Literature DB >> 16687443

Genome-wide expression analysis detects eight genes with robust alterations specific to bipolar I disorder: relevance to neuronal network perturbation.

Noriaki Nakatani1, Eiji Hattori, Tetsuo Ohnishi, Brian Dean, Yoshimi Iwayama, Izuru Matsumoto, Tadafumi Kato, Noriko Osumi, Teruhiko Higuchi, Shin-ichi Niwa, Takeo Yoshikawa.   

Abstract

The limited number of genome-wide transcriptome analyses using the postmortem brains of bipolar disorder sufferers has not produced a clear consensus on the molecular pathways affected by the disorder. To expand the knowledge in this area, we examined the expression levels of more than 12 000 genes in Brodmann's Area (BA), 46 (dorsolateral prefrontal cortex) from bipolar I disorder and control samples using Affymetrix GeneChips. This analysis detected 108 differentially expressed genes in bipolar brains. Validation studies using quantitative RT-PCR on the two original diagnostic cohorts plus tissue from schizophrenic subjects, confirmed the differential expressions of eight genes (RAP1GA1, SST, HLA-DRA, KATNB1, PURA, NDUFV2, STAR and PAFAH1B3) in a bipolar-specific manner and one gene (CCL3) which was downregulated in both bipolar and schizophrenic brains. Of these, protein levels of RAP1GA1 (RAP1 GTPase activating protein 1) showed a trend of increase in BA46 from bipolar brains, in keeping with mRNA transcript levels. Transmission disequilibrium analysis of the nine genes using 43 single nucleotide polymorphisms (SNPs) in 229 National Institute of Mental Health bipolar trios exposed nominal SNP association and modest empirical haplotypic association (P=0.033) between SST (somatostatin) and disease. Finally, gene network analysis using the currently obtained expression data highlighted cellular growth and nervous system development pathways as potential targets in the molecular pathophysiology of bipolar disorder.

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Year:  2006        PMID: 16687443     DOI: 10.1093/hmg/ddl118

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  47 in total

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10.  Combining gene expression, demographic and clinical data in modeling disease: a case study of bipolar disorder and schizophrenia.

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