Systematic review of genome-wide gene expression studies of bipolar disorder

Abstract

BackgroundNumerous genome-wide gene expression studies of bipolar disorder (BP) have been carried out. These studies are heterogeneous, underpowered and use overlapping samples. We conducted a systematic review of these studies to synthesize the current findings.MethodsWe identified all genome-wide gene expression studies on BP in humans. We then carried out a quantitative mega-analysis of studies done with post-mortem brain tissue. We obtained raw data from each study and used standardized procedures to process and analyze the data. We then combined the data and conducted three separate mega-analyses on samples from 1) any region of the brain (9 studies); 2) the prefrontal cortex (PFC) (6 studies); and 3) the hippocampus (2 studies). To minimize heterogeneity across studies, we focused primarily on the most numerous, recent and comprehensive studies.ResultsA total of 30 genome-wide gene expression studies of BP done with blood or brain tissue were identified. We included 10 studies with data on 211 microarrays on 57 unique BP cases and 229 microarrays on 60 unique controls in the quantitative mega-analysis. A total of 382 genes were identified as significantly differentially expressed by the three analyses. Eleven genes survived correction for multiple testing with a q-value < 0.05 in the PFC. Among these were FKBP5 and WFS1, which have been previously implicated in mood disorders. Pathway analyses suggested a role for metallothionein proteins, MAP Kinase phosphotases, and neuropeptides.ConclusionWe provided an up-to-date summary of results from gene expression studies of the brain in BP. Our analyses focused on the highest quality data available and provided results by brain region so that similarities and differences can be examined relative to disease status. The results are available for closer inspection on-line at Metamoodics [http://metamoodics.igm.jhmi.edu/], where investigators can look up any genes of interest and view the current results in their genomic context and in relation to leading findings from other genomic experiments in bipolar disorder.

Transcriptome Sequencing of the Anterior Cingulate in Bipolar Disorder: Dysregulation of G Protein-Coupled Receptors

“…Candidate gene and genome-wide microarray expression analyses of postmortem human brains have shown that transcriptional dysregulation plays a role in the etiology of bipolar disorder (for a review, see reference 3). Some notable genes have been identified through these studies (4,5); however, very few passed corrections for multiple testing, and findings from these studies have largely not been replicated (3,5,6). RNA sequencing, a technique that takes advantage of the recent development of high-throughput sequencing technologies, offers a number of advantages in comparison to previous methodologies, such as microarray studies, in that it provides direct estimates of transcript abundance, as well as nucleotide-level sequence.…”

Transcriptome sequencing implicates dorsal striatum-specific gene network, immune response and energy metabolism pathways in bipolar disorder

“…If the striatum has a role in the molecular pathophysiology of BD, one might expect to find relevant gene expression changes in the striatum of affected individuals. However, transcriptomic analyses of BD using postmortem human brains have largely focused on cortical regions, [20][21][22] with no global gene expression profiling of the dorsal striatum published to date.…”

Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder

“…Consequently, the measured results from such studies of postmortem brain samples could lead to disturbed disease expression profile. This might also be one of the reasons for the heterogeneity that these studies present [12].…”

“…The DE gene list of this study was also compared with the corresponding list from a mega-analysis of genome-wide gene expression studies of post-mortem brain BD samples [12]. The following genes were found to be common: EPHB6, CLEC3B, NID2, RHOBTB3, KLF4, PDE4DIP, and LPCAT2.…”