RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder.

RNA-sequencing (RNA-seq) is a powerful technique to investigate the complexity of gene expression in the human brain. We used RNA-seq to survey the brain transcriptome in high-quality postmortem dorsolateral prefrontal cortex from 11 individuals diagnosed with bipolar disorder (BD) and from 11 age- and gender-matched controls. Deep sequencing was performed, with over 350 million reads per specimen. At a false discovery rate of <5%, we detected five differentially expressed (DE) genes and 12 DE transcripts, most of which have not been previously implicated in BD. Among these, Prominin 1/CD133 and ATP-binding cassette-sub-family G-member2 (ABCG2) have important roles in neuroplasticity. We also show for the first time differential expression of long noncoding RNAs (lncRNAs) in BD. DE transcripts include those of serine/arginine-rich splicing factor 5 (SRSF5) and regulatory factor X4 (RFX4), which along with lncRNAs have a role in mammalian circadian rhythms. The DE genes were significantly enriched for several Gene Ontology categories. Of these, genes involved with GTPase binding were also enriched for BD-associated SNPs from previous genome-wide association studies, suggesting that differential expression of these genes is not simply a consequence of BD or its treatment. Many of these findings were replicated by microarray in an independent sample of 60 cases and controls. These results highlight common pathways for inherited and non-inherited influences on disease risk that may constitute good targets for novel therapies.

Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder.

Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of ∼750,000 high-quality genetic markers on a combined sample of ∼14,000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of ∼17,700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 (LBA1), LMAN2L and PTGFR. In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1, was significant at the P=2.4 × 10(-11) level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63,000 case-control samples would be needed to identify the ∼105 BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.

Replication and meta-analysis of TMEM132D gene variants in panic disorder.

A recent genome-wide association study in patients with panic disorder (PD) identified a risk haplotype consisting of two single-nucleotide polymorphisms (SNPs) (rs7309727 and rs11060369) located in intron 3 of TMEM132D to be associated with PD in three independent samples. Now we report a subsequent confirmation study using five additional PD case-control samples (n = 1670 cases and n = 2266 controls) assembled as part of the Panic Disorder International Consortium (PanIC) study for a total of 2678 cases and 3262 controls in the analysis. In the new independent samples of European ancestry (EA), the association of rs7309727 and the risk haplotype rs7309727-rs11060369 was, indeed, replicated, with the strongest signal coming from patients with primary PD, that is, patients without major psychiatric comorbidities (n = 1038 cases and n = 2411 controls). This finding was paralleled by the results of the meta-analysis across all samples, in which the risk haplotype and rs7309727 reached P-levels of P = 1.4e-8 and P = 1.1e-8, respectively, when restricting the samples to individuals of EA with primary PD. In the Japanese sample no associations with PD could be found. The present results support the initial finding that TMEM132D gene contributes to genetic susceptibility for PD in individuals of EA. Our results also indicate that patient ascertainment and genetic background could be important sources of heterogeneity modifying this association signal in different populations.

Interaction networks of lithium and valproate molecular targets reveal a striking enrichment of apoptosis functional clusters and neurotrophin signaling.

The overall neurobiological mechanisms by which lithium and valproate stabilize mood in bipolar disorder patients have yet to be fully defined. The therapeutic efficacy and dissimilar chemical structures of these medications suggest that they perturb both shared and disparate cellular processes. To investigate key pathways and functional clusters involved in the global action of lithium and valproate, we generated interaction networks formed by well-supported drug targets. Striking functional similarities emerged. Intersecting nodes in lithium and valproate networks highlighted a strong enrichment of apoptosis clusters and neurotrophin signaling. Other enriched pathways included MAPK, ErbB, insulin, VEGF, Wnt and long-term potentiation indicating a widespread effect of both drugs on diverse signaling systems. MAPK1/3 and AKT1/2 were the most preponderant nodes across pathways suggesting a central role in mediating pathway interactions. The convergence of biological responses unveils a functional signature for lithium and valproate that could be key modulators of their therapeutic efficacy.

A systems approach to the biology of mood disorders through network analysis of candidate genes.

Meta analysis of association data of mood disorders has shown evidence for the role of particular genes in genetic risk. Integration of association data from meta analysis with differential expression data in brains of mood disorder patients could heighten the level of support for specific genes. To identify molecular mechanisms that may be disrupted in disease, a systems approach that involves analysis of biological networks created by these selected genes was employed.Interaction networks of hierarchical groupings of selected genes were generated using the Michigan Molecular Interactions (MiMI) software. Large networks were deconvoluted into subclusters of core complexes by using a community clustering program, GLay. Network nodes were functionally annotated in DAVID Bioinformatics Resource to identify enriched pathways and functional clusters. MAPK and beta adrenergic receptor signaling pathways were significantly enriched in the ANK3 and CACNA1C network. The PBRM1 network bolstered the enrichment of chromatin remodeling and transcription regulation functional clusters. Lowering the stringency for inclusion of other genes in network seeds increased network complexity and expanded the recruitment of enriched pathways to include signaling by neurotransmitter and hormone receptors, neurotrophin, ErbB and the cell cycle. We present a strategy to interrogate mechanisms in the cellular system that might be perturbed in disease. Network analysis of meta analysis- generated candidate genes that exhibited differential expression in mood disorder brains identified signaling pathways and functional clusters that may be involved in genetic risk for mood disorders.

Genetic variation in cholinergic muscarinic-2 receptor gene modulates M2 receptor binding in vivo and accounts for reduced binding in bipolar disorder.

Genetic variation in the cholinergic muscarinic-2 (M(2)) receptor gene (CHRM2) has been associated with the risk for developing depression. We previously reported that M(2)-receptor distribution volume (V(T)) was reduced in depressed subjects with bipolar disorder (BD) relative to depressed subjects with major depressive disorder (MDD) and healthy controls (HCs). In this study, we investigated the effects of six single-nucleotide polymorphisms (SNPs) for CHRM2 on M(2)-receptor binding to test the hypotheses that genetic variation in CHRM2 influences M(2)-receptor binding and that a CHRM2 polymorphism underlies the deficits in M(2)-receptor V(T) observed in BD. The M(2)-receptor V(T) was measured using positron emission tomography and [(18)F]FP-TZTP in unmedicated, depressed subjects with BD (n=16) or MDD (n=24) and HCs (n=25), and the effect of genotype on V(T) was assessed. In the controls, one SNP (with identifier rs324650, in which the ancestral allele adenine (A) is replaced with one or two copies of thymine (T), showed a significant allelic effect on V(T) in the pregenual and subgenual anterior cingulate cortices in the direction AA

Homology modeling and QSAR analysis of 1,3,4-thiadiazole and 1,3,4-triazole derivatives as carbonic anhydrase inhibitors.

Carbonic anhydrase (CA) inhibitors are very interesting target for designing anticancer (hypoxic) and antiglaucoma drugs. In the present study, a 3D homology modeling of human carbonic anhydrase-IX (hCA-IX) isozyme, based upon the crystal structure of murine CA-XIVA (PDB CODE 1RJ5) was performed, as no experimental 3D structures are available. A homology model of hCA-IX was developed and validated. To explore the responsible physicochemical properties of 1,3,4-thiadiazole and 1,3,4-triazole derivatives for carbonic anhydrase inhibition, a quantitative structure activity relationship (QSAR) study was performed having hCA-II and hCA-IX inhibitory activity respectively. In hCA-II and hCA-IX inhibitory activities, four significant models with good correlations (> or = 0.945 & > or = 0.926) were obtained; two models (models 1 and 3) were selected based on statistical criterion. The QSAR study revealed that in case of hCA-II, overall increase in size and volume of molecule, introduction of electropositive surfaces might increase the inhibitory activity, whereas in case of hCA-IX, decreasing the hydrophobicity and introduction of electron releasing substituents might increase the hCA-IX inhibitory activity.

Two variants in Ankyrin 3 (ANK3) are independent genetic risk factors for bipolar disorder.

Two recent reports have highlighted ANK3 as a susceptibility gene for bipolar disorder (BD). We first reported association between BD and the ANK3 marker rs9804190 in a genome-wide association study (GWAS) of two independent samples (Baum et al., 2008). Subsequently, a meta-analysis of GWAS data based on samples from the US and the UK reported association with a different ANK3 marker, rs10994336 (Ferreira et al., 2008). The markers lie about 340 kb apart in the gene. Here, we test both markers in additional samples and characterize the contribution of each marker to BD risk. Our previously reported findings at rs9804190, which had been based on DNA pooling, were confirmed by individual genotyping in the National Institute of Mental Health (NIMH) waves 1-4 (P=0.05; odds ratio (OR)=1.24) and German (P=0.0006; OR=1.34) samples. This association was replicated in an independent US sample known as NIMH wave 5 (466 cases, 212 controls; P=0.017; OR=1.38). A random-effects meta-analysis of all three samples was significant (P=3 x 10(-6); OR=1.32), with no heterogeneity. Individual genotyping of rs10994336 revealed a significant association in the German sample (P=0.0001; OR=1.70), and similar ORs in the NIMH 1-4 and NIMH 5 samples that were not significant at the P<0.05 level. Meta-analysis of all three samples supported an association with rs10994336 (P=1.7 x 10(-5); OR=1.54), again with no heterogeneity. There was little linkage disequilibrium between the two markers. Further analysis suggested that each marker contributed independently to BD, with no significant marker x marker interaction. Our findings strongly support ANK3 as a BD susceptibility gene and suggest true allelic heterogeneity.

A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder.

The genetic basis of bipolar disorder has long been thought to be complex, with the potential involvement of multiple genes, but methods to analyze populations with respect to this complexity have only recently become available. We have carried out a genome-wide association study of bipolar disorder by genotyping over 550,000 single-nucleotide polymorphisms (SNPs) in two independent case-control samples of European origin. The initial association screen was performed using pooled DNA, and selected SNPs were confirmed by individual genotyping. While DNA pooling reduces power to detect genetic associations, there is a substantial cost saving and gain in efficiency. A total of 88 SNPs, representing 80 different genes, met the prior criteria for replication in both samples. Effect sizes were modest: no single SNP of large effect was detected. Of 37 SNPs selected for individual genotyping, the strongest association signal was detected at a marker within the first intron of diacylglycerol kinase eta (DGKH; P=1.5 x 10(-8), experiment-wide P<0.01, OR=1.59). This gene encodes DGKH, a key protein in the lithium-sensitive phosphatidyl inositol pathway. This first genome-wide association study of bipolar disorder shows that several genes, each of modest effect, reproducibly influence disease risk. Bipolar disorder may be a polygenic disease.

Findings in an independent sample support an association between bipolar affective disorder and the G72/G30 locus on chromosome 13q33.

Markers near the nested genes G72 and G30 on chromosome 13q33 have been implicated in the etiology of schizophrenia and, recently, bipolar affective disorder (BPAD). Hattori et al (2003) reported that single-nucleotide polymorphisms (SNPs) near the G72/G30 locus were associated with BPAD in a sample of 22 pedigrees, and that SNP haplotypes were associated in a second, larger sample of triads. The present study attempts to replicate this finding in an independent case-control sample. Six SNPs near the G72/G30 locus, including the most strongly associated markers in the previous study, were tested in 139 cases and 113 ethnically matched controls. Significant association was detected between BPAD and two adjacent SNPs (smallest P=0.007; global P=0.024). Haplotype analysis produced additional support for association (smallest P=0.004; global P=0.004). Analysis of 31 unlinked microsatellite markers detected no population stratification in the cases or controls studied. Although the associated alleles and haplotypes differ from those previously reported, these new results provide further evidence, in an independent sample, for an association between BPAD and genetic variation in the vicinity of the genes G72 and G30.

Utility and accuracy of template-directed dye-terminator incorporation with fluorescence-polarization detection for genotyping single nucleotide polymorphisms.

There are little independent data available about how well single nucleotide polymorphism (SNP) genotyping technologies perform in the typical molecular genetics laboratory. We evaluated the utility and accuracy of a widely used technology, template-directed dye-terminator incorporation with fluorescence-polarization detection (FP-TDI), in a sample of 177 SNPs selected solely on the basis of map location. Genotypes were generated without optimization using standard protocols. Overall, 81% of the SNPs we studied generated readable genotypes by FP-TDI. Thirty-two SNPs were genotyped in duplicate by PCR-RFLP orfluorescent dye-terminator sequencing. Out of a total of 631 duplicate genotypes, no true discrepancies were detected. The true error rate has a 95% chance of lying between 0 and 6 out of 1000 genotypes. We also tested for deviations from Hardy-Weinberg Equilibrium in 33 SNPs genotyped in 50 unrelated individuals, and no significant deviations were detected. Our FP-TDI data were readily adaptable to automated genotype calling using our own method of cluster analysis, which assigns a probability score to each genotype call. We conclude that FP-TDI is both efficient and accurate. The method can easily fill the needs of SNP genotyping projects at the scale typically used for regional or candidate-gene association studies.