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A Bird’s Eye View of the Schizophrenia Transcriptome

14 August 2012. A new study takes a novel systems biology approach to microarray transcriptome profiling of schizophrenia postmortem tissue by looking at correlation patterns among genes, rather than single genes alone. Led by Vahram Haroutunian of New York’s Mount Sinai School of Medicine, and published online August 6 in the Archives of General Psychiatry, the study finds that certain groups of genes whose expression covaries, called modules, are altered in schizophrenia. Three modules in particular—the glutamatergic, GABAergic, and oligodendrocyte ones—may be directly involved in schizophrenia pathogenesis, as suggested by their enrichment for genes fingered by a genomewide association study (GWAS). Though the results are based on samples from aged, chronically ill patients, they highlight a way to define groups of genes that work together without relying on preconceived notions about their function.

Expression profiling studies that use microarrays to assess the schizophrenia transcriptome have been plentiful, and changes in functional gene groups, such as oligodendrocyte and metabolic-associated genes, have been identified (Mirnics et al., 2006). However, microarray studies have been plagued by a lack of agreement about the specific genes that are altered, and by only moderate changes in gene expression that are often not statistically significant after correcting for multiple comparisons.

In the current study, Haroutunian’s group has taken a different approach to the analysis of the schizophrenia transcriptome, using a technique called weighted gene coexpression network analysis (WGCNA) (Langfelder and Horvath, 2008). This approach has previously been used to identify perturbed molecular pathways in autism brain tissue (see SRF related news story) and recently in schizophrenia whole blood (de Jong et al., 2012). With the current study, WGCNA comes to postmortem schizophrenia brain tissue for the first time.

WGCNA looks beyond individual genes to identify higher-order relationships among modules. The most highly connected genes within a module form hubs, and the first principal component, termed the module eigengene, serves as a summary of the expression levels for a given module. The module eigengene and the expression levels of hub genes are then used to determine whether a given module is associated with any one of a number of variables, such as a disease, brain region, or genetic variant.

By looking at networks rather than individual genes, WGCNA offers an unbiased way to identify groups of genes altered in illnesses like schizophrenia. As noted by the authors, “such a data reduction strategy has the potential to alleviate the multiple testing correction uncertainties that are encountered in standard gene-centric methodological approaches for microarray data analysis and, hence, decrease the potential for type I and type II statistical errors.”

Making modules from microarrays
First author Panos Roussos and colleagues examined postmortem tissue from four cortical regions (dorsolateral prefrontal, middle temporal, temporopolar, and anterior cingulate) in 21 schizophrenia subjects and 19 controls. Notably, the subjects included in this study were of an advanced age, an average of 81 years and 74 years for control and schizophrenia subjects, respectively.

Microarrays revealed 611 genes that were differentially expressed in schizophrenia, 432 of which were downregulated in the illness. Upon breaking out the expression data by region, only three—middle temporal area, temporopolar area, and anterior cingulate—were significantly altered in schizophrenia. The lack of gene expression changes in the dorsolateral prefrontal cortex, a frequent site of postmortem schizophrenia studies, is in line with a previous report from Haroutunian’s group (Katsel et al., 2005). However, these data are inconsistent with studies from other labs (Fillman et al., 2012), and the authors note that larger pH differences between diagnostic categories in this brain area may be a potential confound.

The researchers identified five network modules—mitochondrial, microglial, oligodendrocyte, glutamatergic, and GABAergic—whose eigengenes were significantly associated with schizophrenia, showing distinct expression profiles from controls. This provides evidence of convergent molecular abnormalities in the illness, and suggests that multiple aspects of cellular functioning are disturbed. In fact, many genes within these modules are already thought to be players in schizophrenia pathology.

Next, they used GWAS data (Ripke et al., 2011) to separate primary transcriptional changes from those due to secondary factors not directly related to disease pathogenesis, with the idea that the modules enriched with genes associated with schizophrenia risk are likely to represent primary etiological changes. Only three of the modules—oligodendrocyte, GABAergic, and glutamatergic—showed a significant association with the schizophrenia GWAS data, suggesting that networks related to these genes may underlie schizophrenia pathophysiology.

When comparing gene network differences among cortical regions, Roussos and colleagues found that substantially fewer genes were differentially expressed among the four brain areas in schizophrenia subjects relative to controls (82 vs. 681 genes), indicating that the gene expression patterns that normally differentiate these cortical areas in healthy subjects are greatly reduced in schizophrenia.

Given the advanced age of the subjects used in this study, whether these findings extend to earlier stages of schizophrenia remains to be seen. By providing a system-level examination of the transcriptome, however, WGCNA offers a new alternative to traditional single gene-based microarray analysis.—Allison A. Curley.

Reference:
Roussos P, Katsel P, Davis KL, Siever LJ, Haroutunian V. A System-Level Transcriptomic Analysis of Schizophrenia Using Postmortem Brain Tissue Samples. Arch Gen Psychiatry . 2012 Aug 6:1-11. Abstract

Comments on News and Primary Papers
Comment by:  Karoly Mirnics, SRF Advisor
Submitted 28 August 2012
Posted 28 August 2012

This is perhaps the best and most comprehensive transcriptome dataset of schizophrenia generated to date. It has multiple strengths, including the use of four different cortical regions, the correlation of genetics with transcriptomics, and the use of strong bioinformatics including WGCNA analysis. The findings are quite revealing. The results suggest that there is a strong, common signature across brain areas BA21, BA32, and BA38 that encompasses genes related to transcription/translation, signal transduction, the cell cycle, cell adhesion, the immune response, apoptosis, and the cytoskeleton.

Perhaps surprisingly, the expression signature was far less prominent in prefrontal cortical area BA 46, which is one of the most affected regions in schizophrenia. However, it was also clear that each brain region had a unique, region-specific schizophrenia signature. In addition, this study reproduced and validated a number of previously reported findings related to oligodendrocyte and mitochondrial transcript deficits. Nevertheless, the results of the current study disagree with the previously reported and replicated outcomes of similar assessments of other cohorts: in this study, GABA system genes were upregulated, and gene ontology categories related to immune response were downregulated in subjects with schizophrenia.

This is certainly noteworthy, and this apparent discrepancy in findings will have to be addressed by future experiments. I wish that the authors had addressed this issue in their discussion. The combined transcriptomics-genetics results suggest that the oligodendrocyte, GABA, and glutamate modules are (at least partially) driven by genetic vulnerability, while other gene expression changes might be secondary/adaptational in nature.

Finally, the study suggests that interregional coexpression is attenuated in schizophrenia. A very similar hypothesis, using WGCNA analysis of samples with autism, has been proposed in autism by the Geschwind laboratory (see Voineagu et al., 2011, and the commentary of Korade and Mirnics, 2011). Voineagu et al. reported that the differential patterns of gene expression that normally distinguish the frontal and temporal cortices are significantly attenuated in the autistic brain, potentially leading to loss of functional specifications across the affected cortical areas. This is certainly worth further exploration, and the schematic hypothesis presented in Figure 5 of the current paper is a logical blueprint that nicely maps out a possible sequence of pathophysiological events in schizophrenia. Still, the actual sequence of the proposed events can be debated at the current time: it is very likely that the cascades proposed by Figure 1 in the commentary by Korade et al. and Figure 5 in the current manuscript will have to be revised as our knowledge accumulates.

References:

Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature . 2011 May 25. Abstract

Korade Z, Mirnics K. Gene expression: the autism disconnect. Nature . 2011 June 15. Abstract

View all comments by Karoly Mirnics

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Comment by:  Roger Boshes
Submitted 10 August 2013
Posted 20 August 2013

These data suggest a "stem" circuit that may be common to many patients with schizophrenia, but subsequent de novo mutations may explain the protean manifestations of the disorder. Alternatively, this prefrontal perturbation may be related to a heritable, i.e., not a somatic, mutation that explains 80 percent heritability but not the protean phenotypic expression of the condition. Finally, it may be the link between schizophrenia and some flavors of autism.

References:

Boshes RA, Manschreck TC, Konigsberg W. Genetics of the schizophrenias: a model accounting for their persistence and myriad phenotypes. Harv Rev Psychiatry. 2012 May-Jun; 20(3):119-29. Abstract

View all comments by Roger Boshes