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Human Molecular Genetics Advance Access originally published online on August 11, 2006
Human Molecular Genetics 2006 15(18):2804-2812; doi:10.1093/hmg/ddl222
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

RGS4 mRNA expression in postmortem human cortex is associated with COMT Val158Met genotype and COMT enzyme activity

Barbara K. Lipska*, Shruti Mitkus, Mark Caruso, Thomas M. Hyde, Jingshan Chen, Radhakrishna Vakkalanka, Richard E. Straub, Daniel R. Weinberger and Joel E. Kleinman

Clinical Brain Disorders Branch, Genes, Cognition and Psychosis Program, National Institute for Mental Health, NIH, DHHS, 9000 Rockville Pike, Bethesda, MD 20892-1385, USA

* To whom correspondence should be addressed at: CBDB, NIMH, 10 Center Drive, Building 10, Room 4N306, Bethesda, MD 20892-1385, USA. Tel: +1 3014969501; Fax: +1 3014022751; Email: lipskab{at}intra.nimh.nih.gov

Received May 25, 2006; Accepted August 3, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 References
 
Linkage, association and postmortem studies have implicated regulator of G-protein signaling 4 (RGS4), which negatively modulates signal transduction at G-protein-coupled receptors, as a candidate schizophrenia susceptibility gene. We compared RGS4 mRNA expression in the dorsolateral prefrontal cortex (DLPFC), between normal controls and patients with schizophrenia in two independent cohorts (>100 subjects each) (the CBDB/NIMH Collection and the Stanley Array Collection), and in the hippocampus in the CBDB/NIMH Collection. We also examined the effects of the four previously identified putative RGS4 risk SNPs (rs10917670, rs951436, rs951439, rs2661319) on RGS4 expression levels in these cohorts. As dopamine signaling is linked to RGS4 expression and there is evidence for statistical epistasis between COMT Val158Met polymorphism and RGS4 alleles, we also examined relationships between the COMT Val158Met genotype and RGS4 expression in the DLPFC. We did not detect a difference in RGS4 expression levels between schizophrenic patients (or bipolar disorder patients in the Stanley Collection) and controls and found no significant association between any of the RGS4 risk SNPs and RGS4 expression. However, COMT Val158Met genotype was associated with prefrontal and hippocampal RGS4 mRNA expression in an allele dose-dependent manner, with carriers of the COMT Val allele showing significantly lower expression than heterozygous individuals or subjects homozygous for the Met allele. Consistent with these genotype effects, RGS4 mRNA was inversely correlated with the COMT enzyme activity in the DLPFC. These data suggest that RGS4 mRNA expression is associated with cortical dopamine signaling and illustrate the importance of genetic and/or environmental background in gene expression studies in schizophrenia.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 References
 
Regulator of G-protein signaling 4 (RGS4) has been implicated as conferring risk for schizophrenia (1). Interest in RGS4 as a schizophrenia susceptibility gene originated from a cDNA microarray study, in which cortical RGS4 expression was significantly decreased in subjects with schizophrenia when compared with pair-matched controls (2). Findings from association studies add to the plausibility of RGS4 as a schizophrenia susceptibility gene. Chowdari et al. (3) identified four SNPs in the RGS4 gene as positively associated with schizophrenia in two family-based samples, although the overtransmitted alleles and haplotypes at these SNPs differed between cohorts. Three of the positive SNPs were located in the 5'-domain upstream of the first exon [SNP1 (rs10917670), SNP4 (rs951436), SNP7 (rs951439)] and one in intron 1 [SNP18 (rs2661319)]. Other groups have found positive associations (4–7) or no associations with schizophrenia (7–9). Allelic variation at SNP4 has been also linked in imaging studies with alterations in functional activation and connectivity evaluated during a working memory task (J.W. Buckholtz and J.H. Callicott, personal communication), as well as with the cortical volume in healthy controls and schizophrenic patients (10) (J.W. Buckholtz and J.H. Callicott, personal communication). A recent meta-analysis suggests only modest and inconclusive evidence for RGS4 as a candidate schizophrenia susceptibility gene (1).

Nevertheless, the biological function of RGS4 makes it a particularly interesting candidate. Signal transduction for a number of G-protein-coupled receptors (GPCRs), many of them implicated in schizophrenia, is negatively modulated by RGS4. RGS4 gene expression is, in turn, regulated by the GPCRs it modulates, e.g. by dopamine receptors (11–13). Although highest expression levels of RGS4 are detected in the neocortex, suggesting that RGS4 may play a more important role in regulating GPCRs in the cortex compared with other, rather sparsely RGS4-expressing subcortical regions (14), changes in the frontal cortex were not extensively studied.

In this study, we evaluated RGS4 mRNA expression levels in the dorsolateral prefrontal cortex (DLPFC) of two large cohorts of normal controls and patients with schizophrenia (the CBDB/NIMH Collection and the Stanley Array Collection) and in the hippocampus (CBDB/NIMH Collection). We also examined the effects of the four putative RGS4 risk SNPs on RGS4 expression levels in these collections. The Val158Met functional polymorphism in COMT, a gene encoding a major enzyme involved in dopamine degradation, has been associated with schizophrenia and with variation in prefrontal function, likely by affecting cortical synaptic dopamine levels (reviewed in 15). Moreover, recent evidence for statistical epistasis between COMT Val158Met polymorphism and RGS4 variants (16) suggests that previously identified RGS4 risk alleles may have greater detrimental effects when present on the COMT Val allele background. Thus, we also examined whether the COMT variants at the Val158Met SNP predict RGS4 expression in the two collections. We did not detect a difference in RGS4 expression levels between patients with schizophrenia (or bipolar disorder patients) and controls and found no significant association between any of the RGS4 risk SNPs and RGS4 expression. We demonstrated, however, that RGS4 expression was inversely correlated with the COMT enzyme activity in the DLPFC and that COMT Val158Met genotype predicted prefrontal and hippocampal RGS4 mRNA expression in an allele dose-dependent manner.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 References
 
RGS4 expression in diagnostic groups
We found no significant difference in normalized RGS4 mRNA expression levels (i.e. the ratios of RGS4 expression to the geometric mean of the three housekeeping genes) between patients with schizophrenia and unaffected controls in the DLPFC gray matter (t=1.09, P=0.29) (Fig. 1A) or in the hippocampus in the CBDB/NIMH sample (t=1.06, P=0.29) (Fig. 1B). There also was no significant difference in RGS4 expression in the DLPFC gray matter between patients with schizophrenia, bipolar disorder and unaffected controls in the Stanley Array Collection (ANOVA: F=1.79, P=0.17) (Fig. 1C). Also, neither non-normalized RGS4 mRNA expression nor the normalizing factor (i.e. the geometric mean of the three housekeeping genes) itself differed significantly between the diagnostic groups. Post hoc analysis revealed, however, that RGS4 mRNA tended to be reduced in bipolar patients in the Stanley Sample Collection (P=0.06).


Figure 2221
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  		Figure 1. Scatterplots of normalized expression levels of RGS4 mRNA (horizontal lines indicate mean values and vertical lines indicate standard deviations) in the DLPFC (A) and hippocampus (B) of patients with schizophrenia and normal controls from the CBDB/NIMH Collection and in the DLPFC of patients with schizophrenia and bipolar disorder and normal controls (C) from the Stanley Array Collection. There were no significant differences between the groups, see text for the detailed results of statistical analysis.

 
In order to examine whether demographic and/or tissue-related factors affected normalized expression of RGS4 in these cohorts and hence whether they should be included as co-variates in the analyses, we performed multiple-regression analyses of normalized RGS4 expression levels using age, sex, pH, RNA quality [RNA integrity number (RIN)] and postmortem interval (PMI) as predicting variables. These analyses revealed that pH was the only factor that significantly contributed to the variance in all three sample collections examined here (standardized regression coefficients ß=0.34, 0.20 and 0.45, for DLPFC and hippocampus from CBDB/NIMH and DLPFC from the Stanley Collection, respectively, P<0.05). Age was inversely correlated with the expression in the DLPFC of both the CBDB/NIMH Collection and the Stanley Array Collection (standardized regression coefficients ß=–0.32 and –0.19, respectively, P<0.02), but had only a marginal effect in the hippocampus (ß=–0.15, P=0.1). In the Stanley DLPFC (for which no RNA quality data were available), sex was another factor identified as uniquely contributing to normalized RGS4 variance (ß=–0.24, P<0.01). When we subsequently used these factors as co-variates in ANCOVAs, we still found no significant effects of diagnosis on RGS4 expression levels in any cohort (all F<1.0, P>0.8).

It should be noted that although absolute comparisons of RGS4 expression between different brain regions are not possible, as the samples from the DLPFC and hippocampus were processed separately, we estimated that the expression of RGS4 was at least an order of magnitude higher in the DLPFC than in the hippocampus. Inter-individual variability in RGS4 expression levels was much higher in the hippocampus than in the DLPFC (coefficient of variation %CV=63.2 and 30.6, respectively), likely reflecting lower accuracy in the quantification of hippocampal levels rather than biological variability.

Effects of RGS4 genotype on RGS4 expression
Four RGS4 SNPs have previously been shown to be associated with schizophrenia (rs10917670, rs951436, rs951439, rs2661319, corresponding to Chowdari's SNPs 1, 4, 7, 18) (3). We now examined whether these SNPs predicted expression of RGS4 mRNA in the gray matter of the DLPFC and the hippocampus in the CBDB/NIMH collection and in the Stanley Collection. None of the SNPs, however, had a significant effect on RGS4 mRNA expression in the whole cohorts in the DLPFC or hippocampus or separately in patients with schizophrenia and normal controls (all P>0.5) (data not shown). In these analyses, we controlled for the potentially confounding effects of pH, age and sex. As the CBDB/NIMH cohort is racially mixed, with a majority of subjects African-American, we also tested whether the effects of polymorphisms previously associated with the disease had differential effects separately in Caucasian and African-American individuals. We again used pH, age and sex as co-variates in these analyses. There was, however, no effect of genotype or race and no genotype by race interaction on RGS4 expression. There was also no effect of genotype in separate racial groups on RGS4 mRNA expression (all F<1.0, all P>0.5) (data not shown).

Effects of COMT Val158Met genotype on RGS4 expression
We further investigated whether the COMT Val158Met genotype, having a significant effect on the COMT enzyme activity and hence, perhaps, on synaptic dopamine levels, is associated with RGS4 mRNA expression in the CBDB/NIMH Collection. In the DLPFC, ANCOVA showed that COMT Val158Met polymorphism predicted RGS4 mRNA expression (F=3.8, P=0.02). Post hoc analysis revealed that there was a Val158Met allele dose effect on RGS4 expression, with individuals homozygous for a Val allele (n=39) having significantly lower expression than heterozygous individuals (by 14.8%, P=0.02, n=45) and Met/Met carriers (by 25.7%, P=0.0009, n=22) (Fig. 2A). There was no COMT Val158Met genotype by diagnosis effect (F=0.18, P=0.8). There was also no genotype by race interaction (F=1.1, P=0.3).


Figure 2222
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  		Figure 2. Scatterplots of normalized expression levels of RGS4 mRNA (horizontal lines indicate mean values and vertical lines indicate standard deviations) in the DLPFC (A) and hippocampus (B) of patients with schizophrenia and normal controls from the CBDB/NIMH Collection and in the DLPFC of patients with schizophrenia and bipolar disorder and normal controls (C) from the Stanley Array Collection grouped according to COMT Val158Met genotype. *P<0.05, significantly different from carriers of Val/Val alleles; see text for the detailed results of statistical analysis.

 
In the hippocampus, the pattern of changes was similar, but the statistical effects were weaker, likely because of the higher variability within the groups (Fig. 2B). Although the overall ANCOVA did not yield a significant result (F=1.8, P=0.1), post hoc tests showed that subjects homozygous for a Val allele (n=40) had significantly lower RGS4 mRNA expression than subjects homozygous for a Met allele (by 44.1%, P=0.02, n=18). There was only a trend for a difference between Val/Val and Val/Met individuals (by 27.6%, P=0.06, n=47).

We then attempted to replicate these findings in the Stanley Array Collection, which consists of Caucasian individuals (i.e. the frequency of a Val allele was lower in this population). In this group, ANOVA showed a trend for a genotype effect (F=2.4, P=0.09), but after co-varying for pH, age and sex, the COMT Val158Met genotype effect was not significant (F=1.1, P=0.2). Post hoc testing, however, revealed similar group differences as in the CBDB/NIMH collection, i.e. subjects homozygous for a Val allele (n=18) showed significantly less RGS4 expression than Met/Met individuals (by 18.0%, P=0.03, n=27), but Val/Val carriers were not different from Val/Met carriers (n=53) (Fig. 2C) (two genotypes were missing in this group). There was no significant genotype by diagnosis interaction (F=1.7, P>0.1).

As the COMT Val158Met genotype is associated with significant differences in the COMT enzyme activity in the DLPFC in the CBDB/NIMH cohort (17), we further examined whether DLPFC RGS4 mRNA levels were related to COMT activity. Indeed, a forward stepwise multiple-regression analysis with age, sex, pH, RNA quality and COMT activity identified COMT activity as a unique, significant contributor to RGS4 expression levels (F=9.9, df=4,84, adjusted R2=0.29, ß =–0.22, P=0.01) and indicated that there was an inverse relationship between COMT activity and RGS4 expression in this sample collection (Fig. 3). This relationship was observed in both diagnostic groups in the CBDB/NIMH Collection. Tissue was not available for measuring COMT activity in the Stanley Collection.


Figure 2223
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  		Figure 3. An inverse linear relationship between normalized expression levels of RGS4 mRNA and relative the COMT enzymatic activity (expressed as DPM per milligram of total protein). A solid line represents a fitted regression line, dotted lines represent the probability (0.95) that the ‘true’ fitted line (in the population) falls between the bands.

 
Effects of chronic antipsychotics on RGS4 expression
There were no significant effects of treatment with antipsychotics on the expression of RGS4 mRNA in the frontal cortex [F(6, 62)=0.6, P=0.7, normalized to NF, a geometric mean of GAPDH, porphobilinogen deaminase (PBGD) and B2M, or to the individual housekeeping genes or without normalization] (data not shown). These results, which are in agreement with the previous data from the monkey (2), suggested that medication did not obscure putative differences between diagnostic groups in our postmortem studies. There were also no differences between drug treatments for any of these housekeeping genes or NF (all F<1.0, all P>0.5).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 References
 
A major finding of this study is that DLPFC RGS4 expression is related to the COMT enzymatic activity and that the COMT Val158Met genotype predicts prefrontal and hippocampal RGS4 mRNA expression in an allele dose-dependent manner, i.e. carriers of the COMT Val allele showed significantly lower expression of RGS4 mRNA than heterozygous individuals and subjects homozygous for the Met allele. These data suggest that RGS4 mRNA expression is related to cortical dopamine signaling and underscore the importance of genetic and/or environmental background in gene expression studies in schizophrenia.

In contrast to a previous report by Mirnics et al. (2) and the most recent report by Erdely et al. (18), we did not detect a difference in RGS4 expression between patients with schizophrenia and unaffected controls in two independent large cohorts. We also did not find a difference in RGS4 expression between controls and patients with bipolar disorder, a group not examined before for RGS4 mRNA expression but linked to RGS4 by a positive haplotype association (3,8). However, this does not in and of itself speak to the reported clinical association (see Introduction and subsequently). There may be several explanations for these discrepant findings. Although we have used much larger cohorts than the previous studies, which examined six matched pairs followed by an additional five pairs (2) and two groups of 20 subjects (18), and we tested two independent cohorts, we used different approaches to quantification and cannot exclude methodological factors that might impact differentially on these techniques. The inconsistency may either be a function of the accuracy of the tests (e.g. one of the potential caveats might be the use of tissue homogenates) or may be related to the effect size of the RGS4 effect within the population. In contrast to Mirnics et al. (2), we opted for the selection of large sample sets over matched pairs, which are sometimes used to reduce the error variance and make tests more sensitive to differences between the groups. Matching, however, may paradoxically induce confounding and introduce a bias in the analysis because the factors affecting expression are not fully understood (19,20). The results from our COMT Val158Met genotype analysis suggest that cohort effects might be critical, e.g. COMT genetic background is an important factor contributing to RGS4 mRNA expression levels. Accordingly, it is possible that patients with schizophrenia in the study of Mirnics et al. (2) may have been enriched for COMT Val genotypes. Although we have found similar effects of COMT Val158Met genotype in two brain regions and two separate collections, there is a possibility of a false positive finding and thus an independent replication of the data should be conducted to confirm our results. The finding of reduced protein levels of RGS4 in the cortical regions of patients with schizophrenia (18) is more difficult to explain. However, in light of the rodent studies, showing that this protein is present at very low levels and is very rapidly degraded (21), there is a possibility that this is a non-specific finding.

Our data are correlational and therefore we cannot conclusively demonstrate causal relations between COMT Val158Met genotype and RGS4 mRNA expression levels. However, it is tempting to speculate that RGS4 expression is influenced by differential dopamine signaling in the COMT Val158Met allelic groups. In the human DLPFC, Met158 homozygotes show approximately one-third less activity than Val158 homozygotes and, as the polymorphisms are codominant, heterozygotes have intermediate levels of the COMT enzymatic activity (17). Here, we have also demonstrated that, predictably, given the significant differences between the COMT genotypic groups in RGS4 expression, COMT activity correlated inversely with RGS4 mRNA expression.

Previous studies provided compelling evidence that COMT plays a prominent role in the catabolism of cortical dopamine (22) and differences in COMT activity can lead to changes in extracellular dopamine, in particular, in the prefrontal cortex. For instance, COMT knockout mice show several-fold increases in baseline frontal dopamine levels compared with wild-type animals (23). Studies with tolcapone, a specific COMT inhibitor (24), show that evoked dopamine overflow increases dramatically in the rat prefrontal cortex after tolcapone treatment (25). Cortical dopamine levels are critical for mediating cognitive function and accordingly individuals with the high activity Val158 COMT show poor performance on prefrontal-dependent tasks and inefficient prefrontal activation (26,27). Finally, higher COMT activity is associated with increased midbrain dopamine synthesis as evidenced in the postmortem (28) and in vivo neuroimaging (29) studies, suggesting that COMT Val158Met polymorphism may affect dopaminergic function also in other brain regions.

Changes in dopamine signaling have been linked to altered expression of RGS4 mRNA in a number of animal studies (12,13,30–33). RGS4 mRNA was shown to be regulated in the rat striatum by dopaminergic agents in a time-dependent manner: D1 antagonists and D2 agonists induce delayed but transient upregulation and D2 antagonists cause downregulation of RGS4 (30). However, other studies found either no changes after direct or indirect dopamine agonists/antagonists or produced conflicting results. The reason for this may be that D1 and D2 receptors regulate RGS4 expression in opposite directions, and thus depending on the region, dose and timing, the overall effects on RGS4 expression may be different (12). Interestingly, and quite surprisingly, D1 knockout mice show no changes in the frontal cortical expression of RGS4 (34) and RGS4-deficient mutant mice show no obvious behavioral abnormalities in tests involving dopaminergic neurotransmission (35). Thus, a complex dopaminergic influence over RGS4 signaling (and vice versa) may reflect a relatively subtle balance between the two systems. Nevertheless, in light of these findings, it is tempting to speculate that RGS4 mRNA expression is downregulated in the carriers of COMT Val158 allele in response, perhaps compensatory, to their low dopaminergic signaling in the DLPFC. However, a precise mechanism of COMT/RGS4 interactions, and whether it even involves dopamine receptors, needs to be determined. For instance, it has recently been reported that striatal dopamine depletion resulted in the upregulation of RGS4 expression in the striatal cholinergic neurons and elevated acetylcholine release but had no effect on D2 dopamine receptors (36). The importance of COMT in the hippocampus, where it is highly expressed, is more elusive (15,37) and remains to be more extensively studied.

There is evidence that COMT and RGS4 may interact at the gene level. The results of statistical calculations indicate that SNPs in RGS4, which show association with the diagnosis of schizophrenia, show enhanced risk effects in the context of COMT genotypes and haplotypes (16). In particular, association with schizophrenia of three SNPs in RGS4 was detected only after considering them in interaction with COMT genotypes or haplotypes. The data suggest that the effects of COMT may exaggerate the risk of several susceptibility genes, including RGS4. The effects are not straightforward, however, and difficult to interpret in terms of underlying biological mechanisms. Our results on the relationship between the COMT enzyme activity and RGS4 expression may offer some basis for speculations about the neurobiology of these interactions. Thus, COMT may impact on the risk of other schizophrenia candidate genes, including RGS4, perhaps by setting the background tone of dopamine signaling, particularly in the prefrontal cortex.

It is also important to note that schizophrenia is a polygenic disorder, involving multiple risk genes of small effect, none of which is necessary or sufficient. They might influence different components of the phenotype and exert their effects in the context of epistasis with other genes as well as potential interactions with epigenetic factors and environment. However, although there is some gene–gene interaction evidence from statistical association data (16), there are not much data yet supporting interactions or convergence at the expression/function level for genes that we and others have shown to be implicated in this disorder, such as DISC1 (38).

Finally, it is also worth noting that COMT polymorphisms interact with environmental factors to increase risk for schizophrenia, e.g. adolescent cannabis users carrying the COMT Val allele show dramatically higher risk for developing schizophrenia (39). Thus, it is also possible that Val homozygotes showing low levels of RGS4 expression share common environmental background [and possibly other common genetic background, as suggested by epistatic effects, see Nicodemus et al. (16)], which determines their molecular profile of RGS4 expression.

In summary, our data suggest that RGS4 mRNA expression is associated with cortical dopamine signaling and illustrate the importance of genetic and, perhaps, also environmental background (as in the example shown earlier) in gene expression studies in schizophrenia.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
References
 
Human postmortem tissue
Postmortem tissue samples were collected at the Clinical Brain Disorders Branch (CBDB) and NIMH from the hippocampus and DLPFC of 73 and 70, respectively, normal controls and 32 and 30, respectively, schizophrenics (as described in 38,40) and at The Stanley Medical Research Institute, courtesy of Drs Michael B. Knable, E. Fuller Torrey, Maree J. Webster, Serge Weis and Robert H. Yolken (Stanley Array Collection described at: http://www.stanleyresearch.org/programs/brain_collection.asp) (Table 1). Briefly, for the DLPFC (Brodmann's areas 9 and 46), gray matter tissue from the middle frontal gyrus was obtained from a coronal slab corresponding to the middle one-third immediately anterior to the genu of the corpus callosum. For the hippocampus, the lateral ventricle and the fimbria fornix were used as the mediodorsal boundary and the subiculum and underlying white matter as the ventral boundary. The adjacent para-hippocampal cortex was not included in the dissection.


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  		Table 1. Characteristics of subject cohorts

 
All brain tissue used in this study was obtained with informed consent from the legal next of kin under the NIMH protocol no. 90-M-0142. Diagnoses were determined by independent reviews of clinical records by two board certified psychiatrists using DSM-IV criteria. Normal subjects have been screened with a psychological autopsy (at least 30 items) by interviews with the next of kin by our staff in addition to being interviewed by medical examiner investigators. All brain tissue from normal controls has been screened by macro- and microscopic neuropathological examinations and toxicology screening. Smoking and substance abuse history was recorded and total daily, lifetime and last dose of neuroleptic medication was calculated for each patient with schizophrenia and converted to chlorpromazine equivalents as described previously (38,40).

RNA extraction and reverse transcription
Tissue was pulverized and stored at –80°C. Total RNA was extracted from 300 mg of tissue using the TRIZOL Reagent (Life Technologies Inc., Grand Island, NY, USA). The yield of total RNA was determined by absorbance at 260 nm. RNA quality was assessed with a high resolution capillary electrophoresis (Agilent Technologies, Palo Alto, CA, USA), and samples showing clearly defined, sharp 18S and 28S ribosomal peaks, 28S/18S ratios >1.2 and RIN ≥4.0 for the DLPFC and ≥3.8 for the hippocampus were included. Total RNA (4 µg) was used in 50 µl of reverse transcriptase reaction to synthesize cDNA, using a SuperScript First-Strand Synthesis System for real-time (RT)-PCR (Invitrogen, Carlsbad, CA, USA).

Probes and primers
Commercial TaqMan probes/primer sets were used for RGS4 (Assays-on-Demand, cat. no. Hs00194501-m1, Applied Biosystems, Foster City, CA, USA), as well as for three endogenous control genes: PBGD, ß-2-microglobulin (B2M) and ß-glucuronidase.

Real-time quantitative PCR
Expression levels of mRNAs were measured by RT quantitative PCR (RT-qPCR), using an ABI Prism 7900 sequence detection system with 384-well format (Applied Biosystems). Each 10 µl reaction contained 900 nM of primer, 250 nM of probe and TaqMan Universal PCR Mastermix (Applied Biosystems) containing Hot Goldstar DNA Polymerase, dNTPs with dUTP, uracil-N-glycosylase, passive reference and 100-200 ng of cDNA template. PCR cycle parameters were 50°C for 2 min, 95°C for 10 min, 40 cycles of 95°C for 15 s and 59 or 60°C for 1 min. PCR data were acquired from the Sequence Detector Software (SDS version 2.0, Applied Biosystems) and quantified by a standard curve method using serial dilutions of pooled cDNA derived from RNA obtained from hippocampi or DLPFC of 10–12 normal control subjects. In each experiment, the R2-value of the curve was more than 0.99, the slope was between –3.2 and –3.5 (amplification efficiency 96–101%) and controls comprising no-template cDNA resulted in no detectable signal. All samples were measured in a single plate for each gene, and their cycles at threshold (Ct) values were in the linear range of the standard curve. All measurements were performed in triplicates. The data were normalized to control genes and a geometric mean of these genes as previously described (38,40).

COMT and RGS4 genotype determination
DNA was extracted from cerebellar brain tissue of subjects, using a protocol by PUREGENE (Gentra Systems, Minneapolis, MN, USA). COMT Val108/158Met genotype was determined by 5'-exonuclease allelic discrimination TaqMan assay (41) that uses the 5'-nuclease activity of Taq DNA polymerase to detect a fluorescent reporter signal generated after PCRs. The details of this assay are described elsewhere (17). We genotyped RGS4 SNP1 (rs10917670), SNP4 (rs951436), SNP7 (rs951439) and one in intron 1 [SNP18 (rs2661319)] and COMT Val108/158Met (rs4680).

Determination of the COMT enzyme activity
The method was performed as described by Chen et al. (17). Briefly, proteins were extracted from the DLPFC using a protease inhibitor–Tris–glycerol extraction buffer (AEBSF 0.024%, aprotinin 0.005%, leupeptin 0.001%, pepstatin A 0.001%, glycerol 50%, Tris 0.6%) (1 g tissue: 10 ml buffer). Five hundred microliters of the substrate mixture containing 10 mM Tris, pH 7.4, 1 mM MgCl2, 1.5 µCi of (3) H-adenosyl-S-methionine (SAM), 10 µM of catechol and 1 µM of DTT was added to each sample containing 100 µg of total protein and incubated at 37°C for 20 min. The reactions were immediately terminated by adding 500 µl of 1 M HCl. The radioisotope-labeled catechol products were quantified without prior separation of the reactants by adding 10 ml of an organic cocktail (Monoflow I, National Diagnostics) and counting the radioactivity solubilized into the cocktail phase. Thus, after the reaction was stopped, the (3) H-methylated catechol was extracted into the organic layer of the mixture (3). H-SAM stayed in the aqueous layer and thus was incapable of emitting energy into the cocktail and producing the signal. The relative the COMT enzyme activity was expressed as radioactivity in disintegrations per minute (DPM) per milligram of total protein.

Determination of the effects of antipsychotic drugs in rats
To test whether chronic exposure to antipsychotic drugs might have affected expression levels in patients with schizophrenia, we measured the expression of RGS4 mRNA in the frontal cortex of rats treated chronically with clozapine and haloperidol as described previously (38). Briefly, rats were randomly assigned to drug treatment groups (8–10 per dose) and given intraperitoneal injections of haloperidol (0.08, 0.6 and 1 mg/kg), clozapine (0.5, 5, 10 mg/kg) or vehicle (0.02% lactic acid) once daily for 28 days. Rats were sacrificed 7 h after the last injection. Frontal cortex without white matter was dissected and frozen at –80°C. RNA was extracted as described earlier for human tissue and mRNA measured by qPCR using the following TaqMan ABI assays: for RGS4 (Rn00568067_m1), GAPDH (Rn99999916_s1), PBGD/HMBS (Rn00565886_m1) and B2M (Rn00560865_m1).

Statistical analyses
Statistical analyses were conducted using Statistica [StatSoft Inc., 2005, STATISTICA (data analysis software system)]version 7.1. (www.statsoft.com). Forward stepwise multiple-regression analyses were used for determining the contribution of demographic, tissue- and disease-related variables to the gene expression levels. Comparisons between diagnostic groups were made using Student's t-tests and ANCOVA with diagnosis as the independent variable and continuous variables as covariates. Effects of genotype variation on gene expression were examined using ANCOVA with genotype and diagnosis as independent variables and demographic/tissue-related factors as co-variates. A co-variate analysis was used in the case of significant contributions of the variable to the gene expression levels. Using Grubb's test, we identified several outlier data points, which were then deleted from the analyses. Deleting these outliers did not change overall statistical results (i.e. there were no effects of diagnosis with or without outliers and the statistical significance of genotype was seen with or without outliers) in any of the data sets.


   ACKNOWLEDGEMENTS
 
This research was supported by the Intramural Research Program of the NIH, NIMH. We thank Mr Robert Fatula, Ms Vesna Imamovic, Ms Jewell King, Mr Chris Mileto and Ms Yeva Snitkovsky for their excellent technical assistance and Ms Amy Deep-Soboslay for her work on the demographic and clinical data. We thank the staff of the Offices of the Chief Medical Examiner of District of Columbia and of Northern Virginia for their assistance. We also thank the families of the deceased, who generously donated brain tissue as well as their time and effort, to make this study possible.

Conflict of Interest statement. None declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 References
 

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