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Schizophrenia is characterized by complex gene expression changes. The transcriptome alterations in the prefrontal cortex have been the subject of several recent postmortem studies which yielded both convergent and divergent findings.
To increase measurement precision, we used a custom-designed DNA microarray platform with long oligonucleotides and multiple probes with replicates. The platform was designed to assess the expression of >1,800 genes specifically chosen because of their hypothesized roles in the pathophysiology of schizophrenia. The gene expression differences in dorsolateral prefrontal cortex samples from 14 matched pairs of schizophrenia and control subjects were analyzed using two technical replicates and four data mining approaches.
In addition to replicating many expression changes in synaptic, oligodendrocyte and signal transduction genes, we uncovered and validated a robust immune/chaperone transcript upregulation in the schizophrenia samples.
We speculate that the overexpression of SERPINA3, IFITM1, IFITM2, IFITM3, CHI3L1, MT2A, CD14, HSPB1, HSPA1B and HSPA1A in schizophrenia subjects represents a long-lasting and correlated signature of an early environmental insult during development that actively contributes to the pathophysiology of prefrontal dysfunction.
Schizophrenia is a severe mental disorder that affects ~1% of the population (Jablensky 2000). Although both genetic and environmental factors play critical roles in the etiology of schizophrenia, the molecular substrate for the pathophysiology of the disease remains elusive and is recognized as complex (Lewis and Levitt 2002). To address this issue, a number of DNA microarray studies have been conducted (for review see (Iwamoto and Kato 2006; Mirnics et al 2006)). These high throughput transcriptome profiling experiments have identified several altered molecular processes in schizophrenia, including downregulation of genes in the GABA and glutamate systems (Mirnics et al 2000; Mirnics et al 2001; Vawter et al 2002), gene expression changes related to synaptic and mitochondrial functions (Altar et al 2005; Iwamoto et al 2005; Middleton et al 2005; Mirnics et al 2000; Prabakaran et al 2004) and a repression of oligodendrocyte mRNAs in the prefrontal cortex (Aston et al 2004; Hakak et al 2001; Pongrac et al 2002; Tkachev et al 2003).
There have been both convergent and divergent results from the transcriptome profiling studies (Mirnics et al 2006). Differences could be attributed to 1) systematic cohort biases arising from brain collection procedures (e.g., hospitalized subjects vs. subjects who died suddenly, divergent inclusion/exclusion criteria, etc.); 2) sample processing differences in RNA isolation procedures, cDNA synthesis, in vitro transcription or fluorescent labeling; 3) microarray platform differences; or 4) differences in segmentation, data extraction, normalization and statistical analyses (Hollingshead et al 2005). Thus, the combined experimental variation and noise in every microarray experiment is considerable, and the microarray data can be less precise and accurate than desired.
Given these limitations, the present study was aimed at analyzing putative schizophrenia-associated molecular pathways at increased measurement precision. We postulated that the ideal microarray would: 1) be built with longer oligonucleotides in order to achieve a higher level of probe specificity; 2) contain multiples probes against the same gene; 3) have a high probe replicate redundancy; and 4) incorporate microarray probes based on published schizophrenia datasets and hypotheses. In addition, we established that the comprehensive microarray experiments would analyze only samples with the highest RNA integrity using two technical replicates. Finally, we utilized several analytical approaches to maximize the identification of convergent data.
The present study reports on a cohort composed of 14 pairs of control and schizophrenic brain samples matched for age, sex and postmortem interval (PMI). We identified significant changes in expression for 67 genes and ESTs between schizophrenia and control samples. In addition to confirming some of the previously reported gene expression changes, we found a highly correlated alteration in the expression of genes involved in immune/chaperone function as part of the transcriptome disturbances in schizophrenia. These results are discussed in the context of the environmental influences that may predispose to the disease.
Fresh-frozen human tissue was obtained from the University of Pittsburgh’s Center for the Neuroscience of Mental Disorders Brain Bank. In this study, 14 pairs of schizophrenia and control post-mortem brains were matched for gender, and as closely as possible for age, race and postmortem interval (PMI). The two groups of normal control (CTR) and schizophrenia (SCZ) subjects (Table 1) did not differ in mean (±SD) age at the time of death (42.21 ± 14.8 and 42.57 ± 12.31 years, respectively), PMI (17.34 ± 5.39 and 17.43 ± 8 hours, respectively), tissue storage time (67.4 ± 24.8 and 73.7 ± 18.2 months, respectively), brain pH (6.88 ± 0.21 and 6.88 ± 0.20, respectively), and RNA integrity number (RIN) number (8.6 ± 0.40 and 8.4 ± 0.67, respectively). Consensus DSM-IV diagnoses for all subjects were made using data from clinical records, toxicology studies and structured interviews with surviving relatives, as described previously (Volk et al 2000). The details of the demographic and clinical features of this subject cohort have been previously described in (Hashimoto et al 2006; Hashimoto et al 2003).
Our custom-designed Nimblegen DNA microarrays were composed of 60-mer single-stranded oligonucleotides synthesized by maskless in situ photolithographic synthesis. Each microarray contained probes against ~1,800 genes. The expression of each gene was assessed by 5 independent probe sequences and each of the probes was printed at 4 technical replicates on a sub-array (Figure 1). Each microarray consisted of 5 sub-arrays, resulting in a total of 100 measurements/gene/array (5 probes × 4 replicate spots × 5 identical sub-arrays). In addition, the experiment was performed in two technical replicates using independent cDNA and cRNA synthesis steps. As a result, our dataset consisted of 56 microarrays.
The ~1,800 gene expression probes were chosen based on 1) previous microarray studies conducted in our laboratory, 2) previously published gene expression and genetic data in schizophrenia, and 3) genes participating in cellular processes implicated in the pathophysiology of PFC dysfunction in schizophrenia. As a result, the list of microarray probes was enriched in GABA, glutamate, synaptic, glial, dopamine, serotonin system and other transcripts. Furthermore, based on previous microarray studies of subjects with schizophrenia and matched controls, an additional ~400 genes with unaltered expression were chosen to ensure proper normalization across the microarrays.
Coronal blocks containing area 9 of the prefrontal cortex (PFC) were cut on a cryostat at 20 μm thickness. The sections were collected into tubes containing Trizol reagent (Invitrogen, Carlsbad, CA) and RNA was isolated according to the manufacturer’s instructions. RNA quality was assessed using an Agilent BioAnalyzer 2100 system (Agilent, Palo Alto, CA). After cDNA synthesis (cDNA SuperScript Custom Kit by Invitrogen Corporation, Carlsbad) and in vitro transcription (MEGAscript T7 Kit, Ambion Inc., Austin), the resulting cRNA was labeled according to Nimblegen recommendations. Hybridization was performed at the Nimblegen Systems facility using established protocols (for further information, see http://www.nimblegen.com/technology/hybe.html).
The microarray images were segmented and analyzed using a customized system named NimbleScan (http://www.nimblegen.com/products/software/nimblescan.html). Data were then normalized and analyzed using Robust Multi-array Analysis (RMA) (Irizarry et al 2003). For analysis purposes and for each gene, the 5 probes were treated as independent measurements and for each probe the result of the 20 measurements/ array were averaged into a single value. In order to define differentially expressed genes between SCZ and CTR brain samples and across the two technical replicates (R1 and R2), data were analyzed using four different strategies (Figure 2):
In this strategy, the normalized expression levels for all 14 SCZ samples and all 14 CTR samples were first averaged and then compared. This analysis was performed for both R1 and R2 independently. A gene was then considered differentially expressed if: 1) it showed an absolute average Log2 ratio (ALR) between SCZ and CTR samples of more than 20 % (|ALR|>0.263; 1.2-fold); 2) at least one gene probe showed a groupwise Student’s t-test significance of p<0.05 between SCZ and CTR samples; and 3) the differential expression was observed in both R1 and R2.
In B, the normalized expression levels for R1 and R2 were averaged for each SCZ and CTR samples. Then, the resulting expression levels for all 14 SCZ and 14 CTR samples were averaged and compared. A gene was then considered differentially expressed if: 1) at least one gene probe showed an absolute average Log2 expression difference between SCZ and CTR of more than 20% (|ALR|>0.263; 1.2-fold); and 2) it showed a groupwise Student’s t-test significance of p<0.05 between SCZ and CTR samples.
In C, the normalized expression levels were compared in a pair wise fashion for each of the 14 SCZ and CTR samples. Then, the results for all 14 pairs were averaged. The same comparison was performed for each R1 and R2 independently. A gene was then considered differentially expressed if: 1) at least one gene probe showed an absolute average Log2 expression difference of more than 20% (|ALR|>0.263; 1.2-fold) between SCZ and CTR samples; 2) it showed a pairwise Student’s t-test significance of p<0.05 between SCZ and CTR samples; and 3) the differential expression was observed in both R1 and R2.
In D, the normalized expression levels for all CTR and SCZ pairs in R1 and R2 were averaged. Then, each matched SCZ and CTR brain samples were compared in a pair wise fashion to determine gene expression differences. A gene was then considered differentially expressed if: 1) at least one probe showed an absolute average Log2 expression difference of more than 20% (|ALR|>0.263; 1.2-fold); and 2) it showed a pairwise Student’s t-test significance of p<0.05 between SCZ and CTR samples.
A gene was considered differentially expressed if it reported significant and unidirectional differential expression in all 4 analysis strategies.
Two-way cluster analyses (genes and samples) were performed on normalized log2 transformed signal levels across the 28 subjects using Euclidian distance analysis in Genes@Work developed by IBM (Lepre et al 2004).
The two potenial confounding factors in our study are antipsychotic medication and ethanol abuse. For post hoc testing for antipsychotic medication effects, we divided the subjects into two groups: A) pairs of subjects where the schizophrenic subjects were off medication at the time of death (Pairs 2, 9 and 13) and B) pairs of subjects where the schizophrenic subjects were on antipsychotic medication at the time of death. For post hoc testing of effects of ethanol dependence on the observed gene expression differences, we also separated the subject pairs into two groups (EtOH group: pairs 5, 9, 11, 13 and 14), this time by history of ethanol dependence and compared the expression ALRs of the two cohorts. For testing of both confounds, we assessed the gene expression of the 20 critically changed genes in across the two newly created pair groups. Data were not available about smoking history of the subjects.
Pearson’s correlation testing was performed on the normalized log2 values for 8 selected genes (SERPINA3, IFITM1, IFITM3, CHI3L1, CD14, HSPA1B, HSPB1 and HSPA1A). The correlation was calculated on the averaged R1R2 data and across all 28 human subjects. We obtained 5 correlation values for each gene (one for each probe). The highest across-gene and within gene correlation is reported.
The false discovery rate (FDR) assessment, a built-in feature of this experimental design, was assessed by establishing how often the independent probes from the same gene reported opposite expression changes. In all of our analyses, two or more different probes against the same gene never reported a statistically significant expression change across the SCZ-CTR comparison in opposite directions (e.g. increase vs. decrease). This suggests a very high measurement precision and a negligible FDR for the differentially expressed genes. However, we acknowledge that our converging data mining strategy may carry an increased amount of type II error (false negative observations), which are not possible to address in current microarray experiments.
For selected genes showing differential expression between CTR and SCZ subjects, cDNA synthesis was performed using two independent reverse transcriptions for each sample with the High Capacity cDNA Archive Kit from Applied Biosystems (Foster City, CA). For each reaction, we used 50 ng of total RNA from each subject. Priming was performed with random hexamers according to the manufacturer’s recommendations. For each sample, amplified product differences were measured with 4 replicates using SYBR Green chemistry-based detection (Mimmack et al 2004). Mitochondrial ATP synthase 6 (MTATP6) and GAPDH were used as the endogenous reference genes, because they did not display significant variation in gene expression between CTR and SCZ samples. The efficiency for each primer set was assessed prior to qPCR measurements, and a primer set was considered valid if its efficiency was between 92 and 100 %. The qPCR reactions were carried out in an ABI Prism 7000 thermal cycler (Applied Biosystems Inc.) using the ABI Prism 7000 SDS software with the automatic baseline and threshold detection options selected. These quantified data were exported to Microsoft Excel for establishing SCZ and CTR ΔCt, determining SCZΔCt - CTRΔCt (ΔΔCt) and significance testing. A one-tailed Student’s t-test was used to determine the significance of the qPCR-reported SCZ-CTR expression differences.
All microarray data will be made publicly available at the authors’ Web site at the time of publication.
In this study, a Nimblegen custom microarray analysis allowed us to compare the expression levels of >1800 different genes, including >400 control genes between SCZ and CTR samples from area 9 of human postmortem PFC. The combination of 4 independent analytical strategies reduced the dataset to 67 transcripts reporting significant differential expression between PFC-harvested samples from SCZ and CTR (|ALR|>0.263; 1.2-fold and pVal<0.05; Table 2). Of these 67 genes, 45 (67%) were found downregulated in SCZ samples compared to CTR samples with a mean ALR value of -0.58 (-1.5-fold) and 22 genes (33%) were found upregulated with a mean ALR value of 0.68 (1.6-fold). These results are in agreement with previous studies reporting a global downregulation of the expression of a variety of functional gene groups in schizophrenia (for review see (Iwamoto and Kato 2006)). The 67 differentially expressed genes between SCZ and CTR samples represented a heterogenic group of transcripts, some of which have been previously associated with the pathophysiological processes in schizophrenia. Downregulation of transferrin (TF), synapsin 2 (SYN2), synaptojanin 1 (SYNJ1), regulator of G-protein signaling 4 (RGS4), mitogen-activated protein kinase 1 (MAPK1), glutamic-oxaloacetic transaminase 1 (GOT1), potassium channel, subfamily K, member 1 (KCNK1) and mu-crystallin (CRYM) have all been previously reported to be downregulated in subjects with schizophrenia (Aston et al 2004; Hakak et al 2001; Iwamoto et al 2005; Middleton et al 2002; Mirnics et al 2000; Pongrac et al 2002; Vawter et al 2002). Finally, this experimental series also confirmed and extended previous findings of altered GABAergic system transcriptome in schizophrenia (Lewis et al 2005). Due to the volume and complexity of these findings, the GABA system related expression changes are the subject of a separate manuscript (Hashimoto et al 2006).
The analyses revealed an unexpected upregulation of genes related to immune function and chaperone system. Of the 19 fully annotated transcripts that reported increased expression in subjects with schizophrenia, 10 (>50%) have been linked to functions in immune/chaperone systems (Table 2). For example, in subjects with schizophrenia >50% expression upregulation was observed for the transcripts encoding 28kDa heat shock protein (HSPB1), 70kDa heat shock protein 1B (HSPA1B), 70kDa heat shock protein 1A (HSPA1A), metallothionein 2A (MT2A), interferon induced transmembrane protein 1 (IFITM1), interferon induced transmembrane protein 3 (IFITM3), CD14 antigen (CD14), chitinase 3-like1 (CHI3L1), serine-cysteine protease inhibitor A3 (SERPINA3) and paired immunoglobulin-like receptor beta (PILRB). These transcripts and their protein products are known to exhibit increased expression in response to cellular stress and/or immune stimulation (Chung et al 2004; Chung et al 2003b; Gosslau and Rensing 2000; Kohler et al 2003; Lewin et al 1991; Mosser et al 2000; Mousseau et al 2000; Penkowa and Hidalgo 2001; Potter et al 2001; Seidberg et al 2003; Shiratori et al 2004; Smith et al 2006; Takekawa and Saito 1998; van Bilsen et al 2004; Xia et al 2006).
A two-way unsupervised clustering (genes and subjects) of the annotated gene probe signal intensities resulted in the separation of the samples in 2 different clusters (Figure 3). Of the 14 SCZ subjects, 9 3clustered together and these subjects clearly were major contributors to the statistical significance across the dataset. In contrast, the remaining 5 SCZ subjects appeared to be indistinguishable from the controls, suggesting a molecular sub-stratification within the SCZ samples. The clustering also yielded an interesting separation along the dimension of expressed genes; here, the immune/chaperone-related transcripts (MT2A, IFITM1, IFITM3, CHI3L1, SERPINA3, CD14, TNC, GADD45G, HSPB1, HSPA1B and HSPA1A) strongly clustered together as a distinct sub-group, suggesting a potential co-regulation of these molecules.
The pre-established criterion of the differentially expressed transcripts needing to fulfill a combination of 4 distinct data analysis strategies resulted in the reporting of only the most reliable expression changes (Table 2). Due 2 to the conservative nature of this data mining approach, however, a number of true differential expressions may have been eliminated from our dataset. In Supplemental Material 1 we report all other non-GABA system related transcripts that were differentially expressed by at least one of the four analysis strategies. Of these additional 170 putatively changed transcripts, ~15% were found changed in previous schizophrenia studies, suggesting that these data may also contain findings for further exploration.
The reported expression findings did not appear to be a result of administration of chronic antipsychotic medication: subjects with schizophrenia that were off medication at time of death showed similar expression changes to subjects on medication at the time of death. For subset of the 21 most changed genes, subjects with schizophrenia on medication showed an average ALR= -0.53 for decreased, and 0.60 for the increased expression (Supplemental Material 2A). Similarly, subjects off medication at the time of death showed for the same genes an average ALR of -0.80 and 1.11, respectively, with an overall correlation of r=0.94 (p<0.01). Furthermore, in an unsupervised clustering of the gene expression changes in Figure 3, two of the three subjects with schizophrenia that were off medication (829s,622s,537s) clustered together with the subjects that were on medication at the time of death. Similarly, sample pairs where the schizophrenic subject had current alcohol abuse/dependence at the time of death showed the similar gene expression changes as the rest of the pairs where the schizophrenic subject had no active EtOH abuse/dependence. The expression differences for the critical genes were highly correlated (r=0.90, p<0.01) between these two groups, with a trend of more robust expression changes in pairs without EtOH abuse/dependence (average ALRs 0.59 vs 0.77 and -0.26 vs -0.77 - Supplemental Material 2B). Thus, based on these data, it appears that antipsychotic medication and EtOH consumption were not confounding factors in our dataset.
In order to validate the microarray findings we selected 12 genes for real-time quantitative PCR (qPCR) analysis. Six of these transcripts were overexpressed (SERPINA3, CHI3L1, HSPB1, MT2A, IFITM1, IFITM3) while six other genes were underexpressed (DIRAS2, CRYM, TF, MOG, MAPK1, RGS4) in the SCZ samples. The agreement between the ΔΔCt of qPCR and DNA microarray ALR datasets was striking: the differential expression for the 12 investigated genes was correlated at r=0.93 (p<0.001) between the two methods (Figure 4).
In addition, as there were no probes against IFITM2 on the microarray, in the context of IFITM1 and IFITM3 transcript increases, we quantified the expression of the third family member, IFITM2. By qPCR measurement the IFITM2 overexpression in the SCZ samples was comparable to that seen to the other two IFITM family members (-ΔΔCt =0.54 or 1.45 fold increase, p<0.05).
The successful verification of the immune/chaperone system transcript findings raised the question of the origin of these robust transcriptome changes. To determine whether the observed transcriptome changes were the result of an active immune process in the brains of subjects with schizophrenia, we investigated the levels of 2-prime,5-prime oligoadenylate synthetase 1 (OAS1), a critical marker of acute viral infection and interferon gamma (IFNγ), a critical first activator of multiple downstream immune cascades, including the OAS system. Subjects with schizophrenia, when compared to matched controls, did not show altered transcript levels of OAS1 or INFγ (data not shown).
The clustered data strongly suggested that the upregulated immune- and HSP-related transcripts may represent a robust, selectively regulated gene expression network. To test this, we performed an expression level cross-correlation of 5 immune genes with 3 HSP family members. This correlational analysis was performed across the whole dataset (28 human PFC samples) for the 5 probes/gene (Figure 5). The data revealed that the expression levels for the 5 immune markers tested (SERPINA3, IFITM1, IFITM3, CHI3L1 and CD14) were highly correlated within the human PFC (max r=0.73-0.93, all p<0.01). Similarly, the 3 HSP family members (HSPA1B, HSPB1 and HSPA1A) showed a high within-group correlation (max r=0.85-0.99, all p<0.01). In contrast, only a weak to moderate correlation was observed between the members of the chaperone and immune genes, suggesting that the modulation of the changed immune-related genes and altered chaperone-related transcripts may occur independently, and not as a direct adaptive relationship between these two systems.
Detailed analysis of the custom DNA microarray data from a cohort of 14 matched pairs of CTR and SCZ brain samples from area 9 of human PFC revealed a number of important observations related to changes in gene expression in schizophrenia. First, using rigorous and convergent statistical approaches, we identified 67 genes that were differentially regulated across CTR and SCZ samples. Second, the number of downregulated transcripts outnumbered the upregulated ones by ~2:1 margin, confirming previous reports that gene expression changes in the PFC of subjects with schizophrenia are predominantly characterized by transcript reductions, rather than increases (Iwamoto and Kato 2006; Mirnics et al 2000). Third, we confirmed many of gene expression changes reported in previous postmortem studies, including downregulation of TF (Hakak et al 2001), RGS4 (Mirnics et al 2001), SYN2, SYNJ1, MAPK1, GOT1, KCNK1 and CRYM (Mirnics et al 2000) and upregulation of CHI3L1 (Chung et al 2003a), HSPB1 (Kuromitsu et al 2001), HSPA1B (Hakak et al 2001) and MT2A (Aston et al 2004). Fourth, we identified an unexpected and strongly correlated upregulation of a subset of genes involved in immune/chaperone function. Fifth, we found that the immune/chaperone signature was primarily present in a subset of subjects with schizophrenia. Although we recognize that chaperones can function in a wide range of roles that are independent of immune processes (e.g. response to cellular stress and signal transduction) (Garcia-Osta et al 2003; Kim et al 2001; Pae et al 2005), in our dataset we favor the interpretation that the observed chaperone and immune changes are of common origin and causally interrelated, and they will be discussed in this context.
The neuroimmune hypothesis of schizophrenia has been debated for decades (Giovannoni and Baker 2003; Hanson and Gottesman 2005; Jones et al 2005; Muller et al 2000; Muller and Schwarz 2006; Patterson 2002; Rothermundt et al 2001; Strous and Shoenfeld 2006). Relative risk for developing schizophrenia is increased more than 2-fold compared to the general population with 2nd trimester infection (reviewed by (Cannon and Clarke 2005)). Additionally, epidemiological, serological, gene expression and pathological findings all suggest an infective-immune component of the disease, albeit replication across different cohorts of patients has been elusive (Rothermundt et al 2001). Nevertheless, the combined evidence suggests an infective-immune predisposition to schizophrenia, and that this predisposition is likely to interact with genetic susceptibility for developing the disease. In this context, the changes related to immune/chaperone functions can represent either a response to an ongoing infective-immune challenge or a long-lasting signature of an immune system challenge that may have acted during brain development, which in the human extends in a lengthy fashion from 1st trimester through puberty. Most of the studies of schizophrenia to date suggest that the observed neuroimmune changes are a long-lasting consequence of a previous infective-immune challenge (for review see (Nawa and Takei 2006; Sperner-Unterweger 2005)). Here, we addressed this more directly by determining that there is no change in the transcript levels of OAS1 and INFγ, two critical markers of acute immune response (Rothwell and Hopkins 1995; Rothwell et al 1996). Thus, we also favor the interpretation of a developmentally-based, long-term alteration in the transcriptome of genes related to immune/chaperone function, although one must be aware that certain pre-mortem life stressors and adverse socio-economic conditions, which are highly prevalent in patients with schizophrenia, may also contribute to some of the observed expression changes.
Long-term consequences of early immune challenge are not unprecedented. Studies in rodents that undergo prenatal or perinatal exposure to immune challenges (such as maternal exposure to polyriboinosinic-polyribocytidilic acid - poly(I:C), a synthetic cytokine inducer), high levels of pro-inflammatory cytokines or viral infections develop post-adolescent behavioral deficits that are similar in nature to clinical manifestations in schizophrenia (Ashdown et al 2006; Meyer et al 2005; Tohmi et al 2004; Zuckerman and Weiner 2005). In the mouse, prenatal exposure in mid-pregnancy to poly(I:C) reduces the number of reelin positive cells in hippocampus (Meyer et al 2006). Furthermore, poly(I:C) administration also causes increased dopamine turnover, prepulse inhibition deficits and cognitive impairments in the adult offspring, and the latter is improved by administration of clozapine (Ozawa et al 2006). Finally, in a rat model, poly(I:C) administration during pregnancy also produced long-lasting pathophysiological changes that are also observed in schizophrenia, including dopaminergic hyperfunction and loss of latent inhibition (Zuckerman et al 2003).
In view of these data, we propose that the transcriptome signature of altered genes related to immune/chaperone function may be a consequence of early life TNF-α, IL-1, IL-6 and/or INFγ brain activation. In this proposed mechanism, the elevated pro-inflammatory cytokine levels during late embryonic development or perinatal period could not only impair normal differentiation and/or refinement of neural connectivity, but also leave behind a specific immune/chaperone signature primarily consisting of altered IFITM, SERPINA3 and HSP transcript increases.
How does elevation of these immune/chaperone system molecules contribute to the symptoms of schizophrenia? The present study does not address this directly, but we speculate that this immune/chaperone signature extends beyond a correlation with an early environmental insult and may actively contribute to the clinical features of the illness. Many immune/chaperone genes are known to be essential for the normal functioning of the CNS, and immune function genes are capable of altering cognitive performance (Blalock et al 2003; Heyser et al 1997; Hoffman et al 1998; Wilson et al 2002; Ziv et al 2006). The causality between the immune/chaperone gene expression changes and altered cognitive performance in schizophrenic patients needs to be addressed in comprehensive clinical studies and in animal models.
In conclusion, our data suggest that a subgroup of subjects with schizophrenia carries an immune/chaperone transcriptome signature of an early environmental insult in the PFC. The identification of this putative immune/chaperone molecular sub-phenotype of schizophrenia, if it is validated across other cohorts of subjects, will allow a separate, mechanistic follow-up in the context of behavioral changes associated with the disease.
We are thankful to Drs. Christine Konradi and Krassimira Garbett for valuable comments on the manuscript. We also thank to Katherine C. Douglas, Annie Bedison and Melissa Macioce for superb technical assistance with the experiments. This work was supported by R01 MH067234 (KM), 2 P50 MH45156 CCNMD Project 2 (KM) and K02 MH070786 (KM).
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