A combined analysis of microarray gene expression studies of the human prefrontal cortex identifies genes implicated in schizophrenia

Journal of Psychiatric Research 46 (2012) 1464e1474

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A combined analysis of microarray gene expression studies of the human prefrontal cortex identifies genes implicated in schizophrenia Josué Pérez-Santiago a,1, Rebeca Diez-Alarcia b, c,1, Luis F. Callado b, c, Jin X. Zhang d, Gursharan Chana e, Cory H. White a, Stephen J. Glatt f, Ming T. Tsuang d, g, Ian P. Everall e, J. Javier Meana b, c, Christopher H. Woelk a, d, * a

Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA Department of Pharmacology, University of the Basque Country (UPV/EHU), E-48940 Leioa, Bizkaia, Spain Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain d Veterans Affairs San Diego Healthcare System, San Diego, CA 92161, USA e Department of Psychiatry, University of Melbourne, Melbourne, Australia f Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY 13210, USA g Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 June 2012 Received in revised form 1 August 2012 Accepted 2 August 2012

Small cohort sizes and modest levels of gene expression changes in brain tissue have plagued the statistical approaches employed in microarray studies investigating the mechanism of schizophrenia. To combat these problems a combined analysis of six prior microarray studies was performed to facilitate the robust statistical analysis of gene expression data from the dorsolateral prefrontal cortex of 107 patients with schizophrenia and 118 healthy subjects. Multivariate permutation tests identified 144 genes that were differentially expressed between schizophrenia and control groups. Seventy of these genes were identified as differentially expressed in at least one component microarray study but none of these individual studies had the power to identify the remaining 74 genes, demonstrating the utility of a combined approach. Gene ontology terms and biological pathways that were significantly enriched for differentially expressed genes were related to neuronal cellecell signaling, mesenchymal induction, and mitogen-activated protein kinase signaling, which have all previously been associated with the etiopathogenesis of schizophrenia. The differential expression of BAG3, C4B, EGR1, MT1X, NEUROD6, SST and S100A8 was confirmed by real-time quantitative PCR in an independent cohort using postmortem human prefrontal cortex samples. Comparison of gene expression between schizophrenic subjects with and without detectable levels of antipsychotics in their blood suggests that the modulation of MT1X and S100A8 may be the result of drug exposure. In conclusion, this combined analysis has resulted in a statistically robust identification of genes whose dysregulation may contribute to the mechanism of schizophrenia. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Schizophrenia Microarray Gene expression Brain Antipsychotic treatment

1. Introduction Gene expression analysis of brain tissue from the dorsolateral prefrontal cortex (DLPFC) using microarray technology has become a standard technique to investigate the mechanisms underpinning schizophrenia (Choi et al., 2008; Garbett et al., 2008; Glatt et al.,

* Corresponding author. University of California San Diego, Department of Medicine, Stein Clinical Research Building, Rm. 326, 9500 Gilman Drive, #0679, La Jolla, CA 92093-0679, USA. Tel.: þ1 858 552 8585x7193; fax: þ1 858 552 7445. E-mail address: [email protected] (C.H. Woelk). 1 Both authors contributed to this manuscript equally. 0022-3956/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jpsychires.2012.08.005

2005; Hakak et al., 2001; Higgs et al., 2006; Maycox et al., 2009; Middleton et al., 2002; Narayan et al., 2008; Prabakaran et al., 2004). Prior microarray studies have suggested that neuronal development, nerve terminal function, myelination, metallothionein expression, G-protein signaling, as well as metabolic and mitochondrial functioning may contribute to the neuropathogenesis of schizophrenia (Choi et al., 2008; Glatt et al., 2005; Maycox et al., 2009). However, the differential regulation of these processes has not been consistently identified from study to study (Glatt et al., 2005) due to the analysis of small sample sizes, modest gene expression changes in brain tissue, differing methods of data analysis, inappropriate statistical methods, failure to account for

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confounding variables, as well as ethnic and demographic disparities between the different subject cohorts analyzed (Choi et al., 2008; Glatt et al., 2005; Prabakaran et al., 2004). Small sample sizes (Garbett et al., 2008; Glatt et al., 2005; Maycox et al., 2009; Narayan et al., 2008; Torkamani et al., 2010) and modest gene expression changes in brain tissue (Hollingshead et al., 2005; Mirnics and Pevsner, 2004) have led to a reduction in power when identifying differentially expressed genes between schizophrenia and healthy control samples (Choi et al., 2008). Furthermore, the majority of patients with schizophrenia are receiving some form of antipsychotic treatment in contrast to treatment-naïve controls, so it is unclear whether differentially expressed genes in previous studies result from the underlying disease or the receipt of pharmacotherapy (Bray, 2008; Woelk et al., 2011). A number of methods have been used to account for such confounding variables including correlating gene expression with lifetime equivalent doses (Choi et al., 2008; Glatt et al., 2005) or comparing medicated to non-medicated groups after stratifying subjects based on each type of pharmacotherapy (Iwamoto et al., 2004). However, a better empirical understanding of the genes whose expression is regulated by different antipsychotic compounds is needed and was assessed in the current study by examining gene expression in fetal brain aggregates treated with haloperidol or clozapine. Many of the issues associated with inconsistent results between prior microarray studies would be addressed through a combined analysis. Therefore, microarray data derived from the analysis of DLPFC samples from schizophrenia and control subjects were compiled into a data set of 225 subjects. Our primary hypothesis was that a data set of this size would allow the robust detection of genes differentially expressed in the DLPFC between schizophrenia and control groups despite low fold changes associated with this compartment. The differential expression of a subset of genes identified through the course of our combined analysis was confirmed by quantitative real-time RT-PCR (RT-qPCR) using a completely independent cohort. Serendipitously, a recently published study by Mistry et al. (2012) performed a similar combined analysis but using differing methodology, and a comparison to these results is discussed. 2. Materials and methods

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Encyclopedia of Genes and Genomes (KEGG) pathway analysis using BRB-Array Tools (Simon et al., 2009), and protein interaction network (PIN) analysis using MetaCoreÔ (GeneGo, St. Joseph, MI, USA). GO and PIN analysis employ a hypergeometric test to identify those GO terms that were significantly overrepresented in the list of differentially expressed genes or determine if a particular gene product was significantly enriched for protein interactions within the set of differentially expressed genes, respectively. Pathway analysis was performed using functional class scoring which analyzes the entire expression data set instead of a list of differentially expressed genes and generates a p-value for the KEGG pathway as opposed to a p-value for each gene (Datson et al., 2010). In all analyses, the FDR associated with multiple testing was corrected using the BenjaminieHochberg method (Benjamini and Hochberg, 1995) and an FDR-corrected p-value <0.05 was considered significant. Further details are available in the Supplementary Methods. 2.3. Identification of haloperidol and clozapine modulated genes The procurement from Advanced Biosciences Resources (ABR, Almeda) and processing of human fetal brain aggregates under approvals from the Human Research Protection Program’s Institutional Review Board at the University of California San Diego were performed as previously described (Chana et al., 2009). Briefly, brain tissue was collected from terminations at 14e18 weeks gestation since this is the optimal time period for neuron and glial survival (Trillo-Pazos et al., 2004). Detailed protocols for culturing human brain aggregates have been described previously (TrilloPazos et al., 2004) and are detailed further in the Supplementary Methods. Groups of 12 mature fetal brain aggregates were exposed to either clozapine (2.0 mM) or haloperidol (0.5 mM) for 3 weeks and a further 12 aggregates were taken for a control group. RNA was extracted from each aggregate using a standard TrizolÒ method (Invitrogen, CA, USA) and deemed to be of high quality based on A260/280 ratios and gel electrophoresis. RNA was then pooled across aggregates for each condition and hybridized to one of three Affymetrix HG-U133 plus 2 GeneChips. Microarray quality control analysis and normalization were performed as described above. Fold changes were calculated by dividing expression levels in the drug treated conditions by those in the control. Further details are available in the Supplementary Methods.

2.1. Microarray quality control, normalization and data analysis 2.4. Quantitative real-time RT-PCR (RT-qPCR) Microarray data from DLPFC samples for 262 subjects (132 patients with schizophrenia and 130 controls) from 6 previously published studies were subjected to quality control analysis as described by Bolstad et al. (2005) leading to the removal of 25 schizophrenia and 12 control microarray samples. Normalization of microarray data was performed separately within each platform using the Guanine Cytosine Robust Multi-Array Analysis (GCRMA) method (Wu et al., 2004) and technical batch effects removed using ComBat (Johnson et al., 2007). Probes that were differentially expressed between the schizophrenia and control groups or whose expression correlated with a continuous variable (i.e. brain pH) were identified using BRB-Array Tools (Simon et al., 2009). Multivariate permutation tests were used to control the false discovery rate (FDR) and set to default confidence thresholds (80% confidence no more than 10 false positives). Further details are available in the Supplementary Methods. 2.2. Gene ontology, pathway and protein network analysis Gene ontology (GO) analysis was performed using the Biological Networks Gene Ontology (BiNGO) tool (Maere et al., 2005), Kyoto

An independent cohort of human DLPFC samples (BA9) from subjects (N ¼ 60) who had died by sudden causes collected at the Basque Institute of Legal Medicine, Bilbao, Spain, in accordance with approved institutional protocols for postmortem human studies, was used to validate differentially expressed genes detected by microarray analysis. This cohort was initially comprised of 20 schizophrenia subjects of whom 16 committed suicide, so that in addition to subjects in the healthy control group (N ¼ 20) who died of natural or accidental causes, a further control group was analyzed consisting of non-schizophrenia subjects (N ¼ 20) with the same number of suicide-completers (N ¼ 16). Toxicological screening for psychotropic drugs and alcohol was carried out in blood and the samples in the schizophrenia group were separated based on whether the subject was receiving antipsychotic therapy (N ¼ 10) or was antipsychotic-free (N ¼ 10) at time of death. RNA extractions were performed using the RiboPureÔ kit (Ambion/Life Technologies, Carlsbad, CA, USA) and following removal of genomic DNA every sample was found to be of sufficient quality as assessed by the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA was then reverse transcribed to cDNA in order to

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perform RT-qPCR analysis using TaqManÒ Gene Expression Assays with the StepOneÔ Plus thermocycler (Applied Biosystems, CA, USA). Changes in gene expression were calculated with the StepOneÔ Plus Software using the relative quantification (RQ) method (2DDCT) with GAPDH as a normalizer. Quality control analysis was performed using inter-quartile range (IQR) versus median plots and led to the removal of samples from 3 controls and 2 non-schizophrenia suicide-completers. RQ values were log2 transformed to satisfy the normality assumption and differences in gene expression between each subject group for a given gene were assessed with a one-way ANOVA followed by a Tukey post hoc test. Further details are available in the Supplementary Methods. 3. Results A combined analysis of microarray data from six previously published individual studies (Table 1) was used to identify genes differentially expressed in the DLPFC between schizophrenia subjects (N ¼ 107) and healthy controls (N ¼ 118, Supplementary Table 1). Schizophrenia and control groups were balanced with respect to gender, age, and postmortem interval, but there was a significant difference in brain pH (Table 2). A total of 160 probes corresponding to 144 unique genes were identified as differentially expressed between the schizophrenia and control groups with high confidence of no more than 10 false positives (Table 3). Seventy of these 144 genes were previously identified in at least one component microarray study demonstrating the reproducibility of the combined analysis. The remaining 74 “novel” genes reflect the increased power derived from our combined analytical approach over the individual studies. The following 4 novel genes with relatively robust fold changes were selected for confirmation of gene expression by RT-qPCR analysis: ATP-binding cassette, subfamily G (WHITE), member 2 (ABCG2), complement component 4B (C4B), neurogenic differentiation 6 (NEUROD6), and somatostatin (SST). Although previously identified by Maycox et al. (2009), S100 calcium binding protein A8 (S100A8) was selected as a target for RT-qPCR confirmation because it exhibited the greatest difference between the schizophrenia and control groups, being upregulated with a fold change of 3.37 in schizophrenia subjects (Table 3). Mistry et al. (2012) recently performed an analysis of seven prior microarray studies of which five overlapped with the studies selected for our combined analysis (Table 1). Although Mistry et al. (2012) analyzed more samples compared to our study (306 vs. 225), they identified a smaller number of differentially expressed genes (95 vs. 144). Encouragingly, when comparing differentially expressed probes between our two studies there were no cases where a probe was shown to be upregulated by Mistry et al. (2012)

and downregulated by our study or vice versa (Supplementary Fig. 1). However, somewhat surprisingly, there was not a large overlap in upregulated (N ¼ 8) or downregulated (N ¼ 11) probes between our two studies. For example, S100A8 was the most differentially expressed gene in our study but was not detected as differentially expressed by Mistry et al. (2012). The possible reasons for these discrepancies are presented in the Discussion. A number of factors that we were unable to examine may have confounded the identification of differentially expressed genes (e.g. recreational drug use and smoking status among others) but the influence of brain pH and antipsychotic-use were investigated further. Quantitative traits analysis in BRB-Array Tools demonstrated that a large number of differentially expressed probes (130/ 160) were significantly correlated with brain pH (FDR-corrected pvalue <0.05, Pearson correlation test) but this association was weak (Supplementary Table 2). For example, the largest absolute value of Pearson’s correlation coefficient (jrj) was 0.383 for acetylCoenzyme A acetyltransferase 2 (ACAT2), which results in an r2 of 0.147, suggesting that only 14.7% of the variability in expression of this gene is associated with variability in brain pH. Half of the microarray studies did not report antipsychotic use for each individual schizophrenia subject in their cohort (Garbett et al., 2008; Glatt et al., 2005; Maycox et al., 2009), however, it is assumed that the majority of schizophrenia patients were being treated with antipsychotic medication (Narayan et al., 2008). It is unclear whether genes differentially expressed between schizophrenia and control groups were the result of altered disease states or due to treatment with antipsychotics. Since it was not possible to correlate gene expression with dose of antipsychotic drug for each subject, a study was performed using fetal brain aggregates treated in vitro with haloperidol or clozapine and fold changes were compared to non-treated aggregates (Supplementary Table 3). With respect to the 160 probes differentially expressed between the schizophrenia and control groups in the combined analysis, 31 of these probes (corresponding to 27 genes) were modulated greater than 2-fold by drug treatment (Supplementary Table 2). Metallothionein 1X (MT1X) was selected for confirmation of gene expression by RTqPCR because it was upregulated by clozapine with a fold change close to the 2-fold cut-off. A number of specific gene ontology (GO) terms related to biological processes were significantly overrepresented in the list of differentially expressed probes (FDR-corrected p-value <0.05) and these included those related to mesenchymal cell differentiation and development, nucleotide biosynthesis and metabolism, as well as the transmission of nerve impulses and synaptic transmission as they relate to cellecell signaling (Supplementary Fig. 2). Functional class scoring was employed to identify those pathways comprised of a large number of genes that are associated with the phenotype

Table 1 Previously published microarray gene expression studies of the dorsolateral prefrontal cortex from schizophrenia subjects and healthy controls used for the combined analysis.a Cohort

BA

No. of SZ samples

No. of control samples

Microarray

GEO accession

Mistry et al. (2012)

Ref.

CCHPC HBB Hungarian SMRI Study 1

10 9 46 46

28 19 12 32

23 27 6 34

HG-U133 plus 2 HG-U133A HG-U133A HG-U133A

GSE17612 No No No

Yes Yes Yes No

SMRI Study 2

46 & 10

11

11

HG-U133A

No

Yes

VBBN

46

30

29

HG-U133 plus 2

GSE21138

Yes

Maycox et al., 2009 Glatt et al., 2005 Garbett et al., 2008 Choi et al., 2008; Higgs et al., 2006 Choi et al., 2008; Higgs et al., 2006 Narayan et al., 2008

a

Microarray refers to the Affymetrix Human Genome (HG) microarray used for analysis. Other abbreviations include: BA, Brodmann’s area; GEO, Gene Expression Omnibus at the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo/); CCHPC, Charing Cross Hospital Prospective Collection; HBB, Harvard Brain Bank; SMRI, Stanley Medical Research Institute; VBBN, Victoria Brain Bank Network.

J. Pérez-Santiago et al. / Journal of Psychiatric Research 46 (2012) 1464e1474 Table 2 Descriptive statistics between individuals in the schizophrenia and control group for which microarray data were available and passed quality control filters.a Statistic

Schizophrenia (N ¼ 107)

Control (N ¼ 118)

P

Male (%) Age (years) PMI (hours) Brain pH

73.83 51.82  18.85 24.11  15.71 6.40  0.31

71.19 49.97  18.01 24.65  14.55 6.48  0.30

0.765 0.505 0.714 0.034

a Values are percentages or mean values  the standard deviation. P, p-values were generated using a Fisher’s Exact test, a t-test or a ManneWhitney test, depending on the distribution of the data, and are in bold when significant (<0.05). PMI refers to postmortem interval.

in a small but consistent way, which is particularly relevant to gene expression data generated from brain tissue where large fold changes are not evident (Datson et al., 2010). In a conservative approach, a pathway was only identified as being significant if it had an FDR-corrected p-value <0.05 for two of the three statistics (Fisher least significance, KolmogoroveSmirnov or Goeman’s Global Test) used to assess functional class scoring. The following four KEGG pathways fulfilled these criteria: MAPK signaling (hsa04010), Alzheimer’s disease (hsa05010), Reductive carboxylate cycle (hsa00720), and Butanoate metabolism (hsa00650). For a complete list of pathways that were significant with an FDRcorrected p-value <0.05 for any one of the three statistics used to assess functional class scoring please refer to Supplementary Table 4. A PIN was generated in MetaCoreÔ to visualize the proteineprotein and proteineDNA interactions of the products of the 144 differentially expressed genes. The majority of genes do not interact at the protein level but a small network was identified (Fig. 1) centered on two major hubs: early growth response 1 (EGR1) and signal transducer and activator of transcription 3 (STAT3). Both EGR1 and STAT3 are transcription factors and EGR1 was downregulated (fold change ¼ 1.31) in schizophrenia while STAT3 was slightly upregulated (fold change ¼ 1.15). In addition, the number of interactions made by EGR1 and STAT3 within the list of 160 differentially expressed probes was significantly enriched (FDR-corrected p-value <0.05, hypergeometric test) with respect to all the known interactions made by these transcription factors. Both of these hubs, EGR1 and STAT3, and their respective target genes, BCL2-associated athanogene 3 (BAG3) and MT1X, were selected for confirmation of gene expression by RT-qPCR analysis. Human brain samples from an independent cohort consisting of 20 schizophrenia subjects, 18 non-schizophrenia suicidecompleters, and 17 controls (Supplementary Table 5), were used to validate differential gene expression by RT-qPCR. The selection of the 9 targets (ABCG2, BAG3, C4B, EGR1, NEUROD6, MT1X, SST, STAT3 and S100A8) for RT-qPCR analysis was justified in the preceding text. There were no variables, including brain pH, that were significantly different between schizophrenia and control groups in this RT-qPCR cohort (Supplementary Table 6). The modulation of gene expression (up or downregulation) of differentially expressed genes detected by microarray analysis was confirmed for the majority of genes (Fig. 2A). The exception was ABCG2, which was downregulated in the schizophrenia group with respect to microarray analysis but slightly upregulated when assessed by RT-qPCR. When this outlier was excluded, differential gene expression detected by microarray analysis correlated with RT-qPCR analysis with an r2 of 0.929 (Pearson correlation coefficient, data not shown). The schizophrenia subjects selected for RT-qPCR contained a large proportion that committed suicide (16/20) whereas the healthy controls expired due to natural or accidental causes. Since

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suicide as a cause of death appears to modulate gene expression (Kim et al., 2007; Sequeira et al., 2012), RT-qPCR analysis was carried out in a further control group, which consisted of nonschizophrenic subjects containing a similar proportion of suicide completers (Supplementary Table 5). The downregulation of EGR1 and SST in the schizophrenia group can be clearly attributed to schizophrenia since these genes were inversely upregulated in the non-schizophrenia suicide-completer group (Fig. 2A). In addition, S100A8 was upregulated to a greater extent (fold change ¼ 5.11) and with statistical significance (p < 0.05, one-way ANOVA with Tukey post hoc test) in the schizophrenia group but not to such an extent (fold change ¼ 1.75) or with significance in the non-schizophrenia suicide-completer group. The schizophrenia group in our RT-qPCR cohort was comprised of 10 subjects showing positive toxicological analysis for antipsychotic drugs at the time of death and 10 subjects with negative toxicological data (Supplementary Table 7). Fold changes were calculated between the antipsychotic treated and antipsychoticfree group for each of the 9 genes selected for RT-qPCR analysis (Fig. 2B). The in vitro drug study with fetal brain aggregates demonstrated that MT1X was upregulated by clozapine greater than 2-fold (Supplementary Table 2). MT1X was also upregulated greater than 2-fold in the antipsychotic treated compared to the antipsychotic-free group as assessed by RT-qPCR analysis (Fig. 2B). The only other gene with a fold change greater than MT1X between the antipsychotic and antipsychotic-free group was S100A8. 4. Discussion A combined analysis of a large number of microarray samples (N ¼ 225) assessing gene expression in the DLPFC from schizophrenia subjects and healthy controls allowed the identification of differentially expressed genes (N ¼ 144) with robust statistical significance despite the modest gene expression changes exhibited in brain tissue. The true power of the combined analysis was revealed with the identification of a large number of differentially expressed genes (N ¼ 74) that did not achieve statistical significance in any of the component studies. Many of these genes have been previously implicated in schizophrenia but were probably not identified in component studies due to the small numbers of samples analyzed. For example, the neuropeptide SST was not identified as differentially expressed between schizophrenic subjects and controls in any of the components studies including Choi et al. (2008). However, Choi et al. (2008) selected SST for analysis by RT-qPCR due to the dysregulation of neuropeptides in schizophrenia (Frederiksen et al., 1991; Gabriel et al., 1996; Hashimoto et al., 2008; Morris et al., 2008) and confirmed that this gene was downregulated. In contrast, our combined analysis confirmed that a statistically significant signal for the downregulation of SST is achievable when analyzing microarray data when a sufficient number of samples are analyzed (Table 3). Finally, it should be noted that the microarray studies combined for analysis used samples from a range of Brodmann’s areas (BA9, 10 and 46, see Table 1). Therefore, a potential weakness of this approach is that genes differentially expressed in specific BAs’ may have been masked but, conversely, the strength of this approach is that a core set of genes that are dysregulated consistently across BAs’ have been identified. The enrichment of GO terms and KEGG pathways for differentially expressed genes was investigated to better understand the higher order biological processes that underpin schizophrenia. Those GO terms and pathways that attained significance confirmed the importance of nerve terminal and metabolic functioning as noted in previous studies (Glatt et al., 2005; Maycox et al., 2009). Specifically, SST belongs to the GO term, transmission of nerve

Gene symbol

Probe-set

Acc. No.

FC

FDR p-value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

S100A8 SERPINA3 C4B ADM BAG3 MT1M DTNA MT1X SLCO4A1 HIG2 TMEM176B CSDA DDIT4 LPL GADD45B FGF2 CNN3 HSPB1 EMP1 CEBPD MT1X IFITM3 TXNIP ALDH1L1 CD99 GMNN LPL C4A IFITM2 ZC3HAV1 ITGB4 CYBA PLOD2 GRAMD1C PALLD ANGPTL4 RYR3 PALLD NQO1 SOX9 MT2A ID4 ICAM2 MT1G FERMT2 EDN1 MT1E CBFB TUBB6 CARTPT CD99 PLOD2 ABCA1 DTNA P4HA1

202917_s_at 202376_at 208451_s_at 202912_at 217911_s_at 217546_at 205741_s_at 204326_x_at 219911_s_at 218507_at 220532_s_at 201160_s_at 202887_s_at 203549_s_at 207574_s_at 204422_s_at 201445_at 201841_s_at 201324_at 203973_s_at 208581_x_at 212203_x_at 201010_s_at 205208_at 201028_s_at 218350_s_at 203548_s_at 214428_x_at 201315_x_at 213051_at 204990_s_at 203028_s_at 202620_s_at 219313_at 200907_s_at 221009_s_at 206306_at 200897_s_at 210519_s_at 202936_s_at 212185_x_at 209291_at 213620_s_at 204745_x_at 209209_s_at 218995_s_at 212859_x_at 202370_s_at 209191_at 206339_at 201029_s_at 202619_s_at 203504_s_at 210611_s_at 207543_s_at

NM_002964 NM_001085 NM_001002029 NM_001124 NM_004281 NM_176870 NM_001392 NM_005952 NM_016354 NM_013332 NM_014020 NM_003651 NM_019058 NM_000237 NM_015675 NM_002006 NM_001839 NM_001540 NM_001423 NM_005195 NM_005952 NM_021034 NM_006472 NM_012190 U82164 NM_015895 NM_000237 K02403 NM_006435 NM_020119 NM_000213 NM_000101 NM_000935 NM_017577 NM_016081 NM_139314 NM_001036 NM_016081 NM_000903 NM_000346 NM_005953 NM_001546 NM_000873 NM_005950 NM_006832 NM_001955 NM_175617 NM_001755 BC002654 NM_004291 NM_002414 NM_000935 NM_005502 U26744 NM_000917

3.37 2.10 1.69 1.60 1.59 1.54 1.52 1.51 1.49 1.43 1.41 1.41 1.41 1.40 1.40 1.40 1.39 1.39 1.38 1.37 1.36 1.34 1.33 1.33 1.32 1.32 1.32 1.31 1.31 1.30 1.30 1.30 1.30 1.30 1.30 1.29 1.29 1.28 1.27 1.27 1.27 1.27 1.26 1.25 1.25 1.25 1.25 1.24 1.24 1.23 1.23 1.23 1.23 1.22 1.22

2.77E-04 1.76E-02 3.50E-03 7.86E-03 2.44E-03 7.86E-03 1.31E-02 1.87E-03 1.48E-02 2.28E-02 1.35E-02 2.26E-02 6.47E-03 3.50E-03 3.87E-02 2.49E-02 2.12E-02 3.09E-03 5.87E-02 2.12E-02 3.50E-03 2.12E-02 2.26E-02 1.35E-02 2.88E-02 4.27E-03 1.48E-02 3.07E-02 3.87E-02 2.49E-02 2.26E-02 4.45E-02 2.07E-02 1.48E-02 2.12E-02 4.81E-02 3.50E-03 4.71E-03 2.26E-02 5.17E-02 9.94E-03 2.43E-02 3.02E-02 3.02E-02 5.84E-02 4.45E-02 1.66E-02 2.12E-02 3.59E-02 2.86E-02 3.88E-02 3.72E-02 2.25E-02 4.96E-02 1.35E-02

CCHPC (Maycox et al., 2009)

HBB (Maycox et al., 2009)

HBB (Glatt et al., 2005)

Hungarian (Garbett et al., 2008)

SMRI (Choi et al., 2008)

VBBN (Narayan et al., 2008)

3.85 e e e 1.40 e e e 1.80 e 1.31 e e e e e e 1.37 e e e e e e e e e e e 1.23 e 1.49 1.17 e e e e e e e e e 1.31 e e e e e e e e e 1.14 e 1.20

e e e e e e e e e e e e e 1.22 e e e e e e e e e e e e e e e e e e e 1.54 1.45 e e e e e e 1.37 e e 1.71 e e e e e e e 1.28 e e

e e e e e e e e e e e e e 1.17 e e 1.37 e e e e e e e e e 1.28 e e e e e e e e e e e e e e e e e e e e e e e e 1.25 e e 1.12

e e e e e e e e e e e e e 1.52 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e

e e e e e e e 1.31 e e e e e e e e e e e e 1.31 e 1.23 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e

e e e 1.68 e e e 1.28 e e e e 1.48 e 1.72 e e 1.73 e 1.46 e e e e e e e e e e e e e e e e e e e e 1.33 e 1.39 1.26 e 1.60 e e e e e e e e e

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No.

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Table 3 Probes identified as differentially expressed between schizophrenia and healthy subjects in the combined analysis of six microarray studies and the congruence of gene modulation with component studies.a

204421_s_at 200916_at 206461_x_at 214722_at 201673_s_at 218304_s_at 212259_s_at 215626_at 203120_at 207643_s_at 219949_at 209210_s_at 217730_at 215201_at 203140_at 208808_s_at 216336_x_at 203430_at 204140_at 201060_x_at 221748_s_at 211270_x_at 212294_at 204550_x_at 207547_s_at 208982_at 209124_at 205073_at 218345_at 200872_at 211964_at 208949_s_at 209747_at 201061_s_at 204683_at 203044_at 200600_at 217734_s_at 208999_at 218854_at 205475_at 205206_at 207181_s_at 200677_at 206583_at 202500_at 201999_s_at 212463_at 202481_at 209600_s_at 212741_at 200685_at 204041_at 208991_at 202330_s_at 200921_s_at 201172_x_at 216331_at 209074_s_at 207071_s_at

M27968 NM_003564 NM_005951 NM_203458 NM_002103 NM_022776 NM_020524 NM_173531 NM_005426 NM_001065 NM_024512 Z24725 NM_022152 NM_031922 NM_001706 BC000903 NM_175617 NM_014320 NM_003596 NM_004099 NM_022648 BC002397 NM_018841 NM_000561 NM_007177 NM_000442 U70451 NM_000775 NM_018487 NM_002966 NM_001846 BC001120 J03241 M81635 NM_000873 NM_014918 NM_002444 NM_018031 NM_001098811 NM_013352 NM_007281 NM_000216 NM_001227 NM_004339 NM_017776 NM_006736 NM_006519 NM_000611 NM_004753 S69189 NM_000240 NM_004768 NM_000898 NM_003150 NM_003362 NM_001731 NM_003945 NM_002206 AL050264 NM_002197

1.22 1.22 1.22 1.22 1.22 1.22 1.21 1.21 1.21 1.21 1.21 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.18 1.18 1.18 1.18 1.18 1.18 1.17 1.17 1.17 1.17 1.17 1.16 1.16 1.16 1.16 1.16 1.16 1.16 1.16 1.15 1.15 1.15 1.15 1.15 1.15 1.14 1.14 1.14 1.14 1.14 1.13

2.17E-02 3.72E-02 2.12E-02 2.88E-02 3.50E-03 4.39E-02 4.57E-02 5.68E-02 4.15E-02 2.43E-02 3.57E-02 5.68E-02 3.07E-02 2.88E-02 1.83E-02 3.58E-02 2.12E-02 3.72E-02 3.16E-02 5.14E-02 4.28E-02 4.03E-02 3.74E-02 3.72E-02 3.94E-02 5.42E-02 3.72E-02 4.90E-02 3.87E-02 4.43E-02 5.09E-02 5.42E-02 3.57E-02 5.83E-02 4.49E-02 2.88E-02 5.42E-02 2.12E-02 2.88E-02 4.96E-02 2.49E-02 4.33E-02 3.94E-02 4.87E-02 2.12E-02 5.09E-02 5.29E-02 3.74E-02 4.33E-02 4.59E-02 5.46E-02 5.54E-02 2.86E-02 4.87E-02 4.33E-02 4.81E-02 5.68E-02 5.83E-02 4.52E-02 5.80E-02

e e e e e e 1.30 1.20 e e e e e e e e e 1.12 e e 1.15 e e e e 1.20 e e e e e e 1.24 e 1.23 e e e 1.17 e e e e e e 1.09 e 1.22 e L1.08 e e e e e 1.19 e e e e

e e e e e e e e e e e 1.30 e e e 1.34 e e e e e e 1.36 e e e e e e e e e e e e e e e 1.31 e 1.21 e e e e e 1.27 e e 1.34 e e e e e e e 1.32 e e

e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e 1.18 e e e e e e e e 1.20 e e e e e 1.14 e e e e

e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e

e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e 1.10 e e e e e 1.06 e e e e e

e 1.35 e e e e e e e e e e 1.50 e e e 1.29 e e e e e e e e e e e e e e e e e 1.39 e e e e e e e e e e e e e e e e e e 1.60 e e e e e e (continued on next page)

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FGF2 TAGLN2 MT1H NOTCH2NL GYS1 OSBPL11 PBXIP1 ZNF100 TP53BP2 TNFRSF1A LRRC2 FERMT2 TMBIM1 REPS1 BCL6 HMGB2 MT1E HEBP2 TPST1 STOM TNS1 PTBP1 GNG12 GSTM1 FAM107A PECAM1 MYD88 CYP2J2 TMEM176A S100A10 COL4A2 LGALS3 TGFB3 STOM ICAM2 CHSY1 MSN WDR6 SEPT8 DSE SCRG1 KAL1 CASP7 PTTG1IP ZNF673 DNAJB2 DYNLT1 CD59 DHRS3 ACOX1 MAOA SFRS11 MAOB STAT3 UNG BTG1 ATP6V0E1 ITGA7 FAM107A ACO1

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56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115

Gene symbol

Probe-set

Acc. No.

FC

FDR p-value

116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160

TPP1 GUF1 NNAT TPBG SCN1B CA4 LBH PROM1 C6orf162 TYRP1 SQLE ETV5 PAH CCKBR FBXO9 MPPED2 FRZB ITM2A ACAT2 EGR4 FRZB DGCR9 SEMA3G GNAL NR4A2 KCNV1 PRPS1 PENK PVALB CYP26B1 C5orf13 RERGL NR4A2 NPY CA4 TAC1 EGR1 NEUROD6 NPTX2 EGR4 SST ABCG2 TNFSF10 AMFR CRH

200743_s_at 218884_s_at 204239_s_at 203476_at 205508_at 206208_at 221011_s_at 204304_s_at 213312_at 205694_at 213562_s_at 203349_s_at 205719_s_at 210381_s_at 210638_s_at 205413_at 203697_at 202747_s_at 209608_s_at 207767_s_at 203698_s_at 215003_at 219689_at 206355_at 204621_s_at 220294_at 208447_s_at 213791_at 205336_at 219825_at 201309_x_at 220276_at 216248_s_at 206001_at 206209_s_at 206552_s_at 201694_s_at 220045_at 213479_at 207768_at 213921_at 209735_at 202688_at 202203_s_at 205630_at

NM_000391 NM_021927 NM_005386 NM_006670 NM_001037 NM_000717 NM_030915 NM_006017 NM_020425 NM_000550 NM_003129 NM_004454 NM_000277 BC000740 AF176704 NM_001584 U91903 NM_004867 BC000408 NM_001965 NM_001463 AA921844 NM_020163 NM_002071 NM_006186 NM_014379 NM_002764 NM_006211 NM_002854 NM_019885 U36189 NM_024730 NM_006186 NM_000905 NM_000717 NM_003182 NM_001964 NM_022728 NM_002523 NM_001965 NM_001048 AF098951 NM_003810 NM_001144 NM_000756

1.13 1.14 1.16 L1.16 L1.16 1.16 L1.16 1.17 1.17 L1.17 L1.19 L1.19 1.19 1.20 L1.20 L1.21 L1.21 1.22 1.22 1.22 L1.23 1.23 L1.23 1.23 L1.23 L1.23 1.23 1.26 1.26 L1.27 L1.27 L1.28 L1.29 L1.30 L1.30 L1.30 L1.31 1.31 L1.33 1.40 1.40 1.52 1.57 L1.64 L1.67

4.59E-02 5.17E-02 4.45E-02 5.42E-02 1.85E-02 3.47E-02 5.19E-02 4.28E-02 5.36E-02 3.72E-02 1.74E-02 2.53E-02 3.72E-02 2.86E-02 1.02E-02 4.71E-03 5.17E-02 2.18E-02 1.31E-02 2.26E-02 3.07E-02 1.48E-02 3.68E-02 3.07E-02 3.57E-02 3.94E-02 4.43E-02 3.07E-02 2.12E-02 3.72E-02 1.35E-02 1.31E-02 1.35E-02 3.94E-02 1.48E-02 2.43E-02 2.88E-02 1.87E-03 3.07E-02 5.86E-02 4.98E-02 4.93E-03 5.54E-04 2.88E-02 1.35E-02

CCHPC (Maycox et al., 2009)

HBB (Maycox et al., 2009)

HBB (Glatt et al., 2005)

Hungarian (Garbett et al., 2008)

SMRI (Choi et al., 2008)

VBBN (Narayan et al., 2008)

e e e e e e e e e L1.23 e L1.23 e e L1.18 L1.20 L1.28 e e e L1.61 e L1.32 e e e e e e L1.39 e e e e L1.15 L1.23 L1.54 e L1.61 e e e e L2.13 L1.75

e e e L1.35 L1.28 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e

e e e e L1.15 e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e

e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e

e e e e e e e e e e e e e e e e L1.07 e e e e e e e L1.16 e e e e e L1.06 e L1.16 e e e e e e e e e e e e

e e e e e e L1.34 e e L1.43 L1.76 L1.38 e e e e e e e e e e e e e L1.28 e e e L1.37 L1.55 L1.53 e L2.22 e L1.21 e e e e e e e e e

a Differentially expressed probes were identified using multivariate permutation tests implemented in BRB-Array Tools using default settings for class comparison analysis (80% confident no more than 10 false positives). Probes are sorted based on the extent of upregulation in schizophrenia subjects. The 160 probes are representative of 144 unique genes. Probes in bold were identified as being differentially expressed between schizophrenia subjects and healthy individuals in both the combined analysis and in at least one component study. Differentially expressed probes 218995_s_at (EDN1), 204683_at (ICAM2), 208991_at (STAT3), and 203349_s_at (ETV5) were matched to the study of Narayan et al. (2008) based on a different probe-set identification number (222802_at, 213620_s_at, 243213_at, and 230102_at, respectively) targeting the same gene. Differentially expressed probe 209600_s_at (ACOX1) matched to the study of Maycox et al. (2009) based on a different probe-set identification number (227962_at) targeting the same gene. The last six columns contain fold change values if the gene was also differentially expressed in a component study. Abbreviations used in this table include: No., refers to a number assigned to each probe based on the magnitude of differential expression; Probe-set, Affymetrix probe-set identification number; Acc. No., GenBank accession number; FC, fold-change where positive values indicate upregulation in the schizophrenia group; FDR p-value, false discovery rate corrected parametric p-values using the BenjaminieHochberg method originally derived from BRB-Array Tools; CCHPC, Charing Cross Hospital Prospective Collection; HBB, Harvard Brain Bank; SMRI, Stanley Medical Research Institute; VBBN, Victorian Brain Bank Network. Note: Glatt et al. (2005) and Maycox et al. (2009) independently analyzed the same microarray data set derived from the HBB.

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No.

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Table 3 (continued )

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1471

Fig. 1. Direct protein interaction network (PIN) constructed using MetaCoreÔ. This network contains a subset of the 144 differentially expressed genes whose corresponding protein products interact at the proteineprotein or proteineDNA level. The key to the right indicates whether the gene was up or downregulated in the schizophrenia versus control group, the type of protein coded for by each gene, and the type of interaction between proteins.

Fig. 2. Gene expression analysis of nine targets by RT-qPCR in an independent cohort of human DLPFC samples. (A) Fold changes were calculated by comparing the expression of genes in the schizophrenia group versus the control group for microarray (gray bars) or RT-qPCR (black bars) data, and the non-schizophrenia suicide-completer group versus the control group for RT-qPCR data (white bars). The asterisk indicates that S100A8 was significantly (p < 0.05) upregulated in schizophrenia versus control samples as determined by a one-way ANOVA with a Tukey post hoc test. (B) Fold changes were calculated by comparing expression of genes in samples from schizophrenia subjects treated with antipsychotic medications to those schizophrenia subjects that were free of medication at time of death. Error bars depict the standard error of the mean and were calculated for fold changes in the RT-qPCR data using the error propagation formula for ratios. For microarray data, error bars were calculated by first converting all values to their natural logarithms and computing the standard error of the mean of the log transformed value such that positive error bars equate to fold change*expSEM1 and negative error bars to fold change*expSEM1.

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impulse, and was confirmed to be downregulated in schizophrenia by RT-qPCR analysis in our independent cohort of postmortem DLPFC samples (Fig. 2A). This inhibitory neuropeptide was previously found to be downregulated in schizophrenia (Choi et al., 2008; Gabriel et al., 1996; Hashimoto et al., 2008; Morris et al., 2008) and has a wide range of physiological functions including the inhibition of endocrine and exocrine secretions, as well as the modulation of neurotransmission, motor and cognitive functions (Bousquet et al., 2004). Furthermore, it is encouraging that the significant GO terms and pathways identified by our combined analysis may provide support for the “two-hit” hypothesis of schizophrenia (Maynard et al., 2001) whereby a prenatal genetic or environmental “first hit” is thought to disrupt some aspect of brain development, which then might increase vulnerability to a “second hit” occurring later in life. Disruptions to cellecell signaling and mesenchymal induction were proposed as potential targets of this first hit and these processes correspond to GO terms that were significantly enriched for differentially expressed genes in our study (Supplementary Fig. 2). Pathways related to stress involving members of the MAPK family were thought to be involved in the second hit and the MAPK signaling pathway was one of the KEGG pathways that attained significance (Supplementary Table 4). When the protein products of differentially expressed genes were used to construct PIN, it suggested that dysregulation in processes controlled by EGR1 and STAT3 may underlie schizophrenia (Fig. 1). The downregulation of EGR1 in schizophrenia subjects was confirmed by RT-qPCR (Fig. 2A) but the upregulation of STAT3 was not confirmed to a similar extent (1.15 fold change by microarray but only 1.04 by RT-qPCR). EGR1 belongs to a family of growth response mediators with roles in neuronal plasticity, and short and long-term memory (Gomez Ravetti et al., 2010), which are also processes associated with schizophrenia (Albus et al., 2006, 2002; Hoff et al., 2005). In agreement with our findings, Yamada and colleagues (Yamada et al., 2007) demonstrated by RT-qPCR that EGR1, 2 and 3 were significantly downregulated in the DLPFC (BA46) of schizophrenia subjects. The effects of brain pH and antipsychotic use on the identification of differentially expressed genes by microarray analysis were investigated but it was not possible to control for other confounders (e.g. recreational drug use and smoking status) that were not measured consistently across component studies. Brain pH was significantly lower in schizophrenia subjects compared to healthy controls (Table 2) but this may not be surprising since a decrease in pH has previously been noted in schizophrenia versus control brains (Prabakaran et al., 2004), possibly due to increased anaerobic respiration and hypoxic conditions in schizophrenia. Brain pH is probably not a serious confounder for this combined analysis since no differentially expressed gene had a correlation coefficient (jrj) greater than 0.6, which is generally viewed as the cut-off for a meaningful correlation (Xu et al., 2008). In addition, when brain pH was removed as a confounder in the cohort used for RT-qPCR analysis (Supplementary Table 6), the direction of gene expression changes by microarray analysis was confirmed for vast majority (89%) of targets (Fig. 2A). Although not possible due to a dramatic reduction in the power to detect differentially expressed genes, future analyses could stratify subjects based on pH when additional microarray data become available and perform similar comparisons of schizophrenia to control groups within strata. Only a small number of differentially expressed genes (27/144) were upregulated more than 2-fold in fetal brain aggregates treated in vitro with haloperidol or clozapine. One such gene, MT1X, which was upregulated 2.21-fold by clozapine treatment, was also found to be upregulated 1.77-fold by RT-qPCR in antipsychotic-treated versus antipsychotic-free schizophrenia subjects (Fig. 2B), albeit not significantly (p ¼ 0.126, t-test). The only other RT-qPCR target

upregulated greater than MT1X in antipsychotic-treated schizophrenia subjects was S100A8 (fold change ¼ 3.56). S100A8 dimerizes with S100A9 to form calprotectin (36 kDa), which influences cellular processes including innate immunity and inflammation (Nacken et al., 2003), and was previously shown to be upregulated in schizophrenia subjects at the protein level (Foster et al., 2006). Calprotectin also appears to be upregulated in the frontal cortices of rats treated with olanzapine (Fatemi et al., 2006), so it is plausible that S100A8 is upregulated by antipsychotic use even though this gene was not found to be upregulated by haloperidol or clozapine in our fetal brain aggregate model. Further studies investigating the effects of antipsychotic treatment on gene expression in the brain using appropriate in vitro or animal models are sorely needed in order to better understand the effects of these drugs on gene expression. Validation of differential gene expression by RT-qPCR in an independent cohort demonstrated that the dysregulation of EGR1, SST and S100A8 could clearly be attributed to schizophrenia and not suicide status (Fig. 2A). It is quite possible that the other genes targeted for RT-qPCR analysis are also relevant to schizophrenia but larger cohorts, preferably not confounded by suicide status, need to be analyzed by RT-qPCR to confirm this. In this respect, C4B and NEUROD6 are important targets for further validation since they may reflect the true power of a combined analysis approach, i.e. the identification of genes for which little or conflicting prior evidence exists associating their expression to the neuropathogenesis of schizophrenia. C4B and NEUROD6 were both significantly differentially expressed between schizophrenia and control groups in combined analysis, not identified within any component microarray study, confirmed by RT-qPCR analysis, and not associated with brain pH (jrj < 0.6) or antipsychotic use (fold change < 2). C4B is a major effector of the innate and adaptive immune system, and has only recently shown to be upregulated in the brains of schizophrenia subjects by RT-qPCR (Chu and Liu, 2010). NEUROD6 is a basic helix-loop-helix (bHLH) transcription factor thought to be involved in the development and maintenance of the mammalian nervous system (Uittenbogaard et al., 2007) that has not been extensively linked to schizophrenia (Struyf et al., 2008). Genes differentially expressed between schizophrenia subjects and healthy controls have been identified through a combined analysis in both our study and the study of Mistry et al. (2012). However, limited overlap was seen in differentially expressed genes between the two studies. The many possible reasons for this discrepancy include the following: (1) analysis of overlapping but non-identical data sets, (2) different normalization procedures (GCRMA vs. RMA), (3) different assessments of microarray data quality, (4) different statistical methods for identifying differentially expressed genes (multivariate permutation tests vs. fixed effects linear models), and (5) different methods for examining the effect of confounding covariates. Regardless, a number of genes that were not identified as differentially expressed by Mistry et al. (2012) (i.e. BAG3, C4B, MT1X, NEUROD6, SST and S100A8), were identified as differentially expressed in our combined microarray analysis and, furthermore, were confirmed in our RT-qPCR analysis in an independent cohort, which gives strong validity to our approach. Finally, Mistry et al. (2012) were unable to construct a PIN from their data but it is encouraging that they also found the transcription factor EGR1 downregulated in schizophrenia, a result that was further confirmed by RT-qPCR in our study. The relevance of other genes that overlapped between the two studies to schizophrenia is presented in Supplementary Table 8. The overlap between differentially expressed genes identified in this study and differentially expressed proteins identified by proteomic approaches was also determined. Prabakaran et al. (2004) performed 2D fluorescence difference gel electrophoresis (2D-

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DIGE) between 10 schizophrenia and 10 control samples to identify the largest number of differentially expressed proteins to date (English et al., 2011). Only one protein from this study, moesin (MSN), overlapped with the differentially expressed genes identified in this work but encouragingly both transcriptomic and proteomic analysis indicated that MSN is upregulated in schizophrenia. This lack of overlap may be due to the low power associated with the small number of samples analyzed by Prabakaran et al. (2004) or to the limited number of proteins that can be discriminated by 2D-DIGE. Future analysis should focus on reconciling differentially expressed genes with differentially expressed proteins especially since proteomic approaches are becoming more comprehensive with respect to the number of proteins that can be detected (e.g. mass spectrometry-based label-free proteomics) (Zhu et al., 2010). Our combined analysis has resulted in a statistically robust identification of genes whose dysregulation may contribute to the mechanism of schizophrenia. Specifically, EGR1 and SST have been corroborated as important genes downregulated in schizophrenia whereas C4B and NEUROD6 may represent new genes contributing to the neuropathogenesis of this disease that require further study. In contrast, the modulation of MT1X and S100A8 may be the result of the treatment of schizophrenia patients with antipsychotics. Furthermore, the power of a combined analysis for identifying genes differentially expressed between schizophrenia subjects and healthy controls with high statistical confidence has been clearly demonstrated. Therefore, researchers who generate microarray data from schizophrenia and other neuropsychiatric cohorts are strongly encouraged to submit their data to public repositories such as the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/ geo/) in order to facilitate even more powerful combined analyses in the future. Funding source This work was performed with the support of the Genomics Core at the UCSD CFAR (AI36214) funded by the U.S. National Institute of Health (NIH) and The James B. Pendleton Charitable Trust. Funding support for this study was made available to JJM by the Spanish Ministry of Science and Innovation (MICINN) (SAF2009/8460), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), and the Basque Government (IT199/07). SJG was supported in part by NIH grants P50MH081755 and R01MH085521, the Sidney R. Baer, Jr. Foundation, and NARSAD: The Brain and Behavior Research Foundation. Contributors JPS collated and analyzed microarray data and assisted in writing the methods section. RDA designed, performed and analyzed data from RT-qPCR experiments as well as helping to draft the manuscript. LFC selected patients for the independent cohort for RT-qPCR analysis. JJM provided access to the independent cohort for RT-qPCR analysis and helped draft the manuscript. JXZ and CS performed microarray and RT-qPCR analysis data analysis, respectively. GC and IPE performed the fetal brain aggregate study. SJG and MTT helped to draft the manuscript. CHW conceived the study, supervised the analyses and drafted the manuscript. All authors have read and approved the final version of the manuscript. Conflict of interest None of the authors associated with this manuscript have any competing interests, financial or non-financial, with respect to the data presented in this body of work.

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Acknowledgments This material is based upon work supported in part by the Department of Veterans Affairs (VA), Veterans Health Administration, Office of Research and Development, but the views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. Gratitude is extended to Drs. Sabine Bahn, Kwang Choi, Brian Dean, Elizabeth Thomas and Ali Torkamani who provided advice during the preparation of this manuscript and to Dr. Karoly Mirnics who supplied microarray gene expression data from the Hungarian cohort analyzed in this paper. The authors wish also to thank the staff members of the Basque Institute of Legal Medicine, Bilbao, for their cooperation in the study. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jpsychires.2012.08.005. References Albus M, Hubmann W, Mohr F, Hecht S, Hinterberger-Weber P, Seitz NN, et al. Neurocognitive functioning in patients with first-episode schizophrenia: results of a prospective 5-year follow-up study. European Archives of Psychiatry and Clinical Neuroscience 2006;256:442e51. Albus M, Hubmann W, Scherer J, Dreikorn B, Hecht S, Sobizack N, et al. A prospective 2-year follow-up study of neurocognitive functioning in patients with first-episode schizophrenia. European Archives of Psychiatry and Clinical Neuroscience 2002;252:262e7. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 1995;57:289e300. Bolstad BM, Collin F, Brettschneider J, Simpson K, Cope L, Irizarry R, et al. Quality assessment of Affymetrix GeneChip data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dutoit S, editors. Bioinformatics and computational biology solutions using R and bioconductor. Heidelberg: Springer; 2005. p. 33e47. Bousquet C, Guillermet J, Vernejoul F, Lahlou H, Buscail L, Susini C. Somatostatin receptors and regulation of cell proliferation. Digestive and Liver Disease 2004; 36(Suppl. 1):S2e7. Bray NJ. Gene expression in the etiology of schizophrenia. Schizophrenia Bulletin 2008;34:412e8. Chana G, Lucero G, Salaria S, Lozach J, Du P, Woelk C, et al. Upregulation of NRG-1 and VAMP-1 in human brain aggregates exposed to clozapine. Schizophrenia Research 2009;113:273e6. Choi KH, Elashoff M, Higgs BW, Song J, Kim S, Sabunciyan S, et al. Putative psychosis genes in the prefrontal cortex: combined analysis of gene expression microarrays. BMC Psychiatry 2008;8:87. Chu TT, Liu Y. An integrated genomic analysis of gene-function correlation on schizophrenia susceptibility genes. Journal of Human Genetics 2010;55: 285e92. Datson NA, Speksnijder N, Mayer JL, Steenbergen PJ, Korobko O, Goeman J, et al. The transcriptional response to chronic stress and glucocorticoid receptor blockade in the hippocampal dentate gyrus. Hippocampus 2010;22:359e71. English JA, Pennington K, Dunn MJ, Cotter DR. The neuroproteomics of schizophrenia. Biological Psychiatry 2011;69:163e72. Fatemi SH, Reutiman TJ, Folsom TD, Bell C, Nos L, Fried P, et al. Chronic olanzapine treatment causes differential expression of genes in frontal cortex of rats as revealed by DNA microarray technique. Neuropsychopharmacology 2006;31: 1888e99. Foster R, Kandanearatchi A, Beasley C, Williams B, Khan N, Fagerhol MK, et al. Calprotectin in microglia from frontal cortex is up-regulated in schizophrenia: evidence for an inflammatory process? European Journal of Neuroscience 2006; 24:3561e6. Frederiksen SO, Ekman R, Gottfries CG, Widerlov E, Jonsson S. Reduced concentrations of galanin, arginine vasopressin, neuropeptide Y and peptide YY in the temporal cortex but not in the hypothalamus of brains from schizophrenics. Acta Psychiatrica Scandinavica 1991;83:273e7. Gabriel SM, Davidson M, Haroutunian V, Powchik P, Bierer LM, Purohit DP, et al. Neuropeptide deficits in schizophrenia vs. Alzheimer’s disease cerebral cortex. Biological Psychiatry 1996;39:82e91. Garbett K, Gal-Chis R, Gaszner G, Lewis DA, Mirnics K. Transcriptome alterations in the prefrontal cortex of subjects with schizophrenia who committed suicide. Neuropsychopharmacologia Hungarica 2008;10:9e14. Glatt SJ, Everall IP, Kremen WS, Corbeil J, Sasik R, Khanlou N, et al. Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia. Proceedings of the National Academy of Sciences of the United States of America 2005;102:15533e8.

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