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. Author manuscript; available in PMC: 2016 May 1.
Published in final edited form as: Schizophr Res. 2015 Mar 7;164(0):100–108. doi: 10.1016/j.schres.2015.02.005

Altered prefrontal cortical MARCKS and PPP1R9A mRNA expression in schizophrenia and bipolar disorder

Glenn T Konopaske 1,2, Sivan Subburaju 1,2, Joseph T Coyle 1,2, Francine M Benes 1,2
PMCID: PMC4409526  NIHMSID: NIHMS665342  PMID: 25757715

Abstract

Background

We previously observed dendritic spine loss in the dorsolateral prefrontal cortex (DLPFC) from schizophrenia and bipolar disorder subjects. In the current study, we sought to determine if the mRNA expression of genes known to regulate the actin cytoskeleton and spines correlated with spine loss.

Methods

Five candidate genes were identified using previously obtained microarray data from the DLPFC from schizophrenia and control subjects. The relative mRNA expression of the genes linked to dendritic spine growth and function, i.e. IGF1R, MARCKS, PPP1R9A, PTPRF, and ARHGEF2, were assessed using quantitative real-time PCR (qRT-PCR) in the DLPFC from a second cohort including schizophrenia, bipolar disorder, and control subjects. Functional pathway analysis was conducted to determine which actin cytoskeleton-regulatory pathways the genes of interest interact with.

Results

MARCKS mRNA expression was increased in both schizophrenia and bipolar disorder subjects. PPP1R9A mRNA expression was increased in bipolar disorder subjects. For IGF1R, mRNA expression did not differ significantly among groups; however, it did show a significant, negative correlation with dendrite length. MARCKS and PPP1R9A mRNA expression did not correlate with spine loss, but interact with NMDA receptor signaling pathways that regulate the actin cytoskeleton and spines.

Conclusions

MARCKS and PPP1R9A might contribute to spine loss in schizophrenia and bipolar disorder through their interactions, possibly indirect ones, with NMDA signaling pathways that regulate spine structure and function.

Keywords: schizophrenia, bipolar disorder, dorsolateral prefrontal cortex, qRT-PCR, postmortem, actin cytoskeleton

1. Introduction

Spines are small protrusions emanating from dendrites of neurons. Most excitatory neurotransmission in the brain occurs at synapses on spines. Thus, spines are central to a myriad of brain functions (Sekino et al., 2007). Reflecting a possible common pathophysiological feature, we recently demonstrated spine loss in the dorsolateral prefrontal cortex (DLPFC) from both schizophrenia (SZ) and bipolar disorder (BD) subjects (Konopaske et al., 2014), suggesting that spine pathology might contribute to clinical features in both disorders. Indeed, alterations in DLPFC activity have been linked to working memory deficits in SZ (Carter et al., 1998; Manoach et al., 2000; Perlstein et al., 2001; Potkin et al., 2009) and emotional processing in BD (Chang et al., 2004; Hassel et al., 2008; Lawrence et al., 2004; Yurgelun-Todd et al., 2000).

Spines are highly dynamic and the actin cytoskeleton plays a pivotal role in their formation, structure, and maintenance (Sekino et al., 2007). Actin exists as either a globular (G-actin) or filamentous (F-actin) form. G-actin is polymerized to form F-actin, which is a major component of the actin cytoskeleton (Sekino et al., 2007; Shirao and Gonzalez-Billault, 2013). The actin cytoskeleton is also high dynamic and regulated by a host of factors, particularly those related to synaptic transmission.

We sought to identify actin cytoskeleton regulatory genes and assess whether their mRNA expression is altered and correlated with spine loss in the DLPFC of SZ and BD subjects (Konopaske et al., 2014). This study was conducted in three stages. First, we identified actin cytoskeleton regulatory genes using previously obtained microarray data. Second, the relative mRNA expression levels for the genes-of-interest were assessed using quantitative real-time PCR (qRT-PCR). We determined if changes in relative mRNA expression correlated with spine loss. For genes having differential mRNA expression in SZ and/or BD, we determined which actin cytoskeleton-regulating pathway(s) the genes of interest interact with using functional analyses. We postulated that the mRNA expression of one or more genes involved in actin cytoskeleton regulation are altered in the DLPFC of subjects with SZ and/or BD and these changes will correlate with the spine loss previously reported (Konopaske et al., 2014).

2. Experiment/Materials and Methods

2.1. Stage 1: Identification of Actin Cytoskeleton Regulatory Genes

To identify genes that regulate the actin cytoskeleton and spines in SZ subjects, we analyzed gene expression data from the National Brain Databank: Brain Tissue Gene Expression Repository (Harvard Brain Tissue Resource Center, HBTRC, McLean Hospital, Belmont, MA). The database contains gene expression data for the DLPFC (BA 9) of subjects in the McLean 66 cohort, which consists of SZ (n=19), BD (n=18), and controls (n=25) matched for age, postmortem interval (PMI), gender, pH, and RNA quality (see Table 1). Tissue processing and mRNA extraction procedures have been described previously (Konradi et al., 2004). The gene expression data were produced using Affymetrix (UH-133A) oligonucleotide microarrays for each case and were analyzed using dChip software (Li and Wong, 2001). A false discovery rate (FDR) was implemented to control for multiple tests. Genes having significantly altered expression levels in SZ subjects relative to controls (FDR-corrected p < 0.05) were further analyzed using GenMAPP (Gladstone Institutes, University of California at San Francisco) and Ingenuity Pathway Analysis (IPA, Qiagen, Redwood City, CA) (Benes et al., 2004; Konradi et al., 2004) to identify actin cytoskeleton regulatory genes. Our analyses identified 37 potential genes. Since some genes might regulate the actin cytoskeleton, but not spines, the literature pertaining to the function of these 37 genes was reviewed. Based on this review, we selected 5 genes for verification with qRT-PCR, namely, IGF1R, MARCKS, PPP1R9A, PTPRF, and ARHGEF2 (see Table 2) (Calabrese and Halpain, 2005; Cheng et al., 2003; Ryan et al., 2005; Zito et al., 2004).

Table 1.

Summary of clinical and demographic data for subjects included in the functional analyses (McLean 66 cohort).

Schizophrenia Bipolar disorder Control p-value
Sex (M/F) 13/6 11/7 17/8
Age 61.7±18 56.3±18.4 57.6±17 n.s.
PMI (hours) 21.6±5.6 21.1±9.7 21±5.4 n.s.
pH 6.4±0.3 6.5±0.2 6.4±0.3 n.s.
18/28s rRNA ratio (total RNA) 1.1±0.5 1±0.3 1.1±0.4 n.s.
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Table 2.

Genes identified in Stage 1 for analysis by qRT-PCR

Gene Name(s) Effect(s) on the Actin Cytoskeleton Citations
IGF1R insulin-like growth factor 1 receptor Modulates cytoskeleton reorganization (Kim and Feldman, 1998)
MARCKS myristoylated alanine-rich protein kinase C substrate F-actin aggregation, filament cross-linking and anchoring to plasma membrane (Calabrese and Halpain, 2005; Hartwig et al., 1992; Li et al., 2008; Yamaguchi et al., 2009)
PPP1R9A neurabin-I, protein phosphatase 1, regulatory (inhibitor) subunit 9A Participates in F-actin polymerization, cross-linking, and cytoskeleton reorganization (Nakanishi et al., 1997; Oliver et al., 2002; Zito et al., 2004)
PTPRF LAR, protein tyrosine phosphatase, receptor type, F Modulates cytoskeleton reorganization (Debant et al., 1996)
ARHGEF2 GEF-H1, rho/rac guanine nucleotide exchange factor (GEF) 2 Participates in actin filament formation and organization (Callow et al., 2005; Glaven et al., 1999; Meiri et al., 2012)
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2.2. Stage 2: qRT-PCR Analyses

2.2.1. qRT-PCR Subjects

Since frozen DLPFC tissue from the McLean 66 cohort is unavailable, tissue from a second cohort, the McLean 75 cohort, was utilized for the qRT-PCR verification study. Frozen, postmortem, human brain tissue samples containing DLPFC (BA 46) were obtained from the HBTRC (McLean Hospital, Belmont, MA). The cohort included subjects with SZ (n=22), BD (n=18), and controls (n=18), which were matched as closely as possible for age, sex, and PMI. Diagnoses were made using Feighner criteria for SZ (Feighner et al., 1972), and DSM-III-R (American Psychiatric Association 1987) for BD, and were based on a review of medical records and a questionnaire completed by the donor's family. Each brain was examined by a neuropathologist for gross and microscopic changes associated with neurodegenerative disorders, cerebrovascular disease, infectious processes, trauma or tumors.

2.2.2. qRT-PCR Tissue Processing

Frozen tissue (~100mg) including all cortical layers and a small portion of white matter was collected into TRIzol (Life Technologies, Grand Island, NY). The tissue was homogenized and total RNA was extracted and purified using the RNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. In-column DNase I digestion was carried out to remove the genomic DNA contamination. Total RNA concentration was measured using a Nanodrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA) and RNA integrity was quantified using an Agilent 2100 Bioanalyzer (RNA 6000 Nano Kit, Agilent Technologies, Santa Clara, CA). Only samples having a RNA integrity number (RIN) > 3 were included. cDNA was made using a high capacity cDNA reverse transcription kit (Applied Biosystems, Grand Island, NY).

2.2.3. qRT-PCR Analyses

Samples from SZ, BD, and control subjects (n=8 per group) were used to analyze 6 commonly used housekeeping genes: PPIA, ACTB, GAPDH, B2M, TFRC, RPLP0, and PGK1. Results from geNorm analyses (qbasePLUS, Biogazelle, Zwijnaarde, Belgium) revealed that GAPDH and ACTB expression had the least variance across the three groups. However, ACTB had considerable variation among BD subjects: therefore GAPDH alone was used as a housekeeping gene. The relative mRNA expression levels of IGF1R, MARCKS, PPP1R9A, PTPRF, and ARHGEF2 were measured in triplicate with qRT-PCR using TaqMan Gene Expression assays (Applied Biosystems, Grand Island, NY) on a Chromo4 Continuous Fluorescence Detection System (Bio-Rad, Hercules, CA). Relative expression levels were calculated using the 2−ΔCt method.

2.4. Stage 3: Functional Pathway Analyses

The interaction networks of genes, found to have differential mRNA expression in SZ and/or BD by qRT-PCR, were analyzed using IPA and revealed that these molecules participate in a host of signaling pathways. The most relevant pathway being the N-methyl-D-aspartate (NMDA)-type glutamate receptor signaling pathways which modulate actin cytoskeletal organization and in turn, spine structure and function (Fukazawa et al., 2003; Halpain et al., 1998; Maletic-Savatic et al., 1999; Okamoto et al., 2004). Based on the existing literature (Cingolani and Goda, 2008; Dillon and Goda, 2005; Tada and Sheng, 2006) and using IPA, signaling pathways linking NMDA receptors and the actin cytoskeleton in spines were constructed. The genes of interest were then added to the network to further elucidate their interactions with the NMDA receptor signaling pathways. To determine if signaling pathways linking NMDA receptors and actin cytoskeleton regulatory molecules in spines have additional alterations in SZ and BD, relevant molecules having differential mRNA expression (absolute fold change > 1.2 and p < 0.05) in SZ and BD subjects were added. These data were previously obtained using microarrays and DLPFC tissue from the McLean 66 cohort described above.

2.5. Antipsychotic Administration in Rats

To assess for the potential effects of antipsychotic medication treatment, 24 adult, male Sprague Dawley rats received haloperidol 1mg/kg/day, clozapine 25mg/kg/day, or sterile saline (n=8 per group) i.p. for 28 days (Linden et al., 2000; Merchant et al., 1994). They were then euthanized 24 hours after the last injection. Frontal cortex (~100mg) was dissected out and processed for qRT-PCR. The geomean of Rpl13 and Ppia mRNA expression levels were used for normalization (Elfving et al., 2008).

2.6. Statistical Analyses

Analysis of subject data by one-way ANOVA revealed no differences for mean age, PMI, storage time, or RIN across groups (p > 0.05). Relative mRNA expression was analyzed across groups using an ANCOVA. To ensure that an optimal statistical model was utilized for each gene, each of the following factors were systematically assessed alone and in combination with other factors using ANCOVA models with diagnosis and RIN included in each model. These included: age, sex, PMI, suicide, alcohol abuse or dependence, substance abuse or dependence, cannabis use, antipsychotic medication treatment in the last year of life, and treatment with lithium or valproic acid at the time of death. An optimized model was selected for each gene using the corrected Akaike's Information Criterion (AICC) (Akaike, 1973; Hurvich and Tsai, 1989) to identify the simplest, best-fitting model in each case. The AICC, which is the AIC corrected for small samples, resolves the “bias-variance tradeoff” in model selection by determining which covariates to include (to remove bias) and which to exclude (to minimize variance). To control for potential experiment-wise errors, a test-wise false-positive error rate was set at 0.05. For any gene having a significant ANCOVA effect for diagnosis (p < 0.05), post-hoc pairwise comparisons were conducted using Dunnett's method to control for multiple comparisons.

Two subjects, one SZ and one BD, had RINs < 3, thus were excluded from the qRT-PCR analyses. In addition, a brain from one SZ subject had evidence of Alzheimer's disease, thus was excluded. Moreover, several SZ (n=3), BD (n=2), and control (n=1) subjects had incomplete substance abuse histories in their medical records and were excluded. Tables 3 and 4 summarize the clinical and demographic data of the SZ (n=17), BD (n=15) and control (n=17) subjects included in the qRT-PCR analyses.

Table 3.

Clinical and demographic data for subjects included in the qRT-PCR analyses (McLean 75 cohort).

Control
Brain Age Sex PMI Cause of Death Medications
1 81 Female 17.5 Colorectal carcinoma amitriptyline
2 78 Female 23 Myocardial infarction none
3 80 Female 24 Cardiac arrest mirtazapine
4 48 Male 24 Myocardial infarction none
5 50 Male 20.5 Myocardial infarction none
6 57 Male 18 Cardiac arrest none
7 49 Male 27 Myocardial infarction none
8 56 Male 24.5 Cardiac arrest none
9 65 Male 21 Cardiac arrest none
10 41 Male 30.5 Cardiopulmonary Arrest none
11 55 Male 22 Cardiac arrest none
12 52 Male 21.5 Cardiac arrest none
13 46 Male 27 Atherosclerotic heart disease none
14 55 Male 22 Myocardial infarction none
15 49 Male 27 Cardiac arrest none
16 36 Female 18 Cardiac arrest lorazepam
17 43 Male 15 Myocardial infarction none
Schizophrenia
18 73 Female 29 Cancer none
19 68 Female 28 Cardiac arrest olanzapine, quetiapine, venlafaxine
20 46 Male 29 Cancer haloperidol
21 48 Male 18 Multiple myeloma perphenazine, quetiapine, sertraline, lorazepam
22 56 Male 22 Head trauma olanzapine, ziprasidone
23 58 Male 25.5 Atherosclerotic heart disease risperidone
24 49 Male 24.5 Acute respiratory distress haloperidol, triheyphenidyl
25 58 Male 26.5 Metastatic lung cancer haloperidol, benztropine
26 56 Female 19 Lung cancer olanzapine, trifluoperazine, valproic acid, lorazepam
27 62 Male 25.5 Sepsis olanzapine, valproic acid
28 68 Male 21.5 Cardiac arrest valproic acid, quetiapine, aripiprazole
29 56 Male 24.5 Asphyxia clozapine, valproic acid, quetiapine, haloperidol
30 55 Male 38.5 Heart failure risperidone, valproic acid, topiramate, benztropine, haloperidol, lorazepam
31 66 Male 16.5 Pneumonia clozapine, risperidone, valproic acid, lorazepam
32 60 Female 17 Cardiopulmonary arrest haloperidol, quetiapine, lithium
33 41 Male 33.5 Hypertensive heart disease chlorpromazine, lithium, benztropine, fluphenazine
34 48 Male 32.5 Suicide risperidone, lithium, amitriptyline
Bipolar disorder
35 86 Female 14 Chronic obstructive pulmonary disease olanzapine, lorazepam, trazodone
36 77 Female 33.5 Pneumonia olanzapine, lorazepam, mirtazapine, gabapentin
37 78 Female 21.5 Cardiac arrest olanzapine, lorazepam, risperidone, gabapentin
38 51 Female 30 Suicide risperidone, ziprasidone, venlafaxine, duloxetine, lorazepam
39 23 Female 24 Suicide risperidone
40 72 Female 21.5 Metastatic breast cancer valproic acid, venlafaxine
41 47 Female 16.5 Major systems failure perphenazine, topiramate, valproic acid, clonazepam
42 80 Female 29.5 Hypernatremia risperidone, valproic acid, citalopram
43 66 Female 25 Suicide quetiapine, valproic acid, escitalopram, clonazepam, mirtazapine
44 70 Male 17.5 Renal failure Lithium
45 76 Female 20 Aspiration pneumonia olanzapine, lithium
46 66 Female 25 Suicide quetiapine, valproic acid, escitalopram, clonazepam, mirtazapine
47 29 Male 20 Suicide risperidone, lithium, benztropine, diazepam
48 52 Female 17 Liver failure olanzapine, lithium, valproic acid
49 63 Male 27 Respiratory failure fluphenazine, quetiapine, lithium, valproic acid
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Table 4.

Summary of clinical and demographic data for subjects included in the qRT-PCR analyses (McLean 75 cohort).

Schizophrenia Bipolar disorder Control p-value
Sex (M/F) 13/4 3/12 13/4
Age 56.9±8.7 63.1±18.9 55.4±13.4 n.s.
PMI (hours) 25.2±6.1 21.9±6.3 22.5±4.1 n.s.
Storage time (months) 105±32.2 93.1±28.1 87.5±19.4 n.s.
RIN 6.5±1.1 6.9±0.7 6.9±0.8 n.s.
Suicide (Y/N) 1/16 4/11 0/17
History of alcohol abuse or dependence (Y/N) 6/11 4/11 2/15
History of cannabis use (Y/N) 6/11 2/13 2/15
History of other substance abuse or dependence (Y/N) 0/17 2/13 2/15
Antipsychotic medication (Y/N) 16/1 13/2 0/17
Valproic acid (Y/N) 6/11 6/9 0/17
Lithium (Y/N) 3/12 6/9 0/17
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The relationship between relative mRNA expression of each gene and basilar dendrite parameters for pyramidal cells in the deep half of layer III in the DLPFC, obtained previously (Konopaske et al., 2014), was assessed for SZ (n=13), BD (n=11), and control (n=16) subjects. A Pearson correlation coefficient was calculated for the relative mRNA expression of each gene and the number of spines per dendrite, spine density, and dendrite length.

The relationship between relative gene expression and clinical variables were assessed in SZ and bipolar groups. The following clinical variables were analyzed: suicide (yes/no), antipsychotic medication treatment in the last year of life (yes/no), lithium treatment at the time of death (yes/no) and valproic acid treatment at the time of death (yes/no). Clinical variables were analyzed with t-tests assuming unequal variances and p-values were corrected using the false discovery rate to control for multiple tests. Statistical analyses were conducted using STATA (v. 12, College Station, TX), and false discovery rate calculations were conducted using QVALUE (v. 1) (Storey, 2002).

3. Results

3.1. qRT-PCR Analyses

The ANCOVA model utilized for each gene is given in Table 5. The relative expression of MARCKS mRNA was increased significantly in both SZ (fold change: 1.6, p=0.01) and BD (fold change: 1.4, p=0.02) subjects. PPP1R9A mRNA expression was increased significantly in BD (fold change: 4.5, p < 0.0005) subjects and non-significantly increased in SZ (fold change: 2.2, p=0.25) subjects. IGF1R mRNA expression was non-significantly increased in both SZ (fold change: 1.7, p=0.18) and BD (fold change: 1.6, p=0.2). The relative expression of PTPRF and ARHGEF2 mRNA did not differ significantly between groups (p > 0.05, see Figure 1).

Table 5.

Summary of relative mRNA expression ANCOVA models.

Gene Main Effect Covariates (in addition to RIN)
IGF1R diagnosis history of alcohol abuse or dependence, age, valproic acid
MARCKS diagnosis PMI
PPP1R9A diagnosis suicide
PTPRF diagnosis valproic acid
ARHGEF2 diagnosis PMI, valproic acid
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Note: models were selected using Akaike's Information Criterion (AICC).

Figure 1.

Figure 1

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Graph depicting the relative mRNA expression levels of IGF1R, MARCKS, PPP1R9A, PTPRF, and ARHGEF2 in the DLPFC of schizophrenia (SZ), bipolar disorder (BD), and control subjects (CON). Error bars represent S.D., ** = p < 0.01, * = p < 0.05.

3.2. Effects of Clinical Variables

There was no difference in the relative mRNA expression of IGF1R, MARCKS, PPP1R9A, PTPRF, or ARHGEF2 between SZ and BD subjects who did and did not commit suicide. In addition, there were no differences in relative mRNA expression of any gene when comparing SZ and BD subjects who were and were not taking antipsychotic medications in the last year of life. Likewise, treatment with either lithium or valproic acid had no effect on the relative mRNA expression of any gene.

3.3. Correlation with Dendritic Parameters

Relative mRNA expression was correlated with previously obtained basilar dendrite parameters of pyramidal cells localized to the deep half of layer III in the DLPFC (Konopaske et al., 2014). The relative expression of IGF1R mRNA had a significant negative correlation with dendrite length (r=−0.35, p=0.03), and a moderate, negative correlation with the number of spines per dendrite which trended toward significance (r=−0.3, p=0.07). The relative expression of MARCKS and PPP1R9A mRNA had moderate, negative correlations with dendrite length, which trended toward significance (r=−0.29, p=0.07 and r=−0.29, p=0.08, respectively). The relative expressions of IGF1R, MARCKS, and PPP1R9A mRNA did not correlate with spine density. Moreover, there was no correlation between MARCKS and PPP1R9A mRNA expression and the number of spines per dendrite. Lastly, the relative mRNA expression of PTPRF and ARHGEF2 did not correlate with any dendritic parameter.

3.4. Functional Pathway Analyses

Functional pathway analyses revealed that MARCKS and PPP1R9A interact with NMDA receptor signaling pathways, which regulate the actin cytoskeleton and spines (Figures 3 and 4). Further analyses in SZ and BP subjects revealed altered mRNA expression of several genes which interact with NMDA signaling pathways. In SZ subjects, the relative mRNA expression of MARCKS, OPHN1, PARD3 and ARHGEF6 was increased (Figure 3). In BP subjects, the expression of MARCKS, PPP1R9A, OPHN1, and CDC42 BPA was increased (Figure 4). However, the expression of DLGAP2, NEFL, and PTK2B was decreased. A comparison of the interaction networks for SZ and BD revealed two genes with altered expression in both disorders, MARCKS and OPHN1.

Figure 3.

Figure 3

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Diagram depicting altered mRNA expression of molecules involved in the regulation of the actin cytoskeleton by NMDA signaling pathways in schizophrenia. Green represents increased mRNA expression, red represents decreased expression and no color represents no significant expression change. The diagram was generated using Ingenuity Pathway Analysis using data from the qRT-PCR study (double circles, McLean 75 cohort) and microarray data obtained from the DLPFC of schizophrenia subjects in the McLean 66 cohort. Both PARD3 (par-3 family cell polarity regulator) and ARHGEF6 (Rac/Cdc42 guanine nucleotide exchange factor (GEF) 6) regulate dendritic spine formation (Node-Langlois et al., 2006; Zhang and Macara, 2006).

Figure 4.

Figure 4

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Diagram depicting molecules involved in actin cytoskeletal regulation mediated by NMDA signaling pathways and having altered mRNA expression in bipolar disorder. Green represents increased mRNA expression, red represents decreased expression and no color represents no significant expression change. The diagram was generated using Ingenuity Pathway Analysis using data from the qRT-PCR study (double circles, McLean 75 cohort) and microarray data obtained from the DLPFC of bipolar disorder subjects in the McLean 66 cohort. Interestingly, mRNA expression levels of CDC42 and KALRN were unchanged in contrast to a previous report (Hill et al., 2006). However, CDC42BPA (CDC42 binding protein kinase alpha (DMPK-like)) mRNA expression was increased. CDC42BPA is a CDC42 effector protein involved in the reorganization of the actin cytoskeleton (Leung et al., 1998). DLGAP2 (Discs, large (Drosophila) homolog-associated protein 2) is a post-synaptic scaffolding protein involved in the regulation of NMDA receptors and dendritic spines (Jiang-Xie et al., 2014). NEFL (neurofilament, light polypeptide) is a post-synaptic structural protein which regulates the surface expression and function of NMDA receptors (Ehlers et al., 1998; Ratnam and Teichberg, 2005). PTK2B (protein tyrosine kinase 2 beta) is involved in the regulation of NMDA receptor activity (Heidinger et al., 2002).

3.5. Antipsychotic Administration in Rats

The relative expression of Marcks and Ppp1r9a mRNA were not changed significantly in the frontal cortex of rats chronically administered haloperidol or clozapine (p > 0.05, see Figure 5).

Figure 5.

Figure 5

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Graph depicting the relative expression of Marcks and Ppp1r9a mRNA in the frontal cortex of rats chronically administered haloperidol (HAL), or clozapine (CLOZ) relative to controls (CON). No differences were detected among groups (p > 0.05).

4. Discussion

The relative expression of MARCKS mRNA was increased significantly in the DLPFC from both SZ and BD subjects. PPP1R9A mRNA expression was increased significantly in BD. The relative expression of IGF1R did not differ significantly among groups. However, it had a significant negative correlation with dendrite length. Although MARCKS and PPP1R9A mRNA expression did not correlate with spine loss, functional pathway analyses reveal that MARCKS and PPP1R9A interact with NMDA receptor signaling pathways, which regulate the actin cytoskeleton. In addition, several members of the NMDA signaling pathways had altered mRNA expression in the DLPFC from SZ and BD subjects, suggesting that altered regulation of the actin cytoskeleton by NMDA signaling might contribute to spine loss in both disorders.

MARCKS is expressed throughout the brain and is localized to axons, dendrites, spines and glial processes (Ouimet et al., 1990). MARCKS regulates the organization of actin filaments. In addition, MARCKS anchors actin filaments to the plasma membrane (Calabrese and Halpain, 2005; Hartwig et al., 1992; Li et al., 2008; Yamaguchi et al., 2009). The current study is consistent with a prior postmortem study finding increased MARCKS mRNA in the DLPFC (Hakak et al., 2001). Interestingly, a second study found decreased MARCKS protein in the DLPFC (Pinner et al., 2014). A knockdown of MARCKS results in spines loss (Calabrese and Halpain, 2005) therefore, decreased MARCKS protein expression might correlate with spine loss. In addition, the increased MARCKS mRNA expression observed in SZ and BD might be a compensation for decreased protein expression.

OPHN1 (oligophrenin 1) is a RhoGAP associated with X-linked mental retardation and other neurological abnormalities (Bergmann et al., 2003; Billuart et al., 1998). OPHN1 is expressed throughout the brain, including dendrites and spines (Govek et al., 2004). Ophn1 knockout mice exhibit phenotypes reminiscent of SZ, especially impaired spatial working memory, enlarged ventricles, and spine pathology (Khelfaoui et al., 2007; Khelfaoui et al., 2009).

PPP1R9A plays an important role in synaptic structure and function, neurite formation, filopodia formation, and spine motility (Nakanishi et al., 1997; Sieburth et al., 2005; Zito et al., 2004), as a result we hypothesized that alterations in PPP1R9A mRNA expression might correlate with spine loss. As it turns out, PPP1R9A did not correlate with spine loss. However, PPP1R9A does play a role in dendritic spine morphology which could be altered in SZ and BP (Penzes et al., 2011; Terry-Lorenzo et al., 2005)

This study has some important limitations. The first involves the lack of correlation between spine loss and mRNA expression. Dendritic spine measurements were conducted in deep layer III of the DLPFC whereas the mRNA expression assessments were conducted across all layers of the grey matter and a small portion of the white matter. Differences in localization might account for the lack of correlation. The second limitation involves the relatively low RINs some of the tissue and the necessity to use one normalizer gene due to the variability of ACTB among BP subjects. Both factors might have increased the variance and masked potential findings (Koppelkamm et al., 2011; Vandesompele et al., 2002).

Antipsychotic medication and mood stabilizer treatment can confound postmortem studies of SZ and BD. We addressed the potential confound of antipsychotics by assessing rats chronically administered haloperidol or clozapine and SZ and BD subjects who were and were not taking antipsychotic medications in the last year of life. Both data suggest that the increased MARCKS and PPP1R9A mRNA expression are not the direct result of antipsychotic medication treatment. Indeed, a prior study showed no effect of a nine months haloperidol administration on Marcks protein levels in rats (Pinner et al., 2014). Lithium and valproic acid have been shown to reduce Marcks protein levels (Lenox et al., 1992; Watson et al., 1998). We assessed SZ and BD subjects who were and were not taking lithium or valproic acid at the time of death. Neither drug appeared to affect the relative mRNA expression of MARCKS or PPP1R9A. Although the effects of antipsychotics and mood stabilizers cannot be completely ruled out, it seems unlikely that the increased MARCKS or PPP1R9A mRNA levels are the result of treatment with these medications.

In conclusion, the relative expression of MARCKS mRNA was increased in both SZ and BD subjects. The relative mRNA expression of PPP1R9A was increased in BD subjects. But, MARCKS and PPP1R9A mRNA expression did not correlate with spine loss observed in both disorders. However, functional pathway analyses of MARCKS and PPP1R9A reveal that they interact with NMDA signaling pathways that regulate the actin cytoskeleton and spines. Multiple members of this interaction network appear to have altered mRNA expression in SZ and BD suggesting that altered actin cytoskeleton regulation by NMDA signaling pathways might contribute to the spine loss observed in both SZ and BD.

Figure 2.

Figure 2

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Schematic depicting the regulation of the actin cytoskeleton by NMDA receptor signaling pathways. Key effector molecules are the RhoGTPases (red) which interact with actin-binding proteins (yellow) via effector molecules. Actin-binding proteins play a key role in regulating the formation, structure and function of actin filaments. NMDA receptor stimulation results in the activation of Ca2+/calmodulin-dependent protein kinase CamKII which phosphorylates and activates downstream targets including the RhoGEFs (green). RhoGEFs, along with RhoGAPs (magenta), modulate the activity of the RhoGTPases. NMDA receptor activation also modulates the activity of the protein phosphatase 1/spinophilin/neurabin 1 (PPP1R9A) complex which regulates the actin cytoskeleton. Protein kinase C (PKC) is activated by NMDA stimulation and in turn, activates MARCKS which also regulates the actin cytoskeleton. In this figure, actin-binding proteins are yellow, RhoGTPases are red, kinases are blue, phosphatases are orange, RhoGEFs are green and RhoGAPs are magenta.

Acknowledgements

The authors would like to thank Susan Konopaske for reviewing this manuscript.

Preliminary data from this manuscript were presented on December, 2013 at the American College of Neuropsychopharmacology and on May, 2014 at the Society for Biological Psychiatry.

Funding support: 1K08MH087640-01A1 (GTK), R01MH05190 and P50MH0G0450 (JTC), R24MH068855 and the William P. and Henry B. Test Endowment (FMB).

Role of funding source

This study was funded by the NIH. The NIH had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Footnotes

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Contributors

Glenn Konopaske participated in all phases of study design, execution, data collection, data analyses, and writing of the manuscript. Sivan Subburaju, Joseph Coyle, and Francine Benes consulted on study design and execution, contributed to data analyses, and helped edit the manuscript.

Potential conflicts of interest: GTK, SS, and FMB-nothing to declare. JTC served as a consultant for Abbvie Laboratories and En Vivo

Financial Disclosures

GTK, SS, and FMB-nothing to declare. JTC served as a consultant for Abbvie Laboratories and Forum Pharmaceuticals.

Conflict of interest

GTK, SS, and FMB-nothing to declare. JTC served as a consultant for Abbvie Laboratories and Forum Pharmaceuticals.

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