci et al. 2007). Trinidad et. al. reported 2159 proteins in PSD isolated from several regions of mouse brain. This higher number of PSD proteins may be attributed to improved instrumentation, synaptic heterogeneity, and/or sample quantity. The Trinidad study used tenfold higher amounts of purified PSD preparation (500 µg, tissues pooled from several animals) in their analyses compared to 50 µg in the current study (Trinidad et al. 2006).

Figure 1
Functional classification of proteins identified in hippocampal proteome

Effects of lithium and valproate on hippocampal postsynaptic proteome

We compared the changes in protein abundance at the postsynaptic density following chronic treatment with lithium or valproate. Based on our statistical criteria (see Materials and Methods), we found that lithium treatment significantly altered the level of 20 proteins (Table 1) (for complete list, see Supplementary Table 2) and valproate significantly affected abundance of 41 proteins (Table 2) (for complete list, see Supplementary Table 3). Seven proteins were significantly altered by both lithium and valproate treatment: metabotropic glutamate receptor 3 (Grm3), ankyrin 3 (Ank3), dynein heavy chain 1 (Dyhc1), and 14-3-3 protein isoforms T, F, E and Z. All seven proteins identified responded similarly, in direction and magnitude, to both mood stabilizers (Table 1 & 2 shown in bold letters). To validate these results, we performed immunoblotting experiments on three selected proteins Ank3 and Grm3 with the same nine PSD fraction sample sets (n=3/treatment group) that underwent mass spectrometry analysis (Figure 2). After testing for antibody specificity and linearity (data not shown), we confirmed that Grm3 and Ank3 abundance were increased, as expected in the PSD fraction by mood stabilizers (Figure 2).

Figure 2
(a) Densitometry from immunoblotting was utilized to confirm quantitative values measured using mass spectrometry. Proteins were measured that were significantly affected by both lithium and valproate treatments and had commercially available antibodies. ...
Table 1
Comparison of protein abundance difference in synapse enriched fraction from hippocampus in lithium treated and non- treated animal
Table 2
Comparison of protein abundance difference in synapse enriched fraction from hippocampus in valproate treated vs control rats

Lithium and valproate specific quantitative changes in hippocampal PSD proteome

There are 13 additional protein concentrations that changed specifically in response to lithium as described in Table 1. These proteins are known from the literature to be implicated in different neurological and psychiatric disorders and are associated with different functions as indicated in the table. The quantitative changes in the protein phosphodiesterase Pde2a and the scaffolding protein Dlg1 are consistent with the observations made by others in the literature (Esposito et al. 2009, Sato et al. 2008) Valproate treatment has more robust effect on altering PSD protein levels in comparison with lithium. There are an additional 35 proteins whose abundance has changed on valproate treatment (Table 2): especially those in different functional categories like cytoskeletal (especially actin dynamics as discussed later), signaling proteins, enzymes, synaptic vesicles and transport. As shown in Table 2, many of these proteins have been implicated in various psychiatric and neurological disorders.

Network analyses

Functional assignment by Ingenuity Pathway Analysis of the 605 identified proteins showed the top five functions as cell-to-cell signaling, cellular organization, morphology, cellular transport, and cell signaling. The top five canonical pathways mapped were oxidative phosphorylation (38 proteins), CREB signaling in neurons (36 proteins), synaptic long term potentiation (28 proteins), glutamate receptor signaling (20 proteins), and calcium signaling (32 proteins). The enrichment of mitochondrial proteins in PSD preparation supports the assignment of oxidative phosphorylation as a top canonical pathway. Network analysis of non-canonical pathways is summarized in Supplementary Table 4, which includes the proteins in each network, their score, and the number of associated focus proteins identified. Ingenuity Pathway Analysis calculates a score for each network that indicates the likelihood that this set of focus genes in a network could be explained by random chance alone. The score is generated by taking into account the number of network eligible molecules, the size of the network, and the total number of molecules in the Ingenuity Knowledge Base that can be included in networks. The score is calculated using a right-tailed Fisher's Exact Test and is displayed as the negative log of that p-value. A score of 6 indicates that there is a 1 in million chance of deriving this network due to random chance.

Discussion

We set out to evaluate the composition of the hippocampal post-synaptic region and how two very different chronic mood stabilizer treatments affect its composition using an unbiased mass spectrometry-based proteomics approach. We found 584 proteins out of 605 proteins in the control and both treatment conditions. These results suggest that chronic lithium and valproate treatments do not promote synthesis or pruning of new protein families, but instead modulate significant increases or decreases in the abundance of proteins present in the postsynaptic proteome as defined by isolation methods commonly used in this field. Although this preparation is enriched with respect to histologically confirmed post-synaptic density proteins, there are some proteins co-isolated that are known to be presynaptic or mitochondrial in origin. The abundance of scaffolding proteins, with the exception of Dlg1 (Disks large homolog 1, or SAP97), did not change significantly following drug treatment (see Supplementary Table 5). The graphical comparison of the log2-fold changes of lithium vs. valproate treatment (Figure 3) reveals that most proteins are centered towards zero in all four quadrants. This suggests that mood stabilizer treatments lead to few, specific quantitative changes in the PSD proteome. Remarkably, there is not a single protein in this scatter plot analysis whose abundance is changed in opposite directions by the two drug treatments (see Quadrants 1 and 3 of Fig. 3) and there are few that show similar changes in abundance (see Quadrants 2 and 4). There are a significant number of proteins that show changes in abundance levels that are specific to either lithium or valproate treatment (points close to the X and Y axis in Fig. 3), suggesting differences in mechanism and pharmacology of these two drugs. The effectiveness of valproate on alteration of protein level, could also be due to its inhibitory action on histone deacetylase, which is thought to epigenetically repress transcription (Phiel et al. 2001, Gottlicher et al. 2001).

Figure 3
Scatter plot analysis of mass spectroscopy results

To get further insight, we analyzed our data set with Ingenuity Pathway Analysis to determine significant non-canonical networks of proteins and corresponding changes distinct to the valproate and lithium treatment. The top five networks identified with this analysis are detailed in Supplementary Table 4. Because it is known that valproate induces growth cone spreading and affects the actin polymerization leading to changes in growth cone morphology, the network illustrating that the abundance of 50 actin interacting proteins change on valproate treatment (Figure 4) is shown. The change in actin cytoskeleton is important for neuronal guidance and may be responsible for establishing new neuronal contacts resulting in changes in the neuronal circuit. In this network of proteins, Cof1(CFL1), Dpysl2, Phactr1, IP3KA (ITPKA) and E41L3 (EPB41L3) are involved in regulation of actin polymerization and dynamics (Figure 4). Though not mapped in Figure 4, Prof2 (1.2 log2-fold change, Table 2) is suggested to interact with the Wave1 complex at the synapse to control the actin polymerization (Pilo Boyl et al. 2007) while Cof1 is implicated in the disassembly of actin filaments (Bellenchi et al. 2007). The role of Dpysl2 is shown to be critical in axon formation and dendrite specification. It has been suggested Dpysl2 plays a role in transport of Sra-1/Wave1 complex to growth cones, augmenting the actin reorganization, and thereby inducing the axon outgrowth and formation (Kawano et al. 2005). The role of IP3KA in calcium transport is well known, but it also contains an F-actin binding domain. Kim et. al. showed that this actin binding domain (independent of IP3KA catalytic activity), regulates the remodeling of dendritic spine actin by the scaffolding Rac protein. (Kim et al. 2009). They also observed the accumulation of IP3KA in the synaptic area after induction of LTP. Phactr1 and E41L3 also physically interact with actin but their role in actin dynamics is still not very clear (Allen et al. 2004, Parra et al. 2000). The expression of E41L3 is significantly increased also on lithium treatment (Table 1). The actin capping protein Caza2 (CAPZA2), regulates the actin polymerization by binding to barbed ends of actin filament (Cooper & Schafer 2000). The loss of capping proteins reduces the cellular motility and their role may be “funneling,” i.e. to regulate actin polymerization at specific time and place (Cooper & Schafer 2000). Our quantitative and network analyses support the pathophysiology of neuronal remodeling and neurogenesis on valproate treatment.

Figure 4
Cellular Assembly and Organization, Cellular Function and Maintenance, Nervous System Development and Function

In lithium treated rats, the expression of the transmembrane protein Igfs8, belonging to immunoglobulin superfamily, increases prominently and significantly (2.5 log2-fold change). Igfs8 is expressed in adult brain and implicated in neurite outgrowth and maintenance of neural networks (Murdoch et al. 2003). Its expression is reduced in glioblastoma and its overexpression inhibits glioblastoma in vivo and in vitro (Kolesnikova et al. 2009). Our data is consistent with the observation that lithium has neuroprotective and neurotrophic effects. In Table 1, the reduced abundance of triose phosphate isomerase (TPIS) after lithium treatment is particularly notable. The therapeutic value of lithium has been attributed to the depletion of inositol in the human brain, although the molecular basis for this depletion is not completely clear. Shi et al. have shown that yeast with a mutation in the gene encoding triphosphate isomerase leads to hypersensitivity to lithium and valproate, inositol auxotrophy and accumulation of dihydroxyacetone phosphate (DHAP), an intermediate in the glycolytic pathway. The authors reasoned that accumulation of DHAP leads to competitive inhibition of myo-inositol phosphate 3 synthetase, which is a likely cause of inositol auxotrophy (Shi et al. 2005). Our data suggest that it is possible that a reduction in inositol during lithium treatment could be due to inhibition of the de novo biosynthetic pathway.

The seven proteins that changed in common after lithium and valproate treatment do not map to any single set of canonical or non-canonical pathways upon Ingenuity Pathway Analysis. However, the role of the 14-3-3 protein group has been implicated in psychiatric disorders previously. Middleton et al. have shown that expression of 14-3-3 gene group is decreased in patients suffering with schizophrenia (Middleton et al. 2005). In support of the transcriptomics data, Martins-de-Souza et al. showed the reduction in 14-3-3 zeta/delta, 14-3-3 gamma and 14-3-3 eta in patients suffering with schizophrenia using differential labeled quantitative proteomic technique (Martins-de-Souza et al. 2009). Two different studies have found evidence of association of two members of this group, 14-3-3F and 14-3-3E with bipolar disorder and schizophrenia (Grover et al. 2009, Ikeda et al. 2008). It was reported that lower levels of 14-3-3E showed developmental defects in hippocampal neurons (Ikeda et al. 2008).

Ankyrin 3 is an adapter protein that regulates the assembly of voltage gated sodium channels. Genome wide association studies (GWAS) have shown that the genetic variant of ANK3 (which encodes ankyrin 3) is significantly associated with risk of bipolar disorders (Phiel et al. 2001, Schulze et al. 2009). Some GWAS imply GRM3 (also known as mGluR3) genetic variants as risk factors for BPD and major depressive disorder (Kato 2007, Fallin et al. 2005). More studies are needed to explore causality between BPD and ANK3 or GRM3 genetic variants. The mGluR2/3 antagonist, LY-341495, increases mobility time in the forced swim test. The mGluR2/3 agonist, LY-379268, treatment mimics nicotine withdraw in induction of reward deficit monitored with intracranial self-stimulation reward thresholds. mGluR2/3 agonists, LY-379268 and LY-354740 attenuate amphetamine induced locomotion (Cartmell et al. 2000, Cartmell et al. 1999), an experimental model for mania and behavioral action of mood stabilizers. Our data suggests both lithium and valproate increase concentrations of Ank3 and mGluR3 in the PSD proteome, supporting the roles of these proteins in mood regulation.

In summary, we used a broad, discovery approach to profile the chronic effects of lithium and valproate on the post-synaptic density-enriched proteome isolated from rat hippocampus. The results support the multiple known protein networks of this synaptosomal preparation with regard to neuronal plasticity, and the notion that dysfunction of this synaptic proteome contributes to BPD and other major psychiatric illnesses. Further, we found that chronic treatment with mood stabilizers regulated levels of PSD proteome proteins linked to several key signaling pathways. Our data specifically support the changes in actin dynamics on valproate treatment. Lithium and valproate treatments increased levels of Ank3 and mGluR3 in the PSD proteome, supporting the roles of these two proteins in mood regulation. Future studies are required to test whether targeting Ank3 or mGluR3 produce lithium-like mood stabilizing effects in animal models and in BPD patients.

Supplementary Material

Nanavati Supplemental Data

Acknowledgments

This work was supported by the Intramural Research Program of the National Institute of Mental Health, NIH (MH000274 and MH000279). The authors received significant advice and assistance from staff in LNT, especially Dr. Jeffrey Kowalak, Anthony J. Makusky, Jason Harrington, and Ronald Finnegan.

Footnotes

Disclosure: The authors report no biomedical financial interests or potential conflicts of interest. At the date of manuscript submission, Dr. Manji and Dr. Chen are employees of Johnson and Johnson Pharmaceutical Research and Development, Titusville, NJ; Dr. Catapano is an employee of George Washington University, Washington, DC, and Dr. Nanavati is an employee of Northwestern University, Evanston, IL. This work was initiated while they were employees of the NIMH.

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